From 5d5ecce0511cfefc5a24c0ba8ad1cd1d4563397c Mon Sep 17 00:00:00 2001 From: yangxue Date: Thu, 25 Apr 2019 16:17:10 +0800 Subject: [PATCH] first commit --- .gitignore | 118 + LICENSE | 21 + README.md | 83 + comparison.png | Bin 0 -> 27825 bytes data/__init__.py | 0 data/io/BDD100K/BDD2VOC.py | 131 + data/io/BDD100K/get_bdd100k_next_batch.py | 95 + data/io/COCO/get_coco_next_batch.py | 61 + data/io/DOAI2019/__init__.py | 0 data/io/DOAI2019/train_crop.py | 238 + data/io/DOTA/get_dota_next_batch.py | 95 + data/io/DOTA/train_crop.py | 357 + data/io/DOTA/val_crop.py | 250 + data/io/__init__.py | 0 data/io/convert_data_to_tfrecord.py | 122 + data/io/convert_data_to_tfrecord_coco.py | 103 + data/io/convert_data_to_tfrecord_voc2012.py | 140 + data/io/image_preprocess.py | 83 + data/io/image_preprocess_multi_gpu.py | 83 + data/io/image_preprocess_multi_gpu_aug.py | 226 + data/io/read_tfrecord.py | 139 + data/io/read_tfrecord_multi_gpu.py | 144 + data/io/read_tfrecord_multi_gpu_aug.py | 152 + 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tools/train_for_coco.py create mode 100644 tools/train_with_placeholder.py diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..02074a3 --- /dev/null +++ b/.gitignore @@ -0,0 +1,118 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +env/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +.installed.cfg +*.egg + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +.hypothesis/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# pyenv +.python-version + +# celery beat schedule file +celerybeat-schedule + +# SageMath parsed files +*.sage.py + +# dotenv +.env + +# virtualenv +.venv +venv/ +ENV/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ + +.pyc +.so +*.data-00000-of-00001 +*.index +*.meta +events.* +checkpoint +.idea/ +__pycache__/ +*.json +*.zip + +*/tools/demos/* +*/output/* +*/data/pretrained_weights/* +*/data/tfrecord/* diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..dda276e --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2018 DetectionTeamUCAS + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/README.md b/README.md new file mode 100644 index 0000000..f0e49f7 --- /dev/null +++ b/README.md @@ -0,0 +1,83 @@ +# Cascade R-CNN: Delving into High Quality Object Detection + +## Abstract +This repo is based on [FPN](https://github.com/DetectionTeamUCAS/FPN_Tensorflow), and completed by [YangXue](https://github.com/yangxue0827). + +## Train on COCO train2017 and test on COCO val2017 (coco minival). +|Model|Backbone|Train Schedule|GPU|Image/GPU|FP16|Box AP(Mask AP)|test stage| +|-----|--------|--------------|---|---------|----|---------------|---| +|Faster (paper)|R50v1-FPN|1X|8X TITAN XP|1|no|38.3|3| +|Faster (ours)|R50v1-FPN|1X|8X 2080 Ti|1|no|38.2|3| +|Faster (Face++)|R50v1-FPN|1X|8X 2080 Ti|2|no|39.1|3| + +![2](comparison.png) + +## My Development Environment +1、python3.5 (anaconda recommend) +2、cuda9.0 **(If you want to use cuda8, please set CUDA9 = False in the cfgs.py file.)** +3、[opencv(cv2)](https://pypi.org/project/opencv-python/) +4、[tfplot](https://github.com/wookayin/tensorflow-plot) +5、tensorflow == 1.12 + +## Download Model +### Pretrain weights +1、Please download [resnet50_v1](http://download.tensorflow.org/models/resnet_v1_50_2016_08_28.tar.gz), [resnet101_v1](http://download.tensorflow.org/models/resnet_v1_101_2016_08_28.tar.gz) pre-trained models on Imagenet, put it to data/pretrained_weights. +2、Or you can choose to use a better backbone, refer to [gluon2TF](https://github.com/yangJirui/gluon2TF). [Pretrain Model Link](https://pan.baidu.com/s/1HF3G5XSxXm7W4pk10RuOlw), password: q4jg. + +### Trained weights +**Select a configuration file in the folder ($PATH_ROOT/libs/configs/) and copy its contents into cfgs.py, then download the corresponding [weights](https://github.com/DetectionTeamUCAS/Models/tree/master/Cascade_FPN_Tensorflow).** + +## Compile +``` +cd $PATH_ROOT/libs/box_utils/cython_utils +python setup.py build_ext --inplace +``` + +## Train + +1、If you want to train your own data, please note: +``` +(1) Modify parameters (such as CLASS_NUM, DATASET_NAME, VERSION, etc.) in $PATH_ROOT/libs/configs/cfgs.py +(2) Add category information in $PATH_ROOT/libs/label_name_dict/lable_dict.py +(3) Add data_name to $PATH_ROOT/data/io/read_tfrecord_multi_gpu.py +``` + +2、make tfrecord +``` +cd $PATH_ROOT/data/io/ +python convert_data_to_tfrecord_coco.py --VOC_dir='/PATH/TO/JSON/FILE/' + --save_name='train' + --dataset='coco' +``` + +3、multi-gpu train +``` +cd $PATH_ROOT/tools +python multi_gpu_train.py +``` + +## Eval +``` +cd $PATH_ROOT/tools +python eval_coco.py --eval_data='/PATH/TO/IMAGES/' + --eval_gt='/PATH/TO/TEST/ANNOTATION/' + --GPU='0' +``` + +## Tensorboard +``` +cd $PATH_ROOT/output/summary +tensorboard --logdir=. +``` +![3](images.png) + +![4](scalars.png) + +## Reference +1、https://github.com/endernewton/tf-faster-rcnn +2、https://github.com/zengarden/light_head_rcnn +3、https://github.com/tensorflow/models/tree/master/research/object_detection +4、https://github.com/CharlesShang/FastMaskRCNN +5、https://github.com/matterport/Mask_RCNN +6、https://github.com/msracver/Deformable-ConvNets +7、https://github.com/tensorpack/tensorpack diff --git a/comparison.png b/comparison.png new file mode 100644 index 0000000000000000000000000000000000000000..f478097e7aa06fed87bbc422cf20d12e4d8e2cd8 GIT binary patch literal 27825 zcmd@6^;=v)(*_6+?h@QR5Q4kA2MZxsaCeu%U4jL73m!bUTL|v%?(S}T$n!qm_wKd3 z*Zu)}ewkr7)2F(-y5z3ACiJs{6bd2%A_xRRk&zbv0s=wKfj|)b@Q}bK%#-uyzzdY4 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z<;I22`^t%8_R5%mL+mYZI-7CqyPN@(Km>VfX7PQ;*TrS!#P35xHYySW)*_s-@CuCr z@wj}%6{cIKCGgV#z{yNnEB9LE&^!g(t-$JX1?AIL`6#7UM6wH4AU7*c4UB_oXkg|P z`lS!oT2dr3h+DIqbxAQBbc(*de!H8wpapi*#Pra;g~b>EpFx~VK04ZMiD@2qZfOn7 zF=!V9jR&xxg|)Z}6F)n94eM(FH+7^s^o`oaG7UbK*@kkJ)N2^i)AufB!pMn7X~pQT(Sb+hTu%5JN^CBLTa{TK3tl6%3C zGu;2th`jf|LC-a-Busyn2dvbi9R$O$_Ylx@P?qb}121h?ftOJ1>Ywn2-|j#B0(WFr z%%k#6BP##MJ9o|YyZjoFYj1S)vx~#lXKPubwdCQyDp9s2Mt?TnWg}nn%2wS!wYbXX zV7eI}&0VKADYVjG^7Gj{>g45RX5LnE*pSh#L8-Eq)| zT5+^WsP~L_jxhd6{ZL`)h}kea{x%wxB^&HpDP8=?$M)IpL!*CAN&f32ydPV;L<#W9 V1iz70aL&YY 0: + shape = image.shape + step = width - overlap + for start_h in range(0, shape[0], step): # 256 + for start_w in range(0, shape[1], step): + boxes = copy.deepcopy(boxes_all) + box = np.zeros_like(boxes_all) + start_h_new = start_h + start_w_new = start_w + if start_h + height > shape[0]: + start_h_new = shape[0] - height + if start_w + width > shape[1]: + start_w_new = shape[1] - width + top_left_row = max(start_h_new, 0) + top_left_col = max(start_w_new, 0) + bottom_right_row = min(start_h + height, shape[0]) + bottom_right_col = min(start_w + width, shape[1]) + + subImage = image[top_left_row:bottom_right_row, top_left_col: bottom_right_col] + + box[:, 0] = boxes[:, 0] - top_left_col + box[:, 2] = boxes[:, 2] - top_left_col + box[:, 4] = boxes[:, 4] - top_left_col + box[:, 6] = boxes[:, 6] - top_left_col + + box[:, 1] = boxes[:, 1] - top_left_row + box[:, 3] = boxes[:, 3] - top_left_row + box[:, 5] = boxes[:, 5] - top_left_row + box[:, 7] = boxes[:, 7] - top_left_row + box[:, 8] = boxes[:, 8] + center_y = 0.25 * (box[:, 1] + box[:, 3] + box[:, 5] + box[:, 7]) + center_x = 0.25 * (box[:, 0] + box[:, 2] + box[:, 4] + box[:, 6]) + # print('center_y', center_y) + # print('center_x', center_x) + # print ('boxes', boxes) + # print ('boxes_all', boxes_all) + # print ('top_left_col', top_left_col, 'top_left_row', top_left_row) + + cond1 = np.intersect1d(np.where(center_y[:] >= 0)[0], np.where(center_x[:] >= 0)[0]) + cond2 = np.intersect1d(np.where(center_y[:] <= (bottom_right_row - top_left_row))[0], + np.where(center_x[:] <= (bottom_right_col - top_left_col))[0]) + idx = np.intersect1d(cond1, cond2) + # idx = np.where(center_y[:]>=0 and center_x[:]>=0 and center_y[:] <= (bottom_right_row - top_left_row) and center_x[:] <= (bottom_right_col - top_left_col))[0] + # save_path, im_width, im_height, objects_axis, label_name + if len(idx) > 0: + xml = os.path.join(save_dir, 'xml', + "%s_%04d_%04d.xml" % (file_idx, top_left_row, top_left_col)) + save_to_xml(xml, subImage.shape[1], subImage.shape[0], box[idx, :], class_list) + # print ('save xml : ', xml) + if subImage.shape[0] > 5 and subImage.shape[1] > 5: + img_path = os.path.join(save_dir, "images1.5") + if not os.path.exists(img_path): + os.makedirs(img_path) + img = os.path.join(save_dir, 'img', + "%s_%04d_%04d.png" % (file_idx, top_left_row, top_left_col)) + cv2.imwrite(img, subImage) + + +print('class_list', len(class_list)) +raw_data = '/data/DOTA/val' +raw_images_dir = os.path.join(raw_data, 'images') +raw_label_dir = os.path.join(raw_data, 'labelTxt-v1.5', 'labelTxt1.5') + +save_dir = '/data/DOTA/DOTA_TOTAL' + +images = [i for i in os.listdir(raw_images_dir) if 'png' in i] +labels = [i for i in os.listdir(raw_label_dir) if 'txt' in i] + +print('find image', len(images)) +print('find label', len(labels)) + +min_length = 1e10 +max_length = 1 + +for idx, img in enumerate(images): + # img = 'P1524.png' + print(idx, 'read image', img) + + # img_data = misc.imread(os.path.join(raw_images_dir, img)) + img_data = cv2.imread(os.path.join(raw_images_dir, img)) + + # if len(img_data.shape) == 2: + # img_data = img_data[:, :, np.newaxis] + # print ('find gray image') + + txt_data = open(os.path.join(raw_label_dir, img.replace('png', 'txt')), 'r').readlines() + # print (idx, len(format_label(txt_data)), img_data.shape) + # if max(img_data.shape[:2]) > max_length: + # max_length = max(img_data.shape[:2]) + # if min(img_data.shape[:2]) < min_length: + # min_length = min(img_data.shape[:2]) + # if idx % 50 ==0: + # print (idx, len(format_label(txt_data)), img_data.shape) + # print (idx, 'min_length', min_length, 'max_length', max_length) + box = format_label(txt_data) + clip_image(img.strip('.png'), img_data, box, 800, 800, overlap=200) diff --git a/data/io/DOTA/get_dota_next_batch.py b/data/io/DOTA/get_dota_next_batch.py new file mode 100644 index 0000000..7ce2c38 --- /dev/null +++ b/data/io/DOTA/get_dota_next_batch.py @@ -0,0 +1,95 @@ +# -*- coding: utf-8 -*- + +from __future__ import absolute_import, print_function, division + +import xml.etree.cElementTree as ET +import cv2 +import numpy as np +import os +from libs.label_name_dict import coco_dict +from libs.label_name_dict.label_dict import * + + +root_path = '/unsullied/sharefs/yangxue/isilon/yangxue/data/DOTA/DOTA_TOTAL/' +xmls = os.listdir(os.path.join(root_path, 'xml_h')) +total_imgs = len(xmls) + +# print (NAME_LABEL_DICT) + + +def read_xml_gtbox_and_label(xml_path): + """ + :param xml_path: the path of voc xml + :return: a list contains gtboxes and labels, shape is [num_of_gtboxes, 5], + and has [xmin, ymin, xmax, ymax, label] in a per row + """ + + tree = ET.parse(xml_path) + root = tree.getroot() + img_width = None + img_height = None + box_list = [] + for child_of_root in root: + # if child_of_root.tag == 'filename': + # assert child_of_root.text == xml_path.split('/')[-1].split('.')[0] \ + # + FLAGS.img_format, 'xml_name and img_name cannot match' + + if child_of_root.tag == 'size': + for child_item in child_of_root: + if child_item.tag == 'width': + img_width = int(child_item.text) + if child_item.tag == 'height': + img_height = int(child_item.text) + + if child_of_root.tag == 'object': + label = None + for child_item in child_of_root: + if child_item.tag == 'name': + label = NAME_LABEL_MAP[child_item.text] + if child_item.tag == 'bndbox': + tmp_box = [] + for node in child_item: + tmp_box.append(int(node.text)) + assert label is not None, 'label is none, error' + tmp_box.append(label) + box_list.append(tmp_box) + + gtbox_label = np.array(box_list, dtype=np.int32) + + return img_height, img_width, gtbox_label + + +def next_img(step): + + if step % total_imgs == 0: + np.random.shuffle(xmls) + xml_name = xmls[step % total_imgs] + img_name = xml_name.replace('.xml', '.jpg') + + img = cv2.imread(os.path.join(root_path, 'img', img_name)) + + img_height, img_width, gtbox_label = read_xml_gtbox_and_label(os.path.join(root_path, 'xml_h', xml_name)) + + gtbox_and_label_list = np.array(gtbox_label, dtype=np.int32) + if gtbox_and_label_list.shape[0] == 0: + return next_img(step+1) + else: + return img_name, img[:, :, ::-1], gtbox_and_label_list + + +if __name__ == '__main__': + + imgid, img, gtbox = next_img(3234) + + print("::") + from libs.box_utils.draw_box_in_img import draw_boxes_with_label_and_scores + + img = draw_boxes_with_label_and_scores(img_array=img, boxes=gtbox[:, :-1], labels=gtbox[:, -1], + scores=np.ones(shape=(len(gtbox), ))) + print ("_----") + + + cv2.imshow("test", img) + cv2.waitKey(0) + + diff --git a/data/io/DOTA/train_crop.py b/data/io/DOTA/train_crop.py new file mode 100644 index 0000000..93ac59c --- /dev/null +++ b/data/io/DOTA/train_crop.py @@ -0,0 +1,357 @@ +import os +import scipy.misc as misc +from xml.dom.minidom import Document +import numpy as np +import copy, cv2 + +def save_rbox_to_xml(save_path, im_width, im_height, objects_axis, label_name): + im_depth = 0 + object_num = len(objects_axis) + doc = Document() + + annotation = doc.createElement('annotation') + doc.appendChild(annotation) + + folder = doc.createElement('folder') + folder_name = doc.createTextNode('DOTA') + folder.appendChild(folder_name) + annotation.appendChild(folder) + + filename = doc.createElement('filename') + filename_name = doc.createTextNode('000024.jpg') + filename.appendChild(filename_name) + annotation.appendChild(filename) + + source = doc.createElement('source') + annotation.appendChild(source) + + database = doc.createElement('database') + database.appendChild(doc.createTextNode('The DOTA Database')) + source.appendChild(database) + + annotation_s = doc.createElement('annotation') + annotation_s.appendChild(doc.createTextNode('PASCAL VOC2007')) + source.appendChild(annotation_s) + + image = doc.createElement('image') + image.appendChild(doc.createTextNode('flickr')) + source.appendChild(image) + + flickrid = doc.createElement('flickrid') + flickrid.appendChild(doc.createTextNode('322409915')) + source.appendChild(flickrid) + + owner = doc.createElement('owner') + annotation.appendChild(owner) + + flickrid_o = doc.createElement('flickrid') + flickrid_o.appendChild(doc.createTextNode('knautia')) + owner.appendChild(flickrid_o) + + name_o = doc.createElement('name') + name_o.appendChild(doc.createTextNode('yang')) + owner.appendChild(name_o) + + + size = doc.createElement('size') + annotation.appendChild(size) + width = doc.createElement('width') + width.appendChild(doc.createTextNode(str(im_width))) + height = doc.createElement('height') + height.appendChild(doc.createTextNode(str(im_height))) + depth = doc.createElement('depth') + depth.appendChild(doc.createTextNode(str(im_depth))) + size.appendChild(width) + size.appendChild(height) + size.appendChild(depth) + segmented = doc.createElement('segmented') + segmented.appendChild(doc.createTextNode('0')) + annotation.appendChild(segmented) + for i in range(object_num): + objects = doc.createElement('object') + annotation.appendChild(objects) + object_name = doc.createElement('name') + object_name.appendChild(doc.createTextNode(label_name[int(objects_axis[i][-1])])) + objects.appendChild(object_name) + pose = doc.createElement('pose') + pose.appendChild(doc.createTextNode('Unspecified')) + objects.appendChild(pose) + truncated = doc.createElement('truncated') + truncated.appendChild(doc.createTextNode('1')) + objects.appendChild(truncated) + difficult = doc.createElement('difficult') + difficult.appendChild(doc.createTextNode('0')) + objects.appendChild(difficult) + bndbox = doc.createElement('bndbox') + objects.appendChild(bndbox) + + x0 = doc.createElement('x0') + x0.appendChild(doc.createTextNode(str((objects_axis[i][0])))) + bndbox.appendChild(x0) + y0 = doc.createElement('y0') + y0.appendChild(doc.createTextNode(str((objects_axis[i][1])))) + bndbox.appendChild(y0) + + x1 = doc.createElement('x1') + x1.appendChild(doc.createTextNode(str((objects_axis[i][2])))) + bndbox.appendChild(x1) + y1 = doc.createElement('y1') + y1.appendChild(doc.createTextNode(str((objects_axis[i][3])))) + bndbox.appendChild(y1) + + x2 = doc.createElement('x2') + x2.appendChild(doc.createTextNode(str((objects_axis[i][4])))) + bndbox.appendChild(x2) + y2 = doc.createElement('y2') + y2.appendChild(doc.createTextNode(str((objects_axis[i][5])))) + bndbox.appendChild(y2) + + x3 = doc.createElement('x3') + x3.appendChild(doc.createTextNode(str((objects_axis[i][6])))) + bndbox.appendChild(x3) + y3 = doc.createElement('y3') + y3.appendChild(doc.createTextNode(str((objects_axis[i][7])))) + bndbox.appendChild(y3) + + f = open(save_path,'w') + f.write(doc.toprettyxml(indent = '')) + f.close() + + +def save_hbox_to_xml(save_path, im_width, im_height, objects_axis, label_name): + im_depth = 0 + object_num = len(objects_axis) + doc = Document() + + annotation = doc.createElement('annotation') + doc.appendChild(annotation) + + folder = doc.createElement('folder') + folder_name = doc.createTextNode('DOTA') + folder.appendChild(folder_name) + annotation.appendChild(folder) + + filename = doc.createElement('filename') + filename_name = doc.createTextNode('000024.jpg') + filename.appendChild(filename_name) + annotation.appendChild(filename) + + source = doc.createElement('source') + annotation.appendChild(source) + + database = doc.createElement('database') + database.appendChild(doc.createTextNode('The DOTA Database')) + source.appendChild(database) + + annotation_s = doc.createElement('annotation') + annotation_s.appendChild(doc.createTextNode('PASCAL VOC2007')) + source.appendChild(annotation_s) + + image = doc.createElement('image') + image.appendChild(doc.createTextNode('flickr')) + source.appendChild(image) + + flickrid = doc.createElement('flickrid') + flickrid.appendChild(doc.createTextNode('322409915')) + source.appendChild(flickrid) + + owner = doc.createElement('owner') + annotation.appendChild(owner) + + flickrid_o = doc.createElement('flickrid') + flickrid_o.appendChild(doc.createTextNode('knautia')) + owner.appendChild(flickrid_o) + + name_o = doc.createElement('name') + name_o.appendChild(doc.createTextNode('yang')) + owner.appendChild(name_o) + + size = doc.createElement('size') + annotation.appendChild(size) + width = doc.createElement('width') + width.appendChild(doc.createTextNode(str(im_width))) + height = doc.createElement('height') + height.appendChild(doc.createTextNode(str(im_height))) + depth = doc.createElement('depth') + depth.appendChild(doc.createTextNode(str(im_depth))) + size.appendChild(width) + size.appendChild(height) + size.appendChild(depth) + segmented = doc.createElement('segmented') + segmented.appendChild(doc.createTextNode('0')) + annotation.appendChild(segmented) + for i in range(object_num): + objects = doc.createElement('object') + annotation.appendChild(objects) + object_name = doc.createElement('name') + object_name.appendChild(doc.createTextNode(label_name[int(objects_axis[i][-1])])) + objects.appendChild(object_name) + pose = doc.createElement('pose') + pose.appendChild(doc.createTextNode('Unspecified')) + objects.appendChild(pose) + truncated = doc.createElement('truncated') + truncated.appendChild(doc.createTextNode('1')) + objects.appendChild(truncated) + difficult = doc.createElement('difficult') + difficult.appendChild(doc.createTextNode('0')) + objects.appendChild(difficult) + bndbox = doc.createElement('bndbox') + objects.appendChild(bndbox) + + x0 = doc.createElement('xmin') + x0.appendChild(doc.createTextNode(str(min(objects_axis[i][0:-1:2])))) + bndbox.appendChild(x0) + y0 = doc.createElement('ymin') + y0.appendChild(doc.createTextNode(str(min(objects_axis[i][1:-1:2])))) + bndbox.appendChild(y0) + + x1 = doc.createElement('xmax') + x1.appendChild(doc.createTextNode(str(max(objects_axis[i][0:-1:2])))) + bndbox.appendChild(x1) + y1 = doc.createElement('ymax') + y1.appendChild(doc.createTextNode(str(max(objects_axis[i][1:-1:2])))) + bndbox.appendChild(y1) + + f = open(save_path, 'w') + f.write(doc.toprettyxml(indent='')) + f.close() + + +class_list = ['plane', 'baseball-diamond', 'bridge', 'ground-track-field', + 'small-vehicle', 'large-vehicle', 'ship', + 'tennis-court', 'basketball-court', + 'storage-tank', 'soccer-ball-field', + 'roundabout', 'harbor', + 'swimming-pool', 'helicopter'] + + +def format_label(txt_list): + format_data = [] + for i in txt_list[2:]: + format_data.append( + [int(xy) for xy in i.split(' ')[:8]] + [class_list.index(i.split(' ')[8])] + # {'x0': int(i.split(' ')[0]), + # 'x1': int(i.split(' ')[2]), + # 'x2': int(i.split(' ')[4]), + # 'x3': int(i.split(' ')[6]), + # 'y1': int(i.split(' ')[1]), + # 'y2': int(i.split(' ')[3]), + # 'y3': int(i.split(' ')[5]), + # 'y4': int(i.split(' ')[7]), + # 'class': class_list.index(i.split(' ')[8]) if i.split(' ')[8] in class_list else 0, + # 'difficulty': int(i.split(' ')[9])} + ) + if i.split(' ')[8] not in class_list : + print ('warning found a new label :', i.split(' ')[8]) + exit() + return np.array(format_data) + + +def clip_image(file_idx, image, boxes_all, width, height): + # print ('image shape', image.shape) + if len(boxes_all) > 0: + shape = image.shape + for start_h in range(0, shape[0], 600): + for start_w in range(0, shape[1], 600): + boxes = copy.deepcopy(boxes_all) + box = np.zeros_like(boxes_all) + start_h_new = start_h + start_w_new = start_w + if start_h + height > shape[0]: + start_h_new = shape[0] - height + if start_w + width > shape[1]: + start_w_new = shape[1] - width + top_left_row = max(start_h_new, 0) + top_left_col = max(start_w_new, 0) + bottom_right_row = min(start_h + height, shape[0]) + bottom_right_col = min(start_w + width, shape[1]) + + subImage = image[top_left_row:bottom_right_row, top_left_col: bottom_right_col] + + box[:, 0] = boxes[:, 0] - top_left_col + box[:, 2] = boxes[:, 2] - top_left_col + box[:, 4] = boxes[:, 4] - top_left_col + box[:, 6] = boxes[:, 6] - top_left_col + + box[:, 1] = boxes[:, 1] - top_left_row + box[:, 3] = boxes[:, 3] - top_left_row + box[:, 5] = boxes[:, 5] - top_left_row + box[:, 7] = boxes[:, 7] - top_left_row + box[:, 8] = boxes[:, 8] + center_y = 0.25*(box[:, 1] + box[:, 3] + box[:, 5] + box[:, 7]) + center_x = 0.25*(box[:, 0] + box[:, 2] + box[:, 4] + box[:, 6]) + # print('center_y', center_y) + # print('center_x', center_x) + # print ('boxes', boxes) + # print ('boxes_all', boxes_all) + # print ('top_left_col', top_left_col, 'top_left_row', top_left_row) + + cond1 = np.intersect1d(np.where(center_y[:]>=0 )[0], np.where(center_x[:]>=0 )[0]) + cond2 = np.intersect1d(np.where(center_y[:] <= (bottom_right_row - top_left_row))[0], + np.where(center_x[:] <= (bottom_right_col - top_left_col))[0]) + idx = np.intersect1d(cond1, cond2) + # idx = np.where(center_y[:]>=0 and center_x[:]>=0 and center_y[:] <= (bottom_right_row - top_left_row) and center_x[:] <= (bottom_right_col - top_left_col))[0] + # save_path, im_width, im_height, objects_axis, label_name + if len(idx) > 0: + xml_r = os.path.join(save_dir, 'xml_r', "%s_%04d_%04d.xml" % (file_idx, top_left_row, top_left_col)) + xml_h = os.path.join(save_dir, 'xml_h', "%s_%04d_%04d.xml" % (file_idx, top_left_row, top_left_col)) + save_rbox_to_xml(xml_r, subImage.shape[1], subImage.shape[0], box[idx, :], class_list) + save_hbox_to_xml(xml_h, subImage.shape[1], subImage.shape[0], box[idx, :], class_list) + # print ('save xml : ', xml) + if subImage.shape[0] > 5 and subImage.shape[1] >5: + img = os.path.join(save_dir, 'img', "%s_%04d_%04d.png" % (file_idx, top_left_row, top_left_col)) + cv2.imwrite(img, subImage) + + +print('class_list', len(class_list)) +raw_data = '/unsullied/sharefs/yangxue/isilon/yangxue/data/DOTA/DOTA_TOTAL/' +raw_images_dir = os.path.join(raw_data, 'images') +raw_label_dir = os.path.join(raw_data, 'labelTxt') + +save_dir = '/unsullied/sharefs/yangxue/isilon/yangxue/data/DOTA/DOTA_TOTAL/' + +images = [i for i in os.listdir(raw_images_dir) if 'png' in i] +labels = [i for i in os.listdir(raw_label_dir) if 'txt' in i] + +print('find image', len(images)) +print('find label', len(labels)) + +min_length = 1e10 +max_length = 1 + +for idx, img in enumerate(images): +# img = 'P1524.png' + print (idx, 'read image', img) + # img_data = misc.imread(os.path.join(raw_images_dir, img)) + img_data = cv2.imread(os.path.join(raw_images_dir, img)) + + # if len(img_data.shape) == 2: + # img_data = img_data[:, :, np.newaxis] + # print ('find gray image') + + txt_data = open(os.path.join(raw_label_dir, img.replace('png', 'txt')), 'r').readlines() + # print (idx, len(format_label(txt_data)), img_data.shape) + # if max(img_data.shape[:2]) > max_length: + # max_length = max(img_data.shape[:2]) + # if min(img_data.shape[:2]) < min_length: + # min_length = min(img_data.shape[:2]) + # if idx % 50 ==0: + # print (idx, len(format_label(txt_data)), img_data.shape) + # print (idx, 'min_length', min_length, 'max_length', max_length) + box = format_label(txt_data) + clip_image(img.strip('.png'), img_data, box, 800, 800) + + +# rm train/images/* && rm train/labeltxt/* + + + + + + + + + + + + diff --git a/data/io/DOTA/val_crop.py b/data/io/DOTA/val_crop.py new file mode 100644 index 0000000..7e55ea6 --- /dev/null +++ b/data/io/DOTA/val_crop.py @@ -0,0 +1,250 @@ +import os +import scipy.misc as misc +from xml.dom.minidom import Document +import numpy as np +import copy, cv2 + +def save_to_xml(save_path, im_width, im_height, objects_axis, label_name): + im_depth = 0 + object_num = len(objects_axis) + doc = Document() + + annotation = doc.createElement('annotation') + doc.appendChild(annotation) + + folder = doc.createElement('folder') + folder_name = doc.createTextNode('VOC2007') + folder.appendChild(folder_name) + annotation.appendChild(folder) + + filename = doc.createElement('filename') + filename_name = doc.createTextNode('000024.jpg') + filename.appendChild(filename_name) + annotation.appendChild(filename) + + source = doc.createElement('source') + annotation.appendChild(source) + + database = doc.createElement('database') + database.appendChild(doc.createTextNode('The VOC2007 Database')) + source.appendChild(database) + + annotation_s = doc.createElement('annotation') + annotation_s.appendChild(doc.createTextNode('PASCAL VOC2007')) + source.appendChild(annotation_s) + + image = doc.createElement('image') + image.appendChild(doc.createTextNode('flickr')) + source.appendChild(image) + + flickrid = doc.createElement('flickrid') + flickrid.appendChild(doc.createTextNode('322409915')) + source.appendChild(flickrid) + + owner = doc.createElement('owner') + annotation.appendChild(owner) + + flickrid_o = doc.createElement('flickrid') + flickrid_o.appendChild(doc.createTextNode('knautia')) + owner.appendChild(flickrid_o) + + name_o = doc.createElement('name') + name_o.appendChild(doc.createTextNode('yang')) + owner.appendChild(name_o) + + + size = doc.createElement('size') + annotation.appendChild(size) + width = doc.createElement('width') + width.appendChild(doc.createTextNode(str(im_width))) + height = doc.createElement('height') + height.appendChild(doc.createTextNode(str(im_height))) + depth = doc.createElement('depth') + depth.appendChild(doc.createTextNode(str(im_depth))) + size.appendChild(width) + size.appendChild(height) + size.appendChild(depth) + segmented = doc.createElement('segmented') + segmented.appendChild(doc.createTextNode('0')) + annotation.appendChild(segmented) + for i in range(object_num): + objects = doc.createElement('object') + annotation.appendChild(objects) + object_name = doc.createElement('name') + object_name.appendChild(doc.createTextNode(label_name[int(objects_axis[i][-1])])) + objects.appendChild(object_name) + pose = doc.createElement('pose') + pose.appendChild(doc.createTextNode('Unspecified')) + objects.appendChild(pose) + truncated = doc.createElement('truncated') + truncated.appendChild(doc.createTextNode('1')) + objects.appendChild(truncated) + difficult = doc.createElement('difficult') + difficult.appendChild(doc.createTextNode('0')) + objects.appendChild(difficult) + bndbox = doc.createElement('bndbox') + objects.appendChild(bndbox) + + x0 = doc.createElement('x0') + x0.appendChild(doc.createTextNode(str((objects_axis[i][0])))) + bndbox.appendChild(x0) + y0 = doc.createElement('y0') + y0.appendChild(doc.createTextNode(str((objects_axis[i][1])))) + bndbox.appendChild(y0) + + x1 = doc.createElement('x1') + x1.appendChild(doc.createTextNode(str((objects_axis[i][2])))) + bndbox.appendChild(x1) + y1 = doc.createElement('y1') + y1.appendChild(doc.createTextNode(str((objects_axis[i][3])))) + bndbox.appendChild(y1) + + x2 = doc.createElement('x2') + x2.appendChild(doc.createTextNode(str((objects_axis[i][4])))) + bndbox.appendChild(x2) + y2 = doc.createElement('y2') + y2.appendChild(doc.createTextNode(str((objects_axis[i][5])))) + bndbox.appendChild(y2) + + x3 = doc.createElement('x3') + x3.appendChild(doc.createTextNode(str((objects_axis[i][6])))) + bndbox.appendChild(x3) + y3 = doc.createElement('y3') + y3.appendChild(doc.createTextNode(str((objects_axis[i][7])))) + bndbox.appendChild(y3) + + f = open(save_path,'w') + f.write(doc.toprettyxml(indent = '')) + f.close() + +class_list = ['plane', 'baseball-diamond', 'bridge', 'ground-track-field', +'small-vehicle', 'large-vehicle', 'ship', +'tennis-court', 'basketball-court', +'storage-tank', 'soccer-ball-field', +'roundabout', 'harbor', +'swimming-pool', 'helicopter'] + + + + +def format_label(txt_list): + format_data = [] + for i in txt_list[2:]: + format_data.append( + [int(xy) for xy in i.split(' ')[:8]] + [class_list.index(i.split(' ')[8])] + # {'x0': int(i.split(' ')[0]), + # 'x1': int(i.split(' ')[2]), + # 'x2': int(i.split(' ')[4]), + # 'x3': int(i.split(' ')[6]), + # 'y1': int(i.split(' ')[1]), + # 'y2': int(i.split(' ')[3]), + # 'y3': int(i.split(' ')[5]), + # 'y4': int(i.split(' ')[7]), + # 'class': class_list.index(i.split(' ')[8]) if i.split(' ')[8] in class_list else 0, + # 'difficulty': int(i.split(' ')[9])} + ) + if i.split(' ')[8] not in class_list : + print ('warning found a new label :', i.split(' ')[8]) + exit() + return np.array(format_data) + +def clip_image(file_idx, image, boxes_all, width, height): + if len(boxes_all) > 0: + # print ('image shape', image.shape) + shape = image.shape + for start_h in range(0, shape[0], 600): + for start_w in range(0, shape[1], 600): + boxes = copy.deepcopy(boxes_all) + box = np.zeros_like(boxes_all) + start_h_new = start_h + start_w_new = start_w + if start_h + height > shape[0]: + start_h_new = shape[0] - height + if start_w + width > shape[1]: + start_w_new = shape[1] - width + top_left_row = max(start_h_new, 0) + top_left_col = max(start_w_new, 0) + bottom_right_row = min(start_h + height, shape[0]) + bottom_right_col = min(start_w + width, shape[1]) + + + subImage = image[top_left_row:bottom_right_row, top_left_col: bottom_right_col] + box[:, 0] = boxes[:, 0] - top_left_col + box[:, 2] = boxes[:, 2] - top_left_col + box[:, 4] = boxes[:, 4] - top_left_col + box[:, 6] = boxes[:, 6] - top_left_col + + box[:, 1] = boxes[:, 1] - top_left_row + box[:, 3] = boxes[:, 3] - top_left_row + box[:, 5] = boxes[:, 5] - top_left_row + box[:, 7] = boxes[:, 7] - top_left_row + box[:, 8] = boxes[:, 8] + center_y = 0.25*(box[:, 1] + box[:, 3] + box[:, 5] + box[:, 7]) + center_x = 0.25*(box[:, 0] + box[:, 2] + box[:, 4] + box[:, 6]) + # print('center_y', center_y) + # print('center_x', center_x) + # print ('boxes', boxes) + # print ('boxes_all', boxes_all) + # print ('top_left_col', top_left_col, 'top_left_row', top_left_row) + + cond1 = np.intersect1d(np.where(center_y[:]>=0 )[0], np.where(center_x[:]>=0 )[0]) + cond2 = np.intersect1d(np.where(center_y[:] <= (bottom_right_row - top_left_row))[0], + np.where(center_x[:] <= (bottom_right_col - top_left_col))[0]) + idx = np.intersect1d(cond1, cond2) + # idx = np.where(center_y[:]>=0 and center_x[:]>=0 and center_y[:] <= (bottom_right_row - top_left_row) and center_x[:] <= (bottom_right_col - top_left_col))[0] + # save_path, im_width, im_height, objects_axis, label_name + if len(idx) > 0: + xml = os.path.join(save_dir, 'labeltxt', "%s_%04d_%04d.xml" % (file_idx, top_left_row, top_left_col)) + save_to_xml(xml, subImage.shape[1], subImage.shape[0], box[idx, :], class_list) + # print ('save xml : ', xml) + if subImage.shape[0] > 5 and subImage.shape[1] >5: + img = os.path.join(save_dir, 'images', "%s_%04d_%04d.png" % (file_idx, top_left_row, top_left_col)) + cv2.imwrite(img, subImage) + + + + +print ('class_list', len(class_list)) +raw_data = '/dataset/DOTA/val/' +raw_images_dir = os.path.join(raw_data, 'images') +raw_label_dir = os.path.join(raw_data, 'labelTxt') + +save_dir = '/dataset/DOTA_clip/val/' + +images = [i for i in os.listdir(raw_images_dir) if 'png' in i] +labels = [i for i in os.listdir(raw_label_dir) if 'txt' in i] + +print ('find image', len(images)) +print ('find label', len(labels)) + +min_length = 1e10 +max_length = 1 + + +for idx, img in enumerate(images): + # img = 'P2330.png' + print (idx, 'read image', img) + # img_data = misc.imread(os.path.join(raw_images_dir, img)) + img_data = cv2.imread(os.path.join(raw_images_dir, img)) + + # if len(img_data.shape) == 2: + # img_data = img_data[:, :, np.newaxis] + # print ('find gray image') + + txt_data = open(os.path.join(raw_label_dir, img.replace('png', 'txt')), 'r').readlines() + # print (idx, len(format_label(txt_data)), img_data.shape) + # if max(img_data.shape[:2]) > max_length: + # max_length = max(img_data.shape[:2]) + # if min(img_data.shape[:2]) < min_length: + # min_length = min(img_data.shape[:2]) + # if idx % 50 ==0: + # print (idx, len(format_label(txt_data)), img_data.shape) + # print (idx, 'min_length', min_length, 'max_length', max_length) + box = format_label(txt_data) + clip_image(img.strip('.png'), img_data, box, 800, 800) + + + + + + diff --git a/data/io/__init__.py b/data/io/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/data/io/convert_data_to_tfrecord.py b/data/io/convert_data_to_tfrecord.py new file mode 100644 index 0000000..b364345 --- /dev/null +++ b/data/io/convert_data_to_tfrecord.py @@ -0,0 +1,122 @@ +# -*- coding: utf-8 -*- +from __future__ import division, print_function, absolute_import +import sys +sys.path.append('../../') +import xml.etree.cElementTree as ET +import numpy as np +import tensorflow as tf +import glob +import cv2 +from libs.label_name_dict.label_dict import * +from help_utils.tools import * + +tf.app.flags.DEFINE_string('VOC_dir', '/data/DOTA/DOTA_TOTAL/', 'Voc dir') +tf.app.flags.DEFINE_string('xml_dir', 'xml', 'xml dir') +tf.app.flags.DEFINE_string('image_dir', 'img', 'image dir') +tf.app.flags.DEFINE_string('save_name', 'train', 'save name') +tf.app.flags.DEFINE_string('save_dir', '../tfrecord/', 'save name') +tf.app.flags.DEFINE_string('img_format', '.png', 'format of image') +tf.app.flags.DEFINE_string('dataset', 'DOAI2019', 'dataset') +FLAGS = tf.app.flags.FLAGS + + +def _int64_feature(value): + return tf.train.Feature(int64_list=tf.train.Int64List(value=[value])) + + +def _bytes_feature(value): + return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value])) + + +def read_xml_gtbox_and_label(xml_path): + """ + :param xml_path: the path of voc xml + :return: a list contains gtboxes and labels, shape is [num_of_gtboxes, 5], + and has [xmin, ymin, xmax, ymax, label] in a per row + """ + + tree = ET.parse(xml_path) + root = tree.getroot() + img_width = None + img_height = None + box_list = [] + for child_of_root in root: + # if child_of_root.tag == 'filename': + # assert child_of_root.text == xml_path.split('/')[-1].split('.')[0] \ + # + FLAGS.img_format, 'xml_name and img_name cannot match' + + if child_of_root.tag == 'size': + for child_item in child_of_root: + if child_item.tag == 'width': + img_width = int(child_item.text) + if child_item.tag == 'height': + img_height = int(child_item.text) + + if child_of_root.tag == 'object': + label = None + for child_item in child_of_root: + if child_item.tag == 'name': + label = NAME_LABEL_MAP[child_item.text] + if child_item.tag == 'bndbox': + tmp_box = [] + for node in child_item: + tmp_box.append(int(node.text)) + assert label is not None, 'label is none, error' + tmp_box.append(label) + box_list.append(tmp_box) + + gtbox_label = np.array(box_list, dtype=np.int32) + + return img_height, img_width, gtbox_label + + +def convert_pascal_to_tfrecord(): + xml_path = FLAGS.VOC_dir + FLAGS.xml_dir + image_path = FLAGS.VOC_dir + FLAGS.image_dir + save_path = FLAGS.save_dir + FLAGS.dataset + '_' + FLAGS.save_name + '.tfrecord' + mkdir(FLAGS.save_dir) + + # writer_options = tf.python_io.TFRecordOptions(tf.python_io.TFRecordCompressionType.ZLIB) + # writer = tf.python_io.TFRecordWriter(path=save_path, options=writer_options) + writer = tf.python_io.TFRecordWriter(path=save_path) + for count, xml in enumerate(glob.glob(xml_path + '/*.xml')): + # to avoid path error in different development platform + xml = xml.replace('\\', '/') + + img_name = xml.split('/')[-1].split('.')[0] + FLAGS.img_format + img_path = image_path + '/' + img_name + + if not os.path.exists(img_path): + print('{} is not exist!'.format(img_path)) + continue + + img_height, img_width, gtbox_label = read_xml_gtbox_and_label(xml) + + # img = np.array(Image.open(img_path)) + img = cv2.imread(img_path)[:, :, ::-1] + + feature = tf.train.Features(feature={ + # do not need encode() in linux + 'img_name': _bytes_feature(img_name.encode()), + # 'img_name': _bytes_feature(img_name), + 'img_height': _int64_feature(img_height), + 'img_width': _int64_feature(img_width), + 'img': _bytes_feature(img.tostring()), + 'gtboxes_and_label': _bytes_feature(gtbox_label.tostring()), + 'num_objects': _int64_feature(gtbox_label.shape[0]) + }) + + example = tf.train.Example(features=feature) + + writer.write(example.SerializeToString()) + + view_bar('Conversion progress', count + 1, len(glob.glob(xml_path + '/*.xml'))) + + print('\nConversion is complete!') + + +if __name__ == '__main__': + # xml_path = '../data/dataset/VOCdevkit/VOC2007/Annotations/000005.xml' + # read_xml_gtbox_and_label(xml_path) + + convert_pascal_to_tfrecord() diff --git a/data/io/convert_data_to_tfrecord_coco.py b/data/io/convert_data_to_tfrecord_coco.py new file mode 100644 index 0000000..205feef --- /dev/null +++ b/data/io/convert_data_to_tfrecord_coco.py @@ -0,0 +1,103 @@ +# -*- coding: utf-8 -*- +from __future__ import division, print_function, absolute_import +import sys +sys.path.append('../../') +import xml.etree.cElementTree as ET +import numpy as np +import tensorflow as tf +import glob +import cv2 +import json +from libs.label_name_dict.label_dict import * +from help_utils.tools import * + +tf.app.flags.DEFINE_string('coco_dir', '/data/COCO/coco_trainvalmini.odgt', 'coco dir') +tf.app.flags.DEFINE_string('save_name', 'train', 'save name') +tf.app.flags.DEFINE_string('save_dir', '../tfrecord/', 'save name') +tf.app.flags.DEFINE_string('dataset', 'coco', 'dataset') +FLAGS = tf.app.flags.FLAGS + + +def _int64_feature(value): + return tf.train.Feature(int64_list=tf.train.Int64List(value=[value])) + + +def _bytes_feature(value): + return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value])) + + +def convert_pascal_to_tfrecord(coco_trainvalmini): + save_path = FLAGS.save_dir + FLAGS.dataset + '_' + FLAGS.save_name + '.tfrecord' + mkdir(FLAGS.save_dir) + + # writer_options = tf.python_io.TFRecordOptions(tf.python_io.TFRecordCompressionType.ZLIB) + # writer = tf.python_io.TFRecordWriter(path=save_path, options=writer_options) + writer = tf.python_io.TFRecordWriter(path=save_path) + + with open(coco_trainvalmini) as f: + files = f.readlines() + + img_count = 0 + gt_count = 0 + + for count, raw_line in enumerate(files): + file = json.loads(raw_line) + img_path = os.path.join('/data/COCO/train2017', file['fpath'].split('_')[-1]) + img_name = file['ID'] + + if not os.path.exists(img_path): + # print('{} is not exist!'.format(img_path)) + img_count += 1 + continue + # img = np.array(Image.open(img_path)) + img = cv2.imread(img_path)[:, :, ::-1] + + if img is None: + continue + + gtboxes = file['gtboxes'] + img_height = file['height'] + img_width = file['width'] + + if len(gtboxes) == 0: + # print('{}: gt is not exist!'.format(img_path)) + gt_count += 1 + continue + + gtbox_label = [] + for gt in gtboxes: + box = gt['box'] + label = gt['tag'] + gtbox_label.append([box[0], box[1], box[0]+box[2], box[1]+box[3], NAME_LABEL_MAP[label]]) + + gtbox_label = np.array(gtbox_label, np.int32) + + feature = tf.train.Features(feature={ + # do not need encode() in linux + 'img_name': _bytes_feature(img_name.encode()), + # 'img_name': _bytes_feature(img_name), + 'img_height': _int64_feature(img_height), + 'img_width': _int64_feature(img_width), + 'img': _bytes_feature(img.tostring()), + 'gtboxes_and_label': _bytes_feature(gtbox_label.tostring()), + 'num_objects': _int64_feature(gtbox_label.shape[0]) + }) + + example = tf.train.Example(features=feature) + + writer.write(example.SerializeToString()) + + view_bar('Conversion progress', count + 1, len(files)) + + print('{} images not exist!'.format(img_count)) + print('{} gts not exist!'.format(gt_count)) + print('\nConversion is complete!') + + +if __name__ == '__main__': + # xml_path = '../data/dataset/VOCdevkit/VOC2007/Annotations/000005.xml' + # read_xml_gtbox_and_label(xml_path) + + # coco_path = '/unsullied/sharefs/_research_detection/GeneralDetection/COCO/data/MSCOCO/odformat/coco_trainvalmini.odgt' + # convert_pascal_to_tfrecord(coco_path) + convert_pascal_to_tfrecord(FLAGS.coco_dir) diff --git a/data/io/convert_data_to_tfrecord_voc2012.py b/data/io/convert_data_to_tfrecord_voc2012.py new file mode 100644 index 0000000..9ef835a --- /dev/null +++ b/data/io/convert_data_to_tfrecord_voc2012.py @@ -0,0 +1,140 @@ +# -*- coding: utf-8 -*- +from __future__ import division, print_function, absolute_import +import sys +sys.path.append('../../') +import xml.etree.cElementTree as ET +import numpy as np +import tensorflow as tf +import glob +import cv2 +from libs.label_name_dict.label_dict import * +from help_utils.tools import * + +tf.app.flags.DEFINE_string('VOC_dir', '/unsullied/sharefs/yangxue/isilon/yangxue/data/VOC2012/VOCdevkit/VOC2012/', 'Voc dir') +tf.app.flags.DEFINE_string('xml_dir', 'Annotations', 'xml dir') +tf.app.flags.DEFINE_string('image_dir', 'JPEGImages', 'image dir') +tf.app.flags.DEFINE_string('save_name', 'train2012', 'save name') +tf.app.flags.DEFINE_string('save_dir', '../tfrecord/', 'save name') +tf.app.flags.DEFINE_string('img_format', '.jpg', 'format of image') +tf.app.flags.DEFINE_string('dataset', 'pascal', 'dataset') +FLAGS = tf.app.flags.FLAGS + + +def _int64_feature(value): + return tf.train.Feature(int64_list=tf.train.Int64List(value=[value])) + + +def _bytes_feature(value): + return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value])) + + +def read_xml_gtbox_and_label(xml_path): + """ + :param xml_path: the path of voc xml + :return: a list contains gtboxes and labels, shape is [num_of_gtboxes, 5], + and has [xmin, ymin, xmax, ymax, label] in a per row + """ + + tree = ET.parse(xml_path) + root = tree.getroot() + img_width = None + img_height = None + box_list = [] + for child_of_root in root: + # if child_of_root.tag == 'filename': + # assert child_of_root.text == xml_path.split('/')[-1].split('.')[0] \ + # + FLAGS.img_format, 'xml_name and img_name cannot match' + + if child_of_root.tag == 'size': + for child_item in child_of_root: + if child_item.tag == 'width': + img_width = int(child_item.text) + if child_item.tag == 'height': + img_height = int(child_item.text) + + if child_of_root.tag == 'object': + label = None + for child_item in child_of_root: + if child_item.tag == 'name': + label = NAME_LABEL_MAP[child_item.text] + if child_item.tag == 'bndbox': + tmp_box = [0, 0, 0, 0] + for node in child_item: + if node.tag == 'xmin': + tmp_box[0] = int(node.text) + if node.tag == 'ymin': + tmp_box[1] = int(node.text) + if node.tag == 'xmax': + tmp_box[2] = int(node.text) + if node.tag == 'ymax': + tmp_box[3] = int(node.text) + assert label is not None, 'label is none, error' + tmp_box.append(label) + box_list.append(tmp_box) + + gtbox_label = np.array(box_list, dtype=np.int32) + + return img_height, img_width, gtbox_label + + +def convert_pascal_to_tfrecord(): + xml_path = FLAGS.VOC_dir + FLAGS.xml_dir + image_path = FLAGS.VOC_dir + FLAGS.image_dir + save_path = FLAGS.save_dir + FLAGS.dataset + '_' + FLAGS.save_name + '.tfrecord' + mkdir(FLAGS.save_dir) + + # writer_options = tf.python_io.TFRecordOptions(tf.python_io.TFRecordCompressionType.ZLIB) + # writer = tf.python_io.TFRecordWriter(path=save_path, options=writer_options) + writer = tf.python_io.TFRecordWriter(path=save_path) + + fr = open('/unsullied/sharefs/yangxue/isilon/yangxue/data/VOC2012/VOCdevkit/VOC2012/ImageSets/Main/trainval.txt', 'r') + lines = fr.readlines() + + real_cnt = 0 + + for count, xml in enumerate(glob.glob(xml_path + '/*.xml')): + # to avoid path error in different development platform + xml = xml.replace('\\', '/') + + tmp = xml.split('/')[-1].split('.')[0] + "\n" + if tmp not in lines: + continue + + img_name = xml.split('/')[-1].split('.')[0] + FLAGS.img_format + img_path = image_path + '/' + img_name + + if not os.path.exists(img_path): + print('{} is not exist!'.format(img_path)) + continue + + img_height, img_width, gtbox_label = read_xml_gtbox_and_label(xml) + + # img = np.array(Image.open(img_path)) + img = cv2.imread(img_path)[:, :, ::-1] + + feature = tf.train.Features(feature={ + # do not need encode() in linux + 'img_name': _bytes_feature(img_name.encode()), + # 'img_name': _bytes_feature(img_name), + 'img_height': _int64_feature(img_height), + 'img_width': _int64_feature(img_width), + 'img': _bytes_feature(img.tostring()), + 'gtboxes_and_label': _bytes_feature(gtbox_label.tostring()), + 'num_objects': _int64_feature(gtbox_label.shape[0]) + }) + + example = tf.train.Example(features=feature) + + writer.write(example.SerializeToString()) + real_cnt += 1 + + view_bar('Conversion progress', count + 1, len(glob.glob(xml_path + '/*.xml'))) + + print('\nConversion is complete! {} images.'.format(real_cnt)) + + +if __name__ == '__main__': + # xml_path = '../data/dataset/VOCdevkit/VOC2007/Annotations/000005.xml' + # read_xml_gtbox_and_label(xml_path) + + convert_pascal_to_tfrecord() diff --git a/data/io/image_preprocess.py b/data/io/image_preprocess.py new file mode 100644 index 0000000..e2abdbe --- /dev/null +++ b/data/io/image_preprocess.py @@ -0,0 +1,83 @@ +# -*- coding: utf-8 -*- + +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import tensorflow as tf + +import numpy as np + + +def max_length_limitation(length, length_limitation): + return tf.cond(tf.less(length, length_limitation), + true_fn=lambda: length, + false_fn=lambda: length_limitation) + + +def short_side_resize(img_tensor, gtboxes_and_label, target_shortside_len, length_limitation=1200): + ''' + + :param img_tensor:[h, w, c], gtboxes_and_label:[-1, 5]. gtboxes: [xmin, ymin, xmax, ymax] + :param target_shortside_len: + :param length_limitation: set max length to avoid OUT OF MEMORY + :return: + ''' + img_h, img_w = tf.shape(img_tensor)[0], tf.shape(img_tensor)[1] + new_h, new_w = tf.cond(tf.less(img_h, img_w), + true_fn=lambda: (target_shortside_len, + max_length_limitation(target_shortside_len * img_w // img_h, length_limitation)), + false_fn=lambda: (max_length_limitation(target_shortside_len * img_h // img_w, length_limitation), + target_shortside_len)) + + img_tensor = tf.expand_dims(img_tensor, axis=0) + img_tensor = tf.image.resize_bilinear(img_tensor, [new_h, new_w]) + + xmin, ymin, xmax, ymax, label = tf.unstack(gtboxes_and_label, axis=1) + + new_xmin, new_ymin = xmin * new_w // img_w, ymin * new_h // img_h + new_xmax, new_ymax = xmax * new_w // img_w, ymax * new_h // img_h + img_tensor = tf.squeeze(img_tensor, axis=0) # ensure image tensor rank is 3 + + return img_tensor, tf.transpose(tf.stack([new_xmin, new_ymin, new_xmax, new_ymax, label], axis=0)) + + +def short_side_resize_for_inference_data(img_tensor, target_shortside_len, length_limitation=1200, is_resize=True): + if is_resize: + img_h, img_w = tf.shape(img_tensor)[0], tf.shape(img_tensor)[1] + + new_h, new_w = tf.cond(tf.less(img_h, img_w), + true_fn=lambda: (target_shortside_len, + max_length_limitation(target_shortside_len * img_w // img_h, length_limitation)), + false_fn=lambda: (max_length_limitation(target_shortside_len * img_h // img_w, length_limitation), + target_shortside_len)) + + img_tensor = tf.expand_dims(img_tensor, axis=0) + img_tensor = tf.image.resize_bilinear(img_tensor, [new_h, new_w]) + + img_tensor = tf.squeeze(img_tensor, axis=0) # ensure image tensor rank is 3 + return img_tensor + + +def flip_left_to_right(img_tensor, gtboxes_and_label): + + h, w = tf.shape(img_tensor)[0], tf.shape(img_tensor)[1] + + img_tensor = tf.image.flip_left_right(img_tensor) + + xmin, ymin, xmax, ymax, label = tf.unstack(gtboxes_and_label, axis=1) + new_xmax = w - xmin + new_xmin = w - xmax + + return img_tensor, tf.transpose(tf.stack([new_xmin, ymin, new_xmax, ymax, label], axis=0)) + + +def random_flip_left_right(img_tensor, gtboxes_and_label): + img_tensor, gtboxes_and_label= tf.cond(tf.less(tf.random_uniform(shape=[], minval=0, maxval=1), 0.5), + lambda: flip_left_to_right(img_tensor, gtboxes_and_label), + lambda: (img_tensor, gtboxes_and_label)) + + return img_tensor, gtboxes_and_label + + + diff --git a/data/io/image_preprocess_multi_gpu.py b/data/io/image_preprocess_multi_gpu.py new file mode 100644 index 0000000..fa25f39 --- /dev/null +++ b/data/io/image_preprocess_multi_gpu.py @@ -0,0 +1,83 @@ +# -*- coding: utf-8 -*- + +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import tensorflow as tf + +import numpy as np + + +def max_length_limitation(length, length_limitation): + return tf.cond(tf.less(length, length_limitation), + true_fn=lambda: length, + false_fn=lambda: length_limitation) + + +def short_side_resize(img_tensor, gtboxes_and_label, target_shortside_len, length_limitation=1200): + ''' + + :param img_tensor:[h, w, c], gtboxes_and_label:[-1, 5]. gtboxes: [xmin, ymin, xmax, ymax] + :param target_shortside_len: + :param length_limitation: set max length to avoid OUT OF MEMORY + :return: + ''' + img_h, img_w = tf.shape(img_tensor)[0], tf.shape(img_tensor)[1] + new_h, new_w = tf.cond(tf.less(img_h, img_w), + true_fn=lambda: (target_shortside_len, + max_length_limitation(target_shortside_len * img_w // img_h, length_limitation)), + false_fn=lambda: (max_length_limitation(target_shortside_len * img_h // img_w, length_limitation), + target_shortside_len)) + + img_tensor = tf.expand_dims(img_tensor, axis=0) + img_tensor = tf.image.resize_bilinear(img_tensor, [new_h, new_w]) + + xmin, ymin, xmax, ymax, label = tf.unstack(gtboxes_and_label, axis=1) + + new_xmin, new_ymin = xmin * new_w // img_w, ymin * new_h // img_h + new_xmax, new_ymax = xmax * new_w // img_w, ymax * new_h // img_h + img_tensor = tf.squeeze(img_tensor, axis=0) # ensure image tensor rank is 3 + + return img_tensor, tf.transpose(tf.stack([new_xmin, new_ymin, new_xmax, new_ymax, label], axis=0)), new_h, new_w + + +def short_side_resize_for_inference_data(img_tensor, target_shortside_len, length_limitation=1200, is_resize=True): + if is_resize: + img_h, img_w = tf.shape(img_tensor)[0], tf.shape(img_tensor)[1] + + new_h, new_w = tf.cond(tf.less(img_h, img_w), + true_fn=lambda: (target_shortside_len, + max_length_limitation(target_shortside_len * img_w // img_h, length_limitation)), + false_fn=lambda: (max_length_limitation(target_shortside_len * img_h // img_w, length_limitation), + target_shortside_len)) + + img_tensor = tf.expand_dims(img_tensor, axis=0) + img_tensor = tf.image.resize_bilinear(img_tensor, [new_h, new_w]) + + img_tensor = tf.squeeze(img_tensor, axis=0) # ensure image tensor rank is 3 + return img_tensor + + +def flip_left_to_right(img_tensor, gtboxes_and_label): + + h, w = tf.shape(img_tensor)[0], tf.shape(img_tensor)[1] + + img_tensor = tf.image.flip_left_right(img_tensor) + + xmin, ymin, xmax, ymax, label = tf.unstack(gtboxes_and_label, axis=1) + new_xmax = w - xmin + new_xmin = w - xmax + + return img_tensor, tf.transpose(tf.stack([new_xmin, ymin, new_xmax, ymax, label], axis=0)) + + +def random_flip_left_right(img_tensor, gtboxes_and_label): + img_tensor, gtboxes_and_label= tf.cond(tf.less(tf.random_uniform(shape=[], minval=0, maxval=1), 0.5), + lambda: flip_left_to_right(img_tensor, gtboxes_and_label), + lambda: (img_tensor, gtboxes_and_label)) + + return img_tensor, gtboxes_and_label + + + diff --git a/data/io/image_preprocess_multi_gpu_aug.py b/data/io/image_preprocess_multi_gpu_aug.py new file mode 100644 index 0000000..72be3b4 --- /dev/null +++ b/data/io/image_preprocess_multi_gpu_aug.py @@ -0,0 +1,226 @@ +# -*- coding: utf-8 -*- + +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import tensorflow as tf +import numpy as np +import cv2 +from libs.label_name_dict.label_dict import NAME_LABEL_MAP + + +def short_side_resize(img_tensor, gtboxes_and_label, target_shortside_len, max_len=1200): + ''' + :param img_tensor:[h, w, c], gtboxes_and_label:[-1, 9] + :param target_shortside_len: + :return: + ''' + + h, w = tf.shape(img_tensor)[0], tf.shape(img_tensor)[1] + + new_h, new_w = tf.cond(tf.less(h, w), + true_fn=lambda: (target_shortside_len, tf.minimum(target_shortside_len * w//h, max_len)), + false_fn=lambda: (tf.minimum(target_shortside_len * h//w, max_len), + target_shortside_len)) + # new_h, new_w = 1200, 1200 + img_tensor = tf.expand_dims(img_tensor, axis=0) + img_tensor = tf.image.resize_bilinear(img_tensor, [new_h, new_w]) + + x1, y1, x2, y2, x3, y3, x4, y4, label = tf.unstack(gtboxes_and_label, axis=1) + + x1, x2, x3, x4 = x1 * new_w//w, x2 * new_w//w, x3 * new_w//w, x4 * new_w//w + y1, y2, y3, y4 = y1 * new_h//h, y2 * new_h//h, y3 * new_h//h, y4 * new_h//h + + img_tensor = tf.squeeze(img_tensor, axis=0) # ensure image tensor rank is 3 + return img_tensor, tf.transpose(tf.stack([x1, y1, x2, y2, x3, y3, x4, y4, label], axis=0)), new_h, new_w + + +def short_side_resize_for_inference_data(img_tensor, target_shortside_len, max_len=1200, is_resize=True): + h, w, = tf.shape(img_tensor)[0], tf.shape(img_tensor)[1] + + img_tensor = tf.expand_dims(img_tensor, axis=0) + + if is_resize: + new_h, new_w = tf.cond(tf.less(h, w), + true_fn=lambda: ( + target_shortside_len, tf.minimum(target_shortside_len * w // h, max_len)), + false_fn=lambda: (tf.minimum(target_shortside_len * h // w, max_len), + target_shortside_len)) + img_tensor = tf.image.resize_bilinear(img_tensor, [new_h, new_w]) + + return img_tensor # [1, h, w, c] + + +def flip_left_right(img_tensor, gtboxes_and_label): + h, w = tf.shape(img_tensor)[0], tf.shape(img_tensor)[1] + img_tensor = tf.image.flip_left_right(img_tensor) + + x1, y1, x2, y2, x3, y3, x4, y4, label = tf.unstack(gtboxes_and_label, axis=1) + new_x1 = w - x1 + new_x2 = w - x2 + new_x3 = w - x3 + new_x4 = w - x4 + return img_tensor, tf.transpose(tf.stack([new_x1, y1, new_x2, y2, new_x3, y3, new_x4, y4, label], axis=0)) + + +def random_flip_left_right(img_tensor, gtboxes_and_label): + + img_tensor, gtboxes_and_label = tf.cond(tf.less(tf.random_uniform(shape=[], minval=0, maxval=1), 0.5), + lambda: flip_left_right(img_tensor, gtboxes_and_label), + lambda: (img_tensor, gtboxes_and_label)) + + return img_tensor, gtboxes_and_label + + +def flip_up_down(img_tensor, gtboxes_and_label): + h, w = tf.shape(img_tensor)[0], tf.shape(img_tensor)[1] + img_tensor = tf.image.flip_up_down(img_tensor) + + x1, y1, x2, y2, x3, y3, x4, y4, label = tf.unstack(gtboxes_and_label, axis=1) + + new_y1 = h - y1 + new_y2 = h - y2 + new_y3 = h - y3 + new_y4 = h - y4 + + return img_tensor, tf.transpose(tf.stack([x1, new_y1, x2, new_y2, x3, new_y3, x4, new_y4, label], axis=0)) + + +def random_flip_up_dowm(img_tensor, gtboxes_and_label): + img_tensor, gtboxes_and_label = tf.cond(tf.less(tf.random_uniform(shape=[], minval=0, maxval=1), 0.5), + lambda: flip_up_down(img_tensor, gtboxes_and_label), + lambda: (img_tensor, gtboxes_and_label)) + + return img_tensor, gtboxes_and_label + + +def random_rgb2gray(img_tensor, gtboxes_and_label): + ''' + :param img_tensor: tf.float32 + :return: + ''' + def rgb2gray(img, gtboxes_and_label): + + label = gtboxes_and_label[:, -1] + if NAME_LABEL_MAP['swimming-pool'] in label: + # do not change color, because swimming-pool need color + return img + + coin = np.random.rand() + if coin < 0.3: + img = np.asarray(img, dtype=np.float32) + r, g, b = img[:, :, 0], img[:, :, 1], img[:, :, 2] + gray = r * 0.299 + g * 0.587 + b * 0.114 + img = np.stack([gray, gray, gray], axis=2) + return img + else: + return img + + h, w, c = tf.shape(img_tensor)[0], tf.shape(img_tensor)[1], tf.shape(img_tensor)[2] + img_tensor = tf.py_func(rgb2gray, + inp=[img_tensor, gtboxes_and_label], + Tout=tf.float32) + img_tensor = tf.reshape(img_tensor, shape=[h, w, c]) + + return img_tensor + + +def rotate_img_np_OLD(img, gtboxes_and_label, r_theta): + h, w, c = img.shape + center = (w // 2, h // 2) + + M = cv2.getRotationMatrix2D(center, r_theta, 1.0) + rotated_img = cv2.warpAffine(img, M, (w, h)) + # print (M) + new_points_list = [] + obj_num = len(gtboxes_and_label) + for st in range(0, 7, 2): + points = gtboxes_and_label[:, st:st+2] + expand_points = np.concatenate((points, np.ones(shape=(obj_num, 1))), axis=1) + new_points = np.dot(M, expand_points.T) + new_points = new_points.T + new_points_list.append(new_points) + gtboxes = np.concatenate(new_points_list, axis=1) + gtboxes_and_label = np.concatenate((gtboxes, gtboxes_and_label[:, -1].reshape(-1, 1)), axis=1) + + x1, y1, x2, y2, x3, y3, x4, y4 = np.split(gtboxes, 8, 1) + + xc = 0.25*(x1+x2+x3+x4) + yc = 0.25*(y1+y2+y3+y4) + + valid = (xc>0) & (yc>0) & (xc', os.path.abspath(pattern)) + + filename_tensorlist = tf.train.match_filenames_once(pattern) + + filename_queue = tf.train.string_input_producer(filename_tensorlist) + + # shortside_len = tf.constant(shortside_len) + # shortside_len = tf.random_shuffle(shortside_len)[0] + + img_name, img, gtboxes_and_label, num_obs = read_and_prepocess_single_img(filename_queue, shortside_len, + is_training=is_training) + img_name_batch, img_batch, gtboxes_and_label_batch, num_obs_batch = \ + tf.train.batch( + [img_name, img, gtboxes_and_label, num_obs], + batch_size=batch_size, + capacity=1, + num_threads=1, + dynamic_pad=True) + return img_name_batch, img_batch, gtboxes_and_label_batch, num_obs_batch + + +if __name__ == '__main__': + os.environ["CUDA_VISIBLE_DEVICES"] = '0' + img_name_batch, img_batch, gtboxes_and_label_batch, num_objects_batch = \ + next_batch(dataset_name=cfgs.DATASET_NAME, # 'pascal', 'coco' + batch_size=cfgs.BATCH_SIZE, + shortside_len=cfgs.IMG_SHORT_SIDE_LEN, + is_training=True) + gtboxes_and_label = tf.reshape(gtboxes_and_label_batch, [-1, 5]) + + init_op = tf.group( + tf.global_variables_initializer(), + tf.local_variables_initializer() + ) + + config = tf.ConfigProto() + config.gpu_options.allow_growth = True + + with tf.Session(config=config) as sess: + sess.run(init_op) + + coord = tf.train.Coordinator() + threads = tf.train.start_queue_runners(sess, coord) + + img_name_batch_, img_batch_, gtboxes_and_label_batch_, num_objects_batch_ \ + = sess.run([img_name_batch, img_batch, gtboxes_and_label_batch, num_objects_batch]) + + print(img_name_batch_) + + print('debug') + + coord.request_stop() + coord.join(threads) diff --git a/data/io/read_tfrecord_multi_gpu.py b/data/io/read_tfrecord_multi_gpu.py new file mode 100644 index 0000000..85a4855 --- /dev/null +++ b/data/io/read_tfrecord_multi_gpu.py @@ -0,0 +1,144 @@ +# -*- coding: utf-8 -*- + +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import tensorflow as tf +import os +import sys +sys.path.append('../../') + +from data.io import image_preprocess_multi_gpu as image_preprocess +from libs.configs import cfgs + + +def read_single_example_and_decode(filename_queue): + + # tfrecord_options = tf.python_io.TFRecordOptions(tf.python_io.TFRecordCompressionType.ZLIB) + + # reader = tf.TFRecordReader(options=tfrecord_options) + reader = tf.TFRecordReader() + _, serialized_example = reader.read(filename_queue) + + features = tf.parse_single_example( + serialized=serialized_example, + features={ + 'img_name': tf.FixedLenFeature([], tf.string), + 'img_height': tf.FixedLenFeature([], tf.int64), + 'img_width': tf.FixedLenFeature([], tf.int64), + 'img': tf.FixedLenFeature([], tf.string), + 'gtboxes_and_label': tf.FixedLenFeature([], tf.string), + 'num_objects': tf.FixedLenFeature([], tf.int64) + } + ) + img_name = features['img_name'] + img_height = tf.cast(features['img_height'], tf.int32) + img_width = tf.cast(features['img_width'], tf.int32) + img = tf.decode_raw(features['img'], tf.uint8) + + img = tf.reshape(img, shape=[img_height, img_width, 3]) + + gtboxes_and_label = tf.decode_raw(features['gtboxes_and_label'], tf.int32) + gtboxes_and_label = tf.reshape(gtboxes_and_label, [-1, 5]) + + num_objects = tf.cast(features['num_objects'], tf.int32) + return img_name, img, gtboxes_and_label, num_objects + + +def read_and_prepocess_single_img(filename_queue, shortside_len, is_training): + + img_name, img, gtboxes_and_label, num_objects = read_single_example_and_decode(filename_queue) + + img = tf.cast(img, tf.float32) + + if is_training: + img, gtboxes_and_label, img_h, img_w = image_preprocess.short_side_resize(img_tensor=img, gtboxes_and_label=gtboxes_and_label, + target_shortside_len=shortside_len, + length_limitation=cfgs.IMG_MAX_LENGTH) + img, gtboxes_and_label = image_preprocess.random_flip_left_right(img_tensor=img, + gtboxes_and_label=gtboxes_and_label) + + else: + img, gtboxes_and_label, img_h, img_w = image_preprocess.short_side_resize(img_tensor=img, gtboxes_and_label=gtboxes_and_label, + target_shortside_len=shortside_len, + length_limitation=cfgs.IMG_MAX_LENGTH) + if cfgs.NET_NAME in ['resnet101_v1d', 'resnet50_v1d']: + img = img / 255 - tf.constant([[cfgs.PIXEL_MEAN_]]) + else: + img = img - tf.constant([[cfgs.PIXEL_MEAN]]) # sub pixel mean at last + return img_name, img, gtboxes_and_label, num_objects, img_h, img_w + + +def next_batch(dataset_name, batch_size, shortside_len, is_training): + ''' + :return: + img_name_batch: shape(1, 1) + img_batch: shape:(1, new_imgH, new_imgW, C) + gtboxes_and_label_batch: shape(1, Num_Of_objects, 5] .each row is [x1, y1, x2, y2, label] + ''' + # assert batch_size == 1, "we only support batch_size is 1.We may support large batch_size in the future" + + if dataset_name not in ['ship', 'spacenet', 'pascal', 'coco', 'bdd100k', 'DOTA', 'DOTA_H']: + raise ValueError('dataSet name must be in pascal, coco spacenet and ship') + + if is_training: + pattern = os.path.join('../data/tfrecord', dataset_name + '_train*') + else: + pattern = os.path.join('../data/tfrecord', dataset_name + '_test*') + + print('tfrecord path is -->', os.path.abspath(pattern)) + + filename_tensorlist = tf.train.match_filenames_once(pattern) + + filename_queue = tf.train.string_input_producer(filename_tensorlist) + + # shortside_len = tf.constant(shortside_len) + # shortside_len = tf.random_shuffle(shortside_len)[0] + + img_name, img, gtboxes_and_label, num_obs, img_h, img_w = read_and_prepocess_single_img(filename_queue, shortside_len, + is_training=is_training) + + img_name_batch, img_batch, gtboxes_and_label_batch, num_obs_batch, img_h_batch, img_w_batch = \ + tf.train.batch( + [img_name, img, gtboxes_and_label, num_obs, img_h, img_w], + batch_size=batch_size, + capacity=16, + num_threads=16, + dynamic_pad=True) + return img_name_batch, img_batch, gtboxes_and_label_batch, num_obs_batch, img_h_batch, img_w_batch + + +if __name__ == '__main__': + os.environ["CUDA_VISIBLE_DEVICES"] = '0,1' + num_gpu = len(cfgs.GPU_GROUP.strip().split(',')) + img_name_batch, img_batch, gtboxes_and_label_batch, num_objects_batch, img_h_batch, img_w_batch = \ + next_batch(dataset_name=cfgs.DATASET_NAME, # 'pascal', 'coco' + batch_size=cfgs.BATCH_SIZE * num_gpu, + shortside_len=cfgs.IMG_SHORT_SIDE_LEN, + is_training=True) + gtboxes_and_label = tf.reshape(gtboxes_and_label_batch, [-1, 5]) + + init_op = tf.group( + tf.global_variables_initializer(), + tf.local_variables_initializer() + ) + + config = tf.ConfigProto() + config.gpu_options.allow_growth = True + + with tf.Session(config=config) as sess: + sess.run(init_op) + + coord = tf.train.Coordinator() + threads = tf.train.start_queue_runners(sess, coord) + + img_name_batch_, img_batch_, gtboxes_and_label_batch_, num_objects_batch_ \ + = sess.run([img_name_batch, img_batch, gtboxes_and_label_batch, num_objects_batch]) + + print(img_name_batch_.shape) + print(img_batch_.shape) + print('debug') + + coord.request_stop() + coord.join(threads) diff --git a/data/io/read_tfrecord_multi_gpu_aug.py b/data/io/read_tfrecord_multi_gpu_aug.py new file mode 100644 index 0000000..e70f779 --- /dev/null +++ b/data/io/read_tfrecord_multi_gpu_aug.py @@ -0,0 +1,152 @@ +# -*- coding: utf-8 -*- + +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import numpy as np +import tensorflow as tf +import os +import sys +sys.path.append('../../') +from data.io import image_preprocess_multi_gpu_aug +from libs.configs import cfgs +from libs.box_utils.boxes_utils import get_horizen_minAreaRectangle + + +def read_single_example_and_decode(filename_queue): + + # tfrecord_options = tf.python_io.TFRecordOptions(tf.python_io.TFRecordCompressionType.ZLIB) + + # reader = tf.TFRecordReader(options=tfrecord_options) + reader = tf.TFRecordReader() + _, serialized_example = reader.read(filename_queue) + + features = tf.parse_single_example( + serialized=serialized_example, + features={ + 'img_name': tf.FixedLenFeature([], tf.string), + 'img_height': tf.FixedLenFeature([], tf.int64), + 'img_width': tf.FixedLenFeature([], tf.int64), + 'img': tf.FixedLenFeature([], tf.string), + 'gtboxes_and_label': tf.FixedLenFeature([], tf.string), + 'num_objects': tf.FixedLenFeature([], tf.int64) + } + ) + img_name = features['img_name'] + img_height = tf.cast(features['img_height'], tf.int32) + img_width = tf.cast(features['img_width'], tf.int32) + img = tf.decode_raw(features['img'], tf.uint8) + + img = tf.reshape(img, shape=[img_height, img_width, 3]) + gtboxes_and_label = tf.decode_raw(features['gtboxes_and_label'], tf.int32) + gtboxes_and_label = tf.reshape(gtboxes_and_label, [-1, 9]) + num_objects = tf.cast(features['num_objects'], tf.int32) + return img_name, img, gtboxes_and_label, num_objects + + +def read_and_prepocess_single_img(filename_queue, shortside_len, is_training): + + img_name, img, gtboxes_and_label, num_objects = read_single_example_and_decode(filename_queue) + + img = tf.cast(img, tf.float32) + if is_training: + # prob is 0.3: convert to gray + img = image_preprocess_multi_gpu_aug.random_rgb2gray(img_tensor=img, gtboxes_and_label=gtboxes_and_label) + + # rotate with 0.5 prob. and if rotate, if will random choose a theta from : tf.range(-90, 90+16, delta=15) + img, gtboxes_and_label = image_preprocess_multi_gpu_aug.random_rotate_img(img_tensor=img, + gtboxes_and_label=gtboxes_and_label) + + img, gtboxes_and_label, img_h, img_w = image_preprocess_multi_gpu_aug.short_side_resize(img_tensor=img, + gtboxes_and_label=gtboxes_and_label, + target_shortside_len=shortside_len, + max_len=cfgs.IMG_MAX_LENGTH) + img, gtboxes_and_label = image_preprocess_multi_gpu_aug.random_flip_left_right(img_tensor=img, + gtboxes_and_label=gtboxes_and_label) + img, gtboxes_and_label = image_preprocess_multi_gpu_aug.random_flip_up_dowm(img_tensor=img, + gtboxes_and_label=gtboxes_and_label) + + else: + img, gtboxes_and_label, img_h, img_w = image_preprocess_multi_gpu_aug.short_side_resize(img_tensor=img, + gtboxes_and_label=gtboxes_and_label, + target_shortside_len=shortside_len) + + gtboxes_and_label = get_horizen_minAreaRectangle(gtboxes_and_label) + if cfgs.NET_NAME in ['resnet101_v1d', 'resnet50_v1d']: + img = img / 255. - tf.constant([cfgs.PIXEL_MEAN_]) + else: + img = img - tf.constant([cfgs.PIXEL_MEAN]) # sub pixel mean at last + return img_name, img, gtboxes_and_label, num_objects, img_h, img_w + + +def next_batch(dataset_name, batch_size, shortside_len, is_training): + ''' + :return: + img_name_batch: shape(1, 1) + img_batch: shape:(1, new_imgH, new_imgW, C) + gtboxes_and_label_batch: shape(1, Num_Of_objects, 5] .each row is [x1, y1, x2, y2, label] + ''' + + if dataset_name not in ['DOAI2019', 'DOTA', 'ship', 'ICDAR2015', 'pascal', 'coco', 'DOTA_TOTAL', 'WIDER']: + raise ValueError('dataSet name must be in pascal, coco spacenet and ship') + + if is_training: + pattern = os.path.join('../data/tfrecord', dataset_name + '_train*') + else: + pattern = os.path.join('../data/tfrecord', dataset_name + '_test*') + + print('tfrecord path is -->', os.path.abspath(pattern)) + + filename_tensorlist = tf.train.match_filenames_once(pattern) + # filename_tensorlist = tf.Print(filename_tensorlist, + # [tf.shape(filename_tensorlist)], summarize=10, message="record_list-->:") + filename_queue = tf.train.string_input_producer(filename_tensorlist) + + shortside_len = tf.constant(shortside_len) + shortside_len = tf.random_shuffle(shortside_len)[0] + + img_name, img, gtboxes_and_label, num_obs, img_h, img_w = read_and_prepocess_single_img(filename_queue, + shortside_len, + is_training=is_training) + img_name_batch, img_batch, gtboxes_and_label_batch , num_obs_batch, img_h_batch, img_w_batch = \ + tf.train.batch( + [img_name, img, gtboxes_and_label, num_obs, img_h, img_w], + batch_size=batch_size, + capacity=16, + num_threads=16, + dynamic_pad=True) + return img_name_batch, img_batch, gtboxes_and_label_batch, num_obs_batch, img_h_batch, img_w_batch + + +if __name__ == '__main__': + os.environ["CUDA_VISIBLE_DEVICES"] = '0' + img_name_batch, img_batch, gtboxes_and_label_batch, num_objects_batch, img_h_batch, img_w_batch = \ + next_batch(dataset_name=cfgs.DATASET_NAME, # 'pascal', 'coco' + batch_size=cfgs.BATCH_SIZE * len(cfgs.GPU_GROUP.strip().split(',')), + shortside_len=cfgs.IMG_SHORT_SIDE_LEN, + is_training=True) + gtboxes_and_label = tf.reshape(gtboxes_and_label_batch, [-1, 9]) + + init_op = tf.group( + tf.global_variables_initializer(), + tf.local_variables_initializer() + ) + + config = tf.ConfigProto() + config.gpu_options.allow_growth = True + + with tf.Session(config=config) as sess: + sess.run(init_op) + + coord = tf.train.Coordinator() + threads = tf.train.start_queue_runners(sess, coord) + + img_name_batch_, img_batch_, gtboxes_and_label_batch_, num_objects_batch_ \ + = sess.run([img_name_batch, img_batch, gtboxes_and_label_batch, num_objects_batch]) + + print(img_name_batch_) + print(img_batch_.shape) + + coord.request_stop() + coord.join(threads) \ No newline at end of file diff --git a/data/lib_coco/PythonAPI/Makefile b/data/lib_coco/PythonAPI/Makefile new file mode 100644 index 0000000..ab5e3d2 --- /dev/null +++ b/data/lib_coco/PythonAPI/Makefile @@ -0,0 +1,9 @@ +all: + # install pycocotools locally + python setup.py build_ext --inplace + rm -rf build + +install: + # install pycocotools to the Python site-packages + python setup.py build_ext install + rm -rf build \ No newline at end of file diff --git a/data/lib_coco/PythonAPI/__init__.py b/data/lib_coco/PythonAPI/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/data/lib_coco/PythonAPI/pycocoDemo.ipynb b/data/lib_coco/PythonAPI/pycocoDemo.ipynb new file mode 100644 index 0000000..c68f242 --- /dev/null +++ b/data/lib_coco/PythonAPI/pycocoDemo.ipynb @@ -0,0 +1,272 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "from pycocotools.coco import COCO\n", + "import numpy as np\n", + "import skimage.io as io\n", + "import matplotlib.pyplot as plt\n", + "import pylab\n", + "pylab.rcParams['figure.figsize'] = (8.0, 10.0)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "dataDir='..'\n", + "dataType='val2017'\n", + "annFile='{}/annotations/instances_{}.json'.format(dataDir,dataType)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading annotations into memory...\n", + "Done (t=0.81s)\n", + "creating index...\n", + "index created!\n" + ] + } + ], + "source": [ + "# initialize COCO api for instance annotations\n", + "coco=COCO(annFile)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "COCO categories: \n", + "person bicycle car motorcycle airplane bus train truck boat traffic light fire hydrant stop sign parking meter bench bird cat dog horse sheep cow elephant bear zebra giraffe backpack umbrella handbag tie suitcase frisbee skis snowboard sports ball kite baseball bat baseball glove skateboard surfboard tennis racket bottle wine glass cup fork knife spoon bowl banana apple sandwich orange broccoli carrot hot dog pizza donut cake chair couch potted plant bed dining table toilet tv laptop mouse remote keyboard cell phone microwave oven toaster sink refrigerator book clock vase scissors teddy bear hair drier toothbrush\n", + "\n", + "COCO supercategories: \n", + "outdoor food indoor appliance sports person animal vehicle furniture accessory electronic kitchen\n" + ] + } + ], + "source": [ + "# display COCO categories and supercategories\n", + "cats = coco.loadCats(coco.getCatIds())\n", + "nms=[cat['name'] for cat in cats]\n", + "print('COCO categories: \\n{}\\n'.format(' '.join(nms)))\n", + "\n", + "nms = set([cat['supercategory'] for cat in cats])\n", + "print('COCO supercategories: \\n{}'.format(' '.join(nms)))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# get all images containing given categories, select one at random\n", + "catIds = coco.getCatIds(catNms=['person','dog','skateboard']);\n", + "imgIds = coco.getImgIds(catIds=catIds );\n", + "imgIds = coco.getImgIds(imgIds = [324158])\n", + "img = coco.loadImgs(imgIds[np.random.randint(0,len(imgIds))])[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Aow8/yiOPP0pbYyO33bif2ekJZtMLvP8Tn+bMG2/wtrvuYnI6Q0NDKwcP3M7h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\npGFMEkXIiBPI5/IwssglJgx9DF0lcIbk8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seZrs+RL45Tm5ymWi0zXqvywx/+OU89+yJ3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\n5Y3rXL92A280pmwYlGyHZ9//LN//7LMcHu6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FqiHOEoSpE/pFmYMMx/juqJCjjQJpub68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A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\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mdfUAaRxiOeolu721Q5ZlbKyv8+yzz5BnGe12G9uq0ERJq+Yh0lh1c0djTEvDkKhYp6yQ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/R9M0qtUqqqpiWdGQqo9EIkSjUR4+fMitW7e4desW+/v7NBstolaMiGHOg2zib8THvv32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\nSUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# load and display image\n", + "# I = io.imread('%s/images/%s/%s'%(dataDir,dataType,img['file_name']))\n", + "# use url to load image\n", + "I = io.imread(img['coco_url'])\n", + "plt.axis('off')\n", + "plt.imshow(I)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Aow8/yiOPP0pbYyO33bif2ekJZtMLvP8Tn+bMG2/wtrvuYnI6Q0NDKwcP3M7h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\npGFMEkXIiBPI5/IwssglJgx9DF0lcIbk8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seZrs+RL45Tm5ymWi0zXqvywx/+OU89+yJ3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\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UouK5F59ie3ubcZZTZBnN+Xned2KRkytnMFWFG1Q4fkAyjRAV9Pa3SHPJ4kKH+9t3cByL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\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FJDjxn7AgLVLZQuQmrE3CQy0sJUKpLdK5xFYxMVMVaFmgSOGbFk2MQH+L6FsgSvHaiwVM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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# load and display instance annotations\n", + "plt.imshow(I); plt.axis('off')\n", + "annIds = coco.getAnnIds(imgIds=img['id'], catIds=catIds, iscrowd=None)\n", + "anns = coco.loadAnns(annIds)\n", + "coco.showAnns(anns)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading annotations into memory...\n", + "Done (t=0.58s)\n", + "creating index...\n", + "index created!\n" + ] + } + ], + "source": [ + "# initialize COCO api for person keypoints annotations\n", + "annFile = '{}/annotations/person_keypoints_{}.json'.format(dataDir,dataType)\n", + "coco_kps=COCO(annFile)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Aow8/yiOPP0pbYyO33bif2ekJZtMLvP8Tn+bMG2/wtrvuYnI6Q0NDKwcP3M7h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\npGFMEkXIiBPI5/IwssglJgx9DF0lcIbk8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seZrs+RL45Tm5ymWi0zXqvywx/+OU89+yJ3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7zKweEhpllibW2NPE+xqyU0XSIIIp4+bSMIAppm8MorryCKBS3t8PCQ4XCIZVnU6zW8NKbf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hRBYnoMkR7d7WFvNTLZQ0Zdzv4ro2o7GJ4WooGmRJwInj92HbLmkSU6t5TDVblGVJMJC7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2I4HtDtOgyHJp2LSzbX21TKBbS0wec//zkefvIJXuCDKLC+so4qyzw7fIrnhqRkhbuvv81w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\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1a2TSKZAk6o06ngTxaIxmqxU0joqPosjICmQzGY4Oj1hdXuXs9ITdVy9IpZOcnZ1Rr9XI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\nAI+JiYmJiTmGxAE8JiYmJibmGPK/APaetT/BH9j/AAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# load and display keypoints annotations\n", + "plt.imshow(I); plt.axis('off')\n", + "ax = plt.gca()\n", + "annIds = coco_kps.getAnnIds(imgIds=img['id'], catIds=catIds, iscrowd=None)\n", + "anns = coco_kps.loadAnns(annIds)\n", + "coco_kps.showAnns(anns)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading annotations into memory...\n", + "Done (t=0.13s)\n", + "creating index...\n", + "index created!\n" + ] + } + ], + "source": [ + "# initialize COCO api for caption annotations\n", + "annFile = '{}/annotations/captions_{}.json'.format(dataDir,dataType)\n", + "coco_caps=COCO(annFile)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "A man is skate boarding down a path and a dog is running by his side.\n", + "A man on a skateboard with a dog outside. \n", + "A person riding a skate board with a dog following beside.\n", + "This man is riding a skateboard behind a dog.\n", + "A man walking his dog on a quiet country road.\n" + ] + }, + { + "data": { + "image/png": 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Aow8/yiOPP0pbYyO33bif2ekJZtMLvP8Tn+bMG2/wtrvuYnI6Q0NDKwcP3M7h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\npGFMEkXIiBPI5/IwssglJgx9DF0lcIbk8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seZrs+RL45Tm5ymWi0zXqvywx/+OU89+yJ3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\n5Y3rXL92A280pmwYlGyHZ9//LN//7LMcHu6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FqiHOEoSpE/pFmYMMx/juqJCjjQJpub68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A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\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mdfUAaRxiOeolu721Q5ZlbKyv8+yzz5BnGe12G9uq0ERJq+Yh0lh1c0djTEvDkKhYp6yQ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/R9M0qtUqqqpiWdGQqo9EIkSjUR4+fMitW7e4desW+/v7NBstolaMiGHOg2zib8THvv32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\nnTyFZVnMT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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# load and display caption annotations\n", + "annIds = coco_caps.getAnnIds(imgIds=img['id']);\n", + "anns = coco_caps.loadAnns(annIds)\n", + "coco_caps.showAnns(anns)\n", + "plt.imshow(I); plt.axis('off'); plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/data/lib_coco/PythonAPI/pycocoEvalDemo.ipynb b/data/lib_coco/PythonAPI/pycocoEvalDemo.ipynb new file mode 100644 index 0000000..8b2ff08 --- /dev/null +++ b/data/lib_coco/PythonAPI/pycocoEvalDemo.ipynb @@ -0,0 +1,168 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "from pycocotools.coco import COCO\n", + "from pycocotools.cocoeval import COCOeval\n", + "import numpy as np\n", + "import skimage.io as io\n", + "import pylab\n", + "pylab.rcParams['figure.figsize'] = (10.0, 8.0)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running demo for *bbox* results.\n" + ] + } + ], + "source": [ + "annType = ['segm','bbox','keypoints']\n", + "annType = annType[1] #specify type here\n", + "prefix = 'person_keypoints' if annType=='keypoints' else 'instances'\n", + "print 'Running demo for *%s* results.'%(annType)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading annotations into memory...\n", + "Done (t=8.01s)\n", + "creating index...\n", + "index created!\n" + ] + } + ], + "source": [ + "#initialize COCO ground truth api\n", + "dataDir='../'\n", + "dataType='val2014'\n", + "annFile = '%s/annotations/%s_%s.json'%(dataDir,prefix,dataType)\n", + "cocoGt=COCO(annFile)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading and preparing results... \n", + "DONE (t=0.05s)\n", + "creating index...\n", + "index created!\n" + ] + } + ], + "source": [ + "#initialize COCO detections api\n", + "resFile='%s/results/%s_%s_fake%s100_results.json'\n", + "resFile = resFile%(dataDir, prefix, dataType, annType)\n", + "cocoDt=cocoGt.loadRes(resFile)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "imgIds=sorted(cocoGt.getImgIds())\n", + "imgIds=imgIds[0:100]\n", + "imgId = imgIds[np.random.randint(100)]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running per image evaluation... \n", + "DONE (t=0.46s).\n", + "Accumulating evaluation results... \n", + "DONE (t=0.38s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.505\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.697\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.573\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.586\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.519\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.501\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.387\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.594\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.595\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.640\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.566\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.564\n" + ] + } + ], + "source": [ + "# running evaluation\n", + "cocoEval = COCOeval(cocoGt,cocoDt,annType)\n", + "cocoEval.params.imgIds = imgIds\n", + "cocoEval.evaluate()\n", + "cocoEval.accumulate()\n", + "cocoEval.summarize()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/data/lib_coco/PythonAPI/pycocotools/__init__.py b/data/lib_coco/PythonAPI/pycocotools/__init__.py new file mode 100644 index 0000000..3f7d85b --- /dev/null +++ b/data/lib_coco/PythonAPI/pycocotools/__init__.py @@ -0,0 +1 @@ +__author__ = 'tylin' diff --git a/data/lib_coco/PythonAPI/pycocotools/_mask.c b/data/lib_coco/PythonAPI/pycocotools/_mask.c new file mode 100644 index 0000000..e0fc353 --- /dev/null +++ b/data/lib_coco/PythonAPI/pycocotools/_mask.c @@ -0,0 +1,16137 @@ +/* Generated by Cython 0.25.2 */ + +#define PY_SSIZE_T_CLEAN +#include "Python.h" +#ifndef Py_PYTHON_H + #error Python headers needed to compile C extensions, please install development version of Python. +#elif PY_VERSION_HEX < 0x02060000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03020000) + #error Cython requires Python 2.6+ or Python 3.2+. +#else +#define CYTHON_ABI "0_25_2" +#include +#ifndef offsetof + #define offsetof(type, member) ( (size_t) & ((type*)0) -> member ) +#endif +#if !defined(WIN32) && !defined(MS_WINDOWS) + #ifndef __stdcall + #define __stdcall + #endif + #ifndef __cdecl + #define __cdecl + #endif + #ifndef __fastcall + #define __fastcall + #endif +#endif +#ifndef DL_IMPORT + #define DL_IMPORT(t) t +#endif +#ifndef DL_EXPORT + #define DL_EXPORT(t) t +#endif +#ifndef HAVE_LONG_LONG + #if PY_VERSION_HEX >= 0x03030000 || (PY_MAJOR_VERSION == 2 && PY_VERSION_HEX >= 0x02070000) + #define HAVE_LONG_LONG + #endif +#endif +#ifndef PY_LONG_LONG + #define PY_LONG_LONG LONG_LONG +#endif +#ifndef Py_HUGE_VAL + #define Py_HUGE_VAL HUGE_VAL +#endif +#ifdef PYPY_VERSION + #define CYTHON_COMPILING_IN_PYPY 1 + #define CYTHON_COMPILING_IN_PYSTON 0 + #define CYTHON_COMPILING_IN_CPYTHON 0 + #undef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 0 + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #undef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 0 + #undef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 0 + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #undef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 1 + #undef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 0 + #undef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 0 + #undef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 0 + #undef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 0 +#elif defined(PYSTON_VERSION) + #define CYTHON_COMPILING_IN_PYPY 0 + #define CYTHON_COMPILING_IN_PYSTON 1 + #define CYTHON_COMPILING_IN_CPYTHON 0 + #ifndef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 1 + #endif + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #undef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 0 + #ifndef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 1 + #endif + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #ifndef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 0 + #endif + #ifndef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 1 + #endif + #ifndef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 1 + #endif + #undef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 0 + #undef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 0 +#else + #define CYTHON_COMPILING_IN_PYPY 0 + #define CYTHON_COMPILING_IN_PYSTON 0 + #define CYTHON_COMPILING_IN_CPYTHON 1 + #ifndef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 1 + #endif + #if PY_MAJOR_VERSION < 3 + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #elif !defined(CYTHON_USE_ASYNC_SLOTS) + #define CYTHON_USE_ASYNC_SLOTS 1 + #endif + #if PY_VERSION_HEX < 0x02070000 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #elif !defined(CYTHON_USE_PYLONG_INTERNALS) + #define CYTHON_USE_PYLONG_INTERNALS 1 + #endif + #ifndef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 1 + #endif + #ifndef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 1 + #endif + #if PY_VERSION_HEX < 0x030300F0 + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #elif !defined(CYTHON_USE_UNICODE_WRITER) + #define CYTHON_USE_UNICODE_WRITER 1 + #endif + #ifndef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 0 + #endif + #ifndef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 1 + #endif + #ifndef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 1 + #endif + #ifndef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 1 + #endif + #ifndef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 1 + #endif +#endif +#if !defined(CYTHON_FAST_PYCCALL) +#define CYTHON_FAST_PYCCALL (CYTHON_FAST_PYCALL && PY_VERSION_HEX >= 0x030600B1) +#endif +#if CYTHON_USE_PYLONG_INTERNALS + #include "longintrepr.h" + #undef SHIFT + #undef BASE + #undef MASK +#endif +#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 && !defined(Py_OptimizeFlag) + #define Py_OptimizeFlag 0 +#endif +#define __PYX_BUILD_PY_SSIZE_T "n" +#define CYTHON_FORMAT_SSIZE_T "z" +#if PY_MAJOR_VERSION < 3 + #define __Pyx_BUILTIN_MODULE_NAME "__builtin__" + #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ + PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) + #define __Pyx_DefaultClassType PyClass_Type +#else + #define __Pyx_BUILTIN_MODULE_NAME "builtins" + #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ + PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) + #define __Pyx_DefaultClassType PyType_Type +#endif +#ifndef Py_TPFLAGS_CHECKTYPES + #define Py_TPFLAGS_CHECKTYPES 0 +#endif +#ifndef Py_TPFLAGS_HAVE_INDEX + #define Py_TPFLAGS_HAVE_INDEX 0 +#endif +#ifndef Py_TPFLAGS_HAVE_NEWBUFFER + #define Py_TPFLAGS_HAVE_NEWBUFFER 0 +#endif +#ifndef Py_TPFLAGS_HAVE_FINALIZE + #define Py_TPFLAGS_HAVE_FINALIZE 0 +#endif +#ifndef METH_FASTCALL + #define METH_FASTCALL 0x80 + typedef PyObject *(*__Pyx_PyCFunctionFast) (PyObject *self, PyObject **args, + Py_ssize_t nargs, PyObject *kwnames); +#else + #define __Pyx_PyCFunctionFast _PyCFunctionFast +#endif +#if CYTHON_FAST_PYCCALL +#define __Pyx_PyFastCFunction_Check(func)\ + ((PyCFunction_Check(func) && (METH_FASTCALL == (PyCFunction_GET_FLAGS(func) & ~(METH_CLASS | METH_STATIC | METH_COEXIST))))) +#else +#define __Pyx_PyFastCFunction_Check(func) 0 +#endif +#if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND) + #define CYTHON_PEP393_ENABLED 1 + #define __Pyx_PyUnicode_READY(op) (likely(PyUnicode_IS_READY(op)) ?\ + 0 : _PyUnicode_Ready((PyObject *)(op))) + #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_LENGTH(u) + #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i) + #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) PyUnicode_MAX_CHAR_VALUE(u) + #define __Pyx_PyUnicode_KIND(u) PyUnicode_KIND(u) + #define __Pyx_PyUnicode_DATA(u) PyUnicode_DATA(u) + #define __Pyx_PyUnicode_READ(k, d, i) PyUnicode_READ(k, d, i) + #define __Pyx_PyUnicode_WRITE(k, d, i, ch) PyUnicode_WRITE(k, d, i, ch) + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : PyUnicode_GET_SIZE(u))) +#else + #define CYTHON_PEP393_ENABLED 0 + #define PyUnicode_1BYTE_KIND 1 + #define PyUnicode_2BYTE_KIND 2 + #define PyUnicode_4BYTE_KIND 4 + #define __Pyx_PyUnicode_READY(op) (0) + #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_SIZE(u) + #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i])) + #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) ((sizeof(Py_UNICODE) == 2) ? 65535 : 1114111) + #define __Pyx_PyUnicode_KIND(u) (sizeof(Py_UNICODE)) + #define __Pyx_PyUnicode_DATA(u) ((void*)PyUnicode_AS_UNICODE(u)) + #define __Pyx_PyUnicode_READ(k, d, i) ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i])) + #define __Pyx_PyUnicode_WRITE(k, d, i, ch) (((void)(k)), ((Py_UNICODE*)d)[i] = ch) + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GET_SIZE(u)) +#endif +#if CYTHON_COMPILING_IN_PYPY + #define __Pyx_PyUnicode_Concat(a, b) PyNumber_Add(a, b) + #define __Pyx_PyUnicode_ConcatSafe(a, b) PyNumber_Add(a, b) +#else + #define __Pyx_PyUnicode_Concat(a, b) PyUnicode_Concat(a, b) + #define __Pyx_PyUnicode_ConcatSafe(a, b) ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ?\ + PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b)) +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyUnicode_Contains) + #define PyUnicode_Contains(u, s) PySequence_Contains(u, s) +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyByteArray_Check) + #define PyByteArray_Check(obj) PyObject_TypeCheck(obj, &PyByteArray_Type) +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Format) + #define PyObject_Format(obj, fmt) PyObject_CallMethod(obj, "__format__", "O", fmt) +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Malloc) + #define PyObject_Malloc(s) PyMem_Malloc(s) + #define PyObject_Free(p) PyMem_Free(p) + #define PyObject_Realloc(p) PyMem_Realloc(p) +#endif +#if CYTHON_COMPILING_IN_PYSTON + #define __Pyx_PyCode_HasFreeVars(co) PyCode_HasFreeVars(co) + #define __Pyx_PyFrame_SetLineNumber(frame, lineno) PyFrame_SetLineNumber(frame, lineno) +#else + #define __Pyx_PyCode_HasFreeVars(co) (PyCode_GetNumFree(co) > 0) + #define __Pyx_PyFrame_SetLineNumber(frame, lineno) (frame)->f_lineno = (lineno) +#endif +#define __Pyx_PyString_FormatSafe(a, b) ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b)) +#define __Pyx_PyUnicode_FormatSafe(a, b) ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b)) +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyString_Format(a, b) PyUnicode_Format(a, b) +#else + #define __Pyx_PyString_Format(a, b) PyString_Format(a, b) +#endif +#if PY_MAJOR_VERSION < 3 && !defined(PyObject_ASCII) + #define PyObject_ASCII(o) PyObject_Repr(o) +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyBaseString_Type PyUnicode_Type + #define PyStringObject PyUnicodeObject + #define PyString_Type PyUnicode_Type + #define PyString_Check PyUnicode_Check + #define PyString_CheckExact PyUnicode_CheckExact +#endif +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj) + #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj) +#else + #define __Pyx_PyBaseString_Check(obj) (PyString_Check(obj) || PyUnicode_Check(obj)) + #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj)) +#endif +#ifndef PySet_CheckExact + #define PySet_CheckExact(obj) (Py_TYPE(obj) == &PySet_Type) +#endif +#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type) +#define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception) +#if PY_MAJOR_VERSION >= 3 + #define PyIntObject PyLongObject + #define PyInt_Type PyLong_Type + #define PyInt_Check(op) PyLong_Check(op) + #define PyInt_CheckExact(op) PyLong_CheckExact(op) + #define PyInt_FromString PyLong_FromString + #define PyInt_FromUnicode PyLong_FromUnicode + #define PyInt_FromLong PyLong_FromLong + #define PyInt_FromSize_t PyLong_FromSize_t + #define PyInt_FromSsize_t PyLong_FromSsize_t + #define PyInt_AsLong PyLong_AsLong + #define PyInt_AS_LONG PyLong_AS_LONG + #define PyInt_AsSsize_t PyLong_AsSsize_t + #define PyInt_AsUnsignedLongMask PyLong_AsUnsignedLongMask + #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask + #define PyNumber_Int PyNumber_Long +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyBoolObject PyLongObject +#endif +#if PY_MAJOR_VERSION >= 3 && CYTHON_COMPILING_IN_PYPY + #ifndef PyUnicode_InternFromString + #define PyUnicode_InternFromString(s) PyUnicode_FromString(s) + #endif +#endif +#if PY_VERSION_HEX < 0x030200A4 + typedef long Py_hash_t; + #define __Pyx_PyInt_FromHash_t PyInt_FromLong + #define __Pyx_PyInt_AsHash_t PyInt_AsLong +#else + #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t + #define __Pyx_PyInt_AsHash_t PyInt_AsSsize_t +#endif +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyMethod_New(func, self, klass) ((self) ? PyMethod_New(func, self) : PyInstanceMethod_New(func)) +#else + #define __Pyx_PyMethod_New(func, self, klass) PyMethod_New(func, self, klass) +#endif +#if CYTHON_USE_ASYNC_SLOTS + #if PY_VERSION_HEX >= 0x030500B1 + #define __Pyx_PyAsyncMethodsStruct PyAsyncMethods + #define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async) + #else + typedef struct { + unaryfunc am_await; + unaryfunc am_aiter; + unaryfunc am_anext; + } __Pyx_PyAsyncMethodsStruct; + #define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved)) + #endif +#else + #define __Pyx_PyType_AsAsync(obj) NULL +#endif +#ifndef CYTHON_RESTRICT + #if defined(__GNUC__) + #define CYTHON_RESTRICT __restrict__ + #elif defined(_MSC_VER) && _MSC_VER >= 1400 + #define CYTHON_RESTRICT __restrict + #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define CYTHON_RESTRICT restrict + #else + #define CYTHON_RESTRICT + #endif +#endif +#ifndef CYTHON_UNUSED +# if defined(__GNUC__) +# if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4)) +# define CYTHON_UNUSED __attribute__ ((__unused__)) +# else +# define CYTHON_UNUSED +# endif +# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER)) +# define CYTHON_UNUSED __attribute__ ((__unused__)) +# else +# define CYTHON_UNUSED +# endif +#endif +#ifndef CYTHON_MAYBE_UNUSED_VAR +# if defined(__cplusplus) + template void CYTHON_MAYBE_UNUSED_VAR( const T& ) { } +# else +# define CYTHON_MAYBE_UNUSED_VAR(x) (void)(x) +# endif +#endif +#ifndef CYTHON_NCP_UNUSED +# if CYTHON_COMPILING_IN_CPYTHON +# define CYTHON_NCP_UNUSED +# else +# define CYTHON_NCP_UNUSED CYTHON_UNUSED +# endif +#endif +#define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None) + +#ifndef CYTHON_INLINE + #if defined(__clang__) + #define CYTHON_INLINE __inline__ __attribute__ ((__unused__)) + #elif defined(__GNUC__) + #define CYTHON_INLINE __inline__ + #elif defined(_MSC_VER) + #define CYTHON_INLINE __inline + #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define CYTHON_INLINE inline + #else + #define CYTHON_INLINE + #endif +#endif + +#if defined(WIN32) || defined(MS_WINDOWS) + #define _USE_MATH_DEFINES +#endif +#include +#ifdef NAN +#define __PYX_NAN() ((float) NAN) +#else +static CYTHON_INLINE float __PYX_NAN() { + float value; + memset(&value, 0xFF, sizeof(value)); + return value; +} +#endif +#if defined(__CYGWIN__) && defined(_LDBL_EQ_DBL) +#define __Pyx_truncl trunc +#else +#define __Pyx_truncl truncl +#endif + + +#define __PYX_ERR(f_index, lineno, Ln_error) \ +{ \ + __pyx_filename = __pyx_f[f_index]; __pyx_lineno = lineno; __pyx_clineno = __LINE__; goto Ln_error; \ +} + +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y) + #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y) +#else + #define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y) + #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceDivide(x,y) +#endif + +#ifndef __PYX_EXTERN_C + #ifdef __cplusplus + #define __PYX_EXTERN_C extern "C" + #else + #define __PYX_EXTERN_C extern + #endif +#endif + +#define __PYX_HAVE__pycocotools___mask +#define __PYX_HAVE_API__pycocotools___mask +#include +#include +#include +#include "numpy/arrayobject.h" +#include "numpy/ufuncobject.h" +#include "maskApi.h" +#ifdef _OPENMP +#include +#endif /* _OPENMP */ + +#ifdef PYREX_WITHOUT_ASSERTIONS +#define CYTHON_WITHOUT_ASSERTIONS +#endif + +typedef struct {PyObject **p; const char *s; const Py_ssize_t n; const char* encoding; + const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; + +#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0 +#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT 0 +#define __PYX_DEFAULT_STRING_ENCODING "" +#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString +#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize +#define __Pyx_uchar_cast(c) ((unsigned char)c) +#define __Pyx_long_cast(x) ((long)x) +#define __Pyx_fits_Py_ssize_t(v, type, is_signed) (\ + (sizeof(type) < sizeof(Py_ssize_t)) ||\ + (sizeof(type) > sizeof(Py_ssize_t) &&\ + likely(v < (type)PY_SSIZE_T_MAX ||\ + v == (type)PY_SSIZE_T_MAX) &&\ + (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||\ + v == (type)PY_SSIZE_T_MIN))) ||\ + (sizeof(type) == sizeof(Py_ssize_t) &&\ + (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\ + v == (type)PY_SSIZE_T_MAX))) ) +#if defined (__cplusplus) && __cplusplus >= 201103L + #include + #define __Pyx_sst_abs(value) std::abs(value) +#elif SIZEOF_INT >= SIZEOF_SIZE_T + #define __Pyx_sst_abs(value) abs(value) +#elif SIZEOF_LONG >= SIZEOF_SIZE_T + #define __Pyx_sst_abs(value) labs(value) +#elif defined (_MSC_VER) && defined (_M_X64) + #define __Pyx_sst_abs(value) _abs64(value) +#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define __Pyx_sst_abs(value) llabs(value) +#elif defined (__GNUC__) + #define __Pyx_sst_abs(value) __builtin_llabs(value) +#else + #define __Pyx_sst_abs(value) ((value<0) ? -value : value) +#endif +static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject*); +static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length); +#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s)) +#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l) +#define __Pyx_PyBytes_FromString PyBytes_FromString +#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*); +#if PY_MAJOR_VERSION < 3 + #define __Pyx_PyStr_FromString __Pyx_PyBytes_FromString + #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize +#else + #define __Pyx_PyStr_FromString __Pyx_PyUnicode_FromString + #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize +#endif +#define __Pyx_PyObject_AsSString(s) ((signed char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsUString(s) ((unsigned char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_FromCString(s) __Pyx_PyObject_FromString((const char*)s) +#define __Pyx_PyBytes_FromCString(s) __Pyx_PyBytes_FromString((const char*)s) +#define __Pyx_PyByteArray_FromCString(s) __Pyx_PyByteArray_FromString((const char*)s) +#define __Pyx_PyStr_FromCString(s) __Pyx_PyStr_FromString((const char*)s) +#define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s) +#if PY_MAJOR_VERSION < 3 +static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) +{ + const Py_UNICODE *u_end = u; + while (*u_end++) ; + return (size_t)(u_end - u - 1); +} +#else +#define __Pyx_Py_UNICODE_strlen Py_UNICODE_strlen +#endif +#define __Pyx_PyUnicode_FromUnicode(u) PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u)) +#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode +#define __Pyx_PyUnicode_AsUnicode PyUnicode_AsUnicode +#define __Pyx_NewRef(obj) (Py_INCREF(obj), obj) +#define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None) +#define __Pyx_PyBool_FromLong(b) ((b) ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False)) +static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*); +static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x); +static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*); +static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t); +#if CYTHON_ASSUME_SAFE_MACROS +#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x)) +#else +#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x) +#endif +#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x)) +#if PY_MAJOR_VERSION >= 3 +#define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x)) +#else +#define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x)) +#endif +#define __Pyx_PyNumber_Float(x) (PyFloat_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Float(x)) +#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII +static int __Pyx_sys_getdefaultencoding_not_ascii; +static int __Pyx_init_sys_getdefaultencoding_params(void) { + PyObject* sys; + PyObject* default_encoding = NULL; + PyObject* ascii_chars_u = NULL; + PyObject* ascii_chars_b = NULL; + const char* default_encoding_c; + sys = PyImport_ImportModule("sys"); + if (!sys) goto bad; + default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL); + Py_DECREF(sys); + if (!default_encoding) goto bad; + default_encoding_c = PyBytes_AsString(default_encoding); + if (!default_encoding_c) goto bad; + if (strcmp(default_encoding_c, "ascii") == 0) { + __Pyx_sys_getdefaultencoding_not_ascii = 0; + } else { + char ascii_chars[128]; + int c; + for (c = 0; c < 128; c++) { + ascii_chars[c] = c; + } + __Pyx_sys_getdefaultencoding_not_ascii = 1; + ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL); + if (!ascii_chars_u) goto bad; + ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL); + if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) { + PyErr_Format( + PyExc_ValueError, + "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.", + default_encoding_c); + goto bad; + } + Py_DECREF(ascii_chars_u); + Py_DECREF(ascii_chars_b); + } + Py_DECREF(default_encoding); + return 0; +bad: + Py_XDECREF(default_encoding); + Py_XDECREF(ascii_chars_u); + Py_XDECREF(ascii_chars_b); + return -1; +} +#endif +#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3 +#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL) +#else +#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL) +#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT +static char* __PYX_DEFAULT_STRING_ENCODING; +static int __Pyx_init_sys_getdefaultencoding_params(void) { + PyObject* sys; + PyObject* default_encoding = NULL; + char* default_encoding_c; + sys = PyImport_ImportModule("sys"); + if (!sys) goto bad; + default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL); + Py_DECREF(sys); + if (!default_encoding) goto bad; + default_encoding_c = PyBytes_AsString(default_encoding); + if (!default_encoding_c) goto bad; + __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c)); + if (!__PYX_DEFAULT_STRING_ENCODING) goto bad; + strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c); + Py_DECREF(default_encoding); + return 0; +bad: + Py_XDECREF(default_encoding); + return -1; +} +#endif +#endif + + +/* Test for GCC > 2.95 */ +#if defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95))) + #define likely(x) __builtin_expect(!!(x), 1) + #define unlikely(x) __builtin_expect(!!(x), 0) +#else /* !__GNUC__ or GCC < 2.95 */ + #define likely(x) (x) + #define unlikely(x) (x) +#endif /* __GNUC__ */ + +static PyObject *__pyx_m; +static PyObject *__pyx_d; +static PyObject *__pyx_b; +static PyObject *__pyx_empty_tuple; +static PyObject *__pyx_empty_bytes; +static PyObject *__pyx_empty_unicode; +static int __pyx_lineno; +static int __pyx_clineno = 0; +static const char * __pyx_cfilenm= __FILE__; +static const char *__pyx_filename; + +/* Header.proto */ +#if !defined(CYTHON_CCOMPLEX) + #if defined(__cplusplus) + #define CYTHON_CCOMPLEX 1 + #elif defined(_Complex_I) + #define CYTHON_CCOMPLEX 1 + #else + #define CYTHON_CCOMPLEX 0 + #endif +#endif +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + #include + #else + #include + #endif +#endif +#if CYTHON_CCOMPLEX && !defined(__cplusplus) && defined(__sun__) && defined(__GNUC__) + #undef _Complex_I + #define _Complex_I 1.0fj +#endif + + +static const char *__pyx_f[] = { + "pycocotools/_mask.pyx", + "__init__.pxd", + "type.pxd", +}; +/* BufferFormatStructs.proto */ +#define IS_UNSIGNED(type) (((type) -1) > 0) +struct __Pyx_StructField_; +#define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0) +typedef struct { + const char* name; + struct __Pyx_StructField_* fields; + size_t size; + size_t arraysize[8]; + int ndim; + char typegroup; + char is_unsigned; + int flags; +} __Pyx_TypeInfo; +typedef struct __Pyx_StructField_ { + __Pyx_TypeInfo* type; + const char* name; + size_t offset; +} __Pyx_StructField; +typedef struct { + __Pyx_StructField* field; + size_t parent_offset; +} __Pyx_BufFmt_StackElem; +typedef struct { + __Pyx_StructField root; + __Pyx_BufFmt_StackElem* head; + size_t fmt_offset; + size_t new_count, enc_count; + size_t struct_alignment; + int is_complex; + char enc_type; + char new_packmode; + char enc_packmode; + char is_valid_array; +} __Pyx_BufFmt_Context; + + +/* "../../../../../softWares/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":725 + * # in Cython to enable them only on the right systems. + * + * ctypedef npy_int8 int8_t # <<<<<<<<<<<<<< + * ctypedef npy_int16 int16_t + * ctypedef npy_int32 int32_t + */ +typedef npy_int8 __pyx_t_5numpy_int8_t; + +/* "../../../../../softWares/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":726 + * + * ctypedef npy_int8 int8_t + * ctypedef npy_int16 int16_t # <<<<<<<<<<<<<< + * ctypedef npy_int32 int32_t + * ctypedef npy_int64 int64_t + */ +typedef npy_int16 __pyx_t_5numpy_int16_t; + +/* "../../../../../softWares/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":727 + * ctypedef npy_int8 int8_t + * ctypedef npy_int16 int16_t + * ctypedef npy_int32 int32_t # <<<<<<<<<<<<<< + * ctypedef npy_int64 int64_t + * #ctypedef npy_int96 int96_t + */ +typedef npy_int32 __pyx_t_5numpy_int32_t; + +/* "../../../../../softWares/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":728 + * ctypedef npy_int16 int16_t + * ctypedef npy_int32 int32_t + * ctypedef npy_int64 int64_t # <<<<<<<<<<<<<< + * #ctypedef npy_int96 int96_t + * #ctypedef npy_int128 int128_t + */ +typedef npy_int64 __pyx_t_5numpy_int64_t; + +/* "../../../../../softWares/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":732 + * #ctypedef npy_int128 int128_t + * + * ctypedef npy_uint8 uint8_t # <<<<<<<<<<<<<< + * ctypedef npy_uint16 uint16_t + * ctypedef npy_uint32 uint32_t + */ +typedef npy_uint8 __pyx_t_5numpy_uint8_t; + +/* "../../../../../softWares/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":733 + * + * ctypedef npy_uint8 uint8_t + * ctypedef npy_uint16 uint16_t # <<<<<<<<<<<<<< + * ctypedef npy_uint32 uint32_t + * ctypedef npy_uint64 uint64_t + */ +typedef npy_uint16 __pyx_t_5numpy_uint16_t; + +/* "../../../../../softWares/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":734 + * ctypedef npy_uint8 uint8_t + * ctypedef npy_uint16 uint16_t + * ctypedef npy_uint32 uint32_t # <<<<<<<<<<<<<< + * ctypedef npy_uint64 uint64_t + * #ctypedef npy_uint96 uint96_t + */ +typedef npy_uint32 __pyx_t_5numpy_uint32_t; + +/* "../../../../../softWares/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":735 + * ctypedef npy_uint16 uint16_t + * ctypedef npy_uint32 uint32_t + * ctypedef npy_uint64 uint64_t # <<<<<<<<<<<<<< + * #ctypedef npy_uint96 uint96_t + * #ctypedef npy_uint128 uint128_t + */ +typedef npy_uint64 __pyx_t_5numpy_uint64_t; + +/* "../../../../../softWares/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":739 + * #ctypedef npy_uint128 uint128_t + * + * ctypedef npy_float32 float32_t # <<<<<<<<<<<<<< + * ctypedef npy_float64 float64_t + * #ctypedef npy_float80 float80_t + */ +typedef npy_float32 __pyx_t_5numpy_float32_t; 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r = NULL; __Pyx_DECREF(tmp);}} while(0) + +/* PyObjectGetAttrStr.proto */ +#if CYTHON_USE_TYPE_SLOTS +static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name) { + PyTypeObject* tp = Py_TYPE(obj); + if (likely(tp->tp_getattro)) + return tp->tp_getattro(obj, attr_name); +#if PY_MAJOR_VERSION < 3 + if (likely(tp->tp_getattr)) + return tp->tp_getattr(obj, PyString_AS_STRING(attr_name)); +#endif + return PyObject_GetAttr(obj, attr_name); +} +#else +#define __Pyx_PyObject_GetAttrStr(o,n) PyObject_GetAttr(o,n) +#endif + +/* GetBuiltinName.proto */ +static PyObject *__Pyx_GetBuiltinName(PyObject *name); + +/* RaiseDoubleKeywords.proto */ +static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name); + +/* ParseKeywords.proto */ +static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[],\ + PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args,\ + const char* function_name); + +/* RaiseArgTupleInvalid.proto */ +static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact, + Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found); + +/* IncludeStringH.proto */ +#include + +/* BytesEquals.proto */ +static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals); + +/* UnicodeEquals.proto */ +static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals); + +/* StrEquals.proto */ +#if PY_MAJOR_VERSION >= 3 +#define __Pyx_PyString_Equals __Pyx_PyUnicode_Equals +#else +#define __Pyx_PyString_Equals __Pyx_PyBytes_Equals +#endif + +/* PyObjectCall.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw); +#else +#define __Pyx_PyObject_Call(func, arg, kw) PyObject_Call(func, arg, kw) +#endif + +/* PyThreadStateGet.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_PyThreadState_declare PyThreadState *__pyx_tstate; +#define __Pyx_PyThreadState_assign __pyx_tstate = PyThreadState_GET(); +#else +#define __Pyx_PyThreadState_declare +#define __Pyx_PyThreadState_assign +#endif + +/* PyErrFetchRestore.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_ErrRestoreWithState(type, value, tb) __Pyx_ErrRestoreInState(PyThreadState_GET(), type, value, tb) +#define __Pyx_ErrFetchWithState(type, value, tb) __Pyx_ErrFetchInState(PyThreadState_GET(), type, value, tb) +#define __Pyx_ErrRestore(type, value, tb) __Pyx_ErrRestoreInState(__pyx_tstate, type, value, tb) +#define __Pyx_ErrFetch(type, value, tb) __Pyx_ErrFetchInState(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb); +static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#else +#define __Pyx_ErrRestoreWithState(type, value, tb) PyErr_Restore(type, value, tb) +#define __Pyx_ErrFetchWithState(type, value, tb) PyErr_Fetch(type, value, tb) +#define __Pyx_ErrRestore(type, value, tb) PyErr_Restore(type, value, tb) +#define __Pyx_ErrFetch(type, value, tb) PyErr_Fetch(type, value, tb) +#endif + +/* RaiseException.proto */ +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause); + +/* ExtTypeTest.proto */ +static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); + +/* ArgTypeTest.proto */ +static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed, + const char *name, int exact); + +/* ListAppend.proto */ +#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS +static CYTHON_INLINE int __Pyx_PyList_Append(PyObject* list, PyObject* x) { + PyListObject* L = (PyListObject*) list; + Py_ssize_t len = Py_SIZE(list); + if (likely(L->allocated > len) & likely(len > (L->allocated >> 1))) { + Py_INCREF(x); + PyList_SET_ITEM(list, len, x); + Py_SIZE(list) = len+1; + return 0; + } + return PyList_Append(list, x); +} +#else +#define __Pyx_PyList_Append(L,x) PyList_Append(L,x) +#endif + +/* PyIntBinop.proto */ +#if !CYTHON_COMPILING_IN_PYPY +static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, long intval, int inplace); +#else +#define __Pyx_PyInt_AddObjC(op1, op2, intval, inplace)\ + (inplace ? PyNumber_InPlaceAdd(op1, op2) : PyNumber_Add(op1, op2)) +#endif + +/* PyIntBinop.proto */ +#if !CYTHON_COMPILING_IN_PYPY +static PyObject* __Pyx_PyInt_EqObjC(PyObject *op1, PyObject *op2, long intval, int inplace); +#else +#define __Pyx_PyInt_EqObjC(op1, op2, intval, inplace)\ + PyObject_RichCompare(op1, op2, Py_EQ) + #endif + +/* GetModuleGlobalName.proto */ +static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name); + +/* PyCFunctionFastCall.proto */ +#if CYTHON_FAST_PYCCALL +static CYTHON_INLINE PyObject *__Pyx_PyCFunction_FastCall(PyObject *func, PyObject **args, Py_ssize_t nargs); +#else +#define __Pyx_PyCFunction_FastCall(func, args, nargs) (assert(0), NULL) +#endif + +/* PyFunctionFastCall.proto */ +#if CYTHON_FAST_PYCALL +#define __Pyx_PyFunction_FastCall(func, args, nargs)\ + __Pyx_PyFunction_FastCallDict((func), (args), (nargs), NULL) +#if 1 || PY_VERSION_HEX < 0x030600B1 +static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, int nargs, PyObject *kwargs); +#else +#define __Pyx_PyFunction_FastCallDict(func, args, nargs, kwargs) _PyFunction_FastCallDict(func, args, nargs, kwargs) +#endif +#endif + +/* PyObjectCallMethO.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg); +#endif + +/* PyObjectCallOneArg.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg); + +/* GetItemInt.proto */ +#define __Pyx_GetItemInt(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ + (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ + __Pyx_GetItemInt_Fast(o, (Py_ssize_t)i, is_list, wraparound, boundscheck) :\ + (is_list ? (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL) :\ + __Pyx_GetItemInt_Generic(o, to_py_func(i)))) +#define __Pyx_GetItemInt_List(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ + (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ + __Pyx_GetItemInt_List_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\ + (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL)) +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, + int wraparound, int boundscheck); +#define __Pyx_GetItemInt_Tuple(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ + (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ + __Pyx_GetItemInt_Tuple_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\ + (PyErr_SetString(PyExc_IndexError, "tuple index out of range"), (PyObject*)NULL)) +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, + int wraparound, int boundscheck); +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j); +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, + int is_list, int wraparound, int boundscheck); + +/* BufferFormatCheck.proto */ +static CYTHON_INLINE int __Pyx_GetBufferAndValidate(Py_buffer* buf, PyObject* obj, + __Pyx_TypeInfo* dtype, int flags, int nd, int cast, __Pyx_BufFmt_StackElem* stack); +static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info); +static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts); +static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, + __Pyx_BufFmt_StackElem* stack, + __Pyx_TypeInfo* type); // PROTO + +/* ListCompAppend.proto */ +#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS +static CYTHON_INLINE int __Pyx_ListComp_Append(PyObject* list, PyObject* x) { + PyListObject* L = (PyListObject*) list; + Py_ssize_t len = Py_SIZE(list); + if (likely(L->allocated > len)) { + Py_INCREF(x); + PyList_SET_ITEM(list, len, x); + Py_SIZE(list) = len+1; + return 0; + } + return PyList_Append(list, x); +} +#else +#define __Pyx_ListComp_Append(L,x) PyList_Append(L,x) +#endif + +/* FetchCommonType.proto */ +static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type); + +/* CythonFunction.proto */ +#define __Pyx_CyFunction_USED 1 +#include +#define __Pyx_CYFUNCTION_STATICMETHOD 0x01 +#define __Pyx_CYFUNCTION_CLASSMETHOD 0x02 +#define __Pyx_CYFUNCTION_CCLASS 0x04 +#define __Pyx_CyFunction_GetClosure(f)\ + (((__pyx_CyFunctionObject *) (f))->func_closure) +#define __Pyx_CyFunction_GetClassObj(f)\ + (((__pyx_CyFunctionObject *) (f))->func_classobj) +#define __Pyx_CyFunction_Defaults(type, f)\ + ((type *)(((__pyx_CyFunctionObject *) (f))->defaults)) +#define __Pyx_CyFunction_SetDefaultsGetter(f, g)\ + ((__pyx_CyFunctionObject *) (f))->defaults_getter = (g) +typedef struct { + PyCFunctionObject func; +#if PY_VERSION_HEX < 0x030500A0 + PyObject *func_weakreflist; +#endif + PyObject *func_dict; + PyObject *func_name; + PyObject *func_qualname; + PyObject *func_doc; + PyObject *func_globals; + PyObject *func_code; + PyObject *func_closure; + PyObject *func_classobj; + void *defaults; + int defaults_pyobjects; + int flags; + PyObject *defaults_tuple; + PyObject *defaults_kwdict; + PyObject *(*defaults_getter)(PyObject *); + PyObject *func_annotations; +} __pyx_CyFunctionObject; +static PyTypeObject *__pyx_CyFunctionType = 0; +#define __Pyx_CyFunction_NewEx(ml, flags, qualname, self, module, globals, code)\ + __Pyx_CyFunction_New(__pyx_CyFunctionType, ml, flags, qualname, self, module, globals, code) +static PyObject *__Pyx_CyFunction_New(PyTypeObject *, PyMethodDef *ml, + int flags, PyObject* qualname, + PyObject *self, + PyObject *module, PyObject *globals, + PyObject* code); +static CYTHON_INLINE void *__Pyx_CyFunction_InitDefaults(PyObject *m, + size_t size, + int pyobjects); +static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsTuple(PyObject *m, + PyObject *tuple); +static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsKwDict(PyObject *m, + PyObject *dict); +static CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *m, + PyObject *dict); +static int __pyx_CyFunction_init(void); + +/* BufferFallbackError.proto */ +static void __Pyx_RaiseBufferFallbackError(void); + +/* None.proto */ +static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t, Py_ssize_t); + +/* BufferIndexError.proto */ +static void __Pyx_RaiseBufferIndexError(int axis); + +#define __Pyx_BufPtrStrided1d(type, buf, i0, s0) (type)((char*)buf + i0 * s0) +/* PySequenceContains.proto */ +static CYTHON_INLINE int __Pyx_PySequence_ContainsTF(PyObject* item, PyObject* seq, int eq) { + int result = PySequence_Contains(seq, item); + return unlikely(result < 0) ? result : (result == (eq == Py_EQ)); +} + +/* DictGetItem.proto */ +#if PY_MAJOR_VERSION >= 3 && !CYTHON_COMPILING_IN_PYPY +static PyObject *__Pyx_PyDict_GetItem(PyObject *d, PyObject* key) { + PyObject *value; + value = PyDict_GetItemWithError(d, key); + if (unlikely(!value)) { + if (!PyErr_Occurred()) { + PyObject* args = PyTuple_Pack(1, key); + if (likely(args)) + PyErr_SetObject(PyExc_KeyError, args); + Py_XDECREF(args); + } + return NULL; + } + Py_INCREF(value); + return value; +} +#else + #define __Pyx_PyDict_GetItem(d, key) PyObject_GetItem(d, key) +#endif + +/* RaiseTooManyValuesToUnpack.proto */ +static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected); + +/* RaiseNeedMoreValuesToUnpack.proto */ +static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index); + +/* RaiseNoneIterError.proto */ +static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void); + +/* SaveResetException.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_ExceptionSave(type, value, tb) __Pyx__ExceptionSave(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#define __Pyx_ExceptionReset(type, value, tb) __Pyx__ExceptionReset(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb); +#else +#define __Pyx_ExceptionSave(type, value, tb) PyErr_GetExcInfo(type, value, tb) +#define __Pyx_ExceptionReset(type, value, tb) PyErr_SetExcInfo(type, value, tb) +#endif + +/* PyErrExceptionMatches.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_PyErr_ExceptionMatches(err) __Pyx_PyErr_ExceptionMatchesInState(__pyx_tstate, err) +static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err); +#else +#define __Pyx_PyErr_ExceptionMatches(err) PyErr_ExceptionMatches(err) +#endif + +/* GetException.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_GetException(type, value, tb) __Pyx__GetException(__pyx_tstate, type, value, tb) +static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#else +static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb); +#endif + +/* Import.proto */ +static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level); + +/* CodeObjectCache.proto */ +typedef struct { + PyCodeObject* code_object; + int code_line; +} __Pyx_CodeObjectCacheEntry; +struct __Pyx_CodeObjectCache { + int count; + int max_count; + __Pyx_CodeObjectCacheEntry* entries; +}; +static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL}; +static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line); +static PyCodeObject *__pyx_find_code_object(int code_line); +static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object); + +/* AddTraceback.proto */ +static void __Pyx_AddTraceback(const char *funcname, int c_line, + int py_line, const char *filename); + +/* BufferStructDeclare.proto */ +typedef struct { + Py_ssize_t shape, strides, suboffsets; +} __Pyx_Buf_DimInfo; +typedef struct { + size_t refcount; + Py_buffer pybuffer; +} __Pyx_Buffer; +typedef struct { + __Pyx_Buffer *rcbuffer; + char *data; + __Pyx_Buf_DimInfo diminfo[8]; +} __Pyx_LocalBuf_ND; + +#if PY_MAJOR_VERSION < 3 + static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags); + static void __Pyx_ReleaseBuffer(Py_buffer *view); +#else + #define __Pyx_GetBuffer PyObject_GetBuffer + #define __Pyx_ReleaseBuffer PyBuffer_Release +#endif + + +/* None.proto */ +static Py_ssize_t __Pyx_zeros[] = {0, 0, 0, 0, 0, 0, 0, 0}; +static Py_ssize_t __Pyx_minusones[] = {-1, -1, -1, -1, -1, -1, -1, -1}; + +/* CIntToPy.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value); + +/* CIntToPy.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_siz(siz value); + +/* CIntToPy.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_Py_intptr_t(Py_intptr_t value); + +/* RealImag.proto */ +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + #define __Pyx_CREAL(z) ((z).real()) + #define __Pyx_CIMAG(z) ((z).imag()) + #else + #define __Pyx_CREAL(z) (__real__(z)) + #define __Pyx_CIMAG(z) (__imag__(z)) + #endif +#else + #define __Pyx_CREAL(z) ((z).real) + #define __Pyx_CIMAG(z) ((z).imag) +#endif +#if defined(__cplusplus) && CYTHON_CCOMPLEX\ + && (defined(_WIN32) || defined(__clang__) || (defined(__GNUC__) && (__GNUC__ >= 5 || __GNUC__ == 4 && __GNUC_MINOR__ >= 4 )) || __cplusplus >= 201103) + #define __Pyx_SET_CREAL(z,x) ((z).real(x)) + #define __Pyx_SET_CIMAG(z,y) ((z).imag(y)) +#else + #define __Pyx_SET_CREAL(z,x) __Pyx_CREAL(z) = (x) + #define __Pyx_SET_CIMAG(z,y) __Pyx_CIMAG(z) = (y) +#endif + +/* Arithmetic.proto */ +#if CYTHON_CCOMPLEX + #define __Pyx_c_eq_float(a, b) ((a)==(b)) + #define __Pyx_c_sum_float(a, b) ((a)+(b)) + #define __Pyx_c_diff_float(a, b) ((a)-(b)) + #define __Pyx_c_prod_float(a, b) ((a)*(b)) + #define __Pyx_c_quot_float(a, b) ((a)/(b)) + #define __Pyx_c_neg_float(a) (-(a)) + #ifdef __cplusplus + #define __Pyx_c_is_zero_float(z) ((z)==(float)0) + #define __Pyx_c_conj_float(z) (::std::conj(z)) + #if 1 + #define __Pyx_c_abs_float(z) (::std::abs(z)) + #define __Pyx_c_pow_float(a, b) (::std::pow(a, b)) + #endif + #else + #define __Pyx_c_is_zero_float(z) ((z)==0) + #define __Pyx_c_conj_float(z) (conjf(z)) + #if 1 + #define __Pyx_c_abs_float(z) (cabsf(z)) + #define __Pyx_c_pow_float(a, b) (cpowf(a, b)) + #endif + #endif +#else + static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex); + static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex); + #if 1 + static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex, __pyx_t_float_complex); + #endif +#endif + +/* Arithmetic.proto */ +#if CYTHON_CCOMPLEX + #define __Pyx_c_eq_double(a, b) ((a)==(b)) + #define __Pyx_c_sum_double(a, b) ((a)+(b)) + #define __Pyx_c_diff_double(a, b) ((a)-(b)) + #define __Pyx_c_prod_double(a, b) ((a)*(b)) + #define __Pyx_c_quot_double(a, b) ((a)/(b)) + #define __Pyx_c_neg_double(a) (-(a)) + #ifdef __cplusplus + #define __Pyx_c_is_zero_double(z) ((z)==(double)0) + #define __Pyx_c_conj_double(z) (::std::conj(z)) + #if 1 + #define __Pyx_c_abs_double(z) (::std::abs(z)) + #define __Pyx_c_pow_double(a, b) (::std::pow(a, b)) + #endif + #else + #define __Pyx_c_is_zero_double(z) ((z)==0) + #define __Pyx_c_conj_double(z) (conj(z)) + #if 1 + #define __Pyx_c_abs_double(z) (cabs(z)) + #define __Pyx_c_pow_double(a, b) (cpow(a, b)) + #endif + #endif +#else + static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex); + static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex); + #if 1 + static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex, __pyx_t_double_complex); + #endif +#endif + +/* CIntToPy.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value); + +/* CIntToPy.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_enum__NPY_TYPES(enum NPY_TYPES value); + +/* CIntFromPy.proto */ +static CYTHON_INLINE siz __Pyx_PyInt_As_siz(PyObject *); + +/* CIntFromPy.proto */ +static CYTHON_INLINE size_t __Pyx_PyInt_As_size_t(PyObject *); + +/* CIntFromPy.proto */ +static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *); + +/* CIntFromPy.proto */ +static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *); + +/* CheckBinaryVersion.proto */ +static int __Pyx_check_binary_version(void); + +/* PyIdentifierFromString.proto */ +#if !defined(__Pyx_PyIdentifier_FromString) +#if PY_MAJOR_VERSION < 3 + #define __Pyx_PyIdentifier_FromString(s) PyString_FromString(s) +#else + #define __Pyx_PyIdentifier_FromString(s) PyUnicode_FromString(s) +#endif +#endif + +/* ModuleImport.proto */ +static PyObject *__Pyx_ImportModule(const char *name); + +/* TypeImport.proto */ +static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict); + +/* InitStrings.proto */ +static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); + + +/* Module declarations from 'cpython.buffer' */ + +/* Module declarations from 'libc.string' */ + +/* Module declarations from 'libc.stdio' */ + +/* Module declarations from '__builtin__' */ + +/* Module declarations from 'cpython.type' */ +static PyTypeObject *__pyx_ptype_7cpython_4type_type = 0; + +/* Module declarations from 'cpython' */ + +/* Module declarations from 'cpython.object' */ + +/* Module declarations from 'cpython.ref' */ + +/* Module declarations from 'libc.stdlib' */ + +/* Module declarations from 'numpy' */ + +/* Module declarations from 'numpy' */ +static PyTypeObject *__pyx_ptype_5numpy_dtype = 0; +static PyTypeObject *__pyx_ptype_5numpy_flatiter = 0; +static PyTypeObject *__pyx_ptype_5numpy_broadcast = 0; +static PyTypeObject *__pyx_ptype_5numpy_ndarray = 0; +static PyTypeObject *__pyx_ptype_5numpy_ufunc = 0; +static CYTHON_INLINE char *__pyx_f_5numpy__util_dtypestring(PyArray_Descr *, char *, char *, int *); /*proto*/ +static CYTHON_INLINE int __pyx_f_5numpy_import_array(void); /*proto*/ + +/* Module declarations from 'pycocotools._mask' */ +static PyTypeObject *__pyx_ptype_11pycocotools_5_mask_RLEs = 0; +static PyTypeObject *__pyx_ptype_11pycocotools_5_mask_Masks = 0; +static __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_5numpy_uint8_t = { "uint8_t", NULL, sizeof(__pyx_t_5numpy_uint8_t), { 0 }, 0, IS_UNSIGNED(__pyx_t_5numpy_uint8_t) ? 'U' : 'I', IS_UNSIGNED(__pyx_t_5numpy_uint8_t), 0 }; +static __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_5numpy_double_t = { "double_t", NULL, sizeof(__pyx_t_5numpy_double_t), { 0 }, 0, 'R', 0, 0 }; +static __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_5numpy_uint32_t = { "uint32_t", NULL, sizeof(__pyx_t_5numpy_uint32_t), { 0 }, 0, IS_UNSIGNED(__pyx_t_5numpy_uint32_t) ? 'U' : 'I', IS_UNSIGNED(__pyx_t_5numpy_uint32_t), 0 }; +#define __Pyx_MODULE_NAME "pycocotools._mask" +int __pyx_module_is_main_pycocotools___mask = 0; + +/* Implementation of 'pycocotools._mask' */ +static PyObject *__pyx_builtin_range; +static PyObject *__pyx_builtin_AttributeError; +static PyObject *__pyx_builtin_enumerate; +static PyObject *__pyx_builtin_ValueError; +static PyObject *__pyx_builtin_RuntimeError; +static PyObject *__pyx_builtin_ImportError; +static const char __pyx_k_F[] = "F"; +static const char __pyx_k_N[] = "N"; +static const char __pyx_k_R[] = "R"; +static const char __pyx_k_a[] = "_a"; +static const char __pyx_k_h[] = "h"; +static const char __pyx_k_i[] = "i"; +static const char __pyx_k_j[] = "j"; +static const char __pyx_k_m[] = "m"; +static const char __pyx_k_n[] = "n"; +static const char __pyx_k_p[] = "p"; +static const char __pyx_k_w[] = "w"; +static const char __pyx_k_Rs[] = "Rs"; +static const char __pyx_k_bb[] = "bb"; +static const char __pyx_k_dt[] = "dt"; +static const char __pyx_k_gt[] = "gt"; +static const char __pyx_k_np[] = "np"; +static const char __pyx_k_a_2[] = "a"; +static const char __pyx_k_all[] = "all"; +static const char __pyx_k_iou[] = "_iou"; +static const char __pyx_k_len[] = "_len"; +static const char __pyx_k_obj[] = "obj"; +static const char __pyx_k_sys[] = "sys"; +static const char __pyx_k_area[] = "area"; +static const char __pyx_k_bb_2[] = "_bb"; +static const char __pyx_k_cnts[] = "cnts"; +static const char __pyx_k_data[] = "data"; +static const char __pyx_k_main[] = "__main__"; +static const char __pyx_k_mask[] = "mask"; +static const char __pyx_k_objs[] = "objs"; +static const char __pyx_k_poly[] = "poly"; +static const char __pyx_k_size[] = "size"; +static const char __pyx_k_test[] = "__test__"; +static const char __pyx_k_utf8[] = "utf8"; +static const char __pyx_k_array[] = "array"; +static const char __pyx_k_bbIou[] = "_bbIou"; +static const char __pyx_k_dtype[] = "dtype"; +static const char __pyx_k_iou_2[] = "iou"; +static const char __pyx_k_isbox[] = "isbox"; +static const char __pyx_k_isrle[] = "isrle"; +static const char __pyx_k_masks[] = "masks"; +static const char __pyx_k_merge[] = "merge"; +static const char __pyx_k_numpy[] = "numpy"; +static const char __pyx_k_order[] = "order"; +static const char __pyx_k_pyobj[] = "pyobj"; +static const char __pyx_k_range[] = "range"; +static const char __pyx_k_shape[] = "shape"; +static const char __pyx_k_uint8[] = "uint8"; +static const char __pyx_k_zeros[] = "zeros"; +static const char __pyx_k_astype[] = "astype"; +static const char __pyx_k_author[] = "__author__"; +static const char __pyx_k_counts[] = "counts"; +static const char __pyx_k_decode[] = "decode"; +static const char __pyx_k_double[] = "double"; +static const char __pyx_k_encode[] = "encode"; +static const char __pyx_k_frBbox[] = "frBbox"; +static const char __pyx_k_frPoly[] = "frPoly"; +static const char __pyx_k_import[] = "__import__"; +static const char __pyx_k_iouFun[] = "_iouFun"; +static const char __pyx_k_rleIou[] = "_rleIou"; +static const char __pyx_k_toBbox[] = "toBbox"; +static const char __pyx_k_ucRles[] = "ucRles"; +static const char __pyx_k_uint32[] = "uint32"; +static const char __pyx_k_iscrowd[] = "iscrowd"; +static const char __pyx_k_np_poly[] = "np_poly"; +static const char __pyx_k_preproc[] = "_preproc"; +static const char __pyx_k_reshape[] = "reshape"; +static const char __pyx_k_rleObjs[] = "rleObjs"; +static const char __pyx_k_tsungyi[] = "tsungyi"; +static const char __pyx_k_c_string[] = "c_string"; +static const char __pyx_k_frString[] = "_frString"; +static const char __pyx_k_toString[] = "_toString"; +static const char __pyx_k_enumerate[] = "enumerate"; +static const char __pyx_k_intersect[] = "intersect"; +static const char __pyx_k_py_string[] = "py_string"; +static const char __pyx_k_pyiscrowd[] = "pyiscrowd"; +static const char __pyx_k_ValueError[] = "ValueError"; +static const char __pyx_k_ImportError[] = "ImportError"; +static const char __pyx_k_frPyObjects[] = "frPyObjects"; +static const char __pyx_k_RuntimeError[] = "RuntimeError"; +static const char __pyx_k_version_info[] = "version_info"; +static const char __pyx_k_AttributeError[] = "AttributeError"; +static const char __pyx_k_PYTHON_VERSION[] = "PYTHON_VERSION"; +static const char __pyx_k_iou_locals__len[] = "iou.._len"; +static const char __pyx_k_frUncompressedRLE[] = "frUncompressedRLE"; +static const char __pyx_k_iou_locals__bbIou[] = "iou.._bbIou"; +static const char __pyx_k_pycocotools__mask[] = "pycocotools._mask"; +static const char __pyx_k_iou_locals__rleIou[] = "iou.._rleIou"; +static const char __pyx_k_iou_locals__preproc[] = "iou.._preproc"; +static const char __pyx_k_input_data_type_not_allowed[] = "input data type not allowed."; +static const char __pyx_k_input_type_is_not_supported[] = "input type is not supported."; +static const char __pyx_k_ndarray_is_not_C_contiguous[] = "ndarray is not C contiguous"; +static const char __pyx_k_Python_version_must_be_2_or_3[] = "Python version must be 2 or 3"; +static const char __pyx_k_home_yjr_PycharmProjects_Faster[] = "/home/yjr/PycharmProjects/Faster-RCNN_TF/data/lib_coco/PythonAPI/pycocotools/_mask.pyx"; +static const char __pyx_k_numpy_core_multiarray_failed_to[] = "numpy.core.multiarray failed to import"; +static const char __pyx_k_numpy_ndarray_input_is_only_for[] = "numpy ndarray input is only for *bounding boxes* and should have Nx4 dimension"; +static const char __pyx_k_unknown_dtype_code_in_numpy_pxd[] = "unknown dtype code in numpy.pxd (%d)"; +static const char __pyx_k_unrecognized_type_The_following[] = "unrecognized type. The following type: RLEs (rle), np.ndarray (box), and list (box) are supported."; +static const char __pyx_k_Format_string_allocated_too_shor[] = "Format string allocated too short, see comment in numpy.pxd"; +static const char __pyx_k_Non_native_byte_order_not_suppor[] = "Non-native byte order not supported"; +static const char __pyx_k_The_dt_and_gt_should_have_the_sa[] = "The dt and gt should have the same data type, either RLEs, list or np.ndarray"; +static const char __pyx_k_list_input_can_be_bounding_box_N[] = "list input can be bounding box (Nx4) or RLEs ([RLE])"; +static const char __pyx_k_ndarray_is_not_Fortran_contiguou[] = "ndarray is not Fortran contiguous"; +static const char __pyx_k_numpy_core_umath_failed_to_impor[] = "numpy.core.umath failed to import"; +static const char __pyx_k_Format_string_allocated_too_shor_2[] = "Format string allocated too short."; +static PyObject *__pyx_n_s_AttributeError; +static PyObject *__pyx_n_s_F; +static PyObject *__pyx_kp_u_Format_string_allocated_too_shor; +static PyObject *__pyx_kp_u_Format_string_allocated_too_shor_2; +static PyObject *__pyx_n_s_ImportError; +static PyObject *__pyx_n_s_N; +static PyObject *__pyx_kp_u_Non_native_byte_order_not_suppor; +static PyObject *__pyx_n_s_PYTHON_VERSION; +static PyObject *__pyx_kp_s_Python_version_must_be_2_or_3; +static PyObject *__pyx_n_s_R; +static PyObject *__pyx_n_s_Rs; +static PyObject *__pyx_n_s_RuntimeError; +static PyObject *__pyx_kp_s_The_dt_and_gt_should_have_the_sa; +static PyObject *__pyx_n_s_ValueError; +static PyObject *__pyx_n_s_a; +static PyObject *__pyx_n_s_a_2; +static PyObject *__pyx_n_s_all; +static PyObject *__pyx_n_s_area; +static PyObject *__pyx_n_s_array; +static PyObject *__pyx_n_s_astype; +static PyObject *__pyx_n_s_author; +static PyObject *__pyx_n_s_bb; +static PyObject *__pyx_n_s_bbIou; +static PyObject *__pyx_n_s_bb_2; +static PyObject *__pyx_n_s_c_string; +static PyObject *__pyx_n_s_cnts; +static PyObject *__pyx_n_s_counts; +static PyObject *__pyx_n_s_data; +static PyObject *__pyx_n_s_decode; +static PyObject *__pyx_n_s_double; +static PyObject *__pyx_n_s_dt; +static PyObject *__pyx_n_s_dtype; +static PyObject *__pyx_n_s_encode; +static PyObject *__pyx_n_s_enumerate; +static PyObject *__pyx_n_s_frBbox; +static PyObject *__pyx_n_s_frPoly; +static PyObject *__pyx_n_s_frPyObjects; +static PyObject *__pyx_n_s_frString; +static PyObject *__pyx_n_s_frUncompressedRLE; +static PyObject *__pyx_n_s_gt; +static PyObject *__pyx_n_s_h; +static PyObject *__pyx_kp_s_home_yjr_PycharmProjects_Faster; +static PyObject *__pyx_n_s_i; +static PyObject *__pyx_n_s_import; +static PyObject *__pyx_kp_s_input_data_type_not_allowed; +static PyObject *__pyx_kp_s_input_type_is_not_supported; +static PyObject *__pyx_n_s_intersect; +static PyObject *__pyx_n_s_iou; +static PyObject *__pyx_n_s_iouFun; +static PyObject *__pyx_n_s_iou_2; +static PyObject *__pyx_n_s_iou_locals__bbIou; +static PyObject *__pyx_n_s_iou_locals__len; +static PyObject *__pyx_n_s_iou_locals__preproc; +static PyObject *__pyx_n_s_iou_locals__rleIou; +static PyObject *__pyx_n_s_isbox; +static PyObject *__pyx_n_s_iscrowd; +static PyObject *__pyx_n_s_isrle; +static PyObject *__pyx_n_s_j; +static PyObject *__pyx_n_s_len; +static PyObject *__pyx_kp_s_list_input_can_be_bounding_box_N; +static PyObject *__pyx_n_s_m; +static PyObject *__pyx_n_s_main; +static PyObject *__pyx_n_s_mask; +static PyObject *__pyx_n_s_masks; +static PyObject *__pyx_n_s_merge; +static PyObject *__pyx_n_s_n; +static PyObject *__pyx_kp_u_ndarray_is_not_C_contiguous; +static PyObject *__pyx_kp_u_ndarray_is_not_Fortran_contiguou; +static PyObject *__pyx_n_s_np; +static PyObject *__pyx_n_s_np_poly; +static PyObject *__pyx_n_s_numpy; +static PyObject *__pyx_kp_s_numpy_core_multiarray_failed_to; +static PyObject *__pyx_kp_s_numpy_core_umath_failed_to_impor; +static PyObject *__pyx_kp_s_numpy_ndarray_input_is_only_for; +static PyObject *__pyx_n_s_obj; +static PyObject *__pyx_n_s_objs; +static PyObject *__pyx_n_s_order; +static PyObject *__pyx_n_s_p; +static PyObject *__pyx_n_s_poly; +static PyObject *__pyx_n_s_preproc; +static PyObject *__pyx_n_s_py_string; +static PyObject *__pyx_n_s_pycocotools__mask; +static PyObject *__pyx_n_s_pyiscrowd; +static PyObject *__pyx_n_s_pyobj; +static PyObject *__pyx_n_s_range; +static PyObject *__pyx_n_s_reshape; +static PyObject *__pyx_n_s_rleIou; +static PyObject *__pyx_n_s_rleObjs; +static PyObject *__pyx_n_s_shape; +static PyObject *__pyx_n_s_size; +static PyObject *__pyx_n_s_sys; +static PyObject *__pyx_n_s_test; +static PyObject *__pyx_n_s_toBbox; +static PyObject *__pyx_n_s_toString; +static PyObject *__pyx_n_s_tsungyi; +static PyObject *__pyx_n_s_ucRles; +static PyObject *__pyx_n_s_uint32; +static PyObject *__pyx_n_s_uint8; +static PyObject *__pyx_kp_u_unknown_dtype_code_in_numpy_pxd; +static PyObject *__pyx_kp_s_unrecognized_type_The_following; +static PyObject *__pyx_n_s_utf8; +static PyObject *__pyx_n_s_version_info; +static PyObject *__pyx_n_s_w; +static PyObject *__pyx_n_s_zeros; +static int __pyx_pf_11pycocotools_5_mask_4RLEs___cinit__(struct __pyx_obj_11pycocotools_5_mask_RLEs *__pyx_v_self, siz __pyx_v_n); /* proto */ +static void __pyx_pf_11pycocotools_5_mask_4RLEs_2__dealloc__(struct __pyx_obj_11pycocotools_5_mask_RLEs *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_11pycocotools_5_mask_4RLEs_4__getattr__(struct __pyx_obj_11pycocotools_5_mask_RLEs *__pyx_v_self, PyObject *__pyx_v_key); /* proto */ +static int __pyx_pf_11pycocotools_5_mask_5Masks___cinit__(struct __pyx_obj_11pycocotools_5_mask_Masks *__pyx_v_self, PyObject *__pyx_v_h, PyObject *__pyx_v_w, PyObject *__pyx_v_n); /* proto */ +static PyObject *__pyx_pf_11pycocotools_5_mask_5Masks_2__array__(struct __pyx_obj_11pycocotools_5_mask_Masks *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_11pycocotools_5_mask__toString(CYTHON_UNUSED PyObject *__pyx_self, struct __pyx_obj_11pycocotools_5_mask_RLEs *__pyx_v_Rs); /* proto */ +static PyObject *__pyx_pf_11pycocotools_5_mask_2_frString(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_rleObjs); /* proto */ +static PyObject *__pyx_pf_11pycocotools_5_mask_4encode(CYTHON_UNUSED PyObject *__pyx_self, PyArrayObject *__pyx_v_mask); 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if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 270, __pyx_L1_error) + __Pyx_GOTREF(__pyx_t_1); + if (PyDict_SetItem(__pyx_d, __pyx_n_s_frUncompressedRLE, __pyx_t_1) < 0) __PYX_ERR(0, 270, __pyx_L1_error) + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + + /* "pycocotools/_mask.pyx":288 + * return objs + * + * def frPyObjects(pyobj, h, w): # <<<<<<<<<<<<<< + * # encode rle from a list of python objects + * if type(pyobj) == np.ndarray: + */ + __pyx_t_1 = PyCFunction_NewEx(&__pyx_mdef_11pycocotools_5_mask_23frPyObjects, NULL, __pyx_n_s_pycocotools__mask); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 288, __pyx_L1_error) + __Pyx_GOTREF(__pyx_t_1); + if (PyDict_SetItem(__pyx_d, __pyx_n_s_frPyObjects, __pyx_t_1) < 0) __PYX_ERR(0, 288, __pyx_L1_error) + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + + /* "pycocotools/_mask.pyx":1 + * # distutils: language = c # <<<<<<<<<<<<<< + * # distutils: sources = ../common/maskApi.c + * + */ + __pyx_t_1 = PyDict_New(); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 1, __pyx_L1_error) + __Pyx_GOTREF(__pyx_t_1); + if (PyDict_SetItem(__pyx_d, __pyx_n_s_test, __pyx_t_1) < 0) __PYX_ERR(0, 1, __pyx_L1_error) + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + + /* "../../../../../softWares/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":997 + * raise ImportError("numpy.core.umath failed to import") + * + * cdef inline int import_ufunc() except -1: # <<<<<<<<<<<<<< + * try: + * _import_umath() + */ + + /*--- Wrapped vars code ---*/ + + goto __pyx_L0; + __pyx_L1_error:; + __Pyx_XDECREF(__pyx_t_1); + __Pyx_XDECREF(__pyx_t_2); + if (__pyx_m) { + if (__pyx_d) { + __Pyx_AddTraceback("init pycocotools._mask", __pyx_clineno, __pyx_lineno, __pyx_filename); + } + Py_DECREF(__pyx_m); __pyx_m = 0; + } else if (!PyErr_Occurred()) { + PyErr_SetString(PyExc_ImportError, "init pycocotools._mask"); + } + __pyx_L0:; + __Pyx_RefNannyFinishContext(); + #if PY_MAJOR_VERSION < 3 + return; + #else + return __pyx_m; + #endif +} + +/* --- Runtime support code --- */ +/* Refnanny */ +#if CYTHON_REFNANNY +static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname) { + PyObject *m = NULL, *p = NULL; + void *r = NULL; + m = PyImport_ImportModule((char *)modname); + if (!m) goto end; + p = PyObject_GetAttrString(m, (char *)"RefNannyAPI"); + if (!p) goto end; + r = PyLong_AsVoidPtr(p); +end: + Py_XDECREF(p); + Py_XDECREF(m); + return (__Pyx_RefNannyAPIStruct *)r; +} +#endif + +/* GetBuiltinName */ +static PyObject *__Pyx_GetBuiltinName(PyObject *name) { + PyObject* result = __Pyx_PyObject_GetAttrStr(__pyx_b, name); + if (unlikely(!result)) { + PyErr_Format(PyExc_NameError, +#if PY_MAJOR_VERSION >= 3 + "name '%U' is not defined", name); +#else + "name '%.200s' is not defined", PyString_AS_STRING(name)); +#endif + } + return result; +} + +/* RaiseDoubleKeywords */ +static void __Pyx_RaiseDoubleKeywordsError( + const char* func_name, + PyObject* kw_name) +{ + PyErr_Format(PyExc_TypeError, + #if PY_MAJOR_VERSION >= 3 + "%s() got multiple values for keyword argument '%U'", func_name, kw_name); + #else + "%s() got multiple values for keyword argument '%s'", func_name, + PyString_AsString(kw_name)); + #endif +} + +/* ParseKeywords */ +static int __Pyx_ParseOptionalKeywords( + PyObject *kwds, + PyObject **argnames[], + PyObject *kwds2, + PyObject *values[], + Py_ssize_t num_pos_args, + const char* function_name) +{ + PyObject *key = 0, *value = 0; + Py_ssize_t pos = 0; + PyObject*** name; + PyObject*** first_kw_arg = argnames + num_pos_args; + while (PyDict_Next(kwds, &pos, &key, &value)) { + name = first_kw_arg; + while (*name && (**name != key)) name++; + if (*name) { + values[name-argnames] = value; + continue; + } + name = first_kw_arg; + #if PY_MAJOR_VERSION < 3 + if (likely(PyString_CheckExact(key)) || likely(PyString_Check(key))) { + while (*name) { + if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key)) + && _PyString_Eq(**name, key)) { + values[name-argnames] = value; + break; + } + name++; + } + if (*name) continue; + else { + PyObject*** argname = argnames; + while (argname != first_kw_arg) { + if ((**argname == key) || ( + (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key)) + && _PyString_Eq(**argname, key))) { + goto arg_passed_twice; + } + argname++; + } + } + } else + #endif + if (likely(PyUnicode_Check(key))) { + while (*name) { + int cmp = (**name == key) ? 0 : + #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 + (PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(key)) ? 1 : + #endif + PyUnicode_Compare(**name, key); + if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; + if (cmp == 0) { + values[name-argnames] = value; + break; + } + name++; + } + if (*name) continue; + else { + PyObject*** argname = argnames; + while (argname != first_kw_arg) { + int cmp = (**argname == key) ? 0 : + #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 + (PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(key)) ? 1 : + #endif + PyUnicode_Compare(**argname, key); + if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; + if (cmp == 0) goto arg_passed_twice; + argname++; + } + } + } else + goto invalid_keyword_type; + if (kwds2) { + if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad; + } else { + goto invalid_keyword; + } + } + return 0; +arg_passed_twice: + __Pyx_RaiseDoubleKeywordsError(function_name, key); + goto bad; +invalid_keyword_type: + PyErr_Format(PyExc_TypeError, + "%.200s() keywords must be strings", function_name); + goto bad; +invalid_keyword: + PyErr_Format(PyExc_TypeError, + #if PY_MAJOR_VERSION < 3 + "%.200s() got an unexpected keyword argument '%.200s'", + function_name, PyString_AsString(key)); + #else + "%s() got an unexpected keyword argument '%U'", + function_name, key); + #endif +bad: + return -1; +} + +/* RaiseArgTupleInvalid */ +static void __Pyx_RaiseArgtupleInvalid( + const char* func_name, + int exact, + Py_ssize_t num_min, + Py_ssize_t num_max, + Py_ssize_t num_found) +{ + Py_ssize_t num_expected; + const char *more_or_less; + if (num_found < num_min) { + num_expected = num_min; + more_or_less = "at least"; + } else { + num_expected = num_max; + more_or_less = "at most"; + } + if (exact) { + more_or_less = "exactly"; + } + PyErr_Format(PyExc_TypeError, + "%.200s() takes %.8s %" CYTHON_FORMAT_SSIZE_T "d positional argument%.1s (%" CYTHON_FORMAT_SSIZE_T "d given)", + func_name, more_or_less, num_expected, + (num_expected == 1) ? "" : "s", num_found); +} + +/* BytesEquals */ +static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals) { +#if CYTHON_COMPILING_IN_PYPY + return PyObject_RichCompareBool(s1, s2, equals); +#else + if (s1 == s2) { + return (equals == Py_EQ); + } else if (PyBytes_CheckExact(s1) & PyBytes_CheckExact(s2)) { + const char *ps1, *ps2; + Py_ssize_t length = PyBytes_GET_SIZE(s1); + if (length != PyBytes_GET_SIZE(s2)) + return (equals == Py_NE); + ps1 = PyBytes_AS_STRING(s1); + ps2 = PyBytes_AS_STRING(s2); + if (ps1[0] != ps2[0]) { + return (equals == Py_NE); + } else if (length == 1) { + return (equals == Py_EQ); + } else { + int result = memcmp(ps1, ps2, (size_t)length); + return (equals == Py_EQ) ? (result == 0) : (result != 0); + } + } else if ((s1 == Py_None) & PyBytes_CheckExact(s2)) { + return (equals == Py_NE); + } else if ((s2 == Py_None) & PyBytes_CheckExact(s1)) { + return (equals == Py_NE); + } else { + int result; + PyObject* py_result = PyObject_RichCompare(s1, s2, equals); + if (!py_result) + return -1; + result = __Pyx_PyObject_IsTrue(py_result); + Py_DECREF(py_result); + return result; + } +#endif +} + +/* UnicodeEquals */ +static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals) { +#if CYTHON_COMPILING_IN_PYPY + return PyObject_RichCompareBool(s1, s2, equals); +#else +#if PY_MAJOR_VERSION < 3 + PyObject* owned_ref = NULL; +#endif + int s1_is_unicode, s2_is_unicode; + if (s1 == s2) { + goto return_eq; + } + s1_is_unicode = PyUnicode_CheckExact(s1); + s2_is_unicode = PyUnicode_CheckExact(s2); +#if PY_MAJOR_VERSION < 3 + if ((s1_is_unicode & (!s2_is_unicode)) && PyString_CheckExact(s2)) { + owned_ref = PyUnicode_FromObject(s2); + if (unlikely(!owned_ref)) + return -1; + s2 = owned_ref; + s2_is_unicode = 1; + } else if ((s2_is_unicode & (!s1_is_unicode)) && PyString_CheckExact(s1)) { + owned_ref = PyUnicode_FromObject(s1); + if (unlikely(!owned_ref)) + return -1; + s1 = owned_ref; + s1_is_unicode = 1; + } else if (((!s2_is_unicode) & (!s1_is_unicode))) { + return __Pyx_PyBytes_Equals(s1, s2, equals); + } +#endif + if (s1_is_unicode & s2_is_unicode) { + Py_ssize_t length; + int kind; + void *data1, *data2; + if (unlikely(__Pyx_PyUnicode_READY(s1) < 0) || unlikely(__Pyx_PyUnicode_READY(s2) < 0)) + return -1; + length = __Pyx_PyUnicode_GET_LENGTH(s1); + if (length != __Pyx_PyUnicode_GET_LENGTH(s2)) { + goto return_ne; + } + kind = __Pyx_PyUnicode_KIND(s1); + if (kind != __Pyx_PyUnicode_KIND(s2)) { + goto return_ne; + } + data1 = __Pyx_PyUnicode_DATA(s1); + data2 = __Pyx_PyUnicode_DATA(s2); + if (__Pyx_PyUnicode_READ(kind, data1, 0) != __Pyx_PyUnicode_READ(kind, data2, 0)) { + goto return_ne; + } else if (length == 1) { + goto return_eq; + } else { + int result = memcmp(data1, data2, (size_t)(length * kind)); + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(owned_ref); + #endif + return (equals == Py_EQ) ? (result == 0) : (result != 0); + } + } else if ((s1 == Py_None) & s2_is_unicode) { + goto return_ne; + } else if ((s2 == Py_None) & s1_is_unicode) { + goto return_ne; + } else { + int result; + PyObject* py_result = PyObject_RichCompare(s1, s2, equals); + if (!py_result) + return -1; + result = __Pyx_PyObject_IsTrue(py_result); + Py_DECREF(py_result); + return result; + } +return_eq: + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(owned_ref); + #endif + return (equals == Py_EQ); +return_ne: + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(owned_ref); + #endif + return (equals == Py_NE); +#endif +} + +/* PyObjectCall */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) { + PyObject *result; + ternaryfunc call = func->ob_type->tp_call; + if (unlikely(!call)) + return PyObject_Call(func, arg, kw); + if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) + return NULL; + result = (*call)(func, arg, kw); + Py_LeaveRecursiveCall(); + if (unlikely(!result) && unlikely(!PyErr_Occurred())) { + PyErr_SetString( + PyExc_SystemError, + "NULL result without error in PyObject_Call"); + } + return result; +} +#endif + +/* PyErrFetchRestore */ +#if CYTHON_FAST_THREAD_STATE +static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { + PyObject *tmp_type, *tmp_value, *tmp_tb; + tmp_type = tstate->curexc_type; + tmp_value = tstate->curexc_value; + tmp_tb = tstate->curexc_traceback; + tstate->curexc_type = type; + tstate->curexc_value = value; + tstate->curexc_traceback = tb; + Py_XDECREF(tmp_type); + Py_XDECREF(tmp_value); + Py_XDECREF(tmp_tb); +} +static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { + *type = tstate->curexc_type; + *value = tstate->curexc_value; + *tb = tstate->curexc_traceback; + tstate->curexc_type = 0; + tstate->curexc_value = 0; + tstate->curexc_traceback = 0; +} +#endif + +/* RaiseException */ +#if PY_MAJOR_VERSION < 3 +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, + CYTHON_UNUSED PyObject *cause) { + __Pyx_PyThreadState_declare + Py_XINCREF(type); + if (!value || value == Py_None) + value = NULL; + else + Py_INCREF(value); + if (!tb || tb == Py_None) + tb = NULL; + else { + Py_INCREF(tb); + if (!PyTraceBack_Check(tb)) { + PyErr_SetString(PyExc_TypeError, + "raise: arg 3 must be a traceback or None"); + goto raise_error; + } + } + if (PyType_Check(type)) { +#if CYTHON_COMPILING_IN_PYPY + if (!value) { + Py_INCREF(Py_None); + value = Py_None; + } +#endif + PyErr_NormalizeException(&type, &value, &tb); + } else { + if (value) { + PyErr_SetString(PyExc_TypeError, + "instance exception may not have a separate value"); + goto raise_error; + } + value = type; + type = (PyObject*) Py_TYPE(type); + Py_INCREF(type); + if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { + PyErr_SetString(PyExc_TypeError, + "raise: exception class must be a subclass of BaseException"); + goto raise_error; + } + } + __Pyx_PyThreadState_assign + __Pyx_ErrRestore(type, value, tb); + return; +raise_error: + Py_XDECREF(value); + Py_XDECREF(type); + Py_XDECREF(tb); + return; +} +#else +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { + PyObject* owned_instance = NULL; + if (tb == Py_None) { + tb = 0; + } else if (tb && !PyTraceBack_Check(tb)) { + PyErr_SetString(PyExc_TypeError, + "raise: arg 3 must be a traceback or None"); + goto bad; + } + if (value == Py_None) + value = 0; + if (PyExceptionInstance_Check(type)) { + if (value) { + PyErr_SetString(PyExc_TypeError, + "instance exception may not have a separate value"); + goto bad; + } + value = type; + type = (PyObject*) Py_TYPE(value); + } else if (PyExceptionClass_Check(type)) { + PyObject *instance_class = NULL; + if (value && PyExceptionInstance_Check(value)) { + instance_class = (PyObject*) Py_TYPE(value); + if (instance_class != type) { + int is_subclass = PyObject_IsSubclass(instance_class, type); + if (!is_subclass) { + instance_class = NULL; + } else if (unlikely(is_subclass == -1)) { + goto bad; + } else { + type = instance_class; + } + } + } + if (!instance_class) { + PyObject *args; + if (!value) + args = PyTuple_New(0); + else if (PyTuple_Check(value)) { + Py_INCREF(value); + args = value; + } else + args = PyTuple_Pack(1, value); + if (!args) + goto bad; + owned_instance = PyObject_Call(type, args, NULL); + Py_DECREF(args); + if (!owned_instance) + goto bad; + value = owned_instance; + if (!PyExceptionInstance_Check(value)) { + PyErr_Format(PyExc_TypeError, + "calling %R should have returned an instance of " + "BaseException, not %R", + type, Py_TYPE(value)); + goto bad; + } + } + } else { + PyErr_SetString(PyExc_TypeError, + "raise: exception class must be a subclass of BaseException"); + goto bad; + } +#if PY_VERSION_HEX >= 0x03030000 + if (cause) { +#else + if (cause && cause != Py_None) { +#endif + PyObject *fixed_cause; + if (cause == Py_None) { + fixed_cause = NULL; + } else if (PyExceptionClass_Check(cause)) { + fixed_cause = PyObject_CallObject(cause, NULL); + if (fixed_cause == NULL) + goto bad; + } else if (PyExceptionInstance_Check(cause)) { + fixed_cause = cause; + Py_INCREF(fixed_cause); + } else { + PyErr_SetString(PyExc_TypeError, + "exception causes must derive from " + "BaseException"); + goto bad; + } + PyException_SetCause(value, fixed_cause); + } + PyErr_SetObject(type, value); + if (tb) { +#if CYTHON_COMPILING_IN_PYPY + PyObject *tmp_type, *tmp_value, *tmp_tb; + PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb); + Py_INCREF(tb); + PyErr_Restore(tmp_type, tmp_value, tb); + Py_XDECREF(tmp_tb); +#else + PyThreadState *tstate = PyThreadState_GET(); + PyObject* tmp_tb = tstate->curexc_traceback; + if (tb != tmp_tb) { + Py_INCREF(tb); + tstate->curexc_traceback = tb; + Py_XDECREF(tmp_tb); + } +#endif + } +bad: + Py_XDECREF(owned_instance); + return; +} +#endif + +/* ExtTypeTest */ + static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { + if (unlikely(!type)) { + PyErr_SetString(PyExc_SystemError, "Missing type object"); + return 0; + } + if (likely(PyObject_TypeCheck(obj, type))) + return 1; + PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s", + Py_TYPE(obj)->tp_name, type->tp_name); + return 0; +} + +/* ArgTypeTest */ + static void __Pyx_RaiseArgumentTypeInvalid(const char* name, PyObject *obj, PyTypeObject *type) { + PyErr_Format(PyExc_TypeError, + "Argument '%.200s' has incorrect type (expected %.200s, got %.200s)", + name, type->tp_name, Py_TYPE(obj)->tp_name); +} +static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed, + const char *name, int exact) +{ + if (unlikely(!type)) { + PyErr_SetString(PyExc_SystemError, "Missing type object"); + return 0; + } + if (none_allowed && obj == Py_None) return 1; + else if (exact) { + if (likely(Py_TYPE(obj) == type)) return 1; + #if PY_MAJOR_VERSION == 2 + else if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1; + #endif + } + else { + if (likely(PyObject_TypeCheck(obj, type))) return 1; + } + __Pyx_RaiseArgumentTypeInvalid(name, obj, type); + return 0; +} + +/* PyIntBinop */ + #if !CYTHON_COMPILING_IN_PYPY +static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, CYTHON_UNUSED long intval, CYTHON_UNUSED int inplace) { + #if PY_MAJOR_VERSION < 3 + if (likely(PyInt_CheckExact(op1))) { + const long b = intval; + long x; + long a = PyInt_AS_LONG(op1); + x = (long)((unsigned long)a + b); + if (likely((x^a) >= 0 || (x^b) >= 0)) + return PyInt_FromLong(x); + return PyLong_Type.tp_as_number->nb_add(op1, op2); + } + #endif + #if CYTHON_USE_PYLONG_INTERNALS + if (likely(PyLong_CheckExact(op1))) { + const long b = intval; + long a, x; +#ifdef HAVE_LONG_LONG + const PY_LONG_LONG llb = intval; + PY_LONG_LONG lla, llx; +#endif + const digit* digits = ((PyLongObject*)op1)->ob_digit; + const Py_ssize_t size = Py_SIZE(op1); + if (likely(__Pyx_sst_abs(size) <= 1)) { + a = likely(size) ? digits[0] : 0; + if (size == -1) a = -a; + } else { + switch (size) { + case -2: + if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + a = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) { + lla = -(PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + case 2: + if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) { + lla = (PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + case -3: + if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + a = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) { + lla = -(PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + case 3: + if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + a = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) { + lla = (PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + case -4: + if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + a = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) { + lla = -(PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + case 4: + if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + a = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) { + lla = (PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + default: return PyLong_Type.tp_as_number->nb_add(op1, op2); + } + } + x = a + b; + return PyLong_FromLong(x); +#ifdef HAVE_LONG_LONG + long_long: + llx = lla + llb; + return PyLong_FromLongLong(llx); +#endif + + + } + #endif + if (PyFloat_CheckExact(op1)) { + const long b = intval; + double a = PyFloat_AS_DOUBLE(op1); + double result; + PyFPE_START_PROTECT("add", return NULL) + result = ((double)a) + (double)b; + PyFPE_END_PROTECT(result) + return PyFloat_FromDouble(result); + } + return (inplace ? PyNumber_InPlaceAdd : PyNumber_Add)(op1, op2); +} +#endif + +/* PyIntBinop */ + #if !CYTHON_COMPILING_IN_PYPY +static PyObject* __Pyx_PyInt_EqObjC(PyObject *op1, PyObject *op2, CYTHON_UNUSED long intval, CYTHON_UNUSED int inplace) { + if (op1 == op2) { + Py_RETURN_TRUE; + } + #if PY_MAJOR_VERSION < 3 + if (likely(PyInt_CheckExact(op1))) { + const long b = intval; + long a = PyInt_AS_LONG(op1); + if (a == b) { + Py_RETURN_TRUE; + } else { + Py_RETURN_FALSE; + } + } + #endif + #if CYTHON_USE_PYLONG_INTERNALS + if (likely(PyLong_CheckExact(op1))) { + const long b = intval; + long a; + const digit* digits = ((PyLongObject*)op1)->ob_digit; + const Py_ssize_t size = Py_SIZE(op1); + if (likely(__Pyx_sst_abs(size) <= 1)) { + a = likely(size) ? digits[0] : 0; + if (size == -1) a = -a; + } else { + switch (size) { + case -2: + if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + a = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; + } + case 2: + if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; + } + case -3: + if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + a = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; + } + case 3: + if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + a = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; + } + case -4: + if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + a = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; + } + case 4: + if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + a = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; + } + #if PyLong_SHIFT < 30 && PyLong_SHIFT != 15 + default: return PyLong_Type.tp_richcompare(op1, op2, Py_EQ); + #else + default: Py_RETURN_FALSE; + #endif + } + } + if (a == b) { + Py_RETURN_TRUE; + } else { + Py_RETURN_FALSE; + } + } + #endif + if (PyFloat_CheckExact(op1)) { + const long b = intval; + double a = PyFloat_AS_DOUBLE(op1); + if ((double)a == (double)b) { + Py_RETURN_TRUE; + } else { + Py_RETURN_FALSE; + } + } + return PyObject_RichCompare(op1, op2, Py_EQ); +} +#endif + +/* GetModuleGlobalName */ + static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name) { + PyObject *result; +#if !CYTHON_AVOID_BORROWED_REFS + result = PyDict_GetItem(__pyx_d, name); + if (likely(result)) { + Py_INCREF(result); + } else { +#else + result = PyObject_GetItem(__pyx_d, name); + if (!result) { + PyErr_Clear(); +#endif + result = __Pyx_GetBuiltinName(name); + } + return result; +} + +/* PyCFunctionFastCall */ + #if CYTHON_FAST_PYCCALL +static CYTHON_INLINE PyObject * __Pyx_PyCFunction_FastCall(PyObject *func_obj, PyObject **args, Py_ssize_t nargs) { + PyCFunctionObject *func = (PyCFunctionObject*)func_obj; + PyCFunction meth = PyCFunction_GET_FUNCTION(func); + PyObject *self = PyCFunction_GET_SELF(func); + assert(PyCFunction_Check(func)); + assert(METH_FASTCALL == (PyCFunction_GET_FLAGS(func) & ~(METH_CLASS | METH_STATIC | METH_COEXIST))); + assert(nargs >= 0); + assert(nargs == 0 || args != NULL); + /* _PyCFunction_FastCallDict() must not be called with an exception set, + because it may clear it (directly or indirectly) and so the + caller loses its exception */ + assert(!PyErr_Occurred()); + return (*((__Pyx_PyCFunctionFast)meth)) (self, args, nargs, NULL); +} +#endif // CYTHON_FAST_PYCCALL + +/* PyFunctionFastCall */ + #if CYTHON_FAST_PYCALL +#include "frameobject.h" +static PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na, + PyObject *globals) { + PyFrameObject *f; + PyThreadState *tstate = PyThreadState_GET(); + PyObject **fastlocals; + Py_ssize_t i; + PyObject *result; + assert(globals != NULL); + /* XXX Perhaps we should create a specialized + PyFrame_New() that doesn't take locals, but does + take builtins without sanity checking them. + */ + assert(tstate != NULL); + f = PyFrame_New(tstate, co, globals, NULL); + if (f == NULL) { + return NULL; + } + fastlocals = f->f_localsplus; + for (i = 0; i < na; i++) { + Py_INCREF(*args); + fastlocals[i] = *args++; + } + result = PyEval_EvalFrameEx(f,0); + ++tstate->recursion_depth; + Py_DECREF(f); + --tstate->recursion_depth; + return result; +} +#if 1 || PY_VERSION_HEX < 0x030600B1 +static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, int nargs, PyObject *kwargs) { + PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func); + PyObject *globals = PyFunction_GET_GLOBALS(func); + PyObject *argdefs = PyFunction_GET_DEFAULTS(func); + PyObject *closure; +#if PY_MAJOR_VERSION >= 3 + PyObject *kwdefs; +#endif + PyObject *kwtuple, **k; + PyObject **d; + Py_ssize_t nd; + Py_ssize_t nk; + PyObject *result; + assert(kwargs == NULL || PyDict_Check(kwargs)); + nk = kwargs ? PyDict_Size(kwargs) : 0; + if (Py_EnterRecursiveCall((char*)" while calling a Python object")) { + return NULL; + } + if ( +#if PY_MAJOR_VERSION >= 3 + co->co_kwonlyargcount == 0 && +#endif + likely(kwargs == NULL || nk == 0) && + co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) { + if (argdefs == NULL && co->co_argcount == nargs) { + result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals); + goto done; + } + else if (nargs == 0 && argdefs != NULL + && co->co_argcount == Py_SIZE(argdefs)) { + /* function called with no arguments, but all parameters have + a default value: use default values as arguments .*/ + args = &PyTuple_GET_ITEM(argdefs, 0); + result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals); + goto done; + } + } + if (kwargs != NULL) { + Py_ssize_t pos, i; + kwtuple = PyTuple_New(2 * nk); + if (kwtuple == NULL) { + result = NULL; + goto done; + } + k = &PyTuple_GET_ITEM(kwtuple, 0); + pos = i = 0; + while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) { + Py_INCREF(k[i]); + Py_INCREF(k[i+1]); + i += 2; + } + nk = i / 2; + } + else { + kwtuple = NULL; + k = NULL; + } + closure = PyFunction_GET_CLOSURE(func); +#if PY_MAJOR_VERSION >= 3 + kwdefs = PyFunction_GET_KW_DEFAULTS(func); +#endif + if (argdefs != NULL) { + d = &PyTuple_GET_ITEM(argdefs, 0); + nd = Py_SIZE(argdefs); + } + else { + d = NULL; + nd = 0; + } +#if PY_MAJOR_VERSION >= 3 + result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL, + args, nargs, + k, (int)nk, + d, (int)nd, kwdefs, closure); +#else + result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL, + args, nargs, + k, (int)nk, + d, (int)nd, closure); +#endif + Py_XDECREF(kwtuple); +done: + Py_LeaveRecursiveCall(); + return result; +} +#endif // CPython < 3.6 +#endif // CYTHON_FAST_PYCALL + +/* PyObjectCallMethO */ + #if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) { + PyObject *self, *result; + PyCFunction cfunc; + cfunc = PyCFunction_GET_FUNCTION(func); + self = PyCFunction_GET_SELF(func); + if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) + return NULL; + result = cfunc(self, arg); + Py_LeaveRecursiveCall(); + if (unlikely(!result) && unlikely(!PyErr_Occurred())) { + PyErr_SetString( + PyExc_SystemError, + "NULL result without error in PyObject_Call"); + } + return result; +} +#endif + +/* PyObjectCallOneArg */ + #if CYTHON_COMPILING_IN_CPYTHON +static PyObject* __Pyx__PyObject_CallOneArg(PyObject *func, PyObject *arg) { + PyObject *result; + PyObject *args = PyTuple_New(1); + if (unlikely(!args)) return NULL; + Py_INCREF(arg); + PyTuple_SET_ITEM(args, 0, arg); + result = __Pyx_PyObject_Call(func, args, NULL); + Py_DECREF(args); + return result; +} +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { +#if CYTHON_FAST_PYCALL + if (PyFunction_Check(func)) { + return __Pyx_PyFunction_FastCall(func, &arg, 1); + } +#endif +#ifdef __Pyx_CyFunction_USED + if (likely(PyCFunction_Check(func) || PyObject_TypeCheck(func, __pyx_CyFunctionType))) { +#else + if (likely(PyCFunction_Check(func))) { +#endif + if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) { + return __Pyx_PyObject_CallMethO(func, arg); +#if CYTHON_FAST_PYCCALL + } else if (PyCFunction_GET_FLAGS(func) & METH_FASTCALL) { + return __Pyx_PyCFunction_FastCall(func, &arg, 1); +#endif + } + } + return __Pyx__PyObject_CallOneArg(func, arg); +} +#else +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { + PyObject *result; + PyObject *args = PyTuple_Pack(1, arg); + if (unlikely(!args)) return NULL; + result = __Pyx_PyObject_Call(func, args, NULL); + Py_DECREF(args); + return result; +} +#endif + +/* GetItemInt */ + static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) { + PyObject *r; + if (!j) return NULL; + r = PyObject_GetItem(o, j); + Py_DECREF(j); + return r; +} +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, + CYTHON_NCP_UNUSED int wraparound, + CYTHON_NCP_UNUSED int boundscheck) { +#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + if (wraparound & unlikely(i < 0)) i += PyList_GET_SIZE(o); + if ((!boundscheck) || likely((0 <= i) & (i < PyList_GET_SIZE(o)))) { + PyObject *r = PyList_GET_ITEM(o, i); + Py_INCREF(r); + return r; + } + return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); +#else + return PySequence_GetItem(o, i); +#endif +} +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, + CYTHON_NCP_UNUSED int wraparound, + CYTHON_NCP_UNUSED int boundscheck) { +#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + if (wraparound & unlikely(i < 0)) i += PyTuple_GET_SIZE(o); + if ((!boundscheck) || likely((0 <= i) & (i < PyTuple_GET_SIZE(o)))) { + PyObject *r = PyTuple_GET_ITEM(o, i); + Py_INCREF(r); + return r; + } + return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); +#else + return PySequence_GetItem(o, i); +#endif +} +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list, + CYTHON_NCP_UNUSED int wraparound, + CYTHON_NCP_UNUSED int boundscheck) { +#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS + if (is_list || PyList_CheckExact(o)) { + Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o); + if ((!boundscheck) || (likely((n >= 0) & (n < PyList_GET_SIZE(o))))) { + PyObject *r = PyList_GET_ITEM(o, n); + Py_INCREF(r); + return r; + } + } + else if (PyTuple_CheckExact(o)) { + Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o); + if ((!boundscheck) || likely((n >= 0) & (n < PyTuple_GET_SIZE(o)))) { + PyObject *r = PyTuple_GET_ITEM(o, n); + Py_INCREF(r); + return r; + } + } else { + PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence; + if (likely(m && m->sq_item)) { + if (wraparound && unlikely(i < 0) && likely(m->sq_length)) { + Py_ssize_t l = m->sq_length(o); + if (likely(l >= 0)) { + i += l; + } else { + if (!PyErr_ExceptionMatches(PyExc_OverflowError)) + return NULL; + PyErr_Clear(); + } + } + return m->sq_item(o, i); + } + } +#else + if (is_list || PySequence_Check(o)) { + return PySequence_GetItem(o, i); + } +#endif + return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); +} + +/* BufferFormatCheck */ + static CYTHON_INLINE int __Pyx_IsLittleEndian(void) { + unsigned int n = 1; + return *(unsigned char*)(&n) != 0; +} +static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, + __Pyx_BufFmt_StackElem* stack, + __Pyx_TypeInfo* type) { + stack[0].field = &ctx->root; + stack[0].parent_offset = 0; + ctx->root.type = type; + ctx->root.name = "buffer dtype"; + ctx->root.offset = 0; + ctx->head = stack; + ctx->head->field = &ctx->root; + ctx->fmt_offset = 0; + ctx->head->parent_offset = 0; + ctx->new_packmode = '@'; + ctx->enc_packmode = '@'; + ctx->new_count = 1; + ctx->enc_count = 0; + ctx->enc_type = 0; + ctx->is_complex = 0; + ctx->is_valid_array = 0; + ctx->struct_alignment = 0; + while (type->typegroup == 'S') { + ++ctx->head; + ctx->head->field = type->fields; + ctx->head->parent_offset = 0; + type = type->fields->type; + } +} +static int __Pyx_BufFmt_ParseNumber(const char** ts) { + int count; + const char* t = *ts; + if (*t < '0' || *t > '9') { + return -1; + } else { + count = *t++ - '0'; + while (*t >= '0' && *t < '9') { + count *= 10; + count += *t++ - '0'; + } + } + *ts = t; + return count; +} +static int __Pyx_BufFmt_ExpectNumber(const char **ts) { + int number = __Pyx_BufFmt_ParseNumber(ts); + if (number == -1) + PyErr_Format(PyExc_ValueError,\ + "Does not understand character buffer dtype format string ('%c')", **ts); + return number; +} +static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) { + PyErr_Format(PyExc_ValueError, + "Unexpected format string character: '%c'", ch); +} +static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) { + switch (ch) { + case 'c': return "'char'"; + case 'b': return "'signed char'"; + case 'B': return "'unsigned char'"; + case 'h': return "'short'"; + case 'H': return "'unsigned short'"; + case 'i': return "'int'"; + case 'I': return "'unsigned int'"; + case 'l': return "'long'"; + case 'L': return "'unsigned long'"; + case 'q': return "'long long'"; + case 'Q': return "'unsigned long long'"; + case 'f': return (is_complex ? "'complex float'" : "'float'"); + case 'd': return (is_complex ? "'complex double'" : "'double'"); + case 'g': return (is_complex ? "'complex long double'" : "'long double'"); + case 'T': return "a struct"; + case 'O': return "Python object"; + case 'P': return "a pointer"; + case 's': case 'p': return "a string"; + case 0: return "end"; + default: return "unparseable format string"; + } +} +static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return 2; + case 'i': case 'I': case 'l': case 'L': return 4; + case 'q': case 'Q': return 8; + case 'f': return (is_complex ? 8 : 4); + case 'd': return (is_complex ? 16 : 8); + case 'g': { + PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g').."); + return 0; + } + case 'O': case 'P': return sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) { + switch (ch) { + case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(short); + case 'i': case 'I': return sizeof(int); + case 'l': case 'L': return sizeof(long); + #ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(PY_LONG_LONG); + #endif + case 'f': return sizeof(float) * (is_complex ? 2 : 1); + case 'd': return sizeof(double) * (is_complex ? 2 : 1); + case 'g': return sizeof(long double) * (is_complex ? 2 : 1); + case 'O': case 'P': return sizeof(void*); + default: { + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } + } +} +typedef struct { char c; short x; } __Pyx_st_short; +typedef struct { char c; int x; } __Pyx_st_int; +typedef struct { char c; long x; } __Pyx_st_long; +typedef struct { char c; float x; } __Pyx_st_float; +typedef struct { char c; double x; } __Pyx_st_double; +typedef struct { char c; long double x; } __Pyx_st_longdouble; +typedef struct { char c; void *x; } __Pyx_st_void_p; +#ifdef HAVE_LONG_LONG +typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong; +#endif +static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, CYTHON_UNUSED int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short); + case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int); + case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long); +#ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG); +#endif + case 'f': return sizeof(__Pyx_st_float) - sizeof(float); + case 'd': return sizeof(__Pyx_st_double) - sizeof(double); + case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double); + case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +/* These are for computing the padding at the end of the struct to align + on the first member of the struct. This will probably the same as above, + but we don't have any guarantees. + */ +typedef struct { short x; char c; } __Pyx_pad_short; +typedef struct { int x; char c; } __Pyx_pad_int; +typedef struct { long x; char c; } __Pyx_pad_long; +typedef struct { float x; char c; } __Pyx_pad_float; +typedef struct { double x; char c; } __Pyx_pad_double; +typedef struct { long double x; char c; } __Pyx_pad_longdouble; +typedef struct { void *x; char c; } __Pyx_pad_void_p; +#ifdef HAVE_LONG_LONG +typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong; +#endif +static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short); + case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int); + case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long); +#ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG); +#endif + case 'f': return sizeof(__Pyx_pad_float) - sizeof(float); + case 'd': return sizeof(__Pyx_pad_double) - sizeof(double); + case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double); + case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) { + switch (ch) { + case 'c': + return 'H'; + case 'b': case 'h': case 'i': + case 'l': case 'q': case 's': case 'p': + return 'I'; + case 'B': case 'H': case 'I': case 'L': case 'Q': + return 'U'; + case 'f': case 'd': case 'g': + return (is_complex ? 'C' : 'R'); + case 'O': + return 'O'; + case 'P': + return 'P'; + default: { + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } + } +} +static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) { + if (ctx->head == NULL || ctx->head->field == &ctx->root) { + const char* expected; + const char* quote; + if (ctx->head == NULL) { + expected = "end"; + quote = ""; + } else { + expected = ctx->head->field->type->name; + quote = "'"; + } + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch, expected %s%s%s but got %s", + quote, expected, quote, + __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex)); + } else { + __Pyx_StructField* field = ctx->head->field; + __Pyx_StructField* parent = (ctx->head - 1)->field; + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'", + field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex), + parent->type->name, field->name); + } +} +static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) { + char group; + size_t size, offset, arraysize = 1; + if (ctx->enc_type == 0) return 0; + if (ctx->head->field->type->arraysize[0]) { + int i, ndim = 0; + if (ctx->enc_type == 's' || ctx->enc_type == 'p') { + ctx->is_valid_array = ctx->head->field->type->ndim == 1; + ndim = 1; + if (ctx->enc_count != ctx->head->field->type->arraysize[0]) { + PyErr_Format(PyExc_ValueError, + "Expected a dimension of size %zu, got %zu", + ctx->head->field->type->arraysize[0], ctx->enc_count); + return -1; + } + } + if (!ctx->is_valid_array) { + PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d", + ctx->head->field->type->ndim, ndim); + return -1; + } + for (i = 0; i < ctx->head->field->type->ndim; i++) { + arraysize *= ctx->head->field->type->arraysize[i]; + } + ctx->is_valid_array = 0; + ctx->enc_count = 1; + } + group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex); + do { + __Pyx_StructField* field = ctx->head->field; + __Pyx_TypeInfo* type = field->type; + if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') { + size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex); + } else { + size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex); + } + if (ctx->enc_packmode == '@') { + size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex); + size_t align_mod_offset; + if (align_at == 0) return -1; + align_mod_offset = ctx->fmt_offset % align_at; + if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset; + if (ctx->struct_alignment == 0) + ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type, + ctx->is_complex); + } + if (type->size != size || type->typegroup != group) { + if (type->typegroup == 'C' && type->fields != NULL) { + size_t parent_offset = ctx->head->parent_offset + field->offset; + ++ctx->head; + ctx->head->field = type->fields; + ctx->head->parent_offset = parent_offset; + continue; + } + if ((type->typegroup == 'H' || group == 'H') && type->size == size) { + } else { + __Pyx_BufFmt_RaiseExpected(ctx); + return -1; + } + } + offset = ctx->head->parent_offset + field->offset; + if (ctx->fmt_offset != offset) { + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected", + (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset); + return -1; + } + ctx->fmt_offset += size; + if (arraysize) + ctx->fmt_offset += (arraysize - 1) * size; + --ctx->enc_count; + while (1) { + if (field == &ctx->root) { + ctx->head = NULL; + if (ctx->enc_count != 0) { + __Pyx_BufFmt_RaiseExpected(ctx); + return -1; + } + break; + } + ctx->head->field = ++field; + if (field->type == NULL) { + --ctx->head; + field = ctx->head->field; + continue; + } else if (field->type->typegroup == 'S') { + size_t parent_offset = ctx->head->parent_offset + field->offset; + if (field->type->fields->type == NULL) continue; + field = field->type->fields; + ++ctx->head; + ctx->head->field = field; + ctx->head->parent_offset = parent_offset; + break; + } else { + break; + } + } + } while (ctx->enc_count); + ctx->enc_type = 0; + ctx->is_complex = 0; + return 0; +} +static CYTHON_INLINE PyObject * +__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp) +{ + const char *ts = *tsp; + int i = 0, number; + int ndim = ctx->head->field->type->ndim; +; + ++ts; + if (ctx->new_count != 1) { + PyErr_SetString(PyExc_ValueError, + "Cannot handle repeated arrays in format string"); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + while (*ts && *ts != ')') { + switch (*ts) { + case ' ': case '\f': case '\r': case '\n': case '\t': case '\v': continue; + default: break; + } + number = __Pyx_BufFmt_ExpectNumber(&ts); + if (number == -1) return NULL; + if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i]) + return PyErr_Format(PyExc_ValueError, + "Expected a dimension of size %zu, got %d", + ctx->head->field->type->arraysize[i], number); + if (*ts != ',' && *ts != ')') + return PyErr_Format(PyExc_ValueError, + "Expected a comma in format string, got '%c'", *ts); + if (*ts == ',') ts++; + i++; + } + if (i != ndim) + return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d", + ctx->head->field->type->ndim, i); + if (!*ts) { + PyErr_SetString(PyExc_ValueError, + "Unexpected end of format string, expected ')'"); + return NULL; + } + ctx->is_valid_array = 1; + ctx->new_count = 1; + *tsp = ++ts; + return Py_None; +} +static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) { + int got_Z = 0; + while (1) { + switch(*ts) { + case 0: + if (ctx->enc_type != 0 && ctx->head == NULL) { + __Pyx_BufFmt_RaiseExpected(ctx); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + if (ctx->head != NULL) { + __Pyx_BufFmt_RaiseExpected(ctx); + return NULL; + } + return ts; + case ' ': + case '\r': + case '\n': + ++ts; + break; + case '<': + if (!__Pyx_IsLittleEndian()) { + PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler"); + return NULL; + } + ctx->new_packmode = '='; + ++ts; + break; + case '>': + case '!': + if (__Pyx_IsLittleEndian()) { + PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler"); + return NULL; + } + ctx->new_packmode = '='; + ++ts; + break; + case '=': + case '@': + case '^': + ctx->new_packmode = *ts++; + break; + case 'T': + { + const char* ts_after_sub; + size_t i, struct_count = ctx->new_count; + size_t struct_alignment = ctx->struct_alignment; + ctx->new_count = 1; + ++ts; + if (*ts != '{') { + PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'"); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_type = 0; + ctx->enc_count = 0; + ctx->struct_alignment = 0; + ++ts; + ts_after_sub = ts; + for (i = 0; i != struct_count; ++i) { + ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts); + if (!ts_after_sub) return NULL; + } + ts = ts_after_sub; + if (struct_alignment) ctx->struct_alignment = struct_alignment; + } + break; + case '}': + { + size_t alignment = ctx->struct_alignment; + ++ts; + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_type = 0; + if (alignment && ctx->fmt_offset % alignment) { + ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment); + } + } + return ts; + case 'x': + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->fmt_offset += ctx->new_count; + ctx->new_count = 1; + ctx->enc_count = 0; + ctx->enc_type = 0; + ctx->enc_packmode = ctx->new_packmode; + ++ts; + break; + case 'Z': + got_Z = 1; + ++ts; + if (*ts != 'f' && *ts != 'd' && *ts != 'g') { + __Pyx_BufFmt_RaiseUnexpectedChar('Z'); + return NULL; + } + case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I': + case 'l': case 'L': case 'q': case 'Q': + case 'f': case 'd': case 'g': + case 'O': case 'p': + if (ctx->enc_type == *ts && got_Z == ctx->is_complex && + ctx->enc_packmode == ctx->new_packmode) { + ctx->enc_count += ctx->new_count; + ctx->new_count = 1; + got_Z = 0; + ++ts; + break; + } + case 's': + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_count = ctx->new_count; + ctx->enc_packmode = ctx->new_packmode; + ctx->enc_type = *ts; + ctx->is_complex = got_Z; + ++ts; + ctx->new_count = 1; + got_Z = 0; + break; + case ':': + ++ts; + while(*ts != ':') ++ts; + ++ts; + break; + case '(': + if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL; + break; + default: + { + int number = __Pyx_BufFmt_ExpectNumber(&ts); + if (number == -1) return NULL; + ctx->new_count = (size_t)number; + } + } + } +} +static CYTHON_INLINE void __Pyx_ZeroBuffer(Py_buffer* buf) { + buf->buf = NULL; + buf->obj = NULL; + buf->strides = __Pyx_zeros; + buf->shape = __Pyx_zeros; + buf->suboffsets = __Pyx_minusones; +} +static CYTHON_INLINE int __Pyx_GetBufferAndValidate( + Py_buffer* buf, PyObject* obj, __Pyx_TypeInfo* dtype, int flags, + int nd, int cast, __Pyx_BufFmt_StackElem* stack) +{ + if (obj == Py_None || obj == NULL) { + __Pyx_ZeroBuffer(buf); + return 0; + } + buf->buf = NULL; + if (__Pyx_GetBuffer(obj, buf, flags) == -1) goto fail; + if (buf->ndim != nd) { + PyErr_Format(PyExc_ValueError, + "Buffer has wrong number of dimensions (expected %d, got %d)", + nd, buf->ndim); + goto fail; + } + if (!cast) { + __Pyx_BufFmt_Context ctx; + __Pyx_BufFmt_Init(&ctx, stack, dtype); + if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail; + } + if ((unsigned)buf->itemsize != dtype->size) { + PyErr_Format(PyExc_ValueError, + "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "d byte%s) does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "d byte%s)", + buf->itemsize, (buf->itemsize > 1) ? "s" : "", + dtype->name, (Py_ssize_t)dtype->size, (dtype->size > 1) ? "s" : ""); + goto fail; + } + if (buf->suboffsets == NULL) buf->suboffsets = __Pyx_minusones; + return 0; +fail:; + __Pyx_ZeroBuffer(buf); + return -1; +} +static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info) { + if (info->buf == NULL) return; + if (info->suboffsets == __Pyx_minusones) info->suboffsets = NULL; + __Pyx_ReleaseBuffer(info); +} + +/* FetchCommonType */ + static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type) { + PyObject* fake_module; + PyTypeObject* cached_type = NULL; + fake_module = PyImport_AddModule((char*) "_cython_" CYTHON_ABI); + if (!fake_module) return NULL; + Py_INCREF(fake_module); + cached_type = (PyTypeObject*) PyObject_GetAttrString(fake_module, type->tp_name); + if (cached_type) { + if (!PyType_Check((PyObject*)cached_type)) { + PyErr_Format(PyExc_TypeError, + "Shared Cython type %.200s is not a type object", + type->tp_name); + goto bad; + } + if (cached_type->tp_basicsize != type->tp_basicsize) { + PyErr_Format(PyExc_TypeError, + "Shared Cython type %.200s has the wrong size, try recompiling", + type->tp_name); + goto bad; + } + } else { + if (!PyErr_ExceptionMatches(PyExc_AttributeError)) goto bad; + PyErr_Clear(); + if (PyType_Ready(type) < 0) goto bad; + if (PyObject_SetAttrString(fake_module, type->tp_name, (PyObject*) type) < 0) + goto bad; + Py_INCREF(type); + cached_type = type; + } +done: + Py_DECREF(fake_module); + return cached_type; +bad: + Py_XDECREF(cached_type); + cached_type = NULL; + goto done; +} + +/* CythonFunction */ + static PyObject * +__Pyx_CyFunction_get_doc(__pyx_CyFunctionObject *op, CYTHON_UNUSED void *closure) +{ + if (unlikely(op->func_doc == NULL)) { + if (op->func.m_ml->ml_doc) { +#if PY_MAJOR_VERSION >= 3 + op->func_doc = PyUnicode_FromString(op->func.m_ml->ml_doc); +#else + op->func_doc = PyString_FromString(op->func.m_ml->ml_doc); +#endif + if (unlikely(op->func_doc == NULL)) + return NULL; + } else { + Py_INCREF(Py_None); + return Py_None; + } + } + Py_INCREF(op->func_doc); + return op->func_doc; +} +static int +__Pyx_CyFunction_set_doc(__pyx_CyFunctionObject *op, PyObject *value) +{ + PyObject *tmp = op->func_doc; + if (value == NULL) { + value = Py_None; + } + Py_INCREF(value); + op->func_doc = value; + Py_XDECREF(tmp); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_name(__pyx_CyFunctionObject *op) +{ + if (unlikely(op->func_name == NULL)) { +#if PY_MAJOR_VERSION >= 3 + op->func_name = PyUnicode_InternFromString(op->func.m_ml->ml_name); +#else + op->func_name = PyString_InternFromString(op->func.m_ml->ml_name); +#endif + if (unlikely(op->func_name == NULL)) + return NULL; + } + Py_INCREF(op->func_name); + return op->func_name; +} +static int +__Pyx_CyFunction_set_name(__pyx_CyFunctionObject *op, PyObject *value) +{ + PyObject *tmp; +#if PY_MAJOR_VERSION >= 3 + if (unlikely(value == NULL || !PyUnicode_Check(value))) { +#else + if (unlikely(value == NULL || !PyString_Check(value))) { +#endif + PyErr_SetString(PyExc_TypeError, + "__name__ must be set to a string object"); + return -1; + } + tmp = op->func_name; + Py_INCREF(value); + op->func_name = value; + Py_XDECREF(tmp); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_qualname(__pyx_CyFunctionObject *op) +{ + Py_INCREF(op->func_qualname); + return op->func_qualname; +} +static int +__Pyx_CyFunction_set_qualname(__pyx_CyFunctionObject *op, PyObject *value) +{ + PyObject *tmp; +#if PY_MAJOR_VERSION >= 3 + if (unlikely(value == NULL || !PyUnicode_Check(value))) { +#else + if (unlikely(value == NULL || !PyString_Check(value))) { +#endif + PyErr_SetString(PyExc_TypeError, + "__qualname__ must be set to a string object"); + return -1; + } + tmp = op->func_qualname; + Py_INCREF(value); + op->func_qualname = value; + Py_XDECREF(tmp); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_self(__pyx_CyFunctionObject *m, CYTHON_UNUSED void *closure) +{ + PyObject *self; + self = m->func_closure; + if (self == NULL) + self = Py_None; + Py_INCREF(self); + return self; +} +static PyObject * +__Pyx_CyFunction_get_dict(__pyx_CyFunctionObject *op) +{ + if (unlikely(op->func_dict == NULL)) { + op->func_dict = PyDict_New(); + if (unlikely(op->func_dict == NULL)) + return NULL; + } + Py_INCREF(op->func_dict); + return op->func_dict; +} +static int +__Pyx_CyFunction_set_dict(__pyx_CyFunctionObject *op, PyObject *value) +{ + PyObject *tmp; + if (unlikely(value == NULL)) { + PyErr_SetString(PyExc_TypeError, + "function's dictionary may not be deleted"); + return -1; + } + if (unlikely(!PyDict_Check(value))) { + PyErr_SetString(PyExc_TypeError, + "setting function's dictionary to a non-dict"); + return -1; + } + tmp = op->func_dict; + Py_INCREF(value); + op->func_dict = value; + Py_XDECREF(tmp); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_globals(__pyx_CyFunctionObject *op) +{ + Py_INCREF(op->func_globals); + return op->func_globals; +} +static PyObject * +__Pyx_CyFunction_get_closure(CYTHON_UNUSED __pyx_CyFunctionObject *op) +{ + Py_INCREF(Py_None); + return Py_None; +} +static PyObject * +__Pyx_CyFunction_get_code(__pyx_CyFunctionObject *op) +{ + PyObject* result = (op->func_code) ? op->func_code : Py_None; + Py_INCREF(result); + return result; +} +static int +__Pyx_CyFunction_init_defaults(__pyx_CyFunctionObject *op) { + int result = 0; + PyObject *res = op->defaults_getter((PyObject *) op); + if (unlikely(!res)) + return -1; + #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + op->defaults_tuple = PyTuple_GET_ITEM(res, 0); + Py_INCREF(op->defaults_tuple); + op->defaults_kwdict = PyTuple_GET_ITEM(res, 1); + Py_INCREF(op->defaults_kwdict); + #else + op->defaults_tuple = PySequence_ITEM(res, 0); + if (unlikely(!op->defaults_tuple)) result = -1; + else { + op->defaults_kwdict = PySequence_ITEM(res, 1); + if (unlikely(!op->defaults_kwdict)) result = -1; + } + #endif + Py_DECREF(res); + return result; +} +static int +__Pyx_CyFunction_set_defaults(__pyx_CyFunctionObject *op, PyObject* value) { + PyObject* tmp; + if (!value) { + value = Py_None; + } else if (value != Py_None && !PyTuple_Check(value)) { + PyErr_SetString(PyExc_TypeError, + "__defaults__ must be set to a tuple object"); + return -1; + } + Py_INCREF(value); + tmp = op->defaults_tuple; + op->defaults_tuple = value; + Py_XDECREF(tmp); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_defaults(__pyx_CyFunctionObject *op) { + PyObject* result = op->defaults_tuple; + if (unlikely(!result)) { + if (op->defaults_getter) { + if (__Pyx_CyFunction_init_defaults(op) < 0) return NULL; + result = op->defaults_tuple; + } else { + result = Py_None; + } + } + Py_INCREF(result); + return result; +} +static int +__Pyx_CyFunction_set_kwdefaults(__pyx_CyFunctionObject *op, PyObject* value) { + PyObject* tmp; + if (!value) { + value = Py_None; + } else if (value != Py_None && !PyDict_Check(value)) { + PyErr_SetString(PyExc_TypeError, + "__kwdefaults__ must be set to a dict object"); + return -1; + } + Py_INCREF(value); + tmp = op->defaults_kwdict; + op->defaults_kwdict = value; + Py_XDECREF(tmp); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_kwdefaults(__pyx_CyFunctionObject *op) { + PyObject* result = op->defaults_kwdict; + if (unlikely(!result)) { + if (op->defaults_getter) { + if (__Pyx_CyFunction_init_defaults(op) < 0) return NULL; + result = op->defaults_kwdict; + } else { + result = Py_None; + } + } + Py_INCREF(result); + return result; +} +static int +__Pyx_CyFunction_set_annotations(__pyx_CyFunctionObject *op, PyObject* value) { + PyObject* tmp; + if (!value || value == Py_None) { + value = NULL; + } else if (!PyDict_Check(value)) { + PyErr_SetString(PyExc_TypeError, + "__annotations__ must be set to a dict object"); + return -1; + } + Py_XINCREF(value); + tmp = op->func_annotations; + op->func_annotations = value; + Py_XDECREF(tmp); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_annotations(__pyx_CyFunctionObject *op) { + PyObject* result = op->func_annotations; + if (unlikely(!result)) { + result = PyDict_New(); + if (unlikely(!result)) return NULL; + op->func_annotations = result; + } + Py_INCREF(result); + return result; +} +static PyGetSetDef __pyx_CyFunction_getsets[] = { + {(char *) "func_doc", (getter)__Pyx_CyFunction_get_doc, (setter)__Pyx_CyFunction_set_doc, 0, 0}, + {(char *) "__doc__", (getter)__Pyx_CyFunction_get_doc, (setter)__Pyx_CyFunction_set_doc, 0, 0}, + {(char *) "func_name", (getter)__Pyx_CyFunction_get_name, (setter)__Pyx_CyFunction_set_name, 0, 0}, + {(char *) "__name__", (getter)__Pyx_CyFunction_get_name, (setter)__Pyx_CyFunction_set_name, 0, 0}, + {(char *) "__qualname__", (getter)__Pyx_CyFunction_get_qualname, (setter)__Pyx_CyFunction_set_qualname, 0, 0}, + {(char *) "__self__", (getter)__Pyx_CyFunction_get_self, 0, 0, 0}, + {(char *) "func_dict", (getter)__Pyx_CyFunction_get_dict, (setter)__Pyx_CyFunction_set_dict, 0, 0}, + {(char *) "__dict__", (getter)__Pyx_CyFunction_get_dict, (setter)__Pyx_CyFunction_set_dict, 0, 0}, + {(char *) "func_globals", (getter)__Pyx_CyFunction_get_globals, 0, 0, 0}, + {(char *) "__globals__", (getter)__Pyx_CyFunction_get_globals, 0, 0, 0}, + {(char *) "func_closure", (getter)__Pyx_CyFunction_get_closure, 0, 0, 0}, + {(char *) "__closure__", (getter)__Pyx_CyFunction_get_closure, 0, 0, 0}, + {(char *) "func_code", (getter)__Pyx_CyFunction_get_code, 0, 0, 0}, + {(char *) "__code__", (getter)__Pyx_CyFunction_get_code, 0, 0, 0}, + {(char *) "func_defaults", (getter)__Pyx_CyFunction_get_defaults, (setter)__Pyx_CyFunction_set_defaults, 0, 0}, + {(char *) "__defaults__", (getter)__Pyx_CyFunction_get_defaults, (setter)__Pyx_CyFunction_set_defaults, 0, 0}, + {(char *) "__kwdefaults__", (getter)__Pyx_CyFunction_get_kwdefaults, (setter)__Pyx_CyFunction_set_kwdefaults, 0, 0}, + {(char *) "__annotations__", (getter)__Pyx_CyFunction_get_annotations, (setter)__Pyx_CyFunction_set_annotations, 0, 0}, + {0, 0, 0, 0, 0} +}; +static PyMemberDef __pyx_CyFunction_members[] = { + {(char *) "__module__", T_OBJECT, offsetof(__pyx_CyFunctionObject, func.m_module), PY_WRITE_RESTRICTED, 0}, + {0, 0, 0, 0, 0} +}; +static PyObject * +__Pyx_CyFunction_reduce(__pyx_CyFunctionObject *m, CYTHON_UNUSED PyObject *args) +{ +#if PY_MAJOR_VERSION >= 3 + return PyUnicode_FromString(m->func.m_ml->ml_name); +#else + return PyString_FromString(m->func.m_ml->ml_name); +#endif +} +static PyMethodDef __pyx_CyFunction_methods[] = { + {"__reduce__", (PyCFunction)__Pyx_CyFunction_reduce, METH_VARARGS, 0}, + {0, 0, 0, 0} +}; +#if PY_VERSION_HEX < 0x030500A0 +#define __Pyx_CyFunction_weakreflist(cyfunc) ((cyfunc)->func_weakreflist) +#else +#define __Pyx_CyFunction_weakreflist(cyfunc) ((cyfunc)->func.m_weakreflist) +#endif +static PyObject *__Pyx_CyFunction_New(PyTypeObject *type, PyMethodDef *ml, int flags, PyObject* qualname, + PyObject *closure, PyObject *module, PyObject* globals, PyObject* code) { + __pyx_CyFunctionObject *op = PyObject_GC_New(__pyx_CyFunctionObject, type); + if (op == NULL) + return NULL; + op->flags = flags; + __Pyx_CyFunction_weakreflist(op) = NULL; + op->func.m_ml = ml; + op->func.m_self = (PyObject *) op; + Py_XINCREF(closure); + op->func_closure = closure; + Py_XINCREF(module); + op->func.m_module = module; + op->func_dict = NULL; + op->func_name = NULL; + Py_INCREF(qualname); + op->func_qualname = qualname; + op->func_doc = NULL; + op->func_classobj = NULL; + op->func_globals = globals; + Py_INCREF(op->func_globals); + Py_XINCREF(code); + op->func_code = code; + op->defaults_pyobjects = 0; + op->defaults = NULL; + op->defaults_tuple = NULL; + op->defaults_kwdict = NULL; + op->defaults_getter = NULL; + op->func_annotations = NULL; + PyObject_GC_Track(op); + return (PyObject *) op; +} +static int +__Pyx_CyFunction_clear(__pyx_CyFunctionObject *m) +{ + Py_CLEAR(m->func_closure); + Py_CLEAR(m->func.m_module); + Py_CLEAR(m->func_dict); + Py_CLEAR(m->func_name); + Py_CLEAR(m->func_qualname); + Py_CLEAR(m->func_doc); + Py_CLEAR(m->func_globals); + Py_CLEAR(m->func_code); + Py_CLEAR(m->func_classobj); + Py_CLEAR(m->defaults_tuple); + Py_CLEAR(m->defaults_kwdict); + Py_CLEAR(m->func_annotations); + if (m->defaults) { + PyObject **pydefaults = __Pyx_CyFunction_Defaults(PyObject *, m); + int i; + for (i = 0; i < m->defaults_pyobjects; i++) + Py_XDECREF(pydefaults[i]); + PyObject_Free(m->defaults); + m->defaults = NULL; + } + return 0; +} +static void __Pyx_CyFunction_dealloc(__pyx_CyFunctionObject *m) +{ + PyObject_GC_UnTrack(m); + if (__Pyx_CyFunction_weakreflist(m) != NULL) + PyObject_ClearWeakRefs((PyObject *) m); + __Pyx_CyFunction_clear(m); + PyObject_GC_Del(m); +} +static int __Pyx_CyFunction_traverse(__pyx_CyFunctionObject *m, visitproc visit, void *arg) +{ + Py_VISIT(m->func_closure); + Py_VISIT(m->func.m_module); + Py_VISIT(m->func_dict); + Py_VISIT(m->func_name); + Py_VISIT(m->func_qualname); + Py_VISIT(m->func_doc); + Py_VISIT(m->func_globals); + Py_VISIT(m->func_code); + Py_VISIT(m->func_classobj); + Py_VISIT(m->defaults_tuple); + Py_VISIT(m->defaults_kwdict); + if (m->defaults) { + PyObject **pydefaults = __Pyx_CyFunction_Defaults(PyObject *, m); + int i; + for (i = 0; i < m->defaults_pyobjects; i++) + Py_VISIT(pydefaults[i]); + } + return 0; +} +static PyObject *__Pyx_CyFunction_descr_get(PyObject *func, PyObject *obj, PyObject *type) +{ + __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; + if (m->flags & __Pyx_CYFUNCTION_STATICMETHOD) { + Py_INCREF(func); + return func; + } + if (m->flags & __Pyx_CYFUNCTION_CLASSMETHOD) { + if (type == NULL) + type = (PyObject *)(Py_TYPE(obj)); + return __Pyx_PyMethod_New(func, type, (PyObject *)(Py_TYPE(type))); + } + if (obj == Py_None) + obj = NULL; + return __Pyx_PyMethod_New(func, obj, type); +} +static PyObject* +__Pyx_CyFunction_repr(__pyx_CyFunctionObject *op) +{ +#if PY_MAJOR_VERSION >= 3 + return PyUnicode_FromFormat("", + op->func_qualname, (void *)op); +#else + return PyString_FromFormat("", + PyString_AsString(op->func_qualname), (void *)op); +#endif +} +static PyObject * __Pyx_CyFunction_CallMethod(PyObject *func, PyObject *self, PyObject *arg, PyObject *kw) { + PyCFunctionObject* f = (PyCFunctionObject*)func; + PyCFunction meth = f->m_ml->ml_meth; + Py_ssize_t size; + switch (f->m_ml->ml_flags & (METH_VARARGS | METH_KEYWORDS | METH_NOARGS | METH_O)) { + case METH_VARARGS: + if (likely(kw == NULL || PyDict_Size(kw) == 0)) + return (*meth)(self, arg); + break; + case METH_VARARGS | METH_KEYWORDS: + return (*(PyCFunctionWithKeywords)meth)(self, arg, kw); + case METH_NOARGS: + if (likely(kw == NULL || PyDict_Size(kw) == 0)) { + size = PyTuple_GET_SIZE(arg); + if (likely(size == 0)) + return (*meth)(self, NULL); + PyErr_Format(PyExc_TypeError, + "%.200s() takes no arguments (%" CYTHON_FORMAT_SSIZE_T "d given)", + f->m_ml->ml_name, size); + return NULL; + } + break; + case METH_O: + if (likely(kw == NULL || PyDict_Size(kw) == 0)) { + size = PyTuple_GET_SIZE(arg); + if (likely(size == 1)) { + PyObject *result, *arg0 = PySequence_ITEM(arg, 0); + if (unlikely(!arg0)) return NULL; + result = (*meth)(self, arg0); + Py_DECREF(arg0); + return result; + } + PyErr_Format(PyExc_TypeError, + "%.200s() takes exactly one argument (%" CYTHON_FORMAT_SSIZE_T "d given)", + f->m_ml->ml_name, size); + return NULL; + } + break; + default: + PyErr_SetString(PyExc_SystemError, "Bad call flags in " + "__Pyx_CyFunction_Call. METH_OLDARGS is no " + "longer supported!"); + return NULL; + } + PyErr_Format(PyExc_TypeError, "%.200s() takes no keyword arguments", + f->m_ml->ml_name); + return NULL; +} +static CYTHON_INLINE PyObject *__Pyx_CyFunction_Call(PyObject *func, PyObject *arg, PyObject *kw) { + return __Pyx_CyFunction_CallMethod(func, ((PyCFunctionObject*)func)->m_self, arg, kw); +} +static PyObject *__Pyx_CyFunction_CallAsMethod(PyObject *func, PyObject *args, PyObject *kw) { + PyObject *result; + __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *) func; + if ((cyfunc->flags & __Pyx_CYFUNCTION_CCLASS) && !(cyfunc->flags & __Pyx_CYFUNCTION_STATICMETHOD)) { + Py_ssize_t argc; + PyObject *new_args; + PyObject *self; + argc = PyTuple_GET_SIZE(args); + new_args = PyTuple_GetSlice(args, 1, argc); + if (unlikely(!new_args)) + return NULL; + self = PyTuple_GetItem(args, 0); + if (unlikely(!self)) { + Py_DECREF(new_args); + return NULL; + } + result = __Pyx_CyFunction_CallMethod(func, self, new_args, kw); + Py_DECREF(new_args); + } else { + result = __Pyx_CyFunction_Call(func, args, kw); + } + return result; +} +static PyTypeObject __pyx_CyFunctionType_type = { + PyVarObject_HEAD_INIT(0, 0) + "cython_function_or_method", + sizeof(__pyx_CyFunctionObject), + 0, + (destructor) __Pyx_CyFunction_dealloc, + 0, + 0, + 0, +#if PY_MAJOR_VERSION < 3 + 0, +#else + 0, +#endif + (reprfunc) __Pyx_CyFunction_repr, + 0, + 0, + 0, + 0, + __Pyx_CyFunction_CallAsMethod, + 0, + 0, + 0, + 0, + Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC, + 0, + (traverseproc) __Pyx_CyFunction_traverse, + (inquiry) __Pyx_CyFunction_clear, + 0, +#if PY_VERSION_HEX < 0x030500A0 + offsetof(__pyx_CyFunctionObject, func_weakreflist), +#else + offsetof(PyCFunctionObject, m_weakreflist), +#endif + 0, + 0, + __pyx_CyFunction_methods, + __pyx_CyFunction_members, + __pyx_CyFunction_getsets, + 0, + 0, + __Pyx_CyFunction_descr_get, + 0, + offsetof(__pyx_CyFunctionObject, func_dict), + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, +#if PY_VERSION_HEX >= 0x030400a1 + 0, +#endif +}; +static int __pyx_CyFunction_init(void) { + __pyx_CyFunctionType = __Pyx_FetchCommonType(&__pyx_CyFunctionType_type); + if (__pyx_CyFunctionType == NULL) { + return -1; + } + return 0; +} +static CYTHON_INLINE void *__Pyx_CyFunction_InitDefaults(PyObject *func, size_t size, int pyobjects) { + __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; + m->defaults = PyObject_Malloc(size); + if (!m->defaults) + return PyErr_NoMemory(); + memset(m->defaults, 0, size); + m->defaults_pyobjects = pyobjects; + return m->defaults; +} +static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsTuple(PyObject *func, PyObject *tuple) { + __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; + m->defaults_tuple = tuple; + Py_INCREF(tuple); +} +static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsKwDict(PyObject *func, PyObject *dict) { + __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; + m->defaults_kwdict = dict; + Py_INCREF(dict); +} +static CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *func, PyObject *dict) { + __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; + m->func_annotations = dict; + Py_INCREF(dict); +} + +/* BufferFallbackError */ + static void __Pyx_RaiseBufferFallbackError(void) { + PyErr_SetString(PyExc_ValueError, + "Buffer acquisition failed on assignment; and then reacquiring the old buffer failed too!"); +} + +/* None */ + static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t a, Py_ssize_t b) { + Py_ssize_t q = a / b; + Py_ssize_t r = a - q*b; + q -= ((r != 0) & ((r ^ b) < 0)); + return q; +} + +/* BufferIndexError */ + static void __Pyx_RaiseBufferIndexError(int axis) { + PyErr_Format(PyExc_IndexError, + "Out of bounds on buffer access (axis %d)", axis); +} + +/* RaiseTooManyValuesToUnpack */ + static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { + PyErr_Format(PyExc_ValueError, + "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); +} + +/* RaiseNeedMoreValuesToUnpack */ + static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { + PyErr_Format(PyExc_ValueError, + "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack", + index, (index == 1) ? "" : "s"); +} + +/* RaiseNoneIterError */ + static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { + PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); +} + +/* SaveResetException */ + #if CYTHON_FAST_THREAD_STATE +static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { + *type = tstate->exc_type; + *value = tstate->exc_value; + *tb = tstate->exc_traceback; + Py_XINCREF(*type); + Py_XINCREF(*value); + Py_XINCREF(*tb); +} +static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { + PyObject *tmp_type, *tmp_value, *tmp_tb; + tmp_type = tstate->exc_type; + tmp_value = tstate->exc_value; + tmp_tb = tstate->exc_traceback; + tstate->exc_type = type; + tstate->exc_value = value; + tstate->exc_traceback = tb; + Py_XDECREF(tmp_type); + Py_XDECREF(tmp_value); + Py_XDECREF(tmp_tb); +} +#endif + +/* PyErrExceptionMatches */ + #if CYTHON_FAST_THREAD_STATE +static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err) { + PyObject *exc_type = tstate->curexc_type; + if (exc_type == err) return 1; + if (unlikely(!exc_type)) return 0; + return PyErr_GivenExceptionMatches(exc_type, err); +} +#endif + +/* GetException */ + #if CYTHON_FAST_THREAD_STATE +static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { +#else +static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) { +#endif + PyObject *local_type, *local_value, *local_tb; +#if CYTHON_FAST_THREAD_STATE + PyObject *tmp_type, *tmp_value, *tmp_tb; + local_type = tstate->curexc_type; + local_value = tstate->curexc_value; + local_tb = tstate->curexc_traceback; + tstate->curexc_type = 0; + tstate->curexc_value = 0; + tstate->curexc_traceback = 0; +#else + PyErr_Fetch(&local_type, &local_value, &local_tb); +#endif + PyErr_NormalizeException(&local_type, &local_value, &local_tb); +#if CYTHON_FAST_THREAD_STATE + if (unlikely(tstate->curexc_type)) +#else + if (unlikely(PyErr_Occurred())) +#endif + goto bad; + #if PY_MAJOR_VERSION >= 3 + if (local_tb) { + if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0)) + goto bad; + } + #endif + Py_XINCREF(local_tb); + Py_XINCREF(local_type); + Py_XINCREF(local_value); + *type = local_type; + *value = local_value; + *tb = local_tb; +#if CYTHON_FAST_THREAD_STATE + tmp_type = tstate->exc_type; + tmp_value = tstate->exc_value; + tmp_tb = tstate->exc_traceback; + tstate->exc_type = local_type; + tstate->exc_value = local_value; + tstate->exc_traceback = local_tb; + Py_XDECREF(tmp_type); + Py_XDECREF(tmp_value); + Py_XDECREF(tmp_tb); +#else + PyErr_SetExcInfo(local_type, local_value, local_tb); +#endif + return 0; +bad: + *type = 0; + *value = 0; + *tb = 0; + Py_XDECREF(local_type); + Py_XDECREF(local_value); + Py_XDECREF(local_tb); + return -1; +} + +/* Import */ + static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { + PyObject *empty_list = 0; + PyObject *module = 0; + PyObject *global_dict = 0; + PyObject *empty_dict = 0; + PyObject *list; + #if PY_VERSION_HEX < 0x03030000 + PyObject *py_import; + py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import); + if (!py_import) + goto bad; + #endif + if (from_list) + list = from_list; + else { + empty_list = PyList_New(0); + if (!empty_list) + goto bad; + list = empty_list; + } + global_dict = PyModule_GetDict(__pyx_m); + if (!global_dict) + goto bad; + empty_dict = PyDict_New(); + if (!empty_dict) + goto bad; + { + #if PY_MAJOR_VERSION >= 3 + if (level == -1) { + if (strchr(__Pyx_MODULE_NAME, '.')) { + #if PY_VERSION_HEX < 0x03030000 + PyObject *py_level = PyInt_FromLong(1); + if (!py_level) + goto bad; + module = PyObject_CallFunctionObjArgs(py_import, + name, global_dict, empty_dict, list, py_level, NULL); + Py_DECREF(py_level); + #else + module = PyImport_ImportModuleLevelObject( + name, global_dict, empty_dict, list, 1); + #endif + if (!module) { + if (!PyErr_ExceptionMatches(PyExc_ImportError)) + goto bad; + PyErr_Clear(); + } + } + level = 0; + } + #endif + if (!module) { + #if PY_VERSION_HEX < 0x03030000 + PyObject *py_level = PyInt_FromLong(level); + if (!py_level) + goto bad; + module = PyObject_CallFunctionObjArgs(py_import, + name, global_dict, empty_dict, list, py_level, NULL); + Py_DECREF(py_level); + #else + module = PyImport_ImportModuleLevelObject( + name, global_dict, empty_dict, list, level); + #endif + } + } +bad: + #if PY_VERSION_HEX < 0x03030000 + Py_XDECREF(py_import); + #endif + Py_XDECREF(empty_list); + Py_XDECREF(empty_dict); + return module; +} + +/* CodeObjectCache */ + static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) { + int start = 0, mid = 0, end = count - 1; + if (end >= 0 && code_line > entries[end].code_line) { + return count; + } + while (start < end) { + mid = start + (end - start) / 2; + if (code_line < entries[mid].code_line) { + end = mid; + } else if (code_line > entries[mid].code_line) { + start = mid + 1; + } else { + return mid; + } + } + if (code_line <= entries[mid].code_line) { + return mid; + } else { + return mid + 1; + } +} +static PyCodeObject *__pyx_find_code_object(int code_line) { + PyCodeObject* code_object; + int pos; + if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) { + return NULL; + } + pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); + if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) { + return NULL; + } + code_object = __pyx_code_cache.entries[pos].code_object; + Py_INCREF(code_object); + return code_object; +} +static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { + int pos, i; + __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries; + if (unlikely(!code_line)) { + return; + } + if (unlikely(!entries)) { + entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry)); + if (likely(entries)) { + __pyx_code_cache.entries = entries; + __pyx_code_cache.max_count = 64; + __pyx_code_cache.count = 1; + entries[0].code_line = code_line; + entries[0].code_object = code_object; + Py_INCREF(code_object); + } + return; + } + pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); + if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) { + PyCodeObject* tmp = entries[pos].code_object; + entries[pos].code_object = code_object; + Py_DECREF(tmp); + return; + } + if (__pyx_code_cache.count == __pyx_code_cache.max_count) { + int new_max = __pyx_code_cache.max_count + 64; + entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( + __pyx_code_cache.entries, (size_t)new_max*sizeof(__Pyx_CodeObjectCacheEntry)); + if (unlikely(!entries)) { + return; + } + __pyx_code_cache.entries = entries; + __pyx_code_cache.max_count = new_max; + } + for (i=__pyx_code_cache.count; i>pos; i--) { + entries[i] = entries[i-1]; + } + entries[pos].code_line = code_line; + entries[pos].code_object = code_object; + __pyx_code_cache.count++; + Py_INCREF(code_object); +} + +/* AddTraceback */ + #include "compile.h" +#include "frameobject.h" +#include "traceback.h" +static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( + const char *funcname, int c_line, + int py_line, const char *filename) { + PyCodeObject *py_code = 0; + PyObject *py_srcfile = 0; + PyObject *py_funcname = 0; + #if PY_MAJOR_VERSION < 3 + py_srcfile = PyString_FromString(filename); + #else + py_srcfile = PyUnicode_FromString(filename); + #endif + if (!py_srcfile) goto bad; + if (c_line) { + #if PY_MAJOR_VERSION < 3 + py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); + #else + py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); + #endif + } + else { + #if PY_MAJOR_VERSION < 3 + py_funcname = PyString_FromString(funcname); + #else + py_funcname = PyUnicode_FromString(funcname); + #endif + } + if (!py_funcname) goto bad; + py_code = __Pyx_PyCode_New( + 0, + 0, + 0, + 0, + 0, + __pyx_empty_bytes, /*PyObject *code,*/ + __pyx_empty_tuple, /*PyObject *consts,*/ + __pyx_empty_tuple, /*PyObject *names,*/ + __pyx_empty_tuple, /*PyObject *varnames,*/ + __pyx_empty_tuple, /*PyObject *freevars,*/ + __pyx_empty_tuple, /*PyObject *cellvars,*/ + py_srcfile, /*PyObject *filename,*/ + py_funcname, /*PyObject *name,*/ + py_line, + __pyx_empty_bytes /*PyObject *lnotab*/ + ); + Py_DECREF(py_srcfile); + Py_DECREF(py_funcname); + return py_code; +bad: + Py_XDECREF(py_srcfile); + Py_XDECREF(py_funcname); + return NULL; +} +static void __Pyx_AddTraceback(const char *funcname, int c_line, + int py_line, const char *filename) { + PyCodeObject *py_code = 0; + PyFrameObject *py_frame = 0; + py_code = __pyx_find_code_object(c_line ? c_line : py_line); + if (!py_code) { + py_code = __Pyx_CreateCodeObjectForTraceback( + funcname, c_line, py_line, filename); + if (!py_code) goto bad; + __pyx_insert_code_object(c_line ? c_line : py_line, py_code); + } + py_frame = PyFrame_New( + PyThreadState_GET(), /*PyThreadState *tstate,*/ + py_code, /*PyCodeObject *code,*/ + __pyx_d, /*PyObject *globals,*/ + 0 /*PyObject *locals*/ + ); + if (!py_frame) goto bad; + __Pyx_PyFrame_SetLineNumber(py_frame, py_line); + PyTraceBack_Here(py_frame); +bad: + Py_XDECREF(py_code); + Py_XDECREF(py_frame); +} + +#if PY_MAJOR_VERSION < 3 +static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) { + if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags); + if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) return __pyx_pw_5numpy_7ndarray_1__getbuffer__(obj, view, flags); + PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name); + return -1; +} +static void __Pyx_ReleaseBuffer(Py_buffer *view) { + PyObject *obj = view->obj; + if (!obj) return; + if (PyObject_CheckBuffer(obj)) { + PyBuffer_Release(view); + return; + } + if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) { __pyx_pw_5numpy_7ndarray_3__releasebuffer__(obj, view); return; } + Py_DECREF(obj); + view->obj = NULL; +} +#endif + + + /* CIntToPy */ + static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { + const long neg_one = (long) -1, const_zero = (long) 0; + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(long) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(long) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); +#endif + } + } else { + if (sizeof(long) <= sizeof(long)) { + return PyInt_FromLong((long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); +#endif + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(long), + little, !is_unsigned); + } +} + +/* CIntFromPyVerify */ + #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\ + __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0) +#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\ + __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1) +#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\ + {\ + func_type value = func_value;\ + if (sizeof(target_type) < sizeof(func_type)) {\ + if (unlikely(value != (func_type) (target_type) value)) {\ + func_type zero = 0;\ + if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\ + return (target_type) -1;\ + if (is_unsigned && unlikely(value < zero))\ + goto raise_neg_overflow;\ + else\ + goto raise_overflow;\ + }\ + }\ + return (target_type) value;\ + } + +/* CIntToPy */ + static CYTHON_INLINE PyObject* __Pyx_PyInt_From_siz(siz value) { + const siz neg_one = (siz) -1, const_zero = (siz) 0; + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(siz) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(siz) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(siz) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); +#endif + } + } else { + if (sizeof(siz) <= sizeof(long)) { + return PyInt_FromLong((long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(siz) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); +#endif + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(siz), + little, !is_unsigned); + } +} + +/* CIntToPy */ + static CYTHON_INLINE PyObject* __Pyx_PyInt_From_Py_intptr_t(Py_intptr_t value) { + const Py_intptr_t neg_one = (Py_intptr_t) -1, const_zero = (Py_intptr_t) 0; + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(Py_intptr_t) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(Py_intptr_t) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(Py_intptr_t) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); +#endif + } + } else { + if (sizeof(Py_intptr_t) <= sizeof(long)) { + return PyInt_FromLong((long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(Py_intptr_t) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); +#endif + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(Py_intptr_t), + little, !is_unsigned); + } +} + +/* Declarations */ + #if CYTHON_CCOMPLEX + #ifdef __cplusplus + static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { + return ::std::complex< float >(x, y); + } + #else + static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { + return x + y*(__pyx_t_float_complex)_Complex_I; + } + #endif +#else + static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { + __pyx_t_float_complex z; + z.real = x; + z.imag = y; + return z; + } +#endif + +/* Arithmetic */ + #if CYTHON_CCOMPLEX +#else + static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + return (a.real == b.real) && (a.imag == b.imag); + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + z.real = a.real + b.real; + z.imag = a.imag + b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + z.real = a.real - b.real; + z.imag = a.imag - b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + z.real = a.real * b.real - a.imag * b.imag; + z.imag = a.real * b.imag + a.imag * b.real; + return z; + } + #if 1 + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + if (b.imag == 0) { + return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real); + } else if (fabsf(b.real) >= fabsf(b.imag)) { + if (b.real == 0 && b.imag == 0) { + return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.imag); + } else { + float r = b.imag / b.real; + float s = 1.0 / (b.real + b.imag * r); + return __pyx_t_float_complex_from_parts( + (a.real + a.imag * r) * s, (a.imag - a.real * r) * s); + } + } else { + float r = b.real / b.imag; + float s = 1.0 / (b.imag + b.real * r); + return __pyx_t_float_complex_from_parts( + (a.real * r + a.imag) * s, (a.imag * r - a.real) * s); + } + } + #else + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + if (b.imag == 0) { + return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real); + } else { + float denom = b.real * b.real + b.imag * b.imag; + return __pyx_t_float_complex_from_parts( + (a.real * b.real + a.imag * b.imag) / denom, + (a.imag * b.real - a.real * b.imag) / denom); + } + } + #endif + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex a) { + __pyx_t_float_complex z; + z.real = -a.real; + z.imag = -a.imag; + return z; + } + static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex a) { + return (a.real == 0) && (a.imag == 0); + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex a) { + __pyx_t_float_complex z; + z.real = a.real; + z.imag = -a.imag; + return z; + } + #if 1 + static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex z) { + #if !defined(HAVE_HYPOT) || defined(_MSC_VER) + return sqrtf(z.real*z.real + z.imag*z.imag); + #else + return hypotf(z.real, z.imag); + #endif + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + float r, lnr, theta, z_r, z_theta; + if (b.imag == 0 && b.real == (int)b.real) { + if (b.real < 0) { + float denom = a.real * a.real + a.imag * a.imag; + a.real = a.real / denom; + a.imag = -a.imag / denom; + b.real = -b.real; + } + switch ((int)b.real) { + case 0: + z.real = 1; + z.imag = 0; + return z; + case 1: + return a; + case 2: + z = __Pyx_c_prod_float(a, a); + return __Pyx_c_prod_float(a, a); + case 3: + z = __Pyx_c_prod_float(a, a); + return __Pyx_c_prod_float(z, a); + case 4: + z = __Pyx_c_prod_float(a, a); + return __Pyx_c_prod_float(z, z); + } + } + if (a.imag == 0) { + if (a.real == 0) { + return a; + } else if (b.imag == 0) { + z.real = powf(a.real, b.real); + z.imag = 0; + return z; + } else if (a.real > 0) { + r = a.real; + theta = 0; + } else { + r = -a.real; + theta = atan2f(0, -1); + } + } else { + r = __Pyx_c_abs_float(a); + theta = atan2f(a.imag, a.real); + } + lnr = logf(r); + z_r = expf(lnr * b.real - theta * b.imag); + z_theta = theta * b.real + lnr * b.imag; + z.real = z_r * cosf(z_theta); + z.imag = z_r * sinf(z_theta); + return z; + } + #endif +#endif + +/* Declarations */ + #if CYTHON_CCOMPLEX + #ifdef __cplusplus + static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { + return ::std::complex< double >(x, y); + } + #else + static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { + return x + y*(__pyx_t_double_complex)_Complex_I; + } + #endif +#else + static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { + __pyx_t_double_complex z; + z.real = x; + z.imag = y; + return z; + } +#endif + +/* Arithmetic */ + #if CYTHON_CCOMPLEX +#else + static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + return (a.real == b.real) && (a.imag == b.imag); + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + z.real = a.real + b.real; + z.imag = a.imag + b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + z.real = a.real - b.real; + z.imag = a.imag - b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + z.real = a.real * b.real - a.imag * b.imag; + z.imag = a.real * b.imag + a.imag * b.real; + return z; + } + #if 1 + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + if (b.imag == 0) { + return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real); + } else if (fabs(b.real) >= fabs(b.imag)) { + if (b.real == 0 && b.imag == 0) { + return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.imag); + } else { + double r = b.imag / b.real; + double s = 1.0 / (b.real + b.imag * r); + return __pyx_t_double_complex_from_parts( + (a.real + a.imag * r) * s, (a.imag - a.real * r) * s); + } + } else { + double r = b.real / b.imag; + double s = 1.0 / (b.imag + b.real * r); + return __pyx_t_double_complex_from_parts( + (a.real * r + a.imag) * s, (a.imag * r - a.real) * s); + } + } + #else + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + if (b.imag == 0) { + return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real); + } else { + double denom = b.real * b.real + b.imag * b.imag; + return __pyx_t_double_complex_from_parts( + (a.real * b.real + a.imag * b.imag) / denom, + (a.imag * b.real - a.real * b.imag) / denom); + } + } + #endif + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex a) { + __pyx_t_double_complex z; + z.real = -a.real; + z.imag = -a.imag; + return z; + } + static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex a) { + return (a.real == 0) && (a.imag == 0); + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex a) { + __pyx_t_double_complex z; + z.real = a.real; + z.imag = -a.imag; + return z; + } + #if 1 + static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex z) { + #if !defined(HAVE_HYPOT) || defined(_MSC_VER) + return sqrt(z.real*z.real + z.imag*z.imag); + #else + return hypot(z.real, z.imag); + #endif + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + double r, lnr, theta, z_r, z_theta; + if (b.imag == 0 && b.real == (int)b.real) { + if (b.real < 0) { + double denom = a.real * a.real + a.imag * a.imag; + a.real = a.real / denom; + a.imag = -a.imag / denom; + b.real = -b.real; + } + switch ((int)b.real) { + case 0: + z.real = 1; + z.imag = 0; + return z; + case 1: + return a; + case 2: + z = __Pyx_c_prod_double(a, a); + return __Pyx_c_prod_double(a, a); + case 3: + z = __Pyx_c_prod_double(a, a); + return __Pyx_c_prod_double(z, a); + case 4: + z = __Pyx_c_prod_double(a, a); + return __Pyx_c_prod_double(z, z); + } + } + if (a.imag == 0) { + if (a.real == 0) { + return a; + } else if (b.imag == 0) { + z.real = pow(a.real, b.real); + z.imag = 0; + return z; + } else if (a.real > 0) { + r = a.real; + theta = 0; + } else { + r = -a.real; + theta = atan2(0, -1); + } + } else { + r = __Pyx_c_abs_double(a); + theta = atan2(a.imag, a.real); + } + lnr = log(r); + z_r = exp(lnr * b.real - theta * b.imag); + z_theta = theta * b.real + lnr * b.imag; + z.real = z_r * cos(z_theta); + z.imag = z_r * sin(z_theta); + return z; + } + #endif +#endif + +/* CIntToPy */ + static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) { + const int neg_one = (int) -1, const_zero = (int) 0; + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(int) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(int) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); +#endif + } + } else { + if (sizeof(int) <= sizeof(long)) { + return PyInt_FromLong((long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); +#endif + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(int), + little, !is_unsigned); + } +} + +/* CIntToPy */ + static CYTHON_INLINE PyObject* __Pyx_PyInt_From_enum__NPY_TYPES(enum NPY_TYPES value) { + const enum NPY_TYPES neg_one = (enum NPY_TYPES) -1, const_zero = (enum NPY_TYPES) 0; + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(enum NPY_TYPES) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(enum NPY_TYPES) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(enum NPY_TYPES) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); +#endif + } + } else { + if (sizeof(enum NPY_TYPES) <= sizeof(long)) { + return PyInt_FromLong((long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(enum NPY_TYPES) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); +#endif + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(enum NPY_TYPES), + little, !is_unsigned); + } +} + +/* CIntFromPy */ + static CYTHON_INLINE siz __Pyx_PyInt_As_siz(PyObject *x) { + const siz neg_one = (siz) -1, const_zero = (siz) 0; + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if (sizeof(siz) < sizeof(long)) { + __PYX_VERIFY_RETURN_INT(siz, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (siz) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (siz) 0; + case 1: __PYX_VERIFY_RETURN_INT(siz, digit, digits[0]) + case 2: + if (8 * sizeof(siz) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(siz, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(siz) >= 2 * PyLong_SHIFT) { + return (siz) (((((siz)digits[1]) << PyLong_SHIFT) | (siz)digits[0])); + } + } + break; + case 3: + if (8 * sizeof(siz) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(siz, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(siz) >= 3 * PyLong_SHIFT) { + return (siz) (((((((siz)digits[2]) << PyLong_SHIFT) | (siz)digits[1]) << PyLong_SHIFT) | (siz)digits[0])); + } + } + break; + case 4: + if (8 * sizeof(siz) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(siz, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(siz) >= 4 * PyLong_SHIFT) { + return (siz) (((((((((siz)digits[3]) << PyLong_SHIFT) | (siz)digits[2]) << PyLong_SHIFT) | (siz)digits[1]) << PyLong_SHIFT) | (siz)digits[0])); + } + } + break; + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (siz) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if (sizeof(siz) <= sizeof(unsigned long)) { + __PYX_VERIFY_RETURN_INT_EXC(siz, unsigned long, PyLong_AsUnsignedLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(siz) <= sizeof(unsigned PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(siz, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) +#endif + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (siz) 0; + case -1: __PYX_VERIFY_RETURN_INT(siz, sdigit, (sdigit) (-(sdigit)digits[0])) + case 1: __PYX_VERIFY_RETURN_INT(siz, digit, +digits[0]) + case -2: + if (8 * sizeof(siz) - 1 > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(siz, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(siz) - 1 > 2 * PyLong_SHIFT) { + return (siz) (((siz)-1)*(((((siz)digits[1]) << PyLong_SHIFT) | (siz)digits[0]))); + } + } + break; + case 2: + if (8 * sizeof(siz) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(siz, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(siz) - 1 > 2 * PyLong_SHIFT) { + return (siz) ((((((siz)digits[1]) << PyLong_SHIFT) | (siz)digits[0]))); + } + } + break; + case -3: + if (8 * sizeof(siz) - 1 > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(siz, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(siz) - 1 > 3 * PyLong_SHIFT) { + return (siz) (((siz)-1)*(((((((siz)digits[2]) << PyLong_SHIFT) | (siz)digits[1]) << PyLong_SHIFT) | (siz)digits[0]))); + } + } + break; + case 3: + if (8 * sizeof(siz) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(siz, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(siz) - 1 > 3 * PyLong_SHIFT) { + return (siz) ((((((((siz)digits[2]) << PyLong_SHIFT) | (siz)digits[1]) << PyLong_SHIFT) | (siz)digits[0]))); + } + } + break; + case -4: + if (8 * sizeof(siz) - 1 > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(siz, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(siz) - 1 > 4 * PyLong_SHIFT) { + return (siz) (((siz)-1)*(((((((((siz)digits[3]) << PyLong_SHIFT) | (siz)digits[2]) << PyLong_SHIFT) | (siz)digits[1]) << PyLong_SHIFT) | (siz)digits[0]))); + } + } + break; + case 4: + if (8 * sizeof(siz) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(siz, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(siz) - 1 > 4 * PyLong_SHIFT) { + return (siz) ((((((((((siz)digits[3]) << PyLong_SHIFT) | (siz)digits[2]) << PyLong_SHIFT) | (siz)digits[1]) << PyLong_SHIFT) | (siz)digits[0]))); + } + } + break; + } +#endif + if (sizeof(siz) <= sizeof(long)) { + __PYX_VERIFY_RETURN_INT_EXC(siz, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(siz) <= sizeof(PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(siz, PY_LONG_LONG, PyLong_AsLongLong(x)) +#endif + } + } + { +#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) + PyErr_SetString(PyExc_RuntimeError, + "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); +#else + siz val; + PyObject *v = __Pyx_PyNumber_IntOrLong(x); + #if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } + #endif + if (likely(v)) { + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + int ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); + Py_DECREF(v); + if (likely(!ret)) + return val; + } +#endif + return (siz) -1; + } + } else { + siz val; + PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); + if (!tmp) return (siz) -1; + val = __Pyx_PyInt_As_siz(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to siz"); + return (siz) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to siz"); + return (siz) -1; +} + +/* CIntFromPy */ + static CYTHON_INLINE size_t __Pyx_PyInt_As_size_t(PyObject *x) { + const size_t neg_one = (size_t) -1, const_zero = (size_t) 0; + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if (sizeof(size_t) < sizeof(long)) { + __PYX_VERIFY_RETURN_INT(size_t, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (size_t) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (size_t) 0; + case 1: __PYX_VERIFY_RETURN_INT(size_t, digit, digits[0]) + case 2: + if (8 * sizeof(size_t) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(size_t, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(size_t) >= 2 * PyLong_SHIFT) { + return (size_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + } + break; + case 3: + if (8 * sizeof(size_t) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(size_t, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(size_t) >= 3 * PyLong_SHIFT) { + return (size_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + } + break; + case 4: + if (8 * sizeof(size_t) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(size_t, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(size_t) >= 4 * PyLong_SHIFT) { + return (size_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + } + break; + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (size_t) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if (sizeof(size_t) <= sizeof(unsigned long)) { + __PYX_VERIFY_RETURN_INT_EXC(size_t, unsigned long, PyLong_AsUnsignedLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(size_t) <= sizeof(unsigned PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(size_t, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) +#endif + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (size_t) 0; + case -1: __PYX_VERIFY_RETURN_INT(size_t, sdigit, (sdigit) (-(sdigit)digits[0])) + case 1: __PYX_VERIFY_RETURN_INT(size_t, digit, +digits[0]) + case -2: + if (8 * sizeof(size_t) - 1 > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(size_t, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(size_t) - 1 > 2 * PyLong_SHIFT) { + return (size_t) (((size_t)-1)*(((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]))); + } + } + break; + case 2: + if (8 * sizeof(size_t) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(size_t, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(size_t) - 1 > 2 * PyLong_SHIFT) { + return (size_t) ((((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]))); + } + } + break; + case -3: + if (8 * sizeof(size_t) - 1 > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(size_t, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(size_t) - 1 > 3 * PyLong_SHIFT) { + return (size_t) (((size_t)-1)*(((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]))); + } + } + break; + case 3: + if (8 * sizeof(size_t) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(size_t, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(size_t) - 1 > 3 * PyLong_SHIFT) { + return (size_t) ((((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]))); + } + } + break; + case -4: + if (8 * sizeof(size_t) - 1 > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(size_t, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(size_t) - 1 > 4 * PyLong_SHIFT) { + return (size_t) (((size_t)-1)*(((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]))); + } + } + break; + case 4: + if (8 * sizeof(size_t) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(size_t, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(size_t) - 1 > 4 * PyLong_SHIFT) { + return (size_t) ((((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]))); + } + } + break; + } +#endif + if (sizeof(size_t) <= sizeof(long)) { + __PYX_VERIFY_RETURN_INT_EXC(size_t, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(size_t) <= sizeof(PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(size_t, PY_LONG_LONG, PyLong_AsLongLong(x)) +#endif + } + } + { +#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) + PyErr_SetString(PyExc_RuntimeError, + "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); +#else + size_t val; + PyObject *v = __Pyx_PyNumber_IntOrLong(x); + #if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } + #endif + if (likely(v)) { + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + int ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); + Py_DECREF(v); + if (likely(!ret)) + return val; + } +#endif + return (size_t) -1; + } + } else { + size_t val; + PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); + if (!tmp) return (size_t) -1; + val = __Pyx_PyInt_As_size_t(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to size_t"); + return (size_t) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to size_t"); + return (size_t) -1; +} + +/* CIntFromPy */ + static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { + const int neg_one = (int) -1, const_zero = (int) 0; + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if (sizeof(int) < sizeof(long)) { + __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (int) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (int) 0; + case 1: __PYX_VERIFY_RETURN_INT(int, digit, digits[0]) + case 2: + if (8 * sizeof(int) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) >= 2 * PyLong_SHIFT) { + return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + case 3: + if (8 * sizeof(int) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) >= 3 * PyLong_SHIFT) { + return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + case 4: + if (8 * sizeof(int) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) >= 4 * PyLong_SHIFT) { + return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (int) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if (sizeof(int) <= sizeof(unsigned long)) { + __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) +#endif + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (int) 0; + case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, (sdigit) (-(sdigit)digits[0])) + case 1: __PYX_VERIFY_RETURN_INT(int, digit, +digits[0]) + case -2: + if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { + return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 2: + if (8 * sizeof(int) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { + return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case -3: + if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { + return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 3: + if (8 * sizeof(int) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { + return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case -4: + if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { + return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 4: + if (8 * sizeof(int) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { + return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + } +#endif + if (sizeof(int) <= sizeof(long)) { + __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x)) +#endif + } + } + { +#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) + PyErr_SetString(PyExc_RuntimeError, + "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); +#else + int val; + PyObject *v = __Pyx_PyNumber_IntOrLong(x); + #if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } + #endif + if (likely(v)) { + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + int ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); + Py_DECREF(v); + if (likely(!ret)) + return val; + } +#endif + return (int) -1; + } + } else { + int val; + PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); + if (!tmp) return (int) -1; + val = __Pyx_PyInt_As_int(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to int"); + return (int) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to int"); + return (int) -1; +} + +/* CIntFromPy */ + static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { + const long neg_one = (long) -1, const_zero = (long) 0; + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if (sizeof(long) < sizeof(long)) { + __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (long) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (long) 0; + case 1: __PYX_VERIFY_RETURN_INT(long, digit, digits[0]) + case 2: + if (8 * sizeof(long) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) >= 2 * PyLong_SHIFT) { + return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + case 3: + if (8 * sizeof(long) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) >= 3 * PyLong_SHIFT) { + return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + case 4: + if (8 * sizeof(long) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) >= 4 * PyLong_SHIFT) { + return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (long) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if (sizeof(long) <= sizeof(unsigned long)) { + __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) +#endif + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (long) 0; + case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, (sdigit) (-(sdigit)digits[0])) + case 1: __PYX_VERIFY_RETURN_INT(long, digit, +digits[0]) + case -2: + if (8 * sizeof(long) - 1 > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 2: + if (8 * sizeof(long) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case -3: + if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 3: + if (8 * sizeof(long) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case -4: + if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 4: + if (8 * sizeof(long) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + } +#endif + if (sizeof(long) <= sizeof(long)) { + __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x)) +#endif + } + } + { +#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) + PyErr_SetString(PyExc_RuntimeError, + "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); +#else + long val; + PyObject *v = __Pyx_PyNumber_IntOrLong(x); + #if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } + #endif + if (likely(v)) { + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + int ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); + Py_DECREF(v); + if (likely(!ret)) + return val; + } +#endif + return (long) -1; + } + } else { + long val; + PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); + if (!tmp) return (long) -1; + val = __Pyx_PyInt_As_long(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to long"); + return (long) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to long"); + return (long) -1; +} + +/* CheckBinaryVersion */ + static int __Pyx_check_binary_version(void) { + char ctversion[4], rtversion[4]; + PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); + PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion()); + if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) { + char message[200]; + PyOS_snprintf(message, sizeof(message), + "compiletime version %s of module '%.100s' " + "does not match runtime version %s", + ctversion, __Pyx_MODULE_NAME, rtversion); + return PyErr_WarnEx(NULL, message, 1); + } + return 0; +} + +/* ModuleImport */ + #ifndef __PYX_HAVE_RT_ImportModule +#define __PYX_HAVE_RT_ImportModule +static PyObject *__Pyx_ImportModule(const char *name) { + PyObject *py_name = 0; + PyObject *py_module = 0; + py_name = __Pyx_PyIdentifier_FromString(name); + if (!py_name) + goto bad; + py_module = PyImport_Import(py_name); + Py_DECREF(py_name); + return py_module; +bad: + Py_XDECREF(py_name); + return 0; +} +#endif + +/* TypeImport */ + #ifndef __PYX_HAVE_RT_ImportType +#define __PYX_HAVE_RT_ImportType +static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, + size_t size, int strict) +{ + PyObject *py_module = 0; + PyObject *result = 0; + PyObject *py_name = 0; + char warning[200]; + Py_ssize_t basicsize; +#ifdef Py_LIMITED_API + PyObject *py_basicsize; +#endif + py_module = __Pyx_ImportModule(module_name); + if (!py_module) + goto bad; + py_name = __Pyx_PyIdentifier_FromString(class_name); + if (!py_name) + goto bad; + result = PyObject_GetAttr(py_module, py_name); + Py_DECREF(py_name); + py_name = 0; + Py_DECREF(py_module); + py_module = 0; + if (!result) + goto bad; + if (!PyType_Check(result)) { + PyErr_Format(PyExc_TypeError, + "%.200s.%.200s is not a type object", + module_name, class_name); + goto bad; + } +#ifndef Py_LIMITED_API + basicsize = ((PyTypeObject *)result)->tp_basicsize; +#else + py_basicsize = PyObject_GetAttrString(result, "__basicsize__"); + if (!py_basicsize) + goto bad; + basicsize = PyLong_AsSsize_t(py_basicsize); + Py_DECREF(py_basicsize); + py_basicsize = 0; + if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred()) + goto bad; +#endif + if (!strict && (size_t)basicsize > size) { + PyOS_snprintf(warning, sizeof(warning), + "%s.%s size changed, may indicate binary incompatibility. Expected %zd, got %zd", + module_name, class_name, basicsize, size); + if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad; + } + else if ((size_t)basicsize != size) { + PyErr_Format(PyExc_ValueError, + "%.200s.%.200s has the wrong size, try recompiling. Expected %zd, got %zd", + module_name, class_name, basicsize, size); + goto bad; + } + return (PyTypeObject *)result; +bad: + Py_XDECREF(py_module); + Py_XDECREF(result); + return NULL; +} +#endif + +/* InitStrings */ + static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { + while (t->p) { + #if PY_MAJOR_VERSION < 3 + if (t->is_unicode) { + *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); + } else if (t->intern) { + *t->p = PyString_InternFromString(t->s); + } else { + *t->p = PyString_FromStringAndSize(t->s, t->n - 1); + } + #else + if (t->is_unicode | t->is_str) { + if (t->intern) { + *t->p = PyUnicode_InternFromString(t->s); + } else if (t->encoding) { + *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL); + } else { + *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); + } + } else { + *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1); + } + #endif + if (!*t->p) + return -1; + ++t; + } + return 0; +} + +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) { + return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str)); +} +static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject* o) { + Py_ssize_t ignore; + return __Pyx_PyObject_AsStringAndSize(o, &ignore); +} +static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) { +#if CYTHON_COMPILING_IN_CPYTHON && (__PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT) + if ( +#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + __Pyx_sys_getdefaultencoding_not_ascii && +#endif + PyUnicode_Check(o)) { +#if PY_VERSION_HEX < 0x03030000 + char* defenc_c; + PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL); + if (!defenc) return NULL; + defenc_c = PyBytes_AS_STRING(defenc); +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + { + char* end = defenc_c + PyBytes_GET_SIZE(defenc); + char* c; + for (c = defenc_c; c < end; c++) { + if ((unsigned char) (*c) >= 128) { + PyUnicode_AsASCIIString(o); + return NULL; + } + } + } +#endif + *length = PyBytes_GET_SIZE(defenc); + return defenc_c; +#else + if (__Pyx_PyUnicode_READY(o) == -1) return NULL; +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + if (PyUnicode_IS_ASCII(o)) { + *length = PyUnicode_GET_LENGTH(o); + return PyUnicode_AsUTF8(o); + } else { + PyUnicode_AsASCIIString(o); + return NULL; + } +#else + return PyUnicode_AsUTF8AndSize(o, length); +#endif +#endif + } else +#endif +#if (!CYTHON_COMPILING_IN_PYPY) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE)) + if (PyByteArray_Check(o)) { + *length = PyByteArray_GET_SIZE(o); + return PyByteArray_AS_STRING(o); + } else +#endif + { + char* result; + int r = PyBytes_AsStringAndSize(o, &result, length); + if (unlikely(r < 0)) { + return NULL; + } else { + return result; + } + } +} +static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { + int is_true = x == Py_True; + if (is_true | (x == Py_False) | (x == Py_None)) return is_true; + else return PyObject_IsTrue(x); +} +static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) { +#if CYTHON_USE_TYPE_SLOTS + PyNumberMethods *m; +#endif + const char *name = NULL; + PyObject *res = NULL; +#if PY_MAJOR_VERSION < 3 + if (PyInt_Check(x) || PyLong_Check(x)) +#else + if (PyLong_Check(x)) +#endif + return __Pyx_NewRef(x); +#if CYTHON_USE_TYPE_SLOTS + m = Py_TYPE(x)->tp_as_number; + #if PY_MAJOR_VERSION < 3 + if (m && m->nb_int) { + name = "int"; + res = PyNumber_Int(x); + } + else if (m && m->nb_long) { + name = "long"; + res = PyNumber_Long(x); + } + #else + if (m && m->nb_int) { + name = "int"; + res = PyNumber_Long(x); + } + #endif +#else + res = PyNumber_Int(x); +#endif + if (res) { +#if PY_MAJOR_VERSION < 3 + if (!PyInt_Check(res) && !PyLong_Check(res)) { +#else + if (!PyLong_Check(res)) { +#endif + PyErr_Format(PyExc_TypeError, + "__%.4s__ returned non-%.4s (type %.200s)", + name, name, Py_TYPE(res)->tp_name); + Py_DECREF(res); + return NULL; + } + } + else if (!PyErr_Occurred()) { + PyErr_SetString(PyExc_TypeError, + "an integer is required"); + } + return res; +} +static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { + Py_ssize_t ival; + PyObject *x; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_CheckExact(b))) { + if (sizeof(Py_ssize_t) >= sizeof(long)) + return PyInt_AS_LONG(b); + else + return PyInt_AsSsize_t(x); + } +#endif + if (likely(PyLong_CheckExact(b))) { + #if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)b)->ob_digit; + const Py_ssize_t size = Py_SIZE(b); + if (likely(__Pyx_sst_abs(size) <= 1)) { + ival = likely(size) ? digits[0] : 0; + if (size == -1) ival = -ival; + return ival; + } else { + switch (size) { + case 2: + if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { + return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -2: + if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case 3: + if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { + return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -3: + if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case 4: + if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { + return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -4: + if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + } + } + #endif + return PyLong_AsSsize_t(b); + } + x = PyNumber_Index(b); + if (!x) return -1; + ival = PyInt_AsSsize_t(x); + Py_DECREF(x); + return ival; +} +static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { + return PyInt_FromSize_t(ival); +} + + +#endif /* Py_PYTHON_H */ diff --git a/data/lib_coco/PythonAPI/pycocotools/_mask.pyx b/data/lib_coco/PythonAPI/pycocotools/_mask.pyx new file mode 100644 index 0000000..d065837 --- /dev/null +++ b/data/lib_coco/PythonAPI/pycocotools/_mask.pyx @@ -0,0 +1,308 @@ +# distutils: language = c +# distutils: sources = ../common/maskApi.c + +#************************************************************************** +# Microsoft COCO Toolbox. version 2.0 +# Data, paper, and tutorials available at: http://mscoco.org/ +# Code written by Piotr Dollar and Tsung-Yi Lin, 2015. +# Licensed under the Simplified BSD License [see coco/license.txt] +#************************************************************************** + +__author__ = 'tsungyi' + +import sys +PYTHON_VERSION = sys.version_info[0] + +# import both Python-level and C-level symbols of Numpy +# the API uses Numpy to interface C and Python +import numpy as np +cimport numpy as np +from libc.stdlib cimport malloc, free + +# intialized Numpy. must do. +np.import_array() + +# import numpy C function +# we use PyArray_ENABLEFLAGS to make Numpy ndarray responsible to memoery management +cdef extern from "numpy/arrayobject.h": + void PyArray_ENABLEFLAGS(np.ndarray arr, int flags) + +# Declare the prototype of the C functions in MaskApi.h +cdef extern from "maskApi.h": + ctypedef unsigned int uint + ctypedef unsigned long siz + ctypedef unsigned char byte + ctypedef double* BB + ctypedef struct RLE: + siz h, + siz w, + siz m, + uint* cnts, + void rlesInit( RLE **R, siz n ) + void rleEncode( RLE *R, const byte *M, siz h, siz w, siz n ) + void rleDecode( const RLE *R, byte *mask, siz n ) + void rleMerge( const RLE *R, RLE *M, siz n, int intersect ) + void rleArea( const RLE *R, siz n, uint *a ) + void rleIou( RLE *dt, RLE *gt, siz m, siz n, byte *iscrowd, double *o ) + void bbIou( BB dt, BB gt, siz m, siz n, byte *iscrowd, double *o ) + void rleToBbox( const RLE *R, BB bb, siz n ) + void rleFrBbox( RLE *R, const BB bb, siz h, siz w, siz n ) + void rleFrPoly( RLE *R, const double *xy, siz k, siz h, siz w ) + char* rleToString( const RLE *R ) + void rleFrString( RLE *R, char *s, siz h, siz w ) + +# python class to wrap RLE array in C +# the class handles the memory allocation and deallocation +cdef class RLEs: + cdef RLE *_R + cdef siz _n + + def __cinit__(self, siz n =0): + rlesInit(&self._R, n) + self._n = n + + # free the RLE array here + def __dealloc__(self): + if self._R is not NULL: + for i in range(self._n): + free(self._R[i].cnts) + free(self._R) + def __getattr__(self, key): + if key == 'n': + return self._n + raise AttributeError(key) + +# python class to wrap Mask array in C +# the class handles the memory allocation and deallocation +cdef class Masks: + cdef byte *_mask + cdef siz _h + cdef siz _w + cdef siz _n + + def __cinit__(self, h, w, n): + self._mask = malloc(h*w*n* sizeof(byte)) + self._h = h + self._w = w + self._n = n + # def __dealloc__(self): + # the memory management of _mask has been passed to np.ndarray + # it doesn't need to be freed here + + # called when passing into np.array() and return an np.ndarray in column-major order + def __array__(self): + cdef np.npy_intp shape[1] + shape[0] = self._h*self._w*self._n + # Create a 1D array, and reshape it to fortran/Matlab column-major array + ndarray = np.PyArray_SimpleNewFromData(1, shape, np.NPY_UINT8, self._mask).reshape((self._h, self._w, self._n), order='F') + # The _mask allocated by Masks is now handled by ndarray + PyArray_ENABLEFLAGS(ndarray, np.NPY_OWNDATA) + return ndarray + +# internal conversion from Python RLEs object to compressed RLE format +def _toString(RLEs Rs): + cdef siz n = Rs.n + cdef bytes py_string + cdef char* c_string + objs = [] + for i in range(n): + c_string = rleToString( &Rs._R[i] ) + py_string = c_string + objs.append({ + 'size': [Rs._R[i].h, Rs._R[i].w], + 'counts': py_string + }) + free(c_string) + return objs + +# internal conversion from compressed RLE format to Python RLEs object +def _frString(rleObjs): + cdef siz n = len(rleObjs) + Rs = RLEs(n) + cdef bytes py_string + cdef char* c_string + for i, obj in enumerate(rleObjs): + if PYTHON_VERSION == 2: + py_string = str(obj['counts']).encode('utf8') + elif PYTHON_VERSION == 3: + py_string = str.encode(obj['counts']) if type(obj['counts']) == str else obj['counts'] + else: + raise Exception('Python version must be 2 or 3') + c_string = py_string + rleFrString( &Rs._R[i], c_string, obj['size'][0], obj['size'][1] ) + return Rs + +# encode mask to RLEs objects +# list of RLE string can be generated by RLEs member function +def encode(np.ndarray[np.uint8_t, ndim=3, mode='fortran'] mask): + h, w, n = mask.shape[0], mask.shape[1], mask.shape[2] + cdef RLEs Rs = RLEs(n) + rleEncode(Rs._R,mask.data,h,w,n) + objs = _toString(Rs) + return objs + +# decode mask from compressed list of RLE string or RLEs object +def decode(rleObjs): + cdef RLEs Rs = _frString(rleObjs) + h, w, n = Rs._R[0].h, Rs._R[0].w, Rs._n + masks = Masks(h, w, n) + rleDecode(Rs._R, masks._mask, n); + return np.array(masks) + +def merge(rleObjs, intersect=0): + cdef RLEs Rs = _frString(rleObjs) + cdef RLEs R = RLEs(1) + rleMerge(Rs._R, R._R, Rs._n, intersect) + obj = _toString(R)[0] + return obj + +def area(rleObjs): + cdef RLEs Rs = _frString(rleObjs) + cdef uint* _a = malloc(Rs._n* sizeof(uint)) + rleArea(Rs._R, Rs._n, _a) + cdef np.npy_intp shape[1] + shape[0] = Rs._n + a = np.array((Rs._n, ), dtype=np.uint8) + a = np.PyArray_SimpleNewFromData(1, shape, np.NPY_UINT32, _a) + PyArray_ENABLEFLAGS(a, np.NPY_OWNDATA) + return a + +# iou computation. support function overload (RLEs-RLEs and bbox-bbox). +def iou( dt, gt, pyiscrowd ): + def _preproc(objs): + if len(objs) == 0: + return objs + if type(objs) == np.ndarray: + if len(objs.shape) == 1: + objs = objs.reshape((objs[0], 1)) + # check if it's Nx4 bbox + if not len(objs.shape) == 2 or not objs.shape[1] == 4: + raise Exception('numpy ndarray input is only for *bounding boxes* and should have Nx4 dimension') + objs = objs.astype(np.double) + elif type(objs) == list: + # check if list is in box format and convert it to np.ndarray + isbox = np.all(np.array([(len(obj)==4) and ((type(obj)==list) or (type(obj)==np.ndarray)) for obj in objs])) + isrle = np.all(np.array([type(obj) == dict for obj in objs])) + if isbox: + objs = np.array(objs, dtype=np.double) + if len(objs.shape) == 1: + objs = objs.reshape((1,objs.shape[0])) + elif isrle: + objs = _frString(objs) + else: + raise Exception('list input can be bounding box (Nx4) or RLEs ([RLE])') + else: + raise Exception('unrecognized type. The following type: RLEs (rle), np.ndarray (box), and list (box) are supported.') + return objs + def _rleIou(RLEs dt, RLEs gt, np.ndarray[np.uint8_t, ndim=1] iscrowd, siz m, siz n, np.ndarray[np.double_t, ndim=1] _iou): + rleIou( dt._R, gt._R, m, n, iscrowd.data, _iou.data ) + def _bbIou(np.ndarray[np.double_t, ndim=2] dt, np.ndarray[np.double_t, ndim=2] gt, np.ndarray[np.uint8_t, ndim=1] iscrowd, siz m, siz n, np.ndarray[np.double_t, ndim=1] _iou): + bbIou( dt.data, gt.data, m, n, iscrowd.data, _iou.data ) + def _len(obj): + cdef siz N = 0 + if type(obj) == RLEs: + N = obj.n + elif len(obj)==0: + pass + elif type(obj) == np.ndarray: + N = obj.shape[0] + return N + # convert iscrowd to numpy array + cdef np.ndarray[np.uint8_t, ndim=1] iscrowd = np.array(pyiscrowd, dtype=np.uint8) + # simple type checking + cdef siz m, n + dt = _preproc(dt) + gt = _preproc(gt) + m = _len(dt) + n = _len(gt) + if m == 0 or n == 0: + return [] + if not type(dt) == type(gt): + raise Exception('The dt and gt should have the same data type, either RLEs, list or np.ndarray') + + # define local variables + cdef double* _iou = 0 + cdef np.npy_intp shape[1] + # check type and assign iou function + if type(dt) == RLEs: + _iouFun = _rleIou + elif type(dt) == np.ndarray: + _iouFun = _bbIou + else: + raise Exception('input data type not allowed.') + _iou = malloc(m*n* sizeof(double)) + iou = np.zeros((m*n, ), dtype=np.double) + shape[0] = m*n + iou = np.PyArray_SimpleNewFromData(1, shape, np.NPY_DOUBLE, _iou) + PyArray_ENABLEFLAGS(iou, np.NPY_OWNDATA) + _iouFun(dt, gt, iscrowd, m, n, iou) + return iou.reshape((m,n), order='F') + +def toBbox( rleObjs ): + cdef RLEs Rs = _frString(rleObjs) + cdef siz n = Rs.n + cdef BB _bb = malloc(4*n* sizeof(double)) + rleToBbox( Rs._R, _bb, n ) + cdef np.npy_intp shape[1] + shape[0] = 4*n + bb = np.array((1,4*n), dtype=np.double) + bb = np.PyArray_SimpleNewFromData(1, shape, np.NPY_DOUBLE, _bb).reshape((n, 4)) + PyArray_ENABLEFLAGS(bb, np.NPY_OWNDATA) + return bb + +def frBbox(np.ndarray[np.double_t, ndim=2] bb, siz h, siz w ): + cdef siz n = bb.shape[0] + Rs = RLEs(n) + rleFrBbox( Rs._R, bb.data, h, w, n ) + objs = _toString(Rs) + return objs + +def frPoly( poly, siz h, siz w ): + cdef np.ndarray[np.double_t, ndim=1] np_poly + n = len(poly) + Rs = RLEs(n) + for i, p in enumerate(poly): + np_poly = np.array(p, dtype=np.double, order='F') + rleFrPoly( &Rs._R[i], np_poly.data, int(len(p)/2), h, w ) + objs = _toString(Rs) + return objs + +def frUncompressedRLE(ucRles, siz h, siz w): + cdef np.ndarray[np.uint32_t, ndim=1] cnts + cdef RLE R + cdef uint *data + n = len(ucRles) + objs = [] + for i in range(n): + Rs = RLEs(1) + cnts = np.array(ucRles[i]['counts'], dtype=np.uint32) + # time for malloc can be saved here but it's fine + data = malloc(len(cnts)* sizeof(uint)) + for j in range(len(cnts)): + data[j] = cnts[j] + R = RLE(ucRles[i]['size'][0], ucRles[i]['size'][1], len(cnts), data) + Rs._R[0] = R + objs.append(_toString(Rs)[0]) + return objs + +def frPyObjects(pyobj, h, w): + # encode rle from a list of python objects + if type(pyobj) == np.ndarray: + objs = frBbox(pyobj, h, w) + elif type(pyobj) == list and len(pyobj[0]) == 4: + objs = frBbox(pyobj, h, w) + elif type(pyobj) == list and len(pyobj[0]) > 4: + objs = frPoly(pyobj, h, w) + elif type(pyobj) == list and type(pyobj[0]) == dict \ + and 'counts' in pyobj[0] and 'size' in pyobj[0]: + objs = frUncompressedRLE(pyobj, h, w) + # encode rle from single python object + elif type(pyobj) == list and len(pyobj) == 4: + objs = frBbox([pyobj], h, w)[0] + elif type(pyobj) == list and len(pyobj) > 4: + objs = frPoly([pyobj], h, w)[0] + elif type(pyobj) == dict and 'counts' in pyobj and 'size' in pyobj: + objs = frUncompressedRLE([pyobj], h, w)[0] + else: + raise Exception('input type is not supported.') + return objs diff --git a/data/lib_coco/PythonAPI/pycocotools/coco.py b/data/lib_coco/PythonAPI/pycocotools/coco.py new file mode 100644 index 0000000..dc9972b --- /dev/null +++ b/data/lib_coco/PythonAPI/pycocotools/coco.py @@ -0,0 +1,433 @@ +__author__ = 'tylin' +__version__ = '2.0' +# Interface for accessing the Microsoft COCO dataset. + +# Microsoft COCO is a large image dataset designed for object detection, +# segmentation, and caption generation. pycocotools is a Python API that +# assists in loading, parsing and visualizing the annotations in COCO. +# Please visit http://mscoco.org/ for more information on COCO, including +# for the data, paper, and tutorials. The exact format of the annotations +# is also described on the COCO website. For example usage of the pycocotools +# please see pycocotools_demo.ipynb. In addition to this API, please download both +# the COCO images and annotations in order to run the demo. + +# An alternative to using the API is to load the annotations directly +# into Python dictionary +# Using the API provides additional utility functions. Note that this API +# supports both *instance* and *caption* annotations. In the case of +# captions not all functions are defined (e.g. categories are undefined). + +# The following API functions are defined: +# COCO - COCO api class that loads COCO annotation file and prepare data structures. +# decodeMask - Decode binary mask M encoded via run-length encoding. +# encodeMask - Encode binary mask M using run-length encoding. +# getAnnIds - Get ann ids that satisfy given filter conditions. +# getCatIds - Get cat ids that satisfy given filter conditions. +# getImgIds - Get img ids that satisfy given filter conditions. +# loadAnns - Load anns with the specified ids. +# loadCats - Load cats with the specified ids. +# loadImgs - Load imgs with the specified ids. +# annToMask - Convert segmentation in an annotation to binary mask. +# showAnns - Display the specified annotations. +# loadRes - Load algorithm results and create API for accessing them. +# download - Download COCO images from mscoco.org server. +# Throughout the API "ann"=annotation, "cat"=category, and "img"=image. +# Help on each functions can be accessed by: "help COCO>function". + +# See also COCO>decodeMask, +# COCO>encodeMask, COCO>getAnnIds, COCO>getCatIds, +# COCO>getImgIds, COCO>loadAnns, COCO>loadCats, +# COCO>loadImgs, COCO>annToMask, COCO>showAnns + +# Microsoft COCO Toolbox. version 2.0 +# Data, paper, and tutorials available at: http://mscoco.org/ +# Code written by Piotr Dollar and Tsung-Yi Lin, 2014. +# Licensed under the Simplified BSD License [see bsd.txt] + +import json +import time +import matplotlib.pyplot as plt +from matplotlib.collections import PatchCollection +from matplotlib.patches import Polygon +import numpy as np +import copy +import itertools +from . import mask as maskUtils +import os +from collections import defaultdict +import sys +PYTHON_VERSION = sys.version_info[0] +if PYTHON_VERSION == 2: + from urllib import urlretrieve +elif PYTHON_VERSION == 3: + from urllib.request import urlretrieve + + +def _isArrayLike(obj): + return hasattr(obj, '__iter__') and hasattr(obj, '__len__') + + +class COCO: + def __init__(self, annotation_file=None): + """ + Constructor of Microsoft COCO helper class for reading and visualizing annotations. + :param annotation_file (str): location of annotation file + :param image_folder (str): location to the folder that hosts images. + :return: + """ + # load dataset + self.dataset,self.anns,self.cats,self.imgs = dict(),dict(),dict(),dict() + self.imgToAnns, self.catToImgs = defaultdict(list), defaultdict(list) + if not annotation_file == None: + print('loading annotations into memory...') + tic = time.time() + dataset = json.load(open(annotation_file, 'r')) + assert type(dataset)==dict, 'annotation file format {} not supported'.format(type(dataset)) + print('Done (t={:0.2f}s)'.format(time.time()- tic)) + self.dataset = dataset + self.createIndex() + + def createIndex(self): + # create index + print('creating index...') + anns, cats, imgs = {}, {}, {} + imgToAnns,catToImgs = defaultdict(list),defaultdict(list) + if 'annotations' in self.dataset: + for ann in self.dataset['annotations']: + imgToAnns[ann['image_id']].append(ann) + anns[ann['id']] = ann + + if 'images' in self.dataset: + for img in self.dataset['images']: + imgs[img['id']] = img + + if 'categories' in self.dataset: + for cat in self.dataset['categories']: + cats[cat['id']] = cat + + if 'annotations' in self.dataset and 'categories' in self.dataset: + for ann in self.dataset['annotations']: + catToImgs[ann['category_id']].append(ann['image_id']) + + print('index created!') + + # create class members + self.anns = anns + self.imgToAnns = imgToAnns + self.catToImgs = catToImgs + self.imgs = imgs + self.cats = cats + + def info(self): + """ + Print information about the annotation file. + :return: + """ + for key, value in self.dataset['info'].items(): + print('{}: {}'.format(key, value)) + + def getAnnIds(self, imgIds=[], catIds=[], areaRng=[], iscrowd=None): + """ + Get ann ids that satisfy given filter conditions. default skips that filter + :param imgIds (int array) : get anns for given imgs + catIds (int array) : get anns for given cats + areaRng (float array) : get anns for given area range (e.g. [0 inf]) + iscrowd (boolean) : get anns for given crowd label (False or True) + :return: ids (int array) : integer array of ann ids + """ + imgIds = imgIds if _isArrayLike(imgIds) else [imgIds] + catIds = catIds if _isArrayLike(catIds) else [catIds] + + if len(imgIds) == len(catIds) == len(areaRng) == 0: + anns = self.dataset['annotations'] + else: + if not len(imgIds) == 0: + lists = [self.imgToAnns[imgId] for imgId in imgIds if imgId in self.imgToAnns] + anns = list(itertools.chain.from_iterable(lists)) + else: + anns = self.dataset['annotations'] + anns = anns if len(catIds) == 0 else [ann for ann in anns if ann['category_id'] in catIds] + anns = anns if len(areaRng) == 0 else [ann for ann in anns if ann['area'] > areaRng[0] and ann['area'] < areaRng[1]] + if not iscrowd == None: + ids = [ann['id'] for ann in anns if ann['iscrowd'] == iscrowd] + else: + ids = [ann['id'] for ann in anns] + return ids + + def getCatIds(self, catNms=[], supNms=[], catIds=[]): + """ + filtering parameters. default skips that filter. + :param catNms (str array) : get cats for given cat names + :param supNms (str array) : get cats for given supercategory names + :param catIds (int array) : get cats for given cat ids + :return: ids (int array) : integer array of cat ids + """ + catNms = catNms if _isArrayLike(catNms) else [catNms] + supNms = supNms if _isArrayLike(supNms) else [supNms] + catIds = catIds if _isArrayLike(catIds) else [catIds] + + if len(catNms) == len(supNms) == len(catIds) == 0: + cats = self.dataset['categories'] + else: + cats = self.dataset['categories'] + cats = cats if len(catNms) == 0 else [cat for cat in cats if cat['name'] in catNms] + cats = cats if len(supNms) == 0 else [cat for cat in cats if cat['supercategory'] in supNms] + cats = cats if len(catIds) == 0 else [cat for cat in cats if cat['id'] in catIds] + ids = [cat['id'] for cat in cats] + return ids + + def getImgIds(self, imgIds=[], catIds=[]): + ''' + Get img ids that satisfy given filter conditions. + :param imgIds (int array) : get imgs for given ids + :param catIds (int array) : get imgs with all given cats + :return: ids (int array) : integer array of img ids + ''' + imgIds = imgIds if _isArrayLike(imgIds) else [imgIds] + catIds = catIds if _isArrayLike(catIds) else [catIds] + + if len(imgIds) == len(catIds) == 0: + ids = self.imgs.keys() + else: + ids = set(imgIds) + for i, catId in enumerate(catIds): + if i == 0 and len(ids) == 0: + ids = set(self.catToImgs[catId]) + else: + ids &= set(self.catToImgs[catId]) + return list(ids) + + def loadAnns(self, ids=[]): + """ + Load anns with the specified ids. + :param ids (int array) : integer ids specifying anns + :return: anns (object array) : loaded ann objects + """ + if _isArrayLike(ids): + return [self.anns[id] for id in ids] + elif type(ids) == int: + return [self.anns[ids]] + + def loadCats(self, ids=[]): + """ + Load cats with the specified ids. + :param ids (int array) : integer ids specifying cats + :return: cats (object array) : loaded cat objects + """ + if _isArrayLike(ids): + return [self.cats[id] for id in ids] + elif type(ids) == int: + return [self.cats[ids]] + + def loadImgs(self, ids=[]): + """ + Load anns with the specified ids. + :param ids (int array) : integer ids specifying img + :return: imgs (object array) : loaded img objects + """ + if _isArrayLike(ids): + return [self.imgs[id] for id in ids] + elif type(ids) == int: + return [self.imgs[ids]] + + def showAnns(self, anns): + """ + Display the specified annotations. + :param anns (array of object): annotations to display + :return: None + """ + if len(anns) == 0: + return 0 + if 'segmentation' in anns[0] or 'keypoints' in anns[0]: + datasetType = 'instances' + elif 'caption' in anns[0]: + datasetType = 'captions' + else: + raise Exception('datasetType not supported') + if datasetType == 'instances': + ax = plt.gca() + ax.set_autoscale_on(False) + polygons = [] + color = [] + for ann in anns: + c = (np.random.random((1, 3))*0.6+0.4).tolist()[0] + if 'segmentation' in ann: + if type(ann['segmentation']) == list: + # polygon + for seg in ann['segmentation']: + poly = np.array(seg).reshape((int(len(seg)/2), 2)) + polygons.append(Polygon(poly)) + color.append(c) + else: + # mask + t = self.imgs[ann['image_id']] + if type(ann['segmentation']['counts']) == list: + rle = maskUtils.frPyObjects([ann['segmentation']], t['height'], t['width']) + else: + rle = [ann['segmentation']] + m = maskUtils.decode(rle) + img = np.ones( (m.shape[0], m.shape[1], 3) ) + if ann['iscrowd'] == 1: + color_mask = np.array([2.0,166.0,101.0])/255 + if ann['iscrowd'] == 0: + color_mask = np.random.random((1, 3)).tolist()[0] + for i in range(3): + img[:,:,i] = color_mask[i] + ax.imshow(np.dstack( (img, m*0.5) )) + if 'keypoints' in ann and type(ann['keypoints']) == list: + # turn skeleton into zero-based index + sks = np.array(self.loadCats(ann['category_id'])[0]['skeleton'])-1 + kp = np.array(ann['keypoints']) + x = kp[0::3] + y = kp[1::3] + v = kp[2::3] + for sk in sks: + if np.all(v[sk]>0): + plt.plot(x[sk],y[sk], linewidth=3, color=c) + plt.plot(x[v>0], y[v>0],'o',markersize=8, markerfacecolor=c, markeredgecolor='k',markeredgewidth=2) + plt.plot(x[v>1], y[v>1],'o',markersize=8, markerfacecolor=c, markeredgecolor=c, markeredgewidth=2) + p = PatchCollection(polygons, facecolor=color, linewidths=0, alpha=0.4) + ax.add_collection(p) + p = PatchCollection(polygons, facecolor='none', edgecolors=color, linewidths=2) + ax.add_collection(p) + elif datasetType == 'captions': + for ann in anns: + print(ann['caption']) + + def loadRes(self, resFile): + """ + Load result file and return a result api object. + :param resFile (str) : file name of result file + :return: res (obj) : result api object + """ + res = COCO() + res.dataset['images'] = [img for img in self.dataset['images']] + + print('Loading and preparing results...') + tic = time.time() + if type(resFile) == str or type(resFile) == unicode: + anns = json.load(open(resFile)) + elif type(resFile) == np.ndarray: + anns = self.loadNumpyAnnotations(resFile) + else: + anns = resFile + assert type(anns) == list, 'results in not an array of objects' + annsImgIds = [ann['image_id'] for ann in anns] + assert set(annsImgIds) == (set(annsImgIds) & set(self.getImgIds())), \ + 'Results do not correspond to current coco set' + if 'caption' in anns[0]: + imgIds = set([img['id'] for img in res.dataset['images']]) & set([ann['image_id'] for ann in anns]) + res.dataset['images'] = [img for img in res.dataset['images'] if img['id'] in imgIds] + for id, ann in enumerate(anns): + ann['id'] = id+1 + elif 'bbox' in anns[0] and not anns[0]['bbox'] == []: + res.dataset['categories'] = copy.deepcopy(self.dataset['categories']) + for id, ann in enumerate(anns): + bb = ann['bbox'] + x1, x2, y1, y2 = [bb[0], bb[0]+bb[2], bb[1], bb[1]+bb[3]] + if not 'segmentation' in ann: + ann['segmentation'] = [[x1, y1, x1, y2, x2, y2, x2, y1]] + ann['area'] = bb[2]*bb[3] + ann['id'] = id+1 + ann['iscrowd'] = 0 + elif 'segmentation' in anns[0]: + res.dataset['categories'] = copy.deepcopy(self.dataset['categories']) + for id, ann in enumerate(anns): + # now only support compressed RLE format as segmentation results + ann['area'] = maskUtils.area(ann['segmentation']) + if not 'bbox' in ann: + ann['bbox'] = maskUtils.toBbox(ann['segmentation']) + ann['id'] = id+1 + ann['iscrowd'] = 0 + elif 'keypoints' in anns[0]: + res.dataset['categories'] = copy.deepcopy(self.dataset['categories']) + for id, ann in enumerate(anns): + s = ann['keypoints'] + x = s[0::3] + y = s[1::3] + x0,x1,y0,y1 = np.min(x), np.max(x), np.min(y), np.max(y) + ann['area'] = (x1-x0)*(y1-y0) + ann['id'] = id + 1 + ann['bbox'] = [x0,y0,x1-x0,y1-y0] + print('DONE (t={:0.2f}s)'.format(time.time()- tic)) + + res.dataset['annotations'] = anns + res.createIndex() + return res + + def download(self, tarDir = None, imgIds = [] ): + ''' + Download COCO images from mscoco.org server. + :param tarDir (str): COCO results directory name + imgIds (list): images to be downloaded + :return: + ''' + if tarDir is None: + print('Please specify target directory') + return -1 + if len(imgIds) == 0: + imgs = self.imgs.values() + else: + imgs = self.loadImgs(imgIds) + N = len(imgs) + if not os.path.exists(tarDir): + os.makedirs(tarDir) + for i, img in enumerate(imgs): + tic = time.time() + fname = os.path.join(tarDir, img['file_name']) + if not os.path.exists(fname): + urlretrieve(img['coco_url'], fname) + print('downloaded {}/{} images (t={:0.1f}s)'.format(i, N, time.time()- tic)) + + def loadNumpyAnnotations(self, data): + """ + Convert result data from a numpy array [Nx7] where each row contains {imageID,x1,y1,w,h,score,class} + :param data (numpy.ndarray) + :return: annotations (python nested list) + """ + print('Converting ndarray to lists...') + assert(type(data) == np.ndarray) + print(data.shape) + assert(data.shape[1] == 7) + N = data.shape[0] + ann = [] + for i in range(N): + if i % 1000000 == 0: + print('{}/{}'.format(i,N)) + ann += [{ + 'image_id' : int(data[i, 0]), + 'bbox' : [ data[i, 1], data[i, 2], data[i, 3], data[i, 4] ], + 'score' : data[i, 5], + 'category_id': int(data[i, 6]), + }] + return ann + + def annToRLE(self, ann): + """ + Convert annotation which can be polygons, uncompressed RLE to RLE. + :return: binary mask (numpy 2D array) + """ + t = self.imgs[ann['image_id']] + h, w = t['height'], t['width'] + segm = ann['segmentation'] + if type(segm) == list: + # polygon -- a single object might consist of multiple parts + # we merge all parts into one mask rle code + rles = maskUtils.frPyObjects(segm, h, w) + rle = maskUtils.merge(rles) + elif type(segm['counts']) == list: + # uncompressed RLE + rle = maskUtils.frPyObjects(segm, h, w) + else: + # rle + rle = ann['segmentation'] + return rle + + def annToMask(self, ann): + """ + Convert annotation which can be polygons, uncompressed RLE, or RLE to binary mask. + :return: binary mask (numpy 2D array) + """ + rle = self.annToRLE(ann) + m = maskUtils.decode(rle) + return m \ No newline at end of file diff --git a/data/lib_coco/PythonAPI/pycocotools/cocoeval.py b/data/lib_coco/PythonAPI/pycocotools/cocoeval.py new file mode 100644 index 0000000..7a4b4ad --- /dev/null +++ b/data/lib_coco/PythonAPI/pycocotools/cocoeval.py @@ -0,0 +1,534 @@ +__author__ = 'tsungyi' + +import numpy as np +import datetime +import time +from collections import defaultdict +from . import mask as maskUtils +import copy + +class COCOeval: + # Interface for evaluating detection on the Microsoft COCO dataset. + # + # The usage for CocoEval is as follows: + # cocoGt=..., cocoDt=... # load dataset and results + # E = CocoEval(cocoGt,cocoDt); # initialize CocoEval object + # E.params.recThrs = ...; # set parameters as desired + # E.evaluate(); # run per image evaluation + # E.accumulate(); # accumulate per image results + # E.summarize(); # display summary metrics of results + # For example usage see evalDemo.m and http://mscoco.org/. + # + # The evaluation parameters are as follows (defaults in brackets): + # imgIds - [all] N img ids to use for evaluation + # catIds - [all] K cat ids to use for evaluation + # iouThrs - [.5:.05:.95] T=10 IoU thresholds for evaluation + # recThrs - [0:.01:1] R=101 recall thresholds for evaluation + # areaRng - [...] A=4 object area ranges for evaluation + # maxDets - [1 10 100] M=3 thresholds on max detections per image + # iouType - ['segm'] set iouType to 'segm', 'bbox' or 'keypoints' + # iouType replaced the now DEPRECATED useSegm parameter. + # useCats - [1] if true use category labels for evaluation + # Note: if useCats=0 category labels are ignored as in proposal scoring. + # Note: multiple areaRngs [Ax2] and maxDets [Mx1] can be specified. + # + # evaluate(): evaluates detections on every image and every category and + # concats the results into the "evalImgs" with fields: + # dtIds - [1xD] id for each of the D detections (dt) + # gtIds - [1xG] id for each of the G ground truths (gt) + # dtMatches - [TxD] matching gt id at each IoU or 0 + # gtMatches - [TxG] matching dt id at each IoU or 0 + # dtScores - [1xD] confidence of each dt + # gtIgnore - [1xG] ignore flag for each gt + # dtIgnore - [TxD] ignore flag for each dt at each IoU + # + # accumulate(): accumulates the per-image, per-category evaluation + # results in "evalImgs" into the dictionary "eval" with fields: + # params - parameters used for evaluation + # date - date evaluation was performed + # counts - [T,R,K,A,M] parameter dimensions (see above) + # precision - [TxRxKxAxM] precision for every evaluation setting + # recall - [TxKxAxM] max recall for every evaluation setting + # Note: precision and recall==-1 for settings with no gt objects. + # + # See also coco, mask, pycocoDemo, pycocoEvalDemo + # + # Microsoft COCO Toolbox. version 2.0 + # Data, paper, and tutorials available at: http://mscoco.org/ + # Code written by Piotr Dollar and Tsung-Yi Lin, 2015. + # Licensed under the Simplified BSD License [see coco/license.txt] + def __init__(self, cocoGt=None, cocoDt=None, iouType='segm'): + ''' + Initialize CocoEval using coco APIs for gt and dt + :param cocoGt: coco object with ground truth annotations + :param cocoDt: coco object with detection results + :return: None + ''' + if not iouType: + print('iouType not specified. use default iouType segm') + self.cocoGt = cocoGt # ground truth COCO API + self.cocoDt = cocoDt # detections COCO API + self.params = {} # evaluation parameters + self.evalImgs = defaultdict(list) # per-image per-category evaluation results [KxAxI] elements + self.eval = {} # accumulated evaluation results + self._gts = defaultdict(list) # gt for evaluation + self._dts = defaultdict(list) # dt for evaluation + self.params = Params(iouType=iouType) # parameters + self._paramsEval = {} # parameters for evaluation + self.stats = [] # result summarization + self.ious = {} # ious between all gts and dts + if not cocoGt is None: + self.params.imgIds = sorted(cocoGt.getImgIds()) + self.params.catIds = sorted(cocoGt.getCatIds()) + + + def _prepare(self): + ''' + Prepare ._gts and ._dts for evaluation based on params + :return: None + ''' + def _toMask(anns, coco): + # modify ann['segmentation'] by reference + for ann in anns: + rle = coco.annToRLE(ann) + ann['segmentation'] = rle + p = self.params + if p.useCats: + gts=self.cocoGt.loadAnns(self.cocoGt.getAnnIds(imgIds=p.imgIds, catIds=p.catIds)) + dts=self.cocoDt.loadAnns(self.cocoDt.getAnnIds(imgIds=p.imgIds, catIds=p.catIds)) + else: + gts=self.cocoGt.loadAnns(self.cocoGt.getAnnIds(imgIds=p.imgIds)) + dts=self.cocoDt.loadAnns(self.cocoDt.getAnnIds(imgIds=p.imgIds)) + + # convert ground truth to mask if iouType == 'segm' + if p.iouType == 'segm': + _toMask(gts, self.cocoGt) + _toMask(dts, self.cocoDt) + # set ignore flag + for gt in gts: + gt['ignore'] = gt['ignore'] if 'ignore' in gt else 0 + gt['ignore'] = 'iscrowd' in gt and gt['iscrowd'] + if p.iouType == 'keypoints': + gt['ignore'] = (gt['num_keypoints'] == 0) or gt['ignore'] + self._gts = defaultdict(list) # gt for evaluation + self._dts = defaultdict(list) # dt for evaluation + for gt in gts: + self._gts[gt['image_id'], gt['category_id']].append(gt) + for dt in dts: + self._dts[dt['image_id'], dt['category_id']].append(dt) + self.evalImgs = defaultdict(list) # per-image per-category evaluation results + self.eval = {} # accumulated evaluation results + + def evaluate(self): + ''' + Run per image evaluation on given images and store results (a list of dict) in self.evalImgs + :return: None + ''' + tic = time.time() + print('Running per image evaluation...') + p = self.params + # add backward compatibility if useSegm is specified in params + if not p.useSegm is None: + p.iouType = 'segm' if p.useSegm == 1 else 'bbox' + print('useSegm (deprecated) is not None. Running {} evaluation'.format(p.iouType)) + print('Evaluate annotation type *{}*'.format(p.iouType)) + p.imgIds = list(np.unique(p.imgIds)) + if p.useCats: + p.catIds = list(np.unique(p.catIds)) + p.maxDets = sorted(p.maxDets) + self.params=p + + self._prepare() + # loop through images, area range, max detection number + catIds = p.catIds if p.useCats else [-1] + + if p.iouType == 'segm' or p.iouType == 'bbox': + computeIoU = self.computeIoU + elif p.iouType == 'keypoints': + computeIoU = self.computeOks + self.ious = {(imgId, catId): computeIoU(imgId, catId) \ + for imgId in p.imgIds + for catId in catIds} + + evaluateImg = self.evaluateImg + maxDet = p.maxDets[-1] + self.evalImgs = [evaluateImg(imgId, catId, areaRng, maxDet) + for catId in catIds + for areaRng in p.areaRng + for imgId in p.imgIds + ] + self._paramsEval = copy.deepcopy(self.params) + toc = time.time() + print('DONE (t={:0.2f}s).'.format(toc-tic)) + + def computeIoU(self, imgId, catId): + p = self.params + if p.useCats: + gt = self._gts[imgId,catId] + dt = self._dts[imgId,catId] + else: + gt = [_ for cId in p.catIds for _ in self._gts[imgId,cId]] + dt = [_ for cId in p.catIds for _ in self._dts[imgId,cId]] + if len(gt) == 0 and len(dt) ==0: + return [] + inds = np.argsort([-d['score'] for d in dt], kind='mergesort') + dt = [dt[i] for i in inds] + if len(dt) > p.maxDets[-1]: + dt=dt[0:p.maxDets[-1]] + + if p.iouType == 'segm': + g = [g['segmentation'] for g in gt] + d = [d['segmentation'] for d in dt] + elif p.iouType == 'bbox': + g = [g['bbox'] for g in gt] + d = [d['bbox'] for d in dt] + else: + raise Exception('unknown iouType for iou computation') + + # compute iou between each dt and gt region + iscrowd = [int(o['iscrowd']) for o in gt] + ious = maskUtils.iou(d,g,iscrowd) + return ious + + def computeOks(self, imgId, catId): + p = self.params + # dimention here should be Nxm + gts = self._gts[imgId, catId] + dts = self._dts[imgId, catId] + inds = np.argsort([-d['score'] for d in dts], kind='mergesort') + dts = [dts[i] for i in inds] + if len(dts) > p.maxDets[-1]: + dts = dts[0:p.maxDets[-1]] + # if len(gts) == 0 and len(dts) == 0: + if len(gts) == 0 or len(dts) == 0: + return [] + ious = np.zeros((len(dts), len(gts))) + sigmas = np.array([.26, .25, .25, .35, .35, .79, .79, .72, .72, .62,.62, 1.07, 1.07, .87, .87, .89, .89])/10.0 + vars = (sigmas * 2)**2 + k = len(sigmas) + # compute oks between each detection and ground truth object + for j, gt in enumerate(gts): + # create bounds for ignore regions(double the gt bbox) + g = np.array(gt['keypoints']) + xg = g[0::3]; yg = g[1::3]; vg = g[2::3] + k1 = np.count_nonzero(vg > 0) + bb = gt['bbox'] + x0 = bb[0] - bb[2]; x1 = bb[0] + bb[2] * 2 + y0 = bb[1] - bb[3]; y1 = bb[1] + bb[3] * 2 + for i, dt in enumerate(dts): + d = np.array(dt['keypoints']) + xd = d[0::3]; yd = d[1::3] + if k1>0: + # measure the per-keypoint distance if keypoints visible + dx = xd - xg + dy = yd - yg + else: + # measure minimum distance to keypoints in (x0,y0) & (x1,y1) + z = np.zeros((k)) + dx = np.max((z, x0-xd),axis=0)+np.max((z, xd-x1),axis=0) + dy = np.max((z, y0-yd),axis=0)+np.max((z, yd-y1),axis=0) + e = (dx**2 + dy**2) / vars / (gt['area']+np.spacing(1)) / 2 + if k1 > 0: + e=e[vg > 0] + ious[i, j] = np.sum(np.exp(-e)) / e.shape[0] + return ious + + def evaluateImg(self, imgId, catId, aRng, maxDet): + ''' + perform evaluation for single category and image + :return: dict (single image results) + ''' + p = self.params + if p.useCats: + gt = self._gts[imgId,catId] + dt = self._dts[imgId,catId] + else: + gt = [_ for cId in p.catIds for _ in self._gts[imgId,cId]] + dt = [_ for cId in p.catIds for _ in self._dts[imgId,cId]] + if len(gt) == 0 and len(dt) ==0: + return None + + for g in gt: + if g['ignore'] or (g['area']aRng[1]): + g['_ignore'] = 1 + else: + g['_ignore'] = 0 + + # sort dt highest score first, sort gt ignore last + gtind = np.argsort([g['_ignore'] for g in gt], kind='mergesort') + gt = [gt[i] for i in gtind] + dtind = np.argsort([-d['score'] for d in dt], kind='mergesort') + dt = [dt[i] for i in dtind[0:maxDet]] + iscrowd = [int(o['iscrowd']) for o in gt] + # load computed ious + ious = self.ious[imgId, catId][:, gtind] if len(self.ious[imgId, catId]) > 0 else self.ious[imgId, catId] + + T = len(p.iouThrs) + G = len(gt) + D = len(dt) + gtm = np.zeros((T,G)) + dtm = np.zeros((T,D)) + gtIg = np.array([g['_ignore'] for g in gt]) + dtIg = np.zeros((T,D)) + if not len(ious)==0: + for tind, t in enumerate(p.iouThrs): + for dind, d in enumerate(dt): + # information about best match so far (m=-1 -> unmatched) + iou = min([t,1-1e-10]) + m = -1 + for gind, g in enumerate(gt): + # if this gt already matched, and not a crowd, continue + if gtm[tind,gind]>0 and not iscrowd[gind]: + continue + # if dt matched to reg gt, and on ignore gt, stop + if m>-1 and gtIg[m]==0 and gtIg[gind]==1: + break + # continue to next gt unless better match made + if ious[dind,gind] < iou: + continue + # if match successful and best so far, store appropriately + iou=ious[dind,gind] + m=gind + # if match made store id of match for both dt and gt + if m ==-1: + continue + dtIg[tind,dind] = gtIg[m] + dtm[tind,dind] = gt[m]['id'] + gtm[tind,m] = d['id'] + # set unmatched detections outside of area range to ignore + a = np.array([d['area']aRng[1] for d in dt]).reshape((1, len(dt))) + dtIg = np.logical_or(dtIg, np.logical_and(dtm==0, np.repeat(a,T,0))) + # store results for given image and category + return { + 'image_id': imgId, + 'category_id': catId, + 'aRng': aRng, + 'maxDet': maxDet, + 'dtIds': [d['id'] for d in dt], + 'gtIds': [g['id'] for g in gt], + 'dtMatches': dtm, + 'gtMatches': gtm, + 'dtScores': [d['score'] for d in dt], + 'gtIgnore': gtIg, + 'dtIgnore': dtIg, + } + + def accumulate(self, p = None): + ''' + Accumulate per image evaluation results and store the result in self.eval + :param p: input params for evaluation + :return: None + ''' + print('Accumulating evaluation results...') + tic = time.time() + if not self.evalImgs: + print('Please run evaluate() first') + # allows input customized parameters + if p is None: + p = self.params + p.catIds = p.catIds if p.useCats == 1 else [-1] + T = len(p.iouThrs) + R = len(p.recThrs) + K = len(p.catIds) if p.useCats else 1 + A = len(p.areaRng) + M = len(p.maxDets) + precision = -np.ones((T,R,K,A,M)) # -1 for the precision of absent categories + recall = -np.ones((T,K,A,M)) + scores = -np.ones((T,R,K,A,M)) + + # create dictionary for future indexing + _pe = self._paramsEval + catIds = _pe.catIds if _pe.useCats else [-1] + setK = set(catIds) + setA = set(map(tuple, _pe.areaRng)) + setM = set(_pe.maxDets) + setI = set(_pe.imgIds) + # get inds to evaluate + k_list = [n for n, k in enumerate(p.catIds) if k in setK] + m_list = [m for n, m in enumerate(p.maxDets) if m in setM] + a_list = [n for n, a in enumerate(map(lambda x: tuple(x), p.areaRng)) if a in setA] + i_list = [n for n, i in enumerate(p.imgIds) if i in setI] + I0 = len(_pe.imgIds) + A0 = len(_pe.areaRng) + # retrieve E at each category, area range, and max number of detections + for k, k0 in enumerate(k_list): + Nk = k0*A0*I0 + for a, a0 in enumerate(a_list): + Na = a0*I0 + for m, maxDet in enumerate(m_list): + E = [self.evalImgs[Nk + Na + i] for i in i_list] + E = [e for e in E if not e is None] + if len(E) == 0: + continue + dtScores = np.concatenate([e['dtScores'][0:maxDet] for e in E]) + + # different sorting method generates slightly different results. + # mergesort is used to be consistent as Matlab implementation. + inds = np.argsort(-dtScores, kind='mergesort') + dtScoresSorted = dtScores[inds] + + dtm = np.concatenate([e['dtMatches'][:,0:maxDet] for e in E], axis=1)[:,inds] + dtIg = np.concatenate([e['dtIgnore'][:,0:maxDet] for e in E], axis=1)[:,inds] + gtIg = np.concatenate([e['gtIgnore'] for e in E]) + npig = np.count_nonzero(gtIg==0 ) + if npig == 0: + continue + tps = np.logical_and( dtm, np.logical_not(dtIg) ) + fps = np.logical_and(np.logical_not(dtm), np.logical_not(dtIg) ) + + tp_sum = np.cumsum(tps, axis=1).astype(dtype=np.float) + fp_sum = np.cumsum(fps, axis=1).astype(dtype=np.float) + for t, (tp, fp) in enumerate(zip(tp_sum, fp_sum)): + tp = np.array(tp) + fp = np.array(fp) + nd = len(tp) + rc = tp / npig + pr = tp / (fp+tp+np.spacing(1)) + q = np.zeros((R,)) + ss = np.zeros((R,)) + + if nd: + recall[t,k,a,m] = rc[-1] + else: + recall[t,k,a,m] = 0 + + # numpy is slow without cython optimization for accessing elements + # use python array gets significant speed improvement + pr = pr.tolist(); q = q.tolist() + + for i in range(nd-1, 0, -1): + if pr[i] > pr[i-1]: + pr[i-1] = pr[i] + + inds = np.searchsorted(rc, p.recThrs, side='left') + try: + for ri, pi in enumerate(inds): + q[ri] = pr[pi] + ss[ri] = dtScoresSorted[pi] + except: + pass + precision[t,:,k,a,m] = np.array(q) + scores[t,:,k,a,m] = np.array(ss) + self.eval = { + 'params': p, + 'counts': [T, R, K, A, M], + 'date': datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), + 'precision': precision, + 'recall': recall, + 'scores': scores, + } + toc = time.time() + print('DONE (t={:0.2f}s).'.format( toc-tic)) + + def summarize(self): + ''' + Compute and display summary metrics for evaluation results. + Note this functin can *only* be applied on the default parameter setting + ''' + def _summarize( ap=1, iouThr=None, areaRng='all', maxDets=100 ): + p = self.params + iStr = ' {:<18} {} @[ IoU={:<9} | area={:>6s} | maxDets={:>3d} ] = {:0.3f}' + titleStr = 'Average Precision' if ap == 1 else 'Average Recall' + typeStr = '(AP)' if ap==1 else '(AR)' + iouStr = '{:0.2f}:{:0.2f}'.format(p.iouThrs[0], p.iouThrs[-1]) \ + if iouThr is None else '{:0.2f}'.format(iouThr) + + aind = [i for i, aRng in enumerate(p.areaRngLbl) if aRng == areaRng] + mind = [i for i, mDet in enumerate(p.maxDets) if mDet == maxDets] + if ap == 1: + # dimension of precision: [TxRxKxAxM] + s = self.eval['precision'] + # IoU + if iouThr is not None: + t = np.where(iouThr == p.iouThrs)[0] + s = s[t] + s = s[:,:,:,aind,mind] + else: + # dimension of recall: [TxKxAxM] + s = self.eval['recall'] + if iouThr is not None: + t = np.where(iouThr == p.iouThrs)[0] + s = s[t] + s = s[:,:,aind,mind] + if len(s[s>-1])==0: + mean_s = -1 + else: + mean_s = np.mean(s[s>-1]) + print(iStr.format(titleStr, typeStr, iouStr, areaRng, maxDets, mean_s)) + return mean_s + def _summarizeDets(): + stats = np.zeros((12,)) + stats[0] = _summarize(1) + stats[1] = _summarize(1, iouThr=.5, maxDets=self.params.maxDets[2]) + stats[2] = _summarize(1, iouThr=.75, maxDets=self.params.maxDets[2]) + stats[3] = _summarize(1, areaRng='small', maxDets=self.params.maxDets[2]) + stats[4] = _summarize(1, areaRng='medium', maxDets=self.params.maxDets[2]) + stats[5] = _summarize(1, areaRng='large', maxDets=self.params.maxDets[2]) + stats[6] = _summarize(0, maxDets=self.params.maxDets[0]) + stats[7] = _summarize(0, maxDets=self.params.maxDets[1]) + stats[8] = _summarize(0, maxDets=self.params.maxDets[2]) + stats[9] = _summarize(0, areaRng='small', maxDets=self.params.maxDets[2]) + stats[10] = _summarize(0, areaRng='medium', maxDets=self.params.maxDets[2]) + stats[11] = _summarize(0, areaRng='large', maxDets=self.params.maxDets[2]) + return stats + def _summarizeKps(): + stats = np.zeros((10,)) + stats[0] = _summarize(1, maxDets=20) + stats[1] = _summarize(1, maxDets=20, iouThr=.5) + stats[2] = _summarize(1, maxDets=20, iouThr=.75) + stats[3] = _summarize(1, maxDets=20, areaRng='medium') + stats[4] = _summarize(1, maxDets=20, areaRng='large') + stats[5] = _summarize(0, maxDets=20) + stats[6] = _summarize(0, maxDets=20, iouThr=.5) + stats[7] = _summarize(0, maxDets=20, iouThr=.75) + stats[8] = _summarize(0, maxDets=20, areaRng='medium') + stats[9] = _summarize(0, maxDets=20, areaRng='large') + return stats + if not self.eval: + raise Exception('Please run accumulate() first') + iouType = self.params.iouType + if iouType == 'segm' or iouType == 'bbox': + summarize = _summarizeDets + elif iouType == 'keypoints': + summarize = _summarizeKps + self.stats = summarize() + + def __str__(self): + self.summarize() + +class Params: + ''' + Params for coco evaluation api + ''' + def setDetParams(self): + self.imgIds = [] + self.catIds = [] + # np.arange causes trouble. the data point on arange is slightly larger than the true value + self.iouThrs = np.linspace(.5, 0.95, np.round((0.95 - .5) / .05) + 1, endpoint=True) + self.recThrs = np.linspace(.0, 1.00, np.round((1.00 - .0) / .01) + 1, endpoint=True) + self.maxDets = [1, 10, 100] + self.areaRng = [[0 ** 2, 1e5 ** 2], [0 ** 2, 32 ** 2], [32 ** 2, 96 ** 2], [96 ** 2, 1e5 ** 2]] + self.areaRngLbl = ['all', 'small', 'medium', 'large'] + self.useCats = 1 + + def setKpParams(self): + self.imgIds = [] + self.catIds = [] + # np.arange causes trouble. the data point on arange is slightly larger than the true value + self.iouThrs = np.linspace(.5, 0.95, np.round((0.95 - .5) / .05) + 1, endpoint=True) + self.recThrs = np.linspace(.0, 1.00, np.round((1.00 - .0) / .01) + 1, endpoint=True) + self.maxDets = [20] + self.areaRng = [[0 ** 2, 1e5 ** 2], [32 ** 2, 96 ** 2], [96 ** 2, 1e5 ** 2]] + self.areaRngLbl = ['all', 'medium', 'large'] + self.useCats = 1 + + def __init__(self, iouType='segm'): + if iouType == 'segm' or iouType == 'bbox': + self.setDetParams() + elif iouType == 'keypoints': + self.setKpParams() + else: + raise Exception('iouType not supported') + self.iouType = iouType + # useSegm is deprecated + self.useSegm = None \ No newline at end of file diff --git a/data/lib_coco/PythonAPI/pycocotools/mask.py b/data/lib_coco/PythonAPI/pycocotools/mask.py new file mode 100644 index 0000000..40853ba --- /dev/null +++ b/data/lib_coco/PythonAPI/pycocotools/mask.py @@ -0,0 +1,103 @@ +__author__ = 'tsungyi' + +import pycocotools._mask as _mask + +# Interface for manipulating masks stored in RLE format. +# +# RLE is a simple yet efficient format for storing binary masks. RLE +# first divides a vector (or vectorized image) into a series of piecewise +# constant regions and then for each piece simply stores the length of +# that piece. For example, given M=[0 0 1 1 1 0 1] the RLE counts would +# be [2 3 1 1], or for M=[1 1 1 1 1 1 0] the counts would be [0 6 1] +# (note that the odd counts are always the numbers of zeros). Instead of +# storing the counts directly, additional compression is achieved with a +# variable bitrate representation based on a common scheme called LEB128. +# +# Compression is greatest given large piecewise constant regions. +# Specifically, the size of the RLE is proportional to the number of +# *boundaries* in M (or for an image the number of boundaries in the y +# direction). Assuming fairly simple shapes, the RLE representation is +# O(sqrt(n)) where n is number of pixels in the object. Hence space usage +# is substantially lower, especially for large simple objects (large n). +# +# Many common operations on masks can be computed directly using the RLE +# (without need for decoding). This includes computations such as area, +# union, intersection, etc. All of these operations are linear in the +# size of the RLE, in other words they are O(sqrt(n)) where n is the area +# of the object. Computing these operations on the original mask is O(n). +# Thus, using the RLE can result in substantial computational savings. +# +# The following API functions are defined: +# encode - Encode binary masks using RLE. +# decode - Decode binary masks encoded via RLE. +# merge - Compute union or intersection of encoded masks. +# iou - Compute intersection over union between masks. +# area - Compute area of encoded masks. +# toBbox - Get bounding boxes surrounding encoded masks. +# frPyObjects - Convert polygon, bbox, and uncompressed RLE to encoded RLE mask. +# +# Usage: +# Rs = encode( masks ) +# masks = decode( Rs ) +# R = merge( Rs, intersect=false ) +# o = iou( dt, gt, iscrowd ) +# a = area( Rs ) +# bbs = toBbox( Rs ) +# Rs = frPyObjects( [pyObjects], h, w ) +# +# In the API the following formats are used: +# Rs - [dict] Run-length encoding of binary masks +# R - dict Run-length encoding of binary mask +# masks - [hxwxn] Binary mask(s) (must have type np.ndarray(dtype=uint8) in column-major order) +# iscrowd - [nx1] list of np.ndarray. 1 indicates corresponding gt image has crowd region to ignore +# bbs - [nx4] Bounding box(es) stored as [x y w h] +# poly - Polygon stored as [[x1 y1 x2 y2...],[x1 y1 ...],...] (2D list) +# dt,gt - May be either bounding boxes or encoded masks +# Both poly and bbs are 0-indexed (bbox=[0 0 1 1] encloses first pixel). +# +# Finally, a note about the intersection over union (iou) computation. +# The standard iou of a ground truth (gt) and detected (dt) object is +# iou(gt,dt) = area(intersect(gt,dt)) / area(union(gt,dt)) +# For "crowd" regions, we use a modified criteria. If a gt object is +# marked as "iscrowd", we allow a dt to match any subregion of the gt. +# Choosing gt' in the crowd gt that best matches the dt can be done using +# gt'=intersect(dt,gt). Since by definition union(gt',dt)=dt, computing +# iou(gt,dt,iscrowd) = iou(gt',dt) = area(intersect(gt,dt)) / area(dt) +# For crowd gt regions we use this modified criteria above for the iou. +# +# To compile run "python setup.py build_ext --inplace" +# Please do not contact us for help with compiling. +# +# Microsoft COCO Toolbox. version 2.0 +# Data, paper, and tutorials available at: http://mscoco.org/ +# Code written by Piotr Dollar and Tsung-Yi Lin, 2015. +# Licensed under the Simplified BSD License [see coco/license.txt] + +iou = _mask.iou +merge = _mask.merge +frPyObjects = _mask.frPyObjects + +def encode(bimask): + if len(bimask.shape) == 3: + return _mask.encode(bimask) + elif len(bimask.shape) == 2: + h, w = bimask.shape + return _mask.encode(bimask.reshape((h, w, 1), order='F'))[0] + +def decode(rleObjs): + if type(rleObjs) == list: + return _mask.decode(rleObjs) + else: + return _mask.decode([rleObjs])[:,:,0] + +def area(rleObjs): + if type(rleObjs) == list: + return _mask.area(rleObjs) + else: + return _mask.area([rleObjs])[0] + +def toBbox(rleObjs): + if type(rleObjs) == list: + return _mask.toBbox(rleObjs) + else: + return _mask.toBbox([rleObjs])[0] \ No newline at end of file diff --git a/data/lib_coco/PythonAPI/setup.py b/data/lib_coco/PythonAPI/setup.py new file mode 100644 index 0000000..dbf0093 --- /dev/null +++ b/data/lib_coco/PythonAPI/setup.py @@ -0,0 +1,27 @@ +from setuptools import setup, Extension +import numpy as np + +# To compile and install locally run "python setup.py build_ext --inplace" +# To install library to Python site-packages run "python setup.py build_ext install" + +ext_modules = [ + Extension( + 'pycocotools._mask', + sources=['../common/maskApi.c', 'pycocotools/_mask.pyx'], + include_dirs = [np.get_include(), '../common'], + extra_compile_args=['-Wno-cpp', '-Wno-unused-function', '-std=c99'], + ) +] + +setup( + name='pycocotools', + packages=['pycocotools'], + package_dir = {'pycocotools': 'pycocotools'}, + install_requires=[ + 'setuptools>=18.0', + 'cython>=0.27.3', + 'matplotlib>=2.1.0' + ], + version='2.0', + ext_modules= ext_modules +) diff --git a/data/lib_coco/__init__.py b/data/lib_coco/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/data/lib_coco/common/gason.cpp b/data/lib_coco/common/gason.cpp new file mode 100644 index 0000000..0f2c00e --- /dev/null +++ b/data/lib_coco/common/gason.cpp @@ -0,0 +1,335 @@ +// https://github.com/vivkin/gason - pulled January 10, 2016 +#include "gason.h" +#include + +#define JSON_ZONE_SIZE 4096 +#define JSON_STACK_SIZE 32 + +const char *jsonStrError(int err) { + switch (err) { +#define XX(no, str) \ + case JSON_##no: \ + return str; + JSON_ERRNO_MAP(XX) +#undef XX + default: + return "unknown"; + } +} + +void *JsonAllocator::allocate(size_t size) { + size = (size + 7) & ~7; + + if (head && head->used + size <= JSON_ZONE_SIZE) { + char *p = (char *)head + head->used; + head->used += size; + return p; + } + + size_t allocSize = sizeof(Zone) + size; + Zone *zone = (Zone *)malloc(allocSize <= JSON_ZONE_SIZE ? JSON_ZONE_SIZE : allocSize); + if (zone == nullptr) + return nullptr; + zone->used = allocSize; + if (allocSize <= JSON_ZONE_SIZE || head == nullptr) { + zone->next = head; + head = zone; + } else { + zone->next = head->next; + head->next = zone; + } + return (char *)zone + sizeof(Zone); +} + +void JsonAllocator::deallocate() { + while (head) { + Zone *next = head->next; + free(head); + head = next; + } +} + +static inline bool isspace(char c) { + return c == ' ' || (c >= '\t' && c <= '\r'); +} + +static inline bool isdelim(char c) { + return c == ',' || c == ':' || c == ']' || c == '}' || isspace(c) || !c; +} + +static inline bool isdigit(char c) { + return c >= '0' && c <= '9'; +} + +static inline bool isxdigit(char c) { + return (c >= '0' && c <= '9') || ((c & ~' ') >= 'A' && (c & ~' ') <= 'F'); +} + +static inline int char2int(char c) { + if (c <= '9') + return c - '0'; + return (c & ~' ') - 'A' + 10; +} + +static double string2double(char *s, char **endptr) { + char ch = *s; + if (ch == '-') + ++s; + + double result = 0; + while (isdigit(*s)) + result = (result * 10) + (*s++ - '0'); + + if (*s == '.') { + ++s; + + double fraction = 1; + while (isdigit(*s)) { + fraction *= 0.1; + result += (*s++ - '0') * fraction; + } + } + + if (*s == 'e' || *s == 'E') { + ++s; + + double base = 10; + if (*s == '+') + ++s; + else if (*s == '-') { + ++s; + base = 0.1; + } + + unsigned int exponent = 0; + while (isdigit(*s)) + exponent = (exponent * 10) + (*s++ - '0'); + + double power = 1; + for (; exponent; exponent >>= 1, base *= base) + if (exponent & 1) + power *= base; + + result *= power; + } + + *endptr = s; + return ch == '-' ? -result : result; +} + +static inline JsonNode *insertAfter(JsonNode *tail, JsonNode *node) { + if (!tail) + return node->next = node; + node->next = tail->next; + tail->next = node; + return node; +} + +static inline JsonValue listToValue(JsonTag tag, JsonNode *tail) { + if (tail) { + auto head = tail->next; + tail->next = nullptr; + return JsonValue(tag, head); + } + return JsonValue(tag, nullptr); +} + +int jsonParse(char *s, char **endptr, JsonValue *value, JsonAllocator &allocator) { + JsonNode *tails[JSON_STACK_SIZE]; + JsonTag tags[JSON_STACK_SIZE]; + char *keys[JSON_STACK_SIZE]; + JsonValue o; + int pos = -1; + bool separator = true; + JsonNode *node; + *endptr = s; + + while (*s) { + while (isspace(*s)) { + ++s; + if (!*s) break; + } + *endptr = s++; + switch (**endptr) { + case '-': + if (!isdigit(*s) && *s != '.') { + *endptr = s; + return JSON_BAD_NUMBER; + } + case '0': + case '1': + case '2': + case '3': + case '4': + case '5': + case '6': + case '7': + case '8': + case '9': + o = JsonValue(string2double(*endptr, &s)); + if (!isdelim(*s)) { + *endptr = s; + return JSON_BAD_NUMBER; + } + break; + case '"': + o = JsonValue(JSON_STRING, s); + for (char *it = s; *s; ++it, ++s) { + int c = *it = *s; + if (c == '\\') { + c = *++s; + switch (c) { + case '\\': + case '"': + case '/': + *it = c; + break; + case 'b': + *it = '\b'; + break; + case 'f': + *it = '\f'; + break; + case 'n': + *it = '\n'; + break; + case 'r': + *it = '\r'; + break; + case 't': + *it = '\t'; + break; + case 'u': + c = 0; + for (int i = 0; i < 4; ++i) { + if (isxdigit(*++s)) { + c = c * 16 + char2int(*s); + } else { + *endptr = s; + return JSON_BAD_STRING; + } + } + if (c < 0x80) { + *it = c; + } else if (c < 0x800) { + *it++ = 0xC0 | (c >> 6); + *it = 0x80 | (c & 0x3F); + } else { + *it++ = 0xE0 | (c >> 12); + *it++ = 0x80 | ((c >> 6) & 0x3F); + *it = 0x80 | (c & 0x3F); + } + break; + default: + *endptr = s; + return JSON_BAD_STRING; + } + } else if ((unsigned int)c < ' ' || c == '\x7F') { + *endptr = s; + return JSON_BAD_STRING; + } else if (c == '"') { + *it = 0; + ++s; + break; + } + } + if (!isdelim(*s)) { + *endptr = s; + return JSON_BAD_STRING; + } + break; + case 't': + if (!(s[0] == 'r' && s[1] == 'u' && s[2] == 'e' && isdelim(s[3]))) + return JSON_BAD_IDENTIFIER; + o = JsonValue(JSON_TRUE); + s += 3; + break; + case 'f': + if (!(s[0] == 'a' && s[1] == 'l' && s[2] == 's' && s[3] == 'e' && isdelim(s[4]))) + return JSON_BAD_IDENTIFIER; + o = JsonValue(JSON_FALSE); + s += 4; + break; + case 'n': + if (!(s[0] == 'u' && s[1] == 'l' && s[2] == 'l' && isdelim(s[3]))) + return JSON_BAD_IDENTIFIER; + o = JsonValue(JSON_NULL); + s += 3; + break; + case ']': + if (pos == -1) + return JSON_STACK_UNDERFLOW; + if (tags[pos] != JSON_ARRAY) + return JSON_MISMATCH_BRACKET; + o = listToValue(JSON_ARRAY, tails[pos--]); + break; + case '}': + if (pos == -1) + return JSON_STACK_UNDERFLOW; + if (tags[pos] != JSON_OBJECT) + return JSON_MISMATCH_BRACKET; + if (keys[pos] != nullptr) + return JSON_UNEXPECTED_CHARACTER; + o = listToValue(JSON_OBJECT, tails[pos--]); + break; + case '[': + if (++pos == JSON_STACK_SIZE) + return JSON_STACK_OVERFLOW; + tails[pos] = nullptr; + tags[pos] = JSON_ARRAY; + keys[pos] = nullptr; + separator = true; + continue; + case '{': + if (++pos == JSON_STACK_SIZE) + return JSON_STACK_OVERFLOW; + tails[pos] = nullptr; + tags[pos] = JSON_OBJECT; + keys[pos] = nullptr; + separator = true; + continue; + case ':': + if (separator || keys[pos] == nullptr) + return JSON_UNEXPECTED_CHARACTER; + separator = true; + continue; + case ',': + if (separator || keys[pos] != nullptr) + return JSON_UNEXPECTED_CHARACTER; + separator = true; + continue; + case '\0': + continue; + default: + return JSON_UNEXPECTED_CHARACTER; + } + + separator = false; + + if (pos == -1) { + *endptr = s; + *value = o; + return JSON_OK; + } + + if (tags[pos] == JSON_OBJECT) { + if (!keys[pos]) { + if (o.getTag() != JSON_STRING) + return JSON_UNQUOTED_KEY; + keys[pos] = o.toString(); + continue; + } + if ((node = (JsonNode *) allocator.allocate(sizeof(JsonNode))) == nullptr) + return JSON_ALLOCATION_FAILURE; + tails[pos] = insertAfter(tails[pos], node); + tails[pos]->key = keys[pos]; + keys[pos] = nullptr; + } else { + if ((node = (JsonNode *) allocator.allocate(sizeof(JsonNode) - sizeof(char *))) == nullptr) + return JSON_ALLOCATION_FAILURE; + tails[pos] = insertAfter(tails[pos], node); + } + tails[pos]->value = o; + } + return JSON_BREAKING_BAD; +} diff --git a/data/lib_coco/common/gason.h b/data/lib_coco/common/gason.h new file mode 100644 index 0000000..2e728d9 --- /dev/null +++ b/data/lib_coco/common/gason.h @@ -0,0 +1,136 @@ +// https://github.com/vivkin/gason - pulled January 10, 2016 +#pragma once + +#include +#include +#include + +enum JsonTag { + JSON_NUMBER = 0, + JSON_STRING, + JSON_ARRAY, + JSON_OBJECT, + JSON_TRUE, + JSON_FALSE, + JSON_NULL = 0xF +}; + +struct JsonNode; + +#define JSON_VALUE_PAYLOAD_MASK 0x00007FFFFFFFFFFFULL +#define JSON_VALUE_NAN_MASK 0x7FF8000000000000ULL +#define JSON_VALUE_TAG_MASK 0xF +#define JSON_VALUE_TAG_SHIFT 47 + +union JsonValue { + uint64_t ival; + double fval; + + JsonValue(double x) + : fval(x) { + } + JsonValue(JsonTag tag = JSON_NULL, void *payload = nullptr) { + assert((uintptr_t)payload <= JSON_VALUE_PAYLOAD_MASK); + ival = JSON_VALUE_NAN_MASK | ((uint64_t)tag << JSON_VALUE_TAG_SHIFT) | (uintptr_t)payload; + } + bool isDouble() const { + return (int64_t)ival <= (int64_t)JSON_VALUE_NAN_MASK; + } + JsonTag getTag() const { + return isDouble() ? JSON_NUMBER : JsonTag((ival >> JSON_VALUE_TAG_SHIFT) & JSON_VALUE_TAG_MASK); + } + uint64_t getPayload() const { + assert(!isDouble()); + return ival & JSON_VALUE_PAYLOAD_MASK; + } + double toNumber() const { + assert(getTag() == JSON_NUMBER); + return fval; + } + char *toString() const { + assert(getTag() == JSON_STRING); + return (char *)getPayload(); + } + JsonNode *toNode() const { + assert(getTag() == JSON_ARRAY || getTag() == JSON_OBJECT); + return (JsonNode *)getPayload(); + } +}; + +struct JsonNode { + JsonValue value; + JsonNode *next; + char *key; +}; + +struct JsonIterator { + JsonNode *p; + + void operator++() { + p = p->next; + } + bool operator!=(const JsonIterator &x) const { + return p != x.p; + } + JsonNode *operator*() const { + return p; + } + JsonNode *operator->() const { + return p; + } +}; + +inline JsonIterator begin(JsonValue o) { + return JsonIterator{o.toNode()}; +} +inline JsonIterator end(JsonValue) { + return JsonIterator{nullptr}; +} + +#define JSON_ERRNO_MAP(XX) \ + XX(OK, "ok") \ + XX(BAD_NUMBER, "bad number") \ + XX(BAD_STRING, "bad string") \ + XX(BAD_IDENTIFIER, "bad identifier") \ + XX(STACK_OVERFLOW, "stack overflow") \ + XX(STACK_UNDERFLOW, "stack underflow") \ + XX(MISMATCH_BRACKET, "mismatch bracket") \ + XX(UNEXPECTED_CHARACTER, "unexpected character") \ + XX(UNQUOTED_KEY, "unquoted key") \ + XX(BREAKING_BAD, "breaking bad") \ + XX(ALLOCATION_FAILURE, "allocation failure") + +enum JsonErrno { +#define XX(no, str) JSON_##no, + JSON_ERRNO_MAP(XX) +#undef XX +}; + +const char *jsonStrError(int err); + +class JsonAllocator { + struct Zone { + Zone *next; + size_t used; + } *head = nullptr; + +public: + JsonAllocator() = default; + JsonAllocator(const JsonAllocator &) = delete; + JsonAllocator &operator=(const JsonAllocator &) = delete; + JsonAllocator(JsonAllocator &&x) : head(x.head) { + x.head = nullptr; + } + JsonAllocator &operator=(JsonAllocator &&x) { + head = x.head; + x.head = nullptr; + return *this; + } + ~JsonAllocator() { + deallocate(); + } + void *allocate(size_t size); + void deallocate(); +}; + +int jsonParse(char *str, char **endptr, JsonValue *value, JsonAllocator &allocator); diff --git a/data/lib_coco/common/maskApi.c b/data/lib_coco/common/maskApi.c new file mode 100644 index 0000000..85e3979 --- /dev/null +++ b/data/lib_coco/common/maskApi.c @@ -0,0 +1,230 @@ +/************************************************************************** +* Microsoft COCO Toolbox. version 2.0 +* Data, paper, and tutorials available at: http://mscoco.org/ +* Code written by Piotr Dollar and Tsung-Yi Lin, 2015. +* Licensed under the Simplified BSD License [see coco/license.txt] +**************************************************************************/ +#include "maskApi.h" +#include +#include + +uint umin( uint a, uint b ) { return (ab) ? a : b; } + +void rleInit( RLE *R, siz h, siz w, siz m, uint *cnts ) { + R->h=h; R->w=w; R->m=m; R->cnts=(m==0)?0:malloc(sizeof(uint)*m); + siz j; if(cnts) for(j=0; jcnts[j]=cnts[j]; +} + +void rleFree( RLE *R ) { + free(R->cnts); R->cnts=0; +} + +void rlesInit( RLE **R, siz n ) { + siz i; *R = (RLE*) malloc(sizeof(RLE)*n); + for(i=0; i0 ) { + c=umin(ca,cb); cc+=c; ct=0; + ca-=c; if(!ca && a0) { + crowd=iscrowd!=NULL && iscrowd[g]; + if(dt[d].h!=gt[g].h || dt[d].w!=gt[g].w) { o[g*m+d]=-1; continue; } + siz ka, kb, a, b; uint c, ca, cb, ct, i, u; int va, vb; + ca=dt[d].cnts[0]; ka=dt[d].m; va=vb=0; + cb=gt[g].cnts[0]; kb=gt[g].m; a=b=1; i=u=0; ct=1; + while( ct>0 ) { + c=umin(ca,cb); if(va||vb) { u+=c; if(va&&vb) i+=c; } ct=0; + ca-=c; if(!ca && athr) keep[j]=0; + } + } +} + +void bbIou( BB dt, BB gt, siz m, siz n, byte *iscrowd, double *o ) { + double h, w, i, u, ga, da; siz g, d; int crowd; + for( g=0; gthr) keep[j]=0; + } + } +} + +void rleToBbox( const RLE *R, BB bb, siz n ) { + siz i; for( i=0; id?1:c=dy && xs>xe) || (dxye); + if(flip) { t=xs; xs=xe; xe=t; t=ys; ys=ye; ye=t; } + s = dx>=dy ? (double)(ye-ys)/dx : (double)(xe-xs)/dy; + if(dx>=dy) for( d=0; d<=dx; d++ ) { + t=flip?dx-d:d; u[m]=t+xs; v[m]=(int)(ys+s*t+.5); m++; + } else for( d=0; d<=dy; d++ ) { + t=flip?dy-d:d; v[m]=t+ys; u[m]=(int)(xs+s*t+.5); m++; + } + } + /* get points along y-boundary and downsample */ + free(x); free(y); k=m; m=0; double xd, yd; + x=malloc(sizeof(int)*k); y=malloc(sizeof(int)*k); + for( j=1; jw-1 ) continue; + yd=(double)(v[j]h) yd=h; yd=ceil(yd); + x[m]=(int) xd; y[m]=(int) yd; m++; + } + /* compute rle encoding given y-boundary points */ + k=m; a=malloc(sizeof(uint)*(k+1)); + for( j=0; j0) b[m++]=a[j++]; else { + j++; if(jm, p=0; long x; int more; + char *s=malloc(sizeof(char)*m*6); + for( i=0; icnts[i]; if(i>2) x-=(long) R->cnts[i-2]; more=1; + while( more ) { + char c=x & 0x1f; x >>= 5; more=(c & 0x10) ? x!=-1 : x!=0; + if(more) c |= 0x20; c+=48; s[p++]=c; + } + } + s[p]=0; return s; +} + +void rleFrString( RLE *R, char *s, siz h, siz w ) { + siz m=0, p=0, k; long x; int more; uint *cnts; + while( s[m] ) m++; cnts=malloc(sizeof(uint)*m); m=0; + while( s[p] ) { + x=0; k=0; more=1; + while( more ) { + char c=s[p]-48; x |= (c & 0x1f) << 5*k; + more = c & 0x20; p++; k++; + if(!more && (c & 0x10)) x |= -1 << 5*k; + } + if(m>2) x+=(long) cnts[m-2]; cnts[m++]=(uint) x; + } + rleInit(R,h,w,m,cnts); free(cnts); +} diff --git a/data/lib_coco/common/maskApi.h b/data/lib_coco/common/maskApi.h new file mode 100644 index 0000000..ebc7892 --- /dev/null +++ b/data/lib_coco/common/maskApi.h @@ -0,0 +1,60 @@ +/************************************************************************** +* Microsoft COCO Toolbox. version 2.0 +* Data, paper, and tutorials available at: http://mscoco.org/ +* Code written by Piotr Dollar and Tsung-Yi Lin, 2015. +* Licensed under the Simplified BSD License [see coco/license.txt] +**************************************************************************/ +#pragma once + +typedef unsigned int uint; +typedef unsigned long siz; +typedef unsigned char byte; +typedef double* BB; +typedef struct { siz h, w, m; uint *cnts; } RLE; + +/* Initialize/destroy RLE. */ +void rleInit( RLE *R, siz h, siz w, siz m, uint *cnts ); +void rleFree( RLE *R ); + +/* Initialize/destroy RLE array. */ +void rlesInit( RLE **R, siz n ); +void rlesFree( RLE **R, siz n ); + +/* Encode binary masks using RLE. */ +void rleEncode( RLE *R, const byte *mask, siz h, siz w, siz n ); + +/* Decode binary masks encoded via RLE. */ +void rleDecode( const RLE *R, byte *mask, siz n ); + +/* Compute union or intersection of encoded masks. */ +void rleMerge( const RLE *R, RLE *M, siz n, int intersect ); + +/* Compute area of encoded masks. */ +void rleArea( const RLE *R, siz n, uint *a ); + +/* Compute intersection over union between masks. */ +void rleIou( RLE *dt, RLE *gt, siz m, siz n, byte *iscrowd, double *o ); + +/* Compute non-maximum suppression between bounding masks */ +void rleNms( RLE *dt, siz n, uint *keep, double thr ); + +/* Compute intersection over union between bounding boxes. */ +void bbIou( BB dt, BB gt, siz m, siz n, byte *iscrowd, double *o ); + +/* Compute non-maximum suppression between bounding boxes */ +void bbNms( BB dt, siz n, uint *keep, double thr ); + +/* Get bounding boxes surrounding encoded masks. */ +void rleToBbox( const RLE *R, BB bb, siz n ); + +/* Convert bounding boxes to encoded masks. */ +void rleFrBbox( RLE *R, const BB bb, siz h, siz w, siz n ); + +/* Convert polygon to encoded mask. */ +void rleFrPoly( RLE *R, const double *xy, siz k, siz h, siz w ); + +/* Get compressed string representation of encoded mask. */ +char* rleToString( const RLE *R ); + +/* Convert from compressed string representation of encoded mask. */ +void rleFrString( RLE *R, char *s, siz h, siz w ); diff --git a/data/lib_coco/get_coco_next_batch.py b/data/lib_coco/get_coco_next_batch.py new file mode 100644 index 0000000..ec56b1a --- /dev/null +++ b/data/lib_coco/get_coco_next_batch.py @@ -0,0 +1,73 @@ +# -*- coding: utf-8 -*- + +from __future__ import absolute_import, print_function, division + +import sys, os +# sys.path.insert(0, os.path.abspath('.')) +sys.path.insert(0, './PythonAPI/') +# sys.path.insert(0, os.path.abspath('data')) +for _ in sys.path: + print (_) +from PythonAPI.pycocotools.coco import COCO +import cv2 +import numpy as np +import os +from libs.label_name_dict import coco_dict + + +annotation_path = '/home/yjr/DataSet/COCO/2017/annotations/instances_train2017.json' +print ("load coco .... it will cost about 17s..") +coco = COCO(annotation_path) + +imgId_list = coco.getImgIds() +imgId_list = np.array(imgId_list) + +total_imgs = len(imgId_list) + +# print (NAME_LABEL_DICT) + + +def next_img(step): + + if step % total_imgs == 0: + np.random.shuffle(imgId_list) + imgid = imgId_list[step % total_imgs] + + imgname = coco.loadImgs(ids=[imgid])[0]['file_name'] + # print (type(imgname), imgname) + img = cv2.imread(os.path.join("/home/yjr/DataSet/COCO/2017/train2017", imgname)) + + annotation = coco.imgToAnns[imgid] + gtbox_and_label_list = [] + for ann in annotation: + box = ann['bbox'] + + box = [box[0], box[1], box[0]+box[2], box[1]+box[3]] # [xmin, ymin, xmax, ymax] + cat_id = ann['category_id'] + cat_name = coco_dict.originID_classes[cat_id] #ID_NAME_DICT[cat_id] + label = coco_dict.NAME_LABEL_MAP[cat_name] + gtbox_and_label_list.append(box + [label]) + gtbox_and_label_list = np.array(gtbox_and_label_list, dtype=np.int32) + # print (img.shape, gtbox_and_label_list.shape) + if gtbox_and_label_list.shape[0] == 0: + return next_img(step+1) + else: + return imgid, img[:, :, ::-1], gtbox_and_label_list + + +if __name__ == '__main__': + + imgid, img, gtbox = next_img(3234) + + print("::") + from libs.box_utils.draw_box_in_img import draw_boxes_with_label_and_scores + + img = draw_boxes_with_label_and_scores(img_array=img, boxes=gtbox[:, :-1], labels=gtbox[:, -1], + scores=np.ones(shape=(len(gtbox), ))) + print ("_----") + + + cv2.imshow("test", img) + cv2.waitKey(0) + + diff --git a/data/pretrained_weights/README.md b/data/pretrained_weights/README.md new file mode 100644 index 0000000..f660730 --- /dev/null +++ b/data/pretrained_weights/README.md @@ -0,0 +1,2 @@ +1、Please download [resnet50_v1](http://download.tensorflow.org/models/resnet_v1_50_2016_08_28.tar.gz), [resnet101_v1](http://download.tensorflow.org/models/resnet_v1_101_2016_08_28.tar.gz) pre-trained models on Imagenet, put it to data/pretrained_weights. +2、Or you can choose to use a better backbone, refer to [gluon2TF](https://github.com/yangJirui/gluon2TF). [Pretrain Model Link](https://pan.baidu.com/s/1HF3G5XSxXm7W4pk10RuOlw), password: q4jg. diff --git a/data/pretrained_weights/mobilenet/README.md b/data/pretrained_weights/mobilenet/README.md new file mode 100644 index 0000000..1158885 --- /dev/null +++ b/data/pretrained_weights/mobilenet/README.md @@ -0,0 +1 @@ +Please download [mobilenet_v2](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.0_224.tgz) pre-trained model on Imagenet, put it to data/pretrained_weights/mobilenet. \ No newline at end of file diff --git a/help_utils/__init__.py b/help_utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/help_utils/tools.py b/help_utils/tools.py new file mode 100644 index 0000000..8b696c8 --- /dev/null +++ b/help_utils/tools.py @@ -0,0 +1,19 @@ +# -*- coding: utf-8 -*- +from __future__ import division, print_function, absolute_import +import math +import sys +import os + + +def view_bar(message, num, total): + rate = num / total + rate_num = int(rate * 40) + rate_nums = math.ceil(rate * 100) + r = '\r%s:[%s%s]%d%%\t%d/%d' % (message, ">" * rate_num, " " * (40 - rate_num), rate_nums, num, total,) + sys.stdout.write(r) + sys.stdout.flush() + + +def mkdir(path): + if not os.path.exists(path): + os.makedirs(path) \ No newline at end of file diff --git a/images.png b/images.png new file mode 100644 index 0000000000000000000000000000000000000000..8829c2c3e5439538293afbec82ecdcfb02978ab2 GIT binary patch literal 748437 zcmb@t1yCK^)-_5_2oWr3aDqDof@=sHcXxMpCqN*$g^jzryGw$FV8Jc8ySu%`xi{zD z|Eqdcf4%Q@RiyXsy?S-`Tyu;$#_S*&DPhD{IImz}U=T$`AaXD;aJ(=uf6cu33w(m4 z(Xt2rd20VrRN=*o7qd&!i(nVqK~Tj(-rC5)S=Y`G#@Nc*(vZ$x-_Fp`%HG7<;oxZ- z7~n08D1=|ZC3$zw-2=_AmGAICf*zlihKhQCct+)mP^@@1@ev$~jE`{6ZYJy(vZh5M zpV<14*Uc!Z%66|=h-p`VoKr^2Sb1}F(PH3uyG-r z9(zhH_hR+LbFWYNz3aMb>q-B{xFn$wIi7wfnCHJXCSba`9s+h&_j>j^8MzT3)hR{6JemnX04HCjI zJ2^O_SnSqOh^Uy-n2KJcv4Ma-pt+=`yH_)PHMu>A_s`r0Q~mXYMN9Un&94lfS9^8n zcz8``*{}CID|hGTHe0lI>4|C*DvH;9ekP9@J&D?NPMa#%c=Q@Yv577R7YSt?Hg<@C 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stride, name='make_anchors'): + ''' + :param base_anchor_size:256 + :param anchor_scales: + :param anchor_ratios: + :param featuremap_height: + :param featuremap_width: + :param stride: + :return: + ''' + with tf.variable_scope(name): + base_anchor = tf.constant([0, 0, base_anchor_size, base_anchor_size], tf.float32) # [x_center, y_center, w, h] + + ws, hs = enum_ratios(enum_scales(base_anchor, anchor_scales), + anchor_ratios) # per locations ws and hs + + # featuremap_height = tf.Print(featuremap_height, + # [featuremap_height, featuremap_width], summarize=10, + # message=name+"_SHAPE***") + + x_centers = tf.range(featuremap_width, dtype=tf.float32) * stride + y_centers = tf.range(featuremap_height, dtype=tf.float32) * stride + + if cfgs.USE_CENTER_OFFSET: + x_centers = x_centers + stride/2. + y_centers = y_centers + stride/2. + + x_centers, y_centers = tf.meshgrid(x_centers, y_centers) + + ws, x_centers = tf.meshgrid(ws, x_centers) + hs, y_centers = tf.meshgrid(hs, y_centers) + + anchor_centers = tf.stack([x_centers, y_centers], 2) + anchor_centers = tf.reshape(anchor_centers, [-1, 2]) + + box_sizes = tf.stack([ws, hs], axis=2) + box_sizes = tf.reshape(box_sizes, [-1, 2]) + # anchors = tf.concat([anchor_centers, box_sizes], axis=1) + anchors = tf.concat([anchor_centers - 0.5*box_sizes, + anchor_centers + 0.5*box_sizes], axis=1) + return anchors + + +def enum_scales(base_anchor, anchor_scales): + + anchor_scales = base_anchor * tf.constant(anchor_scales, dtype=tf.float32, shape=(len(anchor_scales), 1)) + + return anchor_scales + + +def enum_ratios(anchors, anchor_ratios): + ''' + ratio = h /w + :param anchors: + :param anchor_ratios: + :return: + ''' + ws = anchors[:, 2] # for base anchor: w == h + hs = anchors[:, 3] + sqrt_ratios = tf.sqrt(tf.constant(anchor_ratios)) + + ws = tf.reshape(ws / sqrt_ratios[:, tf.newaxis], [-1, 1]) + hs = tf.reshape(hs * sqrt_ratios[:, tf.newaxis], [-1, 1]) + + return hs, ws + + +if __name__ == '__main__': + import os + os.environ["CUDA_VISIBLE_DEVICES"] = '0' + base_anchor_size = 256 + anchor_scales = [1.0] + anchor_ratios = [0.5, 2.0, 1.0] + anchors = make_anchors(base_anchor_size=base_anchor_size, anchor_ratios=anchor_ratios, + anchor_scales=anchor_scales, + featuremap_width=32, + featuremap_height=63, + stride=16) + init = tf.global_variables_initializer() + with tf.Session() as sess: + sess.run(init) + anchor_result = sess.run(anchors) + print (anchor_result.shape) diff --git a/libs/box_utils/boxes_utils.py b/libs/box_utils/boxes_utils.py new file mode 100644 index 0000000..03c5717 --- /dev/null +++ b/libs/box_utils/boxes_utils.py @@ -0,0 +1,170 @@ +# -*- coding: utf-8 -*- + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf + +from libs.box_utils.coordinate_convert import forward_convert + +def ious_calu(boxes_1, boxes_2): + ''' + + :param boxes_1: [N, 4] [xmin, ymin, xmax, ymax] + :param boxes_2: [M, 4] [xmin, ymin. xmax, ymax] + :return: + ''' + boxes_1 = tf.cast(boxes_1, tf.float32) + boxes_2 = tf.cast(boxes_2, tf.float32) + xmin_1, ymin_1, xmax_1, ymax_1 = tf.split(boxes_1, 4, axis=1) # xmin_1 shape is [N, 1].. + xmin_2, ymin_2, xmax_2, ymax_2 = tf.unstack(boxes_2, axis=1) # xmin_2 shape is [M, ].. + + max_xmin = tf.maximum(xmin_1, xmin_2) + min_xmax = tf.minimum(xmax_1, xmax_2) + + max_ymin = tf.maximum(ymin_1, ymin_2) + min_ymax = tf.minimum(ymax_1, ymax_2) + + overlap_h = tf.maximum(0., min_ymax - max_ymin) # avoid h < 0 + overlap_w = tf.maximum(0., min_xmax - max_xmin) + + overlaps = overlap_h * overlap_w + + area_1 = (xmax_1 - xmin_1) * (ymax_1 - ymin_1) # [N, 1] + area_2 = (xmax_2 - xmin_2) * (ymax_2 - ymin_2) # [M, ] + + ious = overlaps / (area_1 + area_2 - overlaps) + + return ious + + +def clip_boxes_to_img_boundaries(decode_boxes, img_shape): + ''' + + :param decode_boxes: + :return: decode boxes, and already clip to boundaries + ''' + + with tf.name_scope('clip_boxes_to_img_boundaries'): + + # xmin, ymin, xmax, ymax = tf.unstack(decode_boxes, axis=1) + xmin = decode_boxes[:, 0] + ymin = decode_boxes[:, 1] + xmax = decode_boxes[:, 2] + ymax = decode_boxes[:, 3] + img_h, img_w = img_shape[1], img_shape[2] + + img_h, img_w = tf.cast(img_h, tf.float32), tf.cast(img_w, tf.float32) + + xmin = tf.maximum(tf.minimum(xmin, img_w-1.), 0.) + ymin = tf.maximum(tf.minimum(ymin, img_h-1.), 0.) + + xmax = tf.maximum(tf.minimum(xmax, img_w-1.), 0.) + ymax = tf.maximum(tf.minimum(ymax, img_h-1.), 0.) + + return tf.transpose(tf.stack([xmin, ymin, xmax, ymax])) + + +def filter_outside_boxes(boxes, img_h, img_w): + ''' + :param anchors:boxes with format [xmin, ymin, xmax, ymax] + :param img_h: height of image + :param img_w: width of image + :return: indices of anchors that inside the image boundary + ''' + + with tf.name_scope('filter_outside_boxes'): + xmin, ymin, xmax, ymax = tf.unstack(boxes, axis=1) + + xmin_index = tf.greater_equal(xmin, 0) + ymin_index = tf.greater_equal(ymin, 0) + xmax_index = tf.less_equal(xmax, tf.cast(img_w, tf.float32)) + ymax_index = tf.less_equal(ymax, tf.cast(img_h, tf.float32)) + + indices = tf.transpose(tf.stack([xmin_index, ymin_index, xmax_index, ymax_index])) + indices = tf.cast(indices, dtype=tf.int32) + indices = tf.reduce_sum(indices, axis=1) + indices = tf.where(tf.equal(indices, 4)) + # indices = tf.equal(indices, 4) + return tf.reshape(indices, [-1]) + + +def padd_boxes_with_zeros(boxes, scores, max_num_of_boxes): + + ''' + num of boxes less than max num of boxes, so it need to pad with zeros[0, 0, 0, 0] + :param boxes: + :param scores: [-1] + :param max_num_of_boxes: + :return: + ''' + + pad_num = tf.cast(max_num_of_boxes, tf.int32) - tf.shape(boxes)[0] + + zero_boxes = tf.zeros(shape=[pad_num, 4], dtype=boxes.dtype) + zero_scores = tf.zeros(shape=[pad_num], dtype=scores.dtype) + + final_boxes = tf.concat([boxes, zero_boxes], axis=0) + + final_scores = tf.concat([scores, zero_scores], axis=0) + + return final_boxes, final_scores + + +def get_horizen_minAreaRectangle(boxs, with_label=True): + + # rpn_proposals_boxes_convert = tf.py_func(forward_convert, + # inp=[boxs, with_label], + # Tout=tf.float32) + if with_label: + rpn_proposals_boxes_convert = tf.reshape(boxs, [-1, 9]) + + boxes_shape = tf.shape(rpn_proposals_boxes_convert) + x_list = tf.strided_slice(rpn_proposals_boxes_convert, begin=[0, 0], end=[boxes_shape[0], boxes_shape[1] - 1], + strides=[1, 2]) + y_list = tf.strided_slice(rpn_proposals_boxes_convert, begin=[0, 1], end=[boxes_shape[0], boxes_shape[1] - 1], + strides=[1, 2]) + + label = tf.unstack(rpn_proposals_boxes_convert, axis=1)[-1] + + y_max = tf.reduce_max(y_list, axis=1) + y_min = tf.reduce_min(y_list, axis=1) + x_max = tf.reduce_max(x_list, axis=1) + x_min = tf.reduce_min(x_list, axis=1) + + ''' The following codes aims to avoid gtbox out_sde''' + + # img_h, img_w = img_shape[0], img_shape[1] + # img_h = tf.cast(img_h, tf.float32) + # img_w = tf.cast(img_w, tf.float32) + # x_min = tf.maximum(x_min, 0) + # y_min = tf.maximum(y_min, 0) + # x_max = tf.minimum(x_max, img_w) + # y_max = tf.minimum(y_max, img_h) + return tf.transpose(tf.stack([x_min, y_min, x_max, y_max, label], axis=0)) + else: + rpn_proposals_boxes_convert = tf.reshape(boxs, [-1, 8]) + + boxes_shape = tf.shape(rpn_proposals_boxes_convert) + x_list = tf.strided_slice(rpn_proposals_boxes_convert, begin=[0, 0], end=[boxes_shape[0], boxes_shape[1]], + strides=[1, 2]) + y_list = tf.strided_slice(rpn_proposals_boxes_convert, begin=[0, 1], end=[boxes_shape[0], boxes_shape[1]], + strides=[1, 2]) + + y_max = tf.reduce_max(y_list, axis=1) + y_min = tf.reduce_min(y_list, axis=1) + x_max = tf.reduce_max(x_list, axis=1) + x_min = tf.reduce_min(x_list, axis=1) + + ''' The following codes aims to avoid gtbox out_sde''' + + # img_h, img_w = img_shape[0], img_shape[1] + # img_h = tf.cast(img_h, tf.float32) + # img_w = tf.cast(img_w, tf.float32) + # x_min = tf.maximum(x_min, 0) + # y_min = tf.maximum(y_min, 0) + # x_max = tf.minimum(x_max, img_w) + # y_max = tf.minimum(y_max, img_h) + + return tf.transpose(tf.stack([x_min, y_min, x_max, y_max], axis=0)) \ No newline at end of file diff --git a/libs/box_utils/coordinate_convert.py b/libs/box_utils/coordinate_convert.py new file mode 100644 index 0000000..8054bef --- /dev/null +++ b/libs/box_utils/coordinate_convert.py @@ -0,0 +1,86 @@ +# -*- coding: utf-8 -*- + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import cv2 +import numpy as np + + +def forward_convert(coordinate, with_label=True): + """ + :param coordinate: format [x_c, y_c, w, h, theta] + :return: format [x1, y1, x2, y2, x3, y3, x4, y4] + """ + boxes = [] + if with_label: + for rect in coordinate: + box = cv2.boxPoints(((rect[0], rect[1]), (rect[2], rect[3]), rect[4])) + box = np.reshape(box, [-1, ]) + boxes.append([box[0], box[1], box[2], box[3], box[4], box[5], box[6], box[7], rect[5]]) + else: + for rect in coordinate: + box = cv2.boxPoints(((rect[0], rect[1]), (rect[2], rect[3]), rect[4])) + box = np.reshape(box, [-1, ]) + boxes.append([box[0], box[1], box[2], box[3], box[4], box[5], box[6], box[7]]) + + return np.array(boxes, dtype=np.float32) + + +def back_forward_convert(coordinate, with_label=True): + """ + :param coordinate: format [x1, y1, x2, y2, x3, y3, x4, y4, (label)] + :param with_label: default True + :return: format [x_c, y_c, w, h, theta, (label)] + """ + + boxes = [] + if with_label: + for rect in coordinate: + box = np.int0(rect[:-1]) + box = box.reshape([4, 2]) + rect1 = cv2.minAreaRect(box) + + x, y, w, h, theta = rect1[0][0], rect1[0][1], rect1[1][0], rect1[1][1], rect1[2] + boxes.append([x, y, w, h, theta, rect[-1]]) + + else: + for rect in coordinate: + box = np.int0(rect) + box = box.reshape([4, 2]) + rect1 = cv2.minAreaRect(box) + + x, y, w, h, theta = rect1[0][0], rect1[0][1], rect1[1][0], rect1[1][1], rect1[2] + boxes.append([x, y, w, h, theta]) + + return np.array(boxes, dtype=np.float32) + + +if __name__ == '__main__': + coord = np.array([[150, 150, 50, 100, -90, 1], + [150, 150, 100, 50, -90, 1], + [150, 150, 50, 100, -45, 1], + [150, 150, 100, 50, -45, 1]]) + + coord1 = np.array([[150, 150, 100, 50, 0], + [150, 150, 100, 50, -90], + [150, 150, 100, 50, 45], + [150, 150, 100, 50, -45]]) + + coord2 = forward_convert(coord) + # coord3 = forward_convert(coord1, mode=-1) + print(coord2) + # print(coord3-coord2) + # coord_label = np.array([[167., 203., 96., 132., 132., 96., 203., 167., 1.]]) + # + # coord4 = back_forward_convert(coord_label, mode=1) + # coord5 = back_forward_convert(coord_label) + + # print(coord4) + # print(coord5) + + # coord3 = coordinate_present_convert(coord, -1) + # print(coord3) + # coord4 = coordinate_present_convert(coord3, mode=1) +# print(coord4) \ No newline at end of file diff --git a/libs/box_utils/cython_utils/Makefile b/libs/box_utils/cython_utils/Makefile new file mode 100644 index 0000000..1e9e686 --- /dev/null +++ b/libs/box_utils/cython_utils/Makefile @@ -0,0 +1,6 @@ +all: + python setup.py build_ext --inplace + rm -rf build +clean: + rm -rf */*.pyc + rm -rf */*.so diff --git a/libs/box_utils/cython_utils/__init__.py b/libs/box_utils/cython_utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/libs/box_utils/cython_utils/bbox.c b/libs/box_utils/cython_utils/bbox.c new file mode 100644 index 0000000..747f59d --- /dev/null +++ b/libs/box_utils/cython_utils/bbox.c @@ -0,0 +1,11128 @@ +/* Generated by Cython 0.25.2 */ + +#define PY_SSIZE_T_CLEAN +#include "Python.h" +#ifndef Py_PYTHON_H + #error Python headers needed to compile C extensions, please install development version of Python. +#elif PY_VERSION_HEX < 0x02060000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03020000) + #error Cython requires Python 2.6+ or Python 3.2+. +#else +#define CYTHON_ABI "0_25_2" +#include +#ifndef offsetof + #define offsetof(type, member) ( (size_t) & ((type*)0) -> member ) +#endif +#if !defined(WIN32) && !defined(MS_WINDOWS) + #ifndef __stdcall + #define __stdcall + #endif + #ifndef __cdecl + #define __cdecl + #endif + #ifndef __fastcall + #define __fastcall + #endif +#endif +#ifndef DL_IMPORT + #define DL_IMPORT(t) t +#endif +#ifndef DL_EXPORT + #define DL_EXPORT(t) t +#endif +#ifndef HAVE_LONG_LONG + #if PY_VERSION_HEX >= 0x03030000 || (PY_MAJOR_VERSION == 2 && PY_VERSION_HEX >= 0x02070000) + #define HAVE_LONG_LONG + #endif +#endif +#ifndef PY_LONG_LONG + #define PY_LONG_LONG LONG_LONG +#endif +#ifndef Py_HUGE_VAL + #define Py_HUGE_VAL HUGE_VAL +#endif +#ifdef PYPY_VERSION + #define CYTHON_COMPILING_IN_PYPY 1 + #define CYTHON_COMPILING_IN_PYSTON 0 + #define CYTHON_COMPILING_IN_CPYTHON 0 + #undef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 0 + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #undef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 0 + #undef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 0 + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #undef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 1 + #undef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 0 + #undef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 0 + #undef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 0 + #undef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 0 +#elif defined(PYSTON_VERSION) + #define CYTHON_COMPILING_IN_PYPY 0 + #define CYTHON_COMPILING_IN_PYSTON 1 + #define CYTHON_COMPILING_IN_CPYTHON 0 + #ifndef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 1 + #endif + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #undef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 0 + #ifndef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 1 + #endif + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #ifndef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 0 + #endif + #ifndef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 1 + #endif + #ifndef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 1 + #endif + #undef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 0 + #undef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 0 +#else + #define CYTHON_COMPILING_IN_PYPY 0 + #define CYTHON_COMPILING_IN_PYSTON 0 + #define CYTHON_COMPILING_IN_CPYTHON 1 + #ifndef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 1 + #endif + #if PY_MAJOR_VERSION < 3 + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #elif !defined(CYTHON_USE_ASYNC_SLOTS) + #define CYTHON_USE_ASYNC_SLOTS 1 + #endif + #if PY_VERSION_HEX < 0x02070000 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #elif !defined(CYTHON_USE_PYLONG_INTERNALS) + #define CYTHON_USE_PYLONG_INTERNALS 1 + #endif + #ifndef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 1 + #endif + #ifndef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 1 + #endif + #if PY_VERSION_HEX < 0x030300F0 + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #elif !defined(CYTHON_USE_UNICODE_WRITER) + #define CYTHON_USE_UNICODE_WRITER 1 + #endif + #ifndef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 0 + #endif + #ifndef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 1 + #endif + #ifndef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 1 + #endif + #ifndef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 1 + #endif + #ifndef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 1 + #endif +#endif +#if !defined(CYTHON_FAST_PYCCALL) +#define CYTHON_FAST_PYCCALL (CYTHON_FAST_PYCALL && PY_VERSION_HEX >= 0x030600B1) +#endif +#if CYTHON_USE_PYLONG_INTERNALS + #include "longintrepr.h" + #undef SHIFT + #undef BASE + #undef MASK +#endif +#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 && !defined(Py_OptimizeFlag) + #define Py_OptimizeFlag 0 +#endif +#define __PYX_BUILD_PY_SSIZE_T "n" +#define CYTHON_FORMAT_SSIZE_T "z" +#if PY_MAJOR_VERSION < 3 + #define __Pyx_BUILTIN_MODULE_NAME "__builtin__" + #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ + PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) + #define __Pyx_DefaultClassType PyClass_Type +#else + #define __Pyx_BUILTIN_MODULE_NAME "builtins" + #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ + PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) + #define __Pyx_DefaultClassType PyType_Type +#endif +#ifndef Py_TPFLAGS_CHECKTYPES + #define Py_TPFLAGS_CHECKTYPES 0 +#endif +#ifndef Py_TPFLAGS_HAVE_INDEX + #define Py_TPFLAGS_HAVE_INDEX 0 +#endif +#ifndef Py_TPFLAGS_HAVE_NEWBUFFER + #define Py_TPFLAGS_HAVE_NEWBUFFER 0 +#endif +#ifndef Py_TPFLAGS_HAVE_FINALIZE + #define Py_TPFLAGS_HAVE_FINALIZE 0 +#endif +#ifndef METH_FASTCALL + #define METH_FASTCALL 0x80 + typedef PyObject *(*__Pyx_PyCFunctionFast) (PyObject *self, PyObject **args, + Py_ssize_t nargs, PyObject *kwnames); +#else + #define __Pyx_PyCFunctionFast _PyCFunctionFast +#endif +#if CYTHON_FAST_PYCCALL +#define __Pyx_PyFastCFunction_Check(func)\ + ((PyCFunction_Check(func) && (METH_FASTCALL == (PyCFunction_GET_FLAGS(func) & ~(METH_CLASS | METH_STATIC | METH_COEXIST))))) +#else +#define __Pyx_PyFastCFunction_Check(func) 0 +#endif +#if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND) + #define CYTHON_PEP393_ENABLED 1 + #define __Pyx_PyUnicode_READY(op) (likely(PyUnicode_IS_READY(op)) ?\ + 0 : _PyUnicode_Ready((PyObject *)(op))) + #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_LENGTH(u) + #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i) + #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) PyUnicode_MAX_CHAR_VALUE(u) + #define __Pyx_PyUnicode_KIND(u) PyUnicode_KIND(u) + #define __Pyx_PyUnicode_DATA(u) PyUnicode_DATA(u) + #define __Pyx_PyUnicode_READ(k, d, i) PyUnicode_READ(k, d, i) + #define __Pyx_PyUnicode_WRITE(k, d, i, ch) PyUnicode_WRITE(k, d, i, ch) + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : PyUnicode_GET_SIZE(u))) +#else + #define CYTHON_PEP393_ENABLED 0 + #define PyUnicode_1BYTE_KIND 1 + #define PyUnicode_2BYTE_KIND 2 + #define PyUnicode_4BYTE_KIND 4 + #define __Pyx_PyUnicode_READY(op) (0) + #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_SIZE(u) + #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i])) + #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) ((sizeof(Py_UNICODE) == 2) ? 65535 : 1114111) + #define __Pyx_PyUnicode_KIND(u) (sizeof(Py_UNICODE)) + #define __Pyx_PyUnicode_DATA(u) ((void*)PyUnicode_AS_UNICODE(u)) + #define __Pyx_PyUnicode_READ(k, d, i) ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i])) + #define __Pyx_PyUnicode_WRITE(k, d, i, ch) (((void)(k)), ((Py_UNICODE*)d)[i] = ch) + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GET_SIZE(u)) +#endif +#if CYTHON_COMPILING_IN_PYPY + #define __Pyx_PyUnicode_Concat(a, b) PyNumber_Add(a, b) + #define __Pyx_PyUnicode_ConcatSafe(a, b) PyNumber_Add(a, b) +#else + #define __Pyx_PyUnicode_Concat(a, b) PyUnicode_Concat(a, b) + #define __Pyx_PyUnicode_ConcatSafe(a, b) ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ?\ + PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b)) +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyUnicode_Contains) + #define PyUnicode_Contains(u, s) PySequence_Contains(u, s) +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyByteArray_Check) + #define PyByteArray_Check(obj) PyObject_TypeCheck(obj, &PyByteArray_Type) +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Format) + #define PyObject_Format(obj, fmt) PyObject_CallMethod(obj, "__format__", "O", fmt) +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Malloc) + #define PyObject_Malloc(s) PyMem_Malloc(s) + #define PyObject_Free(p) PyMem_Free(p) + #define PyObject_Realloc(p) PyMem_Realloc(p) +#endif +#if CYTHON_COMPILING_IN_PYSTON + #define __Pyx_PyCode_HasFreeVars(co) PyCode_HasFreeVars(co) + #define __Pyx_PyFrame_SetLineNumber(frame, lineno) PyFrame_SetLineNumber(frame, lineno) +#else + #define __Pyx_PyCode_HasFreeVars(co) (PyCode_GetNumFree(co) > 0) + #define __Pyx_PyFrame_SetLineNumber(frame, lineno) (frame)->f_lineno = (lineno) +#endif +#define __Pyx_PyString_FormatSafe(a, b) ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b)) +#define __Pyx_PyUnicode_FormatSafe(a, b) ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b)) +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyString_Format(a, b) PyUnicode_Format(a, b) +#else + #define __Pyx_PyString_Format(a, b) PyString_Format(a, b) +#endif +#if PY_MAJOR_VERSION < 3 && !defined(PyObject_ASCII) + #define PyObject_ASCII(o) PyObject_Repr(o) +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyBaseString_Type PyUnicode_Type + #define PyStringObject PyUnicodeObject + #define PyString_Type PyUnicode_Type + #define PyString_Check PyUnicode_Check + #define PyString_CheckExact PyUnicode_CheckExact +#endif +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj) + #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj) +#else + #define __Pyx_PyBaseString_Check(obj) (PyString_Check(obj) || PyUnicode_Check(obj)) + #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj)) +#endif +#ifndef PySet_CheckExact + #define PySet_CheckExact(obj) (Py_TYPE(obj) == &PySet_Type) +#endif +#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type) +#define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception) +#if PY_MAJOR_VERSION >= 3 + #define PyIntObject PyLongObject + #define PyInt_Type PyLong_Type + #define PyInt_Check(op) PyLong_Check(op) + #define PyInt_CheckExact(op) PyLong_CheckExact(op) + #define PyInt_FromString PyLong_FromString + #define PyInt_FromUnicode PyLong_FromUnicode + #define PyInt_FromLong PyLong_FromLong + #define PyInt_FromSize_t PyLong_FromSize_t + #define PyInt_FromSsize_t PyLong_FromSsize_t + #define PyInt_AsLong PyLong_AsLong + #define PyInt_AS_LONG PyLong_AS_LONG + #define PyInt_AsSsize_t PyLong_AsSsize_t + #define PyInt_AsUnsignedLongMask PyLong_AsUnsignedLongMask + #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask + #define PyNumber_Int PyNumber_Long +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyBoolObject PyLongObject +#endif +#if PY_MAJOR_VERSION >= 3 && CYTHON_COMPILING_IN_PYPY + #ifndef PyUnicode_InternFromString + #define PyUnicode_InternFromString(s) PyUnicode_FromString(s) + #endif +#endif +#if PY_VERSION_HEX < 0x030200A4 + typedef long Py_hash_t; + #define __Pyx_PyInt_FromHash_t PyInt_FromLong + #define __Pyx_PyInt_AsHash_t PyInt_AsLong +#else + #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t + #define __Pyx_PyInt_AsHash_t PyInt_AsSsize_t +#endif +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyMethod_New(func, self, klass) ((self) ? PyMethod_New(func, self) : PyInstanceMethod_New(func)) +#else + #define __Pyx_PyMethod_New(func, self, klass) PyMethod_New(func, self, klass) +#endif +#if CYTHON_USE_ASYNC_SLOTS + #if PY_VERSION_HEX >= 0x030500B1 + #define __Pyx_PyAsyncMethodsStruct PyAsyncMethods + #define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async) + #else + typedef struct { + unaryfunc am_await; + unaryfunc am_aiter; + unaryfunc am_anext; + } __Pyx_PyAsyncMethodsStruct; + #define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved)) + #endif +#else + #define __Pyx_PyType_AsAsync(obj) NULL +#endif +#ifndef CYTHON_RESTRICT + #if defined(__GNUC__) + #define CYTHON_RESTRICT __restrict__ + #elif defined(_MSC_VER) && _MSC_VER >= 1400 + #define CYTHON_RESTRICT __restrict + #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define CYTHON_RESTRICT restrict + #else + #define CYTHON_RESTRICT + #endif +#endif +#ifndef CYTHON_UNUSED +# if defined(__GNUC__) +# if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4)) +# define CYTHON_UNUSED __attribute__ ((__unused__)) +# else +# define CYTHON_UNUSED +# endif +# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER)) +# define CYTHON_UNUSED __attribute__ ((__unused__)) +# else +# define CYTHON_UNUSED +# endif +#endif +#ifndef CYTHON_MAYBE_UNUSED_VAR +# if defined(__cplusplus) + template void CYTHON_MAYBE_UNUSED_VAR( const T& ) { } +# else +# define CYTHON_MAYBE_UNUSED_VAR(x) (void)(x) +# endif +#endif +#ifndef CYTHON_NCP_UNUSED +# if CYTHON_COMPILING_IN_CPYTHON +# define CYTHON_NCP_UNUSED +# else +# define CYTHON_NCP_UNUSED CYTHON_UNUSED +# endif +#endif +#define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None) + +#ifndef CYTHON_INLINE + #if defined(__clang__) + #define CYTHON_INLINE __inline__ __attribute__ ((__unused__)) + #elif defined(__GNUC__) + #define CYTHON_INLINE __inline__ + #elif defined(_MSC_VER) + #define CYTHON_INLINE __inline + #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define CYTHON_INLINE inline + #else + #define CYTHON_INLINE + #endif +#endif + +#if defined(WIN32) || defined(MS_WINDOWS) + #define _USE_MATH_DEFINES +#endif +#include +#ifdef NAN +#define __PYX_NAN() ((float) NAN) +#else +static CYTHON_INLINE float __PYX_NAN() { + float value; + memset(&value, 0xFF, sizeof(value)); + return value; +} +#endif +#if defined(__CYGWIN__) && defined(_LDBL_EQ_DBL) +#define __Pyx_truncl trunc +#else +#define __Pyx_truncl truncl +#endif + + +#define __PYX_ERR(f_index, lineno, Ln_error) \ +{ \ + __pyx_filename = __pyx_f[f_index]; __pyx_lineno = lineno; __pyx_clineno = __LINE__; goto Ln_error; \ +} + +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y) + #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y) +#else + #define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y) + #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceDivide(x,y) +#endif + +#ifndef __PYX_EXTERN_C + #ifdef __cplusplus + #define __PYX_EXTERN_C extern "C" + #else + #define __PYX_EXTERN_C extern + #endif +#endif + +#define __PYX_HAVE__cython_bbox +#define __PYX_HAVE_API__cython_bbox +#include +#include +#include +#include "numpy/arrayobject.h" +#include "numpy/ufuncobject.h" +#ifdef _OPENMP +#include +#endif /* _OPENMP */ + +#ifdef PYREX_WITHOUT_ASSERTIONS +#define CYTHON_WITHOUT_ASSERTIONS +#endif + +typedef struct {PyObject **p; const char *s; const Py_ssize_t n; const char* encoding; + const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; + +#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0 +#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT 0 +#define __PYX_DEFAULT_STRING_ENCODING "" +#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString +#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize +#define __Pyx_uchar_cast(c) ((unsigned char)c) +#define __Pyx_long_cast(x) ((long)x) +#define __Pyx_fits_Py_ssize_t(v, type, is_signed) (\ + (sizeof(type) < sizeof(Py_ssize_t)) ||\ + (sizeof(type) > sizeof(Py_ssize_t) &&\ + likely(v < (type)PY_SSIZE_T_MAX ||\ + v == (type)PY_SSIZE_T_MAX) &&\ + (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||\ + v == (type)PY_SSIZE_T_MIN))) ||\ + (sizeof(type) == sizeof(Py_ssize_t) &&\ + (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\ + v == (type)PY_SSIZE_T_MAX))) ) +#if defined (__cplusplus) && __cplusplus >= 201103L + #include + #define __Pyx_sst_abs(value) std::abs(value) +#elif SIZEOF_INT >= SIZEOF_SIZE_T + #define __Pyx_sst_abs(value) abs(value) +#elif SIZEOF_LONG >= SIZEOF_SIZE_T + #define __Pyx_sst_abs(value) labs(value) +#elif defined (_MSC_VER) && defined (_M_X64) + #define __Pyx_sst_abs(value) _abs64(value) +#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define __Pyx_sst_abs(value) llabs(value) +#elif defined (__GNUC__) + #define __Pyx_sst_abs(value) __builtin_llabs(value) +#else + #define __Pyx_sst_abs(value) ((value<0) ? -value : value) +#endif +static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject*); +static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length); +#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s)) +#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l) +#define __Pyx_PyBytes_FromString PyBytes_FromString +#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*); +#if PY_MAJOR_VERSION < 3 + #define __Pyx_PyStr_FromString __Pyx_PyBytes_FromString + #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize +#else + #define __Pyx_PyStr_FromString __Pyx_PyUnicode_FromString + #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize +#endif +#define __Pyx_PyObject_AsSString(s) ((signed char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsUString(s) ((unsigned char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_FromCString(s) __Pyx_PyObject_FromString((const char*)s) +#define __Pyx_PyBytes_FromCString(s) __Pyx_PyBytes_FromString((const char*)s) +#define __Pyx_PyByteArray_FromCString(s) __Pyx_PyByteArray_FromString((const char*)s) +#define __Pyx_PyStr_FromCString(s) __Pyx_PyStr_FromString((const char*)s) +#define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s) +#if PY_MAJOR_VERSION < 3 +static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) +{ + const Py_UNICODE *u_end = u; + while (*u_end++) ; + return (size_t)(u_end - u - 1); +} +#else +#define __Pyx_Py_UNICODE_strlen Py_UNICODE_strlen +#endif +#define __Pyx_PyUnicode_FromUnicode(u) PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u)) +#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode +#define __Pyx_PyUnicode_AsUnicode PyUnicode_AsUnicode +#define __Pyx_NewRef(obj) (Py_INCREF(obj), obj) +#define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None) +#define __Pyx_PyBool_FromLong(b) ((b) ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False)) +static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*); +static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x); +static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*); +static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t); +#if CYTHON_ASSUME_SAFE_MACROS +#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x)) +#else +#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x) +#endif +#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x)) +#if PY_MAJOR_VERSION >= 3 +#define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x)) +#else +#define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x)) +#endif +#define __Pyx_PyNumber_Float(x) (PyFloat_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Float(x)) +#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII +static int __Pyx_sys_getdefaultencoding_not_ascii; +static int __Pyx_init_sys_getdefaultencoding_params(void) { + PyObject* sys; + PyObject* default_encoding = NULL; + PyObject* ascii_chars_u = NULL; + PyObject* ascii_chars_b = NULL; + const char* default_encoding_c; + sys = PyImport_ImportModule("sys"); + if (!sys) goto bad; + default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL); + Py_DECREF(sys); + if (!default_encoding) goto bad; + default_encoding_c = PyBytes_AsString(default_encoding); + if (!default_encoding_c) goto bad; + if (strcmp(default_encoding_c, "ascii") == 0) { + __Pyx_sys_getdefaultencoding_not_ascii = 0; + } else { + char ascii_chars[128]; + int c; + for (c = 0; c < 128; c++) { + ascii_chars[c] = c; + } + __Pyx_sys_getdefaultencoding_not_ascii = 1; + ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL); + if (!ascii_chars_u) goto bad; + ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL); + if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) { + PyErr_Format( + PyExc_ValueError, + "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.", + default_encoding_c); + goto bad; + } + Py_DECREF(ascii_chars_u); + Py_DECREF(ascii_chars_b); + } + Py_DECREF(default_encoding); + return 0; +bad: + Py_XDECREF(default_encoding); + Py_XDECREF(ascii_chars_u); + Py_XDECREF(ascii_chars_b); + return -1; +} +#endif +#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3 +#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL) +#else +#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL) +#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT +static char* __PYX_DEFAULT_STRING_ENCODING; +static int __Pyx_init_sys_getdefaultencoding_params(void) { + PyObject* sys; + PyObject* default_encoding = NULL; + char* default_encoding_c; + sys = PyImport_ImportModule("sys"); + if (!sys) goto bad; + default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL); + Py_DECREF(sys); + if (!default_encoding) goto bad; + default_encoding_c = PyBytes_AsString(default_encoding); + if (!default_encoding_c) goto bad; + __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c)); + if (!__PYX_DEFAULT_STRING_ENCODING) goto bad; + strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c); + Py_DECREF(default_encoding); + return 0; +bad: + Py_XDECREF(default_encoding); + return -1; +} +#endif +#endif + + +/* Test for GCC > 2.95 */ +#if defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95))) + #define likely(x) __builtin_expect(!!(x), 1) + #define unlikely(x) __builtin_expect(!!(x), 0) +#else /* !__GNUC__ or GCC < 2.95 */ + #define likely(x) (x) + #define unlikely(x) (x) +#endif /* __GNUC__ */ + +static PyObject *__pyx_m; +static PyObject *__pyx_d; +static PyObject *__pyx_b; +static PyObject *__pyx_empty_tuple; +static PyObject *__pyx_empty_bytes; +static PyObject *__pyx_empty_unicode; +static int __pyx_lineno; +static int __pyx_clineno = 0; +static const char * __pyx_cfilenm= __FILE__; +static const char *__pyx_filename; + +/* Header.proto */ +#if !defined(CYTHON_CCOMPLEX) + #if defined(__cplusplus) + #define CYTHON_CCOMPLEX 1 + #elif defined(_Complex_I) + #define CYTHON_CCOMPLEX 1 + #else + #define CYTHON_CCOMPLEX 0 + #endif +#endif +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + #include + #else + #include + #endif +#endif +#if CYTHON_CCOMPLEX && !defined(__cplusplus) && defined(__sun__) && defined(__GNUC__) + #undef _Complex_I + #define _Complex_I 1.0fj +#endif + + +static const char *__pyx_f[] = { + "_utils/bbox.pyx", + "__init__.pxd", + "type.pxd", +}; +/* BufferFormatStructs.proto */ +#define IS_UNSIGNED(type) (((type) -1) > 0) +struct __Pyx_StructField_; +#define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0) +typedef struct { + const char* name; + struct __Pyx_StructField_* fields; + size_t size; + size_t arraysize[8]; + int ndim; + char typegroup; + char is_unsigned; + int flags; +} __Pyx_TypeInfo; +typedef struct __Pyx_StructField_ { + __Pyx_TypeInfo* type; + const char* name; + size_t offset; +} __Pyx_StructField; +typedef struct { + __Pyx_StructField* field; + size_t parent_offset; +} __Pyx_BufFmt_StackElem; +typedef struct { + __Pyx_StructField root; + __Pyx_BufFmt_StackElem* head; + size_t fmt_offset; + size_t new_count, enc_count; + size_t struct_alignment; + int is_complex; + char enc_type; + char new_packmode; + char enc_packmode; + char is_valid_array; +} __Pyx_BufFmt_Context; + + +/* "../../../../../softWares/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":725 + * # in Cython to enable them only on the right systems. + * + * ctypedef npy_int8 int8_t # <<<<<<<<<<<<<< + * ctypedef npy_int16 int16_t + * ctypedef npy_int32 int32_t + */ +typedef npy_int8 __pyx_t_5numpy_int8_t; + +/* "../../../../../softWares/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":726 + * + * ctypedef npy_int8 int8_t + * ctypedef npy_int16 int16_t # <<<<<<<<<<<<<< + * ctypedef npy_int32 int32_t + * ctypedef npy_int64 int64_t + */ +typedef npy_int16 __pyx_t_5numpy_int16_t; + +/* "../../../../../softWares/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":727 + * ctypedef npy_int8 int8_t + * ctypedef npy_int16 int16_t + * ctypedef npy_int32 int32_t # <<<<<<<<<<<<<< + * ctypedef npy_int64 int64_t + * #ctypedef npy_int96 int96_t + */ +typedef npy_int32 __pyx_t_5numpy_int32_t; + +/* "../../../../../softWares/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":728 + * ctypedef npy_int16 int16_t + * ctypedef npy_int32 int32_t + * ctypedef npy_int64 int64_t # <<<<<<<<<<<<<< + * #ctypedef npy_int96 int96_t + * #ctypedef npy_int128 int128_t + */ +typedef npy_int64 __pyx_t_5numpy_int64_t; + +/* "../../../../../softWares/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":732 + * #ctypedef npy_int128 int128_t + * + * ctypedef npy_uint8 uint8_t # <<<<<<<<<<<<<< + * ctypedef npy_uint16 uint16_t + * ctypedef npy_uint32 uint32_t + */ +typedef npy_uint8 __pyx_t_5numpy_uint8_t; + +/* "../../../../../softWares/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":733 + * + * ctypedef npy_uint8 uint8_t + * ctypedef npy_uint16 uint16_t # <<<<<<<<<<<<<< + * ctypedef npy_uint32 uint32_t + * ctypedef npy_uint64 uint64_t + */ +typedef npy_uint16 __pyx_t_5numpy_uint16_t; + +/* "../../../../../softWares/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":734 + * ctypedef npy_uint8 uint8_t + * ctypedef npy_uint16 uint16_t + * ctypedef npy_uint32 uint32_t # <<<<<<<<<<<<<< + * ctypedef npy_uint64 uint64_t + * #ctypedef npy_uint96 uint96_t + */ +typedef npy_uint32 __pyx_t_5numpy_uint32_t; + +/* "../../../../../softWares/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":735 + * ctypedef npy_uint16 uint16_t + * ctypedef npy_uint32 uint32_t + * ctypedef npy_uint64 uint64_t # <<<<<<<<<<<<<< + * #ctypedef npy_uint96 uint96_t + * #ctypedef npy_uint128 uint128_t + */ +typedef npy_uint64 __pyx_t_5numpy_uint64_t; + +/* "../../../../../softWares/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":739 + * #ctypedef npy_uint128 uint128_t + * + * ctypedef npy_float32 float32_t # <<<<<<<<<<<<<< + * ctypedef npy_float64 float64_t + * #ctypedef npy_float80 float80_t + */ +typedef npy_float32 __pyx_t_5numpy_float32_t; + +/* "../../../../../softWares/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":740 + * + * ctypedef npy_float32 float32_t + * ctypedef npy_float64 float64_t # <<<<<<<<<<<<<< + * #ctypedef npy_float80 float80_t + * #ctypedef npy_float128 float128_t + */ +typedef npy_float64 __pyx_t_5numpy_float64_t; + +/* "../../../../../softWares/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":749 + * # The int types are mapped a bit surprising -- + * # numpy.int corresponds to 'l' and numpy.long to 'q' + * ctypedef npy_long int_t # <<<<<<<<<<<<<< + * ctypedef npy_longlong long_t + * ctypedef npy_longlong longlong_t + */ +typedef npy_long __pyx_t_5numpy_int_t; + +/* "../../../../../softWares/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":750 + * # numpy.int corresponds to 'l' and numpy.long to 'q' + * ctypedef npy_long int_t + * ctypedef npy_longlong long_t # <<<<<<<<<<<<<< + * ctypedef npy_longlong longlong_t + * + */ +typedef npy_longlong __pyx_t_5numpy_long_t; 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r = NULL; __Pyx_DECREF(tmp);}} while(0) + +/* PyObjectGetAttrStr.proto */ +#if CYTHON_USE_TYPE_SLOTS +static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name) { + PyTypeObject* tp = Py_TYPE(obj); + if (likely(tp->tp_getattro)) + return tp->tp_getattro(obj, attr_name); +#if PY_MAJOR_VERSION < 3 + if (likely(tp->tp_getattr)) + return tp->tp_getattr(obj, PyString_AS_STRING(attr_name)); +#endif + return PyObject_GetAttr(obj, attr_name); +} +#else +#define __Pyx_PyObject_GetAttrStr(o,n) PyObject_GetAttr(o,n) +#endif + +/* GetBuiltinName.proto */ +static PyObject *__Pyx_GetBuiltinName(PyObject *name); + +/* RaiseArgTupleInvalid.proto */ +static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact, + Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found); + +/* RaiseDoubleKeywords.proto */ +static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name); + +/* ParseKeywords.proto */ +static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[],\ + PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args,\ + const char* function_name); + +/* ArgTypeTest.proto */ +static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed, + const char *name, int exact); + +/* BufferFormatCheck.proto */ +static CYTHON_INLINE int __Pyx_GetBufferAndValidate(Py_buffer* buf, PyObject* obj, + __Pyx_TypeInfo* dtype, int flags, int nd, int cast, __Pyx_BufFmt_StackElem* stack); +static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info); +static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts); +static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, + __Pyx_BufFmt_StackElem* stack, + __Pyx_TypeInfo* type); // PROTO + +/* GetModuleGlobalName.proto */ +static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name); + +/* PyObjectCall.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw); +#else +#define __Pyx_PyObject_Call(func, arg, kw) PyObject_Call(func, arg, kw) +#endif + +/* ExtTypeTest.proto */ +static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); + +/* BufferIndexError.proto */ +static void __Pyx_RaiseBufferIndexError(int axis); + +#define __Pyx_BufPtrStrided2d(type, buf, i0, s0, i1, s1) (type)((char*)buf + i0 * s0 + i1 * s1) +/* PyThreadStateGet.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_PyThreadState_declare PyThreadState *__pyx_tstate; +#define __Pyx_PyThreadState_assign __pyx_tstate = PyThreadState_GET(); +#else +#define __Pyx_PyThreadState_declare +#define __Pyx_PyThreadState_assign +#endif + +/* PyErrFetchRestore.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_ErrRestoreWithState(type, value, tb) __Pyx_ErrRestoreInState(PyThreadState_GET(), type, value, tb) +#define __Pyx_ErrFetchWithState(type, value, tb) __Pyx_ErrFetchInState(PyThreadState_GET(), type, value, tb) +#define __Pyx_ErrRestore(type, value, tb) __Pyx_ErrRestoreInState(__pyx_tstate, type, value, tb) +#define __Pyx_ErrFetch(type, value, tb) __Pyx_ErrFetchInState(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb); +static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#else +#define __Pyx_ErrRestoreWithState(type, value, tb) PyErr_Restore(type, value, tb) +#define __Pyx_ErrFetchWithState(type, value, tb) PyErr_Fetch(type, value, tb) +#define __Pyx_ErrRestore(type, value, tb) PyErr_Restore(type, value, tb) +#define __Pyx_ErrFetch(type, value, tb) PyErr_Fetch(type, value, tb) +#endif + +#define __Pyx_BufPtrStrided1d(type, buf, i0, s0) (type)((char*)buf + i0 * s0) +/* RaiseException.proto */ +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause); + +/* DictGetItem.proto */ +#if PY_MAJOR_VERSION >= 3 && !CYTHON_COMPILING_IN_PYPY +static PyObject *__Pyx_PyDict_GetItem(PyObject *d, PyObject* key) { + PyObject *value; + value = PyDict_GetItemWithError(d, key); + if (unlikely(!value)) { + if (!PyErr_Occurred()) { + PyObject* args = PyTuple_Pack(1, key); + if (likely(args)) + PyErr_SetObject(PyExc_KeyError, args); + Py_XDECREF(args); + } + return NULL; + } + Py_INCREF(value); + return value; +} +#else + #define __Pyx_PyDict_GetItem(d, key) PyObject_GetItem(d, key) +#endif + +/* RaiseTooManyValuesToUnpack.proto */ +static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected); + +/* RaiseNeedMoreValuesToUnpack.proto */ +static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index); + +/* RaiseNoneIterError.proto */ +static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void); + +/* SaveResetException.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_ExceptionSave(type, value, tb) __Pyx__ExceptionSave(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#define __Pyx_ExceptionReset(type, value, tb) __Pyx__ExceptionReset(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb); +#else +#define __Pyx_ExceptionSave(type, value, tb) PyErr_GetExcInfo(type, value, tb) +#define __Pyx_ExceptionReset(type, value, tb) PyErr_SetExcInfo(type, value, tb) +#endif + +/* PyErrExceptionMatches.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_PyErr_ExceptionMatches(err) __Pyx_PyErr_ExceptionMatchesInState(__pyx_tstate, err) +static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err); +#else +#define __Pyx_PyErr_ExceptionMatches(err) PyErr_ExceptionMatches(err) +#endif + +/* GetException.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_GetException(type, value, tb) __Pyx__GetException(__pyx_tstate, type, value, tb) +static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#else +static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb); +#endif + +/* Import.proto */ +static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level); + +/* CodeObjectCache.proto */ +typedef struct { + PyCodeObject* code_object; + int code_line; +} __Pyx_CodeObjectCacheEntry; +struct __Pyx_CodeObjectCache { + int count; + int max_count; + __Pyx_CodeObjectCacheEntry* entries; +}; +static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL}; +static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line); +static PyCodeObject *__pyx_find_code_object(int code_line); +static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object); + +/* AddTraceback.proto */ +static void __Pyx_AddTraceback(const char *funcname, int c_line, + int py_line, const char *filename); + +/* BufferStructDeclare.proto */ +typedef struct { + Py_ssize_t shape, strides, suboffsets; +} __Pyx_Buf_DimInfo; +typedef struct { + size_t refcount; + Py_buffer pybuffer; +} __Pyx_Buffer; +typedef struct { + __Pyx_Buffer *rcbuffer; + char *data; + __Pyx_Buf_DimInfo diminfo[8]; +} __Pyx_LocalBuf_ND; + +#if PY_MAJOR_VERSION < 3 + static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags); + static void __Pyx_ReleaseBuffer(Py_buffer *view); +#else + #define __Pyx_GetBuffer PyObject_GetBuffer + #define __Pyx_ReleaseBuffer PyBuffer_Release +#endif + + +/* None.proto */ +static Py_ssize_t __Pyx_zeros[] = {0, 0, 0, 0, 0, 0, 0, 0}; +static Py_ssize_t __Pyx_minusones[] = {-1, -1, -1, -1, -1, -1, -1, -1}; + +/* CIntToPy.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_unsigned_int(unsigned int value); + +/* RealImag.proto */ +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + #define __Pyx_CREAL(z) ((z).real()) + #define __Pyx_CIMAG(z) ((z).imag()) + #else + #define __Pyx_CREAL(z) (__real__(z)) + #define __Pyx_CIMAG(z) (__imag__(z)) + #endif +#else + #define __Pyx_CREAL(z) ((z).real) + #define __Pyx_CIMAG(z) ((z).imag) +#endif +#if defined(__cplusplus) && CYTHON_CCOMPLEX\ + && (defined(_WIN32) || defined(__clang__) || (defined(__GNUC__) && (__GNUC__ >= 5 || __GNUC__ == 4 && __GNUC_MINOR__ >= 4 )) || __cplusplus >= 201103) + #define __Pyx_SET_CREAL(z,x) ((z).real(x)) + #define __Pyx_SET_CIMAG(z,y) ((z).imag(y)) +#else + #define __Pyx_SET_CREAL(z,x) __Pyx_CREAL(z) = (x) + #define __Pyx_SET_CIMAG(z,y) __Pyx_CIMAG(z) = (y) +#endif + +/* Arithmetic.proto */ +#if CYTHON_CCOMPLEX + #define __Pyx_c_eq_float(a, b) ((a)==(b)) + #define __Pyx_c_sum_float(a, b) ((a)+(b)) + #define __Pyx_c_diff_float(a, b) ((a)-(b)) + #define __Pyx_c_prod_float(a, b) ((a)*(b)) + #define __Pyx_c_quot_float(a, b) ((a)/(b)) + #define __Pyx_c_neg_float(a) (-(a)) + #ifdef __cplusplus + #define __Pyx_c_is_zero_float(z) ((z)==(float)0) + #define __Pyx_c_conj_float(z) (::std::conj(z)) + #if 1 + #define __Pyx_c_abs_float(z) (::std::abs(z)) + #define __Pyx_c_pow_float(a, b) (::std::pow(a, b)) + #endif + #else + #define __Pyx_c_is_zero_float(z) ((z)==0) + #define __Pyx_c_conj_float(z) (conjf(z)) + #if 1 + #define __Pyx_c_abs_float(z) (cabsf(z)) + #define __Pyx_c_pow_float(a, b) (cpowf(a, b)) + #endif + #endif +#else + static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex); + static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex); + #if 1 + static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex, __pyx_t_float_complex); + #endif +#endif + +/* Arithmetic.proto */ +#if CYTHON_CCOMPLEX + #define __Pyx_c_eq_double(a, b) ((a)==(b)) + #define __Pyx_c_sum_double(a, b) ((a)+(b)) + #define __Pyx_c_diff_double(a, b) ((a)-(b)) + #define __Pyx_c_prod_double(a, b) ((a)*(b)) + #define __Pyx_c_quot_double(a, b) ((a)/(b)) + #define __Pyx_c_neg_double(a) (-(a)) + #ifdef __cplusplus + #define __Pyx_c_is_zero_double(z) ((z)==(double)0) + #define __Pyx_c_conj_double(z) (::std::conj(z)) + #if 1 + #define __Pyx_c_abs_double(z) (::std::abs(z)) + #define __Pyx_c_pow_double(a, b) (::std::pow(a, b)) + #endif + #else + #define __Pyx_c_is_zero_double(z) ((z)==0) + #define __Pyx_c_conj_double(z) (conj(z)) + #if 1 + #define __Pyx_c_abs_double(z) (cabs(z)) + #define __Pyx_c_pow_double(a, b) (cpow(a, b)) + #endif + #endif +#else + static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex); + static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex); + #if 1 + static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex, __pyx_t_double_complex); + #endif +#endif + +/* CIntToPy.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value); + +/* CIntToPy.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_enum__NPY_TYPES(enum NPY_TYPES value); + +/* CIntFromPy.proto */ +static CYTHON_INLINE unsigned int __Pyx_PyInt_As_unsigned_int(PyObject *); + +/* CIntFromPy.proto */ +static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *); + +/* CIntToPy.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value); + +/* CIntFromPy.proto */ +static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *); + +/* CheckBinaryVersion.proto */ +static int __Pyx_check_binary_version(void); + +/* PyIdentifierFromString.proto */ +#if !defined(__Pyx_PyIdentifier_FromString) +#if PY_MAJOR_VERSION < 3 + #define __Pyx_PyIdentifier_FromString(s) PyString_FromString(s) +#else + #define __Pyx_PyIdentifier_FromString(s) PyUnicode_FromString(s) +#endif +#endif + +/* ModuleImport.proto */ +static PyObject *__Pyx_ImportModule(const char *name); + +/* TypeImport.proto */ +static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict); + +/* InitStrings.proto */ +static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); + + +/* Module declarations from 'cython' */ + +/* Module declarations from 'cpython.buffer' */ + +/* Module declarations from 'libc.string' */ + +/* Module declarations from 'libc.stdio' */ + +/* Module declarations from '__builtin__' */ + +/* Module declarations from 'cpython.type' */ +static PyTypeObject *__pyx_ptype_7cpython_4type_type = 0; + +/* Module declarations from 'cpython' */ + +/* Module declarations from 'cpython.object' */ + +/* Module declarations from 'cpython.ref' */ + +/* Module declarations from 'libc.stdlib' */ + +/* Module declarations from 'numpy' */ + +/* Module declarations from 'numpy' */ +static PyTypeObject *__pyx_ptype_5numpy_dtype = 0; +static PyTypeObject *__pyx_ptype_5numpy_flatiter = 0; +static PyTypeObject *__pyx_ptype_5numpy_broadcast = 0; +static PyTypeObject *__pyx_ptype_5numpy_ndarray = 0; +static PyTypeObject *__pyx_ptype_5numpy_ufunc = 0; +static CYTHON_INLINE char *__pyx_f_5numpy__util_dtypestring(PyArray_Descr *, char *, char *, int *); /*proto*/ + +/* Module declarations from 'cython_bbox' */ +static __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_11cython_bbox_DTYPE_t = { "DTYPE_t", NULL, sizeof(__pyx_t_11cython_bbox_DTYPE_t), { 0 }, 0, 'R', 0, 0 }; +static __Pyx_TypeInfo __Pyx_TypeInfo_long = { "long", NULL, sizeof(long), { 0 }, 0, IS_UNSIGNED(long) ? 'U' : 'I', IS_UNSIGNED(long), 0 }; +#define __Pyx_MODULE_NAME "cython_bbox" +int __pyx_module_is_main_cython_bbox = 0; + +/* Implementation of 'cython_bbox' */ +static PyObject *__pyx_builtin_range; +static PyObject *__pyx_builtin_ValueError; +static PyObject *__pyx_builtin_RuntimeError; +static PyObject *__pyx_builtin_ImportError; +static const char __pyx_k_K[] = "K"; +static const char __pyx_k_N[] = "N"; +static const char __pyx_k_k[] = "k"; +static const char __pyx_k_n[] = "n"; +static const char __pyx_k_ih[] = "ih"; +static const char __pyx_k_iw[] = "iw"; +static const char __pyx_k_np[] = "np"; +static const char __pyx_k_ua[] = "ua"; +static const char __pyx_k_int[] = "int"; +static const char __pyx_k_main[] = "__main__"; +static const char __pyx_k_test[] = "__test__"; +static const char __pyx_k_DTYPE[] = "DTYPE"; +static const char __pyx_k_boxes[] = "boxes"; +static const char __pyx_k_dtype[] = "dtype"; +static const char __pyx_k_float[] = "float"; +static const char __pyx_k_numpy[] = "numpy"; +static const char __pyx_k_range[] = "range"; +static const char __pyx_k_zeros[] = "zeros"; +static const char __pyx_k_import[] = "__import__"; +static const char __pyx_k_overlap[] = "overlap"; +static const char __pyx_k_box_area[] = "box_area"; +static const char __pyx_k_overlaps[] = "overlaps"; +static const char __pyx_k_FG_THRESH[] = "FG_THRESH"; +static const char __pyx_k_ValueError[] = "ValueError"; +static const char __pyx_k_ImportError[] = "ImportError"; +static const char __pyx_k_cython_bbox[] = "cython_bbox"; +static const char __pyx_k_query_boxes[] = "query_boxes"; +static const char __pyx_k_RuntimeError[] = "RuntimeError"; +static const char __pyx_k_max_overlaps[] = "max_overlaps"; +static const char __pyx_k_bbox_overlaps[] = "bbox_overlaps"; +static const char __pyx_k_gt_assignment[] = "gt_assignment"; +static const char __pyx_k_bbox_overlaps_self[] = "bbox_overlaps_self"; +static const char __pyx_k_bbox_overlaps_float[] = "bbox_overlaps_float"; +static const char __pyx_k_bbox_overlaps_ignore[] = "bbox_overlaps_ignore"; +static const char __pyx_k_get_assignment_overlaps[] = "get_assignment_overlaps"; +static const char __pyx_k_ndarray_is_not_C_contiguous[] = "ndarray is not C contiguous"; +static const char __pyx_k_home_yjr_PycharmProjects_Faster[] = "/home/yjr/PycharmProjects/Faster-RCNN_Tensorflow/libs/box_utils/cython_utils/bbox.pyx"; +static const char __pyx_k_numpy_core_multiarray_failed_to[] = "numpy.core.multiarray failed to import"; +static const char __pyx_k_unknown_dtype_code_in_numpy_pxd[] = "unknown dtype code in numpy.pxd (%d)"; +static const char __pyx_k_Format_string_allocated_too_shor[] = "Format string allocated too short, see comment in numpy.pxd"; +static const char __pyx_k_Non_native_byte_order_not_suppor[] = "Non-native byte order not supported"; +static const char __pyx_k_ndarray_is_not_Fortran_contiguou[] = "ndarray is not Fortran contiguous"; +static const char __pyx_k_numpy_core_umath_failed_to_impor[] = "numpy.core.umath failed to import"; +static const char __pyx_k_Format_string_allocated_too_shor_2[] = "Format string allocated too short."; 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+ if (unlikely(!result)) { + PyErr_Format(PyExc_NameError, +#if PY_MAJOR_VERSION >= 3 + "name '%U' is not defined", name); +#else + "name '%.200s' is not defined", PyString_AS_STRING(name)); +#endif + } + return result; +} + +/* RaiseArgTupleInvalid */ +static void __Pyx_RaiseArgtupleInvalid( + const char* func_name, + int exact, + Py_ssize_t num_min, + Py_ssize_t num_max, + Py_ssize_t num_found) +{ + Py_ssize_t num_expected; + const char *more_or_less; + if (num_found < num_min) { + num_expected = num_min; + more_or_less = "at least"; + } else { + num_expected = num_max; + more_or_less = "at most"; + } + if (exact) { + more_or_less = "exactly"; + } + PyErr_Format(PyExc_TypeError, + "%.200s() takes %.8s %" CYTHON_FORMAT_SSIZE_T "d positional argument%.1s (%" CYTHON_FORMAT_SSIZE_T "d given)", + func_name, more_or_less, num_expected, + (num_expected == 1) ? "" : "s", num_found); +} + +/* RaiseDoubleKeywords */ +static void __Pyx_RaiseDoubleKeywordsError( + const char* func_name, + PyObject* kw_name) +{ + PyErr_Format(PyExc_TypeError, + #if PY_MAJOR_VERSION >= 3 + "%s() got multiple values for keyword argument '%U'", func_name, kw_name); + #else + "%s() got multiple values for keyword argument '%s'", func_name, + PyString_AsString(kw_name)); + #endif +} + +/* ParseKeywords */ +static int __Pyx_ParseOptionalKeywords( + PyObject *kwds, + PyObject **argnames[], + PyObject *kwds2, + PyObject *values[], + Py_ssize_t num_pos_args, + const char* function_name) +{ + PyObject *key = 0, *value = 0; + Py_ssize_t pos = 0; + PyObject*** name; + PyObject*** first_kw_arg = argnames + num_pos_args; + while (PyDict_Next(kwds, &pos, &key, &value)) { + name = first_kw_arg; + while (*name && (**name != key)) name++; + if (*name) { + values[name-argnames] = value; + continue; + } + name = first_kw_arg; + #if PY_MAJOR_VERSION < 3 + if (likely(PyString_CheckExact(key)) || likely(PyString_Check(key))) { + while (*name) { + if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key)) + && _PyString_Eq(**name, key)) { + values[name-argnames] = value; + break; + } + name++; + } + if (*name) continue; + else { + PyObject*** argname = argnames; + while (argname != first_kw_arg) { + if ((**argname == key) || ( + (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key)) + && _PyString_Eq(**argname, key))) { + goto arg_passed_twice; + } + argname++; + } + } + } else + #endif + if (likely(PyUnicode_Check(key))) { + while (*name) { + int cmp = (**name == key) ? 0 : + #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 + (PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(key)) ? 1 : + #endif + PyUnicode_Compare(**name, key); + if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; + if (cmp == 0) { + values[name-argnames] = value; + break; + } + name++; + } + if (*name) continue; + else { + PyObject*** argname = argnames; + while (argname != first_kw_arg) { + int cmp = (**argname == key) ? 0 : + #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 + (PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(key)) ? 1 : + #endif + PyUnicode_Compare(**argname, key); + if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; + if (cmp == 0) goto arg_passed_twice; + argname++; + } + } + } else + goto invalid_keyword_type; + if (kwds2) { + if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad; + } else { + goto invalid_keyword; + } + } + return 0; +arg_passed_twice: + __Pyx_RaiseDoubleKeywordsError(function_name, key); + goto bad; +invalid_keyword_type: + PyErr_Format(PyExc_TypeError, + "%.200s() keywords must be strings", function_name); + goto bad; +invalid_keyword: + PyErr_Format(PyExc_TypeError, + #if PY_MAJOR_VERSION < 3 + "%.200s() got an unexpected keyword argument '%.200s'", + function_name, PyString_AsString(key)); + #else + "%s() got an unexpected keyword argument '%U'", + function_name, key); + #endif +bad: + return -1; +} + +/* ArgTypeTest */ +static void __Pyx_RaiseArgumentTypeInvalid(const char* name, PyObject *obj, PyTypeObject *type) { + PyErr_Format(PyExc_TypeError, + "Argument '%.200s' has incorrect type (expected %.200s, got %.200s)", + name, type->tp_name, Py_TYPE(obj)->tp_name); +} +static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed, + const char *name, int exact) +{ + if (unlikely(!type)) { + PyErr_SetString(PyExc_SystemError, "Missing type object"); + return 0; + } + if (none_allowed && obj == Py_None) return 1; + else if (exact) { + if (likely(Py_TYPE(obj) == type)) return 1; + #if PY_MAJOR_VERSION == 2 + else if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1; + #endif + } + else { + if (likely(PyObject_TypeCheck(obj, type))) return 1; + } + __Pyx_RaiseArgumentTypeInvalid(name, obj, type); + return 0; +} + +/* BufferFormatCheck */ +static CYTHON_INLINE int __Pyx_IsLittleEndian(void) { + unsigned int n = 1; + return *(unsigned char*)(&n) != 0; +} +static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, + __Pyx_BufFmt_StackElem* stack, + __Pyx_TypeInfo* type) { + stack[0].field = &ctx->root; + stack[0].parent_offset = 0; + ctx->root.type = type; + ctx->root.name = "buffer dtype"; + ctx->root.offset = 0; + ctx->head = stack; + ctx->head->field = &ctx->root; + ctx->fmt_offset = 0; + ctx->head->parent_offset = 0; + ctx->new_packmode = '@'; + ctx->enc_packmode = '@'; + ctx->new_count = 1; + ctx->enc_count = 0; + ctx->enc_type = 0; + ctx->is_complex = 0; + ctx->is_valid_array = 0; + ctx->struct_alignment = 0; + while (type->typegroup == 'S') { + ++ctx->head; + ctx->head->field = type->fields; + ctx->head->parent_offset = 0; + type = type->fields->type; + } +} +static int __Pyx_BufFmt_ParseNumber(const char** ts) { + int count; + const char* t = *ts; + if (*t < '0' || *t > '9') { + return -1; + } else { + count = *t++ - '0'; + while (*t >= '0' && *t < '9') { + count *= 10; + count += *t++ - '0'; + } + } + *ts = t; + return count; +} +static int __Pyx_BufFmt_ExpectNumber(const char **ts) { + int number = __Pyx_BufFmt_ParseNumber(ts); + if (number == -1) + PyErr_Format(PyExc_ValueError,\ + "Does not understand character buffer dtype format string ('%c')", **ts); + return number; +} +static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) { + PyErr_Format(PyExc_ValueError, + "Unexpected format string character: '%c'", ch); +} +static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) { + switch (ch) { + case 'c': return "'char'"; + case 'b': return "'signed char'"; + case 'B': return "'unsigned char'"; + case 'h': return "'short'"; + case 'H': return "'unsigned short'"; + case 'i': return "'int'"; + case 'I': return "'unsigned int'"; + case 'l': return "'long'"; + case 'L': return "'unsigned long'"; + case 'q': return "'long long'"; + case 'Q': return "'unsigned long long'"; + case 'f': return (is_complex ? "'complex float'" : "'float'"); + case 'd': return (is_complex ? "'complex double'" : "'double'"); + case 'g': return (is_complex ? "'complex long double'" : "'long double'"); + case 'T': return "a struct"; + case 'O': return "Python object"; + case 'P': return "a pointer"; + case 's': case 'p': return "a string"; + case 0: return "end"; + default: return "unparseable format string"; + } +} +static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return 2; + case 'i': case 'I': case 'l': case 'L': return 4; + case 'q': case 'Q': return 8; + case 'f': return (is_complex ? 8 : 4); + case 'd': return (is_complex ? 16 : 8); + case 'g': { + PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g').."); + return 0; + } + case 'O': case 'P': return sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) { + switch (ch) { + case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(short); + case 'i': case 'I': return sizeof(int); + case 'l': case 'L': return sizeof(long); + #ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(PY_LONG_LONG); + #endif + case 'f': return sizeof(float) * (is_complex ? 2 : 1); + case 'd': return sizeof(double) * (is_complex ? 2 : 1); + case 'g': return sizeof(long double) * (is_complex ? 2 : 1); + case 'O': case 'P': return sizeof(void*); + default: { + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } + } +} +typedef struct { char c; short x; } __Pyx_st_short; +typedef struct { char c; int x; } __Pyx_st_int; +typedef struct { char c; long x; } __Pyx_st_long; +typedef struct { char c; float x; } __Pyx_st_float; +typedef struct { char c; double x; } __Pyx_st_double; +typedef struct { char c; long double x; } __Pyx_st_longdouble; +typedef struct { char c; void *x; } __Pyx_st_void_p; +#ifdef HAVE_LONG_LONG +typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong; +#endif +static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, CYTHON_UNUSED int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short); + case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int); + case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long); +#ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG); +#endif + case 'f': return sizeof(__Pyx_st_float) - sizeof(float); + case 'd': return sizeof(__Pyx_st_double) - sizeof(double); + case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double); + case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +/* These are for computing the padding at the end of the struct to align + on the first member of the struct. This will probably the same as above, + but we don't have any guarantees. + */ +typedef struct { short x; char c; } __Pyx_pad_short; +typedef struct { int x; char c; } __Pyx_pad_int; +typedef struct { long x; char c; } __Pyx_pad_long; +typedef struct { float x; char c; } __Pyx_pad_float; +typedef struct { double x; char c; } __Pyx_pad_double; +typedef struct { long double x; char c; } __Pyx_pad_longdouble; +typedef struct { void *x; char c; } __Pyx_pad_void_p; +#ifdef HAVE_LONG_LONG +typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong; +#endif +static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short); + case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int); + case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long); +#ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG); +#endif + case 'f': return sizeof(__Pyx_pad_float) - sizeof(float); + case 'd': return sizeof(__Pyx_pad_double) - sizeof(double); + case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double); + case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) { + switch (ch) { + case 'c': + return 'H'; + case 'b': case 'h': case 'i': + case 'l': case 'q': case 's': case 'p': + return 'I'; + case 'B': case 'H': case 'I': case 'L': case 'Q': + return 'U'; + case 'f': case 'd': case 'g': + return (is_complex ? 'C' : 'R'); + case 'O': + return 'O'; + case 'P': + return 'P'; + default: { + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } + } +} +static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) { + if (ctx->head == NULL || ctx->head->field == &ctx->root) { + const char* expected; + const char* quote; + if (ctx->head == NULL) { + expected = "end"; + quote = ""; + } else { + expected = ctx->head->field->type->name; + quote = "'"; + } + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch, expected %s%s%s but got %s", + quote, expected, quote, + __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex)); + } else { + __Pyx_StructField* field = ctx->head->field; + __Pyx_StructField* parent = (ctx->head - 1)->field; + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'", + field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex), + parent->type->name, field->name); + } +} +static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) { + char group; + size_t size, offset, arraysize = 1; + if (ctx->enc_type == 0) return 0; + if (ctx->head->field->type->arraysize[0]) { + int i, ndim = 0; + if (ctx->enc_type == 's' || ctx->enc_type == 'p') { + ctx->is_valid_array = ctx->head->field->type->ndim == 1; + ndim = 1; + if (ctx->enc_count != ctx->head->field->type->arraysize[0]) { + PyErr_Format(PyExc_ValueError, + "Expected a dimension of size %zu, got %zu", + ctx->head->field->type->arraysize[0], ctx->enc_count); + return -1; + } + } + if (!ctx->is_valid_array) { + PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d", + ctx->head->field->type->ndim, ndim); + return -1; + } + for (i = 0; i < ctx->head->field->type->ndim; i++) { + arraysize *= ctx->head->field->type->arraysize[i]; + } + ctx->is_valid_array = 0; + ctx->enc_count = 1; + } + group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex); + do { + __Pyx_StructField* field = ctx->head->field; + __Pyx_TypeInfo* type = field->type; + if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') { + size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex); + } else { + size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex); + } + if (ctx->enc_packmode == '@') { + size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex); + size_t align_mod_offset; + if (align_at == 0) return -1; + align_mod_offset = ctx->fmt_offset % align_at; + if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset; + if (ctx->struct_alignment == 0) + ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type, + ctx->is_complex); + } + if (type->size != size || type->typegroup != group) { + if (type->typegroup == 'C' && type->fields != NULL) { + size_t parent_offset = ctx->head->parent_offset + field->offset; + ++ctx->head; + ctx->head->field = type->fields; + ctx->head->parent_offset = parent_offset; + continue; + } + if ((type->typegroup == 'H' || group == 'H') && type->size == size) { + } else { + __Pyx_BufFmt_RaiseExpected(ctx); + return -1; + } + } + offset = ctx->head->parent_offset + field->offset; + if (ctx->fmt_offset != offset) { + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected", + (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset); + return -1; + } + ctx->fmt_offset += size; + if (arraysize) + ctx->fmt_offset += (arraysize - 1) * size; + --ctx->enc_count; + while (1) { + if (field == &ctx->root) { + ctx->head = NULL; + if (ctx->enc_count != 0) { + __Pyx_BufFmt_RaiseExpected(ctx); + return -1; + } + break; + } + ctx->head->field = ++field; + if (field->type == NULL) { + --ctx->head; + field = ctx->head->field; + continue; + } else if (field->type->typegroup == 'S') { + size_t parent_offset = ctx->head->parent_offset + field->offset; + if (field->type->fields->type == NULL) continue; + field = field->type->fields; + ++ctx->head; + ctx->head->field = field; + ctx->head->parent_offset = parent_offset; + break; + } else { + break; + } + } + } while (ctx->enc_count); + ctx->enc_type = 0; + ctx->is_complex = 0; + return 0; +} +static CYTHON_INLINE PyObject * +__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp) +{ + const char *ts = *tsp; + int i = 0, number; + int ndim = ctx->head->field->type->ndim; +; + ++ts; + if (ctx->new_count != 1) { + PyErr_SetString(PyExc_ValueError, + "Cannot handle repeated arrays in format string"); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + while (*ts && *ts != ')') { + switch (*ts) { + case ' ': case '\f': case '\r': case '\n': case '\t': case '\v': continue; + default: break; + } + number = __Pyx_BufFmt_ExpectNumber(&ts); + if (number == -1) return NULL; + if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i]) + return PyErr_Format(PyExc_ValueError, + "Expected a dimension of size %zu, got %d", + ctx->head->field->type->arraysize[i], number); + if (*ts != ',' && *ts != ')') + return PyErr_Format(PyExc_ValueError, + "Expected a comma in format string, got '%c'", *ts); + if (*ts == ',') ts++; + i++; + } + if (i != ndim) + return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d", + ctx->head->field->type->ndim, i); + if (!*ts) { + PyErr_SetString(PyExc_ValueError, + "Unexpected end of format string, expected ')'"); + return NULL; + } + ctx->is_valid_array = 1; + ctx->new_count = 1; + *tsp = ++ts; + return Py_None; +} +static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) { + int got_Z = 0; + while (1) { + switch(*ts) { + case 0: + if (ctx->enc_type != 0 && ctx->head == NULL) { + __Pyx_BufFmt_RaiseExpected(ctx); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + if (ctx->head != NULL) { + __Pyx_BufFmt_RaiseExpected(ctx); + return NULL; + } + return ts; + case ' ': + case '\r': + case '\n': + ++ts; + break; + case '<': + if (!__Pyx_IsLittleEndian()) { + PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler"); + return NULL; + } + ctx->new_packmode = '='; + ++ts; + break; + case '>': + case '!': + if (__Pyx_IsLittleEndian()) { + PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler"); + return NULL; + } + ctx->new_packmode = '='; + ++ts; + break; + case '=': + case '@': + case '^': + ctx->new_packmode = *ts++; + break; + case 'T': + { + const char* ts_after_sub; + size_t i, struct_count = ctx->new_count; + size_t struct_alignment = ctx->struct_alignment; + ctx->new_count = 1; + ++ts; + if (*ts != '{') { + PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'"); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_type = 0; + ctx->enc_count = 0; + ctx->struct_alignment = 0; + ++ts; + ts_after_sub = ts; + for (i = 0; i != struct_count; ++i) { + ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts); + if (!ts_after_sub) return NULL; + } + ts = ts_after_sub; + if (struct_alignment) ctx->struct_alignment = struct_alignment; + } + break; + case '}': + { + size_t alignment = ctx->struct_alignment; + ++ts; + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_type = 0; + if (alignment && ctx->fmt_offset % alignment) { + ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment); + } + } + return ts; + case 'x': + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->fmt_offset += ctx->new_count; + ctx->new_count = 1; + ctx->enc_count = 0; + ctx->enc_type = 0; + ctx->enc_packmode = ctx->new_packmode; + ++ts; + break; + case 'Z': + got_Z = 1; + ++ts; + if (*ts != 'f' && *ts != 'd' && *ts != 'g') { + __Pyx_BufFmt_RaiseUnexpectedChar('Z'); + return NULL; + } + case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I': + case 'l': case 'L': case 'q': case 'Q': + case 'f': case 'd': case 'g': + case 'O': case 'p': + if (ctx->enc_type == *ts && got_Z == ctx->is_complex && + ctx->enc_packmode == ctx->new_packmode) { + ctx->enc_count += ctx->new_count; + ctx->new_count = 1; + got_Z = 0; + ++ts; + break; + } + case 's': + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_count = ctx->new_count; + ctx->enc_packmode = ctx->new_packmode; + ctx->enc_type = *ts; + ctx->is_complex = got_Z; + ++ts; + ctx->new_count = 1; + got_Z = 0; + break; + case ':': + ++ts; + while(*ts != ':') ++ts; + ++ts; + break; + case '(': + if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL; + break; + default: + { + int number = __Pyx_BufFmt_ExpectNumber(&ts); + if (number == -1) return NULL; + ctx->new_count = (size_t)number; + } + } + } +} +static CYTHON_INLINE void __Pyx_ZeroBuffer(Py_buffer* buf) { + buf->buf = NULL; + buf->obj = NULL; + buf->strides = __Pyx_zeros; + buf->shape = __Pyx_zeros; + buf->suboffsets = __Pyx_minusones; +} +static CYTHON_INLINE int __Pyx_GetBufferAndValidate( + Py_buffer* buf, PyObject* obj, __Pyx_TypeInfo* dtype, int flags, + int nd, int cast, __Pyx_BufFmt_StackElem* stack) +{ + if (obj == Py_None || obj == NULL) { + __Pyx_ZeroBuffer(buf); + return 0; + } + buf->buf = NULL; + if (__Pyx_GetBuffer(obj, buf, flags) == -1) goto fail; + if (buf->ndim != nd) { + PyErr_Format(PyExc_ValueError, + "Buffer has wrong number of dimensions (expected %d, got %d)", + nd, buf->ndim); + goto fail; + } + if (!cast) { + __Pyx_BufFmt_Context ctx; + __Pyx_BufFmt_Init(&ctx, stack, dtype); + if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail; + } + if ((unsigned)buf->itemsize != dtype->size) { + PyErr_Format(PyExc_ValueError, + "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "d byte%s) does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "d byte%s)", + buf->itemsize, (buf->itemsize > 1) ? "s" : "", + dtype->name, (Py_ssize_t)dtype->size, (dtype->size > 1) ? "s" : ""); + goto fail; + } + if (buf->suboffsets == NULL) buf->suboffsets = __Pyx_minusones; + return 0; +fail:; + __Pyx_ZeroBuffer(buf); + return -1; +} +static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info) { + if (info->buf == NULL) return; + if (info->suboffsets == __Pyx_minusones) info->suboffsets = NULL; + __Pyx_ReleaseBuffer(info); +} + +/* GetModuleGlobalName */ + static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name) { + PyObject *result; +#if !CYTHON_AVOID_BORROWED_REFS + result = PyDict_GetItem(__pyx_d, name); + if (likely(result)) { + Py_INCREF(result); + } else { +#else + result = PyObject_GetItem(__pyx_d, name); + if (!result) { + PyErr_Clear(); +#endif + result = __Pyx_GetBuiltinName(name); + } + return result; +} + +/* PyObjectCall */ + #if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) { + PyObject *result; + ternaryfunc call = func->ob_type->tp_call; + if (unlikely(!call)) + return PyObject_Call(func, arg, kw); + if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) + return NULL; + result = (*call)(func, arg, kw); + Py_LeaveRecursiveCall(); + if (unlikely(!result) && unlikely(!PyErr_Occurred())) { + PyErr_SetString( + PyExc_SystemError, + "NULL result without error in PyObject_Call"); + } + return result; +} +#endif + +/* ExtTypeTest */ + static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { + if (unlikely(!type)) { + PyErr_SetString(PyExc_SystemError, "Missing type object"); + return 0; + } + if (likely(PyObject_TypeCheck(obj, type))) + return 1; + PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s", + Py_TYPE(obj)->tp_name, type->tp_name); + return 0; +} + +/* BufferIndexError */ + static void __Pyx_RaiseBufferIndexError(int axis) { + PyErr_Format(PyExc_IndexError, + "Out of bounds on buffer access (axis %d)", axis); +} + +/* PyErrFetchRestore */ + #if CYTHON_FAST_THREAD_STATE +static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { + PyObject *tmp_type, *tmp_value, *tmp_tb; + tmp_type = tstate->curexc_type; + tmp_value = tstate->curexc_value; + tmp_tb = tstate->curexc_traceback; + tstate->curexc_type = type; + tstate->curexc_value = value; + tstate->curexc_traceback = tb; + Py_XDECREF(tmp_type); + Py_XDECREF(tmp_value); + Py_XDECREF(tmp_tb); +} +static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { + *type = tstate->curexc_type; + *value = tstate->curexc_value; + *tb = tstate->curexc_traceback; + tstate->curexc_type = 0; + tstate->curexc_value = 0; + tstate->curexc_traceback = 0; +} +#endif + +/* RaiseException */ + #if PY_MAJOR_VERSION < 3 +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, + CYTHON_UNUSED PyObject *cause) { + __Pyx_PyThreadState_declare + Py_XINCREF(type); + if (!value || value == Py_None) + value = NULL; + else + Py_INCREF(value); + if (!tb || tb == Py_None) + tb = NULL; + else { + Py_INCREF(tb); + if (!PyTraceBack_Check(tb)) { + PyErr_SetString(PyExc_TypeError, + "raise: arg 3 must be a traceback or None"); + goto raise_error; + } + } + if (PyType_Check(type)) { +#if CYTHON_COMPILING_IN_PYPY + if (!value) { + Py_INCREF(Py_None); + value = Py_None; + } +#endif + PyErr_NormalizeException(&type, &value, &tb); + } else { + if (value) { + PyErr_SetString(PyExc_TypeError, + "instance exception may not have a separate value"); + goto raise_error; + } + value = type; + type = (PyObject*) Py_TYPE(type); + Py_INCREF(type); + if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { + PyErr_SetString(PyExc_TypeError, + "raise: exception class must be a subclass of BaseException"); + goto raise_error; + } + } + __Pyx_PyThreadState_assign + __Pyx_ErrRestore(type, value, tb); + return; +raise_error: + Py_XDECREF(value); + Py_XDECREF(type); + Py_XDECREF(tb); + return; +} +#else +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { + PyObject* owned_instance = NULL; + if (tb == Py_None) { + tb = 0; + } else if (tb && !PyTraceBack_Check(tb)) { + PyErr_SetString(PyExc_TypeError, + "raise: arg 3 must be a traceback or None"); + goto bad; + } + if (value == Py_None) + value = 0; + if (PyExceptionInstance_Check(type)) { + if (value) { + PyErr_SetString(PyExc_TypeError, + "instance exception may not have a separate value"); + goto bad; + } + value = type; + type = (PyObject*) Py_TYPE(value); + } else if (PyExceptionClass_Check(type)) { + PyObject *instance_class = NULL; + if (value && PyExceptionInstance_Check(value)) { + instance_class = (PyObject*) Py_TYPE(value); + if (instance_class != type) { + int is_subclass = PyObject_IsSubclass(instance_class, type); + if (!is_subclass) { + instance_class = NULL; + } else if (unlikely(is_subclass == -1)) { + goto bad; + } else { + type = instance_class; + } + } + } + if (!instance_class) { + PyObject *args; + if (!value) + args = PyTuple_New(0); + else if (PyTuple_Check(value)) { + Py_INCREF(value); + args = value; + } else + args = PyTuple_Pack(1, value); + if (!args) + goto bad; + owned_instance = PyObject_Call(type, args, NULL); + Py_DECREF(args); + if (!owned_instance) + goto bad; + value = owned_instance; + if (!PyExceptionInstance_Check(value)) { + PyErr_Format(PyExc_TypeError, + "calling %R should have returned an instance of " + "BaseException, not %R", + type, Py_TYPE(value)); + goto bad; + } + } + } else { + PyErr_SetString(PyExc_TypeError, + "raise: exception class must be a subclass of BaseException"); + goto bad; + } +#if PY_VERSION_HEX >= 0x03030000 + if (cause) { +#else + if (cause && cause != Py_None) { +#endif + PyObject *fixed_cause; + if (cause == Py_None) { + fixed_cause = NULL; + } else if (PyExceptionClass_Check(cause)) { + fixed_cause = PyObject_CallObject(cause, NULL); + if (fixed_cause == NULL) + goto bad; + } else if (PyExceptionInstance_Check(cause)) { + fixed_cause = cause; + Py_INCREF(fixed_cause); + } else { + PyErr_SetString(PyExc_TypeError, + "exception causes must derive from " + "BaseException"); + goto bad; + } + PyException_SetCause(value, fixed_cause); + } + PyErr_SetObject(type, value); + if (tb) { +#if CYTHON_COMPILING_IN_PYPY + PyObject *tmp_type, *tmp_value, *tmp_tb; + PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb); + Py_INCREF(tb); + PyErr_Restore(tmp_type, tmp_value, tb); + Py_XDECREF(tmp_tb); +#else + PyThreadState *tstate = PyThreadState_GET(); + PyObject* tmp_tb = tstate->curexc_traceback; + if (tb != tmp_tb) { + Py_INCREF(tb); + tstate->curexc_traceback = tb; + Py_XDECREF(tmp_tb); + } +#endif + } +bad: + Py_XDECREF(owned_instance); + return; +} +#endif + +/* RaiseTooManyValuesToUnpack */ + static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { + PyErr_Format(PyExc_ValueError, + "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); +} + +/* RaiseNeedMoreValuesToUnpack */ + static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { + PyErr_Format(PyExc_ValueError, + "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack", + index, (index == 1) ? "" : "s"); +} + +/* RaiseNoneIterError */ + static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { + PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); +} + +/* SaveResetException */ + #if CYTHON_FAST_THREAD_STATE +static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { + *type = tstate->exc_type; + *value = tstate->exc_value; + *tb = tstate->exc_traceback; + Py_XINCREF(*type); + Py_XINCREF(*value); + Py_XINCREF(*tb); +} +static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { + PyObject *tmp_type, *tmp_value, *tmp_tb; + tmp_type = tstate->exc_type; + tmp_value = tstate->exc_value; + tmp_tb = tstate->exc_traceback; + tstate->exc_type = type; + tstate->exc_value = value; + tstate->exc_traceback = tb; + Py_XDECREF(tmp_type); + Py_XDECREF(tmp_value); + Py_XDECREF(tmp_tb); +} +#endif + +/* PyErrExceptionMatches */ + #if CYTHON_FAST_THREAD_STATE +static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err) { + PyObject *exc_type = tstate->curexc_type; + if (exc_type == err) return 1; + if (unlikely(!exc_type)) return 0; + return PyErr_GivenExceptionMatches(exc_type, err); +} +#endif + +/* GetException */ + #if CYTHON_FAST_THREAD_STATE +static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { +#else +static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) { +#endif + PyObject *local_type, *local_value, *local_tb; +#if CYTHON_FAST_THREAD_STATE + PyObject *tmp_type, *tmp_value, *tmp_tb; + local_type = tstate->curexc_type; + local_value = tstate->curexc_value; + local_tb = tstate->curexc_traceback; + tstate->curexc_type = 0; + tstate->curexc_value = 0; + tstate->curexc_traceback = 0; +#else + PyErr_Fetch(&local_type, &local_value, &local_tb); +#endif + PyErr_NormalizeException(&local_type, &local_value, &local_tb); +#if CYTHON_FAST_THREAD_STATE + if (unlikely(tstate->curexc_type)) +#else + if (unlikely(PyErr_Occurred())) +#endif + goto bad; + #if PY_MAJOR_VERSION >= 3 + if (local_tb) { + if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0)) + goto bad; + } + #endif + Py_XINCREF(local_tb); + Py_XINCREF(local_type); + Py_XINCREF(local_value); + *type = local_type; + *value = local_value; + *tb = local_tb; +#if CYTHON_FAST_THREAD_STATE + tmp_type = tstate->exc_type; + tmp_value = tstate->exc_value; + tmp_tb = tstate->exc_traceback; + tstate->exc_type = local_type; + tstate->exc_value = local_value; + tstate->exc_traceback = local_tb; + Py_XDECREF(tmp_type); + Py_XDECREF(tmp_value); + Py_XDECREF(tmp_tb); +#else + PyErr_SetExcInfo(local_type, local_value, local_tb); +#endif + return 0; +bad: + *type = 0; + *value = 0; + *tb = 0; + Py_XDECREF(local_type); + Py_XDECREF(local_value); + Py_XDECREF(local_tb); + return -1; +} + +/* Import */ + static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { + PyObject *empty_list = 0; + PyObject *module = 0; + PyObject *global_dict = 0; + PyObject *empty_dict = 0; + PyObject *list; + #if PY_VERSION_HEX < 0x03030000 + PyObject *py_import; + py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import); + if (!py_import) + goto bad; + #endif + if (from_list) + list = from_list; + else { + empty_list = PyList_New(0); + if (!empty_list) + goto bad; + list = empty_list; + } + global_dict = PyModule_GetDict(__pyx_m); + if (!global_dict) + goto bad; + empty_dict = PyDict_New(); + if (!empty_dict) + goto bad; + { + #if PY_MAJOR_VERSION >= 3 + if (level == -1) { + if (strchr(__Pyx_MODULE_NAME, '.')) { + #if PY_VERSION_HEX < 0x03030000 + PyObject *py_level = PyInt_FromLong(1); + if (!py_level) + goto bad; + module = PyObject_CallFunctionObjArgs(py_import, + name, global_dict, empty_dict, list, py_level, NULL); + Py_DECREF(py_level); + #else + module = PyImport_ImportModuleLevelObject( + name, global_dict, empty_dict, list, 1); + #endif + if (!module) { + if (!PyErr_ExceptionMatches(PyExc_ImportError)) + goto bad; + PyErr_Clear(); + } + } + level = 0; + } + #endif + if (!module) { + #if PY_VERSION_HEX < 0x03030000 + PyObject *py_level = PyInt_FromLong(level); + if (!py_level) + goto bad; + module = PyObject_CallFunctionObjArgs(py_import, + name, global_dict, empty_dict, list, py_level, NULL); + Py_DECREF(py_level); + #else + module = PyImport_ImportModuleLevelObject( + name, global_dict, empty_dict, list, level); + #endif + } + } +bad: + #if PY_VERSION_HEX < 0x03030000 + Py_XDECREF(py_import); + #endif + Py_XDECREF(empty_list); + Py_XDECREF(empty_dict); + return module; +} + +/* CodeObjectCache */ + static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) { + int start = 0, mid = 0, end = count - 1; + if (end >= 0 && code_line > entries[end].code_line) { + return count; + } + while (start < end) { + mid = start + (end - start) / 2; + if (code_line < entries[mid].code_line) { + end = mid; + } else if (code_line > entries[mid].code_line) { + start = mid + 1; + } else { + return mid; + } + } + if (code_line <= entries[mid].code_line) { + return mid; + } else { + return mid + 1; + } +} +static PyCodeObject *__pyx_find_code_object(int code_line) { + PyCodeObject* code_object; + int pos; + if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) { + return NULL; + } + pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); + if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) { + return NULL; + } + code_object = __pyx_code_cache.entries[pos].code_object; + Py_INCREF(code_object); + return code_object; +} +static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { + int pos, i; + __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries; + if (unlikely(!code_line)) { + return; + } + if (unlikely(!entries)) { + entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry)); + if (likely(entries)) { + __pyx_code_cache.entries = entries; + __pyx_code_cache.max_count = 64; + __pyx_code_cache.count = 1; + entries[0].code_line = code_line; + entries[0].code_object = code_object; + Py_INCREF(code_object); + } + return; + } + pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); + if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) { + PyCodeObject* tmp = entries[pos].code_object; + entries[pos].code_object = code_object; + Py_DECREF(tmp); + return; + } + if (__pyx_code_cache.count == __pyx_code_cache.max_count) { + int new_max = __pyx_code_cache.max_count + 64; + entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( + __pyx_code_cache.entries, (size_t)new_max*sizeof(__Pyx_CodeObjectCacheEntry)); + if (unlikely(!entries)) { + return; + } + __pyx_code_cache.entries = entries; + __pyx_code_cache.max_count = new_max; + } + for (i=__pyx_code_cache.count; i>pos; i--) { + entries[i] = entries[i-1]; + } + entries[pos].code_line = code_line; + entries[pos].code_object = code_object; + __pyx_code_cache.count++; + Py_INCREF(code_object); +} + +/* AddTraceback */ + #include "compile.h" +#include "frameobject.h" +#include "traceback.h" +static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( + const char *funcname, int c_line, + int py_line, const char *filename) { + PyCodeObject *py_code = 0; + PyObject *py_srcfile = 0; + PyObject *py_funcname = 0; + #if PY_MAJOR_VERSION < 3 + py_srcfile = PyString_FromString(filename); + #else + py_srcfile = PyUnicode_FromString(filename); + #endif + if (!py_srcfile) goto bad; + if (c_line) { + #if PY_MAJOR_VERSION < 3 + py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); + #else + py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); + #endif + } + else { + #if PY_MAJOR_VERSION < 3 + py_funcname = PyString_FromString(funcname); + #else + py_funcname = PyUnicode_FromString(funcname); + #endif + } + if (!py_funcname) goto bad; + py_code = __Pyx_PyCode_New( + 0, + 0, + 0, + 0, + 0, + __pyx_empty_bytes, /*PyObject *code,*/ + __pyx_empty_tuple, /*PyObject *consts,*/ + __pyx_empty_tuple, /*PyObject *names,*/ + __pyx_empty_tuple, /*PyObject *varnames,*/ + __pyx_empty_tuple, /*PyObject *freevars,*/ + __pyx_empty_tuple, /*PyObject *cellvars,*/ + py_srcfile, /*PyObject *filename,*/ + py_funcname, /*PyObject *name,*/ + py_line, + __pyx_empty_bytes /*PyObject *lnotab*/ + ); + Py_DECREF(py_srcfile); + Py_DECREF(py_funcname); + return py_code; +bad: + Py_XDECREF(py_srcfile); + Py_XDECREF(py_funcname); + return NULL; +} +static void __Pyx_AddTraceback(const char *funcname, int c_line, + int py_line, const char *filename) { + PyCodeObject *py_code = 0; + PyFrameObject *py_frame = 0; + py_code = __pyx_find_code_object(c_line ? c_line : py_line); + if (!py_code) { + py_code = __Pyx_CreateCodeObjectForTraceback( + funcname, c_line, py_line, filename); + if (!py_code) goto bad; + __pyx_insert_code_object(c_line ? c_line : py_line, py_code); + } + py_frame = PyFrame_New( + PyThreadState_GET(), /*PyThreadState *tstate,*/ + py_code, /*PyCodeObject *code,*/ + __pyx_d, /*PyObject *globals,*/ + 0 /*PyObject *locals*/ + ); + if (!py_frame) goto bad; + __Pyx_PyFrame_SetLineNumber(py_frame, py_line); + PyTraceBack_Here(py_frame); +bad: + Py_XDECREF(py_code); + Py_XDECREF(py_frame); +} + +#if PY_MAJOR_VERSION < 3 +static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) { + if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags); + if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) return __pyx_pw_5numpy_7ndarray_1__getbuffer__(obj, view, flags); + PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name); + return -1; +} +static void __Pyx_ReleaseBuffer(Py_buffer *view) { + PyObject *obj = view->obj; + if (!obj) return; + if (PyObject_CheckBuffer(obj)) { + PyBuffer_Release(view); + return; + } + if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) { __pyx_pw_5numpy_7ndarray_3__releasebuffer__(obj, view); return; } + Py_DECREF(obj); + view->obj = NULL; +} +#endif + + + /* CIntToPy */ + static CYTHON_INLINE PyObject* __Pyx_PyInt_From_unsigned_int(unsigned int value) { + const unsigned int neg_one = (unsigned int) -1, const_zero = (unsigned int) 0; + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(unsigned int) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(unsigned int) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(unsigned int) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); +#endif + } + } else { + if (sizeof(unsigned int) <= sizeof(long)) { + return PyInt_FromLong((long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(unsigned int) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); +#endif + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(unsigned int), + little, !is_unsigned); + } +} + +/* CIntFromPyVerify */ + #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\ + __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0) +#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\ + __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1) +#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\ + {\ + func_type value = func_value;\ + if (sizeof(target_type) < sizeof(func_type)) {\ + if (unlikely(value != (func_type) (target_type) value)) {\ + func_type zero = 0;\ + if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\ + return (target_type) -1;\ + if (is_unsigned && unlikely(value < zero))\ + goto raise_neg_overflow;\ + else\ + goto raise_overflow;\ + }\ + }\ + return (target_type) value;\ + } + +/* Declarations */ + #if CYTHON_CCOMPLEX + #ifdef __cplusplus + static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { + return ::std::complex< float >(x, y); + } + #else + static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { + return x + y*(__pyx_t_float_complex)_Complex_I; + } + #endif +#else + static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { + __pyx_t_float_complex z; + z.real = x; + z.imag = y; + return z; + } +#endif + +/* Arithmetic */ + #if CYTHON_CCOMPLEX +#else + static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + return (a.real == b.real) && (a.imag == b.imag); + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + z.real = a.real + b.real; + z.imag = a.imag + b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + z.real = a.real - b.real; + z.imag = a.imag - b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + z.real = a.real * b.real - a.imag * b.imag; + z.imag = a.real * b.imag + a.imag * b.real; + return z; + } + #if 1 + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + if (b.imag == 0) { + return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real); + } else if (fabsf(b.real) >= fabsf(b.imag)) { + if (b.real == 0 && b.imag == 0) { + return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.imag); + } else { + float r = b.imag / b.real; + float s = 1.0 / (b.real + b.imag * r); + return __pyx_t_float_complex_from_parts( + (a.real + a.imag * r) * s, (a.imag - a.real * r) * s); + } + } else { + float r = b.real / b.imag; + float s = 1.0 / (b.imag + b.real * r); + return __pyx_t_float_complex_from_parts( + (a.real * r + a.imag) * s, (a.imag * r - a.real) * s); + } + } + #else + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + if (b.imag == 0) { + return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real); + } else { + float denom = b.real * b.real + b.imag * b.imag; + return __pyx_t_float_complex_from_parts( + (a.real * b.real + a.imag * b.imag) / denom, + (a.imag * b.real - a.real * b.imag) / denom); + } + } + #endif + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex a) { + __pyx_t_float_complex z; + z.real = -a.real; + z.imag = -a.imag; + return z; + } + static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex a) { + return (a.real == 0) && (a.imag == 0); + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex a) { + __pyx_t_float_complex z; + z.real = a.real; + z.imag = -a.imag; + return z; + } + #if 1 + static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex z) { + #if !defined(HAVE_HYPOT) || defined(_MSC_VER) + return sqrtf(z.real*z.real + z.imag*z.imag); + #else + return hypotf(z.real, z.imag); + #endif + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + float r, lnr, theta, z_r, z_theta; + if (b.imag == 0 && b.real == (int)b.real) { + if (b.real < 0) { + float denom = a.real * a.real + a.imag * a.imag; + a.real = a.real / denom; + a.imag = -a.imag / denom; + b.real = -b.real; + } + switch ((int)b.real) { + case 0: + z.real = 1; + z.imag = 0; + return z; + case 1: + return a; + case 2: + z = __Pyx_c_prod_float(a, a); + return __Pyx_c_prod_float(a, a); + case 3: + z = __Pyx_c_prod_float(a, a); + return __Pyx_c_prod_float(z, a); + case 4: + z = __Pyx_c_prod_float(a, a); + return __Pyx_c_prod_float(z, z); + } + } + if (a.imag == 0) { + if (a.real == 0) { + return a; + } else if (b.imag == 0) { + z.real = powf(a.real, b.real); + z.imag = 0; + return z; + } else if (a.real > 0) { + r = a.real; + theta = 0; + } else { + r = -a.real; + theta = atan2f(0, -1); + } + } else { + r = __Pyx_c_abs_float(a); + theta = atan2f(a.imag, a.real); + } + lnr = logf(r); + z_r = expf(lnr * b.real - theta * b.imag); + z_theta = theta * b.real + lnr * b.imag; + z.real = z_r * cosf(z_theta); + z.imag = z_r * sinf(z_theta); + return z; + } + #endif +#endif + +/* Declarations */ + #if CYTHON_CCOMPLEX + #ifdef __cplusplus + static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { + return ::std::complex< double >(x, y); + } + #else + static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { + return x + y*(__pyx_t_double_complex)_Complex_I; + } + #endif +#else + static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { + __pyx_t_double_complex z; + z.real = x; + z.imag = y; + return z; + } +#endif + +/* Arithmetic */ + #if CYTHON_CCOMPLEX +#else + static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + return (a.real == b.real) && (a.imag == b.imag); + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + z.real = a.real + b.real; + z.imag = a.imag + b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + z.real = a.real - b.real; + z.imag = a.imag - b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + z.real = a.real * b.real - a.imag * b.imag; + z.imag = a.real * b.imag + a.imag * b.real; + return z; + } + #if 1 + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + if (b.imag == 0) { + return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real); + } else if (fabs(b.real) >= fabs(b.imag)) { + if (b.real == 0 && b.imag == 0) { + return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.imag); + } else { + double r = b.imag / b.real; + double s = 1.0 / (b.real + b.imag * r); + return __pyx_t_double_complex_from_parts( + (a.real + a.imag * r) * s, (a.imag - a.real * r) * s); + } + } else { + double r = b.real / b.imag; + double s = 1.0 / (b.imag + b.real * r); + return __pyx_t_double_complex_from_parts( + (a.real * r + a.imag) * s, (a.imag * r - a.real) * s); + } + } + #else + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + if (b.imag == 0) { + return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real); + } else { + double denom = b.real * b.real + b.imag * b.imag; + return __pyx_t_double_complex_from_parts( + (a.real * b.real + a.imag * b.imag) / denom, + (a.imag * b.real - a.real * b.imag) / denom); + } + } + #endif + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex a) { + __pyx_t_double_complex z; + z.real = -a.real; + z.imag = -a.imag; + return z; + } + static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex a) { + return (a.real == 0) && (a.imag == 0); + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex a) { + __pyx_t_double_complex z; + z.real = a.real; + z.imag = -a.imag; + return z; + } + #if 1 + static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex z) { + #if !defined(HAVE_HYPOT) || defined(_MSC_VER) + return sqrt(z.real*z.real + z.imag*z.imag); + #else + return hypot(z.real, z.imag); + #endif + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + double r, lnr, theta, z_r, z_theta; + if (b.imag == 0 && b.real == (int)b.real) { + if (b.real < 0) { + double denom = a.real * a.real + a.imag * a.imag; + a.real = a.real / denom; + a.imag = -a.imag / denom; + b.real = -b.real; + } + switch ((int)b.real) { + case 0: + z.real = 1; + z.imag = 0; + return z; + case 1: + return a; + case 2: + z = __Pyx_c_prod_double(a, a); + return __Pyx_c_prod_double(a, a); + case 3: + z = __Pyx_c_prod_double(a, a); + return __Pyx_c_prod_double(z, a); + case 4: + z = __Pyx_c_prod_double(a, a); + return __Pyx_c_prod_double(z, z); + } + } + if (a.imag == 0) { + if (a.real == 0) { + return a; + } else if (b.imag == 0) { + z.real = pow(a.real, b.real); + z.imag = 0; + return z; + } else if (a.real > 0) { + r = a.real; + theta = 0; + } else { + r = -a.real; + theta = atan2(0, -1); + } + } else { + r = __Pyx_c_abs_double(a); + theta = atan2(a.imag, a.real); + } + lnr = log(r); + z_r = exp(lnr * b.real - theta * b.imag); + z_theta = theta * b.real + lnr * b.imag; + z.real = z_r * cos(z_theta); + z.imag = z_r * sin(z_theta); + return z; + } + #endif +#endif + +/* CIntToPy */ + static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) { + const int neg_one = (int) -1, const_zero = (int) 0; + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(int) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(int) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); +#endif + } + } else { + if (sizeof(int) <= sizeof(long)) { + return PyInt_FromLong((long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); +#endif + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(int), + little, !is_unsigned); + } +} + +/* CIntToPy */ + static CYTHON_INLINE PyObject* __Pyx_PyInt_From_enum__NPY_TYPES(enum NPY_TYPES value) { + const enum NPY_TYPES neg_one = (enum NPY_TYPES) -1, const_zero = (enum NPY_TYPES) 0; + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(enum NPY_TYPES) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(enum NPY_TYPES) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(enum NPY_TYPES) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); +#endif + } + } else { + if (sizeof(enum NPY_TYPES) <= sizeof(long)) { + return PyInt_FromLong((long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(enum NPY_TYPES) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); +#endif + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(enum NPY_TYPES), + little, !is_unsigned); + } +} + +/* CIntFromPy */ + static CYTHON_INLINE unsigned int __Pyx_PyInt_As_unsigned_int(PyObject *x) { + const unsigned int neg_one = (unsigned int) -1, const_zero = (unsigned int) 0; + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if (sizeof(unsigned int) < sizeof(long)) { + __PYX_VERIFY_RETURN_INT(unsigned int, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (unsigned int) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (unsigned int) 0; + case 1: __PYX_VERIFY_RETURN_INT(unsigned int, digit, digits[0]) + case 2: + if (8 * sizeof(unsigned int) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(unsigned int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(unsigned int) >= 2 * PyLong_SHIFT) { + return (unsigned int) (((((unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0])); + } + } + break; + case 3: + if (8 * sizeof(unsigned int) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(unsigned int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(unsigned int) >= 3 * PyLong_SHIFT) { + return (unsigned int) (((((((unsigned int)digits[2]) << PyLong_SHIFT) | (unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0])); + } + } + break; + case 4: + if (8 * sizeof(unsigned int) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(unsigned int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(unsigned int) >= 4 * PyLong_SHIFT) { + return (unsigned int) (((((((((unsigned int)digits[3]) << PyLong_SHIFT) | (unsigned int)digits[2]) << PyLong_SHIFT) | (unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0])); + } + } + break; + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (unsigned int) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if (sizeof(unsigned int) <= sizeof(unsigned long)) { + __PYX_VERIFY_RETURN_INT_EXC(unsigned int, unsigned long, PyLong_AsUnsignedLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(unsigned int) <= sizeof(unsigned PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(unsigned int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) +#endif + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (unsigned int) 0; + case -1: __PYX_VERIFY_RETURN_INT(unsigned int, sdigit, (sdigit) (-(sdigit)digits[0])) + case 1: __PYX_VERIFY_RETURN_INT(unsigned int, digit, +digits[0]) + case -2: + if (8 * sizeof(unsigned int) - 1 > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(unsigned int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(unsigned int) - 1 > 2 * PyLong_SHIFT) { + return (unsigned int) (((unsigned int)-1)*(((((unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0]))); + } + } + break; + case 2: + if (8 * sizeof(unsigned int) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(unsigned int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(unsigned int) - 1 > 2 * PyLong_SHIFT) { + return (unsigned int) ((((((unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0]))); + } + } + break; + case -3: + if (8 * sizeof(unsigned int) - 1 > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(unsigned int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(unsigned int) - 1 > 3 * PyLong_SHIFT) { + return (unsigned int) (((unsigned int)-1)*(((((((unsigned int)digits[2]) << PyLong_SHIFT) | (unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0]))); + } + } + break; + case 3: + if (8 * sizeof(unsigned int) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(unsigned int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(unsigned int) - 1 > 3 * PyLong_SHIFT) { + return (unsigned int) ((((((((unsigned int)digits[2]) << PyLong_SHIFT) | (unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0]))); + } + } + break; + case -4: + if (8 * sizeof(unsigned int) - 1 > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(unsigned int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(unsigned int) - 1 > 4 * PyLong_SHIFT) { + return (unsigned int) (((unsigned int)-1)*(((((((((unsigned int)digits[3]) << PyLong_SHIFT) | (unsigned int)digits[2]) << PyLong_SHIFT) | (unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0]))); + } + } + break; + case 4: + if (8 * sizeof(unsigned int) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(unsigned int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(unsigned int) - 1 > 4 * PyLong_SHIFT) { + return (unsigned int) ((((((((((unsigned int)digits[3]) << PyLong_SHIFT) | (unsigned int)digits[2]) << PyLong_SHIFT) | (unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0]))); + } + } + break; + } +#endif + if (sizeof(unsigned int) <= sizeof(long)) { + __PYX_VERIFY_RETURN_INT_EXC(unsigned int, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(unsigned int) <= sizeof(PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(unsigned int, PY_LONG_LONG, PyLong_AsLongLong(x)) +#endif + } + } + { +#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) + PyErr_SetString(PyExc_RuntimeError, + "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); +#else + unsigned int val; + PyObject *v = __Pyx_PyNumber_IntOrLong(x); + #if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } + #endif + if (likely(v)) { + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + int ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); + Py_DECREF(v); + if (likely(!ret)) + return val; + } +#endif + return (unsigned int) -1; + } + } else { + unsigned int val; + PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); + if (!tmp) return (unsigned int) -1; + val = __Pyx_PyInt_As_unsigned_int(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to unsigned int"); + return (unsigned int) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to unsigned int"); + return (unsigned int) -1; +} + +/* CIntFromPy */ + static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { + const int neg_one = (int) -1, const_zero = (int) 0; + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if (sizeof(int) < sizeof(long)) { + __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (int) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (int) 0; + case 1: __PYX_VERIFY_RETURN_INT(int, digit, digits[0]) + case 2: + if (8 * sizeof(int) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) >= 2 * PyLong_SHIFT) { + return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + case 3: + if (8 * sizeof(int) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) >= 3 * PyLong_SHIFT) { + return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + case 4: + if (8 * sizeof(int) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) >= 4 * PyLong_SHIFT) { + return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (int) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if (sizeof(int) <= sizeof(unsigned long)) { + __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) +#endif + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (int) 0; + case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, (sdigit) (-(sdigit)digits[0])) + case 1: __PYX_VERIFY_RETURN_INT(int, digit, +digits[0]) + case -2: + if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { + return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 2: + if (8 * sizeof(int) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { + return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case -3: + if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { + return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 3: + if (8 * sizeof(int) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { + return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case -4: + if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { + return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 4: + if (8 * sizeof(int) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { + return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + } +#endif + if (sizeof(int) <= sizeof(long)) { + __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x)) +#endif + } + } + { +#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) + PyErr_SetString(PyExc_RuntimeError, + "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); +#else + int val; + PyObject *v = __Pyx_PyNumber_IntOrLong(x); + #if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } + #endif + if (likely(v)) { + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + int ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); + Py_DECREF(v); + if (likely(!ret)) + return val; + } +#endif + return (int) -1; + } + } else { + int val; + PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); + if (!tmp) return (int) -1; + val = __Pyx_PyInt_As_int(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to int"); + return (int) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to int"); + return (int) -1; +} + +/* CIntToPy */ + static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { + const long neg_one = (long) -1, const_zero = (long) 0; + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(long) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(long) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); +#endif + } + } else { + if (sizeof(long) <= sizeof(long)) { + return PyInt_FromLong((long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); +#endif + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(long), + little, !is_unsigned); + } +} + +/* CIntFromPy */ + static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { + const long neg_one = (long) -1, const_zero = (long) 0; + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if (sizeof(long) < sizeof(long)) { + __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (long) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (long) 0; + case 1: __PYX_VERIFY_RETURN_INT(long, digit, digits[0]) + case 2: + if (8 * sizeof(long) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) >= 2 * PyLong_SHIFT) { + return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + case 3: + if (8 * sizeof(long) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) >= 3 * PyLong_SHIFT) { + return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + case 4: + if (8 * sizeof(long) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) >= 4 * PyLong_SHIFT) { + return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (long) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if (sizeof(long) <= sizeof(unsigned long)) { + __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) +#endif + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (long) 0; + case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, (sdigit) (-(sdigit)digits[0])) + case 1: __PYX_VERIFY_RETURN_INT(long, digit, +digits[0]) + case -2: + if (8 * sizeof(long) - 1 > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 2: + if (8 * sizeof(long) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case -3: + if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 3: + if (8 * sizeof(long) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case -4: + if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 4: + if (8 * sizeof(long) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + } +#endif + if (sizeof(long) <= sizeof(long)) { + __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x)) +#endif + } + } + { +#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) + PyErr_SetString(PyExc_RuntimeError, + "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); +#else + long val; + PyObject *v = __Pyx_PyNumber_IntOrLong(x); + #if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } + #endif + if (likely(v)) { + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + int ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); + Py_DECREF(v); + if (likely(!ret)) + return val; + } +#endif + return (long) -1; + } + } else { + long val; + PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); + if (!tmp) return (long) -1; + val = __Pyx_PyInt_As_long(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to long"); + return (long) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to long"); + return (long) -1; +} + +/* CheckBinaryVersion */ + static int __Pyx_check_binary_version(void) { + char ctversion[4], rtversion[4]; + PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); + PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion()); + if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) { + char message[200]; + PyOS_snprintf(message, sizeof(message), + "compiletime version %s of module '%.100s' " + "does not match runtime version %s", + ctversion, __Pyx_MODULE_NAME, rtversion); + return PyErr_WarnEx(NULL, message, 1); + } + return 0; +} + +/* ModuleImport */ + #ifndef __PYX_HAVE_RT_ImportModule +#define __PYX_HAVE_RT_ImportModule +static PyObject *__Pyx_ImportModule(const char *name) { + PyObject *py_name = 0; + PyObject *py_module = 0; + py_name = __Pyx_PyIdentifier_FromString(name); + if (!py_name) + goto bad; + py_module = PyImport_Import(py_name); + Py_DECREF(py_name); + return py_module; +bad: + Py_XDECREF(py_name); + return 0; +} +#endif + +/* TypeImport */ + #ifndef __PYX_HAVE_RT_ImportType +#define __PYX_HAVE_RT_ImportType +static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, + size_t size, int strict) +{ + PyObject *py_module = 0; + PyObject *result = 0; + PyObject *py_name = 0; + char warning[200]; + Py_ssize_t basicsize; +#ifdef Py_LIMITED_API + PyObject *py_basicsize; +#endif + py_module = __Pyx_ImportModule(module_name); + if (!py_module) + goto bad; + py_name = __Pyx_PyIdentifier_FromString(class_name); + if (!py_name) + goto bad; + result = PyObject_GetAttr(py_module, py_name); + Py_DECREF(py_name); + py_name = 0; + Py_DECREF(py_module); + py_module = 0; + if (!result) + goto bad; + if (!PyType_Check(result)) { + PyErr_Format(PyExc_TypeError, + "%.200s.%.200s is not a type object", + module_name, class_name); + goto bad; + } +#ifndef Py_LIMITED_API + basicsize = ((PyTypeObject *)result)->tp_basicsize; +#else + py_basicsize = PyObject_GetAttrString(result, "__basicsize__"); + if (!py_basicsize) + goto bad; + basicsize = PyLong_AsSsize_t(py_basicsize); + Py_DECREF(py_basicsize); + py_basicsize = 0; + if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred()) + goto bad; +#endif + if (!strict && (size_t)basicsize > size) { + PyOS_snprintf(warning, sizeof(warning), + "%s.%s size changed, may indicate binary incompatibility. Expected %zd, got %zd", + module_name, class_name, basicsize, size); + if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad; + } + else if ((size_t)basicsize != size) { + PyErr_Format(PyExc_ValueError, + "%.200s.%.200s has the wrong size, try recompiling. Expected %zd, got %zd", + module_name, class_name, basicsize, size); + goto bad; + } + return (PyTypeObject *)result; +bad: + Py_XDECREF(py_module); + Py_XDECREF(result); + return NULL; +} +#endif + +/* InitStrings */ + static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { + while (t->p) { + #if PY_MAJOR_VERSION < 3 + if (t->is_unicode) { + *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); + } else if (t->intern) { + *t->p = PyString_InternFromString(t->s); + } else { + *t->p = PyString_FromStringAndSize(t->s, t->n - 1); + } + #else + if (t->is_unicode | t->is_str) { + if (t->intern) { + *t->p = PyUnicode_InternFromString(t->s); + } else if (t->encoding) { + *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL); + } else { + *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); + } + } else { + *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1); + } + #endif + if (!*t->p) + return -1; + ++t; + } + return 0; +} + +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) { + return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str)); +} +static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject* o) { + Py_ssize_t ignore; + return __Pyx_PyObject_AsStringAndSize(o, &ignore); +} +static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) { +#if CYTHON_COMPILING_IN_CPYTHON && (__PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT) + if ( +#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + __Pyx_sys_getdefaultencoding_not_ascii && +#endif + PyUnicode_Check(o)) { +#if PY_VERSION_HEX < 0x03030000 + char* defenc_c; + PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL); + if (!defenc) return NULL; + defenc_c = PyBytes_AS_STRING(defenc); +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + { + char* end = defenc_c + PyBytes_GET_SIZE(defenc); + char* c; + for (c = defenc_c; c < end; c++) { + if ((unsigned char) (*c) >= 128) { + PyUnicode_AsASCIIString(o); + return NULL; + } + } + } +#endif + *length = PyBytes_GET_SIZE(defenc); + return defenc_c; +#else + if (__Pyx_PyUnicode_READY(o) == -1) return NULL; +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + if (PyUnicode_IS_ASCII(o)) { + *length = PyUnicode_GET_LENGTH(o); + return PyUnicode_AsUTF8(o); + } else { + PyUnicode_AsASCIIString(o); + return NULL; + } +#else + return PyUnicode_AsUTF8AndSize(o, length); +#endif +#endif + } else +#endif +#if (!CYTHON_COMPILING_IN_PYPY) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE)) + if (PyByteArray_Check(o)) { + *length = PyByteArray_GET_SIZE(o); + return PyByteArray_AS_STRING(o); + } else +#endif + { + char* result; + int r = PyBytes_AsStringAndSize(o, &result, length); + if (unlikely(r < 0)) { + return NULL; + } else { + return result; + } + } +} +static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { + int is_true = x == Py_True; + if (is_true | (x == Py_False) | (x == Py_None)) return is_true; + else return PyObject_IsTrue(x); +} +static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) { +#if CYTHON_USE_TYPE_SLOTS + PyNumberMethods *m; +#endif + const char *name = NULL; + PyObject *res = NULL; +#if PY_MAJOR_VERSION < 3 + if (PyInt_Check(x) || PyLong_Check(x)) +#else + if (PyLong_Check(x)) +#endif + return __Pyx_NewRef(x); +#if CYTHON_USE_TYPE_SLOTS + m = Py_TYPE(x)->tp_as_number; + #if PY_MAJOR_VERSION < 3 + if (m && m->nb_int) { + name = "int"; + res = PyNumber_Int(x); + } + else if (m && m->nb_long) { + name = "long"; + res = PyNumber_Long(x); + } + #else + if (m && m->nb_int) { + name = "int"; + res = PyNumber_Long(x); + } + #endif +#else + res = PyNumber_Int(x); +#endif + if (res) { +#if PY_MAJOR_VERSION < 3 + if (!PyInt_Check(res) && !PyLong_Check(res)) { +#else + if (!PyLong_Check(res)) { +#endif + PyErr_Format(PyExc_TypeError, + "__%.4s__ returned non-%.4s (type %.200s)", + name, name, Py_TYPE(res)->tp_name); + Py_DECREF(res); + return NULL; + } + } + else if (!PyErr_Occurred()) { + PyErr_SetString(PyExc_TypeError, + "an integer is required"); + } + return res; +} +static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { + Py_ssize_t ival; + PyObject *x; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_CheckExact(b))) { + if (sizeof(Py_ssize_t) >= sizeof(long)) + return PyInt_AS_LONG(b); + else + return PyInt_AsSsize_t(x); + } +#endif + if (likely(PyLong_CheckExact(b))) { + #if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)b)->ob_digit; + const Py_ssize_t size = Py_SIZE(b); + if (likely(__Pyx_sst_abs(size) <= 1)) { + ival = likely(size) ? digits[0] : 0; + if (size == -1) ival = -ival; + return ival; + } else { + switch (size) { + case 2: + if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { + return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -2: + if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case 3: + if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { + return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -3: + if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case 4: + if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { + return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -4: + if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + } + } + #endif + return PyLong_AsSsize_t(b); + } + x = PyNumber_Index(b); + if (!x) return -1; + ival = PyInt_AsSsize_t(x); + Py_DECREF(x); + return ival; +} +static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { + return PyInt_FromSize_t(ival); +} + + +#endif /* Py_PYTHON_H */ diff --git a/libs/box_utils/cython_utils/bbox.pyx b/libs/box_utils/cython_utils/bbox.pyx new file mode 100644 index 0000000..5b82bdf --- /dev/null +++ b/libs/box_utils/cython_utils/bbox.pyx @@ -0,0 +1,249 @@ +# -------------------------------------------------------- +# Fast R-CNN +# Copyright (c) 2015 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Sergey Karayev +# -------------------------------------------------------- + +cimport cython +import numpy as np +cimport numpy as np + +DTYPE = np.float +ctypedef np.float_t DTYPE_t + +def bbox_overlaps_float( + np.ndarray[DTYPE_t, ndim=2] boxes, + np.ndarray[DTYPE_t, ndim=2] query_boxes): + """ + Parameters + ---------- + boxes: (N, 4) ndarray of float + query_boxes: (K, 4) ndarray of float + Returns + ------- + overlaps: (N, K) ndarray of overlap between boxes and query_boxes + """ + cdef unsigned int N = boxes.shape[0] + cdef unsigned int K = query_boxes.shape[0] + cdef np.ndarray[DTYPE_t, ndim=2] overlaps = np.zeros((N, K), dtype=DTYPE) + cdef DTYPE_t iw, ih, box_area + cdef DTYPE_t ua + cdef unsigned int k, n + for k in range(K): + box_area = ( + (query_boxes[k, 2] - query_boxes[k, 0]) * + (query_boxes[k, 3] - query_boxes[k, 1]) + ) + for n in range(N): + iw = ( + min(boxes[n, 2], query_boxes[k, 2]) - + max(boxes[n, 0], query_boxes[k, 0]) + ) + if iw > 0: + ih = ( + min(boxes[n, 3], query_boxes[k, 3]) - + max(boxes[n, 1], query_boxes[k, 1]) + ) + if ih > 0: + ua = float( + (boxes[n, 2] - boxes[n, 0]) * + (boxes[n, 3] - boxes[n, 1]) + + box_area - iw * ih + ) + # if query_boxes[k, 4] == -1: + # ua = float((boxes[n, 2] - boxes[n, 0]) + # *(boxes[n, 3] - boxes[n, 1])) + # else: + # ua = float( + # (boxes[n, 2] - boxes[n, 0]) * + # (boxes[n, 3] - boxes[n, 1]) + + # box_area - iw * ih + # ) + overlaps[n, k] = iw * ih / ua + return overlaps + +def bbox_overlaps( + np.ndarray[DTYPE_t, ndim=2] boxes, + np.ndarray[DTYPE_t, ndim=2] query_boxes): + """ + Parameters + ---------- + boxes: (N, 4) ndarray of float + query_boxes: (K, 4) ndarray of float + Returns + ------- + overlaps: (N, K) ndarray of overlap between boxes and query_boxes + """ + cdef unsigned int N = boxes.shape[0] + cdef unsigned int K = query_boxes.shape[0] + cdef np.ndarray[DTYPE_t, ndim=2] overlaps = np.zeros((N, K), dtype=DTYPE) + cdef DTYPE_t iw, ih, box_area + cdef DTYPE_t ua + cdef unsigned int k, n + for k in range(K): + box_area = ( + (query_boxes[k, 2] - query_boxes[k, 0] + 1) * + (query_boxes[k, 3] - query_boxes[k, 1] + 1) + ) + for n in range(N): + iw = ( + min(boxes[n, 2], query_boxes[k, 2]) - + max(boxes[n, 0], query_boxes[k, 0]) + 1 + ) + if iw > 0: + ih = ( + min(boxes[n, 3], query_boxes[k, 3]) - + max(boxes[n, 1], query_boxes[k, 1]) + 1 + ) + if ih > 0: + ua = float( + (boxes[n, 2] - boxes[n, 0] + 1) * + (boxes[n, 3] - boxes[n, 1] + 1) + + box_area - iw * ih + ) + overlaps[n, k] = iw * ih / ua + return overlaps + +def bbox_overlaps_self( + np.ndarray[DTYPE_t, ndim=2] boxes, + np.ndarray[DTYPE_t, ndim=2] query_boxes): + """ + Parameters + ---------- + boxes: (N, 4) ndarray of float + query_boxes: (K, 4) ndarray of float + Returns + ------- + overlaps: (N, K) ndarray of overlap between boxes and query_boxes + """ + cdef unsigned int N = boxes.shape[0] + cdef unsigned int K = query_boxes.shape[0] + cdef np.ndarray[DTYPE_t, ndim=2] overlaps = np.zeros((N, K), dtype=DTYPE) + cdef DTYPE_t iw, ih, box_area + cdef DTYPE_t ua + cdef unsigned int k, n + for k in range(K): + box_area = ( + (query_boxes[k, 2] - query_boxes[k, 0] + 1) * + (query_boxes[k, 3] - query_boxes[k, 1] + 1) + ) + for n in range(N): + iw = ( + min(boxes[n, 2], query_boxes[k, 2]) - + max(boxes[n, 0], query_boxes[k, 0]) + 1 + ) + if iw > 0: + ih = ( + min(boxes[n, 3], query_boxes[k, 3]) - + max(boxes[n, 1], query_boxes[k, 1]) + 1 + ) + if ih > 0: + ua = float(box_area) + overlaps[n, k] = iw * ih / ua + return overlaps + + +def bbox_overlaps_ignore( + np.ndarray[DTYPE_t, ndim=2] boxes, + np.ndarray[DTYPE_t, ndim=2] query_boxes): + """ + Parameters + ---------- + boxes: (N, 4) ndarray of float + query_boxes: (K, 4) ndarray of float + Returns + ------- + overlaps: (N, K) ndarray of overlap between boxes and query_boxes + """ + cdef unsigned int N = boxes.shape[0] + cdef unsigned int K = query_boxes.shape[0] + cdef np.ndarray[DTYPE_t, ndim=2] overlaps = np.zeros((N, K), dtype=DTYPE) + cdef DTYPE_t iw, ih, box_area + cdef DTYPE_t ua + cdef unsigned int k, n + for k in range(K): + box_area = ( + (query_boxes[k, 2] - query_boxes[k, 0] + 1) * + (query_boxes[k, 3] - query_boxes[k, 1] + 1) + ) + for n in range(N): + iw = ( + min(boxes[n, 2], query_boxes[k, 2]) - + max(boxes[n, 0], query_boxes[k, 0]) + 1 + ) + if iw > 0: + ih = ( + min(boxes[n, 3], query_boxes[k, 3]) - + max(boxes[n, 1], query_boxes[k, 1]) + 1 + ) + if ih > 0: + if query_boxes[k, 4] == -1: + ua = float((boxes[n, 2] - boxes[n, 0] + 1) + *(boxes[n, 3] - boxes[n, 1] + 1)) + else: + ua = float( + (boxes[n, 2] - boxes[n, 0] + 1) * + (boxes[n, 3] - boxes[n, 1] + 1) + + box_area - iw * ih + ) + overlaps[n, k] = iw * ih / ua + return overlaps + + +def get_assignment_overlaps( + np.ndarray[DTYPE_t, ndim=2] boxes, + np.ndarray[DTYPE_t, ndim=2] query_boxes, + DTYPE_t FG_THRESH): + """ Used for proposal_target_layer_ignore + Parameters + ---------- + boxes: (N, 4) ndarray of float + query_boxes: (K, 4) ndarray of float + Returns + ------- + overlaps: (N, K) ndarray of overlap between boxes and query_boxes + """ + cdef unsigned int N = boxes.shape[0] + cdef unsigned int K = query_boxes.shape[0] + cdef np.ndarray[long, ndim=1] gt_assignment = np.zeros((N,), dtype=np.int) + cdef np.ndarray[DTYPE_t, ndim=1] max_overlaps = np.zeros((N,), dtype=DTYPE) + cdef DTYPE_t iw, ih, box_area + cdef DTYPE_t ua + cdef unsigned int k, n + for k in range(K): + box_area = ( + (query_boxes[k, 2] - query_boxes[k, 0] + 1) * + (query_boxes[k, 3] - query_boxes[k, 1] + 1) + ) + for n in range(N): + iw = ( + min(boxes[n, 2], query_boxes[k, 2]) - + max(boxes[n, 0], query_boxes[k, 0]) + 1 + ) + if iw > 0: + ih = ( + min(boxes[n, 3], query_boxes[k, 3]) - + max(boxes[n, 1], query_boxes[k, 1]) + 1 + ) + if ih > 0: + if query_boxes[k, 4] == -1: + ua = float((boxes[n, 2] - boxes[n, 0] + 1) + *(boxes[n, 3] - boxes[n, 1] + 1)) + overlap = iw * ih / ua + if overlap > max_overlaps[n]: + if query_boxes[gt_assignment[n], 4] == -1 or max_overlaps[n] < FG_THRESH: + max_overlaps[n] = overlap + gt_assignment[n] = k + else: + ua = float( + (boxes[n, 2] - boxes[n, 0] + 1) * + (boxes[n, 3] - boxes[n, 1] + 1) + + box_area - iw * ih + ) + overlap = iw * ih / ua + if overlap > max_overlaps[n]: + max_overlaps[n] = overlap + gt_assignment[n] = k + #overlaps[n, k] = overlap + return gt_assignment, max_overlaps diff --git a/libs/box_utils/cython_utils/nms.c b/libs/box_utils/cython_utils/nms.c new file mode 100644 index 0000000..54934ea --- /dev/null +++ b/libs/box_utils/cython_utils/nms.c @@ -0,0 +1,9561 @@ +/* Generated by Cython 0.25.2 */ + +#define PY_SSIZE_T_CLEAN +#include "Python.h" +#ifndef Py_PYTHON_H + #error Python headers needed to compile C extensions, please install development version of Python. +#elif PY_VERSION_HEX < 0x02060000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03020000) + #error Cython requires Python 2.6+ or Python 3.2+. +#else +#define CYTHON_ABI "0_25_2" +#include +#ifndef offsetof + #define offsetof(type, member) ( (size_t) & ((type*)0) -> member ) +#endif +#if !defined(WIN32) && !defined(MS_WINDOWS) + #ifndef __stdcall + #define __stdcall + #endif + #ifndef __cdecl + #define __cdecl + #endif + #ifndef __fastcall + #define __fastcall + #endif +#endif +#ifndef DL_IMPORT + #define DL_IMPORT(t) t +#endif +#ifndef DL_EXPORT + #define DL_EXPORT(t) t +#endif +#ifndef HAVE_LONG_LONG + #if PY_VERSION_HEX >= 0x03030000 || (PY_MAJOR_VERSION == 2 && PY_VERSION_HEX >= 0x02070000) + #define HAVE_LONG_LONG + #endif +#endif +#ifndef PY_LONG_LONG + #define PY_LONG_LONG LONG_LONG +#endif +#ifndef Py_HUGE_VAL + #define Py_HUGE_VAL HUGE_VAL +#endif +#ifdef PYPY_VERSION + #define CYTHON_COMPILING_IN_PYPY 1 + #define CYTHON_COMPILING_IN_PYSTON 0 + #define CYTHON_COMPILING_IN_CPYTHON 0 + #undef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 0 + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #undef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 0 + #undef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 0 + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #undef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 1 + #undef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 0 + #undef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 0 + #undef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 0 + #undef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 0 +#elif defined(PYSTON_VERSION) + #define CYTHON_COMPILING_IN_PYPY 0 + #define CYTHON_COMPILING_IN_PYSTON 1 + #define CYTHON_COMPILING_IN_CPYTHON 0 + #ifndef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 1 + #endif + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #undef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 0 + #ifndef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 1 + #endif + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #ifndef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 0 + #endif + #ifndef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 1 + #endif + #ifndef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 1 + #endif + #undef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 0 + #undef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 0 +#else + #define CYTHON_COMPILING_IN_PYPY 0 + #define CYTHON_COMPILING_IN_PYSTON 0 + #define CYTHON_COMPILING_IN_CPYTHON 1 + #ifndef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 1 + #endif + #if PY_MAJOR_VERSION < 3 + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #elif !defined(CYTHON_USE_ASYNC_SLOTS) + #define CYTHON_USE_ASYNC_SLOTS 1 + #endif + #if PY_VERSION_HEX < 0x02070000 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #elif !defined(CYTHON_USE_PYLONG_INTERNALS) + #define CYTHON_USE_PYLONG_INTERNALS 1 + #endif + #ifndef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 1 + #endif + #ifndef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 1 + #endif + #if PY_VERSION_HEX < 0x030300F0 + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #elif !defined(CYTHON_USE_UNICODE_WRITER) + #define CYTHON_USE_UNICODE_WRITER 1 + #endif + #ifndef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 0 + #endif + #ifndef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 1 + #endif + #ifndef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 1 + #endif + #ifndef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 1 + #endif + #ifndef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 1 + #endif +#endif +#if !defined(CYTHON_FAST_PYCCALL) +#define CYTHON_FAST_PYCCALL (CYTHON_FAST_PYCALL && PY_VERSION_HEX >= 0x030600B1) +#endif +#if CYTHON_USE_PYLONG_INTERNALS + #include "longintrepr.h" + #undef SHIFT + #undef BASE + #undef MASK +#endif +#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 && !defined(Py_OptimizeFlag) + #define Py_OptimizeFlag 0 +#endif +#define __PYX_BUILD_PY_SSIZE_T "n" +#define CYTHON_FORMAT_SSIZE_T "z" +#if PY_MAJOR_VERSION < 3 + #define __Pyx_BUILTIN_MODULE_NAME "__builtin__" + #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ + PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) + #define __Pyx_DefaultClassType PyClass_Type +#else + #define __Pyx_BUILTIN_MODULE_NAME "builtins" + #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ + PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) + #define __Pyx_DefaultClassType PyType_Type +#endif +#ifndef Py_TPFLAGS_CHECKTYPES + #define Py_TPFLAGS_CHECKTYPES 0 +#endif +#ifndef Py_TPFLAGS_HAVE_INDEX + #define Py_TPFLAGS_HAVE_INDEX 0 +#endif +#ifndef Py_TPFLAGS_HAVE_NEWBUFFER + #define Py_TPFLAGS_HAVE_NEWBUFFER 0 +#endif +#ifndef Py_TPFLAGS_HAVE_FINALIZE + #define Py_TPFLAGS_HAVE_FINALIZE 0 +#endif +#ifndef METH_FASTCALL + #define METH_FASTCALL 0x80 + typedef PyObject *(*__Pyx_PyCFunctionFast) (PyObject *self, PyObject **args, + Py_ssize_t nargs, PyObject *kwnames); +#else + #define __Pyx_PyCFunctionFast _PyCFunctionFast +#endif +#if CYTHON_FAST_PYCCALL +#define __Pyx_PyFastCFunction_Check(func)\ + ((PyCFunction_Check(func) && (METH_FASTCALL == (PyCFunction_GET_FLAGS(func) & ~(METH_CLASS | METH_STATIC | METH_COEXIST))))) +#else +#define __Pyx_PyFastCFunction_Check(func) 0 +#endif +#if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND) + #define CYTHON_PEP393_ENABLED 1 + #define __Pyx_PyUnicode_READY(op) (likely(PyUnicode_IS_READY(op)) ?\ + 0 : _PyUnicode_Ready((PyObject *)(op))) + #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_LENGTH(u) + #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i) + #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) PyUnicode_MAX_CHAR_VALUE(u) + #define __Pyx_PyUnicode_KIND(u) PyUnicode_KIND(u) + #define __Pyx_PyUnicode_DATA(u) PyUnicode_DATA(u) + #define __Pyx_PyUnicode_READ(k, d, i) PyUnicode_READ(k, d, i) + #define __Pyx_PyUnicode_WRITE(k, d, i, ch) PyUnicode_WRITE(k, d, i, ch) + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : PyUnicode_GET_SIZE(u))) +#else + #define CYTHON_PEP393_ENABLED 0 + #define PyUnicode_1BYTE_KIND 1 + #define PyUnicode_2BYTE_KIND 2 + #define PyUnicode_4BYTE_KIND 4 + #define __Pyx_PyUnicode_READY(op) (0) + #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_SIZE(u) + #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i])) + #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) ((sizeof(Py_UNICODE) == 2) ? 65535 : 1114111) + #define __Pyx_PyUnicode_KIND(u) (sizeof(Py_UNICODE)) + #define __Pyx_PyUnicode_DATA(u) ((void*)PyUnicode_AS_UNICODE(u)) + #define __Pyx_PyUnicode_READ(k, d, i) ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i])) + #define __Pyx_PyUnicode_WRITE(k, d, i, ch) (((void)(k)), ((Py_UNICODE*)d)[i] = ch) + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GET_SIZE(u)) +#endif +#if CYTHON_COMPILING_IN_PYPY + #define __Pyx_PyUnicode_Concat(a, b) PyNumber_Add(a, b) + #define __Pyx_PyUnicode_ConcatSafe(a, b) PyNumber_Add(a, b) +#else + #define __Pyx_PyUnicode_Concat(a, b) PyUnicode_Concat(a, b) + #define __Pyx_PyUnicode_ConcatSafe(a, b) ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ?\ + PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b)) +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyUnicode_Contains) + #define PyUnicode_Contains(u, s) PySequence_Contains(u, s) +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyByteArray_Check) + #define PyByteArray_Check(obj) PyObject_TypeCheck(obj, &PyByteArray_Type) +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Format) + #define PyObject_Format(obj, fmt) PyObject_CallMethod(obj, "__format__", "O", fmt) +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Malloc) + #define PyObject_Malloc(s) PyMem_Malloc(s) + #define PyObject_Free(p) PyMem_Free(p) + #define PyObject_Realloc(p) PyMem_Realloc(p) +#endif +#if CYTHON_COMPILING_IN_PYSTON + #define __Pyx_PyCode_HasFreeVars(co) PyCode_HasFreeVars(co) + #define __Pyx_PyFrame_SetLineNumber(frame, lineno) PyFrame_SetLineNumber(frame, lineno) +#else + #define __Pyx_PyCode_HasFreeVars(co) (PyCode_GetNumFree(co) > 0) + #define __Pyx_PyFrame_SetLineNumber(frame, lineno) (frame)->f_lineno = (lineno) +#endif +#define __Pyx_PyString_FormatSafe(a, b) ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b)) +#define __Pyx_PyUnicode_FormatSafe(a, b) ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b)) +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyString_Format(a, b) PyUnicode_Format(a, b) +#else + #define __Pyx_PyString_Format(a, b) PyString_Format(a, b) +#endif +#if PY_MAJOR_VERSION < 3 && !defined(PyObject_ASCII) + #define PyObject_ASCII(o) PyObject_Repr(o) +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyBaseString_Type PyUnicode_Type + #define PyStringObject PyUnicodeObject + #define PyString_Type PyUnicode_Type + #define PyString_Check PyUnicode_Check + #define PyString_CheckExact PyUnicode_CheckExact +#endif +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj) + #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj) +#else + #define __Pyx_PyBaseString_Check(obj) (PyString_Check(obj) || PyUnicode_Check(obj)) + #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj)) +#endif +#ifndef PySet_CheckExact + #define PySet_CheckExact(obj) (Py_TYPE(obj) == &PySet_Type) +#endif +#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type) +#define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception) +#if PY_MAJOR_VERSION >= 3 + #define PyIntObject PyLongObject + #define PyInt_Type PyLong_Type + #define PyInt_Check(op) PyLong_Check(op) + #define PyInt_CheckExact(op) PyLong_CheckExact(op) + #define PyInt_FromString PyLong_FromString + #define PyInt_FromUnicode PyLong_FromUnicode + #define PyInt_FromLong PyLong_FromLong + #define PyInt_FromSize_t PyLong_FromSize_t + #define PyInt_FromSsize_t PyLong_FromSsize_t + #define PyInt_AsLong PyLong_AsLong + #define PyInt_AS_LONG PyLong_AS_LONG + #define PyInt_AsSsize_t PyLong_AsSsize_t + #define PyInt_AsUnsignedLongMask PyLong_AsUnsignedLongMask + #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask + #define PyNumber_Int PyNumber_Long +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyBoolObject PyLongObject +#endif +#if PY_MAJOR_VERSION >= 3 && CYTHON_COMPILING_IN_PYPY + #ifndef PyUnicode_InternFromString + #define PyUnicode_InternFromString(s) PyUnicode_FromString(s) + #endif +#endif +#if PY_VERSION_HEX < 0x030200A4 + typedef long Py_hash_t; + #define __Pyx_PyInt_FromHash_t PyInt_FromLong + #define __Pyx_PyInt_AsHash_t PyInt_AsLong +#else + #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t + #define __Pyx_PyInt_AsHash_t PyInt_AsSsize_t +#endif +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyMethod_New(func, self, klass) ((self) ? PyMethod_New(func, self) : PyInstanceMethod_New(func)) +#else + #define __Pyx_PyMethod_New(func, self, klass) PyMethod_New(func, self, klass) +#endif +#if CYTHON_USE_ASYNC_SLOTS + #if PY_VERSION_HEX >= 0x030500B1 + #define __Pyx_PyAsyncMethodsStruct PyAsyncMethods + #define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async) + #else + typedef struct { + unaryfunc am_await; + unaryfunc am_aiter; + unaryfunc am_anext; + } __Pyx_PyAsyncMethodsStruct; + #define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved)) + #endif +#else + #define __Pyx_PyType_AsAsync(obj) NULL +#endif +#ifndef CYTHON_RESTRICT + #if defined(__GNUC__) + #define CYTHON_RESTRICT __restrict__ + #elif defined(_MSC_VER) && _MSC_VER >= 1400 + #define CYTHON_RESTRICT __restrict + #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define CYTHON_RESTRICT restrict + #else + #define CYTHON_RESTRICT + #endif +#endif +#ifndef CYTHON_UNUSED +# if defined(__GNUC__) +# if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4)) +# define CYTHON_UNUSED __attribute__ ((__unused__)) +# else +# define CYTHON_UNUSED +# endif +# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER)) +# define CYTHON_UNUSED __attribute__ ((__unused__)) +# else +# define CYTHON_UNUSED +# endif +#endif +#ifndef CYTHON_MAYBE_UNUSED_VAR +# if defined(__cplusplus) + template void CYTHON_MAYBE_UNUSED_VAR( const T& ) { } +# else +# define CYTHON_MAYBE_UNUSED_VAR(x) (void)(x) +# endif +#endif +#ifndef CYTHON_NCP_UNUSED +# if CYTHON_COMPILING_IN_CPYTHON +# define CYTHON_NCP_UNUSED +# else +# define CYTHON_NCP_UNUSED CYTHON_UNUSED +# endif +#endif +#define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None) + +#ifndef CYTHON_INLINE + #if defined(__clang__) + #define CYTHON_INLINE __inline__ __attribute__ ((__unused__)) + #elif defined(__GNUC__) + #define CYTHON_INLINE __inline__ + #elif defined(_MSC_VER) + #define CYTHON_INLINE __inline + #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define CYTHON_INLINE inline + #else + #define CYTHON_INLINE + #endif +#endif + +#if defined(WIN32) || defined(MS_WINDOWS) + #define _USE_MATH_DEFINES +#endif +#include +#ifdef NAN +#define __PYX_NAN() ((float) NAN) +#else +static CYTHON_INLINE float __PYX_NAN() { + float value; + memset(&value, 0xFF, sizeof(value)); + return value; +} +#endif +#if defined(__CYGWIN__) && defined(_LDBL_EQ_DBL) +#define __Pyx_truncl trunc +#else +#define __Pyx_truncl truncl +#endif + + +#define __PYX_ERR(f_index, lineno, Ln_error) \ +{ \ + __pyx_filename = __pyx_f[f_index]; __pyx_lineno = lineno; __pyx_clineno = __LINE__; goto Ln_error; \ +} + +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y) + #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y) +#else + #define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y) + #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceDivide(x,y) +#endif + +#ifndef __PYX_EXTERN_C + #ifdef __cplusplus + #define __PYX_EXTERN_C extern "C" + #else + #define __PYX_EXTERN_C extern + #endif +#endif + +#define __PYX_HAVE__cython_nms +#define __PYX_HAVE_API__cython_nms +#include +#include +#include +#include "numpy/arrayobject.h" +#include "numpy/ufuncobject.h" +#ifdef _OPENMP +#include +#endif /* _OPENMP */ + +#ifdef PYREX_WITHOUT_ASSERTIONS +#define CYTHON_WITHOUT_ASSERTIONS +#endif + +typedef struct {PyObject **p; const char *s; const Py_ssize_t n; const char* encoding; + const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; + +#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0 +#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT 0 +#define __PYX_DEFAULT_STRING_ENCODING "" +#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString +#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize +#define __Pyx_uchar_cast(c) ((unsigned char)c) +#define __Pyx_long_cast(x) ((long)x) +#define __Pyx_fits_Py_ssize_t(v, type, is_signed) (\ + (sizeof(type) < sizeof(Py_ssize_t)) ||\ + (sizeof(type) > sizeof(Py_ssize_t) &&\ + likely(v < (type)PY_SSIZE_T_MAX ||\ + v == (type)PY_SSIZE_T_MAX) &&\ + (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||\ + v == (type)PY_SSIZE_T_MIN))) ||\ + (sizeof(type) == sizeof(Py_ssize_t) &&\ + (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\ + v == (type)PY_SSIZE_T_MAX))) ) +#if defined (__cplusplus) && __cplusplus >= 201103L + #include + #define __Pyx_sst_abs(value) std::abs(value) +#elif SIZEOF_INT >= SIZEOF_SIZE_T + #define __Pyx_sst_abs(value) abs(value) +#elif SIZEOF_LONG >= SIZEOF_SIZE_T + #define __Pyx_sst_abs(value) labs(value) +#elif defined (_MSC_VER) && defined (_M_X64) + #define __Pyx_sst_abs(value) _abs64(value) +#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define __Pyx_sst_abs(value) llabs(value) +#elif defined (__GNUC__) + #define __Pyx_sst_abs(value) __builtin_llabs(value) +#else + #define __Pyx_sst_abs(value) ((value<0) ? -value : value) +#endif +static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject*); +static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length); +#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s)) +#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l) +#define __Pyx_PyBytes_FromString PyBytes_FromString +#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*); +#if PY_MAJOR_VERSION < 3 + #define __Pyx_PyStr_FromString __Pyx_PyBytes_FromString + #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize +#else + #define __Pyx_PyStr_FromString __Pyx_PyUnicode_FromString + #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize +#endif +#define __Pyx_PyObject_AsSString(s) ((signed char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsUString(s) ((unsigned char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_FromCString(s) __Pyx_PyObject_FromString((const char*)s) +#define __Pyx_PyBytes_FromCString(s) __Pyx_PyBytes_FromString((const char*)s) +#define __Pyx_PyByteArray_FromCString(s) __Pyx_PyByteArray_FromString((const char*)s) +#define __Pyx_PyStr_FromCString(s) __Pyx_PyStr_FromString((const char*)s) +#define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s) +#if PY_MAJOR_VERSION < 3 +static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) +{ + const Py_UNICODE *u_end = u; + while (*u_end++) ; + return (size_t)(u_end - u - 1); +} +#else +#define __Pyx_Py_UNICODE_strlen Py_UNICODE_strlen +#endif +#define __Pyx_PyUnicode_FromUnicode(u) PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u)) +#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode +#define __Pyx_PyUnicode_AsUnicode PyUnicode_AsUnicode +#define __Pyx_NewRef(obj) (Py_INCREF(obj), obj) +#define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None) +#define __Pyx_PyBool_FromLong(b) ((b) ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False)) +static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*); +static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x); +static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*); +static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t); +#if CYTHON_ASSUME_SAFE_MACROS +#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x)) +#else +#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x) +#endif +#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x)) +#if PY_MAJOR_VERSION >= 3 +#define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x)) +#else +#define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x)) +#endif +#define __Pyx_PyNumber_Float(x) (PyFloat_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Float(x)) +#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII +static int __Pyx_sys_getdefaultencoding_not_ascii; +static int __Pyx_init_sys_getdefaultencoding_params(void) { + PyObject* sys; + PyObject* default_encoding = NULL; + PyObject* ascii_chars_u = NULL; + PyObject* ascii_chars_b = NULL; + const char* default_encoding_c; + sys = PyImport_ImportModule("sys"); + if (!sys) goto bad; + default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL); + Py_DECREF(sys); + if (!default_encoding) goto bad; + default_encoding_c = PyBytes_AsString(default_encoding); + if (!default_encoding_c) goto bad; + if (strcmp(default_encoding_c, "ascii") == 0) { + __Pyx_sys_getdefaultencoding_not_ascii = 0; + } else { + char ascii_chars[128]; + int c; + for (c = 0; c < 128; c++) { + ascii_chars[c] = c; + } + __Pyx_sys_getdefaultencoding_not_ascii = 1; + ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL); + if (!ascii_chars_u) goto bad; + ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL); + if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) { + PyErr_Format( + PyExc_ValueError, + "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.", + default_encoding_c); + goto bad; + } + Py_DECREF(ascii_chars_u); + Py_DECREF(ascii_chars_b); + } + Py_DECREF(default_encoding); + return 0; +bad: + Py_XDECREF(default_encoding); + Py_XDECREF(ascii_chars_u); + Py_XDECREF(ascii_chars_b); + return -1; +} +#endif +#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3 +#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL) +#else +#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL) +#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT +static char* __PYX_DEFAULT_STRING_ENCODING; +static int __Pyx_init_sys_getdefaultencoding_params(void) { + PyObject* sys; + PyObject* default_encoding = NULL; + char* default_encoding_c; + sys = PyImport_ImportModule("sys"); + if (!sys) goto bad; + default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL); + Py_DECREF(sys); + if (!default_encoding) goto bad; + default_encoding_c = PyBytes_AsString(default_encoding); + if (!default_encoding_c) goto bad; + __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c)); + if (!__PYX_DEFAULT_STRING_ENCODING) goto bad; + strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c); + Py_DECREF(default_encoding); + return 0; +bad: + Py_XDECREF(default_encoding); + return -1; +} +#endif +#endif + + +/* Test for GCC > 2.95 */ +#if defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95))) + #define likely(x) __builtin_expect(!!(x), 1) + #define unlikely(x) __builtin_expect(!!(x), 0) +#else /* !__GNUC__ or GCC < 2.95 */ + #define likely(x) (x) + #define unlikely(x) (x) +#endif /* __GNUC__ */ + +static PyObject *__pyx_m; +static PyObject *__pyx_d; +static PyObject *__pyx_b; +static PyObject *__pyx_empty_tuple; +static PyObject *__pyx_empty_bytes; +static PyObject *__pyx_empty_unicode; +static int __pyx_lineno; +static int __pyx_clineno = 0; +static const char * __pyx_cfilenm= __FILE__; +static const char *__pyx_filename; + +/* Header.proto */ +#if !defined(CYTHON_CCOMPLEX) + #if defined(__cplusplus) + #define CYTHON_CCOMPLEX 1 + #elif defined(_Complex_I) + #define CYTHON_CCOMPLEX 1 + #else + #define CYTHON_CCOMPLEX 0 + #endif +#endif +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + #include + #else + #include + #endif +#endif +#if CYTHON_CCOMPLEX && !defined(__cplusplus) && defined(__sun__) && defined(__GNUC__) + #undef _Complex_I + #define _Complex_I 1.0fj +#endif + + +static const char *__pyx_f[] = { + "_utils/nms.pyx", + "__init__.pxd", + "type.pxd", +}; +/* BufferFormatStructs.proto */ +#define IS_UNSIGNED(type) (((type) -1) > 0) +struct __Pyx_StructField_; +#define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0) +typedef struct { + const char* name; + struct __Pyx_StructField_* fields; + size_t size; + size_t arraysize[8]; + int ndim; + char typegroup; + char is_unsigned; + int flags; +} __Pyx_TypeInfo; +typedef struct __Pyx_StructField_ { + __Pyx_TypeInfo* type; + const char* name; + size_t offset; +} __Pyx_StructField; +typedef struct { + __Pyx_StructField* field; + size_t parent_offset; +} __Pyx_BufFmt_StackElem; +typedef struct { + __Pyx_StructField root; + __Pyx_BufFmt_StackElem* head; + size_t fmt_offset; + size_t new_count, enc_count; + size_t struct_alignment; + int is_complex; + char enc_type; + char new_packmode; + char enc_packmode; + char is_valid_array; +} __Pyx_BufFmt_Context; + + +/* "../../../../../softWares/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":725 + * # in Cython to enable them only on the right systems. + * + * ctypedef npy_int8 int8_t # <<<<<<<<<<<<<< + * ctypedef npy_int16 int16_t + * ctypedef npy_int32 int32_t + */ +typedef npy_int8 __pyx_t_5numpy_int8_t; + +/* "../../../../../softWares/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":726 + * + * ctypedef npy_int8 int8_t + * ctypedef npy_int16 int16_t # <<<<<<<<<<<<<< + * ctypedef npy_int32 int32_t + * ctypedef npy_int64 int64_t + */ +typedef npy_int16 __pyx_t_5numpy_int16_t; + +/* "../../../../../softWares/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":727 + * ctypedef npy_int8 int8_t + * ctypedef npy_int16 int16_t + * ctypedef npy_int32 int32_t # <<<<<<<<<<<<<< + * ctypedef npy_int64 int64_t + * #ctypedef npy_int96 int96_t + */ +typedef npy_int32 __pyx_t_5numpy_int32_t; + +/* "../../../../../softWares/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":728 + * ctypedef npy_int16 int16_t + * ctypedef npy_int32 int32_t + * ctypedef npy_int64 int64_t # <<<<<<<<<<<<<< + * #ctypedef npy_int96 int96_t + * #ctypedef npy_int128 int128_t + */ +typedef npy_int64 __pyx_t_5numpy_int64_t; + +/* "../../../../../softWares/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":732 + * #ctypedef npy_int128 int128_t + * + * ctypedef npy_uint8 uint8_t # <<<<<<<<<<<<<< + * ctypedef npy_uint16 uint16_t + * ctypedef npy_uint32 uint32_t + */ +typedef npy_uint8 __pyx_t_5numpy_uint8_t; + +/* "../../../../../softWares/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":733 + * + * ctypedef npy_uint8 uint8_t + * ctypedef npy_uint16 uint16_t # <<<<<<<<<<<<<< + * ctypedef npy_uint32 uint32_t + * ctypedef npy_uint64 uint64_t + */ +typedef npy_uint16 __pyx_t_5numpy_uint16_t; + +/* "../../../../../softWares/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":734 + * ctypedef npy_uint8 uint8_t + * ctypedef npy_uint16 uint16_t + * ctypedef npy_uint32 uint32_t # <<<<<<<<<<<<<< + * ctypedef npy_uint64 uint64_t + * #ctypedef npy_uint96 uint96_t + */ +typedef npy_uint32 __pyx_t_5numpy_uint32_t; + +/* "../../../../../softWares/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":735 + * ctypedef npy_uint16 uint16_t + * ctypedef npy_uint32 uint32_t + * ctypedef npy_uint64 uint64_t # <<<<<<<<<<<<<< + * #ctypedef npy_uint96 uint96_t + * #ctypedef npy_uint128 uint128_t + */ +typedef npy_uint64 __pyx_t_5numpy_uint64_t; + +/* "../../../../../softWares/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":739 + * #ctypedef npy_uint128 uint128_t + * + * ctypedef npy_float32 float32_t # <<<<<<<<<<<<<< + * ctypedef npy_float64 float64_t + * #ctypedef npy_float80 float80_t + */ +typedef npy_float32 __pyx_t_5numpy_float32_t; 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r = NULL; __Pyx_DECREF(tmp);}} while(0) + +/* PyObjectGetAttrStr.proto */ +#if CYTHON_USE_TYPE_SLOTS +static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name) { + PyTypeObject* tp = Py_TYPE(obj); + if (likely(tp->tp_getattro)) + return tp->tp_getattro(obj, attr_name); +#if PY_MAJOR_VERSION < 3 + if (likely(tp->tp_getattr)) + return tp->tp_getattr(obj, PyString_AS_STRING(attr_name)); +#endif + return PyObject_GetAttr(obj, attr_name); +} +#else +#define __Pyx_PyObject_GetAttrStr(o,n) PyObject_GetAttr(o,n) +#endif + +/* GetBuiltinName.proto */ +static PyObject *__Pyx_GetBuiltinName(PyObject *name); + +/* RaiseArgTupleInvalid.proto */ +static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact, + Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found); + +/* RaiseDoubleKeywords.proto */ +static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name); + +/* ParseKeywords.proto */ +static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[],\ + PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args,\ + const char* function_name); + +/* ArgTypeTest.proto */ +static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed, + const char *name, int exact); + +/* BufferFormatCheck.proto */ +static CYTHON_INLINE int __Pyx_GetBufferAndValidate(Py_buffer* buf, PyObject* obj, + __Pyx_TypeInfo* dtype, int flags, int nd, int cast, __Pyx_BufFmt_StackElem* stack); +static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info); +static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts); +static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, + __Pyx_BufFmt_StackElem* stack, + __Pyx_TypeInfo* type); // PROTO + +/* ExtTypeTest.proto */ +static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); + +/* PyIntBinop.proto */ +#if !CYTHON_COMPILING_IN_PYPY +static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, long intval, int inplace); +#else +#define __Pyx_PyInt_AddObjC(op1, op2, intval, inplace)\ + (inplace ? PyNumber_InPlaceAdd(op1, op2) : PyNumber_Add(op1, op2)) +#endif + +/* PyCFunctionFastCall.proto */ +#if CYTHON_FAST_PYCCALL +static CYTHON_INLINE PyObject *__Pyx_PyCFunction_FastCall(PyObject *func, PyObject **args, Py_ssize_t nargs); +#else +#define __Pyx_PyCFunction_FastCall(func, args, nargs) (assert(0), NULL) +#endif + +/* PyFunctionFastCall.proto */ +#if CYTHON_FAST_PYCALL +#define __Pyx_PyFunction_FastCall(func, args, nargs)\ + __Pyx_PyFunction_FastCallDict((func), (args), (nargs), NULL) +#if 1 || PY_VERSION_HEX < 0x030600B1 +static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, int nargs, PyObject *kwargs); +#else +#define __Pyx_PyFunction_FastCallDict(func, args, nargs, kwargs) _PyFunction_FastCallDict(func, args, nargs, kwargs) +#endif +#endif + +/* PyObjectCall.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw); +#else +#define __Pyx_PyObject_Call(func, arg, kw) PyObject_Call(func, arg, kw) +#endif + +/* PyObjectCallMethO.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg); +#endif + +/* PyObjectCallOneArg.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg); + +/* PyObjectCallNoArg.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func); +#else +#define __Pyx_PyObject_CallNoArg(func) __Pyx_PyObject_Call(func, __pyx_empty_tuple, NULL) +#endif + +/* GetModuleGlobalName.proto */ +static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name); + +/* BufferIndexError.proto */ +static void __Pyx_RaiseBufferIndexError(int axis); + +#define __Pyx_BufPtrStrided1d(type, buf, i0, s0) (type)((char*)buf + i0 * s0) +/* ListAppend.proto */ +#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS +static CYTHON_INLINE int __Pyx_PyList_Append(PyObject* list, PyObject* x) { + PyListObject* L = (PyListObject*) list; + Py_ssize_t len = Py_SIZE(list); + if (likely(L->allocated > len) & likely(len > (L->allocated >> 1))) { + Py_INCREF(x); + PyList_SET_ITEM(list, len, x); + Py_SIZE(list) = len+1; + return 0; + } + return PyList_Append(list, x); +} +#else +#define __Pyx_PyList_Append(L,x) PyList_Append(L,x) +#endif + +/* PyThreadStateGet.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_PyThreadState_declare PyThreadState *__pyx_tstate; +#define __Pyx_PyThreadState_assign __pyx_tstate = PyThreadState_GET(); +#else +#define __Pyx_PyThreadState_declare +#define __Pyx_PyThreadState_assign +#endif + +/* PyErrFetchRestore.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_ErrRestoreWithState(type, value, tb) __Pyx_ErrRestoreInState(PyThreadState_GET(), type, value, tb) +#define __Pyx_ErrFetchWithState(type, value, tb) __Pyx_ErrFetchInState(PyThreadState_GET(), type, value, tb) +#define __Pyx_ErrRestore(type, value, tb) __Pyx_ErrRestoreInState(__pyx_tstate, type, value, tb) +#define __Pyx_ErrFetch(type, value, tb) __Pyx_ErrFetchInState(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb); +static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#else +#define __Pyx_ErrRestoreWithState(type, value, tb) PyErr_Restore(type, value, tb) +#define __Pyx_ErrFetchWithState(type, value, tb) PyErr_Fetch(type, value, tb) +#define __Pyx_ErrRestore(type, value, tb) PyErr_Restore(type, value, tb) +#define __Pyx_ErrFetch(type, value, tb) PyErr_Fetch(type, value, tb) +#endif + +/* RaiseException.proto */ +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause); + +/* DictGetItem.proto */ +#if PY_MAJOR_VERSION >= 3 && !CYTHON_COMPILING_IN_PYPY +static PyObject *__Pyx_PyDict_GetItem(PyObject *d, PyObject* key) { + PyObject *value; + value = PyDict_GetItemWithError(d, key); + if (unlikely(!value)) { + if (!PyErr_Occurred()) { + PyObject* args = PyTuple_Pack(1, key); + if (likely(args)) + PyErr_SetObject(PyExc_KeyError, args); + Py_XDECREF(args); + } + return NULL; + } + Py_INCREF(value); + return value; +} +#else + #define __Pyx_PyDict_GetItem(d, key) PyObject_GetItem(d, key) +#endif + +/* RaiseTooManyValuesToUnpack.proto */ +static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected); + +/* RaiseNeedMoreValuesToUnpack.proto */ +static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index); + +/* RaiseNoneIterError.proto */ +static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void); + +/* SaveResetException.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_ExceptionSave(type, value, tb) __Pyx__ExceptionSave(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#define __Pyx_ExceptionReset(type, value, tb) __Pyx__ExceptionReset(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb); +#else +#define __Pyx_ExceptionSave(type, value, tb) PyErr_GetExcInfo(type, value, tb) +#define __Pyx_ExceptionReset(type, value, tb) PyErr_SetExcInfo(type, value, tb) +#endif + +/* PyErrExceptionMatches.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_PyErr_ExceptionMatches(err) __Pyx_PyErr_ExceptionMatchesInState(__pyx_tstate, err) +static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err); +#else +#define __Pyx_PyErr_ExceptionMatches(err) PyErr_ExceptionMatches(err) +#endif + +/* GetException.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_GetException(type, value, tb) __Pyx__GetException(__pyx_tstate, type, value, tb) +static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#else +static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb); +#endif + +/* Import.proto */ +static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level); + +/* CodeObjectCache.proto */ +typedef struct { + PyCodeObject* code_object; + int code_line; +} __Pyx_CodeObjectCacheEntry; +struct __Pyx_CodeObjectCache { + int count; + int max_count; + __Pyx_CodeObjectCacheEntry* entries; +}; +static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL}; +static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line); +static PyCodeObject *__pyx_find_code_object(int code_line); +static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object); + +/* AddTraceback.proto */ +static void __Pyx_AddTraceback(const char *funcname, int c_line, + int py_line, const char *filename); + +/* BufferStructDeclare.proto */ +typedef struct { + Py_ssize_t shape, strides, suboffsets; +} __Pyx_Buf_DimInfo; +typedef struct { + size_t refcount; + Py_buffer pybuffer; +} __Pyx_Buffer; +typedef struct { + __Pyx_Buffer *rcbuffer; + char *data; + __Pyx_Buf_DimInfo diminfo[8]; +} __Pyx_LocalBuf_ND; + +#if PY_MAJOR_VERSION < 3 + static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags); + static void __Pyx_ReleaseBuffer(Py_buffer *view); +#else + #define __Pyx_GetBuffer PyObject_GetBuffer + #define __Pyx_ReleaseBuffer PyBuffer_Release +#endif + + +/* None.proto */ +static Py_ssize_t __Pyx_zeros[] = {0, 0, 0, 0, 0, 0, 0, 0}; +static Py_ssize_t __Pyx_minusones[] = {-1, -1, -1, -1, -1, -1, -1, -1}; + +/* CIntToPy.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value); + +/* CIntToPy.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value); + +/* RealImag.proto */ +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + #define __Pyx_CREAL(z) ((z).real()) + #define __Pyx_CIMAG(z) ((z).imag()) + #else + #define __Pyx_CREAL(z) (__real__(z)) + #define __Pyx_CIMAG(z) (__imag__(z)) + #endif +#else + #define __Pyx_CREAL(z) ((z).real) + #define __Pyx_CIMAG(z) ((z).imag) +#endif +#if defined(__cplusplus) && CYTHON_CCOMPLEX\ + && (defined(_WIN32) || defined(__clang__) || (defined(__GNUC__) && (__GNUC__ >= 5 || __GNUC__ == 4 && __GNUC_MINOR__ >= 4 )) || __cplusplus >= 201103) + #define __Pyx_SET_CREAL(z,x) ((z).real(x)) + #define __Pyx_SET_CIMAG(z,y) ((z).imag(y)) +#else + #define __Pyx_SET_CREAL(z,x) __Pyx_CREAL(z) = (x) + #define __Pyx_SET_CIMAG(z,y) __Pyx_CIMAG(z) = (y) +#endif + +/* Arithmetic.proto */ +#if CYTHON_CCOMPLEX + #define __Pyx_c_eq_float(a, b) ((a)==(b)) + #define __Pyx_c_sum_float(a, b) ((a)+(b)) + #define __Pyx_c_diff_float(a, b) ((a)-(b)) + #define __Pyx_c_prod_float(a, b) ((a)*(b)) + #define __Pyx_c_quot_float(a, b) ((a)/(b)) + #define __Pyx_c_neg_float(a) (-(a)) + #ifdef __cplusplus + #define __Pyx_c_is_zero_float(z) ((z)==(float)0) + #define __Pyx_c_conj_float(z) (::std::conj(z)) + #if 1 + #define __Pyx_c_abs_float(z) (::std::abs(z)) + #define __Pyx_c_pow_float(a, b) (::std::pow(a, b)) + #endif + #else + #define __Pyx_c_is_zero_float(z) ((z)==0) + #define __Pyx_c_conj_float(z) (conjf(z)) + #if 1 + #define __Pyx_c_abs_float(z) (cabsf(z)) + #define __Pyx_c_pow_float(a, b) (cpowf(a, b)) + #endif + #endif +#else + static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex); + static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex); + #if 1 + static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex, __pyx_t_float_complex); + #endif +#endif + +/* Arithmetic.proto */ +#if CYTHON_CCOMPLEX + #define __Pyx_c_eq_double(a, b) ((a)==(b)) + #define __Pyx_c_sum_double(a, b) ((a)+(b)) + #define __Pyx_c_diff_double(a, b) ((a)-(b)) + #define __Pyx_c_prod_double(a, b) ((a)*(b)) + #define __Pyx_c_quot_double(a, b) ((a)/(b)) + #define __Pyx_c_neg_double(a) (-(a)) + #ifdef __cplusplus + #define __Pyx_c_is_zero_double(z) ((z)==(double)0) + #define __Pyx_c_conj_double(z) (::std::conj(z)) + #if 1 + #define __Pyx_c_abs_double(z) (::std::abs(z)) + #define __Pyx_c_pow_double(a, b) (::std::pow(a, b)) + #endif + #else + #define __Pyx_c_is_zero_double(z) ((z)==0) + #define __Pyx_c_conj_double(z) (conj(z)) + #if 1 + #define __Pyx_c_abs_double(z) (cabs(z)) + #define __Pyx_c_pow_double(a, b) (cpow(a, b)) + #endif + #endif +#else + static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex); + static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex); + #if 1 + static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex, __pyx_t_double_complex); + #endif +#endif + +/* CIntToPy.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_enum__NPY_TYPES(enum NPY_TYPES value); + +/* CIntFromPy.proto */ +static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *); + +/* CIntFromPy.proto */ +static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *); + +/* CheckBinaryVersion.proto */ +static int __Pyx_check_binary_version(void); + +/* PyIdentifierFromString.proto */ +#if !defined(__Pyx_PyIdentifier_FromString) +#if PY_MAJOR_VERSION < 3 + #define __Pyx_PyIdentifier_FromString(s) PyString_FromString(s) +#else + #define __Pyx_PyIdentifier_FromString(s) PyUnicode_FromString(s) +#endif +#endif + +/* ModuleImport.proto */ +static PyObject *__Pyx_ImportModule(const char *name); + +/* TypeImport.proto */ +static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict); + +/* InitStrings.proto */ +static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); + + +/* Module declarations from 'cpython.buffer' */ + +/* Module declarations from 'libc.string' */ + +/* Module declarations from 'libc.stdio' */ + +/* Module declarations from '__builtin__' */ + +/* Module declarations from 'cpython.type' */ +static PyTypeObject *__pyx_ptype_7cpython_4type_type = 0; + +/* Module declarations from 'cpython' */ + +/* Module declarations from 'cpython.object' */ + +/* Module declarations from 'cpython.ref' */ + +/* Module declarations from 'libc.stdlib' */ + +/* Module declarations from 'numpy' */ + +/* Module declarations from 'numpy' */ +static PyTypeObject *__pyx_ptype_5numpy_dtype = 0; +static PyTypeObject *__pyx_ptype_5numpy_flatiter = 0; +static PyTypeObject *__pyx_ptype_5numpy_broadcast = 0; +static PyTypeObject *__pyx_ptype_5numpy_ndarray = 0; +static PyTypeObject *__pyx_ptype_5numpy_ufunc = 0; +static CYTHON_INLINE char *__pyx_f_5numpy__util_dtypestring(PyArray_Descr *, char *, char *, int *); /*proto*/ + +/* Module declarations from 'cython_nms' */ +static CYTHON_INLINE __pyx_t_5numpy_float32_t __pyx_f_10cython_nms_max(__pyx_t_5numpy_float32_t, __pyx_t_5numpy_float32_t); /*proto*/ +static CYTHON_INLINE __pyx_t_5numpy_float32_t __pyx_f_10cython_nms_min(__pyx_t_5numpy_float32_t, __pyx_t_5numpy_float32_t); /*proto*/ +static __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_5numpy_float32_t = { "float32_t", NULL, sizeof(__pyx_t_5numpy_float32_t), { 0 }, 0, 'R', 0, 0 }; +static __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_5numpy_int_t = { "int_t", NULL, sizeof(__pyx_t_5numpy_int_t), { 0 }, 0, IS_UNSIGNED(__pyx_t_5numpy_int_t) ? 'U' : 'I', IS_UNSIGNED(__pyx_t_5numpy_int_t), 0 }; +#define __Pyx_MODULE_NAME "cython_nms" +int __pyx_module_is_main_cython_nms = 0; + +/* Implementation of 'cython_nms' */ +static PyObject *__pyx_builtin_range; +static PyObject *__pyx_builtin_ValueError; +static PyObject *__pyx_builtin_RuntimeError; +static PyObject *__pyx_builtin_ImportError; +static const char __pyx_k_h[] = "h"; +static const char __pyx_k_i[] = "_i"; +static const char __pyx_k_j[] = "_j"; +static const char __pyx_k_w[] = "w"; +static const char __pyx_k_np[] = "np"; +static const char __pyx_k_x1[] = "x1"; +static const char __pyx_k_x2[] = "x2"; +static const char __pyx_k_y1[] = "y1"; +static const char __pyx_k_y2[] = "y2"; +static const char __pyx_k_i_2[] = "i"; +static const char __pyx_k_int[] = "int"; +static const char __pyx_k_ix1[] = "ix1"; +static const char __pyx_k_ix2[] = "ix2"; +static const char __pyx_k_iy1[] = "iy1"; +static const char __pyx_k_iy2[] = "iy2"; +static const char __pyx_k_j_2[] = "j"; +static const char __pyx_k_nms[] = "nms"; +static const char __pyx_k_ovr[] = "ovr"; +static const char __pyx_k_xx1[] = "xx1"; +static const char __pyx_k_xx2[] = "xx2"; +static const char __pyx_k_yy1[] = "yy1"; +static const char __pyx_k_yy2[] = "yy2"; +static const char __pyx_k_dets[] = "dets"; +static const char __pyx_k_keep[] = "keep"; +static const char __pyx_k_main[] = "__main__"; +static const char __pyx_k_ovr1[] = "ovr1"; +static const char __pyx_k_ovr2[] = "ovr2"; +static const char __pyx_k_test[] = "__test__"; +static const char __pyx_k_areas[] = "areas"; +static const char __pyx_k_dtype[] = "dtype"; +static const char __pyx_k_iarea[] = "iarea"; +static const char __pyx_k_inter[] = "inter"; +static const char __pyx_k_ndets[] = "ndets"; +static const char __pyx_k_numpy[] = "numpy"; +static const char __pyx_k_order[] = "order"; +static const char __pyx_k_range[] = "range"; +static const char __pyx_k_zeros[] = "zeros"; +static const char __pyx_k_import[] = "__import__"; +static const char __pyx_k_scores[] = "scores"; +static const char __pyx_k_thresh[] = "thresh"; +static const char __pyx_k_argsort[] = "argsort"; +static const char __pyx_k_nms_new[] = "nms_new"; +static const char __pyx_k_ValueError[] = "ValueError"; +static const char __pyx_k_cython_nms[] = "cython_nms"; +static const char __pyx_k_suppressed[] = "suppressed"; +static const char __pyx_k_ImportError[] = "ImportError"; +static const char __pyx_k_RuntimeError[] = "RuntimeError"; +static const char __pyx_k_ndarray_is_not_C_contiguous[] = "ndarray is not C contiguous"; +static const char __pyx_k_home_yjr_PycharmProjects_Faster[] = "/home/yjr/PycharmProjects/Faster-RCNN_Tensorflow/libs/box_utils/cython_utils/nms.pyx"; +static const char __pyx_k_numpy_core_multiarray_failed_to[] = "numpy.core.multiarray failed to import"; +static const char __pyx_k_unknown_dtype_code_in_numpy_pxd[] = "unknown dtype code in numpy.pxd (%d)"; +static const char __pyx_k_Format_string_allocated_too_shor[] = "Format string allocated too short, see comment in numpy.pxd"; +static const char __pyx_k_Non_native_byte_order_not_suppor[] = "Non-native byte order not supported"; +static const char __pyx_k_ndarray_is_not_Fortran_contiguou[] = "ndarray is not Fortran contiguous"; +static const char __pyx_k_numpy_core_umath_failed_to_impor[] = "numpy.core.umath failed to import"; +static const char __pyx_k_Format_string_allocated_too_shor_2[] = "Format string allocated too short."; +static PyObject *__pyx_kp_u_Format_string_allocated_too_shor; +static PyObject *__pyx_kp_u_Format_string_allocated_too_shor_2; +static PyObject *__pyx_n_s_ImportError; +static PyObject *__pyx_kp_u_Non_native_byte_order_not_suppor; +static PyObject *__pyx_n_s_RuntimeError; +static PyObject *__pyx_n_s_ValueError; +static PyObject *__pyx_n_s_areas; +static PyObject *__pyx_n_s_argsort; +static PyObject *__pyx_n_s_cython_nms; +static PyObject *__pyx_n_s_dets; +static PyObject *__pyx_n_s_dtype; +static PyObject *__pyx_n_s_h; +static PyObject *__pyx_kp_s_home_yjr_PycharmProjects_Faster; +static PyObject *__pyx_n_s_i; +static PyObject *__pyx_n_s_i_2; +static PyObject *__pyx_n_s_iarea; +static PyObject *__pyx_n_s_import; 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if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 8, __pyx_L1_error) + __Pyx_GOTREF(__pyx_t_1); + if (PyDict_SetItem(__pyx_d, __pyx_n_s_np, __pyx_t_1) < 0) __PYX_ERR(0, 8, __pyx_L1_error) + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + + /* "nms.pyx":17 + * return a if a <= b else b + * + * def nms(np.ndarray[np.float32_t, ndim=2] dets, np.float thresh): # <<<<<<<<<<<<<< + * cdef np.ndarray[np.float32_t, ndim=1] x1 = dets[:, 0] + * cdef np.ndarray[np.float32_t, ndim=1] y1 = dets[:, 1] + */ + __pyx_t_1 = PyCFunction_NewEx(&__pyx_mdef_10cython_nms_1nms, NULL, __pyx_n_s_cython_nms); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 17, __pyx_L1_error) + __Pyx_GOTREF(__pyx_t_1); + if (PyDict_SetItem(__pyx_d, __pyx_n_s_nms, __pyx_t_1) < 0) __PYX_ERR(0, 17, __pyx_L1_error) + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + + /* "nms.pyx":70 + * return keep + * + * def nms_new(np.ndarray[np.float32_t, ndim=2] dets, np.float thresh): # <<<<<<<<<<<<<< + * cdef np.ndarray[np.float32_t, ndim=1] x1 = dets[:, 0] + * cdef np.ndarray[np.float32_t, ndim=1] y1 = dets[:, 1] + */ + __pyx_t_1 = PyCFunction_NewEx(&__pyx_mdef_10cython_nms_3nms_new, NULL, __pyx_n_s_cython_nms); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 70, __pyx_L1_error) + __Pyx_GOTREF(__pyx_t_1); + if (PyDict_SetItem(__pyx_d, __pyx_n_s_nms_new, __pyx_t_1) < 0) __PYX_ERR(0, 70, __pyx_L1_error) + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + + /* "nms.pyx":1 + * # -------------------------------------------------------- # <<<<<<<<<<<<<< + * # Fast R-CNN + * # Copyright (c) 2015 Microsoft + */ + __pyx_t_1 = PyDict_New(); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 1, __pyx_L1_error) + __Pyx_GOTREF(__pyx_t_1); + if (PyDict_SetItem(__pyx_d, __pyx_n_s_test, __pyx_t_1) < 0) __PYX_ERR(0, 1, __pyx_L1_error) + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + + /* "../../../../../softWares/anaconda/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":997 + * raise ImportError("numpy.core.umath failed to import") + * + * cdef inline int import_ufunc() except -1: # <<<<<<<<<<<<<< + * try: + * _import_umath() + */ + + /*--- Wrapped vars code ---*/ + + goto __pyx_L0; + __pyx_L1_error:; + __Pyx_XDECREF(__pyx_t_1); + if (__pyx_m) { + if (__pyx_d) { + __Pyx_AddTraceback("init cython_nms", __pyx_clineno, __pyx_lineno, __pyx_filename); + } + Py_DECREF(__pyx_m); __pyx_m = 0; + } else if (!PyErr_Occurred()) { + PyErr_SetString(PyExc_ImportError, "init cython_nms"); + } + __pyx_L0:; + __Pyx_RefNannyFinishContext(); + #if PY_MAJOR_VERSION < 3 + return; + #else + return __pyx_m; + #endif +} + +/* --- Runtime support code --- */ +/* Refnanny */ +#if CYTHON_REFNANNY +static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname) { + PyObject *m = NULL, *p = NULL; + void *r = NULL; + m = PyImport_ImportModule((char *)modname); + if (!m) goto end; + p = PyObject_GetAttrString(m, (char *)"RefNannyAPI"); + if (!p) goto end; + r = PyLong_AsVoidPtr(p); +end: + Py_XDECREF(p); + Py_XDECREF(m); + return (__Pyx_RefNannyAPIStruct *)r; +} +#endif + +/* GetBuiltinName */ +static PyObject *__Pyx_GetBuiltinName(PyObject *name) { + PyObject* result = __Pyx_PyObject_GetAttrStr(__pyx_b, name); + if (unlikely(!result)) { + PyErr_Format(PyExc_NameError, +#if PY_MAJOR_VERSION >= 3 + "name '%U' is not defined", name); +#else + "name '%.200s' is not defined", PyString_AS_STRING(name)); +#endif + } + return result; +} + +/* RaiseArgTupleInvalid */ +static void __Pyx_RaiseArgtupleInvalid( + const char* func_name, + int exact, + Py_ssize_t num_min, + Py_ssize_t num_max, + Py_ssize_t num_found) +{ + Py_ssize_t num_expected; + const char *more_or_less; + if (num_found < num_min) { + num_expected = num_min; + more_or_less = "at least"; + } else { + num_expected = num_max; + more_or_less = "at most"; + } + if (exact) { + more_or_less = "exactly"; + } + PyErr_Format(PyExc_TypeError, + "%.200s() takes %.8s %" CYTHON_FORMAT_SSIZE_T "d positional argument%.1s (%" CYTHON_FORMAT_SSIZE_T "d given)", + func_name, more_or_less, num_expected, + (num_expected == 1) ? "" : "s", num_found); +} + +/* RaiseDoubleKeywords */ +static void __Pyx_RaiseDoubleKeywordsError( + const char* func_name, + PyObject* kw_name) +{ + PyErr_Format(PyExc_TypeError, + #if PY_MAJOR_VERSION >= 3 + "%s() got multiple values for keyword argument '%U'", func_name, kw_name); + #else + "%s() got multiple values for keyword argument '%s'", func_name, + PyString_AsString(kw_name)); + #endif +} + +/* ParseKeywords */ +static int __Pyx_ParseOptionalKeywords( + PyObject *kwds, + PyObject **argnames[], + PyObject *kwds2, + PyObject *values[], + Py_ssize_t num_pos_args, + const char* function_name) +{ + PyObject *key = 0, *value = 0; + Py_ssize_t pos = 0; + PyObject*** name; + PyObject*** first_kw_arg = argnames + num_pos_args; + while (PyDict_Next(kwds, &pos, &key, &value)) { + name = first_kw_arg; + while (*name && (**name != key)) name++; + if (*name) { + values[name-argnames] = value; + continue; + } + name = first_kw_arg; + #if PY_MAJOR_VERSION < 3 + if (likely(PyString_CheckExact(key)) || likely(PyString_Check(key))) { + while (*name) { + if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key)) + && _PyString_Eq(**name, key)) { + values[name-argnames] = value; + break; + } + name++; + } + if (*name) continue; + else { + PyObject*** argname = argnames; + while (argname != first_kw_arg) { + if ((**argname == key) || ( + (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key)) + && _PyString_Eq(**argname, key))) { + goto arg_passed_twice; + } + argname++; + } + } + } else + #endif + if (likely(PyUnicode_Check(key))) { + while (*name) { + int cmp = (**name == key) ? 0 : + #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 + (PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(key)) ? 1 : + #endif + PyUnicode_Compare(**name, key); + if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; + if (cmp == 0) { + values[name-argnames] = value; + break; + } + name++; + } + if (*name) continue; + else { + PyObject*** argname = argnames; + while (argname != first_kw_arg) { + int cmp = (**argname == key) ? 0 : + #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 + (PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(key)) ? 1 : + #endif + PyUnicode_Compare(**argname, key); + if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; + if (cmp == 0) goto arg_passed_twice; + argname++; + } + } + } else + goto invalid_keyword_type; + if (kwds2) { + if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad; + } else { + goto invalid_keyword; + } + } + return 0; +arg_passed_twice: + __Pyx_RaiseDoubleKeywordsError(function_name, key); + goto bad; +invalid_keyword_type: + PyErr_Format(PyExc_TypeError, + "%.200s() keywords must be strings", function_name); + goto bad; +invalid_keyword: + PyErr_Format(PyExc_TypeError, + #if PY_MAJOR_VERSION < 3 + "%.200s() got an unexpected keyword argument '%.200s'", + function_name, PyString_AsString(key)); + #else + "%s() got an unexpected keyword argument '%U'", + function_name, key); + #endif +bad: + return -1; +} + +/* ArgTypeTest */ +static void __Pyx_RaiseArgumentTypeInvalid(const char* name, PyObject *obj, PyTypeObject *type) { + PyErr_Format(PyExc_TypeError, + "Argument '%.200s' has incorrect type (expected %.200s, got %.200s)", + name, type->tp_name, Py_TYPE(obj)->tp_name); +} +static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed, + const char *name, int exact) +{ + if (unlikely(!type)) { + PyErr_SetString(PyExc_SystemError, "Missing type object"); + return 0; + } + if (none_allowed && obj == Py_None) return 1; + else if (exact) { + if (likely(Py_TYPE(obj) == type)) return 1; + #if PY_MAJOR_VERSION == 2 + else if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1; + #endif + } + else { + if (likely(PyObject_TypeCheck(obj, type))) return 1; + } + __Pyx_RaiseArgumentTypeInvalid(name, obj, type); + return 0; +} + +/* BufferFormatCheck */ +static CYTHON_INLINE int __Pyx_IsLittleEndian(void) { + unsigned int n = 1; + return *(unsigned char*)(&n) != 0; +} +static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, + __Pyx_BufFmt_StackElem* stack, + __Pyx_TypeInfo* type) { + stack[0].field = &ctx->root; + stack[0].parent_offset = 0; + ctx->root.type = type; + ctx->root.name = "buffer dtype"; + ctx->root.offset = 0; + ctx->head = stack; + ctx->head->field = &ctx->root; + ctx->fmt_offset = 0; + ctx->head->parent_offset = 0; + ctx->new_packmode = '@'; + ctx->enc_packmode = '@'; + ctx->new_count = 1; + ctx->enc_count = 0; + ctx->enc_type = 0; + ctx->is_complex = 0; + ctx->is_valid_array = 0; + ctx->struct_alignment = 0; + while (type->typegroup == 'S') { + ++ctx->head; + ctx->head->field = type->fields; + ctx->head->parent_offset = 0; + type = type->fields->type; + } +} +static int __Pyx_BufFmt_ParseNumber(const char** ts) { + int count; + const char* t = *ts; + if (*t < '0' || *t > '9') { + return -1; + } else { + count = *t++ - '0'; + while (*t >= '0' && *t < '9') { + count *= 10; + count += *t++ - '0'; + } + } + *ts = t; + return count; +} +static int __Pyx_BufFmt_ExpectNumber(const char **ts) { + int number = __Pyx_BufFmt_ParseNumber(ts); + if (number == -1) + PyErr_Format(PyExc_ValueError,\ + "Does not understand character buffer dtype format string ('%c')", **ts); + return number; +} +static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) { + PyErr_Format(PyExc_ValueError, + "Unexpected format string character: '%c'", ch); +} +static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) { + switch (ch) { + case 'c': return "'char'"; + case 'b': return "'signed char'"; + case 'B': return "'unsigned char'"; + case 'h': return "'short'"; + case 'H': return "'unsigned short'"; + case 'i': return "'int'"; + case 'I': return "'unsigned int'"; + case 'l': return "'long'"; + case 'L': return "'unsigned long'"; + case 'q': return "'long long'"; + case 'Q': return "'unsigned long long'"; + case 'f': return (is_complex ? "'complex float'" : "'float'"); + case 'd': return (is_complex ? "'complex double'" : "'double'"); + case 'g': return (is_complex ? "'complex long double'" : "'long double'"); + case 'T': return "a struct"; + case 'O': return "Python object"; + case 'P': return "a pointer"; + case 's': case 'p': return "a string"; + case 0: return "end"; + default: return "unparseable format string"; + } +} +static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return 2; + case 'i': case 'I': case 'l': case 'L': return 4; + case 'q': case 'Q': return 8; + case 'f': return (is_complex ? 8 : 4); + case 'd': return (is_complex ? 16 : 8); + case 'g': { + PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g').."); + return 0; + } + case 'O': case 'P': return sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) { + switch (ch) { + case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(short); + case 'i': case 'I': return sizeof(int); + case 'l': case 'L': return sizeof(long); + #ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(PY_LONG_LONG); + #endif + case 'f': return sizeof(float) * (is_complex ? 2 : 1); + case 'd': return sizeof(double) * (is_complex ? 2 : 1); + case 'g': return sizeof(long double) * (is_complex ? 2 : 1); + case 'O': case 'P': return sizeof(void*); + default: { + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } + } +} +typedef struct { char c; short x; } __Pyx_st_short; +typedef struct { char c; int x; } __Pyx_st_int; +typedef struct { char c; long x; } __Pyx_st_long; +typedef struct { char c; float x; } __Pyx_st_float; +typedef struct { char c; double x; } __Pyx_st_double; +typedef struct { char c; long double x; } __Pyx_st_longdouble; +typedef struct { char c; void *x; } __Pyx_st_void_p; +#ifdef HAVE_LONG_LONG +typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong; +#endif +static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, CYTHON_UNUSED int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short); + case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int); + case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long); +#ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG); +#endif + case 'f': return sizeof(__Pyx_st_float) - sizeof(float); + case 'd': return sizeof(__Pyx_st_double) - sizeof(double); + case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double); + case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +/* These are for computing the padding at the end of the struct to align + on the first member of the struct. This will probably the same as above, + but we don't have any guarantees. + */ +typedef struct { short x; char c; } __Pyx_pad_short; +typedef struct { int x; char c; } __Pyx_pad_int; +typedef struct { long x; char c; } __Pyx_pad_long; +typedef struct { float x; char c; } __Pyx_pad_float; +typedef struct { double x; char c; } __Pyx_pad_double; +typedef struct { long double x; char c; } __Pyx_pad_longdouble; +typedef struct { void *x; char c; } __Pyx_pad_void_p; +#ifdef HAVE_LONG_LONG +typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong; +#endif +static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short); + case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int); + case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long); +#ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG); +#endif + case 'f': return sizeof(__Pyx_pad_float) - sizeof(float); + case 'd': return sizeof(__Pyx_pad_double) - sizeof(double); + case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double); + case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) { + switch (ch) { + case 'c': + return 'H'; + case 'b': case 'h': case 'i': + case 'l': case 'q': case 's': case 'p': + return 'I'; + case 'B': case 'H': case 'I': case 'L': case 'Q': + return 'U'; + case 'f': case 'd': case 'g': + return (is_complex ? 'C' : 'R'); + case 'O': + return 'O'; + case 'P': + return 'P'; + default: { + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } + } +} +static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) { + if (ctx->head == NULL || ctx->head->field == &ctx->root) { + const char* expected; + const char* quote; + if (ctx->head == NULL) { + expected = "end"; + quote = ""; + } else { + expected = ctx->head->field->type->name; + quote = "'"; + } + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch, expected %s%s%s but got %s", + quote, expected, quote, + __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex)); + } else { + __Pyx_StructField* field = ctx->head->field; + __Pyx_StructField* parent = (ctx->head - 1)->field; + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'", + field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex), + parent->type->name, field->name); + } +} +static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) { + char group; + size_t size, offset, arraysize = 1; + if (ctx->enc_type == 0) return 0; + if (ctx->head->field->type->arraysize[0]) { + int i, ndim = 0; + if (ctx->enc_type == 's' || ctx->enc_type == 'p') { + ctx->is_valid_array = ctx->head->field->type->ndim == 1; + ndim = 1; + if (ctx->enc_count != ctx->head->field->type->arraysize[0]) { + PyErr_Format(PyExc_ValueError, + "Expected a dimension of size %zu, got %zu", + ctx->head->field->type->arraysize[0], ctx->enc_count); + return -1; + } + } + if (!ctx->is_valid_array) { + PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d", + ctx->head->field->type->ndim, ndim); + return -1; + } + for (i = 0; i < ctx->head->field->type->ndim; i++) { + arraysize *= ctx->head->field->type->arraysize[i]; + } + ctx->is_valid_array = 0; + ctx->enc_count = 1; + } + group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex); + do { + __Pyx_StructField* field = ctx->head->field; + __Pyx_TypeInfo* type = field->type; + if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') { + size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex); + } else { + size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex); + } + if (ctx->enc_packmode == '@') { + size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex); + size_t align_mod_offset; + if (align_at == 0) return -1; + align_mod_offset = ctx->fmt_offset % align_at; + if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset; + if (ctx->struct_alignment == 0) + ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type, + ctx->is_complex); + } + if (type->size != size || type->typegroup != group) { + if (type->typegroup == 'C' && type->fields != NULL) { + size_t parent_offset = ctx->head->parent_offset + field->offset; + ++ctx->head; + ctx->head->field = type->fields; + ctx->head->parent_offset = parent_offset; + continue; + } + if ((type->typegroup == 'H' || group == 'H') && type->size == size) { + } else { + __Pyx_BufFmt_RaiseExpected(ctx); + return -1; + } + } + offset = ctx->head->parent_offset + field->offset; + if (ctx->fmt_offset != offset) { + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected", + (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset); + return -1; + } + ctx->fmt_offset += size; + if (arraysize) + ctx->fmt_offset += (arraysize - 1) * size; + --ctx->enc_count; + while (1) { + if (field == &ctx->root) { + ctx->head = NULL; + if (ctx->enc_count != 0) { + __Pyx_BufFmt_RaiseExpected(ctx); + return -1; + } + break; + } + ctx->head->field = ++field; + if (field->type == NULL) { + --ctx->head; + field = ctx->head->field; + continue; + } else if (field->type->typegroup == 'S') { + size_t parent_offset = ctx->head->parent_offset + field->offset; + if (field->type->fields->type == NULL) continue; + field = field->type->fields; + ++ctx->head; + ctx->head->field = field; + ctx->head->parent_offset = parent_offset; + break; + } else { + break; + } + } + } while (ctx->enc_count); + ctx->enc_type = 0; + ctx->is_complex = 0; + return 0; +} +static CYTHON_INLINE PyObject * +__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp) +{ + const char *ts = *tsp; + int i = 0, number; + int ndim = ctx->head->field->type->ndim; +; + ++ts; + if (ctx->new_count != 1) { + PyErr_SetString(PyExc_ValueError, + "Cannot handle repeated arrays in format string"); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + while (*ts && *ts != ')') { + switch (*ts) { + case ' ': case '\f': case '\r': case '\n': case '\t': case '\v': continue; + default: break; + } + number = __Pyx_BufFmt_ExpectNumber(&ts); + if (number == -1) return NULL; + if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i]) + return PyErr_Format(PyExc_ValueError, + "Expected a dimension of size %zu, got %d", + ctx->head->field->type->arraysize[i], number); + if (*ts != ',' && *ts != ')') + return PyErr_Format(PyExc_ValueError, + "Expected a comma in format string, got '%c'", *ts); + if (*ts == ',') ts++; + i++; + } + if (i != ndim) + return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d", + ctx->head->field->type->ndim, i); + if (!*ts) { + PyErr_SetString(PyExc_ValueError, + "Unexpected end of format string, expected ')'"); + return NULL; + } + ctx->is_valid_array = 1; + ctx->new_count = 1; + *tsp = ++ts; + return Py_None; +} +static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) { + int got_Z = 0; + while (1) { + switch(*ts) { + case 0: + if (ctx->enc_type != 0 && ctx->head == NULL) { + __Pyx_BufFmt_RaiseExpected(ctx); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + if (ctx->head != NULL) { + __Pyx_BufFmt_RaiseExpected(ctx); + return NULL; + } + return ts; + case ' ': + case '\r': + case '\n': + ++ts; + break; + case '<': + if (!__Pyx_IsLittleEndian()) { + PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler"); + return NULL; + } + ctx->new_packmode = '='; + ++ts; + break; + case '>': + case '!': + if (__Pyx_IsLittleEndian()) { + PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler"); + return NULL; + } + ctx->new_packmode = '='; + ++ts; + break; + case '=': + case '@': + case '^': + ctx->new_packmode = *ts++; + break; + case 'T': + { + const char* ts_after_sub; + size_t i, struct_count = ctx->new_count; + size_t struct_alignment = ctx->struct_alignment; + ctx->new_count = 1; + ++ts; + if (*ts != '{') { + PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'"); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_type = 0; + ctx->enc_count = 0; + ctx->struct_alignment = 0; + ++ts; + ts_after_sub = ts; + for (i = 0; i != struct_count; ++i) { + ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts); + if (!ts_after_sub) return NULL; + } + ts = ts_after_sub; + if (struct_alignment) ctx->struct_alignment = struct_alignment; + } + break; + case '}': + { + size_t alignment = ctx->struct_alignment; + ++ts; + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_type = 0; + if (alignment && ctx->fmt_offset % alignment) { + ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment); + } + } + return ts; + case 'x': + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->fmt_offset += ctx->new_count; + ctx->new_count = 1; + ctx->enc_count = 0; + ctx->enc_type = 0; + ctx->enc_packmode = ctx->new_packmode; + ++ts; + break; + case 'Z': + got_Z = 1; + ++ts; + if (*ts != 'f' && *ts != 'd' && *ts != 'g') { + __Pyx_BufFmt_RaiseUnexpectedChar('Z'); + return NULL; + } + case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I': + case 'l': case 'L': case 'q': case 'Q': + case 'f': case 'd': case 'g': + case 'O': case 'p': + if (ctx->enc_type == *ts && got_Z == ctx->is_complex && + ctx->enc_packmode == ctx->new_packmode) { + ctx->enc_count += ctx->new_count; + ctx->new_count = 1; + got_Z = 0; + ++ts; + break; + } + case 's': + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_count = ctx->new_count; + ctx->enc_packmode = ctx->new_packmode; + ctx->enc_type = *ts; + ctx->is_complex = got_Z; + ++ts; + ctx->new_count = 1; + got_Z = 0; + break; + case ':': + ++ts; + while(*ts != ':') ++ts; + ++ts; + break; + case '(': + if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL; + break; + default: + { + int number = __Pyx_BufFmt_ExpectNumber(&ts); + if (number == -1) return NULL; + ctx->new_count = (size_t)number; + } + } + } +} +static CYTHON_INLINE void __Pyx_ZeroBuffer(Py_buffer* buf) { + buf->buf = NULL; + buf->obj = NULL; + buf->strides = __Pyx_zeros; + buf->shape = __Pyx_zeros; + buf->suboffsets = __Pyx_minusones; +} +static CYTHON_INLINE int __Pyx_GetBufferAndValidate( + Py_buffer* buf, PyObject* obj, __Pyx_TypeInfo* dtype, int flags, + int nd, int cast, __Pyx_BufFmt_StackElem* stack) +{ + if (obj == Py_None || obj == NULL) { + __Pyx_ZeroBuffer(buf); + return 0; + } + buf->buf = NULL; + if (__Pyx_GetBuffer(obj, buf, flags) == -1) goto fail; + if (buf->ndim != nd) { + PyErr_Format(PyExc_ValueError, + "Buffer has wrong number of dimensions (expected %d, got %d)", + nd, buf->ndim); + goto fail; + } + if (!cast) { + __Pyx_BufFmt_Context ctx; + __Pyx_BufFmt_Init(&ctx, stack, dtype); + if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail; + } + if ((unsigned)buf->itemsize != dtype->size) { + PyErr_Format(PyExc_ValueError, + "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "d byte%s) does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "d byte%s)", + buf->itemsize, (buf->itemsize > 1) ? "s" : "", + dtype->name, (Py_ssize_t)dtype->size, (dtype->size > 1) ? "s" : ""); + goto fail; + } + if (buf->suboffsets == NULL) buf->suboffsets = __Pyx_minusones; + return 0; +fail:; + __Pyx_ZeroBuffer(buf); + return -1; +} +static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info) { + if (info->buf == NULL) return; + if (info->suboffsets == __Pyx_minusones) info->suboffsets = NULL; + __Pyx_ReleaseBuffer(info); +} + +/* ExtTypeTest */ + static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { + if (unlikely(!type)) { + PyErr_SetString(PyExc_SystemError, "Missing type object"); + return 0; + } + if (likely(PyObject_TypeCheck(obj, type))) + return 1; + PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s", + Py_TYPE(obj)->tp_name, type->tp_name); + return 0; +} + +/* PyIntBinop */ + #if !CYTHON_COMPILING_IN_PYPY +static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, CYTHON_UNUSED long intval, CYTHON_UNUSED int inplace) { + #if PY_MAJOR_VERSION < 3 + if (likely(PyInt_CheckExact(op1))) { + const long b = intval; + long x; + long a = PyInt_AS_LONG(op1); + x = (long)((unsigned long)a + b); + if (likely((x^a) >= 0 || (x^b) >= 0)) + return PyInt_FromLong(x); + return PyLong_Type.tp_as_number->nb_add(op1, op2); + } + #endif + #if CYTHON_USE_PYLONG_INTERNALS + if (likely(PyLong_CheckExact(op1))) { + const long b = intval; + long a, x; +#ifdef HAVE_LONG_LONG + const PY_LONG_LONG llb = intval; + PY_LONG_LONG lla, llx; +#endif + const digit* digits = ((PyLongObject*)op1)->ob_digit; + const Py_ssize_t size = Py_SIZE(op1); + if (likely(__Pyx_sst_abs(size) <= 1)) { + a = likely(size) ? digits[0] : 0; + if (size == -1) a = -a; + } else { + switch (size) { + case -2: + if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + a = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) { + lla = -(PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + case 2: + if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) { + lla = (PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + case -3: + if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + a = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) { + lla = -(PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + case 3: + if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + a = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) { + lla = (PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + case -4: + if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + a = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) { + lla = -(PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + case 4: + if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + a = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) { + lla = (PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + default: return PyLong_Type.tp_as_number->nb_add(op1, op2); + } + } + x = a + b; + return PyLong_FromLong(x); +#ifdef HAVE_LONG_LONG + long_long: + llx = lla + llb; + return PyLong_FromLongLong(llx); +#endif + + + } + #endif + if (PyFloat_CheckExact(op1)) { + const long b = intval; + double a = PyFloat_AS_DOUBLE(op1); + double result; + PyFPE_START_PROTECT("add", return NULL) + result = ((double)a) + (double)b; + PyFPE_END_PROTECT(result) + return PyFloat_FromDouble(result); + } + return (inplace ? PyNumber_InPlaceAdd : PyNumber_Add)(op1, op2); +} +#endif + +/* PyCFunctionFastCall */ + #if CYTHON_FAST_PYCCALL +static CYTHON_INLINE PyObject * __Pyx_PyCFunction_FastCall(PyObject *func_obj, PyObject **args, Py_ssize_t nargs) { + PyCFunctionObject *func = (PyCFunctionObject*)func_obj; + PyCFunction meth = PyCFunction_GET_FUNCTION(func); + PyObject *self = PyCFunction_GET_SELF(func); + assert(PyCFunction_Check(func)); + assert(METH_FASTCALL == (PyCFunction_GET_FLAGS(func) & ~(METH_CLASS | METH_STATIC | METH_COEXIST))); + assert(nargs >= 0); + assert(nargs == 0 || args != NULL); + /* _PyCFunction_FastCallDict() must not be called with an exception set, + because it may clear it (directly or indirectly) and so the + caller loses its exception */ + assert(!PyErr_Occurred()); + return (*((__Pyx_PyCFunctionFast)meth)) (self, args, nargs, NULL); +} +#endif // CYTHON_FAST_PYCCALL + +/* PyFunctionFastCall */ + #if CYTHON_FAST_PYCALL +#include "frameobject.h" +static PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na, + PyObject *globals) { + PyFrameObject *f; + PyThreadState *tstate = PyThreadState_GET(); + PyObject **fastlocals; + Py_ssize_t i; + PyObject *result; + assert(globals != NULL); + /* XXX Perhaps we should create a specialized + PyFrame_New() that doesn't take locals, but does + take builtins without sanity checking them. + */ + assert(tstate != NULL); + f = PyFrame_New(tstate, co, globals, NULL); + if (f == NULL) { + return NULL; + } + fastlocals = f->f_localsplus; + for (i = 0; i < na; i++) { + Py_INCREF(*args); + fastlocals[i] = *args++; + } + result = PyEval_EvalFrameEx(f,0); + ++tstate->recursion_depth; + Py_DECREF(f); + --tstate->recursion_depth; + return result; +} +#if 1 || PY_VERSION_HEX < 0x030600B1 +static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, int nargs, PyObject *kwargs) { + PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func); + PyObject *globals = PyFunction_GET_GLOBALS(func); + PyObject *argdefs = PyFunction_GET_DEFAULTS(func); + PyObject *closure; +#if PY_MAJOR_VERSION >= 3 + PyObject *kwdefs; +#endif + PyObject *kwtuple, **k; + PyObject **d; + Py_ssize_t nd; + Py_ssize_t nk; + PyObject *result; + assert(kwargs == NULL || PyDict_Check(kwargs)); + nk = kwargs ? PyDict_Size(kwargs) : 0; + if (Py_EnterRecursiveCall((char*)" while calling a Python object")) { + return NULL; + } + if ( +#if PY_MAJOR_VERSION >= 3 + co->co_kwonlyargcount == 0 && +#endif + likely(kwargs == NULL || nk == 0) && + co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) { + if (argdefs == NULL && co->co_argcount == nargs) { + result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals); + goto done; + } + else if (nargs == 0 && argdefs != NULL + && co->co_argcount == Py_SIZE(argdefs)) { + /* function called with no arguments, but all parameters have + a default value: use default values as arguments .*/ + args = &PyTuple_GET_ITEM(argdefs, 0); + result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals); + goto done; + } + } + if (kwargs != NULL) { + Py_ssize_t pos, i; + kwtuple = PyTuple_New(2 * nk); + if (kwtuple == NULL) { + result = NULL; + goto done; + } + k = &PyTuple_GET_ITEM(kwtuple, 0); + pos = i = 0; + while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) { + Py_INCREF(k[i]); + Py_INCREF(k[i+1]); + i += 2; + } + nk = i / 2; + } + else { + kwtuple = NULL; + k = NULL; + } + closure = PyFunction_GET_CLOSURE(func); +#if PY_MAJOR_VERSION >= 3 + kwdefs = PyFunction_GET_KW_DEFAULTS(func); +#endif + if (argdefs != NULL) { + d = &PyTuple_GET_ITEM(argdefs, 0); + nd = Py_SIZE(argdefs); + } + else { + d = NULL; + nd = 0; + } +#if PY_MAJOR_VERSION >= 3 + result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL, + args, nargs, + k, (int)nk, + d, (int)nd, kwdefs, closure); +#else + result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL, + args, nargs, + k, (int)nk, + d, (int)nd, closure); +#endif + Py_XDECREF(kwtuple); +done: + Py_LeaveRecursiveCall(); + return result; +} +#endif // CPython < 3.6 +#endif // CYTHON_FAST_PYCALL + +/* PyObjectCall */ + #if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) { + PyObject *result; + ternaryfunc call = func->ob_type->tp_call; + if (unlikely(!call)) + return PyObject_Call(func, arg, kw); + if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) + return NULL; + result = (*call)(func, arg, kw); + Py_LeaveRecursiveCall(); + if (unlikely(!result) && unlikely(!PyErr_Occurred())) { + PyErr_SetString( + PyExc_SystemError, + "NULL result without error in PyObject_Call"); + } + return result; +} +#endif + +/* PyObjectCallMethO */ + #if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) { + PyObject *self, *result; + PyCFunction cfunc; + cfunc = PyCFunction_GET_FUNCTION(func); + self = PyCFunction_GET_SELF(func); + if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) + return NULL; + result = cfunc(self, arg); + Py_LeaveRecursiveCall(); + if (unlikely(!result) && unlikely(!PyErr_Occurred())) { + PyErr_SetString( + PyExc_SystemError, + "NULL result without error in PyObject_Call"); + } + return result; +} +#endif + +/* PyObjectCallOneArg */ + #if CYTHON_COMPILING_IN_CPYTHON +static PyObject* __Pyx__PyObject_CallOneArg(PyObject *func, PyObject *arg) { + PyObject *result; + PyObject *args = PyTuple_New(1); + if (unlikely(!args)) return NULL; + Py_INCREF(arg); + PyTuple_SET_ITEM(args, 0, arg); + result = __Pyx_PyObject_Call(func, args, NULL); + Py_DECREF(args); + return result; +} +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { +#if CYTHON_FAST_PYCALL + if (PyFunction_Check(func)) { + return __Pyx_PyFunction_FastCall(func, &arg, 1); + } +#endif +#ifdef __Pyx_CyFunction_USED + if (likely(PyCFunction_Check(func) || PyObject_TypeCheck(func, __pyx_CyFunctionType))) { +#else + if (likely(PyCFunction_Check(func))) { +#endif + if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) { + return __Pyx_PyObject_CallMethO(func, arg); +#if CYTHON_FAST_PYCCALL + } else if (PyCFunction_GET_FLAGS(func) & METH_FASTCALL) { + return __Pyx_PyCFunction_FastCall(func, &arg, 1); +#endif + } + } + return __Pyx__PyObject_CallOneArg(func, arg); +} +#else +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { + PyObject *result; + PyObject *args = PyTuple_Pack(1, arg); + if (unlikely(!args)) return NULL; + result = __Pyx_PyObject_Call(func, args, NULL); + Py_DECREF(args); + return result; +} +#endif + +/* PyObjectCallNoArg */ + #if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func) { +#if CYTHON_FAST_PYCALL + if (PyFunction_Check(func)) { + return __Pyx_PyFunction_FastCall(func, NULL, 0); + } +#endif +#ifdef __Pyx_CyFunction_USED + if (likely(PyCFunction_Check(func) || PyObject_TypeCheck(func, __pyx_CyFunctionType))) { +#else + if (likely(PyCFunction_Check(func))) { +#endif + if (likely(PyCFunction_GET_FLAGS(func) & METH_NOARGS)) { + return __Pyx_PyObject_CallMethO(func, NULL); + } + } + return __Pyx_PyObject_Call(func, __pyx_empty_tuple, NULL); +} +#endif + +/* GetModuleGlobalName */ + static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name) { + PyObject *result; +#if !CYTHON_AVOID_BORROWED_REFS + result = PyDict_GetItem(__pyx_d, name); + if (likely(result)) { + Py_INCREF(result); + } else { +#else + result = PyObject_GetItem(__pyx_d, name); + if (!result) { + PyErr_Clear(); +#endif + result = __Pyx_GetBuiltinName(name); + } + return result; +} + +/* BufferIndexError */ + static void __Pyx_RaiseBufferIndexError(int axis) { + PyErr_Format(PyExc_IndexError, + "Out of bounds on buffer access (axis %d)", axis); +} + +/* PyErrFetchRestore */ + #if CYTHON_FAST_THREAD_STATE +static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { + PyObject *tmp_type, *tmp_value, *tmp_tb; + tmp_type = tstate->curexc_type; + tmp_value = tstate->curexc_value; + tmp_tb = tstate->curexc_traceback; + tstate->curexc_type = type; + tstate->curexc_value = value; + tstate->curexc_traceback = tb; + Py_XDECREF(tmp_type); + Py_XDECREF(tmp_value); + Py_XDECREF(tmp_tb); +} +static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { + *type = tstate->curexc_type; + *value = tstate->curexc_value; + *tb = tstate->curexc_traceback; + tstate->curexc_type = 0; + tstate->curexc_value = 0; + tstate->curexc_traceback = 0; +} +#endif + +/* RaiseException */ + #if PY_MAJOR_VERSION < 3 +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, + CYTHON_UNUSED PyObject *cause) { + __Pyx_PyThreadState_declare + Py_XINCREF(type); + if (!value || value == Py_None) + value = NULL; + else + Py_INCREF(value); + if (!tb || tb == Py_None) + tb = NULL; + else { + Py_INCREF(tb); + if (!PyTraceBack_Check(tb)) { + PyErr_SetString(PyExc_TypeError, + "raise: arg 3 must be a traceback or None"); + goto raise_error; + } + } + if (PyType_Check(type)) { +#if CYTHON_COMPILING_IN_PYPY + if (!value) { + Py_INCREF(Py_None); + value = Py_None; + } +#endif + PyErr_NormalizeException(&type, &value, &tb); + } else { + if (value) { + PyErr_SetString(PyExc_TypeError, + "instance exception may not have a separate value"); + goto raise_error; + } + value = type; + type = (PyObject*) Py_TYPE(type); + Py_INCREF(type); + if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { + PyErr_SetString(PyExc_TypeError, + "raise: exception class must be a subclass of BaseException"); + goto raise_error; + } + } + __Pyx_PyThreadState_assign + __Pyx_ErrRestore(type, value, tb); + return; +raise_error: + Py_XDECREF(value); + Py_XDECREF(type); + Py_XDECREF(tb); + return; +} +#else +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { + PyObject* owned_instance = NULL; + if (tb == Py_None) { + tb = 0; + } else if (tb && !PyTraceBack_Check(tb)) { + PyErr_SetString(PyExc_TypeError, + "raise: arg 3 must be a traceback or None"); + goto bad; + } + if (value == Py_None) + value = 0; + if (PyExceptionInstance_Check(type)) { + if (value) { + PyErr_SetString(PyExc_TypeError, + "instance exception may not have a separate value"); + goto bad; + } + value = type; + type = (PyObject*) Py_TYPE(value); + } else if (PyExceptionClass_Check(type)) { + PyObject *instance_class = NULL; + if (value && PyExceptionInstance_Check(value)) { + instance_class = (PyObject*) Py_TYPE(value); + if (instance_class != type) { + int is_subclass = PyObject_IsSubclass(instance_class, type); + if (!is_subclass) { + instance_class = NULL; + } else if (unlikely(is_subclass == -1)) { + goto bad; + } else { + type = instance_class; + } + } + } + if (!instance_class) { + PyObject *args; + if (!value) + args = PyTuple_New(0); + else if (PyTuple_Check(value)) { + Py_INCREF(value); + args = value; + } else + args = PyTuple_Pack(1, value); + if (!args) + goto bad; + owned_instance = PyObject_Call(type, args, NULL); + Py_DECREF(args); + if (!owned_instance) + goto bad; + value = owned_instance; + if (!PyExceptionInstance_Check(value)) { + PyErr_Format(PyExc_TypeError, + "calling %R should have returned an instance of " + "BaseException, not %R", + type, Py_TYPE(value)); + goto bad; + } + } + } else { + PyErr_SetString(PyExc_TypeError, + "raise: exception class must be a subclass of BaseException"); + goto bad; + } +#if PY_VERSION_HEX >= 0x03030000 + if (cause) { +#else + if (cause && cause != Py_None) { +#endif + PyObject *fixed_cause; + if (cause == Py_None) { + fixed_cause = NULL; + } else if (PyExceptionClass_Check(cause)) { + fixed_cause = PyObject_CallObject(cause, NULL); + if (fixed_cause == NULL) + goto bad; + } else if (PyExceptionInstance_Check(cause)) { + fixed_cause = cause; + Py_INCREF(fixed_cause); + } else { + PyErr_SetString(PyExc_TypeError, + "exception causes must derive from " + "BaseException"); + goto bad; + } + PyException_SetCause(value, fixed_cause); + } + PyErr_SetObject(type, value); + if (tb) { +#if CYTHON_COMPILING_IN_PYPY + PyObject *tmp_type, *tmp_value, *tmp_tb; + PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb); + Py_INCREF(tb); + PyErr_Restore(tmp_type, tmp_value, tb); + Py_XDECREF(tmp_tb); +#else + PyThreadState *tstate = PyThreadState_GET(); + PyObject* tmp_tb = tstate->curexc_traceback; + if (tb != tmp_tb) { + Py_INCREF(tb); + tstate->curexc_traceback = tb; + Py_XDECREF(tmp_tb); + } +#endif + } +bad: + Py_XDECREF(owned_instance); + return; +} +#endif + +/* RaiseTooManyValuesToUnpack */ + static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { + PyErr_Format(PyExc_ValueError, + "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); +} + +/* RaiseNeedMoreValuesToUnpack */ + static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { + PyErr_Format(PyExc_ValueError, + "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack", + index, (index == 1) ? "" : "s"); +} + +/* RaiseNoneIterError */ + static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { + PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); +} + +/* SaveResetException */ + #if CYTHON_FAST_THREAD_STATE +static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { + *type = tstate->exc_type; + *value = tstate->exc_value; + *tb = tstate->exc_traceback; + Py_XINCREF(*type); + Py_XINCREF(*value); + Py_XINCREF(*tb); +} +static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { + PyObject *tmp_type, *tmp_value, *tmp_tb; + tmp_type = tstate->exc_type; + tmp_value = tstate->exc_value; + tmp_tb = tstate->exc_traceback; + tstate->exc_type = type; + tstate->exc_value = value; + tstate->exc_traceback = tb; + Py_XDECREF(tmp_type); + Py_XDECREF(tmp_value); + Py_XDECREF(tmp_tb); +} +#endif + +/* PyErrExceptionMatches */ + #if CYTHON_FAST_THREAD_STATE +static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err) { + PyObject *exc_type = tstate->curexc_type; + if (exc_type == err) return 1; + if (unlikely(!exc_type)) return 0; + return PyErr_GivenExceptionMatches(exc_type, err); +} +#endif + +/* GetException */ + #if CYTHON_FAST_THREAD_STATE +static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { +#else +static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) { +#endif + PyObject *local_type, *local_value, *local_tb; +#if CYTHON_FAST_THREAD_STATE + PyObject *tmp_type, *tmp_value, *tmp_tb; + local_type = tstate->curexc_type; + local_value = tstate->curexc_value; + local_tb = tstate->curexc_traceback; + tstate->curexc_type = 0; + tstate->curexc_value = 0; + tstate->curexc_traceback = 0; +#else + PyErr_Fetch(&local_type, &local_value, &local_tb); +#endif + PyErr_NormalizeException(&local_type, &local_value, &local_tb); +#if CYTHON_FAST_THREAD_STATE + if (unlikely(tstate->curexc_type)) +#else + if (unlikely(PyErr_Occurred())) +#endif + goto bad; + #if PY_MAJOR_VERSION >= 3 + if (local_tb) { + if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0)) + goto bad; + } + #endif + Py_XINCREF(local_tb); + Py_XINCREF(local_type); + Py_XINCREF(local_value); + *type = local_type; + *value = local_value; + *tb = local_tb; +#if CYTHON_FAST_THREAD_STATE + tmp_type = tstate->exc_type; + tmp_value = tstate->exc_value; + tmp_tb = tstate->exc_traceback; + tstate->exc_type = local_type; + tstate->exc_value = local_value; + tstate->exc_traceback = local_tb; + Py_XDECREF(tmp_type); + Py_XDECREF(tmp_value); + Py_XDECREF(tmp_tb); +#else + PyErr_SetExcInfo(local_type, local_value, local_tb); +#endif + return 0; +bad: + *type = 0; + *value = 0; + *tb = 0; + Py_XDECREF(local_type); + Py_XDECREF(local_value); + Py_XDECREF(local_tb); + return -1; +} + +/* Import */ + static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { + PyObject *empty_list = 0; + PyObject *module = 0; + PyObject *global_dict = 0; + PyObject *empty_dict = 0; + PyObject *list; + #if PY_VERSION_HEX < 0x03030000 + PyObject *py_import; + py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import); + if (!py_import) + goto bad; + #endif + if (from_list) + list = from_list; + else { + empty_list = PyList_New(0); + if (!empty_list) + goto bad; + list = empty_list; + } + global_dict = PyModule_GetDict(__pyx_m); + if (!global_dict) + goto bad; + empty_dict = PyDict_New(); + if (!empty_dict) + goto bad; + { + #if PY_MAJOR_VERSION >= 3 + if (level == -1) { + if (strchr(__Pyx_MODULE_NAME, '.')) { + #if PY_VERSION_HEX < 0x03030000 + PyObject *py_level = PyInt_FromLong(1); + if (!py_level) + goto bad; + module = PyObject_CallFunctionObjArgs(py_import, + name, global_dict, empty_dict, list, py_level, NULL); + Py_DECREF(py_level); + #else + module = PyImport_ImportModuleLevelObject( + name, global_dict, empty_dict, list, 1); + #endif + if (!module) { + if (!PyErr_ExceptionMatches(PyExc_ImportError)) + goto bad; + PyErr_Clear(); + } + } + level = 0; + } + #endif + if (!module) { + #if PY_VERSION_HEX < 0x03030000 + PyObject *py_level = PyInt_FromLong(level); + if (!py_level) + goto bad; + module = PyObject_CallFunctionObjArgs(py_import, + name, global_dict, empty_dict, list, py_level, NULL); + Py_DECREF(py_level); + #else + module = PyImport_ImportModuleLevelObject( + name, global_dict, empty_dict, list, level); + #endif + } + } +bad: + #if PY_VERSION_HEX < 0x03030000 + Py_XDECREF(py_import); + #endif + Py_XDECREF(empty_list); + Py_XDECREF(empty_dict); + return module; +} + +/* CodeObjectCache */ + static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) { + int start = 0, mid = 0, end = count - 1; + if (end >= 0 && code_line > entries[end].code_line) { + return count; + } + while (start < end) { + mid = start + (end - start) / 2; + if (code_line < entries[mid].code_line) { + end = mid; + } else if (code_line > entries[mid].code_line) { + start = mid + 1; + } else { + return mid; + } + } + if (code_line <= entries[mid].code_line) { + return mid; + } else { + return mid + 1; + } +} +static PyCodeObject *__pyx_find_code_object(int code_line) { + PyCodeObject* code_object; + int pos; + if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) { + return NULL; + } + pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); + if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) { + return NULL; + } + code_object = __pyx_code_cache.entries[pos].code_object; + Py_INCREF(code_object); + return code_object; +} +static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { + int pos, i; + __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries; + if (unlikely(!code_line)) { + return; + } + if (unlikely(!entries)) { + entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry)); + if (likely(entries)) { + __pyx_code_cache.entries = entries; + __pyx_code_cache.max_count = 64; + __pyx_code_cache.count = 1; + entries[0].code_line = code_line; + entries[0].code_object = code_object; + Py_INCREF(code_object); + } + return; + } + pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); + if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) { + PyCodeObject* tmp = entries[pos].code_object; + entries[pos].code_object = code_object; + Py_DECREF(tmp); + return; + } + if (__pyx_code_cache.count == __pyx_code_cache.max_count) { + int new_max = __pyx_code_cache.max_count + 64; + entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( + __pyx_code_cache.entries, (size_t)new_max*sizeof(__Pyx_CodeObjectCacheEntry)); + if (unlikely(!entries)) { + return; + } + __pyx_code_cache.entries = entries; + __pyx_code_cache.max_count = new_max; + } + for (i=__pyx_code_cache.count; i>pos; i--) { + entries[i] = entries[i-1]; + } + entries[pos].code_line = code_line; + entries[pos].code_object = code_object; + __pyx_code_cache.count++; + Py_INCREF(code_object); +} + +/* AddTraceback */ + #include "compile.h" +#include "frameobject.h" +#include "traceback.h" +static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( + const char *funcname, int c_line, + int py_line, const char *filename) { + PyCodeObject *py_code = 0; + PyObject *py_srcfile = 0; + PyObject *py_funcname = 0; + #if PY_MAJOR_VERSION < 3 + py_srcfile = PyString_FromString(filename); + #else + py_srcfile = PyUnicode_FromString(filename); + #endif + if (!py_srcfile) goto bad; + if (c_line) { + #if PY_MAJOR_VERSION < 3 + py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); + #else + py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); + #endif + } + else { + #if PY_MAJOR_VERSION < 3 + py_funcname = PyString_FromString(funcname); + #else + py_funcname = PyUnicode_FromString(funcname); + #endif + } + if (!py_funcname) goto bad; + py_code = __Pyx_PyCode_New( + 0, + 0, + 0, + 0, + 0, + __pyx_empty_bytes, /*PyObject *code,*/ + __pyx_empty_tuple, /*PyObject *consts,*/ + __pyx_empty_tuple, /*PyObject *names,*/ + __pyx_empty_tuple, /*PyObject *varnames,*/ + __pyx_empty_tuple, /*PyObject *freevars,*/ + __pyx_empty_tuple, /*PyObject *cellvars,*/ + py_srcfile, /*PyObject *filename,*/ + py_funcname, /*PyObject *name,*/ + py_line, + __pyx_empty_bytes /*PyObject *lnotab*/ + ); + Py_DECREF(py_srcfile); + Py_DECREF(py_funcname); + return py_code; +bad: + Py_XDECREF(py_srcfile); + Py_XDECREF(py_funcname); + return NULL; +} +static void __Pyx_AddTraceback(const char *funcname, int c_line, + int py_line, const char *filename) { + PyCodeObject *py_code = 0; + PyFrameObject *py_frame = 0; + py_code = __pyx_find_code_object(c_line ? c_line : py_line); + if (!py_code) { + py_code = __Pyx_CreateCodeObjectForTraceback( + funcname, c_line, py_line, filename); + if (!py_code) goto bad; + __pyx_insert_code_object(c_line ? c_line : py_line, py_code); + } + py_frame = PyFrame_New( + PyThreadState_GET(), /*PyThreadState *tstate,*/ + py_code, /*PyCodeObject *code,*/ + __pyx_d, /*PyObject *globals,*/ + 0 /*PyObject *locals*/ + ); + if (!py_frame) goto bad; + __Pyx_PyFrame_SetLineNumber(py_frame, py_line); + PyTraceBack_Here(py_frame); +bad: + Py_XDECREF(py_code); + Py_XDECREF(py_frame); +} + +#if PY_MAJOR_VERSION < 3 +static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) { + if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags); + if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) return __pyx_pw_5numpy_7ndarray_1__getbuffer__(obj, view, flags); + PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name); + return -1; +} +static void __Pyx_ReleaseBuffer(Py_buffer *view) { + PyObject *obj = view->obj; + if (!obj) return; + if (PyObject_CheckBuffer(obj)) { + PyBuffer_Release(view); + return; + } + if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) { __pyx_pw_5numpy_7ndarray_3__releasebuffer__(obj, view); return; } + Py_DECREF(obj); + view->obj = NULL; +} +#endif + + + /* CIntToPy */ + static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) { + const int neg_one = (int) -1, const_zero = (int) 0; + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(int) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(int) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); +#endif + } + } else { + if (sizeof(int) <= sizeof(long)) { + return PyInt_FromLong((long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); +#endif + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(int), + little, !is_unsigned); + } +} + +/* CIntFromPyVerify */ + #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\ + __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0) +#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\ + __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1) +#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\ + {\ + func_type value = func_value;\ + if (sizeof(target_type) < sizeof(func_type)) {\ + if (unlikely(value != (func_type) (target_type) value)) {\ + func_type zero = 0;\ + if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\ + return (target_type) -1;\ + if (is_unsigned && unlikely(value < zero))\ + goto raise_neg_overflow;\ + else\ + goto raise_overflow;\ + }\ + }\ + return (target_type) value;\ + } + +/* CIntToPy */ + static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { + const long neg_one = (long) -1, const_zero = (long) 0; + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(long) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(long) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); +#endif + } + } else { + if (sizeof(long) <= sizeof(long)) { + return PyInt_FromLong((long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); +#endif + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(long), + little, !is_unsigned); + } +} + +/* Declarations */ + #if CYTHON_CCOMPLEX + #ifdef __cplusplus + static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { + return ::std::complex< float >(x, y); + } + #else + static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { + return x + y*(__pyx_t_float_complex)_Complex_I; + } + #endif +#else + static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { + __pyx_t_float_complex z; + z.real = x; + z.imag = y; + return z; + } +#endif + +/* Arithmetic */ + #if CYTHON_CCOMPLEX +#else + static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + return (a.real == b.real) && (a.imag == b.imag); + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + z.real = a.real + b.real; + z.imag = a.imag + b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + z.real = a.real - b.real; + z.imag = a.imag - b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + z.real = a.real * b.real - a.imag * b.imag; + z.imag = a.real * b.imag + a.imag * b.real; + return z; + } + #if 1 + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + if (b.imag == 0) { + return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real); + } else if (fabsf(b.real) >= fabsf(b.imag)) { + if (b.real == 0 && b.imag == 0) { + return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.imag); + } else { + float r = b.imag / b.real; + float s = 1.0 / (b.real + b.imag * r); + return __pyx_t_float_complex_from_parts( + (a.real + a.imag * r) * s, (a.imag - a.real * r) * s); + } + } else { + float r = b.real / b.imag; + float s = 1.0 / (b.imag + b.real * r); + return __pyx_t_float_complex_from_parts( + (a.real * r + a.imag) * s, (a.imag * r - a.real) * s); + } + } + #else + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + if (b.imag == 0) { + return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real); + } else { + float denom = b.real * b.real + b.imag * b.imag; + return __pyx_t_float_complex_from_parts( + (a.real * b.real + a.imag * b.imag) / denom, + (a.imag * b.real - a.real * b.imag) / denom); + } + } + #endif + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex a) { + __pyx_t_float_complex z; + z.real = -a.real; + z.imag = -a.imag; + return z; + } + static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex a) { + return (a.real == 0) && (a.imag == 0); + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex a) { + __pyx_t_float_complex z; + z.real = a.real; + z.imag = -a.imag; + return z; + } + #if 1 + static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex z) { + #if !defined(HAVE_HYPOT) || defined(_MSC_VER) + return sqrtf(z.real*z.real + z.imag*z.imag); + #else + return hypotf(z.real, z.imag); + #endif + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + float r, lnr, theta, z_r, z_theta; + if (b.imag == 0 && b.real == (int)b.real) { + if (b.real < 0) { + float denom = a.real * a.real + a.imag * a.imag; + a.real = a.real / denom; + a.imag = -a.imag / denom; + b.real = -b.real; + } + switch ((int)b.real) { + case 0: + z.real = 1; + z.imag = 0; + return z; + case 1: + return a; + case 2: + z = __Pyx_c_prod_float(a, a); + return __Pyx_c_prod_float(a, a); + case 3: + z = __Pyx_c_prod_float(a, a); + return __Pyx_c_prod_float(z, a); + case 4: + z = __Pyx_c_prod_float(a, a); + return __Pyx_c_prod_float(z, z); + } + } + if (a.imag == 0) { + if (a.real == 0) { + return a; + } else if (b.imag == 0) { + z.real = powf(a.real, b.real); + z.imag = 0; + return z; + } else if (a.real > 0) { + r = a.real; + theta = 0; + } else { + r = -a.real; + theta = atan2f(0, -1); + } + } else { + r = __Pyx_c_abs_float(a); + theta = atan2f(a.imag, a.real); + } + lnr = logf(r); + z_r = expf(lnr * b.real - theta * b.imag); + z_theta = theta * b.real + lnr * b.imag; + z.real = z_r * cosf(z_theta); + z.imag = z_r * sinf(z_theta); + return z; + } + #endif +#endif + +/* Declarations */ + #if CYTHON_CCOMPLEX + #ifdef __cplusplus + static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { + return ::std::complex< double >(x, y); + } + #else + static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { + return x + y*(__pyx_t_double_complex)_Complex_I; + } + #endif +#else + static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { + __pyx_t_double_complex z; + z.real = x; + z.imag = y; + return z; + } +#endif + +/* Arithmetic */ + #if CYTHON_CCOMPLEX +#else + static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + return (a.real == b.real) && (a.imag == b.imag); + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + z.real = a.real + b.real; + z.imag = a.imag + b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + z.real = a.real - b.real; + z.imag = a.imag - b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + z.real = a.real * b.real - a.imag * b.imag; + z.imag = a.real * b.imag + a.imag * b.real; + return z; + } + #if 1 + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + if (b.imag == 0) { + return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real); + } else if (fabs(b.real) >= fabs(b.imag)) { + if (b.real == 0 && b.imag == 0) { + return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.imag); + } else { + double r = b.imag / b.real; + double s = 1.0 / (b.real + b.imag * r); + return __pyx_t_double_complex_from_parts( + (a.real + a.imag * r) * s, (a.imag - a.real * r) * s); + } + } else { + double r = b.real / b.imag; + double s = 1.0 / (b.imag + b.real * r); + return __pyx_t_double_complex_from_parts( + (a.real * r + a.imag) * s, (a.imag * r - a.real) * s); + } + } + #else + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + if (b.imag == 0) { + return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real); + } else { + double denom = b.real * b.real + b.imag * b.imag; + return __pyx_t_double_complex_from_parts( + (a.real * b.real + a.imag * b.imag) / denom, + (a.imag * b.real - a.real * b.imag) / denom); + } + } + #endif + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex a) { + __pyx_t_double_complex z; + z.real = -a.real; + z.imag = -a.imag; + return z; + } + static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex a) { + return (a.real == 0) && (a.imag == 0); + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex a) { + __pyx_t_double_complex z; + z.real = a.real; + z.imag = -a.imag; + return z; + } + #if 1 + static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex z) { + #if !defined(HAVE_HYPOT) || defined(_MSC_VER) + return sqrt(z.real*z.real + z.imag*z.imag); + #else + return hypot(z.real, z.imag); + #endif + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + double r, lnr, theta, z_r, z_theta; + if (b.imag == 0 && b.real == (int)b.real) { + if (b.real < 0) { + double denom = a.real * a.real + a.imag * a.imag; + a.real = a.real / denom; + a.imag = -a.imag / denom; + b.real = -b.real; + } + switch ((int)b.real) { + case 0: + z.real = 1; + z.imag = 0; + return z; + case 1: + return a; + case 2: + z = __Pyx_c_prod_double(a, a); + return __Pyx_c_prod_double(a, a); + case 3: + z = __Pyx_c_prod_double(a, a); + return __Pyx_c_prod_double(z, a); + case 4: + z = __Pyx_c_prod_double(a, a); + return __Pyx_c_prod_double(z, z); + } + } + if (a.imag == 0) { + if (a.real == 0) { + return a; + } else if (b.imag == 0) { + z.real = pow(a.real, b.real); + z.imag = 0; + return z; + } else if (a.real > 0) { + r = a.real; + theta = 0; + } else { + r = -a.real; + theta = atan2(0, -1); + } + } else { + r = __Pyx_c_abs_double(a); + theta = atan2(a.imag, a.real); + } + lnr = log(r); + z_r = exp(lnr * b.real - theta * b.imag); + z_theta = theta * b.real + lnr * b.imag; + z.real = z_r * cos(z_theta); + z.imag = z_r * sin(z_theta); + return z; + } + #endif +#endif + +/* CIntToPy */ + static CYTHON_INLINE PyObject* __Pyx_PyInt_From_enum__NPY_TYPES(enum NPY_TYPES value) { + const enum NPY_TYPES neg_one = (enum NPY_TYPES) -1, const_zero = (enum NPY_TYPES) 0; + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(enum NPY_TYPES) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(enum NPY_TYPES) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(enum NPY_TYPES) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); +#endif + } + } else { + if (sizeof(enum NPY_TYPES) <= sizeof(long)) { + return PyInt_FromLong((long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(enum NPY_TYPES) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); +#endif + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(enum NPY_TYPES), + little, !is_unsigned); + } +} + +/* CIntFromPy */ + static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { + const int neg_one = (int) -1, const_zero = (int) 0; + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if (sizeof(int) < sizeof(long)) { + __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (int) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (int) 0; + case 1: __PYX_VERIFY_RETURN_INT(int, digit, digits[0]) + case 2: + if (8 * sizeof(int) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) >= 2 * PyLong_SHIFT) { + return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + case 3: + if (8 * sizeof(int) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) >= 3 * PyLong_SHIFT) { + return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + case 4: + if (8 * sizeof(int) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) >= 4 * PyLong_SHIFT) { + return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (int) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if (sizeof(int) <= sizeof(unsigned long)) { + __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) +#endif + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (int) 0; + case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, (sdigit) (-(sdigit)digits[0])) + case 1: __PYX_VERIFY_RETURN_INT(int, digit, +digits[0]) + case -2: + if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { + return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 2: + if (8 * sizeof(int) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { + return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case -3: + if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { + return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 3: + if (8 * sizeof(int) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { + return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case -4: + if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { + return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 4: + if (8 * sizeof(int) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { + return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + } +#endif + if (sizeof(int) <= sizeof(long)) { + __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x)) +#endif + } + } + { +#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) + PyErr_SetString(PyExc_RuntimeError, + "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); +#else + int val; + PyObject *v = __Pyx_PyNumber_IntOrLong(x); + #if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } + #endif + if (likely(v)) { + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + int ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); + Py_DECREF(v); + if (likely(!ret)) + return val; + } +#endif + return (int) -1; + } + } else { + int val; + PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); + if (!tmp) return (int) -1; + val = __Pyx_PyInt_As_int(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to int"); + return (int) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to int"); + return (int) -1; +} + +/* CIntFromPy */ + static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { + const long neg_one = (long) -1, const_zero = (long) 0; + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if (sizeof(long) < sizeof(long)) { + __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (long) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (long) 0; + case 1: __PYX_VERIFY_RETURN_INT(long, digit, digits[0]) + case 2: + if (8 * sizeof(long) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) >= 2 * PyLong_SHIFT) { + return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + case 3: + if (8 * sizeof(long) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) >= 3 * PyLong_SHIFT) { + return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + case 4: + if (8 * sizeof(long) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) >= 4 * PyLong_SHIFT) { + return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (long) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if (sizeof(long) <= sizeof(unsigned long)) { + __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) +#endif + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (long) 0; + case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, (sdigit) (-(sdigit)digits[0])) + case 1: __PYX_VERIFY_RETURN_INT(long, digit, +digits[0]) + case -2: + if (8 * sizeof(long) - 1 > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 2: + if (8 * sizeof(long) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case -3: + if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 3: + if (8 * sizeof(long) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case -4: + if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 4: + if (8 * sizeof(long) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + } +#endif + if (sizeof(long) <= sizeof(long)) { + __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x)) +#endif + } + } + { +#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) + PyErr_SetString(PyExc_RuntimeError, + "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); +#else + long val; + PyObject *v = __Pyx_PyNumber_IntOrLong(x); + #if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } + #endif + if (likely(v)) { + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + int ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); + Py_DECREF(v); + if (likely(!ret)) + return val; + } +#endif + return (long) -1; + } + } else { + long val; + PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); + if (!tmp) return (long) -1; + val = __Pyx_PyInt_As_long(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to long"); + return (long) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to long"); + return (long) -1; +} + +/* CheckBinaryVersion */ + static int __Pyx_check_binary_version(void) { + char ctversion[4], rtversion[4]; + PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); + PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion()); + if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) { + char message[200]; + PyOS_snprintf(message, sizeof(message), + "compiletime version %s of module '%.100s' " + "does not match runtime version %s", + ctversion, __Pyx_MODULE_NAME, rtversion); + return PyErr_WarnEx(NULL, message, 1); + } + return 0; +} + +/* ModuleImport */ + #ifndef __PYX_HAVE_RT_ImportModule +#define __PYX_HAVE_RT_ImportModule +static PyObject *__Pyx_ImportModule(const char *name) { + PyObject *py_name = 0; + PyObject *py_module = 0; + py_name = __Pyx_PyIdentifier_FromString(name); + if (!py_name) + goto bad; + py_module = PyImport_Import(py_name); + Py_DECREF(py_name); + return py_module; +bad: + Py_XDECREF(py_name); + return 0; +} +#endif + +/* TypeImport */ + #ifndef __PYX_HAVE_RT_ImportType +#define __PYX_HAVE_RT_ImportType +static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, + size_t size, int strict) +{ + PyObject *py_module = 0; + PyObject *result = 0; + PyObject *py_name = 0; + char warning[200]; + Py_ssize_t basicsize; +#ifdef Py_LIMITED_API + PyObject *py_basicsize; +#endif + py_module = __Pyx_ImportModule(module_name); + if (!py_module) + goto bad; + py_name = __Pyx_PyIdentifier_FromString(class_name); + if (!py_name) + goto bad; + result = PyObject_GetAttr(py_module, py_name); + Py_DECREF(py_name); + py_name = 0; + Py_DECREF(py_module); + py_module = 0; + if (!result) + goto bad; + if (!PyType_Check(result)) { + PyErr_Format(PyExc_TypeError, + "%.200s.%.200s is not a type object", + module_name, class_name); + goto bad; + } +#ifndef Py_LIMITED_API + basicsize = ((PyTypeObject *)result)->tp_basicsize; +#else + py_basicsize = PyObject_GetAttrString(result, "__basicsize__"); + if (!py_basicsize) + goto bad; + basicsize = PyLong_AsSsize_t(py_basicsize); + Py_DECREF(py_basicsize); + py_basicsize = 0; + if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred()) + goto bad; +#endif + if (!strict && (size_t)basicsize > size) { + PyOS_snprintf(warning, sizeof(warning), + "%s.%s size changed, may indicate binary incompatibility. Expected %zd, got %zd", + module_name, class_name, basicsize, size); + if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad; + } + else if ((size_t)basicsize != size) { + PyErr_Format(PyExc_ValueError, + "%.200s.%.200s has the wrong size, try recompiling. Expected %zd, got %zd", + module_name, class_name, basicsize, size); + goto bad; + } + return (PyTypeObject *)result; +bad: + Py_XDECREF(py_module); + Py_XDECREF(result); + return NULL; +} +#endif + +/* InitStrings */ + static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { + while (t->p) { + #if PY_MAJOR_VERSION < 3 + if (t->is_unicode) { + *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); + } else if (t->intern) { + *t->p = PyString_InternFromString(t->s); + } else { + *t->p = PyString_FromStringAndSize(t->s, t->n - 1); + } + #else + if (t->is_unicode | t->is_str) { + if (t->intern) { + *t->p = PyUnicode_InternFromString(t->s); + } else if (t->encoding) { + *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL); + } else { + *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); + } + } else { + *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1); + } + #endif + if (!*t->p) + return -1; + ++t; + } + return 0; +} + +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) { + return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str)); +} +static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject* o) { + Py_ssize_t ignore; + return __Pyx_PyObject_AsStringAndSize(o, &ignore); +} +static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) { +#if CYTHON_COMPILING_IN_CPYTHON && (__PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT) + if ( +#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + __Pyx_sys_getdefaultencoding_not_ascii && +#endif + PyUnicode_Check(o)) { +#if PY_VERSION_HEX < 0x03030000 + char* defenc_c; + PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL); + if (!defenc) return NULL; + defenc_c = PyBytes_AS_STRING(defenc); +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + { + char* end = defenc_c + PyBytes_GET_SIZE(defenc); + char* c; + for (c = defenc_c; c < end; c++) { + if ((unsigned char) (*c) >= 128) { + PyUnicode_AsASCIIString(o); + return NULL; + } + } + } +#endif + *length = PyBytes_GET_SIZE(defenc); + return defenc_c; +#else + if (__Pyx_PyUnicode_READY(o) == -1) return NULL; +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + if (PyUnicode_IS_ASCII(o)) { + *length = PyUnicode_GET_LENGTH(o); + return PyUnicode_AsUTF8(o); + } else { + PyUnicode_AsASCIIString(o); + return NULL; + } +#else + return PyUnicode_AsUTF8AndSize(o, length); +#endif +#endif + } else +#endif +#if (!CYTHON_COMPILING_IN_PYPY) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE)) + if (PyByteArray_Check(o)) { + *length = PyByteArray_GET_SIZE(o); + return PyByteArray_AS_STRING(o); + } else +#endif + { + char* result; + int r = PyBytes_AsStringAndSize(o, &result, length); + if (unlikely(r < 0)) { + return NULL; + } else { + return result; + } + } +} +static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { + int is_true = x == Py_True; + if (is_true | (x == Py_False) | (x == Py_None)) return is_true; + else return PyObject_IsTrue(x); +} +static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) { +#if CYTHON_USE_TYPE_SLOTS + PyNumberMethods *m; +#endif + const char *name = NULL; + PyObject *res = NULL; +#if PY_MAJOR_VERSION < 3 + if (PyInt_Check(x) || PyLong_Check(x)) +#else + if (PyLong_Check(x)) +#endif + return __Pyx_NewRef(x); +#if CYTHON_USE_TYPE_SLOTS + m = Py_TYPE(x)->tp_as_number; + #if PY_MAJOR_VERSION < 3 + if (m && m->nb_int) { + name = "int"; + res = PyNumber_Int(x); + } + else if (m && m->nb_long) { + name = "long"; + res = PyNumber_Long(x); + } + #else + if (m && m->nb_int) { + name = "int"; + res = PyNumber_Long(x); + } + #endif +#else + res = PyNumber_Int(x); +#endif + if (res) { +#if PY_MAJOR_VERSION < 3 + if (!PyInt_Check(res) && !PyLong_Check(res)) { +#else + if (!PyLong_Check(res)) { +#endif + PyErr_Format(PyExc_TypeError, + "__%.4s__ returned non-%.4s (type %.200s)", + name, name, Py_TYPE(res)->tp_name); + Py_DECREF(res); + return NULL; + } + } + else if (!PyErr_Occurred()) { + PyErr_SetString(PyExc_TypeError, + "an integer is required"); + } + return res; +} +static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { + Py_ssize_t ival; + PyObject *x; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_CheckExact(b))) { + if (sizeof(Py_ssize_t) >= sizeof(long)) + return PyInt_AS_LONG(b); + else + return PyInt_AsSsize_t(x); + } +#endif + if (likely(PyLong_CheckExact(b))) { + #if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)b)->ob_digit; + const Py_ssize_t size = Py_SIZE(b); + if (likely(__Pyx_sst_abs(size) <= 1)) { + ival = likely(size) ? digits[0] : 0; + if (size == -1) ival = -ival; + return ival; + } else { + switch (size) { + case 2: + if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { + return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -2: + if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case 3: + if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { + return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -3: + if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case 4: + if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { + return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -4: + if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + } + } + #endif + return PyLong_AsSsize_t(b); + } + x = PyNumber_Index(b); + if (!x) return -1; + ival = PyInt_AsSsize_t(x); + Py_DECREF(x); + return ival; +} +static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { + return PyInt_FromSize_t(ival); +} + + +#endif /* Py_PYTHON_H */ diff --git a/libs/box_utils/cython_utils/nms.pyx b/libs/box_utils/cython_utils/nms.pyx new file mode 100644 index 0000000..344f66d --- /dev/null +++ b/libs/box_utils/cython_utils/nms.pyx @@ -0,0 +1,123 @@ +# -------------------------------------------------------- +# Fast R-CNN +# Copyright (c) 2015 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ross Girshick +# -------------------------------------------------------- + +import numpy as np +cimport numpy as np + +cdef inline np.float32_t max(np.float32_t a, np.float32_t b): + return a if a >= b else b + +cdef inline np.float32_t min(np.float32_t a, np.float32_t b): + return a if a <= b else b + +def nms(np.ndarray[np.float32_t, ndim=2] dets, np.float thresh): + cdef np.ndarray[np.float32_t, ndim=1] x1 = dets[:, 0] + cdef np.ndarray[np.float32_t, ndim=1] y1 = dets[:, 1] + cdef np.ndarray[np.float32_t, ndim=1] x2 = dets[:, 2] + cdef np.ndarray[np.float32_t, ndim=1] y2 = dets[:, 3] + cdef np.ndarray[np.float32_t, ndim=1] scores = dets[:, 4] + + cdef np.ndarray[np.float32_t, ndim=1] areas = (x2 - x1 + 1) * (y2 - y1 + 1) + cdef np.ndarray[np.int_t, ndim=1] order = scores.argsort()[::-1] + + cdef int ndets = dets.shape[0] + cdef np.ndarray[np.int_t, ndim=1] suppressed = \ + np.zeros((ndets), dtype=np.int) + + # nominal indices + cdef int _i, _j + # sorted indices + cdef int i, j + # temp variables for box i's (the box currently under consideration) + cdef np.float32_t ix1, iy1, ix2, iy2, iarea + # variables for computing overlap with box j (lower scoring box) + cdef np.float32_t xx1, yy1, xx2, yy2 + cdef np.float32_t w, h + cdef np.float32_t inter, ovr + + keep = [] + for _i in range(ndets): + i = order[_i] + if suppressed[i] == 1: + continue + keep.append(i) + ix1 = x1[i] + iy1 = y1[i] + ix2 = x2[i] + iy2 = y2[i] + iarea = areas[i] + for _j in range(_i + 1, ndets): + j = order[_j] + if suppressed[j] == 1: + continue + xx1 = max(ix1, x1[j]) + yy1 = max(iy1, y1[j]) + xx2 = min(ix2, x2[j]) + yy2 = min(iy2, y2[j]) + w = max(0.0, xx2 - xx1 + 1) + h = max(0.0, yy2 - yy1 + 1) + inter = w * h + ovr = inter / (iarea + areas[j] - inter) + if ovr >= thresh: + suppressed[j] = 1 + + return keep + +def nms_new(np.ndarray[np.float32_t, ndim=2] dets, np.float thresh): + cdef np.ndarray[np.float32_t, ndim=1] x1 = dets[:, 0] + cdef np.ndarray[np.float32_t, ndim=1] y1 = dets[:, 1] + cdef np.ndarray[np.float32_t, ndim=1] x2 = dets[:, 2] + cdef np.ndarray[np.float32_t, ndim=1] y2 = dets[:, 3] + cdef np.ndarray[np.float32_t, ndim=1] scores = dets[:, 4] + + cdef np.ndarray[np.float32_t, ndim=1] areas = (x2 - x1 + 1) * (y2 - y1 + 1) + cdef np.ndarray[np.int_t, ndim=1] order = scores.argsort()[::-1] + + cdef int ndets = dets.shape[0] + cdef np.ndarray[np.int_t, ndim=1] suppressed = \ + np.zeros((ndets), dtype=np.int) + + # nominal indices + cdef int _i, _j + # sorted indices + cdef int i, j + # temp variables for box i's (the box currently under consideration) + cdef np.float32_t ix1, iy1, ix2, iy2, iarea + # variables for computing overlap with box j (lower scoring box) + cdef np.float32_t xx1, yy1, xx2, yy2 + cdef np.float32_t w, h + cdef np.float32_t inter, ovr + + keep = [] + for _i in range(ndets): + i = order[_i] + if suppressed[i] == 1: + continue + keep.append(i) + ix1 = x1[i] + iy1 = y1[i] + ix2 = x2[i] + iy2 = y2[i] + iarea = areas[i] + for _j in range(_i + 1, ndets): + j = order[_j] + if suppressed[j] == 1: + continue + xx1 = max(ix1, x1[j]) + yy1 = max(iy1, y1[j]) + xx2 = min(ix2, x2[j]) + yy2 = min(iy2, y2[j]) + w = max(0.0, xx2 - xx1 + 1) + h = max(0.0, yy2 - yy1 + 1) + inter = w * h + ovr = inter / (iarea + areas[j] - inter) + ovr1 = inter / iarea + ovr2 = inter / areas[j] + if ovr >= thresh or ovr1 > 0.95 or ovr2 > 0.95: + suppressed[j] = 1 + + return keep diff --git a/libs/box_utils/cython_utils/setup.py b/libs/box_utils/cython_utils/setup.py new file mode 100644 index 0000000..1227d34 --- /dev/null +++ b/libs/box_utils/cython_utils/setup.py @@ -0,0 +1,133 @@ +# -------------------------------------------------------- +# Fast R-CNN +# Copyright (c) 2015 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ross Girshick +# -------------------------------------------------------- + +import os +from os.path import join as pjoin +import numpy as np +from distutils.core import setup +from distutils.extension import Extension +from Cython.Distutils import build_ext + +def find_in_path(name, path): + "Find a file in a search path" + #adapted fom http://code.activestate.com/recipes/52224-find-a-file-given-a-search-path/ + for dir in path.split(os.pathsep): + binpath = pjoin(dir, name) + if os.path.exists(binpath): + return os.path.abspath(binpath) + return None + +def locate_cuda(): + """Locate the CUDA environment on the system + + Returns a dict with keys 'home', 'nvcc', 'include', and 'lib64' + and values giving the absolute path to each directory. + + Starts by looking for the CUDAHOME env variable. If not found, everything + is based on finding 'nvcc' in the PATH. + """ + + # first check if the CUDAHOME env variable is in use + if 'CUDAHOME' in os.environ: + home = os.environ['CUDAHOME'] + nvcc = pjoin(home, 'bin', 'nvcc') + else: + # otherwise, search the PATH for NVCC + default_path = pjoin(os.sep, 'usr', 'local', 'cuda', 'bin') + nvcc = find_in_path('nvcc', os.environ['PATH'] + os.pathsep + default_path) + if nvcc is None: + raise EnvironmentError('The nvcc binary could not be ' + 'located in your $PATH. Either add it to your path, or set $CUDAHOME') + home = os.path.dirname(os.path.dirname(nvcc)) + + cudaconfig = {'home':home, 'nvcc':nvcc, + 'include': pjoin(home, 'include'), + 'lib64': pjoin(home, 'lib64')} + for k, v in cudaconfig.items(): + if not os.path.exists(v): + raise EnvironmentError('The CUDA %s path could not be located in %s' % (k, v)) + + return cudaconfig +CUDA = locate_cuda() + +# Obtain the numpy include directory. This logic works across numpy versions. +try: + numpy_include = np.get_include() +except AttributeError: + numpy_include = np.get_numpy_include() + +def customize_compiler_for_nvcc(self): + """inject deep into distutils to customize how the dispatch + to gcc/nvcc works. + + If you subclass UnixCCompiler, it's not trivial to get your subclass + injected in, and still have the right customizations (i.e. + distutils.sysconfig.customize_compiler) run on it. So instead of going + the OO route, I have this. Note, it's kindof like a wierd functional + subclassing going on.""" + + # tell the compiler it can processes .cu + self.src_extensions.append('.cu') + + # save references to the default compiler_so and _comple methods + default_compiler_so = self.compiler_so + super = self._compile + + # now redefine the _compile method. This gets executed for each + # object but distutils doesn't have the ability to change compilers + # based on source extension: we add it. + def _compile(obj, src, ext, cc_args, extra_postargs, pp_opts): + print(extra_postargs) + if os.path.splitext(src)[1] == '.cu': + # use the cuda for .cu files + self.set_executable('compiler_so', CUDA['nvcc']) + # use only a subset of the extra_postargs, which are 1-1 translated + # from the extra_compile_args in the Extension class + postargs = extra_postargs['nvcc'] + else: + postargs = extra_postargs['gcc'] + + super(obj, src, ext, cc_args, postargs, pp_opts) + # reset the default compiler_so, which we might have changed for cuda + self.compiler_so = default_compiler_so + + # inject our redefined _compile method into the class + self._compile = _compile + +# run the customize_compiler +class custom_build_ext(build_ext): + def build_extensions(self): + customize_compiler_for_nvcc(self.compiler) + build_ext.build_extensions(self) + +ext_modules = [ + Extension( + "cython_bbox", + ["bbox.pyx"], + extra_compile_args={'gcc': ["-Wno-cpp", "-Wno-unused-function"]}, + include_dirs = [numpy_include] + ), + Extension( + "cython_nms", + ["nms.pyx"], + extra_compile_args={'gcc': ["-Wno-cpp", "-Wno-unused-function"]}, + include_dirs = [numpy_include] + ) + # Extension( + # "cpu_nms", + # ["cpu_nms.pyx"], + # extra_compile_args={'gcc': ["-Wno-cpp", "-Wno-unused-function"]}, + # include_dirs = [numpy_include] + # ) +] + +setup( + name='tf_faster_rcnn', + ext_modules=ext_modules, + # inject our custom trigger + cmdclass={'build_ext': custom_build_ext}, +) diff --git a/libs/box_utils/draw_box_in_img.py b/libs/box_utils/draw_box_in_img.py new file mode 100644 index 0000000..27e896c --- /dev/null +++ b/libs/box_utils/draw_box_in_img.py @@ -0,0 +1,162 @@ +# -*- coding: utf-8 -*- + +from __future__ import absolute_import, print_function, division + +import numpy as np + +from PIL import Image, ImageDraw, ImageFont +import cv2 + +from libs.configs import cfgs +from libs.label_name_dict.label_dict import LABEl_NAME_MAP +NOT_DRAW_BOXES = 0 +ONLY_DRAW_BOXES = -1 +ONLY_DRAW_BOXES_WITH_SCORES = -2 + +STANDARD_COLORS = [ + 'AliceBlue', 'Chartreuse', 'Aqua', 'Aquamarine', 'Azure', 'Beige', 'Bisque', + 'BlanchedAlmond', 'BlueViolet', 'BurlyWood', 'CadetBlue', 'AntiqueWhite', + 'Chocolate', 'Coral', 'CornflowerBlue', 'Cornsilk', 'Crimson', 'Cyan', + 'DarkCyan', 'DarkGoldenRod', 'DarkGrey', 'DarkKhaki', 'DarkOrange', + 'DarkOrchid', 'DarkSalmon', 'DarkSeaGreen', 'DarkTurquoise', 'DarkViolet', + 'DeepPink', 'DeepSkyBlue', 'DodgerBlue', 'FireBrick', 'FloralWhite', + 'ForestGreen', 'Fuchsia', 'Gainsboro', 'GhostWhite', 'Gold', 'GoldenRod', + 'Salmon', 'Tan', 'HoneyDew', 'HotPink', 'IndianRed', 'Ivory', 'Khaki', + 'Lavender', 'LavenderBlush', 'LawnGreen', 'LemonChiffon', 'LightBlue', + 'LightCoral', 'LightCyan', 'LightGoldenRodYellow', 'LightGray', 'LightGrey', + 'LightGreen', 'LightPink', 'LightSalmon', 'LightSeaGreen', 'LightSkyBlue', + 'LightSlateGray', 'LightSlateGrey', 'LightSteelBlue', 'LightYellow', 'Lime', + 'LimeGreen', 'Linen', 'Magenta', 'MediumAquaMarine', 'MediumOrchid', + 'MediumPurple', 'MediumSeaGreen', 'MediumSlateBlue', 'MediumSpringGreen', + 'MediumTurquoise', 'MediumVioletRed', 'MintCream', 'MistyRose', 'Moccasin', + 'NavajoWhite', 'OldLace', 'Olive', 'OliveDrab', 'Orange', 'OrangeRed', + 'Orchid', 'PaleGoldenRod', 'PaleGreen', 'PaleTurquoise', 'PaleVioletRed', + 'PapayaWhip', 'PeachPuff', 'Peru', 'Pink', 'Plum', 'PowderBlue', 'Purple', + 'Red', 'RosyBrown', 'RoyalBlue', 'SaddleBrown', 'Green', 'SandyBrown', + 'SeaGreen', 'SeaShell', 'Sienna', 'Silver', 'SkyBlue', 'SlateBlue', + 'SlateGray', 'SlateGrey', 'Snow', 'SpringGreen', 'SteelBlue', 'GreenYellow', + 'Teal', 'Thistle', 'Tomato', 'Turquoise', 'Violet', 'Wheat', 'White', + 'WhiteSmoke', 'Yellow', 'YellowGreen', 'LightBlue', 'LightGreen' +] +FONT = ImageFont.load_default() + + +def draw_a_rectangel_in_img(draw_obj, box, color, width): + ''' + use draw lines to draw rectangle. since the draw_rectangle func can not modify the width of rectangle + :param draw_obj: + :param box: [x1, y1, x2, y2] + :return: + ''' + x1, y1, x2, y2 = box[0], box[1], box[2], box[3] + top_left, top_right = (x1, y1), (x2, y1) + bottom_left, bottom_right = (x1, y2), (x2, y2) + + draw_obj.line(xy=[top_left, top_right], + fill=color, + width=width) + draw_obj.line(xy=[top_left, bottom_left], + fill=color, + width=width) + draw_obj.line(xy=[bottom_left, bottom_right], + fill=color, + width=width) + draw_obj.line(xy=[top_right, bottom_right], + fill=color, + width=width) + + +def only_draw_scores(draw_obj, box, score, color): + + x, y = box[0], box[1] + draw_obj.rectangle(xy=[x, y, x+60, y+10], + fill=color) + draw_obj.text(xy=(x, y), + text="obj:" +str(round(score, 2)), + fill='black', + font=FONT) + + +def draw_label_with_scores(draw_obj, box, label, score, color): + x, y = box[0], box[1] + draw_obj.rectangle(xy=[x, y, x + 60, y + 10], + fill=color) + + txt = LABEl_NAME_MAP[label] + ':' + str(round(score, 2)) + draw_obj.text(xy=(x, y), + text=txt, + fill='black', + font=FONT) + + +def draw_boxes_with_label_and_scores(img_array, boxes, labels, scores, in_graph=True): + if in_graph: + if cfgs.NET_NAME in ['resnet101_v1d', 'resnet50_v1d']: + img_array = (img_array * np.array(cfgs.PIXEL_STD) + np.array(cfgs.PIXEL_MEAN_)) * 255 + else: + img_array = img_array + np.array(cfgs.PIXEL_MEAN) + img_array.astype(np.float32) + boxes = boxes.astype(np.int64) + labels = labels.astype(np.int32) + img_array = np.array(img_array * 255 / np.max(img_array), dtype=np.uint8) + + img_obj = Image.fromarray(img_array) + raw_img_obj = img_obj.copy() + + draw_obj = ImageDraw.Draw(img_obj) + num_of_objs = 0 + for box, a_label, a_score in zip(boxes, labels, scores): + + if a_label != NOT_DRAW_BOXES: + num_of_objs += 1 + draw_a_rectangel_in_img(draw_obj, box, color=STANDARD_COLORS[a_label], width=3) + if a_label == ONLY_DRAW_BOXES: # -1 + continue + elif a_label == ONLY_DRAW_BOXES_WITH_SCORES: # -2 + only_draw_scores(draw_obj, box, a_score, color='White') + continue + else: + draw_label_with_scores(draw_obj, box, a_label, a_score, color='White') + + out_img_obj = Image.blend(raw_img_obj, img_obj, alpha=0.7) + + return np.array(out_img_obj) + + +if __name__ == '__main__': + img_array = cv2.imread("/home/yjr/PycharmProjects/FPN_TF/tools/inference_image/2.jpg") + img_array = np.array(img_array, np.float32) - np.array(cfgs.PIXEL_MEAN) + boxes = np.array( + [[200, 200, 500, 500], + [300, 300, 400, 400], + [200, 200, 400, 400]] + ) + + # test only draw boxes + labes = np.ones(shape=[len(boxes), ], dtype=np.float32) * ONLY_DRAW_BOXES + scores = np.zeros_like(labes) + imm = draw_boxes_with_label_and_scores(img_array, boxes, labes ,scores) + # imm = np.array(imm) + + cv2.imshow("te", imm) + + # test only draw scores + labes = np.ones(shape=[len(boxes), ], dtype=np.float32) * ONLY_DRAW_BOXES_WITH_SCORES + scores = np.random.rand((len(boxes))) * 10 + imm2 = draw_boxes_with_label_and_scores(img_array, boxes, labes, scores) + + cv2.imshow("te2", imm2) + # test draw label and scores + + labels = np.arange(1, 4) + imm3 = draw_boxes_with_label_and_scores(img_array, boxes, labels, scores) + cv2.imshow("te3", imm3) + + cv2.waitKey(0) + + + + + + + diff --git a/libs/box_utils/encode_and_decode.py b/libs/box_utils/encode_and_decode.py new file mode 100644 index 0000000..66092b2 --- /dev/null +++ b/libs/box_utils/encode_and_decode.py @@ -0,0 +1,165 @@ +# -*- coding: utf-8 -*- + +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import tensorflow as tf +import numpy as np + + + +# def encode_boxes(ex_rois, gt_rois, scale_factor=None): +# ex_widths = ex_rois[:, 2] - ex_rois[:, 0] + 1.0 +# ex_heights = ex_rois[:, 3] - ex_rois[:, 1] + 1.0 +# ex_ctr_x = ex_rois[:, 0] + 0.5 * ex_widths +# ex_ctr_y = ex_rois[:, 1] + 0.5 * ex_heights +# +# gt_widths = gt_rois[:, 2] - gt_rois[:, 0] + 1.0 +# gt_heights = gt_rois[:, 3] - gt_rois[:, 1] + 1.0 +# gt_ctr_x = gt_rois[:, 0] + 0.5 * gt_widths +# gt_ctr_y = gt_rois[:, 1] + 0.5 * gt_heights +# +# targets_dx = (gt_ctr_x - ex_ctr_x) / ex_widths +# targets_dy = (gt_ctr_y - ex_ctr_y) / ex_heights +# targets_dw = np.log(gt_widths / ex_widths) +# targets_dh = np.log(gt_heights / ex_heights) +# +# if scale_factor: +# targets_dx = targets_dx * scale_factor[0] +# targets_dy = targets_dy * scale_factor[1] +# targets_dw = targets_dw * scale_factor[2] +# targets_dh = targets_dh * scale_factor[3] +# +# targets = np.vstack( +# (targets_dx, targets_dy, targets_dw, targets_dh)).transpose() +# return targets +# +# +# def _concat_new_axis(t1, t2, t3, t4, axis): +# return tf.concat( +# [tf.expand_dims(t1, -1), tf.expand_dims(t2, -1), +# tf.expand_dims(t3, -1), tf.expand_dims(t4, -1)], axis=axis) +# +# +# def decode_boxes(boxes, deltas, scale_factor=None): +# widths = boxes[:, 2] - boxes[:, 0] + 1.0 +# heights = boxes[:, 3] - boxes[:, 1] + 1.0 +# ctr_x = tf.expand_dims(boxes[:, 0] + 0.5 * widths, -1) +# ctr_y = tf.expand_dims(boxes[:, 1] + 0.5 * heights, -1) +# +# dx = deltas[:, 0::4] +# dy = deltas[:, 1::4] +# dw = deltas[:, 2::4] +# dh = deltas[:, 3::4] +# +# if scale_factor: +# dx /= scale_factor[0] +# dy /= scale_factor[1] +# dw /= scale_factor[2] +# dh /= scale_factor[3] +# +# widths = tf.expand_dims(widths, -1) +# heights = tf.expand_dims(heights, -1) +# +# pred_ctr_x = dx * widths + ctr_x +# pred_ctr_y = dy * heights + ctr_y +# pred_w = tf.exp(dw) * widths +# pred_h = tf.exp(dh) * heights +# +# # x1 +# # pred_boxes[:, 0::4] = pred_ctr_x - 0.5 * pred_w +# pred_x1 = pred_ctr_x - 0.5 * pred_w +# # y1 +# # pred_boxes[:, 1::4] = pred_ctr_y - 0.5 * pred_h +# pred_y1 = pred_ctr_y - 0.5 * pred_h +# # x2 +# # pred_boxes[:, 2::4] = pred_ctr_x + 0.5 * pred_w +# pred_x2 = pred_ctr_x + 0.5 * pred_w +# # y2 +# # pred_boxes[:, 3::4] = pred_ctr_y + 0.5 * pred_h +# pred_y2 = pred_ctr_y + 0.5 * pred_h +# +# pred_boxes = _concat_new_axis(pred_x1, pred_y1, pred_x2, pred_y2, 2) +# pred_boxes = tf.reshape(pred_boxes, (tf.shape(pred_boxes)[0], -1)) +# return pred_boxes + + +def decode_boxes(encoded_boxes, reference_boxes, scale_factors=None): + ''' + + :param encoded_boxes:[N, 4] + :param reference_boxes: [N, 4] . + :param scale_factors: use for scale. + + in the first stage, reference_boxes are anchors + in the second stage, reference boxes are proposals(decode) produced by first stage + :return:decode boxes [N, 4] + ''' + + t_xcenter, t_ycenter, t_w, t_h = tf.unstack(encoded_boxes, axis=1) + if scale_factors: + t_xcenter /= scale_factors[0] + t_ycenter /= scale_factors[1] + t_w /= scale_factors[2] + t_h /= scale_factors[3] + + reference_xmin, reference_ymin, reference_xmax, reference_ymax = tf.unstack(reference_boxes, axis=1) + # reference boxes are anchors in the first stage + + reference_xcenter = (reference_xmin + reference_xmax) / 2. + reference_ycenter = (reference_ymin + reference_ymax) / 2. + reference_w = reference_xmax - reference_xmin + reference_h = reference_ymax - reference_ymin + + predict_xcenter = t_xcenter * reference_w + reference_xcenter + predict_ycenter = t_ycenter * reference_h + reference_ycenter + predict_w = tf.exp(t_w) * reference_w + predict_h = tf.exp(t_h) * reference_h + + predict_xmin = predict_xcenter - predict_w / 2. + predict_xmax = predict_xcenter + predict_w / 2. + predict_ymin = predict_ycenter - predict_h / 2. + predict_ymax = predict_ycenter + predict_h / 2. + + return tf.transpose(tf.stack([predict_xmin, predict_ymin, + predict_xmax, predict_ymax])) + + +def encode_boxes(unencode_boxes, reference_boxes, scale_factors=None): + ''' + + :param unencode_boxes: [-1, 4] + :param reference_boxes: [-1, 4] + :return: encode_boxes [-1, 4] + ''' + + xmin, ymin, xmax, ymax = unencode_boxes[:, 0], unencode_boxes[:, 1], unencode_boxes[:, 2], unencode_boxes[:, 3] + + reference_xmin, reference_ymin, reference_xmax, reference_ymax = reference_boxes[:, 0], reference_boxes[:, 1], \ + reference_boxes[:, 2], reference_boxes[:, 3] + + x_center = (xmin + xmax) / 2. + y_center = (ymin + ymax) / 2. + w = xmax - xmin + 1e-8 + h = ymax - ymin + 1e-8 + + reference_xcenter = (reference_xmin + reference_xmax) / 2. + reference_ycenter = (reference_ymin + reference_ymax) / 2. + reference_w = reference_xmax - reference_xmin + 1e-8 + reference_h = reference_ymax - reference_ymin + 1e-8 + + # w + 1e-8 to avoid NaN in division and log below + + t_xcenter = (x_center - reference_xcenter) / reference_w + t_ycenter = (y_center - reference_ycenter) / reference_h + t_w = np.log(w/reference_w) + t_h = np.log(h/reference_h) + + if scale_factors: + t_xcenter *= scale_factors[0] + t_ycenter *= scale_factors[1] + t_w *= scale_factors[2] + t_h *= scale_factors[3] + + return np.transpose(np.stack([t_xcenter, t_ycenter, t_w, t_h], axis=0)) diff --git a/libs/box_utils/iou.py b/libs/box_utils/iou.py new file mode 100644 index 0000000..fe866d0 --- /dev/null +++ b/libs/box_utils/iou.py @@ -0,0 +1,74 @@ +# -*- coding: utf-8 -*- + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + + +import tensorflow as tf +import numpy as np + + +def iou_calculate(boxes_1, boxes_2): + + with tf.name_scope('iou_caculate'): + xmin_1, ymin_1, xmax_1, ymax_1 = boxes_1[:, 0], boxes_1[:, 1], boxes_1[:, 2], boxes_1[:, 3] + + xmin_2, ymin_2, xmax_2, ymax_2 = boxes_2[:, 0], boxes_2[:, 1], boxes_2[:, 2], boxes_2[:, 3] + + max_xmin = tf.maximum(xmin_1, xmin_2) + min_xmax = tf.minimum(xmax_1, xmax_2) + + max_ymin = tf.maximum(ymin_1, ymin_2) + min_ymax = tf.minimum(ymax_1, ymax_2) + + overlap_h = tf.maximum(0., min_ymax - max_ymin) # avoid h < 0 + overlap_w = tf.maximum(0., min_xmax - max_xmin) + + overlaps = overlap_h * overlap_w + + area_1 = (xmax_1 - xmin_1) * (ymax_1 - ymin_1) # [N, 1] + area_2 = (xmax_2 - xmin_2) * (ymax_2 - ymin_2) # [M, ] + + iou = overlaps / (area_1 + area_2 - overlaps) + + return iou + + +def iou_calculate1(boxes_1, boxes_2): + xmin_1, ymin_1, xmax_1, ymax_1 = boxes_1[:, 0], boxes_1[:, 1], boxes_1[:, 2], boxes_1[:, 3] + + xmin_2, ymin_2, xmax_2, ymax_2 = boxes_2[:, 0], boxes_2[:, 1], boxes_2[:, 2], boxes_2[:, 3] + + max_xmin = np.maximum(xmin_1, xmin_2) + min_xmax = np.minimum(xmax_1, xmax_2) + + max_ymin = np.maximum(ymin_1, ymin_2) + min_ymax = np.minimum(ymax_1, ymax_2) + + overlap_h = np.maximum(0., min_ymax - max_ymin) # avoid h < 0 + overlap_w = np.maximum(0., min_xmax - max_xmin) + + overlaps = overlap_h * overlap_w + + area_1 = (xmax_1 - xmin_1) * (ymax_1 - ymin_1) # [N, 1] + area_2 = (xmax_2 - xmin_2) * (ymax_2 - ymin_2) # [M, ] + + iou = overlaps / (area_1 + area_2 - overlaps) + + return iou + + +if __name__ == '__main__': + import os + os.environ["CUDA_VISIBLE_DEVICES"] = '13' + boxes1 = np.array([[50, 50, 100, 300], + [60, 60, 100, 200]], np.float32) + + boxes2 = np.array([[50, 50, 100, 300], + [200, 200, 100, 200]], np.float32) + + print(iou_calculate1(boxes1, boxes2)) + + + diff --git a/libs/box_utils/nms.py b/libs/box_utils/nms.py new file mode 100644 index 0000000..5ae9b8b --- /dev/null +++ b/libs/box_utils/nms.py @@ -0,0 +1,38 @@ +# -*- coding: utf-8 -*- + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + + +def py_cpu_nms(dets, thresh, max_output_size): + """Pure Python NMS baseline.""" + x1 = dets[:, 0] + y1 = dets[:, 1] + x2 = dets[:, 2] + y2 = dets[:, 3] + scores = dets[:, 4] + + areas = (x2 - x1 + 1) * (y2 - y1 + 1) + order = scores.argsort()[::-1] + keep = [] + while order.size > 0: + if len(keep) >= max_output_size: + break + i = order[0] + keep.append(i) + xx1 = np.maximum(x1[i], x1[order[1:]]) + yy1 = np.maximum(y1[i], y1[order[1:]]) + xx2 = np.minimum(x2[i], x2[order[1:]]) + yy2 = np.minimum(y2[i], y2[order[1:]]) + + w = np.maximum(0.0, xx2 - xx1 + 1) + h = np.maximum(0.0, yy2 - yy1 + 1) + inter = w * h + ovr = inter / (areas[i] + areas[order[1:]] - inter) + inds = np.where(ovr <= thresh)[0] + order = order[inds + 1] + + return np.array(keep, np.int64) diff --git a/libs/box_utils/show_box_in_tensor.py b/libs/box_utils/show_box_in_tensor.py new file mode 100644 index 0000000..37afda0 --- /dev/null +++ b/libs/box_utils/show_box_in_tensor.py @@ -0,0 +1,70 @@ +# -*- coding: utf-8 -*- + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf +import numpy as np +import cv2 +from libs.label_name_dict.label_dict import LABEl_NAME_MAP + +from libs.configs import cfgs + +from libs.box_utils import draw_box_in_img + +def only_draw_boxes(img_batch, boxes): + + boxes = tf.stop_gradient(boxes) + img_tensor = tf.squeeze(img_batch, 0) + img_tensor = tf.cast(img_tensor, tf.float32) + labels = tf.ones(shape=(tf.shape(boxes)[0], ), dtype=tf.int32) * draw_box_in_img.ONLY_DRAW_BOXES + scores = tf.zeros_like(labels, dtype=tf.float32) + img_tensor_with_boxes = tf.py_func(draw_box_in_img.draw_boxes_with_label_and_scores, + inp=[img_tensor, boxes, labels, scores], + Tout=tf.uint8) + img_tensor_with_boxes = tf.reshape(img_tensor_with_boxes, tf.shape(img_batch)) # [batch_size, h, w, c] + + return img_tensor_with_boxes + +def draw_boxes_with_scores(img_batch, boxes, scores): + + boxes = tf.stop_gradient(boxes) + scores = tf.stop_gradient(scores) + + img_tensor = tf.squeeze(img_batch, 0) + img_tensor = tf.cast(img_tensor, tf.float32) + labels = tf.ones(shape=(tf.shape(boxes)[0],), dtype=tf.int32) * draw_box_in_img.ONLY_DRAW_BOXES_WITH_SCORES + img_tensor_with_boxes = tf.py_func(draw_box_in_img.draw_boxes_with_label_and_scores, + inp=[img_tensor, boxes, labels, scores], + Tout=[tf.uint8]) + img_tensor_with_boxes = tf.reshape(img_tensor_with_boxes, tf.shape(img_batch)) + return img_tensor_with_boxes + +def draw_boxes_with_categories(img_batch, boxes, labels): + boxes = tf.stop_gradient(boxes) + + img_tensor = tf.squeeze(img_batch, 0) + img_tensor = tf.cast(img_tensor, tf.float32) + scores = tf.ones(shape=(tf.shape(boxes)[0],), dtype=tf.float32) + img_tensor_with_boxes = tf.py_func(draw_box_in_img.draw_boxes_with_label_and_scores, + inp=[img_tensor, boxes, labels, scores], + Tout=[tf.uint8]) + img_tensor_with_boxes = tf.reshape(img_tensor_with_boxes, tf.shape(img_batch)) + return img_tensor_with_boxes + +def draw_boxes_with_categories_and_scores(img_batch, boxes, labels, scores): + boxes = tf.stop_gradient(boxes) + scores = tf.stop_gradient(scores) + + img_tensor = tf.squeeze(img_batch, 0) + img_tensor = tf.cast(img_tensor, tf.float32) + img_tensor_with_boxes = tf.py_func(draw_box_in_img.draw_boxes_with_label_and_scores, + inp=[img_tensor, boxes, labels, scores], + Tout=[tf.uint8]) + img_tensor_with_boxes = tf.reshape(img_tensor_with_boxes, tf.shape(img_batch)) + return img_tensor_with_boxes + +if __name__ == "__main__": + print (1) + diff --git a/libs/box_utils/tf_ops.py b/libs/box_utils/tf_ops.py new file mode 100644 index 0000000..86d945a --- /dev/null +++ b/libs/box_utils/tf_ops.py @@ -0,0 +1,57 @@ +# -*- coding:utf-8 -*- + +from __future__ import absolute_import, print_function, division + +import tensorflow as tf + +''' +all of these ops are derived from tenosrflow Object Detection API +''' +def indices_to_dense_vector(indices, + size, + indices_value=1., + default_value=0, + dtype=tf.float32): + """Creates dense vector with indices set to specific (the para "indices_value" ) and rest to zeros. + + This function exists because it is unclear if it is safe to use + tf.sparse_to_dense(indices, [size], 1, validate_indices=False) + with indices which are not ordered. + This function accepts a dynamic size (e.g. tf.shape(tensor)[0]) + + Args: + indices: 1d Tensor with integer indices which are to be set to + indices_values. + size: scalar with size (integer) of output Tensor. + indices_value: values of elements specified by indices in the output vector + default_value: values of other elements in the output vector. + dtype: data type. + + Returns: + dense 1D Tensor of shape [size] with indices set to indices_values and the + rest set to default_value. + """ + size = tf.to_int32(size) + zeros = tf.ones([size], dtype=dtype) * default_value + values = tf.ones_like(indices, dtype=dtype) * indices_value + + return tf.dynamic_stitch([tf.range(size), tf.to_int32(indices)], + [zeros, values]) + + + + +def test_plt(): + from PIL import Image + import matplotlib.pyplot as plt + import numpy as np + + a = np.random.rand(20, 30) + print (a.shape) + # plt.subplot() + b = plt.imshow(a) + plt.show() + + +if __name__ == '__main__': + test_plt() diff --git a/libs/configs/COCO/__init__.py b/libs/configs/COCO/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/libs/configs/COCO/cfgs_res50_1x_coco_v1.py b/libs/configs/COCO/cfgs_res50_1x_coco_v1.py new file mode 100644 index 0000000..a1aefdc --- /dev/null +++ b/libs/configs/COCO/cfgs_res50_1x_coco_v1.py @@ -0,0 +1,129 @@ +# -*- coding: utf-8 -*- +from __future__ import division, print_function, absolute_import +import os +import tensorflow as tf +''' +gluoncv backbone + multi_gpu + +''' + +# ------------------------------------------------ +VERSION = 'Cascade_FPN_Res50_COCO_1x_20190416_v1' +NET_NAME = 'resnet50_v1d' +ADD_BOX_IN_TENSORBOARD = True + +# ---------------------------------------- System_config +ROOT_PATH = os.path.abspath('../') +print(20*"++--") +print(ROOT_PATH) +GPU_GROUP = "0,1,2,3,4,5,6,7" +NUM_GPU = len(GPU_GROUP.strip().split(',')) +SHOW_TRAIN_INFO_INTE = 10 +SMRY_ITER = 200 +SAVE_WEIGHTS_INTE = 80000 + +SUMMARY_PATH = ROOT_PATH + '/output/summary' +TEST_SAVE_PATH = ROOT_PATH + '/tools/test_result' +INFERENCE_IMAGE_PATH = ROOT_PATH + '/tools/inference_image' +INFERENCE_SAVE_PATH = ROOT_PATH + '/tools/inference_results' + +if NET_NAME.startswith("resnet"): + weights_name = NET_NAME +elif NET_NAME.startswith("MobilenetV2"): + weights_name = "mobilenet/mobilenet_v2_1.0_224" +else: + raise NotImplementedError + +PRETRAINED_CKPT = ROOT_PATH + '/data/pretrained_weights/' + weights_name + '.ckpt' +TRAINED_CKPT = os.path.join(ROOT_PATH, 'output/trained_weights') + +EVALUATE_DIR = ROOT_PATH + '/output/evaluate_result_pickle/' + +# ------------------------------------------ Train config +RESTORE_FROM_RPN = False +IS_FILTER_OUTSIDE_BOXES = False +FIXED_BLOCKS = 0 # allow 0~3 +FREEZE_BLOCKS = [True, False, False, False, False] # for gluoncv backbone +USE_07_METRIC = True +CUDA9 = True +EVAL_THRESHOLD = 0.5 + +RPN_LOCATION_LOSS_WEIGHT = 1. +RPN_CLASSIFICATION_LOSS_WEIGHT = 1.0 + +FAST_RCNN_LOCATION_LOSS_WEIGHT = 1.0 +FAST_RCNN_CLASSIFICATION_LOSS_WEIGHT = 1.0 +RPN_SIGMA = 3.0 +FASTRCNN_SIGMA = 1.0 + +MUTILPY_BIAS_GRADIENT = None # 2.0 # if None, will not multipy +GRADIENT_CLIPPING_BY_NORM = None # 10.0 if None, will not clip + +EPSILON = 1e-5 +MOMENTUM = 0.9 +BATCH_SIZE = 1 +WARM_SETP = int(0.25 * SAVE_WEIGHTS_INTE) +LR = 5e-4 * 2 * 1.25 * NUM_GPU * BATCH_SIZE +DECAY_STEP = [11*SAVE_WEIGHTS_INTE, 16*SAVE_WEIGHTS_INTE, 20*SAVE_WEIGHTS_INTE] # 50000, 70000 +MAX_ITERATION = 20*SAVE_WEIGHTS_INTE + +# -------------------------------------------- Data_preprocess_config +DATASET_NAME = 'coco' # 'pascal', 'coco' +PIXEL_MEAN = [123.68, 116.779, 103.939] # R, G, B. In tf, channel is RGB. In openCV, channel is BGR +PIXEL_MEAN_ = [0.485, 0.456, 0.406] +PIXEL_STD = [0.229, 0.224, 0.225] # R, G, B. In tf, channel is RGB. In openCV, channel is BGR +IMG_SHORT_SIDE_LEN = 800 +IMG_MAX_LENGTH = 1333 +CLASS_NUM = 80 + + +# --------------------------------------------- Network_config +INITIALIZER = tf.random_normal_initializer(mean=0.0, stddev=0.01) +BBOX_INITIALIZER = tf.random_normal_initializer(mean=0.0, stddev=0.001) +WEIGHT_DECAY = 0.00004 if NET_NAME.startswith('Mobilenet') else 0.0001 +IS_ASSIGN = True + +# ---------------------------------------------Anchor config +USE_CENTER_OFFSET = True +LEVLES = ['P2', 'P3', 'P4', 'P5', 'P6'] +BASE_ANCHOR_SIZE_LIST = [32, 64, 128, 256, 512] +ANCHOR_STRIDE_LIST = [4, 8, 16, 32, 64] +ANCHOR_SCALES = [1.0] +ANCHOR_RATIOS = [0.5, 1., 2.0] +ROI_SCALE_FACTORS = [[10., 10., 5.0, 5.0], [10., 10., 5.0, 5.0], [10., 10., 5.0, 5.0]] +ANCHOR_SCALE_FACTORS = None # [10., 10., 5.0, 5.0] + +# --------------------------------------------FPN config +SHARE_HEADS = True +KERNEL_SIZE = 3 +RPN_IOU_POSITIVE_THRESHOLD = 0.7 +RPN_IOU_NEGATIVE_THRESHOLD = 0.3 +TRAIN_RPN_CLOOBER_POSITIVES = False + +RPN_MINIBATCH_SIZE = 256 +RPN_POSITIVE_RATE = 0.5 +RPN_NMS_IOU_THRESHOLD = 0.7 +RPN_TOP_K_NMS_TRAIN = 12000 +RPN_MAXIMUM_PROPOSAL_TARIN = 2000 + +RPN_TOP_K_NMS_TEST = 6000 +RPN_MAXIMUM_PROPOSAL_TEST = 1000 + +# -------------------------------------------Fast-RCNN config +ROI_SIZE = 14 +ROI_POOL_KERNEL_SIZE = 2 +USE_DROPOUT = False +KEEP_PROB = 1.0 +SHOW_SCORE_THRSHOLD = 0.6 # only show in tensorboard + +FAST_RCNN_NMS_IOU_THRESHOLD = 0.5 # 0.6 +FAST_RCNN_NMS_MAX_BOXES_PER_CLASS = 100 +FAST_RCNN_IOU_POSITIVE_THRESHOLD = 0.5 +FAST_RCNN_IOU_NEGATIVE_THRESHOLD = 0.0 # 0.1 < IOU < 0.5 is negative +FAST_RCNN_MINIBATCH_SIZE = 512 # if is -1, that is train with OHEM +FAST_RCNN_POSITIVE_RATE = 0.25 + +ADD_GTBOXES_TO_TRAIN = False + + + diff --git a/libs/configs/COCO/cfgs_res50_1x_coco_v2.py b/libs/configs/COCO/cfgs_res50_1x_coco_v2.py new file mode 100644 index 0000000..fd0e3bb --- /dev/null +++ b/libs/configs/COCO/cfgs_res50_1x_coco_v2.py @@ -0,0 +1,129 @@ +# -*- coding: utf-8 -*- +from __future__ import division, print_function, absolute_import +import os +import tensorflow as tf +''' +gluoncv backbone + multi_gpu + +''' + +# ------------------------------------------------ +VERSION = 'Cascade_FPN_Res50_COCO_1x_20190420_v2' +NET_NAME = 'resnet50_v1d' +ADD_BOX_IN_TENSORBOARD = True + +# ---------------------------------------- System_config +ROOT_PATH = os.path.abspath('../') +print(20*"++--") +print(ROOT_PATH) +GPU_GROUP = "0,1,2,3,4,5,6,7" +NUM_GPU = len(GPU_GROUP.strip().split(',')) +SHOW_TRAIN_INFO_INTE = 10 +SMRY_ITER = 200 +SAVE_WEIGHTS_INTE = 80000 + +SUMMARY_PATH = ROOT_PATH + '/output/summary' +TEST_SAVE_PATH = ROOT_PATH + '/tools/test_result' +INFERENCE_IMAGE_PATH = ROOT_PATH + '/tools/inference_image' +INFERENCE_SAVE_PATH = ROOT_PATH + '/tools/inference_results' + +if NET_NAME.startswith("resnet"): + weights_name = NET_NAME +elif NET_NAME.startswith("MobilenetV2"): + weights_name = "mobilenet/mobilenet_v2_1.0_224" +else: + raise NotImplementedError + +PRETRAINED_CKPT = ROOT_PATH + '/data/pretrained_weights/' + weights_name + '.ckpt' +TRAINED_CKPT = os.path.join(ROOT_PATH, 'output/trained_weights') + +EVALUATE_DIR = ROOT_PATH + '/output/evaluate_result_pickle/' + +# ------------------------------------------ Train config +RESTORE_FROM_RPN = False +IS_FILTER_OUTSIDE_BOXES = False +FIXED_BLOCKS = 0 # allow 0~3 +FREEZE_BLOCKS = [True, False, False, False, False] # for gluoncv backbone +USE_07_METRIC = True +CUDA9 = True +EVAL_THRESHOLD = 0.5 + +RPN_LOCATION_LOSS_WEIGHT = 1. +RPN_CLASSIFICATION_LOSS_WEIGHT = 1.0 + +FAST_RCNN_LOCATION_LOSS_WEIGHT = 1.0 +FAST_RCNN_CLASSIFICATION_LOSS_WEIGHT = 1.0 +RPN_SIGMA = 3.0 +FASTRCNN_SIGMA = 1.0 + +MUTILPY_BIAS_GRADIENT = None # 2.0 # if None, will not multipy +GRADIENT_CLIPPING_BY_NORM = None # 10.0 if None, will not clip + +EPSILON = 1e-5 +MOMENTUM = 0.9 +BATCH_SIZE = 1 +WARM_SETP = int(0.25 * SAVE_WEIGHTS_INTE) +LR = 5e-4 * 2 * 1.25 * NUM_GPU * BATCH_SIZE +DECAY_STEP = [11*SAVE_WEIGHTS_INTE, 16*SAVE_WEIGHTS_INTE, 20*SAVE_WEIGHTS_INTE] # 50000, 70000 +MAX_ITERATION = 20*SAVE_WEIGHTS_INTE + +# -------------------------------------------- Data_preprocess_config +DATASET_NAME = 'coco' # 'pascal', 'coco' +PIXEL_MEAN = [123.68, 116.779, 103.939] # R, G, B. In tf, channel is RGB. In openCV, channel is BGR +PIXEL_MEAN_ = [0.485, 0.456, 0.406] +PIXEL_STD = [0.229, 0.224, 0.225] # R, G, B. In tf, channel is RGB. In openCV, channel is BGR +IMG_SHORT_SIDE_LEN = 800 +IMG_MAX_LENGTH = 1333 +CLASS_NUM = 80 + + +# --------------------------------------------- Network_config +INITIALIZER = tf.random_normal_initializer(mean=0.0, stddev=0.01) +BBOX_INITIALIZER = tf.random_normal_initializer(mean=0.0, stddev=0.001) +WEIGHT_DECAY = 0.00004 if NET_NAME.startswith('Mobilenet') else 0.0001 +IS_ASSIGN = True + +# ---------------------------------------------Anchor config +USE_CENTER_OFFSET = True +LEVLES = ['P2', 'P3', 'P4', 'P5', 'P6'] +BASE_ANCHOR_SIZE_LIST = [32, 64, 128, 256, 512] +ANCHOR_STRIDE_LIST = [4, 8, 16, 32, 64] +ANCHOR_SCALES = [1.0] +ANCHOR_RATIOS = [0.5, 1., 2.0] +ROI_SCALE_FACTORS = [[10., 10., 5.0, 5.0], [20., 20., 10.0, 10.0], [30., 30., 15.0, 15.0]] +ANCHOR_SCALE_FACTORS = None # [10., 10., 5.0, 5.0] + +# --------------------------------------------FPN config +SHARE_HEADS = True +KERNEL_SIZE = 3 +RPN_IOU_POSITIVE_THRESHOLD = 0.7 +RPN_IOU_NEGATIVE_THRESHOLD = 0.3 +TRAIN_RPN_CLOOBER_POSITIVES = False + +RPN_MINIBATCH_SIZE = 256 +RPN_POSITIVE_RATE = 0.5 +RPN_NMS_IOU_THRESHOLD = 0.7 +RPN_TOP_K_NMS_TRAIN = 12000 +RPN_MAXIMUM_PROPOSAL_TARIN = 2000 + +RPN_TOP_K_NMS_TEST = 6000 +RPN_MAXIMUM_PROPOSAL_TEST = 1000 + +# -------------------------------------------Fast-RCNN config +ROI_SIZE = 14 +ROI_POOL_KERNEL_SIZE = 2 +USE_DROPOUT = False +KEEP_PROB = 1.0 +SHOW_SCORE_THRSHOLD = 0.6 # only show in tensorboard + +FAST_RCNN_NMS_IOU_THRESHOLD = 0.5 # 0.6 +FAST_RCNN_NMS_MAX_BOXES_PER_CLASS = 100 +FAST_RCNN_IOU_POSITIVE_THRESHOLD = 0.5 +FAST_RCNN_IOU_NEGATIVE_THRESHOLD = 0.0 # 0.1 < IOU < 0.5 is negative +FAST_RCNN_MINIBATCH_SIZE = 512 # if is -1, that is train with OHEM +FAST_RCNN_POSITIVE_RATE = 0.25 + +ADD_GTBOXES_TO_TRAIN = False + + + diff --git a/libs/configs/COCO/cfgs_res50_1x_coco_v3.py b/libs/configs/COCO/cfgs_res50_1x_coco_v3.py new file mode 100644 index 0000000..0ac9b86 --- /dev/null +++ b/libs/configs/COCO/cfgs_res50_1x_coco_v3.py @@ -0,0 +1,129 @@ +# -*- coding: utf-8 -*- +from __future__ import division, print_function, absolute_import +import os +import tensorflow as tf +''' +gluoncv backbone + multi_gpu + +''' + +# ------------------------------------------------ +VERSION = 'Cascade_FPN_Res50_COCO_1x_20190421_v3' +NET_NAME = 'resnet50_v1d' +ADD_BOX_IN_TENSORBOARD = True + +# ---------------------------------------- System_config +ROOT_PATH = os.path.abspath('../') +print(20*"++--") +print(ROOT_PATH) +GPU_GROUP = "0,1,2,3,4,5,6,7" +NUM_GPU = len(GPU_GROUP.strip().split(',')) +SHOW_TRAIN_INFO_INTE = 20 +SMRY_ITER = 200 +SAVE_WEIGHTS_INTE = 80000 + +SUMMARY_PATH = ROOT_PATH + '/output/summary' +TEST_SAVE_PATH = ROOT_PATH + '/tools/test_result' +INFERENCE_IMAGE_PATH = ROOT_PATH + '/tools/inference_image' +INFERENCE_SAVE_PATH = ROOT_PATH + '/tools/inference_results' + +if NET_NAME.startswith("resnet"): + weights_name = NET_NAME +elif NET_NAME.startswith("MobilenetV2"): + weights_name = "mobilenet/mobilenet_v2_1.0_224" +else: + raise NotImplementedError + +PRETRAINED_CKPT = ROOT_PATH + '/data/pretrained_weights/' + weights_name + '.ckpt' +TRAINED_CKPT = os.path.join(ROOT_PATH, 'output/trained_weights') + +EVALUATE_DIR = ROOT_PATH + '/output/evaluate_result_pickle/' + +# ------------------------------------------ Train config +RESTORE_FROM_RPN = False +IS_FILTER_OUTSIDE_BOXES = False +FIXED_BLOCKS = 0 # allow 0~3 +FREEZE_BLOCKS = [True, False, False, False, False] # for gluoncv backbone +USE_07_METRIC = True +CUDA9 = True +EVAL_THRESHOLD = 0.5 + +RPN_LOCATION_LOSS_WEIGHT = 1. +RPN_CLASSIFICATION_LOSS_WEIGHT = 1.0 + +FAST_RCNN_LOCATION_LOSS_WEIGHT = 1.0 +FAST_RCNN_CLASSIFICATION_LOSS_WEIGHT = 1.0 +RPN_SIGMA = 3.0 +FASTRCNN_SIGMA = 1.0 + +MUTILPY_BIAS_GRADIENT = None # 2.0 # if None, will not multipy +GRADIENT_CLIPPING_BY_NORM = None # 10.0 if None, will not clip + +EPSILON = 1e-5 +MOMENTUM = 0.9 +BATCH_SIZE = 1 +WARM_SETP = int(0.25 * SAVE_WEIGHTS_INTE) +LR = 5e-4 * 2 * 1.25 * NUM_GPU * BATCH_SIZE +DECAY_STEP = [11*SAVE_WEIGHTS_INTE, 16*SAVE_WEIGHTS_INTE, 20*SAVE_WEIGHTS_INTE] # 50000, 70000 +MAX_ITERATION = 20*SAVE_WEIGHTS_INTE + +# -------------------------------------------- Data_preprocess_config +DATASET_NAME = 'coco' # 'pascal', 'coco' +PIXEL_MEAN = [123.68, 116.779, 103.939] # R, G, B. In tf, channel is RGB. In openCV, channel is BGR +PIXEL_MEAN_ = [0.485, 0.456, 0.406] +PIXEL_STD = [0.229, 0.224, 0.225] # R, G, B. In tf, channel is RGB. In openCV, channel is BGR +IMG_SHORT_SIDE_LEN = 800 +IMG_MAX_LENGTH = 1333 +CLASS_NUM = 80 + + +# --------------------------------------------- Network_config +INITIALIZER = tf.random_normal_initializer(mean=0.0, stddev=0.01) +BBOX_INITIALIZER = tf.random_normal_initializer(mean=0.0, stddev=0.001) +WEIGHT_DECAY = 0.00004 if NET_NAME.startswith('Mobilenet') else 0.0001 +IS_ASSIGN = True + +# ---------------------------------------------Anchor config +USE_CENTER_OFFSET = True +LEVLES = ['P2', 'P3', 'P4', 'P5', 'P6'] +BASE_ANCHOR_SIZE_LIST = [32, 64, 128, 256, 512] +ANCHOR_STRIDE_LIST = [4, 8, 16, 32, 64] +ANCHOR_SCALES = [1.0] +ANCHOR_RATIOS = [0.5, 1., 2.0] +ROI_SCALE_FACTORS = [[10., 10., 5.0, 5.0], [20., 20., 10.0, 10.0], [40., 40., 20.0, 20.0]] +ANCHOR_SCALE_FACTORS = [10., 10., 5.0, 5.0] + +# --------------------------------------------FPN config +SHARE_HEADS = True +KERNEL_SIZE = 3 +RPN_IOU_POSITIVE_THRESHOLD = 0.7 +RPN_IOU_NEGATIVE_THRESHOLD = 0.3 +TRAIN_RPN_CLOOBER_POSITIVES = False + +RPN_MINIBATCH_SIZE = 256 +RPN_POSITIVE_RATE = 0.5 +RPN_NMS_IOU_THRESHOLD = 0.7 +RPN_TOP_K_NMS_TRAIN = 12000 +RPN_MAXIMUM_PROPOSAL_TARIN = 2000 + +RPN_TOP_K_NMS_TEST = 6000 +RPN_MAXIMUM_PROPOSAL_TEST = 1000 + +# -------------------------------------------Fast-RCNN config +ROI_SIZE = 14 +ROI_POOL_KERNEL_SIZE = 2 +USE_DROPOUT = False +KEEP_PROB = 1.0 +SHOW_SCORE_THRSHOLD = 0.6 # only show in tensorboard + +FAST_RCNN_NMS_IOU_THRESHOLD = 0.5 # 0.6 +FAST_RCNN_NMS_MAX_BOXES_PER_CLASS = 100 +FAST_RCNN_IOU_POSITIVE_THRESHOLD = 0.5 +FAST_RCNN_IOU_NEGATIVE_THRESHOLD = 0.0 # 0.1 < IOU < 0.5 is negative +FAST_RCNN_MINIBATCH_SIZE = 512 # if is -1, that is train with OHEM +FAST_RCNN_POSITIVE_RATE = 0.25 + +ADD_GTBOXES_TO_TRAIN = False + + + diff --git a/libs/configs/__init__.py b/libs/configs/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/libs/configs/cfgs.py b/libs/configs/cfgs.py new file mode 100644 index 0000000..ae9597a --- /dev/null +++ b/libs/configs/cfgs.py @@ -0,0 +1,125 @@ +# -*- coding: utf-8 -*- +from __future__ import division, print_function, absolute_import +import os +import tensorflow as tf + +# ------------------------------------------------ +VERSION = 'Cascade_FPN_Res50_COCO_1x_20190420_v2' +NET_NAME = 'resnet_v1_50' +ADD_BOX_IN_TENSORBOARD = True + +# ---------------------------------------- System_config +ROOT_PATH = os.path.abspath('../') +print(20*"++--") +print(ROOT_PATH) +GPU_GROUP = "0,1,2,3,4,5,6,7" +NUM_GPU = len(GPU_GROUP.strip().split(',')) +SHOW_TRAIN_INFO_INTE = 10 +SMRY_ITER = 200 +SAVE_WEIGHTS_INTE = 80000 + +SUMMARY_PATH = ROOT_PATH + '/output/summary' +TEST_SAVE_PATH = ROOT_PATH + '/tools/test_result' +INFERENCE_IMAGE_PATH = ROOT_PATH + '/tools/inference_image' +INFERENCE_SAVE_PATH = ROOT_PATH + '/tools/inference_results' + +if NET_NAME.startswith("resnet"): + weights_name = NET_NAME +elif NET_NAME.startswith("MobilenetV2"): + weights_name = "mobilenet/mobilenet_v2_1.0_224" +else: + raise NotImplementedError + +PRETRAINED_CKPT = ROOT_PATH + '/data/pretrained_weights/' + weights_name + '.ckpt' +TRAINED_CKPT = os.path.join(ROOT_PATH, 'output/trained_weights') + +EVALUATE_DIR = ROOT_PATH + '/output/evaluate_result_pickle/' + +# ------------------------------------------ Train config +RESTORE_FROM_RPN = False +IS_FILTER_OUTSIDE_BOXES = False +FIXED_BLOCKS = 0 # allow 0~3 +FREEZE_BLOCKS = [True, False, False, False, False] # for gluoncv backbone +USE_07_METRIC = True +CUDA9 = True +EVAL_THRESHOLD = 0.5 + +RPN_LOCATION_LOSS_WEIGHT = 1. +RPN_CLASSIFICATION_LOSS_WEIGHT = 1.0 + +FAST_RCNN_LOCATION_LOSS_WEIGHT = 1.0 +FAST_RCNN_CLASSIFICATION_LOSS_WEIGHT = 1.0 +RPN_SIGMA = 3.0 +FASTRCNN_SIGMA = 1.0 + +MUTILPY_BIAS_GRADIENT = None # 2.0 # if None, will not multipy +GRADIENT_CLIPPING_BY_NORM = None # 10.0 if None, will not clip + +EPSILON = 1e-5 +MOMENTUM = 0.9 +BATCH_SIZE = 1 +WARM_SETP = int(0.25 * SAVE_WEIGHTS_INTE) +LR = 5e-4 * 2 * 1.25 * NUM_GPU * BATCH_SIZE +DECAY_STEP = [11*SAVE_WEIGHTS_INTE, 16*SAVE_WEIGHTS_INTE, 20*SAVE_WEIGHTS_INTE] # 50000, 70000 +MAX_ITERATION = 20*SAVE_WEIGHTS_INTE + +# -------------------------------------------- Data_preprocess_config +DATASET_NAME = 'coco' # 'pascal', 'coco' +PIXEL_MEAN = [123.68, 116.779, 103.939] # R, G, B. In tf, channel is RGB. In openCV, channel is BGR +PIXEL_MEAN_ = [0.485, 0.456, 0.406] +PIXEL_STD = [0.229, 0.224, 0.225] # R, G, B. In tf, channel is RGB. In openCV, channel is BGR +IMG_SHORT_SIDE_LEN = 800 +IMG_MAX_LENGTH = 1333 +CLASS_NUM = 80 + + +# --------------------------------------------- Network_config +INITIALIZER = tf.random_normal_initializer(mean=0.0, stddev=0.01) +BBOX_INITIALIZER = tf.random_normal_initializer(mean=0.0, stddev=0.001) +WEIGHT_DECAY = 0.00004 if NET_NAME.startswith('Mobilenet') else 0.0001 +IS_ASSIGN = True + +# ---------------------------------------------Anchor config +USE_CENTER_OFFSET = True +LEVLES = ['P2', 'P3', 'P4', 'P5', 'P6'] +BASE_ANCHOR_SIZE_LIST = [32, 64, 128, 256, 512] +ANCHOR_STRIDE_LIST = [4, 8, 16, 32, 64] +ANCHOR_SCALES = [1.0] +ANCHOR_RATIOS = [0.5, 1., 2.0] +ROI_SCALE_FACTORS = [[10., 10., 5.0, 5.0], [20., 20., 10.0, 10.0], [30., 30., 15.0, 15.0]] +ANCHOR_SCALE_FACTORS = None # [10., 10., 5.0, 5.0] + +# --------------------------------------------FPN config +SHARE_HEADS = True +KERNEL_SIZE = 3 +RPN_IOU_POSITIVE_THRESHOLD = 0.7 +RPN_IOU_NEGATIVE_THRESHOLD = 0.3 +TRAIN_RPN_CLOOBER_POSITIVES = False + +RPN_MINIBATCH_SIZE = 256 +RPN_POSITIVE_RATE = 0.5 +RPN_NMS_IOU_THRESHOLD = 0.7 +RPN_TOP_K_NMS_TRAIN = 12000 +RPN_MAXIMUM_PROPOSAL_TARIN = 2000 + +RPN_TOP_K_NMS_TEST = 6000 +RPN_MAXIMUM_PROPOSAL_TEST = 1000 + +# -------------------------------------------Fast-RCNN config +ROI_SIZE = 14 +ROI_POOL_KERNEL_SIZE = 2 +USE_DROPOUT = False +KEEP_PROB = 1.0 +SHOW_SCORE_THRSHOLD = 0.6 # only show in tensorboard + +FAST_RCNN_NMS_IOU_THRESHOLD = 0.5 # 0.6 +FAST_RCNN_NMS_MAX_BOXES_PER_CLASS = 100 +FAST_RCNN_IOU_POSITIVE_THRESHOLD = 0.5 +FAST_RCNN_IOU_NEGATIVE_THRESHOLD = 0.0 # 0.1 < IOU < 0.5 is negative +FAST_RCNN_MINIBATCH_SIZE = 512 # if is -1, that is train with OHEM +FAST_RCNN_POSITIVE_RATE = 0.25 + +ADD_GTBOXES_TO_TRAIN = False + + + diff --git a/libs/detection_oprations/__init__.py b/libs/detection_oprations/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/libs/detection_oprations/anchor_target_layer_without_boxweight.py b/libs/detection_oprations/anchor_target_layer_without_boxweight.py new file mode 100644 index 0000000..e0f2908 --- /dev/null +++ b/libs/detection_oprations/anchor_target_layer_without_boxweight.py @@ -0,0 +1,124 @@ +# -------------------------------------------------------- +# Faster R-CNN +# Copyright (c) 2015 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ross Girshick and Xinlei Chen +# -------------------------------------------------------- +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +from libs.configs import cfgs +import numpy as np +import numpy.random as npr +from libs.box_utils.cython_utils.cython_bbox import bbox_overlaps +from libs.box_utils import encode_and_decode + + +def anchor_target_layer( + gt_boxes, img_shape, all_anchors, is_restrict_bg=False): + """Same as the anchor target layer in original Fast/er RCNN """ + + total_anchors = all_anchors.shape[0] + img_h, img_w = img_shape[1], img_shape[2] + gt_boxes = gt_boxes[:, :-1] # remove class label + + # allow boxes to sit over the edge by a small amount + _allowed_border = 0 + + # only keep anchors inside the image + if cfgs.IS_FILTER_OUTSIDE_BOXES: + inds_inside = np.where( + (all_anchors[:, 0] >= -_allowed_border) & + (all_anchors[:, 1] >= -_allowed_border) & + (all_anchors[:, 2] < img_w + _allowed_border) & # width + (all_anchors[:, 3] < img_h + _allowed_border) # height + )[0] + else: + inds_inside = range(all_anchors.shape[0]) + + anchors = all_anchors[inds_inside, :] + + # label: 1 is positive, 0 is negative, -1 is dont care + labels = np.empty((len(inds_inside),), dtype=np.float32) + labels.fill(-1) + + # overlaps between the anchors and the gt boxes + overlaps = bbox_overlaps( + np.ascontiguousarray(anchors, dtype=np.float), + np.ascontiguousarray(gt_boxes, dtype=np.float)) + + argmax_overlaps = overlaps.argmax(axis=1) + max_overlaps = overlaps[np.arange(len(inds_inside)), argmax_overlaps] + gt_argmax_overlaps = overlaps.argmax(axis=0) + gt_max_overlaps = overlaps[ + gt_argmax_overlaps, np.arange(overlaps.shape[1])] + gt_argmax_overlaps = np.where(overlaps == gt_max_overlaps)[0] + + if not cfgs.TRAIN_RPN_CLOOBER_POSITIVES: + labels[max_overlaps < cfgs.RPN_IOU_NEGATIVE_THRESHOLD] = 0 + + labels[gt_argmax_overlaps] = 1 + labels[max_overlaps >= cfgs.RPN_IOU_POSITIVE_THRESHOLD] = 1 + + if cfgs.TRAIN_RPN_CLOOBER_POSITIVES: + labels[max_overlaps < cfgs.RPN_IOU_NEGATIVE_THRESHOLD] = 0 + + num_fg = int(cfgs.RPN_MINIBATCH_SIZE * cfgs.RPN_POSITIVE_RATE) + fg_inds = np.where(labels == 1)[0] + if len(fg_inds) > num_fg: + disable_inds = npr.choice( + fg_inds, size=(len(fg_inds) - num_fg), replace=False) + labels[disable_inds] = -1 + + num_bg = cfgs.RPN_MINIBATCH_SIZE - np.sum(labels == 1) + if is_restrict_bg: + num_bg = max(num_bg, num_fg * 1.5) + bg_inds = np.where(labels == 0)[0] + if len(bg_inds) > num_bg: + disable_inds = npr.choice( + bg_inds, size=(len(bg_inds) - num_bg), replace=False) + labels[disable_inds] = -1 + + bbox_targets = _compute_targets(anchors, gt_boxes[argmax_overlaps, :]) + + # map up to original set of anchors + labels = _unmap(labels, total_anchors, inds_inside, fill=-1) + bbox_targets = _unmap(bbox_targets, total_anchors, inds_inside, fill=0) + + # labels = labels.reshape((1, height, width, A)) + rpn_labels = labels.reshape((-1, 1)) + + # bbox_targets + bbox_targets = bbox_targets.reshape((-1, 4)) + rpn_bbox_targets = bbox_targets + + return rpn_labels, rpn_bbox_targets + + +def _unmap(data, count, inds, fill=0): + """ Unmap a subset of item (data) back to the original set of items (of + size count) """ + if len(data.shape) == 1: + ret = np.empty((count,), dtype=np.float32) + ret.fill(fill) + ret[inds] = data + else: + ret = np.empty((count,) + data.shape[1:], dtype=np.float32) + ret.fill(fill) + ret[inds, :] = data + return ret + + +def _compute_targets(ex_rois, gt_rois): + """Compute bounding-box regression targets for an image.""" + # targets = bbox_transform(ex_rois, gt_rois[:, :4]).astype( + # np.float32, copy=False) + targets = encode_and_decode.encode_boxes(unencode_boxes=gt_rois, + reference_boxes=ex_rois, + scale_factors=cfgs.ANCHOR_SCALE_FACTORS) + # targets = encode_and_decode.encode_boxes(ex_rois=ex_rois, + # gt_rois=gt_rois, + # scale_factor=None) + return targets diff --git a/libs/detection_oprations/proposal_opr.py b/libs/detection_oprations/proposal_opr.py new file mode 100644 index 0000000..7673efc --- /dev/null +++ b/libs/detection_oprations/proposal_opr.py @@ -0,0 +1,62 @@ +# encoding: utf-8 + +from libs.configs import cfgs +from libs.box_utils import encode_and_decode +from libs.box_utils import boxes_utils +import tensorflow as tf +import numpy as np + + +def postprocess_rpn_proposals(rpn_bbox_pred, rpn_cls_prob, img_shape, anchors, is_training): + ''' + + :param rpn_bbox_pred: [-1, 4] + :param rpn_cls_prob: [-1, 2] + :param img_shape: + :param anchors:[-1, 4] + :param is_training: + :return: + ''' + + if is_training: + pre_nms_topN = cfgs.RPN_TOP_K_NMS_TRAIN + post_nms_topN = cfgs.RPN_MAXIMUM_PROPOSAL_TARIN + # pre_nms_topN = cfgs.FPN_TOP_K_PER_LEVEL_TRAIN + # post_nms_topN = pre_nms_topN + else: + pre_nms_topN = cfgs.RPN_TOP_K_NMS_TEST + post_nms_topN = cfgs.RPN_MAXIMUM_PROPOSAL_TEST + # pre_nms_topN = cfgs.FPN_TOP_K_PER_LEVEL_TEST + # post_nms_topN = pre_nms_topN + + nms_thresh = cfgs.RPN_NMS_IOU_THRESHOLD + + cls_prob = rpn_cls_prob[:, 1] + + # 1. decode boxes + decode_boxes = encode_and_decode.decode_boxes(encoded_boxes=rpn_bbox_pred, + reference_boxes=anchors, + scale_factors=cfgs.ANCHOR_SCALE_FACTORS) + + # 2. clip to img boundaries + decode_boxes = boxes_utils.clip_boxes_to_img_boundaries(decode_boxes=decode_boxes, + img_shape=img_shape) + + # 3. get top N to NMS + if pre_nms_topN > 0: + pre_nms_topN = tf.minimum(pre_nms_topN, tf.shape(decode_boxes)[0], name='avoid_unenough_boxes') + cls_prob, top_k_indices = tf.nn.top_k(cls_prob, k=pre_nms_topN) + decode_boxes = tf.gather(decode_boxes, top_k_indices) + + # 4. NMS + keep = tf.image.non_max_suppression( + boxes=decode_boxes, + scores=cls_prob, + max_output_size=post_nms_topN, + iou_threshold=nms_thresh) + + final_boxes = tf.gather(decode_boxes, keep) + final_probs = tf.gather(cls_prob, keep) + + return final_boxes, final_probs + diff --git a/libs/detection_oprations/proposal_target_layer.py b/libs/detection_oprations/proposal_target_layer.py new file mode 100644 index 0000000..b77ea87 --- /dev/null +++ b/libs/detection_oprations/proposal_target_layer.py @@ -0,0 +1,162 @@ +# -------------------------------------------------------- +# Faster R-CNN +# Copyright (c) 2015 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ross Girshick +# -------------------------------------------------------- +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +from libs.configs import cfgs +import numpy as np +import numpy.random as npr + +from libs.box_utils import encode_and_decode +from libs.box_utils.cython_utils.cython_bbox import bbox_overlaps + + +def proposal_target_layer(rpn_rois, gt_boxes, fg_threshold): + """ + Assign object detection proposals to ground-truth targets. Produces proposal + classification labels and bounding-box regression targets. + """ + # Proposal ROIs (x1, y1, x2, y2) coming from RPN + # gt_boxes (x1, y1, x2, y2, label) + + if cfgs.ADD_GTBOXES_TO_TRAIN: + + num_jitter = 10 + jitter_gtboxes = [] + for n in range(num_jitter): + tmp_gtboxes = gt_boxes[:, :-1] + 0 + w = gt_boxes[:, 2] - gt_boxes[:, 0] + h = gt_boxes[:, 3] - gt_boxes[:, 1] + scale = np.random.uniform(-0.2, 0.2, 1) + delta_w = np.reshape(w * scale, [-1, 1]) + delta_h = np.reshape(h * scale, [-1, 1]) + tmp_gtboxes[:, 0::2] = tmp_gtboxes[:, 0::2] + delta_w + tmp_gtboxes[:, 1::2] = tmp_gtboxes[:, 1::2] + delta_h + jitter_gtboxes.append(tmp_gtboxes) + jitter_gtboxes = np.concatenate(jitter_gtboxes, axis=0) + all_rois = np.vstack((rpn_rois, jitter_gtboxes)) + # all_rois = np.vstack((rpn_rois, gt_boxes[:, :-1])) + else: + all_rois = rpn_rois + + rois_per_image = np.inf if cfgs.FAST_RCNN_MINIBATCH_SIZE == -1 else cfgs.FAST_RCNN_MINIBATCH_SIZE + + fg_rois_per_image = np.round(cfgs.FAST_RCNN_POSITIVE_RATE * rois_per_image) + + # Sample rois with classification labels and bounding box regression + labels, rois, bbox_targets, gtboxes = _sample_rois(all_rois, gt_boxes, fg_rois_per_image, + rois_per_image, cfgs.CLASS_NUM+1, fg_threshold) + + rois = rois.reshape(-1, 4) + labels = labels.reshape(-1) + bbox_targets = bbox_targets.reshape(-1, (cfgs.CLASS_NUM+1) * 4) + gtboxes = gtboxes.reshape(-1, 4) + + return rois, labels, bbox_targets, gtboxes + + +def _get_bbox_regression_labels(bbox_target_data, num_classes): + """Bounding-box regression targets (bbox_target_data) are stored in a + compact form N x (class, tx, ty, tw, th) + + This function expands those targets into the 4-of-4*K representation used + by the network (i.e. only one class has non-zero targets). + + Returns: + bbox_target (ndarray): N x 4K blob of regression targets + """ + + clss = bbox_target_data[:, 0] + bbox_targets = np.zeros((clss.size, 4 * num_classes), dtype=np.float32) + inds = np.where(clss > 0)[0] + for ind in inds: + cls = clss[ind] + start = int(4 * cls) + end = start + 4 + bbox_targets[ind, start:end] = bbox_target_data[ind, 1:] + + return bbox_targets + + +def _compute_targets(ex_rois, gt_rois, labels): + """Compute bounding-box regression targets for an image. + that is : [label, tx, ty, tw, th] + """ + + assert ex_rois.shape[0] == gt_rois.shape[0] + assert ex_rois.shape[1] == 4 + assert gt_rois.shape[1] == 4 + + targets = encode_and_decode.encode_boxes(unencode_boxes=gt_rois, + reference_boxes=ex_rois, + scale_factors=cfgs.ROI_SCALE_FACTORS) + # targets = encode_and_decode.encode_boxes(ex_rois=ex_rois, + # gt_rois=gt_rois, + # scale_factor=cfgs.ROI_SCALE_FACTORS) + + return np.hstack( + (labels[:, np.newaxis], targets)).astype(np.float32, copy=False) + + +def _sample_rois(all_rois, gt_boxes, fg_rois_per_image, + rois_per_image, num_classes, fg_threshold): + """Generate a random sample of RoIs comprising foreground and background + examples. + + all_rois shape is [-1, 4] + gt_boxes shape is [-1, 5]. that is [x1, y1, x2, y2, label] + """ + # overlaps: (rois x gt_boxes) + overlaps = bbox_overlaps( + np.ascontiguousarray(all_rois, dtype=np.float), + np.ascontiguousarray(gt_boxes[:, :-1], dtype=np.float)) + gt_assignment = overlaps.argmax(axis=1) + max_overlaps = overlaps.max(axis=1) + labels = gt_boxes[gt_assignment, -1] + + # Select foreground RoIs as those with >= FG_THRESH overlap + # fg_inds = np.where(max_overlaps >= cfgs.FAST_RCNN_IOU_POSITIVE_THRESHOLD)[0] + fg_inds = np.where(max_overlaps >= fg_threshold)[0] + # Guard against the case when an image has fewer than fg_rois_per_image + # Select background RoIs as those within [BG_THRESH_LO, BG_THRESH_HI) + bg_inds = np.where((max_overlaps < cfgs.FAST_RCNN_IOU_POSITIVE_THRESHOLD) & + (max_overlaps >= cfgs.FAST_RCNN_IOU_NEGATIVE_THRESHOLD))[0] + # print("first fileter, fg_size: {} || bg_size: {}".format(fg_inds.shape, bg_inds.shape)) + # Guard against the case when an image has fewer than fg_rois_per_image + # foreground RoIs + fg_rois_per_this_image = min(fg_rois_per_image, fg_inds.size) + + # Sample foreground regions without replacement + if fg_inds.size > 0: + fg_inds = npr.choice(fg_inds, size=int(fg_rois_per_this_image), replace=False) + # Compute number of background RoIs to take from this image (guarding + # against there being fewer than desired) + bg_rois_per_this_image = rois_per_image - fg_rois_per_this_image + bg_rois_per_this_image = min(bg_rois_per_this_image, bg_inds.size) + # Sample background regions without replacement + if bg_inds.size > 0: + bg_inds = npr.choice(bg_inds, size=int(bg_rois_per_this_image), replace=False) + + # print("second fileter, fg_size: {} || bg_size: {}".format(fg_inds.shape, bg_inds.shape)) + # The indices that we're selecting (both fg and bg) + keep_inds = np.append(fg_inds, bg_inds) + + + # Select sampled values from various arrays: + labels = labels[keep_inds] + + # Clamp labels for the background RoIs to 0 + labels[int(fg_rois_per_this_image):] = 0 + rois = all_rois[keep_inds] + + bbox_target_data = _compute_targets( + rois, gt_boxes[gt_assignment[keep_inds], :-1], labels) + + bbox_targets = \ + _get_bbox_regression_labels(bbox_target_data, num_classes) + + return labels, rois, bbox_targets, gt_boxes[gt_assignment[keep_inds], :-1] diff --git a/libs/detection_oprations/proposal_target_layer_cascade.py b/libs/detection_oprations/proposal_target_layer_cascade.py new file mode 100644 index 0000000..9753e29 --- /dev/null +++ b/libs/detection_oprations/proposal_target_layer_cascade.py @@ -0,0 +1,162 @@ +# -------------------------------------------------------- +# Faster R-CNN +# Copyright (c) 2015 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ross Girshick +# -------------------------------------------------------- +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +from libs.configs import cfgs +import numpy as np +import numpy.random as npr + +from libs.box_utils import encode_and_decode +from libs.box_utils.cython_utils.cython_bbox import bbox_overlaps + + +def proposal_target_layer(rpn_rois, gt_boxes, fg_threshold, stage): + """ + Assign object detection proposals to ground-truth targets. Produces proposal + classification labels and bounding-box regression targets. + """ + # Proposal ROIs (x1, y1, x2, y2) coming from RPN + # gt_boxes (x1, y1, x2, y2, label) + + if cfgs.ADD_GTBOXES_TO_TRAIN: + + num_jitter = 10 + jitter_gtboxes = [] + for n in range(num_jitter): + tmp_gtboxes = gt_boxes[:, :-1] + 0 + w = gt_boxes[:, 2] - gt_boxes[:, 0] + h = gt_boxes[:, 3] - gt_boxes[:, 1] + scale = np.random.uniform(-0.2, 0.2, 1) + delta_w = np.reshape(w * scale, [-1, 1]) + delta_h = np.reshape(h * scale, [-1, 1]) + tmp_gtboxes[:, 0::2] = tmp_gtboxes[:, 0::2] + delta_w + tmp_gtboxes[:, 1::2] = tmp_gtboxes[:, 1::2] + delta_h + jitter_gtboxes.append(tmp_gtboxes) + jitter_gtboxes = np.concatenate(jitter_gtboxes, axis=0) + all_rois = np.vstack((rpn_rois, jitter_gtboxes)) + # all_rois = np.vstack((rpn_rois, gt_boxes[:, :-1])) + else: + all_rois = rpn_rois + + rois_per_image = np.inf if cfgs.FAST_RCNN_MINIBATCH_SIZE == -1 else cfgs.FAST_RCNN_MINIBATCH_SIZE + + fg_rois_per_image = np.round(cfgs.FAST_RCNN_POSITIVE_RATE * rois_per_image) + + # Sample rois with classification labels and bounding box regression + labels, rois, bbox_targets, gtboxes = _sample_rois(all_rois, gt_boxes, fg_rois_per_image, + rois_per_image, cfgs.CLASS_NUM+1, fg_threshold, stage) + + rois = rois.reshape(-1, 4) + labels = labels.reshape(-1) + bbox_targets = bbox_targets.reshape(-1, (cfgs.CLASS_NUM+1) * 4) + gtboxes = gtboxes.reshape(-1, 4) + + return rois, labels, bbox_targets, gtboxes + + +def _get_bbox_regression_labels(bbox_target_data, num_classes): + """Bounding-box regression targets (bbox_target_data) are stored in a + compact form N x (class, tx, ty, tw, th) + + This function expands those targets into the 4-of-4*K representation used + by the network (i.e. only one class has non-zero targets). + + Returns: + bbox_target (ndarray): N x 4K blob of regression targets + """ + + clss = bbox_target_data[:, 0] + bbox_targets = np.zeros((clss.size, 4 * num_classes), dtype=np.float32) + inds = np.where(clss > 0)[0] + for ind in inds: + cls = clss[ind] + start = int(4 * cls) + end = start + 4 + bbox_targets[ind, start:end] = bbox_target_data[ind, 1:] + + return bbox_targets + + +def _compute_targets(ex_rois, gt_rois, labels, stage): + """Compute bounding-box regression targets for an image. + that is : [label, tx, ty, tw, th] + """ + + assert ex_rois.shape[0] == gt_rois.shape[0] + assert ex_rois.shape[1] == 4 + assert gt_rois.shape[1] == 4 + + targets = encode_and_decode.encode_boxes(unencode_boxes=gt_rois, + reference_boxes=ex_rois, + scale_factors=cfgs.ROI_SCALE_FACTORS[stage-1]) + # targets = encode_and_decode.encode_boxes(ex_rois=ex_rois, + # gt_rois=gt_rois, + # scale_factor=cfgs.ROI_SCALE_FACTORS) + + return np.hstack( + (labels[:, np.newaxis], targets)).astype(np.float32, copy=False) + + +def _sample_rois(all_rois, gt_boxes, fg_rois_per_image, + rois_per_image, num_classes, fg_threshold, stage): + """Generate a random sample of RoIs comprising foreground and background + examples. + + all_rois shape is [-1, 4] + gt_boxes shape is [-1, 5]. that is [x1, y1, x2, y2, label] + """ + # overlaps: (rois x gt_boxes) + overlaps = bbox_overlaps( + np.ascontiguousarray(all_rois, dtype=np.float), + np.ascontiguousarray(gt_boxes[:, :-1], dtype=np.float)) + gt_assignment = overlaps.argmax(axis=1) + max_overlaps = overlaps.max(axis=1) + labels = gt_boxes[gt_assignment, -1] + + # Select foreground RoIs as those with >= FG_THRESH overlap + # fg_inds = np.where(max_overlaps >= cfgs.FAST_RCNN_IOU_POSITIVE_THRESHOLD)[0] + fg_inds = np.where(max_overlaps >= fg_threshold)[0] + # Guard against the case when an image has fewer than fg_rois_per_image + # Select background RoIs as those within [BG_THRESH_LO, BG_THRESH_HI) + bg_inds = np.where((max_overlaps < cfgs.FAST_RCNN_IOU_POSITIVE_THRESHOLD) & + (max_overlaps >= cfgs.FAST_RCNN_IOU_NEGATIVE_THRESHOLD))[0] + # print("first fileter, fg_size: {} || bg_size: {}".format(fg_inds.shape, bg_inds.shape)) + # Guard against the case when an image has fewer than fg_rois_per_image + # foreground RoIs + fg_rois_per_this_image = min(fg_rois_per_image, fg_inds.size) + + # Sample foreground regions without replacement + if fg_inds.size > 0: + fg_inds = npr.choice(fg_inds, size=int(fg_rois_per_this_image), replace=False) + # Compute number of background RoIs to take from this image (guarding + # against there being fewer than desired) + bg_rois_per_this_image = rois_per_image - fg_rois_per_this_image + bg_rois_per_this_image = min(bg_rois_per_this_image, bg_inds.size) + # Sample background regions without replacement + if bg_inds.size > 0: + bg_inds = npr.choice(bg_inds, size=int(bg_rois_per_this_image), replace=False) + + # print("second fileter, fg_size: {} || bg_size: {}".format(fg_inds.shape, bg_inds.shape)) + # The indices that we're selecting (both fg and bg) + keep_inds = np.append(fg_inds, bg_inds) + + + # Select sampled values from various arrays: + labels = labels[keep_inds] + + # Clamp labels for the background RoIs to 0 + labels[int(fg_rois_per_this_image):] = 0 + rois = all_rois[keep_inds] + + bbox_target_data = _compute_targets( + rois, gt_boxes[gt_assignment[keep_inds], :-1], labels, stage) + + bbox_targets = \ + _get_bbox_regression_labels(bbox_target_data, num_classes) + + return labels, rois, bbox_targets, gt_boxes[gt_assignment[keep_inds], :-1] diff --git a/libs/detection_oprations/proposal_target_layer_cascade_.py b/libs/detection_oprations/proposal_target_layer_cascade_.py new file mode 100644 index 0000000..0db36a0 --- /dev/null +++ b/libs/detection_oprations/proposal_target_layer_cascade_.py @@ -0,0 +1,161 @@ +# -------------------------------------------------------- +# Faster R-CNN +# Copyright (c) 2015 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ross Girshick +# -------------------------------------------------------- +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +from libs.configs import cfgs +import numpy as np +import numpy.random as npr + +from libs.box_utils import encode_and_decode +from libs.box_utils.cython_utils.cython_bbox import bbox_overlaps + + +def proposal_target_layer(rpn_rois, gt_boxes, fg_threshold, stage): + """ + Assign object detection proposals to ground-truth targets. Produces proposal + classification labels and bounding-box regression targets. + """ + # Proposal ROIs (x1, y1, x2, y2) coming from RPN + # gt_boxes (x1, y1, x2, y2, label) + + if cfgs.ADD_GTBOXES_TO_TRAIN: + + num_jitter = 10 + jitter_gtboxes = [] + for n in range(num_jitter): + tmp_gtboxes = gt_boxes[:, :-1] + 0 + w = gt_boxes[:, 2] - gt_boxes[:, 0] + h = gt_boxes[:, 3] - gt_boxes[:, 1] + scale = np.random.uniform(-0.2, 0.2, 1) + delta_w = np.reshape(w * scale, [-1, 1]) + delta_h = np.reshape(h * scale, [-1, 1]) + tmp_gtboxes[:, 0::2] = tmp_gtboxes[:, 0::2] + delta_w + tmp_gtboxes[:, 1::2] = tmp_gtboxes[:, 1::2] + delta_h + jitter_gtboxes.append(tmp_gtboxes) + jitter_gtboxes = np.concatenate(jitter_gtboxes, axis=0) + all_rois = np.vstack((rpn_rois, jitter_gtboxes)) + # all_rois = np.vstack((rpn_rois, gt_boxes[:, :-1])) + else: + all_rois = rpn_rois + + rois_per_image = np.inf if cfgs.FAST_RCNN_MINIBATCH_SIZE == -1 else cfgs.FAST_RCNN_MINIBATCH_SIZE + + fg_rois_per_image = np.round(cfgs.FAST_RCNN_POSITIVE_RATE * rois_per_image) + + # Sample rois with classification labels and bounding box regression + labels, rois, bbox_targets, gtboxes = _sample_rois(all_rois, gt_boxes, fg_rois_per_image, + rois_per_image, 1, fg_threshold, stage) + + rois = rois.reshape(-1, 4) + labels = labels.reshape(-1) + bbox_targets = bbox_targets.reshape(-1, 4) + gtboxes = gtboxes.reshape(-1, 4) + + return rois, labels, bbox_targets, gtboxes + + +def _get_bbox_regression_labels(bbox_target_data, num_classes): + """Bounding-box regression targets (bbox_target_data) are stored in a + compact form N x (class, tx, ty, tw, th) + + This function expands those targets into the 4-of-4*K representation used + by the network (i.e. only one class has non-zero targets). + + Returns: + bbox_target (ndarray): N x 4K blob of regression targets + """ + + clss = bbox_target_data[:, 0] + bbox_targets = np.zeros((clss.size, 4 * num_classes), dtype=np.float32) + inds = np.where(clss > 0)[0] + for ind in inds: + # cls = clss[ind] + start = 0 # int(4 * cls) + end = start + 4 + bbox_targets[ind, start:end] = bbox_target_data[ind, 1:] + + return bbox_targets + + +def _compute_targets(ex_rois, gt_rois, labels, stage): + """Compute bounding-box regression targets for an image. + that is : [label, tx, ty, tw, th] + """ + + assert ex_rois.shape[0] == gt_rois.shape[0] + assert ex_rois.shape[1] == 4 + assert gt_rois.shape[1] == 4 + + targets = encode_and_decode.encode_boxes(unencode_boxes=gt_rois, + reference_boxes=ex_rois, + scale_factors=cfgs.ROI_SCALE_FACTORS[stage-1]) + # targets = encode_and_decode.encode_boxes(ex_rois=ex_rois, + # gt_rois=gt_rois, + # scale_factor=cfgs.ROI_SCALE_FACTORS) + + return np.hstack( + (labels[:, np.newaxis], targets)).astype(np.float32, copy=False) + + +def _sample_rois(all_rois, gt_boxes, fg_rois_per_image, + rois_per_image, num_classes, fg_threshold, stage): + """Generate a random sample of RoIs comprising foreground and background + examples. + + all_rois shape is [-1, 4] + gt_boxes shape is [-1, 5]. that is [x1, y1, x2, y2, label] + """ + # overlaps: (rois x gt_boxes) + overlaps = bbox_overlaps( + np.ascontiguousarray(all_rois, dtype=np.float), + np.ascontiguousarray(gt_boxes[:, :-1], dtype=np.float)) + gt_assignment = overlaps.argmax(axis=1) + max_overlaps = overlaps.max(axis=1) + labels = gt_boxes[gt_assignment, -1] + + # Select foreground RoIs as those with >= FG_THRESH overlap + # fg_inds = np.where(max_overlaps >= cfgs.FAST_RCNN_IOU_POSITIVE_THRESHOLD)[0] + fg_inds = np.where(max_overlaps >= fg_threshold)[0] + # Guard against the case when an image has fewer than fg_rois_per_image + # Select background RoIs as those within [BG_THRESH_LO, BG_THRESH_HI) + bg_inds = np.where((max_overlaps < cfgs.FAST_RCNN_IOU_POSITIVE_THRESHOLD) & + (max_overlaps >= cfgs.FAST_RCNN_IOU_NEGATIVE_THRESHOLD))[0] + # print("first fileter, fg_size: {} || bg_size: {}".format(fg_inds.shape, bg_inds.shape)) + # Guard against the case when an image has fewer than fg_rois_per_image + # foreground RoIs + fg_rois_per_this_image = min(fg_rois_per_image, fg_inds.size) + + # Sample foreground regions without replacement + if fg_inds.size > 0: + fg_inds = npr.choice(fg_inds, size=int(fg_rois_per_this_image), replace=False) + # Compute number of background RoIs to take from this image (guarding + # against there being fewer than desired) + bg_rois_per_this_image = rois_per_image - fg_rois_per_this_image + bg_rois_per_this_image = min(bg_rois_per_this_image, bg_inds.size) + # Sample background regions without replacement + if bg_inds.size > 0: + bg_inds = npr.choice(bg_inds, size=int(bg_rois_per_this_image), replace=False) + + # print("second fileter, fg_size: {} || bg_size: {}".format(fg_inds.shape, bg_inds.shape)) + # The indices that we're selecting (both fg and bg) + keep_inds = np.append(fg_inds, bg_inds) + + # Select sampled values from various arrays: + labels = labels[keep_inds] + + # Clamp labels for the background RoIs to 0 + labels[int(fg_rois_per_this_image):] = 0 + rois = all_rois[keep_inds] + + bbox_target_data = _compute_targets( + rois, gt_boxes[gt_assignment[keep_inds], :-1], labels, stage) + + bbox_targets = \ + _get_bbox_regression_labels(bbox_target_data, num_classes) + + return labels, rois, bbox_targets, gt_boxes[gt_assignment[keep_inds], :-1] diff --git a/libs/export_pbs/__init__.py b/libs/export_pbs/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/libs/export_pbs/exportPb.py b/libs/export_pbs/exportPb.py new file mode 100644 index 0000000..3381924 --- /dev/null +++ b/libs/export_pbs/exportPb.py @@ -0,0 +1,92 @@ +# -*- coding: utf-8 -*- + +from __future__ import absolute_import, print_function, division + +import os, sys +import tensorflow as tf +import tensorflow.contrib.slim as slim +from tensorflow.python.tools import freeze_graph + +sys.path.append('../../') +from data.io.image_preprocess import short_side_resize_for_inference_data +from libs.configs import cfgs +from libs.networks import build_whole_network + +CKPT_PATH = '/home/yangxue/isilon/yangxue/code/yxdet/FPN_TF_DEV/output/trained_weights/FPN_Res50_COCO_20190211_v18/voc_1599999model.ckpt' +OUT_DIR = '../../output/Pbs' +PB_NAME = 'FPN_Res50_COCO.pb' + + +def build_detection_graph(): + # 1. preprocess img + img_plac = tf.placeholder(dtype=tf.uint8, shape=[None, None, 3], + name='input_img') # is RGB. not GBR + raw_shape = tf.shape(img_plac) + raw_h, raw_w = tf.to_float(raw_shape[0]), tf.to_float(raw_shape[1]) + + img_batch = tf.cast(img_plac, tf.float32) + img_batch = short_side_resize_for_inference_data(img_tensor=img_batch, + target_shortside_len=cfgs.IMG_SHORT_SIDE_LEN, + length_limitation=cfgs.IMG_MAX_LENGTH) + if cfgs.NET_NAME in ['resnet101_v1d', 'resnet50_v1d']: + img_batch = (img_batch / 255 - tf.constant(cfgs.PIXEL_MEAN_)) / tf.constant(cfgs.PIXEL_STD) + else: + img_batch = img_batch - tf.constant(cfgs.PIXEL_MEAN) + + img_batch = tf.expand_dims(img_batch, axis=0) # [1, None, None, 3] + + det_net = build_whole_network.DetectionNetwork(base_network_name=cfgs.NET_NAME, + is_training=False) + + detected_boxes, detection_scores, detection_category = det_net.build_whole_detection_network( + input_img_batch=img_batch, + gtboxes_batch=None) + + xmin, ymin, xmax, ymax = detected_boxes[:, 0], detected_boxes[:, 1], \ + detected_boxes[:, 2], detected_boxes[:, 3] + + resized_shape = tf.shape(img_batch) + resized_h, resized_w = tf.to_float(resized_shape[1]), tf.to_float(resized_shape[2]) + + xmin = xmin * raw_w / resized_w + xmax = xmax * raw_w / resized_w + + ymin = ymin * raw_h / resized_h + ymax = ymax * raw_h / resized_h + + boxes = tf.transpose(tf.stack([xmin, ymin, xmax, ymax])) + dets = tf.concat([tf.reshape(detection_category, [-1, 1]), + tf.reshape(detection_scores, [-1, 1]), + boxes], axis=1, name='DetResults') + + return dets + + +def export_frozenPB(): + + tf.reset_default_graph() + + dets = build_detection_graph() + + saver = tf.train.Saver() + + with tf.Session() as sess: + print("we have restred the weights from =====>>\n", CKPT_PATH) + saver.restore(sess, CKPT_PATH) + + tf.train.write_graph(sess.graph_def, OUT_DIR, PB_NAME) + freeze_graph.freeze_graph(input_graph=os.path.join(OUT_DIR, PB_NAME), + input_saver='', + input_binary=False, + input_checkpoint=CKPT_PATH, + output_node_names="DetResults", + restore_op_name="save/restore_all", + filename_tensor_name='save/Const:0', + output_graph=os.path.join(OUT_DIR, PB_NAME.replace('.pb', '_Frozen.pb')), + clear_devices=False, + initializer_nodes='') + + +if __name__ == '__main__': + os.environ["CUDA_VISIBLE_DEVICES"] = '' + export_frozenPB() diff --git a/libs/export_pbs/test_TensorRT.py b/libs/export_pbs/test_TensorRT.py new file mode 100644 index 0000000..6bd005e --- /dev/null +++ b/libs/export_pbs/test_TensorRT.py @@ -0,0 +1,93 @@ +# -*- coding:utf-8 -*- + +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import os, sys +import tensorflow as tf +import tensorflow.contrib.tensorrt as trt +import time +import cv2 +import argparse +import numpy as np +sys.path.append('../../') + +from data.io.image_preprocess import short_side_resize_for_inference_data +from libs.configs import cfgs +from libs.networks import build_whole_network +from libs.box_utils import draw_box_in_img +from help_utils import tools + + +def load_graph(frozen_graph_file): + + # we parse the graph_def file + with tf.gfile.GFile(frozen_graph_file, 'rb') as f: + graph_def = tf.GraphDef() + graph_def.ParseFromString(f.read()) + + # we load the graph_def in the default graph + + graph_def = trt.create_inference_graph(graph_def, ["DetResults"], + max_batch_size=1000, + max_workspace_size_bytes=(1 << 10)*10000, + precision_mode="INT8", + maximum_cached_engines=10) # Get optimized graph + + # graph_def = trt.calib_graph_to_infer_graph(graph_def) + tf.reset_default_graph() + with tf.Graph().as_default() as graph: + tf.import_graph_def(graph_def, + input_map=None, + return_elements=None, + name="", + op_dict=None, + producer_op_list=None) + return graph + + +def test(frozen_graph_path, test_dir): + + graph = load_graph(frozen_graph_path) + print("we are testing ====>>>>", frozen_graph_path) + + img = graph.get_tensor_by_name("input_img:0") + dets = graph.get_tensor_by_name("DetResults:0") + + gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.5) + config = tf.ConfigProto(gpu_options=gpu_options) + + with tf.Session(graph=graph, config=config) as sess: + for img_path in os.listdir(test_dir): + a_img = cv2.imread(os.path.join(test_dir, img_path))[:, :, ::-1] + st = time.time() + dets_val = sess.run(dets, feed_dict={img: a_img}) + end = time.time() + + show_indices = dets_val[:, 1] >= 0.5 + dets_val = dets_val[show_indices] + final_detections = draw_box_in_img.draw_boxes_with_label_and_scores(a_img, + boxes=dets_val[:, 2:], + labels=dets_val[:, 0], + scores=dets_val[:, 1]) + cv2.imwrite(img_path, + final_detections[:, :, ::-1]) + print("%s cost time: %f" % (img_path, end - st)) + + +if __name__ == '__main__': + os.environ["CUDA_VISIBLE_DEVICES"] = '0' + test('/home/yangxue/isilon/yangxue/code/yxdet/FPN_TF_DEV/output/Pbs/FPN_Res50_COCO_Frozen.pb', + '/unsullied/sharefs/yangxue/isilon/yangxue/data/COCO/train2017') + + + + + + + + + + + diff --git a/libs/export_pbs/test_exportPb.py b/libs/export_pbs/test_exportPb.py new file mode 100644 index 0000000..1a77b26 --- /dev/null +++ b/libs/export_pbs/test_exportPb.py @@ -0,0 +1,79 @@ +# -*- coding:utf-8 -*- + +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import os, sys +import tensorflow as tf +import tensorflow.contrib.tensorrt as trt +import time +import cv2 +import argparse +import numpy as np +sys.path.append('../../') + +from data.io.image_preprocess import short_side_resize_for_inference_data +from libs.configs import cfgs +from libs.networks import build_whole_network +from libs.box_utils import draw_box_in_img +from help_utils import tools + + +def load_graph(frozen_graph_file): + + # we parse the graph_def file + with tf.gfile.GFile(frozen_graph_file, 'rb') as f: + graph_def = tf.GraphDef() + graph_def.ParseFromString(f.read()) + + with tf.Graph().as_default() as graph: + tf.import_graph_def(graph_def, + input_map=None, + return_elements=None, + name="", + op_dict=None, + producer_op_list=None) + return graph + + +def test(frozen_graph_path, test_dir): + + graph = load_graph(frozen_graph_path) + print("we are testing ====>>>>", frozen_graph_path) + + img = graph.get_tensor_by_name("input_img:0") + dets = graph.get_tensor_by_name("DetResults:0") + + with tf.Session(graph=graph) as sess: + for img_path in os.listdir(test_dir): + a_img = cv2.imread(os.path.join(test_dir, img_path))[:, :, ::-1] + st = time.time() + dets_val = sess.run(dets, feed_dict={img: a_img}) + + show_indices = dets_val[:, 1] >= 0.5 + dets_val = dets_val[show_indices] + final_detections = draw_box_in_img.draw_boxes_with_label_and_scores(a_img, + boxes=dets_val[:, 2:], + labels=dets_val[:, 0], + scores=dets_val[:, 1]) + cv2.imwrite(img_path, + final_detections[:, :, ::-1]) + print("%s cost time: %f" % (img_path, time.time() - st)) + + +if __name__ == '__main__': + os.environ["CUDA_VISIBLE_DEVICES"] = '0' + test('/home/yangxue/isilon/yangxue/code/yxdet/FPN_TF_DEV/output/Pbs/FPN_Res50_COCO_Frozen.pb', + '/unsullied/sharefs/yangxue/isilon/yangxue/data/COCO/train2017') + + + + + + + + + + + diff --git a/libs/gluon2TF/.gitignore b/libs/gluon2TF/.gitignore new file mode 100644 index 0000000..aaa7c14 --- /dev/null +++ b/libs/gluon2TF/.gitignore @@ -0,0 +1,122 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +env/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +.installed.cfg +*.egg + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +.hypothesis/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# pyenv +.python-version + +# celery beat schedule file +celerybeat-schedule + +# SageMath parsed files +*.sage.py + +# dotenv +.env + +# virtualenv +.venv +venv/ +ENV/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ + +.pyc + +# pycharm files +.idea/* + +# vis files +*.png* +*.jpg* +*events.out.tfevents* +*.ckpt* +*.tfrecord* +*.params +*.zip +tf_ckpts/ + +# folder +tools/demos/* +tools/txt_output/* +output/* + diff --git a/libs/gluon2TF/README.md b/libs/gluon2TF/README.md new file mode 100644 index 0000000..c19e102 --- /dev/null +++ b/libs/gluon2TF/README.md @@ -0,0 +1,87 @@ +# Convert ResNets weights from GluonCV to Tensorflow + +## Abstract +GluonCV released some new resnet pre-training weights and designed some new resnets (such as resnet_v1_b, resnet_v1_d, refer [this](https://arxiv.org/pdf/1812.01187.pdf) for detail). + +This project reproduces the resnet in glouncv by Tensorflow and attempts to convert the pre-training weights in glouncv to the Tensorflow CheckPoints. +At present, we have completed the conversion of resnet50_v1_b, resnet101_v1_b, resnet50_v1_d, resnet101_v1_d, +and the 1000-dimensional Logits error rate is controlled within the range of 1e-5. +(We welcome you to submit PR to support more models.) + +We also try to transfer these weights to object detection (using FPN as the baseline, the specific detection code we will post [here](https://github.com/DetectionTeamUCAS/FPN_Tensorflow_DEV).), +and **train on voc07trainVal (excluding voc2012 dataset), test in voc07test**. The results are as follows: + +## Comparison + +### use_voc2007_metric +| Models | mAP | sheep | horse | bicycle | bottle | cow | sofa | bus | dog | cat | person | train | diningtable | aeroplane | car | pottedplant | tvmonitor | chair | bird | boat | motorbike | +|------------|:---:|:--:|:--:|:--:|:---:|:--:|:--:|:--:|:--:|:--:|:--:|:---:|:--:|:--:|:--:|:--:|:---:|:--:|:--:|:--:|:--:| +|[Faster-RCNN](https://github.com/DetectionTeamUCAS/Faster-RCNN_Tensorflow) resnet101_v1(original)|74.63|76.35|86.18|79.87|58.73|83.4|74.75|80.03|85.4|86.55|78.24|76.07|70.89|78.52|86.26|47.80|76.34|52.14|78.06|58.90|78.04| +|FPN resnet101_v1(original)|76.14|74.63|85.13|81.67|63.79|82.43|77.83|83.07|86.45|85.82|81.08|81.01|71.22|80.01|86.30|48.05|73.89|56.99|78.33|62.91|82.24| +|FPN resnet101_v1_d|77.98|78.01|87.48|85.34|65.42|84.56|74.42|82.97|87.87|87.34|82.14|84.44|70.32|80.64|88.6|51.9|76.59|59.31|81.19|67.84|83.1| + + +**FPN_resnet101_v1_d is transfer from GluonCV** + +**FPN_resnet101_v1(original) is official resnet in [tensorflow/models](https://github.com/tensorflow/models/tree/master/research/slim/nets)** + +## My Development Environment +1、python2.7 (anaconda recommend) + +2、cuda9.0 + +3、[opencv(cv2)](https://pypi.org/project/opencv-python/) + +4、mxnet-cu90 (1.3.0) + +5、tensorflow == 1.10 + +6、[GlounCV](https://gluon-cv.mxnet.io/) + +## Download MxNet GluonCV PreTrained Weights + +``` +cd $PATH_ROOT/resnet +(modify the resnet version in the main function of download_mxnet_resnet_weights.py.) +python download_mxnet_resnet_weights.py +``` + + +## Convert MxNet Weights To Tensorflow CheckPoint and caculate Erros + +modify the main function in gluon2TF/resnet/test_resnet.py as following, and then run it +``` +MODEL_NAME = 'resnet101_v1d' (modify the version as u want) +Mxnet_Weights_PATH = '../mxnet_weights/resnet101_v1d-1b2b825f.params' (remember modify the path) + +cal_erro(img_path='../demo_img/person.jpg', + use_tf_ckpt=False, + ckpt_path='../tf_ckpts/%s.ckpt' % MODEL_NAME, + save_ckpt=True) +``` + +Just run it : +``` +cd $PATH_ROOT/resnet +python test_resnet +``` + +## caculate Erros between the converted tensorflow chenckpoints and Mxnet GluonCV Weights + +modify the main function in gluon2TF/resnet/test_resnet.py as following, and then run it +``` +MODEL_NAME = 'resnet101_v1d' (modify the version as u want) +Mxnet_Weights_PATH = '../mxnet_weights/resnet101_v1d-1b2b825f.params' (remember modify the path) + +cal_erro(img_path='../demo_img/person.jpg', + use_tf_ckpt=True, + ckpt_path='../tf_ckpts/%s.ckpt' % MODEL_NAME, + save_ckpt=False) +``` + +Just run it : +``` +cd $PATH_ROOT/resnet +python test_resnet +``` + diff --git a/libs/gluon2TF/mxnet_weights/mxnet_weights_namefile.py b/libs/gluon2TF/mxnet_weights/mxnet_weights_namefile.py new file mode 100644 index 0000000..1ec690e --- /dev/null +++ b/libs/gluon2TF/mxnet_weights/mxnet_weights_namefile.py @@ -0,0 +1,562 @@ + + +# for resNet 50::: +''' +name: bn1.beta || shape: (64,) || dtype: float32 +name: 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(128,) || dtype: float32 +name: layer2.2.bn2.running_mean || shape: (128,) || dtype: float32 +name: layer2.2.bn2.running_var || shape: (128,) || dtype: float32 +name: layer2.2.bn3.beta || shape: (512,) || dtype: float32 +name: layer2.2.bn3.gamma || shape: (512,) || dtype: float32 +name: layer2.2.bn3.running_mean || shape: (512,) || dtype: float32 +name: layer2.2.bn3.running_var || shape: (512,) || dtype: float32 +name: layer2.2.conv1.weight || shape: (128, 512, 1, 1) || dtype: float32 +name: layer2.2.conv2.weight || shape: (128, 128, 3, 3) || dtype: float32 +name: layer2.2.conv3.weight || shape: (512, 128, 1, 1) || dtype: float32 +name: layer2.3.bn1.beta || shape: (128,) || dtype: float32 +name: layer2.3.bn1.gamma || shape: (128,) || dtype: float32 +name: layer2.3.bn1.running_mean || shape: (128,) || dtype: float32 +name: layer2.3.bn1.running_var || shape: (128,) || dtype: float32 +name: layer2.3.bn2.beta || shape: (128,) || dtype: float32 +name: layer2.3.bn2.gamma || shape: (128,) || dtype: float32 +name: layer2.3.bn2.running_mean || shape: (128,) || dtype: float32 +name: layer2.3.bn2.running_var || shape: (128,) || dtype: float32 +name: layer2.3.bn3.beta || shape: (512,) || dtype: float32 +name: layer2.3.bn3.gamma || shape: (512,) || dtype: float32 +name: layer2.3.bn3.running_mean || shape: (512,) || dtype: float32 +name: layer2.3.bn3.running_var || shape: (512,) || dtype: float32 +name: layer2.3.conv1.weight || shape: (128, 512, 1, 1) || dtype: float32 +name: layer2.3.conv2.weight || shape: (128, 128, 3, 3) || dtype: float32 +name: layer2.3.conv3.weight || shape: (512, 128, 1, 1) || dtype: float32 +name: layer3.0.bn1.beta || shape: (256,) || dtype: float32 +name: layer3.0.bn1.gamma || shape: (256,) || dtype: float32 +name: layer3.0.bn1.running_mean || shape: (256,) || dtype: float32 +name: layer3.0.bn1.running_var || shape: (256,) || dtype: float32 +name: layer3.0.bn2.beta || shape: (256,) || dtype: float32 +name: layer3.0.bn2.gamma || shape: (256,) || dtype: float32 +name: layer3.0.bn2.running_mean || shape: (256,) || dtype: float32 +name: layer3.0.bn2.running_var || shape: (256,) || dtype: float32 +name: layer3.0.bn3.beta || shape: (1024,) || dtype: float32 +name: layer3.0.bn3.gamma || shape: (1024,) || dtype: float32 +name: layer3.0.bn3.running_mean || shape: (1024,) || dtype: float32 +name: layer3.0.bn3.running_var || shape: (1024,) || dtype: float32 +name: layer3.0.conv1.weight || shape: (256, 512, 1, 1) || dtype: float32 +name: layer3.0.conv2.weight || shape: (256, 256, 3, 3) || dtype: float32 +name: layer3.0.conv3.weight || shape: (1024, 256, 1, 1) || dtype: float32 +name: layer3.0.downsample.1.weight || shape: (1024, 512, 1, 1) || dtype: float32 +name: layer3.0.downsample.2.beta || shape: (1024,) || dtype: float32 +name: layer3.0.downsample.2.gamma || shape: (1024,) || dtype: float32 +name: layer3.0.downsample.2.running_mean || shape: (1024,) || dtype: float32 +name: layer3.0.downsample.2.running_var || shape: (1024,) || dtype: float32 +name: layer3.1.bn1.beta || shape: (256,) || dtype: float32 +name: layer3.1.bn1.gamma || shape: (256,) || dtype: float32 +name: layer3.1.bn1.running_mean || shape: (256,) || dtype: float32 +name: layer3.1.bn1.running_var || shape: (256,) || dtype: float32 +name: layer3.1.bn2.beta || shape: (256,) || dtype: float32 +name: layer3.1.bn2.gamma || shape: (256,) || dtype: float32 +name: layer3.1.bn2.running_mean || shape: (256,) || dtype: float32 +name: layer3.1.bn2.running_var || shape: (256,) || dtype: float32 +name: layer3.1.bn3.beta || shape: (1024,) || dtype: float32 +name: layer3.1.bn3.gamma || shape: (1024,) || dtype: float32 +name: layer3.1.bn3.running_mean || shape: (1024,) || dtype: float32 +name: layer3.1.bn3.running_var || shape: (1024,) || dtype: float32 +name: layer3.1.conv1.weight || shape: (256, 1024, 1, 1) || dtype: float32 +name: layer3.1.conv2.weight || shape: (256, 256, 3, 3) || dtype: float32 +name: layer3.1.conv3.weight || shape: (1024, 256, 1, 1) || dtype: float32 +name: layer3.2.bn1.beta || shape: (256,) || dtype: float32 +name: layer3.2.bn1.gamma || shape: (256,) || dtype: float32 +name: layer3.2.bn1.running_mean || shape: (256,) || dtype: float32 +name: layer3.2.bn1.running_var || shape: (256,) || dtype: float32 +name: layer3.2.bn2.beta || shape: (256,) || dtype: float32 +name: layer3.2.bn2.gamma || shape: (256,) || dtype: float32 +name: layer3.2.bn2.running_mean || shape: (256,) || dtype: float32 +name: layer3.2.bn2.running_var || shape: (256,) || dtype: float32 +name: layer3.2.bn3.beta || shape: (1024,) || dtype: float32 +name: layer3.2.bn3.gamma || shape: (1024,) || dtype: float32 +name: layer3.2.bn3.running_mean || shape: (1024,) || dtype: float32 +name: layer3.2.bn3.running_var || shape: (1024,) || dtype: float32 +name: layer3.2.conv1.weight || shape: (256, 1024, 1, 1) || dtype: float32 +name: layer3.2.conv2.weight || shape: (256, 256, 3, 3) || dtype: float32 +name: layer3.2.conv3.weight || shape: (1024, 256, 1, 1) || dtype: float32 +name: layer3.3.bn1.beta || shape: (256,) || dtype: float32 +name: layer3.3.bn1.gamma || shape: (256,) || dtype: float32 +name: layer3.3.bn1.running_mean || shape: (256,) || dtype: float32 +name: layer3.3.bn1.running_var || shape: (256,) || dtype: float32 +name: layer3.3.bn2.beta || shape: (256,) || dtype: float32 +name: layer3.3.bn2.gamma || shape: (256,) || dtype: float32 +name: layer3.3.bn2.running_mean || shape: (256,) || dtype: float32 +name: layer3.3.bn2.running_var || shape: (256,) || dtype: float32 +name: layer3.3.bn3.beta || shape: (1024,) || dtype: float32 +name: layer3.3.bn3.gamma || shape: (1024,) || dtype: float32 +name: layer3.3.bn3.running_mean || shape: (1024,) || dtype: float32 +name: layer3.3.bn3.running_var || shape: (1024,) || dtype: float32 +name: layer3.3.conv1.weight || shape: (256, 1024, 1, 1) || dtype: float32 +name: layer3.3.conv2.weight || shape: (256, 256, 3, 3) || dtype: float32 +name: layer3.3.conv3.weight || shape: (1024, 256, 1, 1) || dtype: float32 +name: layer3.4.bn1.beta || shape: (256,) || dtype: float32 +name: layer3.4.bn1.gamma || shape: (256,) || dtype: float32 +name: layer3.4.bn1.running_mean || shape: (256,) || dtype: float32 +name: layer3.4.bn1.running_var || shape: (256,) || dtype: float32 +name: layer3.4.bn2.beta || shape: (256,) || dtype: float32 +name: layer3.4.bn2.gamma || shape: (256,) || dtype: float32 +name: layer3.4.bn2.running_mean || shape: (256,) || dtype: float32 +name: layer3.4.bn2.running_var || shape: (256,) || dtype: float32 +name: layer3.4.bn3.beta || shape: (1024,) || dtype: float32 +name: layer3.4.bn3.gamma || shape: (1024,) || dtype: float32 +name: layer3.4.bn3.running_mean || shape: (1024,) || dtype: float32 +name: layer3.4.bn3.running_var || shape: (1024,) || dtype: float32 +name: layer3.4.conv1.weight || shape: (256, 1024, 1, 1) || dtype: float32 +name: layer3.4.conv2.weight || shape: (256, 256, 3, 3) || dtype: float32 +name: layer3.4.conv3.weight || shape: (1024, 256, 1, 1) || dtype: float32 +name: layer3.5.bn1.beta || shape: (256,) || dtype: float32 +name: layer3.5.bn1.gamma || shape: (256,) || dtype: float32 +name: layer3.5.bn1.running_mean || shape: (256,) || dtype: float32 +name: layer3.5.bn1.running_var || shape: (256,) || dtype: float32 +name: layer3.5.bn2.beta || shape: (256,) || dtype: float32 +name: layer3.5.bn2.gamma || shape: (256,) || dtype: float32 +name: layer3.5.bn2.running_mean || shape: (256,) || dtype: float32 +name: layer3.5.bn2.running_var || shape: (256,) || dtype: float32 +name: layer3.5.bn3.beta || shape: (1024,) || dtype: float32 +name: layer3.5.bn3.gamma || shape: (1024,) || dtype: float32 +name: layer3.5.bn3.running_mean || shape: (1024,) || dtype: float32 +name: layer3.5.bn3.running_var || shape: (1024,) || dtype: float32 +name: layer3.5.conv1.weight || shape: (256, 1024, 1, 1) || dtype: float32 +name: layer3.5.conv2.weight || shape: (256, 256, 3, 3) || dtype: float32 +name: layer3.5.conv3.weight || shape: (1024, 256, 1, 1) || dtype: float32 +name: layer4.0.bn1.beta || shape: (512,) || dtype: float32 +name: layer4.0.bn1.gamma || shape: (512,) || dtype: float32 +name: layer4.0.bn1.running_mean || shape: (512,) || dtype: float32 +name: layer4.0.bn1.running_var || shape: (512,) || dtype: float32 +name: layer4.0.bn2.beta || shape: (512,) || dtype: float32 +name: layer4.0.bn2.gamma || shape: (512,) || dtype: float32 +name: layer4.0.bn2.running_mean || shape: (512,) || dtype: float32 +name: layer4.0.bn2.running_var || shape: (512,) || dtype: float32 +name: layer4.0.bn3.beta || shape: (2048,) || dtype: float32 +name: layer4.0.bn3.gamma || shape: (2048,) || dtype: float32 +name: layer4.0.bn3.running_mean || shape: (2048,) || dtype: float32 +name: layer4.0.bn3.running_var || shape: (2048,) || dtype: float32 +name: layer4.0.conv1.weight || shape: (512, 1024, 1, 1) || dtype: float32 +name: layer4.0.conv2.weight || shape: (512, 512, 3, 3) || dtype: float32 +name: layer4.0.conv3.weight || shape: (2048, 512, 1, 1) || dtype: float32 +name: layer4.0.downsample.1.weight || shape: (2048, 1024, 1, 1) || dtype: float32 +name: layer4.0.downsample.2.beta || shape: (2048,) || dtype: float32 +name: layer4.0.downsample.2.gamma || shape: (2048,) || dtype: float32 +name: layer4.0.downsample.2.running_mean || shape: (2048,) || dtype: float32 +name: layer4.0.downsample.2.running_var || shape: (2048,) || dtype: float32 +name: layer4.1.bn1.beta || shape: (512,) || dtype: float32 +name: layer4.1.bn1.gamma || shape: (512,) || dtype: float32 +name: layer4.1.bn1.running_mean || shape: (512,) || dtype: float32 +name: layer4.1.bn1.running_var || shape: (512,) || dtype: float32 +name: layer4.1.bn2.beta || shape: (512,) || dtype: float32 +name: layer4.1.bn2.gamma || shape: (512,) || dtype: float32 +name: layer4.1.bn2.running_mean || shape: (512,) || dtype: float32 +name: layer4.1.bn2.running_var || shape: (512,) || dtype: float32 +name: layer4.1.bn3.beta || shape: (2048,) || dtype: float32 +name: layer4.1.bn3.gamma || shape: (2048,) || dtype: float32 +name: layer4.1.bn3.running_mean || shape: (2048,) || dtype: float32 +name: layer4.1.bn3.running_var || shape: (2048,) || dtype: float32 +name: layer4.1.conv1.weight || shape: (512, 2048, 1, 1) || dtype: float32 +name: layer4.1.conv2.weight || shape: (512, 512, 3, 3) || dtype: float32 +name: layer4.1.conv3.weight || shape: (2048, 512, 1, 1) || dtype: float32 +name: layer4.2.bn1.beta || shape: (512,) || dtype: float32 +name: layer4.2.bn1.gamma || shape: (512,) || dtype: float32 +name: layer4.2.bn1.running_mean || shape: (512,) || dtype: float32 +name: layer4.2.bn1.running_var || shape: (512,) || dtype: float32 +name: layer4.2.bn2.beta || shape: (512,) || dtype: float32 +name: layer4.2.bn2.gamma || shape: (512,) || dtype: float32 +name: layer4.2.bn2.running_mean || shape: (512,) || dtype: float32 +name: layer4.2.bn2.running_var || shape: (512,) || dtype: float32 +name: layer4.2.bn3.beta || shape: (2048,) || dtype: float32 +name: layer4.2.bn3.gamma || shape: (2048,) || dtype: float32 +name: layer4.2.bn3.running_mean || shape: (2048,) || dtype: float32 +name: layer4.2.bn3.running_var || shape: (2048,) || dtype: float32 +name: layer4.2.conv1.weight || shape: (512, 2048, 1, 1) || dtype: float32 +name: layer4.2.conv2.weight || shape: (512, 512, 3, 3) || dtype: float32 +name: layer4.2.conv3.weight || shape: (2048, 512, 1, 1) || dtype: float32 +''' + +''' +C1/conv0/BatchNorm/beta :: conv1.1.beta +C1/conv0/BatchNorm/gamma :: conv1.1.gamma +C1/conv0/BatchNorm/moving_mean :: conv1.1.running_mean +C1/conv0/BatchNorm/moving_variance :: conv1.1.running_var +C1/conv0/weights :: conv1.0.weight +C1/conv1/beta :: conv1.4.beta +C1/conv1/gamma :: conv1.4.gamma +C1/conv1/moving_mean :: conv1.4.running_mean +C1/conv1/moving_variance :: conv1.4.running_var +C1/conv1/weights :: conv1.3.weight +C1/conv2/beta :: bn1.beta +C1/conv2/gamma :: bn1.gamma +C1/conv2/moving_mean :: bn1.running_mean +C1/conv2/moving_variance :: bn1.running_var +C1/conv2/weights :: conv1.6.weight +C2/bottleneck_0/conv0/beta :: layer1.0.bn1.beta +C2/bottleneck_0/conv0/gamma :: layer1.0.bn1.gamma +C2/bottleneck_0/conv0/moving_mean :: layer1.0.bn1.running_mean +C2/bottleneck_0/conv0/moving_variance :: layer1.0.bn1.running_var +C2/bottleneck_0/conv0/weights :: layer1.0.conv1.weight +C2/bottleneck_0/conv1/beta :: layer1.0.bn2.beta +C2/bottleneck_0/conv1/gamma :: layer1.0.bn2.gamma +C2/bottleneck_0/conv1/moving_mean :: layer1.0.bn2.running_mean +C2/bottleneck_0/conv1/moving_variance :: layer1.0.bn2.running_var +C2/bottleneck_0/conv1/weights :: layer1.0.conv2.weight +C2/bottleneck_0/conv2/beta :: layer1.0.bn3.beta +C2/bottleneck_0/conv2/gamma :: layer1.0.bn3.gamma +C2/bottleneck_0/conv2/moving_mean :: layer1.0.bn3.running_mean +C2/bottleneck_0/conv2/moving_variance :: layer1.0.bn3.running_var +C2/bottleneck_0/conv2/weights :: layer1.0.conv3.weight +C2/bottleneck_0/shortcut/beta :: layer1.0.downsample.2.beta +C2/bottleneck_0/shortcut/gamma :: layer1.0.downsample.2.gamma +C2/bottleneck_0/shortcut/moving_mean :: layer1.0.downsample.2.running_mean +C2/bottleneck_0/shortcut/moving_variance :: layer1.0.downsample.2.running_var +C2/bottleneck_0/shortcut/weights :: layer1.0.downsample.1.weight +C2/bottleneck_1/conv0/beta :: layer1.1.bn1.beta +C2/bottleneck_1/conv0/gamma :: layer1.1.bn1.gamma +C2/bottleneck_1/conv0/moving_mean :: layer1.1.bn1.running_mean +C2/bottleneck_1/conv0/moving_variance :: layer1.1.bn1.running_var +C2/bottleneck_1/conv0/weights :: layer1.1.conv1.weight +C2/bottleneck_1/conv1/beta :: layer1.1.bn2.beta +C2/bottleneck_1/conv1/gamma :: layer1.1.bn2.gamma +C2/bottleneck_1/conv1/moving_mean :: layer1.1.bn2.running_mean +C2/bottleneck_1/conv1/moving_variance :: layer1.1.bn2.running_var +C2/bottleneck_1/conv1/weights :: layer1.1.conv2.weight +C2/bottleneck_1/conv2/beta :: layer1.1.bn3.beta +C2/bottleneck_1/conv2/gamma :: layer1.1.bn3.gamma +C2/bottleneck_1/conv2/moving_mean :: layer1.1.bn3.running_mean +C2/bottleneck_1/conv2/moving_variance :: layer1.1.bn3.running_var +C2/bottleneck_1/conv2/weights :: layer1.1.conv3.weight +C2/bottleneck_2/conv0/beta :: layer1.2.bn1.beta +C2/bottleneck_2/conv0/gamma :: layer1.2.bn1.gamma +C2/bottleneck_2/conv0/moving_mean :: layer1.2.bn1.running_mean +C2/bottleneck_2/conv0/moving_variance :: layer1.2.bn1.running_var +C2/bottleneck_2/conv0/weights :: layer1.2.conv1.weight +C2/bottleneck_2/conv1/beta :: layer1.2.bn2.beta +C2/bottleneck_2/conv1/gamma :: layer1.2.bn2.gamma +C2/bottleneck_2/conv1/moving_mean :: layer1.2.bn2.running_mean +C2/bottleneck_2/conv1/moving_variance :: layer1.2.bn2.running_var +C2/bottleneck_2/conv1/weights :: layer1.2.conv2.weight +C2/bottleneck_2/conv2/beta :: layer1.2.bn3.beta +C2/bottleneck_2/conv2/gamma :: layer1.2.bn3.gamma +C2/bottleneck_2/conv2/moving_mean :: layer1.2.bn3.running_mean +C2/bottleneck_2/conv2/moving_variance :: layer1.2.bn3.running_var +C2/bottleneck_2/conv2/weights :: layer1.2.conv3.weight +C3/bottleneck_0/conv0/beta :: layer2.0.bn1.beta +C3/bottleneck_0/conv0/gamma :: layer2.0.bn1.gamma +C3/bottleneck_0/conv0/moving_mean :: layer2.0.bn1.running_mean +C3/bottleneck_0/conv0/moving_variance :: layer2.0.bn1.running_var +C3/bottleneck_0/conv0/weights :: layer2.0.conv1.weight +C3/bottleneck_0/conv1/beta :: layer2.0.bn2.beta +C3/bottleneck_0/conv1/gamma :: layer2.0.bn2.gamma +C3/bottleneck_0/conv1/moving_mean :: layer2.0.bn2.running_mean +C3/bottleneck_0/conv1/moving_variance :: layer2.0.bn2.running_var +C3/bottleneck_0/conv1/weights :: layer2.0.conv2.weight +C3/bottleneck_0/conv2/beta :: layer2.0.bn3.beta +C3/bottleneck_0/conv2/gamma :: layer2.0.bn3.gamma +C3/bottleneck_0/conv2/moving_mean :: layer2.0.bn3.running_mean +C3/bottleneck_0/conv2/moving_variance :: layer2.0.bn3.running_var +C3/bottleneck_0/conv2/weights :: layer2.0.conv3.weight +C3/bottleneck_0/shortcut/beta :: layer2.0.downsample.2.beta +C3/bottleneck_0/shortcut/gamma :: layer2.0.downsample.2.gamma +C3/bottleneck_0/shortcut/moving_mean :: layer2.0.downsample.2.running_mean +C3/bottleneck_0/shortcut/moving_variance :: layer2.0.downsample.2.running_var +C3/bottleneck_0/shortcut/weights :: layer2.0.downsample.1.weight +C3/bottleneck_1/conv0/beta :: layer2.1.bn1.beta +C3/bottleneck_1/conv0/gamma :: layer2.1.bn1.gamma +C3/bottleneck_1/conv0/moving_mean :: layer2.1.bn1.running_mean +C3/bottleneck_1/conv0/moving_variance :: layer2.1.bn1.running_var +C3/bottleneck_1/conv0/weights :: layer2.1.conv1.weight +C3/bottleneck_1/conv1/beta :: layer2.1.bn2.beta +C3/bottleneck_1/conv1/gamma :: layer2.1.bn2.gamma +C3/bottleneck_1/conv1/moving_mean :: layer2.1.bn2.running_mean +C3/bottleneck_1/conv1/moving_variance :: layer2.1.bn2.running_var +C3/bottleneck_1/conv1/weights :: layer2.1.conv2.weight +C3/bottleneck_1/conv2/beta :: layer2.1.bn3.beta +C3/bottleneck_1/conv2/gamma :: layer2.1.bn3.gamma +C3/bottleneck_1/conv2/moving_mean :: layer2.1.bn3.running_mean +C3/bottleneck_1/conv2/moving_variance :: layer2.1.bn3.running_var +C3/bottleneck_1/conv2/weights :: layer2.1.conv3.weight +C3/bottleneck_2/conv0/beta :: layer2.2.bn1.beta +C3/bottleneck_2/conv0/gamma :: layer2.2.bn1.gamma +C3/bottleneck_2/conv0/moving_mean :: layer2.2.bn1.running_mean +C3/bottleneck_2/conv0/moving_variance :: layer2.2.bn1.running_var +C3/bottleneck_2/conv0/weights :: layer2.2.conv1.weight +C3/bottleneck_2/conv1/beta :: layer2.2.bn2.beta +C3/bottleneck_2/conv1/gamma :: layer2.2.bn2.gamma +C3/bottleneck_2/conv1/moving_mean :: layer2.2.bn2.running_mean +C3/bottleneck_2/conv1/moving_variance :: layer2.2.bn2.running_var +C3/bottleneck_2/conv1/weights :: layer2.2.conv2.weight +C3/bottleneck_2/conv2/beta :: layer2.2.bn3.beta +C3/bottleneck_2/conv2/gamma :: layer2.2.bn3.gamma +C3/bottleneck_2/conv2/moving_mean :: layer2.2.bn3.running_mean +C3/bottleneck_2/conv2/moving_variance :: layer2.2.bn3.running_var +C3/bottleneck_2/conv2/weights :: layer2.2.conv3.weight +C3/bottleneck_3/conv0/beta :: layer2.3.bn1.beta +C3/bottleneck_3/conv0/gamma :: layer2.3.bn1.gamma +C3/bottleneck_3/conv0/moving_mean :: layer2.3.bn1.running_mean +C3/bottleneck_3/conv0/moving_variance :: layer2.3.bn1.running_var +C3/bottleneck_3/conv0/weights :: layer2.3.conv1.weight +C3/bottleneck_3/conv1/beta :: layer2.3.bn2.beta +C3/bottleneck_3/conv1/gamma :: layer2.3.bn2.gamma +C3/bottleneck_3/conv1/moving_mean :: layer2.3.bn2.running_mean +C3/bottleneck_3/conv1/moving_variance :: layer2.3.bn2.running_var +C3/bottleneck_3/conv1/weights :: layer2.3.conv2.weight +C3/bottleneck_3/conv2/beta :: layer2.3.bn3.beta +C3/bottleneck_3/conv2/gamma :: layer2.3.bn3.gamma +C3/bottleneck_3/conv2/moving_mean :: layer2.3.bn3.running_mean +C3/bottleneck_3/conv2/moving_variance :: layer2.3.bn3.running_var +C3/bottleneck_3/conv2/weights :: layer2.3.conv3.weight +C4/bottleneck_0/conv0/beta :: layer3.0.bn1.beta +C4/bottleneck_0/conv0/gamma :: layer3.0.bn1.gamma +C4/bottleneck_0/conv0/moving_mean :: layer3.0.bn1.running_mean +C4/bottleneck_0/conv0/moving_variance :: layer3.0.bn1.running_var +C4/bottleneck_0/conv0/weights :: layer3.0.conv1.weight +C4/bottleneck_0/conv1/beta :: layer3.0.bn2.beta +C4/bottleneck_0/conv1/gamma :: layer3.0.bn2.gamma +C4/bottleneck_0/conv1/moving_mean :: layer3.0.bn2.running_mean +C4/bottleneck_0/conv1/moving_variance :: layer3.0.bn2.running_var +C4/bottleneck_0/conv1/weights :: layer3.0.conv2.weight +C4/bottleneck_0/conv2/beta :: layer3.0.bn3.beta +C4/bottleneck_0/conv2/gamma :: layer3.0.bn3.gamma +C4/bottleneck_0/conv2/moving_mean :: layer3.0.bn3.running_mean +C4/bottleneck_0/conv2/moving_variance :: layer3.0.bn3.running_var +C4/bottleneck_0/conv2/weights :: layer3.0.conv3.weight +C4/bottleneck_0/shortcut/beta :: layer3.0.downsample.2.beta +C4/bottleneck_0/shortcut/gamma :: layer3.0.downsample.2.gamma +C4/bottleneck_0/shortcut/moving_mean :: layer3.0.downsample.2.running_mean +C4/bottleneck_0/shortcut/moving_variance :: layer3.0.downsample.2.running_var +C4/bottleneck_0/shortcut/weights :: layer3.0.downsample.1.weight +C4/bottleneck_1/conv0/beta :: layer3.1.bn1.beta +C4/bottleneck_1/conv0/gamma :: layer3.1.bn1.gamma +C4/bottleneck_1/conv0/moving_mean :: layer3.1.bn1.running_mean +C4/bottleneck_1/conv0/moving_variance :: layer3.1.bn1.running_var +C4/bottleneck_1/conv0/weights :: layer3.1.conv1.weight +C4/bottleneck_1/conv1/beta :: layer3.1.bn2.beta +C4/bottleneck_1/conv1/gamma :: layer3.1.bn2.gamma +C4/bottleneck_1/conv1/moving_mean :: layer3.1.bn2.running_mean +C4/bottleneck_1/conv1/moving_variance :: layer3.1.bn2.running_var +C4/bottleneck_1/conv1/weights :: layer3.1.conv2.weight +C4/bottleneck_1/conv2/beta :: layer3.1.bn3.beta +C4/bottleneck_1/conv2/gamma :: layer3.1.bn3.gamma +C4/bottleneck_1/conv2/moving_mean :: layer3.1.bn3.running_mean +C4/bottleneck_1/conv2/moving_variance :: layer3.1.bn3.running_var +C4/bottleneck_1/conv2/weights :: layer3.1.conv3.weight +C4/bottleneck_2/conv0/beta :: layer3.2.bn1.beta +C4/bottleneck_2/conv0/gamma :: layer3.2.bn1.gamma +C4/bottleneck_2/conv0/moving_mean :: layer3.2.bn1.running_mean +C4/bottleneck_2/conv0/moving_variance :: layer3.2.bn1.running_var +C4/bottleneck_2/conv0/weights :: layer3.2.conv1.weight +C4/bottleneck_2/conv1/beta :: layer3.2.bn2.beta +C4/bottleneck_2/conv1/gamma :: layer3.2.bn2.gamma +C4/bottleneck_2/conv1/moving_mean :: layer3.2.bn2.running_mean +C4/bottleneck_2/conv1/moving_variance :: layer3.2.bn2.running_var +C4/bottleneck_2/conv1/weights :: layer3.2.conv2.weight +C4/bottleneck_2/conv2/beta :: layer3.2.bn3.beta +C4/bottleneck_2/conv2/gamma :: layer3.2.bn3.gamma +C4/bottleneck_2/conv2/moving_mean :: layer3.2.bn3.running_mean +C4/bottleneck_2/conv2/moving_variance :: layer3.2.bn3.running_var +C4/bottleneck_2/conv2/weights :: layer3.2.conv3.weight +C4/bottleneck_3/conv0/beta :: layer3.3.bn1.beta +C4/bottleneck_3/conv0/gamma :: layer3.3.bn1.gamma +C4/bottleneck_3/conv0/moving_mean :: layer3.3.bn1.running_mean +C4/bottleneck_3/conv0/moving_variance :: layer3.3.bn1.running_var +C4/bottleneck_3/conv0/weights :: layer3.3.conv1.weight +C4/bottleneck_3/conv1/beta :: layer3.3.bn2.beta +C4/bottleneck_3/conv1/gamma :: layer3.3.bn2.gamma +C4/bottleneck_3/conv1/moving_mean :: layer3.3.bn2.running_mean +C4/bottleneck_3/conv1/moving_variance :: layer3.3.bn2.running_var +C4/bottleneck_3/conv1/weights :: layer3.3.conv2.weight +C4/bottleneck_3/conv2/beta :: layer3.3.bn3.beta +C4/bottleneck_3/conv2/gamma :: layer3.3.bn3.gamma +C4/bottleneck_3/conv2/moving_mean :: layer3.3.bn3.running_mean +C4/bottleneck_3/conv2/moving_variance :: layer3.3.bn3.running_var +C4/bottleneck_3/conv2/weights :: layer3.3.conv3.weight +C4/bottleneck_4/conv0/beta :: layer3.4.bn1.beta +C4/bottleneck_4/conv0/gamma :: layer3.4.bn1.gamma +C4/bottleneck_4/conv0/moving_mean :: layer3.4.bn1.running_mean +C4/bottleneck_4/conv0/moving_variance :: layer3.4.bn1.running_var +C4/bottleneck_4/conv0/weights :: layer3.4.conv1.weight +C4/bottleneck_4/conv1/beta :: layer3.4.bn2.beta +C4/bottleneck_4/conv1/gamma :: layer3.4.bn2.gamma +C4/bottleneck_4/conv1/moving_mean :: layer3.4.bn2.running_mean +C4/bottleneck_4/conv1/moving_variance :: layer3.4.bn2.running_var +C4/bottleneck_4/conv1/weights :: layer3.4.conv2.weight +C4/bottleneck_4/conv2/beta :: layer3.4.bn3.beta +C4/bottleneck_4/conv2/gamma :: layer3.4.bn3.gamma +C4/bottleneck_4/conv2/moving_mean :: layer3.4.bn3.running_mean +C4/bottleneck_4/conv2/moving_variance :: layer3.4.bn3.running_var +C4/bottleneck_4/conv2/weights :: layer3.4.conv3.weight +C4/bottleneck_5/conv0/beta :: layer3.5.bn1.beta +C4/bottleneck_5/conv0/gamma :: layer3.5.bn1.gamma +C4/bottleneck_5/conv0/moving_mean :: layer3.5.bn1.running_mean +C4/bottleneck_5/conv0/moving_variance :: layer3.5.bn1.running_var +C4/bottleneck_5/conv0/weights :: layer3.5.conv1.weight +C4/bottleneck_5/conv1/beta :: layer3.5.bn2.beta +C4/bottleneck_5/conv1/gamma :: layer3.5.bn2.gamma +C4/bottleneck_5/conv1/moving_mean :: layer3.5.bn2.running_mean +C4/bottleneck_5/conv1/moving_variance :: layer3.5.bn2.running_var +C4/bottleneck_5/conv1/weights :: layer3.5.conv2.weight +C4/bottleneck_5/conv2/beta :: layer3.5.bn3.beta +C4/bottleneck_5/conv2/gamma :: layer3.5.bn3.gamma +C4/bottleneck_5/conv2/moving_mean :: layer3.5.bn3.running_mean +C4/bottleneck_5/conv2/moving_variance :: layer3.5.bn3.running_var +C4/bottleneck_5/conv2/weights :: layer3.5.conv3.weight +C5/bottleneck_0/conv0/beta :: layer4.0.bn1.beta +C5/bottleneck_0/conv0/gamma :: layer4.0.bn1.gamma +C5/bottleneck_0/conv0/moving_mean :: layer4.0.bn1.running_mean +C5/bottleneck_0/conv0/moving_variance :: layer4.0.bn1.running_var +C5/bottleneck_0/conv0/weights :: layer4.0.conv1.weight +C5/bottleneck_0/conv1/beta :: layer4.0.bn2.beta +C5/bottleneck_0/conv1/gamma :: layer4.0.bn2.gamma +C5/bottleneck_0/conv1/moving_mean :: layer4.0.bn2.running_mean +C5/bottleneck_0/conv1/moving_variance :: layer4.0.bn2.running_var +C5/bottleneck_0/conv1/weights :: layer4.0.conv2.weight +C5/bottleneck_0/conv2/beta :: layer4.0.bn3.beta +C5/bottleneck_0/conv2/gamma :: layer4.0.bn3.gamma +C5/bottleneck_0/conv2/moving_mean :: layer4.0.bn3.running_mean +C5/bottleneck_0/conv2/moving_variance :: layer4.0.bn3.running_var +C5/bottleneck_0/conv2/weights :: layer4.0.conv3.weight +C5/bottleneck_0/shortcut/beta :: layer4.0.downsample.2.beta +C5/bottleneck_0/shortcut/gamma :: layer4.0.downsample.2.gamma +C5/bottleneck_0/shortcut/moving_mean :: layer4.0.downsample.2.running_mean +C5/bottleneck_0/shortcut/moving_variance :: layer4.0.downsample.2.running_var +C5/bottleneck_0/shortcut/weights :: layer4.0.downsample.1.weight +C5/bottleneck_1/conv0/beta :: layer4.1.bn1.beta +C5/bottleneck_1/conv0/gamma :: layer4.1.bn1.gamma +C5/bottleneck_1/conv0/moving_mean :: layer4.1.bn1.running_mean +C5/bottleneck_1/conv0/moving_variance :: layer4.1.bn1.running_var +C5/bottleneck_1/conv0/weights :: layer4.1.conv1.weight +C5/bottleneck_1/conv1/beta :: layer4.1.bn2.beta +C5/bottleneck_1/conv1/gamma :: layer4.1.bn2.gamma +C5/bottleneck_1/conv1/moving_mean :: layer4.1.bn2.running_mean +C5/bottleneck_1/conv1/moving_variance :: layer4.1.bn2.running_var +C5/bottleneck_1/conv1/weights :: layer4.1.conv2.weight +C5/bottleneck_1/conv2/beta :: layer4.1.bn3.beta +C5/bottleneck_1/conv2/gamma :: layer4.1.bn3.gamma +C5/bottleneck_1/conv2/moving_mean :: layer4.1.bn3.running_mean +C5/bottleneck_1/conv2/moving_variance :: layer4.1.bn3.running_var +C5/bottleneck_1/conv2/weights :: layer4.1.conv3.weight +C5/bottleneck_2/conv0/beta :: layer4.2.bn1.beta +C5/bottleneck_2/conv0/gamma :: layer4.2.bn1.gamma +C5/bottleneck_2/conv0/moving_mean :: layer4.2.bn1.running_mean +C5/bottleneck_2/conv0/moving_variance :: layer4.2.bn1.running_var +C5/bottleneck_2/conv0/weights :: layer4.2.conv1.weight +C5/bottleneck_2/conv1/beta :: layer4.2.bn2.beta +C5/bottleneck_2/conv1/gamma :: layer4.2.bn2.gamma +C5/bottleneck_2/conv1/moving_mean :: layer4.2.bn2.running_mean +C5/bottleneck_2/conv1/moving_variance :: layer4.2.bn2.running_var +C5/bottleneck_2/conv1/weights :: layer4.2.conv2.weight +C5/bottleneck_2/conv2/beta :: layer4.2.bn3.beta +C5/bottleneck_2/conv2/gamma :: layer4.2.bn3.gamma +C5/bottleneck_2/conv2/moving_mean :: layer4.2.bn3.running_mean +C5/bottleneck_2/conv2/moving_variance :: layer4.2.bn3.running_var +C5/bottleneck_2/conv2/weights :: layer4.2.conv3.weight +logits/biases :: fc.bias +logits/weights :: fc.weight +''' \ No newline at end of file diff --git a/libs/gluon2TF/mxnet_weights/readme.txt b/libs/gluon2TF/mxnet_weights/readme.txt new file mode 100644 index 0000000..ffab729 --- /dev/null +++ b/libs/gluon2TF/mxnet_weights/readme.txt @@ -0,0 +1 @@ +the dir place mxnet_weights diff --git a/libs/gluon2TF/resnet/__init__.py b/libs/gluon2TF/resnet/__init__.py new file mode 100644 index 0000000..8d1c8b6 --- /dev/null +++ b/libs/gluon2TF/resnet/__init__.py @@ -0,0 +1 @@ + diff --git a/libs/gluon2TF/resnet/download_mxnet_resnet_weights.py b/libs/gluon2TF/resnet/download_mxnet_resnet_weights.py new file mode 100644 index 0000000..e8fba61 --- /dev/null +++ b/libs/gluon2TF/resnet/download_mxnet_resnet_weights.py @@ -0,0 +1,159 @@ +# -*- coding: utf-8 -*- +import os +import requests +import hashlib +import zipfile +from tqdm import tqdm + +def check_sha1(filename, sha1_hash): + """Check whether the sha1 hash of the file content matches the expected hash. + Parameters + ---------- + filename : str + Path to the file. + sha1_hash : str + Expected sha1 hash in hexadecimal digits. + Returns + ------- + bool + Whether the file content matches the expected hash. + """ + sha1 = hashlib.sha1() + with open(filename, 'rb') as f: + while True: + data = f.read(1048576) + if not data: + break + sha1.update(data) + + sha1_file = sha1.hexdigest() + l = min(len(sha1_file), len(sha1_hash)) + return sha1.hexdigest()[0:l] == sha1_hash[0:l] + + +def download(url, path=None, overwrite=False, sha1_hash=None): + """Download an given URL + Parameters + ---------- + url : str + URL to download + path : str, optional + Destination path to store downloaded file. By default stores to the + current directory with same name as in url. + overwrite : bool, optional + Whether to overwrite destination file if already exists. + sha1_hash : str, optional + Expected sha1 hash in hexadecimal digits. Will ignore existing file when hash is specified + but doesn't match. + Returns + ------- + str + The file path of the downloaded file. + """ + if path is None: + fname = url.split('/')[-1] + else: + path = os.path.expanduser(path) + if os.path.isdir(path): + fname = os.path.join(path, url.split('/')[-1]) + else: + fname = path + + if overwrite or not os.path.exists(fname) or (sha1_hash and not check_sha1(fname, sha1_hash)): + dirname = os.path.dirname(os.path.abspath(os.path.expanduser(fname))) + if not os.path.exists(dirname): + os.makedirs(dirname) + + print('Downloading %s from %s...'%(fname, url)) + r = requests.get(url, stream=True) + if r.status_code != 200: + raise RuntimeError("Failed downloading url %s"%url) + total_length = r.headers.get('content-length') + with open(fname, 'wb') as f: + if total_length is None: # no content length header + for chunk in r.iter_content(chunk_size=1024): + if chunk: # filter out keep-alive new chunks + f.write(chunk) + else: + total_length = int(total_length) + for chunk in tqdm(r.iter_content(chunk_size=1024), + total=int(total_length / 1024. + 0.5), + unit='KB', unit_scale=False, dynamic_ncols=True): + f.write(chunk) + + if sha1_hash and not check_sha1(fname, sha1_hash): + raise UserWarning('File {} is downloaded but the content hash does not match. ' \ + 'The repo may be outdated or download may be incomplete. ' \ + 'If the "repo_url" is overridden, consider switching to ' \ + 'the default repo.'.format(fname)) + + return fname + + +def download_mxnet_weights(name, tag=None, root='../mxnet_weights'): + _model_sha1 = {name: checksum for checksum, name in [ + ('2d9d980c990442f826f20781ed039851e78dabe3', 'resnet18_v1b'), + ('8e16b84814e84f64d897854003f049872991eaa6', 'resnet34_v1b'), + ('0ecdba34691be172036ddf244ff1b2eade75ffde', 'resnet50_v1b'), + ('a455932aa95cb7dcfa05fd040b9b5a5660733c39', 'resnet101_v1b'), + ('a5a61ee1ce5ab7c09720775b223360f3c60e211d', 'resnet152_v1b'), + ('2a4e070854db538595cc7ee02e1a914bdd49ca02', 'resnet50_v1c'), + ('064858f23f9878bfbbe378a88ccb25d612b149a1', 'resnet101_v1c'), + ('75babab699e1c93f5da3c1ce4fd0092d1075f9a0', 'resnet152_v1c'), + ('117a384ecf61490eb31ea147eb0e61e6d2b8a449', 'resnet50_v1d'), + ('1b2b825feff86b0354642a4ab59f9b6e35e47338', 'resnet101_v1d'), + ('cddbc86ff24a5544f57242ded0acb14ef1fbd437', 'resnet152_v1d') + ]} + + apache_repo_url = 'https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/' + _url_format = '{repo_url}gluon/models/{file_name}.zip' + + def short_hash(name): + if name not in _model_sha1: + raise ValueError('Pretrained model for {name} is not available.'.format(name=name)) + return _model_sha1[name][:8] + + use_tag = isinstance(tag, str) + if use_tag: + file_name = '{name}-{short_hash}'.format(name=name, + short_hash=tag) + else: + file_name = '{name}-{short_hash}'.format(name=name, + short_hash=short_hash(name)) + root = os.path.expanduser(root) + file_path = os.path.join(root, file_name + '.params') + if use_tag: + sha1_hash = tag + else: + sha1_hash = _model_sha1[name] + if os.path.exists(file_path): + if check_sha1(file_path, sha1_hash): + return file_path + else: + print('Mismatch in the content of model file detected. Downloading again.') + else: + print('Model file is not found. Downloading.') + + if not os.path.exists(root): + os.makedirs(root) + + zip_file_path = os.path.join(root, file_name + '.zip') + repo_url = os.environ.get('MXNET_GLUON_REPO', apache_repo_url) + if repo_url[-1] != '/': + repo_url = repo_url + '/' + download(_url_format.format(repo_url=repo_url, file_name=file_name), + path=zip_file_path, + overwrite=True) + with zipfile.ZipFile(zip_file_path) as zf: + zf.extractall(root) + os.remove(zip_file_path) + + if check_sha1(file_path, sha1_hash): + return file_path + else: + raise ValueError('Downloaded file has different hash. Please try again.') + + +if __name__ == '__main__': + download_mxnet_weights('resnet%d_v%db' % (101, 1), + tag=True) \ No newline at end of file diff --git a/libs/gluon2TF/resnet/parse_mxnet_weights.py b/libs/gluon2TF/resnet/parse_mxnet_weights.py new file mode 100644 index 0000000..8de1fc1 --- /dev/null +++ b/libs/gluon2TF/resnet/parse_mxnet_weights.py @@ -0,0 +1,65 @@ +# -*- coding: utf-8 -*- + +from __future__ import absolute_import, print_function, division +import mxnet.ndarray as nd +import numpy as np + + +def read_mxnet_weights(path, show=False): + + # assert os.path.exists(path), "path erro: {}".format(path) + + name_MxnetArray_dict = nd.load(path) + + name_array_dict = {} + for name in sorted(name_MxnetArray_dict.keys()): + mxnet_array = name_MxnetArray_dict[name] + array = mxnet_array.asnumpy() + + if show: + print ("name: {} || shape: {} || dtype: {}".format(name, array.shape, array.dtype)) + + if name.endswith("weight"): + if name.endswith("fc.weight"): + array = np.transpose(array, [1, 0]) + else: + array = np.transpose(array, [2, 3, 1, 0]) + # (out_channel, in_channel, k, k)(mxnet) --> (k, k, in_channel, out_channel)(tf) + # (32, 3, 3, 3)-->(3, 3, 3, 32) + name_array_dict[name] = array + + return name_array_dict + + +def check_mxnet_names(mxnet_tf_map, mxnetName_array_dict): + + for key1, key2 in zip(sorted(mxnet_tf_map.keys()), sorted(mxnetName_array_dict.keys())): + assert key1 == key2, "key in mxnet_array_dict and mxnet_tf_map do not equal, details are :\n" \ + "key1 in mxnet_tf_map: {}\n"\ + "key2 in mxnet_array dict: {}".format(key1, key2) + if len(mxnetName_array_dict) == len(mxnet_tf_map): + print("all mxnet names are mapped") + + +def check_tf_vars(tf_mxnet_map, mxnetName_array_dict, tf_model_vars, scope='resnet50_v1_d'): + + tf_nake_names = sorted([var.op.name.split("%s/" % scope)[1] for var in tf_model_vars]) + # check_name + for tf_name, name2 in zip(tf_nake_names, sorted(tf_mxnet_map.keys())): + assert tf_name == name2, "key in tf_model_vars and tf_mxnet_map do not equal, details are :\n" \ + "tf_name in tf_model_vars: {}\n" \ + "name2 in tf_mxnet_maps: {}".format(tf_name, name2) + print("all tf_model_var can find matched name in tf_mxnet_map") + + # check shape + for var in tf_model_vars: + name = var.op.name.split("%s/"%scope)[1] + array = mxnetName_array_dict[tf_mxnet_map[name]] + + assert var.shape == array.shape, "var in tf_model_vars and mxnet_arrays shape do not equal, details are :\n" \ + "tf_var in tf_model_vars: {}\n" \ + "name in tf_mxnet_maps: {}, shape is : {}".format(var, tf_mxnet_map[name], + array.shape) + print("All tf_model_var shapes match the shape of arrays in mxnet_array_dict...") + + diff --git a/libs/gluon2TF/resnet/resnet.py b/libs/gluon2TF/resnet/resnet.py new file mode 100644 index 0000000..8b570eb --- /dev/null +++ b/libs/gluon2TF/resnet/resnet.py @@ -0,0 +1,80 @@ +# -*- coding: utf-8 -*- + + +from __future__ import absolute_import, print_function, division +import numpy as np +import tensorflow as tf +import tensorflow.contrib.slim as slim +from resnet_utils import get_resnet_v1_d, get_resnet_v1_b +from parse_mxnet_weights import read_mxnet_weights, check_mxnet_names, check_tf_vars +import weights_map +import os + +BottleNeck_NUM_DICT = { + 'resnet50_v1b': [3, 4, 6, 3], + 'resnet101_v1b': [3, 4, 23, 3], + 'resnet50_v1d': [3, 4, 6, 3], + 'resnet101_v1d': [3, 4, 23, 3] +} + +BASE_CHANNELS_DICT = { + 'resnet50_v1b': [64, 128, 256, 512], + 'resnet101_v1b': [64, 128, 256, 512], + 'resnet50_v1d': [64, 128, 256, 512], + 'resnet101_v1d': [64, 128, 256, 512] +} + + +def create_resotre_op(scope, mxnet_weights_path): + + mxnetName_array_dict = read_mxnet_weights(mxnet_weights_path, show=False) + + tf_mxnet_map, mxnet_tf_map = \ + weights_map.get_map(scope=scope, + bottleneck_nums=BottleNeck_NUM_DICT[scope], show_mxnettf=False, show_tfmxnet=False) + + tf_model_vars = slim.get_model_variables(scope) + + # # check name and var + check_mxnet_names(mxnet_tf_map, mxnetName_array_dict=mxnetName_array_dict) + check_tf_vars(tf_mxnet_map, mxnetName_array_dict, tf_model_vars, scope=scope) + # # + + assign_ops = [] + + for var in tf_model_vars: + name = var.op.name.split('%s/' % scope)[1] + new_val = tf.constant(mxnetName_array_dict[tf_mxnet_map[name]]) + sub_assign_op = tf.assign(var, value=new_val) + + assign_ops.append(sub_assign_op) + + assign_op = tf.group(*assign_ops) + + return assign_op + + +def build_resnet(img_batch=None, scope='resnet50_v1d', is_training=True, freeze_norm=False, num_cls=1000): + if img_batch is None: + np.random.seed(30) + img_batch = np.random.rand(1, 224, 224, 3) # H, W, C + img_batch = tf.constant(img_batch, dtype=tf.float32) + + print("Please Ensure the img is in NHWC") + + if scope.endswith('b'): + get_resnet_fn = get_resnet_v1_b + elif scope.endswith('d'): + get_resnet_fn = get_resnet_v1_d + + logits = get_resnet_fn(input_x=img_batch, scope=scope, + bottleneck_nums=BottleNeck_NUM_DICT[scope], + base_channels=BASE_CHANNELS_DICT[scope], + is_training=is_training, freeze_norm=freeze_norm, num_cls=num_cls) + + return logits + + +if __name__ == "__main__": + build_resnet() + create_resotre_op() diff --git a/libs/gluon2TF/resnet/resnet_utils.py b/libs/gluon2TF/resnet/resnet_utils.py new file mode 100644 index 0000000..d6b6644 --- /dev/null +++ b/libs/gluon2TF/resnet/resnet_utils.py @@ -0,0 +1,254 @@ +# -*- coding: utf-8 -*- +from __future__ import absolute_import, division, print_function +import tensorflow as tf +import tensorflow.contrib.slim as slim +import math + +DATA_FORMAT = "NHWC" +DEBUG = False +debug_dict = {} + + +def resnet_arg_scope(freeze_norm, is_training=True, weight_decay=0.0001, + batch_norm_decay=0.9, batch_norm_epsilon=1e-5, batch_norm_scale=True): + + batch_norm_params = { + 'is_training': (not freeze_norm) and is_training, 'decay': batch_norm_decay, + 'epsilon': batch_norm_epsilon, 'scale': batch_norm_scale, + 'trainable': (not freeze_norm) and is_training, + 'updates_collections': tf.GraphKeys.UPDATE_OPS, + 'data_format': DATA_FORMAT + } + with slim.arg_scope( + [slim.conv2d], + weights_regularizer=slim.l2_regularizer(weight_decay), + weights_initializer=slim.variance_scaling_initializer(), + trainable=is_training, + activation_fn=tf.nn.relu, + normalizer_fn=slim.batch_norm, + normalizer_params=batch_norm_params): + with slim.arg_scope([slim.batch_norm], **batch_norm_params) as arg_sc: + return arg_sc + + +def stem_7x7(net, scope="C1"): + + with tf.variable_scope(scope): + net = tf.pad(net, paddings=[[0, 0], [3, 3], [3, 3], [0, 0]]) # pad for data + net = slim.conv2d(net, num_outputs=64, kernel_size=[7, 7], stride=2, + padding="VALID", biases_initializer=None, data_format=DATA_FORMAT, + scope="conv0") + if DEBUG: + debug_dict['conv_7x7_bn_relu'] = tf.transpose(net, [0, 3, 1, 2]) # NHWC --> NCHW + net = tf.pad(net, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]) + net = slim.max_pool2d(net, kernel_size=[3, 3], stride=2, padding="VALID", data_format=DATA_FORMAT) + return net + + +def stem_stack_3x3(net, input_channel=32, scope="C1"): + with tf.variable_scope(scope): + net = tf.pad(net, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]) + net = slim.conv2d(net, num_outputs=input_channel, kernel_size=[3, 3], stride=2, + padding="VALID", biases_initializer=None, data_format=DATA_FORMAT, + scope='conv0') + net = tf.pad(net, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]) + net = slim.conv2d(net, num_outputs=input_channel, kernel_size=[3, 3], stride=1, + padding="VALID", biases_initializer=None, data_format=DATA_FORMAT, + scope='conv1') + net = tf.pad(net, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]) + net = slim.conv2d(net, num_outputs=input_channel*2, kernel_size=[3, 3], stride=1, + padding="VALID", biases_initializer=None, data_format=DATA_FORMAT, + scope='conv2') + net = tf.pad(net, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]) + net = slim.max_pool2d(net, kernel_size=[3, 3], stride=2, padding="VALID", data_format=DATA_FORMAT) + return net + + +def bottleneck_v1b(input_x, base_channel, scope, stride=1, projection=False, avg_down=True): + ''' + for bottleneck_v1b: reduce spatial dim in conv_3x3 with stride 2. + ''' + with tf.variable_scope(scope): + if DEBUG: + debug_dict[input_x.op.name] = tf.transpose(input_x, [0, 3, 1, 2]) + net = slim.conv2d(input_x, num_outputs=base_channel, kernel_size=[1, 1], stride=1, + padding="VALID", biases_initializer=None, data_format=DATA_FORMAT, + scope='conv0') + if DEBUG: + debug_dict[net.op.name] = tf.transpose(net, [0, 3, 1, 2]) + + net = tf.pad(net, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]) + if DEBUG: + debug_dict[net.op.name] = tf.transpose(net, [0, 3, 1, 2]) + + net = slim.conv2d(net, num_outputs=base_channel, kernel_size=[3, 3], stride=stride, + padding="VALID", biases_initializer=None, data_format=DATA_FORMAT, + scope='conv1') + if DEBUG: + debug_dict[net.op.name] = tf.transpose(net, [0, 3, 1, 2]) + net = slim.conv2d(net, num_outputs=base_channel * 4, kernel_size=[1, 1], stride=1, + padding="VALID", biases_initializer=None, data_format=DATA_FORMAT, + activation_fn=None, scope='conv2') + if DEBUG: + debug_dict[net.op.name] = tf.transpose(net, [0, 3, 1, 2]) + # Note that : gamma in the last conv should be init with 0. + # But we just reload params from mxnet, so don't specific batch norm initializer + if projection: + + if avg_down: # design for resnet_v1d + ''' + In GluonCV, padding is "ceil mode". Here we use "SAME" to replace it, which may cause Erros. + And the erro will grow with depth of resnet. e.g. res101 erro > res50 erro + ''' + shortcut = slim.avg_pool2d(input_x, kernel_size=[stride, stride], stride=stride, padding="SAME", + data_format=DATA_FORMAT) + if DEBUG: + debug_dict[shortcut.op.name] = tf.transpose(shortcut, [0, 3, 1, 2]) + + shortcut = slim.conv2d(shortcut, num_outputs=base_channel*4, kernel_size=[1, 1], + stride=1, padding="VALID", biases_initializer=None, data_format=DATA_FORMAT, + activation_fn=None, + scope='shortcut') + if DEBUG: + debug_dict[shortcut.op.name] = tf.transpose(shortcut, [0, 3, 1, 2]) + # shortcut should have batch norm. + else: + shortcut = slim.conv2d(input_x, num_outputs=base_channel * 4, kernel_size=[1, 1], + stride=stride, padding="VALID", biases_initializer=None, activation_fn=None, + data_format=DATA_FORMAT, + scope='shortcut') + if DEBUG: + debug_dict[shortcut.op.name] = tf.transpose(shortcut, [0, 3, 1, 2]) + else: + shortcut = tf.identity(input_x, name='shortcut/Identity') + if DEBUG: + debug_dict[shortcut.op.name] = tf.transpose(shortcut, [0, 3, 1, 2]) + + net = net + shortcut + if DEBUG: + debug_dict[net.op.name] = tf.transpose(net, [0, 3, 1, 2]) + net = tf.nn.relu(net) + if DEBUG: + debug_dict[net.op.name] = tf.transpose(net, [0, 3, 1, 2]) + return net + + +def make_block(net, base_channel, bottleneck_nums, scope, avg_down=True, spatial_downsample=False): + with tf.variable_scope(scope): + first_stride = 2 if spatial_downsample else 1 + + net = bottleneck_v1b(input_x=net, base_channel=base_channel,scope='bottleneck_0', + stride=first_stride, avg_down=avg_down, projection=True) + for i in range(1, bottleneck_nums): + net = bottleneck_v1b(input_x=net, base_channel=base_channel, scope="bottleneck_%d" % i, + stride=1, avg_down=avg_down, projection=False) + return net + + +def get_resnet_v1_b_base(input_x, freeze_norm, scope="resnet50_v1b", bottleneck_nums=[3, 4, 6, 3], base_channels=[64, 128, 256, 512], + freeze=[True, False, False, False, False], is_training=True): + + assert len(bottleneck_nums) == len(base_channels), "bottleneck num should same as base_channels size" + assert len(freeze) == len(bottleneck_nums) +1, "should satisfy:: len(freeze) == len(bottleneck_nums) + 1" + feature_dict = {} + with tf.variable_scope(scope): + with slim.arg_scope(resnet_arg_scope(is_training=(not freeze[0]) and is_training, + freeze_norm=freeze_norm)): + net = stem_7x7(net=input_x, scope="C1") + feature_dict["C1"] = net + for i in range(2, len(bottleneck_nums)+2): + spatial_downsample = False if i == 2 else True + with slim.arg_scope(resnet_arg_scope(is_training=(not freeze[i-1]) and is_training, + freeze_norm=freeze_norm)): + net = make_block(net=net, base_channel=base_channels[i-2], + bottleneck_nums=bottleneck_nums[i-2], + scope="C%d" % i, + avg_down=False, spatial_downsample=spatial_downsample) + feature_dict["C%d" % i] = net + + return net, feature_dict + + +def get_resnet_v1_b(input_x, + scope="resnet50_v1b", bottleneck_nums=[3, 4, 6, 3], + base_channels=[64, 128, 256, 512], + freeze=[True, False, False, False, False], + is_training=True, freeze_norm=False, + num_cls=1000, dropout=False): + + net, fet_dict = get_resnet_v1_b_base(input_x=input_x, scope=scope, bottleneck_nums=bottleneck_nums, base_channels=base_channels, + freeze=freeze, is_training=is_training, freeze_norm=freeze_norm) + with tf.variable_scope(scope): + # net shape : [B, H, W, C] + if DATA_FORMAT.strip() == "NCHW": + net = tf.reduce_mean(net, axis=[2, 3], name="global_avg_pooling", + keep_dims=True) # [B, C, 1, 1] + elif DATA_FORMAT.strip() == "NHWC": + net = tf.reduce_mean(net, axis=[1, 2], name="global_avg_pooling", + keep_dims=True) # [B, 1, 1, C] + else: + raise ValueError("Data Format Erro...") + + net = slim.flatten(net, scope='flatten') + if dropout: + net = slim.dropout(net, keep_prob=0.5, is_training=is_training) + logits = slim.fully_connected(net, num_outputs=num_cls, activation_fn=None, scope='logits') + return logits + + +def get_resnet_v1_d_base(input_x, freeze_norm, scope="resnet50_v1d", bottleneck_nums=[3, 4, 6, 3], base_channels=[64, 128, 256, 512], + freeze=[True, False, False, False, False], is_training=True): + + assert len(bottleneck_nums) == len(base_channels), "bottleneck num should same as base_channels size" + assert len(freeze) == len(bottleneck_nums) +1, "should satisfy:: len(freeze) == len(bottleneck_nums) + 1" + feature_dict = {} + with tf.variable_scope(scope): + with slim.arg_scope(resnet_arg_scope(is_training=(not freeze[0]) and is_training, + freeze_norm=freeze_norm)): + net = stem_stack_3x3(net=input_x, input_channel=32, scope="C1") + feature_dict["C1"] = net + # print (net) + for i in range(2, len(bottleneck_nums)+2): + spatial_downsample = False if i == 2 else True + with slim.arg_scope(resnet_arg_scope(is_training=(not freeze[i-1]) and is_training, + freeze_norm=freeze_norm)): + net = make_block(net=net, base_channel=base_channels[i-2], + bottleneck_nums=bottleneck_nums[i-2], + scope="C%d" % i, + avg_down=True, spatial_downsample=spatial_downsample) + feature_dict["C%d" % i] = net + + return net, feature_dict + + +def get_resnet_v1_d(input_x, + scope="resnet50_v1d", bottleneck_nums=[3, 4, 6, 3], + base_channels=[64, 128, 256, 512], + freeze=[True, False, False, False, False], + is_training=True, freeze_norm=False, + num_cls=1000, dropout=False): + + net, fet_dict = get_resnet_v1_d_base(input_x=input_x, scope=scope, bottleneck_nums=bottleneck_nums, base_channels=base_channels, + freeze=freeze, is_training=is_training, freeze_norm=freeze_norm) + with tf.variable_scope(scope): + # net shape : [B, H, W, C] + if DATA_FORMAT.strip() == "NCHW": + net = tf.reduce_mean(net, axis=[2, 3], name="global_avg_pooling", + keep_dims=True) # [B, C, 1, 1] + elif DATA_FORMAT.strip() == "NHWC": + net = tf.reduce_mean(net, axis=[1, 2], name="global_avg_pooling", + keep_dims=True) # [B, 1, 1, C] + else: + raise ValueError("Data Format Erro...") + + net = slim.flatten(net, scope='flatten') + if dropout: + net = slim.dropout(net, keep_prob=0.5, is_training=is_training) + logits = slim.fully_connected(net, num_outputs=num_cls, activation_fn=None, scope='logits') + return logits + + + + + + diff --git a/libs/gluon2TF/resnet/resnet_utils_NCHW.py b/libs/gluon2TF/resnet/resnet_utils_NCHW.py new file mode 100644 index 0000000..46b5098 --- /dev/null +++ b/libs/gluon2TF/resnet/resnet_utils_NCHW.py @@ -0,0 +1,237 @@ +# -*- coding: utf-8 -*- +from __future__ import absolute_import, division, print_function +import tensorflow as tf +import tensorflow.contrib.slim as slim +import math + +DATA_FORMAT = "NCHW" # to match data format for mxnet +debug_dict = {} +def resnet_arg_scope(freeze_norm, is_training=True, weight_decay=0.0001, + batch_norm_decay=0.9, batch_norm_epsilon=1e-5, batch_norm_scale=True): + + batch_norm_params = { + 'is_training': (not freeze_norm) and is_training, 'decay': batch_norm_decay, + 'epsilon': batch_norm_epsilon, 'scale': batch_norm_scale, + 'trainable': (not freeze_norm) and is_training, + 'updates_collections': tf.GraphKeys.UPDATE_OPS, + 'data_format': DATA_FORMAT + } + with slim.arg_scope( + [slim.conv2d], + weights_regularizer=slim.l2_regularizer(weight_decay), + weights_initializer=slim.variance_scaling_initializer(), + trainable=is_training, + activation_fn=tf.nn.relu, + normalizer_fn=slim.batch_norm, + normalizer_params=batch_norm_params): + with slim.arg_scope([slim.batch_norm], **batch_norm_params) as arg_sc: + return arg_sc + + +def stem_7x7(net, scope="C1"): + + with tf.variable_scope(scope): + net = tf.pad(net, paddings=[[0, 0], [0, 0], [3, 3], [3, 3]]) # pad for data + net = slim.conv2d(net, num_outputs=64, kernel_size=[7, 7], stride=2, + padding="VALID", biases_initializer=None, data_format=DATA_FORMAT, + scope="conv0", normalizer_fn=None, activation_fn=None) + debug_dict['conv_7x7'] = net + with tf.variable_scope('conv0') as scope: + net = slim.batch_norm(net) + debug_dict['conv_7x7_bn'] = net + net = tf.nn.relu(net) + debug_dict['conv_7x7_bn_relu'] = net + + net = tf.pad(net, paddings=[[0, 0], [0, 0], [1, 1], [1, 1]]) + net = slim.max_pool2d(net, kernel_size=[3, 3], stride=2, padding="VALID", data_format=DATA_FORMAT) + return net + +def stem_stack_3x3(net, input_channel=32, scope="C1"): + with tf.variable_scope(scope): + net = tf.pad(net, paddings=[[0, 0], [0, 0], [1, 1], [1, 1]]) + net = slim.conv2d(net, num_outputs=input_channel, kernel_size=[3, 3], stride=2, + padding="VALID", biases_initializer=None, data_format=DATA_FORMAT, + scope='conv0') + net = tf.pad(net, paddings=[[0, 0], [0, 0], [1, 1], [1, 1]]) + net = slim.conv2d(net, num_outputs=input_channel, kernel_size=[3, 3], stride=1, + padding="VALID", biases_initializer=None, data_format=DATA_FORMAT, + scope='conv1') + net = tf.pad(net, paddings=[[0, 0], [0, 0], [1, 1], [1, 1]]) + net = slim.conv2d(net, num_outputs=input_channel*2, kernel_size=[3, 3], stride=1, + padding="VALID", biases_initializer=None, data_format=DATA_FORMAT, + scope='conv2') + net = tf.pad(net, paddings=[[0, 0], [0, 0], [1, 1], [1, 1]]) + net = slim.max_pool2d(net, kernel_size=[3, 3], stride=2, padding="VALID", data_format=DATA_FORMAT) + return net + + +def bottleneck_v1b(input_x, base_channel, scope, stride=1, projection=False, avg_down=True): + ''' + for bottleneck_v1b: reduce spatial dim in conv_3x3 with stride 2. + ''' + with tf.variable_scope(scope): + debug_dict[input_x.op.name] = input_x + net = slim.conv2d(input_x, num_outputs=base_channel, kernel_size=[1, 1], stride=1, + padding="VALID", biases_initializer=None, data_format=DATA_FORMAT, + scope='conv0') + debug_dict[net.op.name] = net + net = tf.pad(net, paddings=[[0, 0], [0, 0], [1, 1], [1, 1]]) + debug_dict[net.op.name] = net + net = slim.conv2d(net, num_outputs=base_channel, kernel_size=[3, 3], stride=stride, + padding="VALID", biases_initializer=None, data_format=DATA_FORMAT, + scope='conv1') + debug_dict[net.op.name] = net + net = slim.conv2d(net, num_outputs=base_channel * 4, kernel_size=[1, 1], stride=1, + padding="VALID", biases_initializer=None, data_format=DATA_FORMAT, + activation_fn=None, scope='conv2') + debug_dict[net.op.name] = net + # Note that : gamma in the last conv should be init with 0. + # But we just reload params from mxnet, so don't specific batch norm initializer + if projection: + + if avg_down: # design for resnet_v1d + # pad = 1 # int(math.floor((stride - 1)/2.0)) + # input_x = tf.pad(input_x, paddings=[[0, 0], [0, 0], [pad, pad], [pad, pad]]) + shortcut = slim.avg_pool2d(input_x, kernel_size=[stride, stride], stride=stride, padding="SAME", + data_format=DATA_FORMAT) + debug_dict[shortcut.op.name] = shortcut + shortcut = slim.conv2d(shortcut, num_outputs=base_channel*4, kernel_size=[1, 1], + stride=1, padding="VALID", biases_initializer=None, data_format=DATA_FORMAT, + activation_fn=None, + scope='shortcut') + debug_dict[shortcut.op.name] = shortcut + # shortcut should have batch norm. + else: + shortcut = slim.conv2d(input_x, num_outputs=base_channel * 4, kernel_size=[1, 1], + stride=stride, padding="VALID", biases_initializer=None, activation_fn=None, + data_format=DATA_FORMAT, + scope='shortcut') + debug_dict[shortcut.op.name] = shortcut + else: + shortcut = tf.identity(input_x, name='shortcut/Relu') + debug_dict[shortcut.op.name] = shortcut + + net = net + shortcut + debug_dict[net.op.name] = net + net = tf.nn.relu(net) + debug_dict[net.op.name] = net + return net + + +def make_block(net, base_channel, bottleneck_nums, scope, avg_down=True, spatial_downsample=False): + with tf.variable_scope(scope): + first_stride = 2 if spatial_downsample else 1 + + net = bottleneck_v1b(input_x=net, base_channel=base_channel,scope='bottleneck_0', + stride=first_stride, avg_down=avg_down, projection=True) + for i in range(1, bottleneck_nums): + net = bottleneck_v1b(input_x=net, base_channel=base_channel, scope="bottleneck_%d" % i, + stride=1, avg_down=avg_down, projection=False) + return net + + +def get_resnet_v1_b_base(input_x, freeze_norm, scope="resnet50_v1b", bottleneck_nums=[3, 4, 6, 3], base_channels=[64, 128, 256, 512], + freeze=[True, False, False, False, False], is_training=True): + + assert len(bottleneck_nums) == len(base_channels), "bottleneck num should same as base_channels size" + assert len(freeze) == len(bottleneck_nums) +1, "should satisfy:: len(freeze) == len(bottleneck_nums) + 1" + feature_dict = {} + with tf.variable_scope(scope): + with slim.arg_scope(resnet_arg_scope(is_training=(not freeze[0]) and is_training, + freeze_norm=freeze_norm)): + net = stem_7x7(net=input_x, scope="C1") + feature_dict["C1"] = net + for i in range(2, len(bottleneck_nums)+2): + spatial_downsample = False if i == 2 else True + with slim.arg_scope(resnet_arg_scope(is_training=(not freeze[i-2]) and is_training, + freeze_norm=freeze_norm)): + net = make_block(net=net, base_channel=base_channels[i-2], + bottleneck_nums=bottleneck_nums[i-2], + scope="C%d" % i, + avg_down=False, spatial_downsample=spatial_downsample) + feature_dict["C%d" % i] = net + + return net, feature_dict + + +def get_resnet_v1_b(input_x, + scope="resnet50_v1b", bottleneck_nums=[3, 4, 6, 3], + base_channels=[64, 128, 256, 512], + freeze=[True, False, False, False, False], + is_training=True, freeze_norm=False, + num_cls=1000, dropout=False): + + net, fet_dict = get_resnet_v1_b_base(input_x=input_x, scope=scope, bottleneck_nums=bottleneck_nums, base_channels=base_channels, + freeze=freeze, is_training=is_training, freeze_norm=freeze_norm) + with tf.variable_scope(scope): + # net shape : [B, C, H, W] + if DATA_FORMAT.strip() == "NCHW": + net = tf.reduce_mean(net, axis=[2, 3], name="global_avg_pooling", + keep_dims=True) # [B, C, 1, 1] + elif DATA_FORMAT.strip() == "NHWC": + net = tf.reduce_mean(net, axis=[1, 2], name="global_avg_pooling", + keep_dims=True) # [B, 1, 1, C] + else: + raise ValueError("Data Format Erro...") + + net = slim.flatten(net, scope='flatten') + if dropout: + net = slim.dropout(net, keep_prob=0.5, is_training=is_training) + logits = slim.fully_connected(net, num_outputs=num_cls, activation_fn=None, scope='logits') + return logits + + +def get_resnet_v1_d_base(input_x, freeze_norm, scope="resnet50_v1d", bottleneck_nums=[3, 4, 6, 3], base_channels=[64, 128, 256, 512], + freeze=[True, False, False, False, False], is_training=True): + + assert len(bottleneck_nums) == len(base_channels), "bottleneck num should same as base_channels size" + assert len(freeze) == len(bottleneck_nums) +1, "should satisfy:: len(freeze) == len(bottleneck_nums) + 1" + feature_dict = {} + with tf.variable_scope(scope): + with slim.arg_scope(resnet_arg_scope(is_training=(not freeze[0]) and is_training, + freeze_norm=freeze_norm)): + net = stem_stack_3x3(net=input_x, input_channel=32, scope="C1") + feature_dict["C1"] = net + for i in range(2, len(bottleneck_nums)+2): + spatial_downsample = False if i == 2 else True + with slim.arg_scope(resnet_arg_scope(is_training=(not freeze[i-2]) and is_training, + freeze_norm=freeze_norm)): + net = make_block(net=net, base_channel=base_channels[i-2], + bottleneck_nums=bottleneck_nums[i-2], + scope="C%d" % i, + avg_down=True, spatial_downsample=spatial_downsample) + feature_dict["C%d" % i] = net + + return net, feature_dict + +def get_resnet_v1_d(input_x, + scope="resnet50_v1d", bottleneck_nums=[3, 4, 6, 3], + base_channels=[64, 128, 256, 512], + freeze=[True, False, False, False, False], + is_training=True, freeze_norm=False, + num_cls=1000, dropout=False): + + net, fet_dict = get_resnet_v1_d_base(input_x=input_x, scope=scope, bottleneck_nums=bottleneck_nums, base_channels=base_channels, + freeze=freeze, is_training=is_training, freeze_norm=freeze_norm) + with tf.variable_scope(scope): + # net shape : [B, C, H, W] + if DATA_FORMAT.strip() == "NCHW": + net = tf.reduce_mean(net, axis=[2, 3], name="global_avg_pooling", + keep_dims=True) # [B, C, 1, 1] + elif DATA_FORMAT.strip() == "NHWC": + net = tf.reduce_mean(net, axis=[1, 2], name="global_avg_pooling", + keep_dims=True) # [B, 1, 1, C] + else: + raise ValueError("Data Format Erro...") + + net = slim.flatten(net, scope='flatten') + if dropout: + net = slim.dropout(net, keep_prob=0.5, is_training=is_training) + logits = slim.fully_connected(net, num_outputs=num_cls, activation_fn=None, scope='logits') + return logits + + + + + + diff --git a/libs/gluon2TF/resnet/some_test.py b/libs/gluon2TF/resnet/some_test.py new file mode 100644 index 0000000..689d10c --- /dev/null +++ b/libs/gluon2TF/resnet/some_test.py @@ -0,0 +1,50 @@ +# -*- coding: utf-8 -*- + +import mxnet as mx +from mxnet.gluon import nn +from mxnet import ndarray as nd +import tensorflow.contrib.slim as slim +import tensorflow as tf +import numpy as np +from test_resnet import mxnet_process_img +# 卷积层 +# 输入输出的数据格式是： batch * channel * height * width +# 权重格式：output_channels * in_channels * height * width +np.random.seed(30) + +# w = nd.array(np.random.rand(2, 3, 3, 3)) +w = nd.load('/home/yjr/MxNet_Codes/gluon-cv/scripts/gloun2TF/mxnet_weights/resnet50_v1b-0ecdba34.params')['conv1.weight'] # [64, 3, 7, 7] +# w = nd.arange(9*2).reshape((2, 1, 3, 3)) +data = nd.array(np.random.rand(1, 3, 224, 224)) +# data, _ = mxnet_process_img('../demo_img/person.jpg') +# data = nd.arange(6*6).reshape((1, 1, 6, 6)) + +# 卷积运算 +out = nd.Convolution(data, w, no_bias=True, + kernel=(7, 7), + stride=(2, 2), + num_filter=64, + pad=(3, 3)) + + + +def tf_conv(data, w): + + data = tf.constant(data.asnumpy()) + data = tf.pad(data, paddings=[[0, 0], [0, 0], [3, 3], [3, 3]]) + tf_out = slim.conv2d(data, num_outputs=64, kernel_size=[7, 7], padding='VALID', stride=2, + biases_initializer=None, data_format='NCHW', normalizer_fn=None, activation_fn=None) + tf_w = tf.constant(np.transpose(w.asnumpy(), [2, 3, 1, 0])) + # tf_w = + model_vars = slim.get_model_variables() + assign_op = tf.assign(model_vars[0], tf_w) + + with tf.Session() as sess: + sess.run(assign_op) + print(sess.run(tf_out)) + + +if __name__ == '__main__': + tf_conv(data, w=w) + print "mxnet_out: ", out + print 20 * "+" \ No newline at end of file diff --git a/libs/gluon2TF/resnet/test_resnet.py b/libs/gluon2TF/resnet/test_resnet.py new file mode 100644 index 0000000..b76c2dc --- /dev/null +++ b/libs/gluon2TF/resnet/test_resnet.py @@ -0,0 +1,132 @@ +# -*- coding: utf-8 -*- + +from __future__ import absolute_import, division, print_function + +import tensorflow as tf +import tensorflow.contrib.slim as slim + +from mxnet import nd, image +import numpy as np +from mxnet.gluon.data.vision import transforms +from gluoncv.model_zoo import get_model +from gluoncv.data.transforms.presets.imagenet import transform_eval +from resnet import build_resnet, create_resotre_op +from resnet_utils import DEBUG +import os + + +# MODEL_NAME = 'resnet50_v1d' +# Mxnet_Weights_PATH = '../mxnet_weights/resnet50_v1d-117a384e.params' + + +MODEL_NAME = 'resnet101_v1b' +# Mxnet_Weights_PATH = '../mxnet_weights/resnet101_v1d-1b2b825f.params' +Mxnet_Weights_PATH = '../mxnet_weights/resnet101_v1b-a455932a.params' + +def mxnet_process_img(path): + # Load Images + img = image.imread(path) + + # Transform + img = transform_eval(img) + img_arr = img.asnumpy() + if len(img_arr) == 3: + img_arr = np.expand_dims(img_arr, axis=0) + img_tf_tensor = tf.constant(img_arr) + + # np.random.seed(30) + # img = nd.array(np.random.randn(1, 3, 600, 800)) + # img_tf_tensor = tf.constant(img.asnumpy()) + img_tf_tensor = tf.transpose(img_tf_tensor, [0, 2, 3, 1]) + return img, img_tf_tensor + +# +def mxnet_infer(img): + + model_name = MODEL_NAME + net = get_model(model_name, pretrained=False) + net.load_parameters(Mxnet_Weights_PATH) + pred = net(img) + + # print (pred.shape, pred.dtype) + pred = pred.asnumpy() + return pred + + +def tf_infer(img, save_ckpt=True, restore_from_tfckpt=False, ckpt_path=None): + + pred_tensor = build_resnet(img_batch=img, scope=MODEL_NAME, + is_training=False, freeze_norm=True, num_cls=1000) + + if restore_from_tfckpt: + print("restore weights from tf_CKPT") + assert not ckpt_path is None, "ckpt_path is None, Erro" + restore_op = tf.train.Saver() + else: + print('restore weights from MxnetWeights') + restore_op = create_resotre_op(MODEL_NAME, Mxnet_Weights_PATH) + + if DEBUG: + from resnet_utils import debug_dict + print (debug_dict) + assert len(debug_dict) >=3, "debug_dict size erro, len is :{}".format(len(debug_dict)) + + if save_ckpt: + save_dir = '../tf_ckpts' + if not os.path.exists(save_dir): + os.mkdir(save_dir) + saver = tf.train.Saver(max_to_keep=30) + save_ckpt = os.path.join(save_dir, '%s.ckpt' % MODEL_NAME) + + with tf.Session() as sess: + if restore_from_tfckpt: + restore_op.restore(sess, ckpt_path) + else: + sess.run(restore_op) + if DEBUG: + name_val = {} + for name in debug_dict.keys(): + name_val[name] = sess.run(debug_dict[name]) + pred = sess.run(pred_tensor) + if save_ckpt: + saver.save(sess, save_ckpt) + + return pred + + +def cal_erro(img_path, use_tf_ckpt=False, ckpt_path=None, save_ckpt=False): + + mxnet_img, tf_img = mxnet_process_img(img_path) + + mxnet_pred = mxnet_infer(mxnet_img) + + mxnet_pred = np.squeeze(mxnet_pred, axis=0) + tf_pred = tf_infer(tf_img, restore_from_tfckpt=use_tf_ckpt, ckpt_path=ckpt_path, save_ckpt=save_ckpt) + tf_pred = np.squeeze(tf_pred, axis=0) + assert mxnet_pred.shape == tf_pred.shape, "mxnet_pred shape Do Not equal with tf_pred shape" + + argmax_mxnet = np.argmax(mxnet_pred) + argmax_tf = np.argmax(tf_pred) + + erro = np.linalg.norm(tf_pred-mxnet_pred) + for i, (m, t) in enumerate(zip(mxnet_pred, tf_pred)): + if i == 5: + break + print ("mxnet|tf==>>{} | {} ".format(m, t)) + + print ('total_erro-->', erro) + print ('erro_rate-->', erro/np.linalg.norm(mxnet_pred)) + print ("argmax_mxnet: {} || tf_argmx: {}".format(argmax_mxnet, argmax_tf)) + + +if __name__ == '__main__': + + # cal_erro(img_path='../demo_img/person.jpg', + # use_tf_ckpt=False, + # ckpt_path=None, + # save_ckpt=True) + cal_erro(img_path='../demo_img/person.jpg', + use_tf_ckpt=True, + ckpt_path='../tf_ckpts/%s.ckpt' % MODEL_NAME, + save_ckpt=False) + print (20*"++") diff --git a/libs/gluon2TF/resnet/weights_map.py b/libs/gluon2TF/resnet/weights_map.py new file mode 100644 index 0000000..10167b2 --- /dev/null +++ b/libs/gluon2TF/resnet/weights_map.py @@ -0,0 +1,179 @@ +# -*-coding: utf-8 -*- + +from __future__ import absolute_import, print_function, division +# from tensorflow.python.tools import inspect_checkpoint as chkp +# +# # print all tensors in checkpoint file +# chkp.print_tensors_in_checkpoint_file("/tmp/model.ckpt", tensor_name='', all_tensors=True) + + +tf_mxnet_map = {} + +tf_mxnet_prefix_map = {"weights": "weight", + "moving_mean": "running_mean", + "moving_variance": "running_var", + "beta": "beta", + "gamma": "gamma"} +def update_logitis(): + tf_mxnet_map["logits/weights"] = 'fc.weight' + tf_mxnet_map["logits/biases"] = 'fc.bias' + + +def update_C1_resnet_v1_b(): + tmp_map = {"C1/conv0/weights": "conv1.weight", + "C1/conv0/BatchNorm/beta": "bn1.beta", + "C1/conv0/BatchNorm/gamma": "bn1.gamma", + "C1/conv0/BatchNorm/moving_mean": "bn1.running_mean", + "C1/conv0/BatchNorm/moving_variance": "bn1.running_var"} + tf_mxnet_map.update(tmp_map) + + +def update_C1_resnet_v1_d(): + tmp_map = {"C1/conv0/weights": "conv1.0.weight", + "C1/conv0/BatchNorm/beta": "conv1.1.beta", + "C1/conv0/BatchNorm/gamma": "conv1.1.gamma", + "C1/conv0/BatchNorm/moving_mean": "conv1.1.running_mean", + "C1/conv0/BatchNorm/moving_variance": "conv1.1.running_var", + "C1/conv1/weights": "conv1.3.weight", + "C1/conv2/weights": "conv1.6.weight"} + tf_mxnet_map.update(tmp_map) + + tf_prefix = "C1/conv1/BatchNorm/" + for key in tf_mxnet_prefix_map.keys(): + if key != 'weights': + tf_mxnet_map[tf_prefix+key] = "conv1.4." + tf_mxnet_prefix_map[key] + + tf_prefix = "C1/conv2/BatchNorm/" + for key in tf_mxnet_prefix_map.keys(): + if key != 'weights': + tf_mxnet_map[tf_prefix+key] = "bn1." + tf_mxnet_prefix_map[key] + + +def update_C2345(scope, bottleneck_nums): + + ''' + bottleneck nums :[3, 4, 6, 3] for res 50 + ''' + for layer, num in enumerate(bottleneck_nums): + + layer += 2 # 0->C2; 1->C3...3->C5 + for i in range(num): + for j in range(3): + tf_prefix = "C%d/bottleneck_%d/conv%d/" % (layer, i, j) + for key in tf_mxnet_prefix_map.keys(): + if key == 'weights': + tf_mxnet_map[tf_prefix + key] = "layer%d.%d.conv%d." % (layer-1, i, j + 1) + tf_mxnet_prefix_map[key] + else: + tf_mxnet_map[tf_prefix + "BatchNorm/" + key] = "layer%d.%d.bn%d." % (layer-1, i, j + 1) + tf_mxnet_prefix_map[key] + if i == 0: + tf_prefix = "C%d/bottleneck_%d/shortcut/" % (layer, i) + for key in tf_mxnet_prefix_map.keys(): + index = 1 + if scope.endswith('b'): + index = 0 + if key == 'weights': + tf_mxnet_map[tf_prefix + key] = "layer%d.%d.downsample.%d." % (layer-1, i, index) + tf_mxnet_prefix_map[key] + else: + tf_mxnet_map[tf_prefix + "BatchNorm/" + key] = "layer%d.%d.downsample.%d." % (layer-1, i, index+1) + tf_mxnet_prefix_map[key] + + + +# def update_C2(bottleneck_num): +# for i in range(bottleneck_num): +# for j in range(3): +# tf_prefix = "C2/bottleneck_%d/conv%d/" % (i, j) +# for key in tf_mxnet_prefix_map.keys(): +# if key == 'weights': +# tf_mxnet_map[tf_prefix+key] = "layer1.%d.conv%d." % (i, j+1) + tf_mxnet_prefix_map[key] +# else: +# tf_mxnet_map[tf_prefix + key] = "layer1.%d.bn%d." % (i, j + 1) + tf_mxnet_prefix_map[key] +# if i==0: +# tf_prefix = "C2/bottleneck_%d/shortcut/" % i +# for key in tf_mxnet_prefix_map.keys(): +# if key == 'weights': +# tf_mxnet_map[tf_prefix + key] = "layer1.%d.downsample.1." % i + tf_mxnet_prefix_map[key] +# else: +# tf_mxnet_map[tf_prefix + key] = "layer1.%d.downsample.2." % i + tf_mxnet_prefix_map[key] +# +# def update_C3(bottleneck_num): +# +# for i in range(bottleneck_num): +# for j in range(3): +# tf_prefix = "C3/bottleneck_%d/conv%d/" % (i, j) +# for key in tf_mxnet_prefix_map.keys(): +# if key == 'weights': +# tf_mxnet_map[tf_prefix+key] = "layer2.%d.conv%d." % (i, j+1) + tf_mxnet_prefix_map[key] +# else: +# tf_mxnet_map[tf_prefix + key] = "layer2.%d.bn%d." % (i, j + 1) + tf_mxnet_prefix_map[key] +# if i == 0: +# tf_prefix = "C3/bottleneck_%d/shortcut/" % i +# for key in tf_mxnet_prefix_map.keys(): +# if key == 'weights': +# tf_mxnet_map[tf_prefix + key] = "layer2.%d.downsample.1." % i + tf_mxnet_prefix_map[key] +# else: +# tf_mxnet_map[tf_prefix + key] = "layer2.%d.downsample.2." % i + tf_mxnet_prefix_map[key] +# +# def update_C4(bottleneck_num): +# for i in range(bottleneck_num): +# for j in range(3): +# tf_prefix = "C4/bottleneck_%d/conv%d/" % (i, j) +# for key in tf_mxnet_prefix_map.keys(): +# if key == 'weights': +# tf_mxnet_map[tf_prefix+key] = "layer3.%d.conv%d." % (i, j+1) + tf_mxnet_prefix_map[key] +# else: +# tf_mxnet_map[tf_prefix + key] = "layer3.%d.bn%d." % (i, j + 1) + tf_mxnet_prefix_map[key] +# if i == 0: +# tf_prefix = "C4/bottleneck_%d/shortcut/" % i +# for key in tf_mxnet_prefix_map.keys(): +# if key == 'weights': +# tf_mxnet_map[tf_prefix + key] = "layer3.%d.downsample.1." % i + tf_mxnet_prefix_map[key] +# else: +# tf_mxnet_map[tf_prefix + key] = "layer3.%d.downsample.2." % i + tf_mxnet_prefix_map[key] +# +# def update_C5(bottleneck_num): +# for i in range(bottleneck_num): +# for j in range(3): +# tf_prefix = "C5/bottleneck_%d/conv%d/" % (i, j) +# for key in tf_mxnet_prefix_map.keys(): +# if key == 'weights': +# tf_mxnet_map[tf_prefix+key] = "layer4.%d.conv%d." % (i, j+1) + tf_mxnet_prefix_map[key] +# else: +# tf_mxnet_map[tf_prefix + key] = "layer4.%d.bn%d." % (i, j + 1) + tf_mxnet_prefix_map[key] +# if i == 0: +# tf_prefix = "C5/bottleneck_%d/shortcut/" % i +# for key in tf_mxnet_prefix_map.keys(): +# if key == 'weights': +# tf_mxnet_map[tf_prefix + key] = "layer4.%d.downsample.1." % i + tf_mxnet_prefix_map[key] +# else: +# tf_mxnet_map[tf_prefix + key] = "layer4.%d.downsample.2." % i + tf_mxnet_prefix_map[key] + + +def get_map(scope, bottleneck_nums, show_mxnettf=True, show_tfmxnet=True): + + if scope.endswith('b'): + update_C1_resnet_v1_b() + elif scope.endswith('d'): + update_C1_resnet_v1_d() + + update_C2345(scope, bottleneck_nums) + update_logitis() + + mxnet_tf_map = {} + for tf_name, mxnet_name in tf_mxnet_map.items(): + mxnet_tf_map[mxnet_name] = tf_name + + if show_mxnettf: + for key in sorted(mxnet_tf_map.keys()): + print ("{} :: {}".format(key, mxnet_tf_map[key])) + print(20*"===") + + if show_tfmxnet: + for key in sorted(tf_mxnet_map.keys()): + print("{} :: {}".format(key, tf_mxnet_map[key])) + print(20 * "===") + + return tf_mxnet_map, mxnet_tf_map + + +if __name__ == "__main__": + get_map(bottleneck_nums=[3, 4, 6, 3]) diff --git a/libs/label_name_dict/__init__.py b/libs/label_name_dict/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/libs/label_name_dict/coco_dict.py b/libs/label_name_dict/coco_dict.py new file mode 100644 index 0000000..d4c190f --- /dev/null +++ b/libs/label_name_dict/coco_dict.py @@ -0,0 +1,54 @@ +# -*- coding: utf-8 -*- + +from __future__ import absolute_import, print_function, division + +class_names = [ + 'back_ground', 'person', 'bicycle', 'car', 'motorcycle', + 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', + 'fire hydrant', 'stop sign', 'parking meter', 'bench', + 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', + 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', + 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', + 'sports ball', 'kite', 'baseball bat', 'baseball glove', + 'skateboard', 'surfboard', 'tennis racket', 'bottle', + 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', + 'banana', 'apple', 'sandwich', 'orange', 'broccoli', + 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', + 'couch', 'potted plant', 'bed', 'dining table', 'toilet', + 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', + 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', + 'book', 'clock', 'vase', 'scissors', 'teddy bear', + 'hair drier', 'toothbrush'] + + +classes_originID = { + 'person': 1, 'bicycle': 2, 'car': 3, 'motorcycle': 4, + 'airplane': 5, 'bus': 6, 'train': 7, 'truck': 8, 'boat': 9, + 'traffic light': 10, 'fire hydrant': 11, 'stop sign': 13, + 'parking meter': 14, 'bench': 15, 'bird': 16, 'cat': 17, + 'dog': 18, 'horse': 19, 'sheep': 20, 'cow': 21, 'elephant': 22, + 'bear': 23, 'zebra': 24, 'giraffe': 25, 'backpack': 27, + 'umbrella': 28, 'handbag': 31, 'tie': 32, 'suitcase': 33, + 'frisbee': 34, 'skis': 35, 'snowboard': 36, 'sports ball': 37, + 'kite': 38, 'baseball bat': 39, 'baseball glove': 40, + 'skateboard': 41, 'surfboard': 42, 'tennis racket': 43, + 'bottle': 44, 'wine glass': 46, 'cup': 47, 'fork': 48, + 'knife': 49, 'spoon': 50, 'bowl': 51, 'banana': 52, 'apple': 53, + 'sandwich': 54, 'orange': 55, 'broccoli': 56, 'carrot': 57, + 'hot dog': 58, 'pizza': 59, 'donut': 60, 'cake': 61, + 'chair': 62, 'couch': 63, 'potted plant': 64, 'bed': 65, + 'dining table': 67, 'toilet': 70, 'tv': 72, 'laptop': 73, + 'mouse': 74, 'remote': 75, 'keyboard': 76, 'cell phone': 77, + 'microwave': 78, 'oven': 79, 'toaster': 80, 'sink': 81, + 'refrigerator': 82, 'book': 84, 'clock': 85, 'vase': 86, + 'scissors': 87, 'teddy bear': 88, 'hair drier': 89, + 'toothbrush': 90} + +originID_classes = {item: key for key, item in classes_originID.items()} +NAME_LABEL_MAP = dict(zip(class_names, range(len(class_names)))) +LABEL_NAME_MAP = dict(zip(range(len(class_names)), class_names)) + +# print (originID_classes) + + + diff --git a/libs/label_name_dict/label_dict.py b/libs/label_name_dict/label_dict.py new file mode 100644 index 0000000..0f679aa --- /dev/null +++ b/libs/label_name_dict/label_dict.py @@ -0,0 +1,164 @@ +# -*- coding: utf-8 -*- +from __future__ import division, print_function, absolute_import + +from libs.configs import cfgs + + +class_names = [ + 'back_ground', 'person', 'bicycle', 'car', 'motorcycle', + 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', + 'fire hydrant', 'stop sign', 'parking meter', 'bench', + 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', + 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', + 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', + 'sports ball', 'kite', 'baseball bat', 'baseball glove', + 'skateboard', 'surfboard', 'tennis racket', 'bottle', + 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', + 'banana', 'apple', 'sandwich', 'orange', 'broccoli', + 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', + 'couch', 'potted plant', 'bed', 'dining table', 'toilet', + 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', + 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', + 'book', 'clock', 'vase', 'scissors', 'teddy bear', + 'hair drier', 'toothbrush'] + +classes_originID = { + 'person': 1, 'bicycle': 2, 'car': 3, 'motorcycle': 4, + 'airplane': 5, 'bus': 6, 'train': 7, 'truck': 8, 'boat': 9, + 'traffic light': 10, 'fire hydrant': 11, 'stop sign': 13, + 'parking meter': 14, 'bench': 15, 'bird': 16, 'cat': 17, + 'dog': 18, 'horse': 19, 'sheep': 20, 'cow': 21, 'elephant': 22, + 'bear': 23, 'zebra': 24, 'giraffe': 25, 'backpack': 27, + 'umbrella': 28, 'handbag': 31, 'tie': 32, 'suitcase': 33, + 'frisbee': 34, 'skis': 35, 'snowboard': 36, 'sports ball': 37, + 'kite': 38, 'baseball bat': 39, 'baseball glove': 40, + 'skateboard': 41, 'surfboard': 42, 'tennis racket': 43, + 'bottle': 44, 'wine glass': 46, 'cup': 47, 'fork': 48, + 'knife': 49, 'spoon': 50, 'bowl': 51, 'banana': 52, 'apple': 53, + 'sandwich': 54, 'orange': 55, 'broccoli': 56, 'carrot': 57, + 'hot dog': 58, 'pizza': 59, 'donut': 60, 'cake': 61, + 'chair': 62, 'couch': 63, 'potted plant': 64, 'bed': 65, + 'dining table': 67, 'toilet': 70, 'tv': 72, 'laptop': 73, + 'mouse': 74, 'remote': 75, 'keyboard': 76, 'cell phone': 77, + 'microwave': 78, 'oven': 79, 'toaster': 80, 'sink': 81, + 'refrigerator': 82, 'book': 84, 'clock': 85, 'vase': 86, + 'scissors': 87, 'teddy bear': 88, 'hair drier': 89, + 'toothbrush': 90} + + +def get_coco_label_dict(): + originID_classes = {item: key for key, item in classes_originID.items()} + NAME_LABEL_MAP = dict(zip(class_names, range(len(class_names)))) + return NAME_LABEL_MAP + +if cfgs.DATASET_NAME == 'ship': + NAME_LABEL_MAP = { + 'back_ground': 0, + 'ship': 1 + } +elif cfgs.DATASET_NAME == 'aeroplane': + NAME_LABEL_MAP = { + 'back_ground': 0, + 'aeroplane': 1 + } +elif cfgs.DATASET_NAME == 'WIDER': + NAME_LABEL_MAP = { + 'back_ground': 0, + 'face': 1 + } +elif cfgs.DATASET_NAME == 'icdar': + NAME_LABEL_MAP = { + 'back_ground': 0, + 'text': 1 + } +elif cfgs.DATASET_NAME.startswith('DOTA'): + NAME_LABEL_MAP = { + 'back_ground': 0, + 'roundabout': 1, + 'tennis-court': 2, + 'swimming-pool': 3, + 'storage-tank': 4, + 'soccer-ball-field': 5, + 'small-vehicle': 6, + 'ship': 7, + 'plane': 8, + 'large-vehicle': 9, + 'helicopter': 10, + 'harbor': 11, + 'ground-track-field': 12, + 'bridge': 13, + 'basketball-court': 14, + 'baseball-diamond': 15 + } +elif cfgs.DATASET_NAME.startswith('DOAI2019'): + NAME_LABEL_MAP = { + 'back_ground': 0, + 'turntable': 1, + 'tennis-court': 2, + 'swimming-pool': 3, + 'storage-tank': 4, + 'soccer-ball-field': 5, + 'small-vehicle': 6, + 'ship': 7, + 'plane': 8, + 'large-vehicle': 9, + 'helicopter': 10, + 'harbor': 11, + 'ground-track-field': 12, + 'bridge': 13, + 'basketball-court': 14, + 'baseball-diamond': 15, + 'container-crane': 16 + } +elif cfgs.DATASET_NAME == 'coco': + NAME_LABEL_MAP = get_coco_label_dict() +elif cfgs.DATASET_NAME == 'pascal': + NAME_LABEL_MAP = { + 'back_ground': 0, + 'aeroplane': 1, + 'bicycle': 2, + 'bird': 3, + 'boat': 4, + 'bottle': 5, + 'bus': 6, + 'car': 7, + 'cat': 8, + 'chair': 9, + 'cow': 10, + 'diningtable': 11, + 'dog': 12, + 'horse': 13, + 'motorbike': 14, + 'person': 15, + 'pottedplant': 16, + 'sheep': 17, + 'sofa': 18, + 'train': 19, + 'tvmonitor': 20 + } +elif cfgs.DATASET_NAME == 'bdd100k': + NAME_LABEL_MAP = { + 'back_ground': 0, + 'bus': 1, + 'traffic light': 2, + 'traffic sign': 3, + 'person': 4, + 'bike': 5, + 'truck': 6, + 'motor': 7, + 'car': 8, + 'train': 9, + 'rider': 10 + } +else: + assert 'please set label dict!' + + +def get_label_name_map(): + reverse_dict = {} + for name, label in NAME_LABEL_MAP.items(): + reverse_dict[label] = name + return reverse_dict + + +LABEl_NAME_MAP = get_label_name_map() \ No newline at end of file diff --git a/libs/label_name_dict/remote_sensing_dict.py b/libs/label_name_dict/remote_sensing_dict.py new file mode 100644 index 0000000..5257bc9 --- /dev/null +++ b/libs/label_name_dict/remote_sensing_dict.py @@ -0,0 +1,15 @@ +# -*- coding: utf-8 -*- + +NAME_LABEL_MAP = { + 'back_ground': 0, + 'building': 1 +} + + +def get_label_name_map(): + reverse_dict = {} + for name, label in NAME_LABEL_MAP.items(): + reverse_dict[label] = name + return reverse_dict + +LABEl_NAME_MAP = get_label_name_map() \ No newline at end of file diff --git a/libs/losses/__init__.py b/libs/losses/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/libs/losses/losses.py b/libs/losses/losses.py new file mode 100644 index 0000000..fccc4b6 --- /dev/null +++ b/libs/losses/losses.py @@ -0,0 +1,209 @@ +# -*- coding: utf-8 -*- + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf +from libs.box_utils import encode_and_decode +from libs.configs import cfgs + + +def _smooth_l1_loss_base(bbox_pred, bbox_targets, sigma=1.0): + ''' + + :param bbox_pred: [-1, 4] in RPN. [-1, cls_num+1, 4] in Fast-rcnn + :param bbox_targets: shape is same as bbox_pred + :param sigma: + :return: + ''' + sigma_2 = sigma**2 + + box_diff = bbox_pred - bbox_targets + + abs_box_diff = tf.abs(box_diff) + + smoothL1_sign = tf.stop_gradient( + tf.to_float(tf.less(abs_box_diff, 1. / sigma_2))) + loss_box = tf.pow(box_diff, 2) * (sigma_2 / 2.0) * smoothL1_sign \ + + (abs_box_diff - (0.5 / sigma_2)) * (1.0 - smoothL1_sign) + return loss_box + + +def smooth_l1_loss_rpn(bbox_pred, bbox_targets, label, sigma=1.0): + ''' + + :param bbox_pred: [-1, 4] + :param bbox_targets: [-1, 4] + :param label: [-1] + :param sigma: + :return: + ''' + value = _smooth_l1_loss_base(bbox_pred, bbox_targets, sigma=sigma) + value = tf.reduce_sum(value, axis=1) # to sum in axis 1 + rpn_positive = tf.where(tf.greater(label, 0)) + + # rpn_select = tf.stop_gradient(rpn_select) # to avoid + selected_value = tf.gather(value, rpn_positive) + non_ignored_mask = tf.stop_gradient( + 1.0 - tf.to_float(tf.equal(label, -1))) # positve is 1.0 others is 0.0 + + bbox_loss = tf.reduce_sum(selected_value) / tf.maximum(1.0, tf.reduce_sum(non_ignored_mask)) + + return bbox_loss + + +def smooth_l1_loss_rcnn(bbox_pred, bbox_targets, label, num_classes, sigma=1.0): + ''' + + :param bbox_pred: [-1, (cfgs.CLS_NUM +1) * 4] + :param bbox_targets:[-1, (cfgs.CLS_NUM +1) * 4] + :param label:[-1] + :param num_classes: + :param sigma: + :return: + ''' + + outside_mask = tf.stop_gradient(tf.to_float(tf.greater(label, 0))) + + bbox_pred = tf.reshape(bbox_pred, [-1, num_classes, 4]) + bbox_targets = tf.reshape(bbox_targets, [-1, num_classes, 4]) + + value = _smooth_l1_loss_base(bbox_pred, + bbox_targets, + sigma=sigma) + value = tf.reduce_sum(value, 2) + value = tf.reshape(value, [-1, num_classes]) + + inside_mask = tf.one_hot(tf.reshape(label, [-1, 1]), + depth=num_classes, axis=1) + + inside_mask = tf.stop_gradient( + tf.to_float(tf.reshape(inside_mask, [-1, num_classes]))) + + normalizer = tf.to_float(tf.shape(bbox_pred)[0]) + bbox_loss = tf.reduce_sum( + tf.reduce_sum(value * inside_mask, 1)*outside_mask) / normalizer + + return bbox_loss + + +def smooth_l1_loss_rcnn_iou(bbox_pred, bbox_targets, label, ious, num_classes, sigma=1.0): + ''' + + :param bbox_pred: [-1, (cfgs.CLS_NUM +1) * 4] + :param bbox_targets:[-1, (cfgs.CLS_NUM +1) * 4] + :param label:[-1] + :param num_classes: + :param sigma: + :return: + ''' + + outside_mask = tf.stop_gradient(tf.to_float(tf.greater(label, 0))) + + ious = tf.reshape(ious, [-1, ]) + ious = tf.stop_gradient(ious) + + bbox_pred = tf.reshape(bbox_pred, [-1, num_classes, 4]) + bbox_targets = tf.reshape(bbox_targets, [-1, num_classes, 4]) + + value = _smooth_l1_loss_base(bbox_pred, + bbox_targets, + sigma=sigma) + value = tf.reduce_sum(value, 2) + value = tf.reshape(value, [-1, num_classes]) + + inside_mask = tf.one_hot(tf.reshape(label, [-1, 1]), + depth=num_classes, axis=1) + + inside_mask = tf.stop_gradient( + tf.to_float(tf.reshape(inside_mask, [-1, num_classes]))) + + normalizer = tf.to_float(tf.shape(bbox_pred)[0]) + + tmp = tf.reduce_sum(value * inside_mask, 1) + tmp = tf.stop_gradient(tmp) + iou_factor = (1 - ious) / tmp + iou_factor = tf.stop_gradient(iou_factor) + + bbox_loss = tf.reduce_sum( + tf.reduce_sum(value * inside_mask, 1)*outside_mask*iou_factor) / normalizer + + return bbox_loss + + +def iou_loss_(label, ious): + outside_mask = tf.stop_gradient(tf.to_float(tf.greater(label, 0))) + + ious = tf.reshape(ious, [-1, ]) + + normalizer = tf.to_float(tf.shape(ious)[0]) + + bbox_loss = tf.reduce_sum(-tf.log(ious+1e-5) * outside_mask) / normalizer + + return bbox_loss + + +def iou_loss(bbox_pred, bbox_targets, gtbox, label, num_classes): + """ + :param bbox_pred: [-1, (cfgs.CLS_NUM +1) * 4] + :param bbox_targets: [-1, (cfgs.CLS_NUM +1) * 4] + :param gtbox: [-1, 4] + :param label: [-1] + :param num_classes: + :return: + """ + + gtbox = tf.tile(gtbox, [1, num_classes]) + bbox_pred = tf.reshape(bbox_pred, [-1, 4]) + bbox_targets = tf.reshape(bbox_targets, [-1, 4]) + gtbox = tf.reshape(gtbox, [-1, 4]) + pred_box = encode_and_decode.decode_boxes(bbox_pred, gtbox, scale_factors=cfgs.ROI_SCALE_FACTORS) + gt_box = encode_and_decode.decode_boxes(bbox_targets, gtbox, scale_factors=cfgs.ROI_SCALE_FACTORS) + + inside_mask = tf.one_hot(tf.reshape(label, [-1, 1]), + depth=num_classes, axis=1) + + inside_mask = tf.reshape(inside_mask, [-1, ]) + iou = iou_calculate(pred_box, gt_box) + iou_loss = tf.reduce_mean(-tf.log(iou*inside_mask+1e-5)) + + pred = tf.cast(tf.greater(iou, 0.5), tf.float32) + pred = tf.reshape(pred, [-1, num_classes]) + pred_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=pred, labels=label)) + + loss = iou_loss * 0.1 + pred_loss * 0.0 + return loss + + +def iou_calculate(boxes_1, boxes_2): + + with tf.name_scope('iou_caculate'): + + xmin_1, ymin_1, xmax_1, ymax_1 = tf.unstack(boxes_1, axis=1) # ymin_1 shape is [N, 1].. + + xmin_2, ymin_2, xmax_2, ymax_2 = tf.unstack(boxes_2, axis=1) # ymin_2 shape is [M, ].. + + max_xmin = tf.maximum(xmin_1, xmin_2) + min_xmax = tf.minimum(xmax_1, xmax_2) + + max_ymin = tf.maximum(ymin_1, ymin_2) + min_ymax = tf.minimum(ymax_1, ymax_2) + + overlap_h = tf.maximum(0., min_ymax - max_ymin) # avoid h < 0 + overlap_w = tf.maximum(0., min_xmax - max_xmin) + + overlaps = overlap_h * overlap_w + + area_1 = (xmax_1 - xmin_1) * (ymax_1 - ymin_1) # [N, 1] + area_2 = (xmax_2 - xmin_2) * (ymax_2 - ymin_2) # [M, ] + + iou = overlaps / (area_1 + area_2 - overlaps) + + return iou + + +def sum_ohem_loss(cls_score, label, bbox_pred, bbox_targets, + nr_ohem_sampling, nr_classes, sigma=1.0): + + raise NotImplementedError('not implement Now. YJR will implemetn in the future') \ No newline at end of file diff --git a/libs/networks/__init__.py b/libs/networks/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/libs/networks/build_whole_network.py b/libs/networks/build_whole_network.py new file mode 100644 index 0000000..197359b --- /dev/null +++ b/libs/networks/build_whole_network.py @@ -0,0 +1,749 @@ +# -*-coding: utf-8 -*- + +from __future__ import absolute_import, division, print_function + +import os +import tensorflow as tf +import tensorflow.contrib.slim as slim +import numpy as np + +from libs.networks import resnet, resnet_gluoncv +from libs.networks import mobilenet_v2 +from libs.box_utils import encode_and_decode +from libs.box_utils import boxes_utils +from libs.box_utils import anchor_utils +from libs.configs import cfgs +from libs.losses import losses +from libs.box_utils import show_box_in_tensor +from libs.detection_oprations.proposal_opr import postprocess_rpn_proposals +from libs.detection_oprations.anchor_target_layer_without_boxweight import anchor_target_layer +from libs.detection_oprations.proposal_target_layer_cascade import proposal_target_layer + + +class DetectionNetwork(object): + + def __init__(self, base_network_name, is_training): + + self.base_network_name = base_network_name + self.is_training = is_training + self.num_anchors_per_location = len(cfgs.ANCHOR_SCALES) * len(cfgs.ANCHOR_RATIOS) + + def build_base_network(self, input_img_batch): + + if self.base_network_name.startswith('resnet_v1'): + return resnet.resnet_base(input_img_batch, scope_name=self.base_network_name, is_training=self.is_training) + elif self.base_network_name in ['resnet101_v1d', 'resnet50_v1d']: + return resnet_gluoncv.resnet_base(input_img_batch, scope_name=self.base_network_name, + is_training=self.is_training) + elif self.base_network_name.startswith('MobilenetV2'): + return mobilenet_v2.mobilenetv2_base(input_img_batch, is_training=self.is_training) + + else: + raise ValueError('Sry, we only support resnet or mobilenet_v2') + + def postprocess_cascadercnn(self, rois, bbox_pred, cls_score, stage): + ''' + :param rois:[-1, 4] + :param bbox_ppred: bbox_ppred: [-1, 4] + :param scores: [-1, 1] + :return: + ''' + # rois = tf.stop_gradient(rois) + # bbox_pred = tf.stop_gradient(bbox_pred) + bbox_pred_ins = tf.reshape(bbox_pred, [-1, cfgs.CLASS_NUM + 1, 4]) + + # only keep a box which score is the bigest + keep_abox = tf.argmax(cls_score, axis=1) + keep_inds = tf.reshape(tf.transpose(tf.stack([tf.cumsum(tf.ones_like(keep_abox)) - 1, keep_abox])), + [-1, 2]) + bbox_pred_fliter = tf.reshape(tf.gather_nd(bbox_pred_ins, keep_inds), [-1, 4]) + + # decode boxes + decoded_boxes = encode_and_decode.decode_boxes(encoded_boxes=bbox_pred_fliter, + reference_boxes=rois, + scale_factors=cfgs.ROI_SCALE_FACTORS[stage-1]) + + return decoded_boxes + + def postprocess_fastrcnn(self, rois, bbox_ppred, scores, img_shape): + ''' + + :param rois:[-1, 4] + :param bbox_ppred: [-1, (cfgs.Class_num+1) * 4] + :param scores: [-1, cfgs.Class_num + 1] + :return: + ''' + + with tf.name_scope('postprocess_fastrcnn'): + rois = tf.stop_gradient(rois) + scores = tf.stop_gradient(scores) + bbox_ppred = tf.reshape(bbox_ppred, [-1, cfgs.CLASS_NUM + 1, 4]) + bbox_ppred = tf.stop_gradient(bbox_ppred) + + bbox_pred_list = tf.unstack(bbox_ppred, axis=1) + score_list = tf.unstack(scores, axis=1) + + allclasses_boxes = [] + allclasses_scores = [] + categories = [] + for i in range(1, cfgs.CLASS_NUM + 1): + # 1. decode boxes in each class + tmp_encoded_box = bbox_pred_list[i] + tmp_score = score_list[i] + tmp_decoded_boxes = encode_and_decode.decode_boxes(encoded_boxes=tmp_encoded_box, + reference_boxes=rois, + scale_factors=cfgs.ROI_SCALE_FACTORS[-1]) + # tmp_decoded_boxes = encode_and_decode.decode_boxes(boxes=rois, + # deltas=tmp_encoded_box, + # scale_factor=cfgs.ROI_SCALE_FACTORS) + + # 2. clip to img boundaries + tmp_decoded_boxes = boxes_utils.clip_boxes_to_img_boundaries(decode_boxes=tmp_decoded_boxes, + img_shape=img_shape) + + # 3. NMS + keep = tf.image.non_max_suppression( + boxes=tmp_decoded_boxes, + scores=tmp_score, + max_output_size=cfgs.FAST_RCNN_NMS_MAX_BOXES_PER_CLASS, + iou_threshold=cfgs.FAST_RCNN_NMS_IOU_THRESHOLD) + + perclass_boxes = tf.gather(tmp_decoded_boxes, keep) + perclass_scores = tf.gather(tmp_score, keep) + + allclasses_boxes.append(perclass_boxes) + allclasses_scores.append(perclass_scores) + categories.append(tf.ones_like(perclass_scores) * i) + + final_boxes = tf.concat(allclasses_boxes, axis=0) + final_scores = tf.concat(allclasses_scores, axis=0) + final_category = tf.concat(categories, axis=0) + + if self.is_training: + ''' + in training. We should show the detecitons in the tensorboard. So we add this. + ''' + kept_indices = tf.reshape(tf.where(tf.greater_equal(final_scores, cfgs.SHOW_SCORE_THRSHOLD)), [-1]) + + final_boxes = tf.gather(final_boxes, kept_indices) + final_scores = tf.gather(final_scores, kept_indices) + final_category = tf.gather(final_category, kept_indices) + + return final_boxes, final_scores, final_category + + def roi_pooling(self, feature_maps, rois, img_shape, scope): + ''' + Here use roi warping as roi_pooling + + :param featuremaps_dict: feature map to crop + :param rois: shape is [-1, 4]. [x1, y1, x2, y2] + :return: + ''' + + with tf.variable_scope('ROI_Warping_' + scope): + img_h, img_w = tf.cast(img_shape[1], tf.float32), tf.cast(img_shape[2], tf.float32) + N = tf.shape(rois)[0] + x1, y1, x2, y2 = tf.unstack(rois, axis=1) + + normalized_x1 = x1 / img_w + normalized_x2 = x2 / img_w + normalized_y1 = y1 / img_h + normalized_y2 = y2 / img_h + + normalized_rois = tf.transpose( + tf.stack([normalized_y1, normalized_x1, normalized_y2, normalized_x2]), name='get_normalized_rois') + + normalized_rois = tf.stop_gradient(normalized_rois) + + cropped_roi_features = tf.image.crop_and_resize(feature_maps, normalized_rois, + box_ind=tf.zeros(shape=[N, ], + dtype=tf.int32), + crop_size=[cfgs.ROI_SIZE, cfgs.ROI_SIZE], + name='CROP_AND_RESIZE' + ) + roi_features = slim.max_pool2d(cropped_roi_features, + [cfgs.ROI_POOL_KERNEL_SIZE, cfgs.ROI_POOL_KERNEL_SIZE], + stride=cfgs.ROI_POOL_KERNEL_SIZE) + + return roi_features + + def build_fastrcnn(self, P_list, rois_list, img_shape): + + with tf.variable_scope('Fast-RCNN'): + # 5. ROI Pooling + with tf.variable_scope('rois_pooling'): + pooled_features_list = [] + for level_name, p, rois in zip(cfgs.LEVLES, P_list, rois_list): # exclude P6_rois + # p = tf.Print(p, [tf.shape(p)], summarize=10, message=level_name+'SHPAE***') + pooled_features = self.roi_pooling(feature_maps=p, rois=rois, img_shape=img_shape, + scope=level_name) + pooled_features_list.append(pooled_features) + + pooled_features = tf.concat(pooled_features_list, axis=0) # [minibatch_size, H, W, C] + + # 6. inferecne rois in Fast-RCNN to obtain fc_flatten features + if self.base_network_name.startswith('resnet'): + fc_flatten = resnet.restnet_head(inputs=pooled_features, + is_training=self.is_training, + scope_name=self.base_network_name) + elif self.base_network_name.startswith('Mobile'): + fc_flatten = mobilenet_v2.mobilenetv2_head(inputs=pooled_features, + is_training=self.is_training) + else: + raise NotImplementedError('only support resnet and mobilenet') + + # 7. cls and reg in Fast-RCNN + with slim.arg_scope([slim.fully_connected], weights_regularizer=slim.l2_regularizer(cfgs.WEIGHT_DECAY)): + + cls_score = slim.fully_connected(fc_flatten, + num_outputs=cfgs.CLASS_NUM + 1, + weights_initializer=cfgs.INITIALIZER, + activation_fn=None, trainable=self.is_training, + scope='cls_fc') + + bbox_pred = slim.fully_connected(fc_flatten, + num_outputs=(cfgs.CLASS_NUM + 1) * 4, + activation_fn=None, trainable=self.is_training, + weights_initializer=tf.random_normal_initializer(mean=0.0, stddev=0.001), + scope='reg_fc') + + # for convient. It also produce (cls_num +1) bboxes + cls_score = tf.reshape(cls_score, [-1, cfgs.CLASS_NUM + 1]) + bbox_pred = tf.reshape(bbox_pred, [-1, 4 * (cfgs.CLASS_NUM + 1)]) + + return bbox_pred, cls_score + + def assign_levels(self, all_rois, labels=None, bbox_targets=None): + ''' + + :param all_rois: + :param labels: + :param bbox_targets: + :return: + ''' + with tf.name_scope('assign_levels'): + # all_rois = tf.Print(all_rois, [tf.shape(all_rois)], summarize=10, message='ALL_ROIS_SHAPE*****') + xmin, ymin, xmax, ymax = tf.unstack(all_rois, axis=1) + + h = tf.maximum(0., ymax - ymin) + w = tf.maximum(0., xmax - xmin) + + levels = tf.floor(4. + tf.log(tf.sqrt(w * h + 1e-8) / 224.0) / tf.log(2.)) # 4 + log_2(***) + # use floor instead of round + + min_level = int(cfgs.LEVLES[0][-1]) + max_level = min(5, int(cfgs.LEVLES[-1][-1])) + levels = tf.maximum(levels, tf.ones_like(levels) * min_level) # level minimum is 2 + levels = tf.minimum(levels, tf.ones_like(levels) * max_level) # level maximum is 5 + + levels = tf.stop_gradient(tf.reshape(levels, [-1])) + + def get_rois(levels, level_i, rois, labels, bbox_targets): + + level_i_indices = tf.reshape(tf.where(tf.equal(levels, level_i)), [-1]) + # level_i_indices = tf.Print(level_i_indices, [tf.shape(tf.where(tf.equal(levels, level_i)))[0]], message="SHAPE%d***"%level_i, + # summarize=10) + tf.summary.scalar('LEVEL/LEVEL_%d_rois_NUM' % level_i, tf.shape(level_i_indices)[0]) + level_i_rois = tf.gather(rois, level_i_indices) + + if self.is_training: + if cfgs.CUDA9: + # Note: for cuda 9 + level_i_rois = tf.stop_gradient(level_i_rois) + level_i_labels = tf.gather(labels, level_i_indices) + + level_i_targets = tf.gather(bbox_targets, level_i_indices) + else: + + # Note: for cuda 8 + level_i_rois = tf.stop_gradient(tf.concat([level_i_rois, [[0, 0, 0., 0.]]], axis=0)) + # to avoid the num of level i rois is 0.0, which will broken the BP in tf + + level_i_labels = tf.gather(labels, level_i_indices) + level_i_labels = tf.stop_gradient(tf.concat([level_i_labels, [0]], axis=0)) + + level_i_targets = tf.gather(bbox_targets, level_i_indices) + level_i_targets = tf.stop_gradient(tf.concat([level_i_targets, + tf.zeros(shape=(1, 4 * (cfgs.CLASS_NUM + 1)), + dtype=tf.float32)], axis=0)) + + return level_i_rois, level_i_labels, level_i_targets + else: + if not cfgs.CUDA9: + # Note: for cuda 8 + level_i_rois = tf.concat([level_i_rois, [[0, 0, 0., 0.]]], axis=0) + return level_i_rois, None, None + + rois_list = [] + labels_list = [] + targets_list = [] + for i in range(min_level, max_level + 1): + P_i_rois, P_i_labels, P_i_targets = get_rois(levels, level_i=i, rois=all_rois, + labels=labels, + bbox_targets=bbox_targets) + rois_list.append(P_i_rois) + labels_list.append(P_i_labels) + targets_list.append(P_i_targets) + + if self.is_training: + all_labels = tf.concat(labels_list, axis=0) + all_targets = tf.concat(targets_list, axis=0) + return rois_list, all_labels, all_targets + else: + return rois_list # [P2_rois, P3_rois, P4_rois, P5_rois] Note: P6 do not assign rois + + def add_anchor_img_smry(self, img, anchors, labels): + + positive_anchor_indices = tf.reshape(tf.where(tf.greater_equal(labels, 1)), [-1]) + negative_anchor_indices = tf.reshape(tf.where(tf.equal(labels, 0)), [-1]) + + positive_anchor = tf.gather(anchors, positive_anchor_indices) + negative_anchor = tf.gather(anchors, negative_anchor_indices) + + pos_in_img = show_box_in_tensor.only_draw_boxes(img_batch=img, + boxes=positive_anchor) + neg_in_img = show_box_in_tensor.only_draw_boxes(img_batch=img, + boxes=negative_anchor) + + tf.summary.image('positive_anchor', pos_in_img) + tf.summary.image('negative_anchors', neg_in_img) + + def add_roi_batch_img_smry(self, img, rois, labels): + positive_roi_indices = tf.reshape(tf.where(tf.greater_equal(labels, 1)), [-1]) + + negative_roi_indices = tf.reshape(tf.where(tf.equal(labels, 0)), [-1]) + + pos_roi = tf.gather(rois, positive_roi_indices) + neg_roi = tf.gather(rois, negative_roi_indices) + + pos_in_img = show_box_in_tensor.only_draw_boxes(img_batch=img, + boxes=pos_roi) + neg_in_img = show_box_in_tensor.only_draw_boxes(img_batch=img, + boxes=neg_roi) + tf.summary.image('pos_rois', pos_in_img) + tf.summary.image('neg_rois', neg_in_img) + + def build_loss(self, rpn_box_pred, rpn_bbox_targets, rpn_cls_score, rpn_labels, + bbox_pred, bbox_targets, cls_score, labels): + ''' + + :param rpn_box_pred: [-1, 4] + :param rpn_bbox_targets: [-1, 4] + :param rpn_cls_score: [-1] + :param rpn_labels: [-1] + :param bbox_pred: [-1, 4*(cls_num+1)] + :param bbox_targets: [-1, 4*(cls_num+1)] + :param cls_score: [-1, cls_num+1] + :param labels: [-1] + :return: + ''' + with tf.variable_scope('build_loss'): + + rpn_cls_loss = 0.0 + rpn_bbox_loss = 0.0 + if rpn_box_pred is not None: + with tf.variable_scope('rpn_loss'): + + rpn_bbox_loss = losses.smooth_l1_loss_rpn(bbox_pred=rpn_box_pred, + bbox_targets=rpn_bbox_targets, + label=rpn_labels, + sigma=cfgs.RPN_SIGMA) + + rpn_select = tf.reshape(tf.where(tf.not_equal(rpn_labels, -1)), [-1]) + rpn_cls_score = tf.reshape(tf.gather(rpn_cls_score, rpn_select), [-1, 2]) + rpn_labels = tf.reshape(tf.gather(rpn_labels, rpn_select), [-1]) + rpn_cls_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=rpn_cls_score, + labels=rpn_labels)) + + rpn_cls_loss = rpn_cls_loss * cfgs.RPN_CLASSIFICATION_LOSS_WEIGHT + rpn_bbox_loss = rpn_bbox_loss * cfgs.RPN_LOCATION_LOSS_WEIGHT + + with tf.variable_scope('FastRCNN_loss'): + if not cfgs.FAST_RCNN_MINIBATCH_SIZE == -1: + bbox_loss = losses.smooth_l1_loss_rcnn(bbox_pred=bbox_pred, + bbox_targets=bbox_targets, + label=labels, + num_classes=cfgs.CLASS_NUM + 1, + sigma=cfgs.FASTRCNN_SIGMA) + + cls_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits( + logits=cls_score, + labels=labels)) # beacause already sample before + else: + ''' + applying OHEM here + ''' + print(20 * "@@") + print("@@" + 10 * " " + "TRAIN WITH OHEM ...") + print(20 * "@@") + cls_loss = bbox_loss = losses.sum_ohem_loss( + cls_score=cls_score, + label=labels, + bbox_targets=bbox_targets, + nr_ohem_sampling=128, + nr_classes=cfgs.CLASS_NUM + 1) + cls_loss = cls_loss * cfgs.FAST_RCNN_CLASSIFICATION_LOSS_WEIGHT + # bbox_loss = bbox_loss * cfgs.FAST_RCNN_LOCATION_LOSS_WEIGHT + loss_dict = { + 'rpn_cls_loss': rpn_cls_loss, + 'rpn_loc_loss': rpn_bbox_loss, + 'fastrcnn_cls_loss': cls_loss, + 'fastrcnn_loc_loss': bbox_loss + } + return loss_dict + + def cascade_rcnn(self, cascade_rois, gtboxes_batch, input_img_batch, P_list, + img_shape, fg_threshold, fpn_box_pred, fpn_bbox_targets, + fpn_cls_score, fpn_labels, stage=None): + + with tf.variable_scope('cascade_stage{}'.format(stage)): + if self.is_training: + # with tf.control_dependencies([fpn_labels]): + with tf.variable_scope('sample_RCNN_minibatch'): + cascade_rois, labels, bbox_targets, gtboxes = \ + tf.py_func(proposal_target_layer, + [cascade_rois, gtboxes_batch, fg_threshold, stage], + [tf.float32, tf.float32, tf.float32, tf.float32]) + cascade_rois = tf.reshape(cascade_rois, [-1, 4]) + labels = tf.to_int32(labels) + labels = tf.reshape(labels, [-1]) + bbox_targets = tf.reshape(bbox_targets, [-1, 4 * (cfgs.CLASS_NUM + 1)]) + self.add_roi_batch_img_smry(input_img_batch, cascade_rois, labels) + if self.is_training: + rois_list, labels, bbox_targets = self.assign_levels(all_rois=cascade_rois, + labels=labels, + bbox_targets=bbox_targets) + else: + rois_list = self.assign_levels(all_rois=cascade_rois) + + # ----------------------------------------------------------------------------------------------------# + # Fast-RCNN # + # ----------------------------------------------------------------------------------------------------# + + # 5. build Fast-RCNN + bbox_pred, cls_score = self.build_fastrcnn(P_list=P_list, + rois_list=rois_list, + img_shape=img_shape) + + cls_prob = slim.softmax(cls_score, 'cls_prob') + + # ----------------------------------------------add smry---------------------------------------------- + if self.is_training: + cls_category = tf.argmax(cls_prob, axis=1) + fast_acc = tf.reduce_mean(tf.to_float(tf.equal(cls_category, tf.to_int64(labels)))) + tf.summary.scalar('ACC/fast_acc', fast_acc) + + cascade_rois = tf.concat(rois_list, axis=0, name='concat_rois') + # 6. postprocess_fastrcnn + if stage != 3: + cascade_rois = self.postprocess_cascadercnn(rois=cascade_rois, + bbox_pred=bbox_pred, + cls_score=cls_score, + stage=stage) + + if self.is_training: + loss_dict = self.build_loss(rpn_box_pred=None, + rpn_bbox_targets=None, + rpn_cls_score=None, + rpn_labels=None, + bbox_pred=bbox_pred, + bbox_targets=bbox_targets, + cls_score=cls_score, + labels=labels) + return cascade_rois, bbox_pred, cls_prob, loss_dict + else: + return cascade_rois, bbox_pred, cls_prob + + else: + if self.is_training: + loss_dict = self.build_loss(rpn_box_pred=fpn_box_pred, + rpn_bbox_targets=fpn_bbox_targets, + rpn_cls_score=fpn_cls_score, + rpn_labels=fpn_labels, + bbox_pred=bbox_pred, + bbox_targets=bbox_targets, + cls_score=cls_score, + labels=labels) + return cascade_rois, bbox_pred, cls_prob, loss_dict + else: + return cascade_rois, bbox_pred, cls_prob + + def build_whole_detection_network(self, input_img_batch, gtboxes_batch): + + if self.is_training: + # ensure shape is [M, 5] + gtboxes_batch = tf.reshape(gtboxes_batch, [-1, 5]) + gtboxes_batch = tf.cast(gtboxes_batch, tf.float32) + + img_shape = tf.shape(input_img_batch) + + # 1. build base network + P_list = self.build_base_network(input_img_batch) + + # 2. build rpn + with tf.variable_scope('build_rpn', + regularizer=slim.l2_regularizer(cfgs.WEIGHT_DECAY)): + + fpn_cls_score = [] + fpn_box_pred = [] + for level_name, p in zip(cfgs.LEVLES, P_list): + if cfgs.SHARE_HEADS: + reuse_flag = None if level_name == cfgs.LEVLES[0] else True + scope_list = ['rpn_conv/3x3', 'rpn_cls_score', 'rpn_bbox_pred'] + else: + reuse_flag = None + scope_list = ['rpn_conv/3x3_%s' % level_name, 'rpn_cls_score_%s' % level_name, + 'rpn_bbox_pred_%s' % level_name] + rpn_conv3x3 = slim.conv2d( + p, 512, [3, 3], + trainable=self.is_training, weights_initializer=cfgs.INITIALIZER, padding="SAME", + activation_fn=tf.nn.relu, + scope=scope_list[0], + reuse=reuse_flag) + rpn_cls_score = slim.conv2d(rpn_conv3x3, self.num_anchors_per_location * 2, [1, 1], stride=1, + trainable=self.is_training, weights_initializer=cfgs.INITIALIZER, + activation_fn=None, padding="VALID", + scope=scope_list[1], + reuse=reuse_flag) + rpn_box_pred = slim.conv2d(rpn_conv3x3, self.num_anchors_per_location * 4, [1, 1], stride=1, + trainable=self.is_training, weights_initializer=cfgs.BBOX_INITIALIZER, + activation_fn=None, padding="VALID", + scope=scope_list[2], + reuse=reuse_flag) + rpn_box_pred = tf.reshape(rpn_box_pred, [-1, 4]) + rpn_cls_score = tf.reshape(rpn_cls_score, [-1, 2]) + + fpn_cls_score.append(rpn_cls_score) + fpn_box_pred.append(rpn_box_pred) + + fpn_cls_score = tf.concat(fpn_cls_score, axis=0, name='fpn_cls_score') + fpn_box_pred = tf.concat(fpn_box_pred, axis=0, name='fpn_box_pred') + fpn_cls_prob = slim.softmax(fpn_cls_score, scope='fpn_cls_prob') + + # 3. generate_anchors + all_anchors = [] + for i in range(len(cfgs.LEVLES)): + level_name, p = cfgs.LEVLES[i], P_list[i] + + p_h, p_w = tf.shape(p)[1], tf.shape(p)[2] + + featuremap_height = tf.cast(p_h, tf.float32) + featuremap_width = tf.cast(p_w, tf.float32) + anchors = anchor_utils.make_anchors(base_anchor_size=cfgs.BASE_ANCHOR_SIZE_LIST[i], + anchor_scales=cfgs.ANCHOR_SCALES, + anchor_ratios=cfgs.ANCHOR_RATIOS, + featuremap_height=featuremap_height, + featuremap_width=featuremap_width, + stride=cfgs.ANCHOR_STRIDE_LIST[i], + name="make_anchors_for%s" % level_name) + all_anchors.append(anchors) + all_anchors = tf.concat(all_anchors, axis=0, name='all_anchors_of_FPN') + + # 4. postprocess rpn proposals. such as: decode, clip, NMS + with tf.variable_scope('postprocess_FPN'): + cascade_rois, roi_scores = postprocess_rpn_proposals(rpn_bbox_pred=fpn_box_pred, + rpn_cls_prob=fpn_cls_prob, + img_shape=img_shape, + anchors=all_anchors, + is_training=self.is_training) + # rois shape [-1, 4] + # +++++++++++++++++++++++++++++++++++++add img smry+++++++++++++++++++++++++++++++++++++++++++++++++++++++ + + if self.is_training: + score_gre_05 = tf.reshape(tf.where(tf.greater_equal(roi_scores, 0.5)), [-1]) + score_gre_05_rois = tf.gather(cascade_rois, score_gre_05) + score_gre_05_score = tf.gather(roi_scores, score_gre_05) + score_gre_05_in_img = show_box_in_tensor.draw_boxes_with_scores(img_batch=input_img_batch, + boxes=score_gre_05_rois, + scores=score_gre_05_score) + tf.summary.image('score_greater_05_rois', score_gre_05_in_img) + # ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ + + if self.is_training: + with tf.variable_scope('sample_anchors_minibatch'): + fpn_labels, fpn_bbox_targets = \ + tf.py_func( + anchor_target_layer, + [gtboxes_batch, img_shape, all_anchors], + [tf.float32, tf.float32]) + fpn_bbox_targets = tf.reshape(fpn_bbox_targets, [-1, 4]) + fpn_labels = tf.to_int32(fpn_labels, name="to_int32") + fpn_labels = tf.reshape(fpn_labels, [-1]) + self.add_anchor_img_smry(input_img_batch, all_anchors, fpn_labels) + + # --------------------------------------add smry----------------------------------------------------------- + + fpn_cls_category = tf.argmax(fpn_cls_prob, axis=1) + kept_rpppn = tf.reshape(tf.where(tf.not_equal(fpn_labels, -1)), [-1]) + fpn_cls_category = tf.gather(fpn_cls_category, kept_rpppn) + acc = tf.reduce_mean(tf.to_float(tf.equal(fpn_cls_category, + tf.to_int64(tf.gather(fpn_labels, kept_rpppn))))) + tf.summary.scalar('ACC/fpn_accuracy', acc) + + # cascade rcnn + total_loss_dict = {} + fg_thresholds = [0.5, 0.6, 0.7] + for i in range(len(fg_thresholds)-1): + if self.is_training: + cascade_rois, _, _, loss_dict = self.cascade_rcnn(cascade_rois, gtboxes_batch, input_img_batch, + P_list, + img_shape, fg_thresholds[i], + fpn_box_pred, fpn_bbox_targets, fpn_cls_score, fpn_labels, + stage=i + 1) + + for k in loss_dict.keys(): + if k not in total_loss_dict.keys(): + total_loss_dict[k] = loss_dict[k] + else: + total_loss_dict[k] += loss_dict[k] + else: + cascade_rois, _, _ = self.cascade_rcnn(cascade_rois, gtboxes_batch, input_img_batch, + P_list, + img_shape, fg_thresholds[i], + fpn_box_pred, None, + fpn_cls_score, None, + stage=i+1) + if self.is_training: + cascade_rois, bbox_pred, cls_prob, loss_dict = self.cascade_rcnn(cascade_rois, gtboxes_batch, + input_img_batch, + P_list, + img_shape, fg_thresholds[-1], + fpn_box_pred, fpn_bbox_targets, + fpn_cls_score, fpn_labels, + stage=len(fg_thresholds)) + + final_bbox, final_scores, final_category = self.postprocess_fastrcnn(rois=cascade_rois, + bbox_ppred=bbox_pred, + scores=cls_prob, + img_shape=img_shape) + + for k in loss_dict.keys(): + if k not in total_loss_dict.keys(): + total_loss_dict[k] = loss_dict[k] + else: + total_loss_dict[k] += loss_dict[k] + return final_bbox, final_scores, final_category, total_loss_dict + else: + cascade_rois, bbox_pred, cls_prob_stage3 = self.cascade_rcnn(cascade_rois, gtboxes_batch, + input_img_batch, + P_list, + img_shape, fg_thresholds[-1], + fpn_box_pred, None, + fpn_cls_score, None, + stage=len(fg_thresholds)) + cascade_rois_ = self.postprocess_cascadercnn(rois=cascade_rois, + bbox_pred=bbox_pred, + cls_score=cls_prob_stage3, + stage=len(fg_thresholds)) + + with slim.arg_scope([slim.conv2d, slim.conv2d_in_plane, + slim.conv2d_transpose, slim.separable_conv2d, slim.fully_connected], + reuse=True): + _, _, cls_prob_stage1 = self.cascade_rcnn(cascade_rois_, gtboxes_batch, + input_img_batch, + P_list, + img_shape, fg_thresholds[0], + fpn_box_pred, None, + fpn_cls_score, None, + stage=1) + + _, _, cls_prob_stage2 = self.cascade_rcnn(cascade_rois_, gtboxes_batch, + input_img_batch, + P_list, + img_shape, fg_thresholds[1], + fpn_box_pred, None, + fpn_cls_score, None, + stage=2) + + cls_prob = 0.8 * cls_prob_stage3 + 0.1 * cls_prob_stage2 + 0.1 * cls_prob_stage1 + + final_bbox, final_scores, final_category = self.postprocess_fastrcnn(rois=cascade_rois, + bbox_ppred=bbox_pred, + scores=cls_prob, + img_shape=img_shape) + + return final_bbox, final_scores, final_category + + def get_restorer(self): + checkpoint_path = tf.train.latest_checkpoint(os.path.join(cfgs.TRAINED_CKPT, cfgs.VERSION)) + + if checkpoint_path != None: + restorer = tf.train.Saver() + print("model restore from :", checkpoint_path) + else: + checkpoint_path = cfgs.PRETRAINED_CKPT + print("model restore from pretrained mode, path is :", checkpoint_path) + + model_variables = slim.get_model_variables() + + # for var in model_variables: + # print(var.name) + # print(20*"__++__++__") + + def name_in_ckpt_rpn(var): + return var.op.name + + def name_in_ckpt_fastrcnn_head(var): + ''' + Fast-RCNN/resnet_v1_50/block4 -->resnet_v1_50/block4 + Fast-RCNN/MobilenetV2/** -- > MobilenetV2 ** + :param var: + :return: + ''' + return '/'.join(var.op.name.split('/')[1:]) + + nameInCkpt_Var_dict = {} + for var in model_variables: + if var.name.startswith(self.base_network_name): + var_name_in_ckpt = name_in_ckpt_rpn(var) + nameInCkpt_Var_dict[var_name_in_ckpt] = var + restore_variables = nameInCkpt_Var_dict + for key, item in restore_variables.items(): + print("var_in_graph: ", item.name) + print("var_in_ckpt: ", key) + print(20 * "___") + restorer = tf.train.Saver(restore_variables) + print(20 * "****") + print("restore from pretrained_weighs in IMAGE_NET") + return restorer, checkpoint_path + + def get_gradients(self, optimizer, loss): + ''' + + :param optimizer: + :param loss: + :return: + + return vars and grads that not be fixed + ''' + + # if cfgs.FIXED_BLOCKS > 0: + # trainable_vars = tf.trainable_variables() + # # trained_vars = slim.get_trainable_variables() + # start_names = [cfgs.NET_NAME + '/block%d'%i for i in range(1, cfgs.FIXED_BLOCKS+1)] + \ + # [cfgs.NET_NAME + '/conv1'] + # start_names = tuple(start_names) + # trained_var_list = [] + # for var in trainable_vars: + # if not var.name.startswith(start_names): + # trained_var_list.append(var) + # # slim.learning.train() + # grads = optimizer.compute_gradients(loss, var_list=trained_var_list) + # return grads + # else: + # return optimizer.compute_gradients(loss) + return optimizer.compute_gradients(loss) + + def enlarge_gradients_for_bias(self, gradients): + + final_gradients = [] + with tf.variable_scope("Gradient_Mult") as scope: + for grad, var in gradients: + scale = 1.0 + if cfgs.MUTILPY_BIAS_GRADIENT and './biases' in var.name: + scale = scale * cfgs.MUTILPY_BIAS_GRADIENT + if not np.allclose(scale, 1.0): + grad = tf.multiply(grad, scale) + final_gradients.append((grad, var)) + return final_gradients + diff --git a/libs/networks/build_whole_network_cascade.py b/libs/networks/build_whole_network_cascade.py new file mode 100644 index 0000000..1d15137 --- /dev/null +++ b/libs/networks/build_whole_network_cascade.py @@ -0,0 +1,661 @@ +# -*-coding: utf-8 -*- + +from __future__ import absolute_import, division, print_function + +import os +import tensorflow as tf +import tensorflow.contrib.slim as slim +import numpy as np + +from libs.networks import resnet, resnet_gluoncv +from libs.networks import mobilenet_v2 +from libs.box_utils import encode_and_decode +from libs.box_utils import boxes_utils +from libs.box_utils import anchor_utils +from libs.configs import cfgs +from libs.losses import losses +from libs.box_utils import show_box_in_tensor +from libs.detection_oprations.proposal_opr import postprocess_rpn_proposals +from libs.detection_oprations.anchor_target_layer_without_boxweight import anchor_target_layer +from libs.detection_oprations.proposal_target_layer_cascade_ import proposal_target_layer + + +class DetectionNetwork(object): + + def __init__(self, base_network_name, is_training): + + self.base_network_name = base_network_name + self.is_training = is_training + self.num_anchors_per_location = len(cfgs.ANCHOR_SCALES) * len(cfgs.ANCHOR_RATIOS) + + def build_base_network(self, input_img_batch): + + if self.base_network_name.startswith('resnet_v1'): + return resnet.resnet_base(input_img_batch, scope_name=self.base_network_name, is_training=self.is_training) + elif self.base_network_name in ['resnet101_v1d', 'resnet50_v1d']: + return resnet_gluoncv.resnet_base(input_img_batch, scope_name=self.base_network_name, + is_training=self.is_training) + elif self.base_network_name.startswith('MobilenetV2'): + return mobilenet_v2.mobilenetv2_base(input_img_batch, is_training=self.is_training) + + else: + raise ValueError('Sry, we only support resnet or mobilenet_v2') + + + def postprocess_fastrcnn(self, rois, bbox_ppred, scores, img_shape): + ''' + + :param rois:[-1, 4] + :param bbox_ppred: [-1, (cfgs.Class_num+1) * 4] + :param scores: [-1, cfgs.Class_num + 1] + :return: + ''' + + with tf.name_scope('postprocess_fastrcnn'): + rois = tf.stop_gradient(rois) + scores = tf.stop_gradient(scores) + bbox_ppred = tf.reshape(bbox_ppred, [-1, 4]) + bbox_ppred = tf.stop_gradient(bbox_ppred) + + score_list = tf.unstack(scores, axis=1) + + allclasses_boxes = [] + allclasses_scores = [] + categories = [] + for i in range(1, cfgs.CLASS_NUM + 1): + # 1. decode boxes in each class + tmp_score = score_list[i] + tmp_decoded_boxes = encode_and_decode.decode_boxes(encoded_boxes=bbox_ppred, + reference_boxes=rois, + scale_factors=cfgs.ROI_SCALE_FACTORS[-1]) + # tmp_decoded_boxes = encode_and_decode.decode_boxes(boxes=rois, + # deltas=tmp_encoded_box, + # scale_factor=cfgs.ROI_SCALE_FACTORS) + + # 2. clip to img boundaries + tmp_decoded_boxes = boxes_utils.clip_boxes_to_img_boundaries(decode_boxes=tmp_decoded_boxes, + img_shape=img_shape) + + # 3. NMS + keep = tf.image.non_max_suppression( + boxes=tmp_decoded_boxes, + scores=tmp_score, + max_output_size=cfgs.FAST_RCNN_NMS_MAX_BOXES_PER_CLASS, + iou_threshold=cfgs.FAST_RCNN_NMS_IOU_THRESHOLD) + + perclass_boxes = tf.gather(tmp_decoded_boxes, keep) + perclass_scores = tf.gather(tmp_score, keep) + + allclasses_boxes.append(perclass_boxes) + allclasses_scores.append(perclass_scores) + categories.append(tf.ones_like(perclass_scores) * i) + + final_boxes = tf.concat(allclasses_boxes, axis=0) + final_scores = tf.concat(allclasses_scores, axis=0) + final_category = tf.concat(categories, axis=0) + + if self.is_training: + ''' + in training. We should show the detecitons in the tensorboard. So we add this. + ''' + kept_indices = tf.reshape(tf.where(tf.greater_equal(final_scores, cfgs.SHOW_SCORE_THRSHOLD)), [-1]) + + final_boxes = tf.gather(final_boxes, kept_indices) + final_scores = tf.gather(final_scores, kept_indices) + final_category = tf.gather(final_category, kept_indices) + + return final_boxes, final_scores, final_category + + def roi_pooling(self, feature_maps, rois, img_shape, scope): + ''' + Here use roi warping as roi_pooling + + :param featuremaps_dict: feature map to crop + :param rois: shape is [-1, 4]. [x1, y1, x2, y2] + :return: + ''' + + with tf.variable_scope('ROI_Warping_' + scope): + img_h, img_w = tf.cast(img_shape[1], tf.float32), tf.cast(img_shape[2], tf.float32) + N = tf.shape(rois)[0] + x1, y1, x2, y2 = tf.unstack(rois, axis=1) + + normalized_x1 = x1 / img_w + normalized_x2 = x2 / img_w + normalized_y1 = y1 / img_h + normalized_y2 = y2 / img_h + + normalized_rois = tf.transpose( + tf.stack([normalized_y1, normalized_x1, normalized_y2, normalized_x2]), name='get_normalized_rois') + + normalized_rois = tf.stop_gradient(normalized_rois) + + cropped_roi_features = tf.image.crop_and_resize(feature_maps, normalized_rois, + box_ind=tf.zeros(shape=[N, ], + dtype=tf.int32), + crop_size=[cfgs.ROI_SIZE, cfgs.ROI_SIZE], + name='CROP_AND_RESIZE' + ) + roi_features = slim.max_pool2d(cropped_roi_features, + [cfgs.ROI_POOL_KERNEL_SIZE, cfgs.ROI_POOL_KERNEL_SIZE], + stride=cfgs.ROI_POOL_KERNEL_SIZE) + + return roi_features + + def build_fastrcnn(self, P_list, rois_list, img_shape): + + with tf.variable_scope('Fast-RCNN'): + # 5. ROI Pooling + with tf.variable_scope('rois_pooling'): + pooled_features_list = [] + for level_name, p, rois in zip(cfgs.LEVLES, P_list, rois_list): # exclude P6_rois + # p = tf.Print(p, [tf.shape(p)], summarize=10, message=level_name+'SHPAE***') + pooled_features = self.roi_pooling(feature_maps=p, rois=rois, img_shape=img_shape, + scope=level_name) + pooled_features_list.append(pooled_features) + + pooled_features = tf.concat(pooled_features_list, axis=0) # [minibatch_size, H, W, C] + + # 6. inferecne rois in Fast-RCNN to obtain fc_flatten features + if self.base_network_name.startswith('resnet'): + fc_flatten = resnet.restnet_head(inputs=pooled_features, + is_training=self.is_training, + scope_name=self.base_network_name) + elif self.base_network_name.startswith('Mobile'): + fc_flatten = mobilenet_v2.mobilenetv2_head(inputs=pooled_features, + is_training=self.is_training) + else: + raise NotImplementedError('only support resnet and mobilenet') + + # 7. cls and reg in Fast-RCNN + with slim.arg_scope([slim.fully_connected], weights_regularizer=slim.l2_regularizer(cfgs.WEIGHT_DECAY)): + + cls_score = slim.fully_connected(fc_flatten, + num_outputs=cfgs.CLASS_NUM + 1, + weights_initializer=cfgs.INITIALIZER, + activation_fn=None, trainable=self.is_training, + scope='cls_fc') + + bbox_pred = slim.fully_connected(fc_flatten, + num_outputs=4, + activation_fn=None, trainable=self.is_training, + weights_initializer=tf.random_normal_initializer(mean=0.0, stddev=0.001), + scope='reg_fc') + + # for convient. It also produce (cls_num +1) bboxes + cls_score = tf.reshape(cls_score, [-1, cfgs.CLASS_NUM + 1]) + bbox_pred = tf.reshape(bbox_pred, [-1, 4]) + + return bbox_pred, cls_score + + def assign_levels(self, all_rois, labels=None, bbox_targets=None): + ''' + + :param all_rois: + :param labels: + :param bbox_targets: + :return: + ''' + with tf.name_scope('assign_levels'): + # all_rois = tf.Print(all_rois, [tf.shape(all_rois)], summarize=10, message='ALL_ROIS_SHAPE*****') + xmin, ymin, xmax, ymax = tf.unstack(all_rois, axis=1) + + h = tf.maximum(0., ymax - ymin) + w = tf.maximum(0., xmax - xmin) + + levels = tf.floor(4. + tf.log(tf.sqrt(w * h + 1e-8) / 224.0) / tf.log(2.)) # 4 + log_2(***) + # use floor instead of round + + min_level = int(cfgs.LEVLES[0][-1]) + max_level = min(5, int(cfgs.LEVLES[-1][-1])) + levels = tf.maximum(levels, tf.ones_like(levels) * min_level) # level minimum is 2 + levels = tf.minimum(levels, tf.ones_like(levels) * max_level) # level maximum is 5 + + levels = tf.stop_gradient(tf.reshape(levels, [-1])) + + def get_rois(levels, level_i, rois, labels, bbox_targets): + + level_i_indices = tf.reshape(tf.where(tf.equal(levels, level_i)), [-1]) + # level_i_indices = tf.Print(level_i_indices, [tf.shape(tf.where(tf.equal(levels, level_i)))[0]], message="SHAPE%d***"%level_i, + # summarize=10) + tf.summary.scalar('LEVEL/LEVEL_%d_rois_NUM' % level_i, tf.shape(level_i_indices)[0]) + level_i_rois = tf.gather(rois, level_i_indices) + + if self.is_training: + if cfgs.CUDA9: + # Note: for cuda 9 + level_i_rois = tf.stop_gradient(level_i_rois) + level_i_labels = tf.gather(labels, level_i_indices) + + level_i_targets = tf.gather(bbox_targets, level_i_indices) + else: + + # Note: for cuda 8 + level_i_rois = tf.stop_gradient(tf.concat([level_i_rois, [[0, 0, 0., 0.]]], axis=0)) + # to avoid the num of level i rois is 0.0, which will broken the BP in tf + + level_i_labels = tf.gather(labels, level_i_indices) + level_i_labels = tf.stop_gradient(tf.concat([level_i_labels, [0]], axis=0)) + + level_i_targets = tf.gather(bbox_targets, level_i_indices) + level_i_targets = tf.stop_gradient(tf.concat([level_i_targets, + tf.zeros(shape=(1, 4 * (cfgs.CLASS_NUM + 1)), + dtype=tf.float32)], axis=0)) + + return level_i_rois, level_i_labels, level_i_targets + else: + if not cfgs.CUDA9: + # Note: for cuda 8 + level_i_rois = tf.concat([level_i_rois, [[0, 0, 0., 0.]]], axis=0) + return level_i_rois, None, None + + rois_list = [] + labels_list = [] + targets_list = [] + for i in range(min_level, max_level + 1): + P_i_rois, P_i_labels, P_i_targets = get_rois(levels, level_i=i, rois=all_rois, + labels=labels, + bbox_targets=bbox_targets) + rois_list.append(P_i_rois) + labels_list.append(P_i_labels) + targets_list.append(P_i_targets) + + if self.is_training: + all_labels = tf.concat(labels_list, axis=0) + all_targets = tf.concat(targets_list, axis=0) + return rois_list, all_labels, all_targets + else: + return rois_list # [P2_rois, P3_rois, P4_rois, P5_rois] Note: P6 do not assign rois + + def add_anchor_img_smry(self, img, anchors, labels): + + positive_anchor_indices = tf.reshape(tf.where(tf.greater_equal(labels, 1)), [-1]) + negative_anchor_indices = tf.reshape(tf.where(tf.equal(labels, 0)), [-1]) + + positive_anchor = tf.gather(anchors, positive_anchor_indices) + negative_anchor = tf.gather(anchors, negative_anchor_indices) + + pos_in_img = show_box_in_tensor.only_draw_boxes(img_batch=img, + boxes=positive_anchor) + neg_in_img = show_box_in_tensor.only_draw_boxes(img_batch=img, + boxes=negative_anchor) + + tf.summary.image('positive_anchor', pos_in_img) + tf.summary.image('negative_anchors', neg_in_img) + + def add_roi_batch_img_smry(self, img, rois, labels): + positive_roi_indices = tf.reshape(tf.where(tf.greater_equal(labels, 1)), [-1]) + + negative_roi_indices = tf.reshape(tf.where(tf.equal(labels, 0)), [-1]) + + pos_roi = tf.gather(rois, positive_roi_indices) + neg_roi = tf.gather(rois, negative_roi_indices) + + pos_in_img = show_box_in_tensor.only_draw_boxes(img_batch=img, + boxes=pos_roi) + neg_in_img = show_box_in_tensor.only_draw_boxes(img_batch=img, + boxes=neg_roi) + tf.summary.image('pos_rois', pos_in_img) + tf.summary.image('neg_rois', neg_in_img) + + def build_loss(self, rpn_box_pred, rpn_bbox_targets, rpn_cls_score, rpn_labels, + bbox_pred, bbox_targets, cls_score, labels): + ''' + + :param rpn_box_pred: [-1, 4] + :param rpn_bbox_targets: [-1, 4] + :param rpn_cls_score: [-1] + :param rpn_labels: [-1] + :param bbox_pred: [-1, 4*(cls_num+1)] + :param bbox_targets: [-1, 4*(cls_num+1)] + :param cls_score: [-1, cls_num+1] + :param labels: [-1] + :return: + ''' + with tf.variable_scope('build_loss'): + + rpn_cls_loss = 0.0 + rpn_bbox_loss = 0.0 + if rpn_box_pred is not None: + with tf.variable_scope('rpn_loss'): + + rpn_bbox_loss = losses.smooth_l1_loss_rpn(bbox_pred=rpn_box_pred, + bbox_targets=rpn_bbox_targets, + label=rpn_labels, + sigma=cfgs.RPN_SIGMA) + + rpn_select = tf.reshape(tf.where(tf.not_equal(rpn_labels, -1)), [-1]) + rpn_cls_score = tf.reshape(tf.gather(rpn_cls_score, rpn_select), [-1, 2]) + rpn_labels = tf.reshape(tf.gather(rpn_labels, rpn_select), [-1]) + rpn_cls_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=rpn_cls_score, + labels=rpn_labels)) + + rpn_cls_loss = rpn_cls_loss * cfgs.RPN_CLASSIFICATION_LOSS_WEIGHT + rpn_bbox_loss = rpn_bbox_loss * cfgs.RPN_LOCATION_LOSS_WEIGHT + + with tf.variable_scope('FastRCNN_loss'): + if not cfgs.FAST_RCNN_MINIBATCH_SIZE == -1: + bbox_loss = losses.smooth_l1_loss_rcnn(bbox_pred=bbox_pred, + bbox_targets=bbox_targets, + label=labels, + num_classes=1, + sigma=cfgs.FASTRCNN_SIGMA) + + cls_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits( + logits=cls_score, + labels=labels)) # beacause already sample before + else: + ''' + applying OHEM here + ''' + print(20 * "@@") + print("@@" + 10 * " " + "TRAIN WITH OHEM ...") + print(20 * "@@") + cls_loss = bbox_loss = losses.sum_ohem_loss( + cls_score=cls_score, + label=labels, + bbox_targets=bbox_targets, + nr_ohem_sampling=128, + nr_classes=cfgs.CLASS_NUM + 1) + cls_loss = cls_loss * cfgs.FAST_RCNN_CLASSIFICATION_LOSS_WEIGHT + # bbox_loss = bbox_loss * cfgs.FAST_RCNN_LOCATION_LOSS_WEIGHT + loss_dict = { + 'rpn_cls_loss': rpn_cls_loss, + 'rpn_loc_loss': rpn_bbox_loss, + 'fastrcnn_cls_loss': cls_loss, + 'fastrcnn_loc_loss': bbox_loss + } + return loss_dict + + def cascade_rcnn(self, cascade_rois, gtboxes_batch, input_img_batch, P_list, + img_shape, fg_threshold, fpn_box_pred, fpn_bbox_targets, + fpn_cls_score, fpn_labels, stage=None): + + with tf.variable_scope('cascade_stage{}'.format(stage)): + if self.is_training: + # with tf.control_dependencies([fpn_labels]): + with tf.variable_scope('sample_RCNN_minibatch'): + cascade_rois, labels, bbox_targets, gtboxes = \ + tf.py_func(proposal_target_layer, + [cascade_rois, gtboxes_batch, fg_threshold, stage], + [tf.float32, tf.float32, tf.float32, tf.float32]) + cascade_rois = tf.reshape(cascade_rois, [-1, 4]) + labels = tf.to_int32(labels) + labels = tf.reshape(labels, [-1]) + bbox_targets = tf.reshape(bbox_targets, [-1, 4]) + self.add_roi_batch_img_smry(input_img_batch, cascade_rois, labels) + if self.is_training: + rois_list, labels, bbox_targets = self.assign_levels(all_rois=cascade_rois, + labels=labels, + bbox_targets=bbox_targets) + else: + rois_list = self.assign_levels(all_rois=cascade_rois) + + # ----------------------------------------------------------------------------------------------------# + # Fast-RCNN # + # ----------------------------------------------------------------------------------------------------# + + # 5. build Fast-RCNN + bbox_pred, cls_score = self.build_fastrcnn(P_list=P_list, + rois_list=rois_list, + img_shape=img_shape) + + cls_prob = slim.softmax(cls_score, 'cls_prob') + + # ----------------------------------------------add smry---------------------------------------------- + if self.is_training: + cls_category = tf.argmax(cls_prob, axis=1) + fast_acc = tf.reduce_mean(tf.to_float(tf.equal(cls_category, tf.to_int64(labels)))) + tf.summary.scalar('ACC/fast_acc', fast_acc) + + cascade_rois = tf.concat(rois_list, axis=0, name='concat_rois') + # 6. postprocess_fastrcnn + if self.is_training: + loss_dict = self.build_loss(rpn_box_pred=fpn_box_pred, + rpn_bbox_targets=fpn_bbox_targets, + rpn_cls_score=fpn_cls_score, + rpn_labels=fpn_labels, + bbox_pred=bbox_pred, + bbox_targets=bbox_targets, + cls_score=cls_score, + labels=labels) + return cascade_rois, bbox_pred, cls_prob, loss_dict + else: + return cascade_rois, bbox_pred, cls_prob + + def build_whole_detection_network(self, input_img_batch, gtboxes_batch): + + if self.is_training: + # ensure shape is [M, 5] + gtboxes_batch = tf.reshape(gtboxes_batch, [-1, 5]) + gtboxes_batch = tf.cast(gtboxes_batch, tf.float32) + + img_shape = tf.shape(input_img_batch) + + # 1. build base network + P_list = self.build_base_network(input_img_batch) + + # 2. build rpn + with tf.variable_scope('build_rpn', + regularizer=slim.l2_regularizer(cfgs.WEIGHT_DECAY)): + + fpn_cls_score = [] + fpn_box_pred = [] + for level_name, p in zip(cfgs.LEVLES, P_list): + if cfgs.SHARE_HEADS: + reuse_flag = None if level_name == cfgs.LEVLES[0] else True + scope_list = ['rpn_conv/3x3', 'rpn_cls_score', 'rpn_bbox_pred'] + else: + reuse_flag = None + scope_list = ['rpn_conv/3x3_%s' % level_name, 'rpn_cls_score_%s' % level_name, + 'rpn_bbox_pred_%s' % level_name] + rpn_conv3x3 = slim.conv2d( + p, 512, [3, 3], + trainable=self.is_training, weights_initializer=cfgs.INITIALIZER, padding="SAME", + activation_fn=tf.nn.relu, + scope=scope_list[0], + reuse=reuse_flag) + rpn_cls_score = slim.conv2d(rpn_conv3x3, self.num_anchors_per_location * 2, [1, 1], stride=1, + trainable=self.is_training, weights_initializer=cfgs.INITIALIZER, + activation_fn=None, padding="VALID", + scope=scope_list[1], + reuse=reuse_flag) + rpn_box_pred = slim.conv2d(rpn_conv3x3, self.num_anchors_per_location * 4, [1, 1], stride=1, + trainable=self.is_training, weights_initializer=cfgs.BBOX_INITIALIZER, + activation_fn=None, padding="VALID", + scope=scope_list[2], + reuse=reuse_flag) + rpn_box_pred = tf.reshape(rpn_box_pred, [-1, 4]) + rpn_cls_score = tf.reshape(rpn_cls_score, [-1, 2]) + + fpn_cls_score.append(rpn_cls_score) + fpn_box_pred.append(rpn_box_pred) + + fpn_cls_score = tf.concat(fpn_cls_score, axis=0, name='fpn_cls_score') + fpn_box_pred = tf.concat(fpn_box_pred, axis=0, name='fpn_box_pred') + fpn_cls_prob = slim.softmax(fpn_cls_score, scope='fpn_cls_prob') + + # 3. generate_anchors + all_anchors = [] + for i in range(len(cfgs.LEVLES)): + level_name, p = cfgs.LEVLES[i], P_list[i] + + p_h, p_w = tf.shape(p)[1], tf.shape(p)[2] + + featuremap_height = tf.cast(p_h, tf.float32) + featuremap_width = tf.cast(p_w, tf.float32) + anchors = anchor_utils.make_anchors(base_anchor_size=cfgs.BASE_ANCHOR_SIZE_LIST[i], + anchor_scales=cfgs.ANCHOR_SCALES, + anchor_ratios=cfgs.ANCHOR_RATIOS, + featuremap_height=featuremap_height, + featuremap_width=featuremap_width, + stride=cfgs.ANCHOR_STRIDE_LIST[i], + name="make_anchors_for%s" % level_name) + all_anchors.append(anchors) + all_anchors = tf.concat(all_anchors, axis=0, name='all_anchors_of_FPN') + + # 4. postprocess rpn proposals. such as: decode, clip, NMS + with tf.variable_scope('postprocess_FPN'): + rois, roi_scores = postprocess_rpn_proposals(rpn_bbox_pred=fpn_box_pred, + rpn_cls_prob=fpn_cls_prob, + img_shape=img_shape, + anchors=all_anchors, + is_training=self.is_training) + # rois shape [-1, 4] + # +++++++++++++++++++++++++++++++++++++add img smry+++++++++++++++++++++++++++++++++++++++++++++++++++++++ + + if self.is_training: + score_gre_05 = tf.reshape(tf.where(tf.greater_equal(roi_scores, 0.5)), [-1]) + score_gre_05_rois = tf.gather(rois, score_gre_05) + score_gre_05_score = tf.gather(roi_scores, score_gre_05) + score_gre_05_in_img = show_box_in_tensor.draw_boxes_with_scores(img_batch=input_img_batch, + boxes=score_gre_05_rois, + scores=score_gre_05_score) + tf.summary.image('score_greater_05_rois', score_gre_05_in_img) + # ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ + + if self.is_training: + with tf.variable_scope('sample_anchors_minibatch'): + fpn_labels, fpn_bbox_targets = \ + tf.py_func( + anchor_target_layer, + [gtboxes_batch, img_shape, all_anchors], + [tf.float32, tf.float32]) + fpn_bbox_targets = tf.reshape(fpn_bbox_targets, [-1, 4]) + fpn_labels = tf.to_int32(fpn_labels, name="to_int32") + fpn_labels = tf.reshape(fpn_labels, [-1]) + self.add_anchor_img_smry(input_img_batch, all_anchors, fpn_labels) + + # --------------------------------------add smry----------------------------------------------------------- + + fpn_cls_category = tf.argmax(fpn_cls_prob, axis=1) + kept_rpppn = tf.reshape(tf.where(tf.not_equal(fpn_labels, -1)), [-1]) + fpn_cls_category = tf.gather(fpn_cls_category, kept_rpppn) + acc = tf.reduce_mean(tf.to_float(tf.equal(fpn_cls_category, + tf.to_int64(tf.gather(fpn_labels, kept_rpppn))))) + tf.summary.scalar('ACC/fpn_accuracy', acc) + + # cascade rcnn + total_loss_dict = {} + cascade_bbox_pred = [] + cascade_cls_prob = [] + cascade_rois = [] + fg_thresholds = [0.5, 0.6, 0.7] + for i in range(len(fg_thresholds)): + if self.is_training: + rois, bbox_pred, cls_prob, loss_dict = self.cascade_rcnn(rois, gtboxes_batch, + input_img_batch, + P_list, img_shape, fg_thresholds[i], + fpn_box_pred, fpn_bbox_targets, + fpn_cls_score, + fpn_labels, stage=i + 1) + + for k in loss_dict.keys(): + if k not in total_loss_dict.keys(): + total_loss_dict[k] = loss_dict[k] + else: + total_loss_dict[k] += loss_dict[k] + else: + rois, bbox_pred, cls_prob = self.cascade_rcnn(rois, gtboxes_batch, input_img_batch, + P_list, + img_shape, fg_thresholds[i], + fpn_box_pred, None, + fpn_cls_score, None, + stage=i + 1) + cascade_bbox_pred.append(bbox_pred) + cascade_cls_prob.append(cls_prob) + cascade_rois.append(rois) + + final_bbox, final_scores, final_category = self.postprocess_fastrcnn(rois=cascade_rois[-1], + bbox_ppred=cascade_bbox_pred[-1], + scores=cascade_cls_prob[-1], + img_shape=img_shape) + + if self.is_training: + return final_bbox, final_scores, final_category, total_loss_dict + else: + return final_bbox, final_scores, final_category + + def get_restorer(self): + checkpoint_path = tf.train.latest_checkpoint(os.path.join(cfgs.TRAINED_CKPT, cfgs.VERSION)) + + if checkpoint_path != None: + restorer = tf.train.Saver() + print("model restore from :", checkpoint_path) + else: + checkpoint_path = cfgs.PRETRAINED_CKPT + print("model restore from pretrained mode, path is :", checkpoint_path) + + model_variables = slim.get_model_variables() + + # for var in model_variables: + # print(var.name) + # print(20*"__++__++__") + + def name_in_ckpt_rpn(var): + return var.op.name + + def name_in_ckpt_fastrcnn_head(var): + ''' + Fast-RCNN/resnet_v1_50/block4 -->resnet_v1_50/block4 + Fast-RCNN/MobilenetV2/** -- > MobilenetV2 ** + :param var: + :return: + ''' + return '/'.join(var.op.name.split('/')[1:]) + + nameInCkpt_Var_dict = {} + for var in model_variables: + if var.name.startswith(self.base_network_name): + var_name_in_ckpt = name_in_ckpt_rpn(var) + nameInCkpt_Var_dict[var_name_in_ckpt] = var + restore_variables = nameInCkpt_Var_dict + for key, item in restore_variables.items(): + print("var_in_graph: ", item.name) + print("var_in_ckpt: ", key) + print(20 * "___") + restorer = tf.train.Saver(restore_variables) + print(20 * "****") + print("restore from pretrained_weighs in IMAGE_NET") + return restorer, checkpoint_path + + def get_gradients(self, optimizer, loss): + ''' + + :param optimizer: + :param loss: + :return: + + return vars and grads that not be fixed + ''' + + # if cfgs.FIXED_BLOCKS > 0: + # trainable_vars = tf.trainable_variables() + # # trained_vars = slim.get_trainable_variables() + # start_names = [cfgs.NET_NAME + '/block%d'%i for i in range(1, cfgs.FIXED_BLOCKS+1)] + \ + # [cfgs.NET_NAME + '/conv1'] + # start_names = tuple(start_names) + # trained_var_list = [] + # for var in trainable_vars: + # if not var.name.startswith(start_names): + # trained_var_list.append(var) + # # slim.learning.train() + # grads = optimizer.compute_gradients(loss, var_list=trained_var_list) + # return grads + # else: + # return optimizer.compute_gradients(loss) + return optimizer.compute_gradients(loss) + + def enlarge_gradients_for_bias(self, gradients): + + final_gradients = [] + with tf.variable_scope("Gradient_Mult") as scope: + for grad, var in gradients: + scale = 1.0 + if cfgs.MUTILPY_BIAS_GRADIENT and './biases' in var.name: + scale = scale * cfgs.MUTILPY_BIAS_GRADIENT + if not np.allclose(scale, 1.0): + grad = tf.multiply(grad, scale) + final_gradients.append((grad, var)) + return final_gradients + diff --git a/libs/networks/layer.py b/libs/networks/layer.py new file mode 100644 index 0000000..aea655f --- /dev/null +++ b/libs/networks/layer.py @@ -0,0 +1,179 @@ +# -*-coding: utf-8 -*- + +from __future__ import absolute_import, division, print_function + +import tensorflow as tf +import tensorflow.contrib.slim as slim + +from libs.configs import cfgs + + +def build_attention(inputs, is_training, scope): + with tf.variable_scope(scope): + attention_conv3x3_1 = slim.conv2d(inputs, 256, [3, 3], + trainable=is_training, + weights_initializer=cfgs.INITIALIZER, + activation_fn=tf.nn.relu, + scope='attention_conv/3x3_1') + attention_conv3x3_2 = slim.conv2d(attention_conv3x3_1, 256, [3, 3], + trainable=is_training, + weights_initializer=cfgs.INITIALIZER, + activation_fn=tf.nn.relu, + scope='attention_conv/3x3_2') + attention_conv3x3_3 = slim.conv2d(attention_conv3x3_2, 256, [3, 3], + trainable=is_training, + weights_initializer=cfgs.INITIALIZER, + activation_fn=tf.nn.relu, + scope='attention_conv/3x3_3') + attention_conv3x3_4 = slim.conv2d(attention_conv3x3_3, 256, [3, 3], + trainable=is_training, + weights_initializer=cfgs.INITIALIZER, + activation_fn=tf.nn.relu, + scope='attention_conv/3x3_4') + attention_conv3x3_5 = slim.conv2d(attention_conv3x3_4, 2, [3, 3], + trainable=is_training, + weights_initializer=cfgs.INITIALIZER, + activation_fn=None, + scope='attention_conv/3x3_5') + return attention_conv3x3_5 + + +def build_inception(inputs, is_training): + with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d], + stride=1, padding='SAME'): + with tf.variable_scope('Branch_0'): + branch_0 = slim.conv2d(inputs, 384, [1, 1], + trainable=is_training, + weights_initializer=cfgs.INITIALIZER, + activation_fn=tf.nn.relu, + scope='conv2d_0a_1x1') + with tf.variable_scope('Branch_1'): + branch_1 = slim.conv2d(inputs, 192, [1, 1], + trainable=is_training, + weights_initializer=cfgs.INITIALIZER, + activation_fn=tf.nn.relu, + scope='conv2d_0a_1x1') + branch_1 = slim.conv2d(branch_1, 224, [1, 7], + trainable=is_training, + weights_initializer=cfgs.INITIALIZER, + activation_fn=tf.nn.relu, + scope='conv2d_0b_1x7') + branch_1 = slim.conv2d(branch_1, 256, [7, 1], + trainable=is_training, + weights_initializer=cfgs.INITIALIZER, + activation_fn=tf.nn.relu, + scope='conv2d_0c_7x1') + with tf.variable_scope('Branch_2'): + branch_2 = slim.conv2d(inputs, 192, [1, 1], + trainable=is_training, + weights_initializer=cfgs.INITIALIZER, + activation_fn=tf.nn.relu, + scope='conv2d_0a_1x1') + branch_2 = slim.conv2d(branch_2, 192, [7, 1], + trainable=is_training, + weights_initializer=cfgs.INITIALIZER, + activation_fn=tf.nn.relu, + scope='conv2d_0b_7x1') + branch_2 = slim.conv2d(branch_2, 224, [1, 7], + trainable=is_training, + weights_initializer=cfgs.INITIALIZER, + activation_fn=tf.nn.relu, + scope='Conv2d_0c_1x7') + branch_2 = slim.conv2d(branch_2, 224, [7, 1], + trainable=is_training, + weights_initializer=cfgs.INITIALIZER, + activation_fn=tf.nn.relu, + scope='conv2d_0d_7x1') + branch_2 = slim.conv2d(branch_2, 256, [1, 7], + trainable=is_training, + weights_initializer=cfgs.INITIALIZER, + activation_fn=tf.nn.relu, + scope='conv2d_0e_1x7') + with tf.variable_scope('Branch_3'): + branch_3 = slim.avg_pool2d(inputs, [3, 3], scope='avgPool_0a_3x3') + branch_3 = slim.conv2d(branch_3, 128, [1, 1], + trainable=is_training, + weights_initializer=cfgs.INITIALIZER, + activation_fn=tf.nn.relu, + scope='conv2d_0b_1x1') + inception_out = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) + return inception_out + + +def build_inception_attention(inputs, is_training): + """Builds Inception-B block for Inception v4 network.""" + # By default use stride=1 and SAME padding + inception_out = build_inception(inputs, is_training) + + inception_attention_out = slim.conv2d(inception_out, 2, [3, 3], + trainable=is_training, + weights_initializer=cfgs.INITIALIZER, + activation_fn=None, + scope='inception_attention_out') + return inception_attention_out + + +def build_context(inputs, is_training): + conv3x3 = slim.conv2d(inputs, 512, [3, 3], + trainable=is_training, + weights_initializer=cfgs.INITIALIZER, + activation_fn=None, + scope='conv3x3') + + conv3x3_dimred = slim.conv2d(inputs, 256, [3, 3], + trainable=is_training, + weights_initializer=cfgs.INITIALIZER, + activation_fn=tf.nn.relu, + scope='conv3x3_dimred') + conv3x3_5x5 = slim.conv2d(conv3x3_dimred, 256, [3, 3], + trainable=is_training, + weights_initializer=cfgs.INITIALIZER, + activation_fn=None, + scope='conv3x3_5x5') + + conv3x3_7x7_1 = slim.conv2d(conv3x3_dimred, 256, [3, 3], + trainable=is_training, + weights_initializer=cfgs.INITIALIZER, + activation_fn=tf.nn.relu, + scope='conv3x3_7x7_1') + + conv3x3_7x7 = slim.conv2d(conv3x3_7x7_1, 256, [3, 3], + trainable=is_training, + weights_initializer=cfgs.INITIALIZER, + activation_fn=None, + scope='conv3x3_7x7') + + concat_layer = tf.concat([conv3x3, conv3x3_5x5, conv3x3_7x7], axis=-1) + + outputs = tf.nn.relu(concat_layer) + return outputs + + +def squeeze_excitation_layer(input_x, out_dim, ratio, layer_name, is_training, mode): + with tf.name_scope(layer_name): + if mode == 'avg': + # Global_Average_Pooling + squeeze = tf.reduce_mean(input_x, [1, 2]) + else: + squeeze = tf.reduce_max(input_x, [1, 2]) + + excitation = slim.fully_connected(inputs=squeeze, + num_outputs=out_dim // ratio, + weights_initializer=cfgs.BBOX_INITIALIZER, + activation_fn=tf.nn.relu, + trainable=is_training, + scope=layer_name+'_fully_connected1') + + excitation = slim.fully_connected(inputs=excitation, + num_outputs=out_dim, + weights_initializer=cfgs.BBOX_INITIALIZER, + activation_fn=tf.nn.sigmoid, + trainable=is_training, + scope=layer_name + '_fully_connected2') + + excitation = tf.reshape(excitation, [-1, 1, 1, out_dim]) + + # scale = input_x * excitation + + return excitation + diff --git a/libs/networks/mobilenet/README.md b/libs/networks/mobilenet/README.md new file mode 100644 index 0000000..ba62406 --- /dev/null +++ b/libs/networks/mobilenet/README.md @@ -0,0 +1,12 @@ +# Mobilenet V2 +This folder contains building code for Mobilenet V2, based on +[Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation] +(https://arxiv.org/abs/1801.04381) + +# Pretrained model +TODO + +# Example +TODO + + diff --git a/libs/networks/mobilenet/__init__.py b/libs/networks/mobilenet/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/libs/networks/mobilenet/conv_blocks.py b/libs/networks/mobilenet/conv_blocks.py new file mode 100644 index 0000000..029bd11 --- /dev/null +++ b/libs/networks/mobilenet/conv_blocks.py @@ -0,0 +1,352 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Convolution blocks for mobilenet.""" +import contextlib +import functools + +import tensorflow as tf + +slim = tf.contrib.slim + + +def _fixed_padding(inputs, kernel_size, rate=1): + """Pads the input along the spatial dimensions independently of input size. + + Pads the input such that if it was used in a convolution with 'VALID' padding, + the output would have the same dimensions as if the unpadded input was used + in a convolution with 'SAME' padding. + + Args: + inputs: A tensor of size [batch, height_in, width_in, channels]. + kernel_size: The kernel to be used in the conv2d or max_pool2d operation. + rate: An integer, rate for atrous convolution. + + Returns: + output: A tensor of size [batch, height_out, width_out, channels] with the + input, either intact (if kernel_size == 1) or padded (if kernel_size > 1). + """ + kernel_size_effective = [kernel_size[0] + (kernel_size[0] - 1) * (rate - 1), + kernel_size[0] + (kernel_size[0] - 1) * (rate - 1)] + pad_total = [kernel_size_effective[0] - 1, kernel_size_effective[1] - 1] + pad_beg = [pad_total[0] // 2, pad_total[1] // 2] + pad_end = [pad_total[0] - pad_beg[0], pad_total[1] - pad_beg[1]] + padded_inputs = tf.pad(inputs, [[0, 0], [pad_beg[0], pad_end[0]], + [pad_beg[1], pad_end[1]], [0, 0]]) + return padded_inputs + + +def _make_divisible(v, divisor, min_value=None): + if min_value is None: + min_value = divisor + new_v = max(min_value, int(v + divisor / 2) // divisor * divisor) + # Make sure that round down does not go down by more than 10%. + if new_v < 0.9 * v: + new_v += divisor + return new_v + + +def _split_divisible(num, num_ways, divisible_by=8): + """Evenly splits num, num_ways so each piece is a multiple of divisible_by.""" + assert num % divisible_by == 0 + assert num / num_ways >= divisible_by + # Note: want to round down, we adjust each split to match the total. + base = num // num_ways // divisible_by * divisible_by + result = [] + accumulated = 0 + for i in range(num_ways): + r = base + while accumulated + r < num * (i + 1) / num_ways: + r += divisible_by + result.append(r) + accumulated += r + assert accumulated == num + return result + + +@contextlib.contextmanager +def _v1_compatible_scope_naming(scope): + if scope is None: # Create uniqified separable blocks. + with tf.variable_scope(None, default_name='separable') as s, \ + tf.name_scope(s.original_name_scope): + yield '' + else: + # We use scope_depthwise, scope_pointwise for compatibility with V1 ckpts. + # which provide numbered scopes. + scope += '_' + yield scope + + +@slim.add_arg_scope +def split_separable_conv2d(input_tensor, + num_outputs, + scope=None, + normalizer_fn=None, + stride=1, + rate=1, + endpoints=None, + use_explicit_padding=False): + """Separable mobilenet V1 style convolution. + + Depthwise convolution, with default non-linearity, + followed by 1x1 depthwise convolution. This is similar to + slim.separable_conv2d, but differs in tha it applies batch + normalization and non-linearity to depthwise. This matches + the basic building of Mobilenet Paper + (https://arxiv.org/abs/1704.04861) + + Args: + input_tensor: input + num_outputs: number of outputs + scope: optional name of the scope. Note if provided it will use + scope_depthwise for deptwhise, and scope_pointwise for pointwise. + normalizer_fn: which normalizer function to use for depthwise/pointwise + stride: stride + rate: output rate (also known as dilation rate) + endpoints: optional, if provided, will export additional tensors to it. + use_explicit_padding: Use 'VALID' padding for convolutions, but prepad + inputs so that the output dimensions are the same as if 'SAME' padding + were used. + + Returns: + output tesnor + """ + + with _v1_compatible_scope_naming(scope) as scope: + dw_scope = scope + 'depthwise' + endpoints = endpoints if endpoints is not None else {} + kernel_size = [3, 3] + padding = 'SAME' + if use_explicit_padding: + padding = 'VALID' + input_tensor = _fixed_padding(input_tensor, kernel_size, rate) + net = slim.separable_conv2d( + input_tensor, + None, + kernel_size, + depth_multiplier=1, + stride=stride, + rate=rate, + normalizer_fn=normalizer_fn, + padding=padding, + scope=dw_scope) + + endpoints[dw_scope] = net + + pw_scope = scope + 'pointwise' + net = slim.conv2d( + net, + num_outputs, [1, 1], + stride=1, + normalizer_fn=normalizer_fn, + scope=pw_scope) + endpoints[pw_scope] = net + return net + + +def expand_input_by_factor(n, divisible_by=8): + return lambda num_inputs, **_: _make_divisible(num_inputs * n, divisible_by) + + +@slim.add_arg_scope +def expanded_conv(input_tensor, + num_outputs, + expansion_size=expand_input_by_factor(6), + stride=1, + rate=1, + kernel_size=(3, 3), + residual=True, + normalizer_fn=None, + split_projection=1, + split_expansion=1, + expansion_transform=None, + depthwise_location='expansion', + depthwise_channel_multiplier=1, + endpoints=None, + use_explicit_padding=False, + scope=None): + """Depthwise Convolution Block with expansion. + + Builds a composite convolution that has the following structure + expansion (1x1) -> depthwise (kernel_size) -> projection (1x1) + + Args: + input_tensor: input + num_outputs: number of outputs in the final layer. + expansion_size: the size of expansion, could be a constant or a callable. + If latter it will be provided 'num_inputs' as an input. For forward + compatibility it should accept arbitrary keyword arguments. + Default will expand the input by factor of 6. + stride: depthwise stride + rate: depthwise rate + kernel_size: depthwise kernel + residual: whether to include residual connection between input + and output. + normalizer_fn: batchnorm or otherwise + split_projection: how many ways to split projection operator + (that is conv expansion->bottleneck) + split_expansion: how many ways to split expansion op + (that is conv bottleneck->expansion) ops will keep depth divisible + by this value. + expansion_transform: Optional function that takes expansion + as a single input and returns output. + depthwise_location: where to put depthwise covnvolutions supported + values None, 'input', 'output', 'expansion' + depthwise_channel_multiplier: depthwise channel multiplier: + each input will replicated (with different filters) + that many times. So if input had c channels, + output will have c x depthwise_channel_multpilier. + endpoints: An optional dictionary into which intermediate endpoints are + placed. The keys "expansion_output", "depthwise_output", + "projection_output" and "expansion_transform" are always populated, even + if the corresponding functions are not invoked. + use_explicit_padding: Use 'VALID' padding for convolutions, but prepad + inputs so that the output dimensions are the same as if 'SAME' padding + were used. + scope: optional scope. + + Returns: + Tensor of depth num_outputs + + Raises: + TypeError: on inval + """ + with tf.variable_scope(scope, default_name='expanded_conv') as s, \ + tf.name_scope(s.original_name_scope): + prev_depth = input_tensor.get_shape().as_list()[3] + if depthwise_location not in [None, 'input', 'output', 'expansion']: + raise TypeError('%r is unknown value for depthwise_location' % + depthwise_location) + padding = 'SAME' + if use_explicit_padding: + padding = 'VALID' + depthwise_func = functools.partial( + slim.separable_conv2d, + num_outputs=None, + kernel_size=kernel_size, + depth_multiplier=depthwise_channel_multiplier, + stride=stride, + rate=rate, + normalizer_fn=normalizer_fn, + padding=padding, + scope='depthwise') + # b1 -> b2 * r -> b2 + # i -> (o * r) (bottleneck) -> o + input_tensor = tf.identity(input_tensor, 'input') + net = input_tensor + + if depthwise_location == 'input': + if use_explicit_padding: + net = _fixed_padding(net, kernel_size, rate) + net = depthwise_func(net, activation_fn=None) + + if callable(expansion_size): + inner_size = expansion_size(num_inputs=prev_depth) + else: + inner_size = expansion_size + + if inner_size > net.shape[3]: + net = split_conv( + net, + inner_size, + num_ways=split_expansion, + scope='expand', + stride=1, + normalizer_fn=normalizer_fn) + net = tf.identity(net, 'expansion_output') + if endpoints is not None: + endpoints['expansion_output'] = net + + if depthwise_location == 'expansion': + if use_explicit_padding: + net = _fixed_padding(net, kernel_size, rate) + net = depthwise_func(net) + + net = tf.identity(net, name='depthwise_output') + if endpoints is not None: + endpoints['depthwise_output'] = net + if expansion_transform: + net = expansion_transform(expansion_tensor=net, input_tensor=input_tensor) + # Note in contrast with expansion, we always have + # projection to produce the desired output size. + net = split_conv( + net, + num_outputs, + num_ways=split_projection, + stride=1, + scope='project', + normalizer_fn=normalizer_fn, + activation_fn=tf.identity) + if endpoints is not None: + endpoints['projection_output'] = net + if depthwise_location == 'output': + if use_explicit_padding: + net = _fixed_padding(net, kernel_size, rate) + net = depthwise_func(net, activation_fn=None) + + if callable(residual): # custom residual + net = residual(input_tensor=input_tensor, output_tensor=net) + elif (residual and + # stride check enforces that we don't add residuals when spatial + # dimensions are None + stride == 1 and + # Depth matches + net.get_shape().as_list()[3] == + input_tensor.get_shape().as_list()[3]): + net += input_tensor + return tf.identity(net, name='output') + + +def split_conv(input_tensor, + num_outputs, + num_ways, + scope, + divisible_by=8, + **kwargs): + """Creates a split convolution. + + Split convolution splits the input and output into + 'num_blocks' blocks of approximately the same size each, + and only connects $i$-th input to $i$ output. + + Args: + input_tensor: input tensor + num_outputs: number of output filters + num_ways: num blocks to split by. + scope: scope for all the operators. + divisible_by: make sure that every part is divisiable by this. + **kwargs: will be passed directly into conv2d operator + Returns: + tensor + """ + b = input_tensor.get_shape().as_list()[3] + + if num_ways == 1 or min(b // num_ways, + num_outputs // num_ways) < divisible_by: + # Don't do any splitting if we end up with less than 8 filters + # on either side. + return slim.conv2d(input_tensor, num_outputs, [1, 1], scope=scope, **kwargs) + + outs = [] + input_splits = _split_divisible(b, num_ways, divisible_by=divisible_by) + output_splits = _split_divisible( + num_outputs, num_ways, divisible_by=divisible_by) + inputs = tf.split(input_tensor, input_splits, axis=3, name='split_' + scope) + base = scope + for i, (input_tensor, out_size) in enumerate(zip(inputs, output_splits)): + scope = base + '_part_%d' % (i,) + n = slim.conv2d(input_tensor, out_size, [1, 1], scope=scope, **kwargs) + n = tf.identity(n, scope + '_output') + outs.append(n) + return tf.concat(outs, 3, name=scope + '_concat') diff --git a/libs/networks/mobilenet/mobilenet.py b/libs/networks/mobilenet/mobilenet.py new file mode 100644 index 0000000..3c8ca50 --- /dev/null +++ b/libs/networks/mobilenet/mobilenet.py @@ -0,0 +1,433 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Mobilenet Base Class.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +import collections +import contextlib +import copy +import os + +import tensorflow as tf + + +slim = tf.contrib.slim + + +@slim.add_arg_scope +def apply_activation(x, name=None, activation_fn=None): + return activation_fn(x, name=name) if activation_fn else x + + +def _fixed_padding(inputs, kernel_size, rate=1): + """Pads the input along the spatial dimensions independently of input size. + + Pads the input such that if it was used in a convolution with 'VALID' padding, + the output would have the same dimensions as if the unpadded input was used + in a convolution with 'SAME' padding. + + Args: + inputs: A tensor of size [batch, height_in, width_in, channels]. + kernel_size: The kernel to be used in the conv2d or max_pool2d operation. + rate: An integer, rate for atrous convolution. + + Returns: + output: A tensor of size [batch, height_out, width_out, channels] with the + input, either intact (if kernel_size == 1) or padded (if kernel_size > 1). + """ + kernel_size_effective = [kernel_size[0] + (kernel_size[0] - 1) * (rate - 1), + kernel_size[0] + (kernel_size[0] - 1) * (rate - 1)] + pad_total = [kernel_size_effective[0] - 1, kernel_size_effective[1] - 1] + pad_beg = [pad_total[0] // 2, pad_total[1] // 2] + pad_end = [pad_total[0] - pad_beg[0], pad_total[1] - pad_beg[1]] + padded_inputs = tf.pad(inputs, [[0, 0], [pad_beg[0], pad_end[0]], + [pad_beg[1], pad_end[1]], [0, 0]]) + return padded_inputs + + +def _make_divisible(v, divisor, min_value=None): + if min_value is None: + min_value = divisor + new_v = max(min_value, int(v + divisor / 2) // divisor * divisor) + # Make sure that round down does not go down by more than 10%. + if new_v < 0.9 * v: + new_v += divisor + return new_v + + +@contextlib.contextmanager +def _set_arg_scope_defaults(defaults): + """Sets arg scope defaults for all items present in defaults. + + Args: + defaults: dictionary/list of pairs, containing a mapping from + function to a dictionary of default args. + + Yields: + context manager where all defaults are set. + """ + if hasattr(defaults, 'items'): + items = defaults.items() + else: + items = defaults + if not items: + yield + else: + func, default_arg = items[0] + with slim.arg_scope(func, **default_arg): + with _set_arg_scope_defaults(items[1:]): + yield + + +@slim.add_arg_scope +def depth_multiplier(output_params, + multiplier, + divisible_by=8, + min_depth=8, + **unused_kwargs): + if 'num_outputs' not in output_params: + return + d = output_params['num_outputs'] + output_params['num_outputs'] = _make_divisible(d * multiplier, divisible_by, + min_depth) + + +_Op = collections.namedtuple('Op', ['op', 'params', 'multiplier_func']) + + +def op(opfunc, **params): + multiplier = params.pop('multiplier_transorm', depth_multiplier) + return _Op(opfunc, params=params, multiplier_func=multiplier) + + +@slim.add_arg_scope +def mobilenet_base( # pylint: disable=invalid-name + inputs, + conv_defs, + multiplier=1.0, + final_endpoint=None, + output_stride=None, + use_explicit_padding=False, + scope=None, + is_training=False): + """Mobilenet base network. + + Constructs a network from inputs to the given final endpoint. By default + the network is constructed in inference mode. To create network + in training mode use: + + with slim.arg_scope(mobilenet.training_scope()): + logits, endpoints = mobilenet_base(...) + + Args: + inputs: a tensor of shape [batch_size, height, width, channels]. + conv_defs: A list of op(...) layers specifying the net architecture. + multiplier: Float multiplier for the depth (number of channels) + for all convolution ops. The value must be greater than zero. Typical + usage will be to set this value in (0, 1) to reduce the number of + parameters or computation cost of the model. + final_endpoint: The name of last layer, for early termination for + for V1-based networks: last layer is "layer_14", for V2: "layer_20" + output_stride: An integer that specifies the requested ratio of input to + output spatial resolution. If not None, then we invoke atrous convolution + if necessary to prevent the network from reducing the spatial resolution + of the activation maps. Allowed values are 1 or any even number, excluding + zero. Typical values are 8 (accurate fully convolutional mode), 16 + (fast fully convolutional mode), and 32 (classification mode). + + NOTE- output_stride relies on all consequent operators to support dilated + operators via "rate" parameter. This might require wrapping non-conv + operators to operate properly. + + use_explicit_padding: Use 'VALID' padding for convolutions, but prepad + inputs so that the output dimensions are the same as if 'SAME' padding + were used. + scope: optional variable scope. + is_training: How to setup batch_norm and other ops. Note: most of the time + this does not need be set directly. Use mobilenet.training_scope() to set + up training instead. This parameter is here for backward compatibility + only. It is safe to set it to the value matching + training_scope(is_training=...). It is also safe to explicitly set + it to False, even if there is outer training_scope set to to training. + (The network will be built in inference mode). + Returns: + tensor_out: output tensor. + end_points: a set of activations for external use, for example summaries or + losses. + + Raises: + ValueError: depth_multiplier <= 0, or the target output_stride is not + allowed. + """ + if multiplier <= 0: + raise ValueError('multiplier is not greater than zero.') + + # Set conv defs defaults and overrides. + conv_defs_defaults = conv_defs.get('defaults', {}) + conv_defs_overrides = conv_defs.get('overrides', {}) + if use_explicit_padding: + conv_defs_overrides = copy.deepcopy(conv_defs_overrides) + conv_defs_overrides[ + (slim.conv2d, slim.separable_conv2d)] = {'padding': 'VALID'} + + if output_stride is not None: + if output_stride == 0 or (output_stride > 1 and output_stride % 2): + raise ValueError('Output stride must be None, 1 or a multiple of 2.') + + # a) Set the tensorflow scope + # b) set padding to default: note we might consider removing this + # since it is also set by mobilenet_scope + # c) set all defaults + # d) set all extra overrides. + with _scope_all(scope, default_scope='Mobilenet'), \ + slim.arg_scope([slim.batch_norm], is_training=is_training), \ + _set_arg_scope_defaults(conv_defs_defaults), \ + _set_arg_scope_defaults(conv_defs_overrides): + # The current_stride variable keeps track of the output stride of the + # activations, i.e., the running product of convolution strides up to the + # current network layer. This allows us to invoke atrous convolution + # whenever applying the next convolution would result in the activations + # having output stride larger than the target output_stride. + current_stride = 1 + + # The atrous convolution rate parameter. + rate = 1 + + net = inputs + # Insert default parameters before the base scope which includes + # any custom overrides set in mobilenet. + end_points = {} + scopes = {} + for i, opdef in enumerate(conv_defs['spec']): + params = dict(opdef.params) + opdef.multiplier_func(params, multiplier) + stride = params.get('stride', 1) + if output_stride is not None and current_stride == output_stride: + # If we have reached the target output_stride, then we need to employ + # atrous convolution with stride=1 and multiply the atrous rate by the + # current unit's stride for use in subsequent layers. + layer_stride = 1 + layer_rate = rate + rate *= stride + else: + layer_stride = stride + layer_rate = 1 + current_stride *= stride + # Update params. + params['stride'] = layer_stride + # Only insert rate to params if rate > 1. + if layer_rate > 1: + params['rate'] = layer_rate + # Set padding + if use_explicit_padding: + if 'kernel_size' in params: + net = _fixed_padding(net, params['kernel_size'], layer_rate) + else: + params['use_explicit_padding'] = True + + end_point = 'layer_%d' % (i + 1) + try: + net = opdef.op(net, **params) + except Exception: + print('Failed to create op %i: %r params: %r' % (i, opdef, params)) + raise + end_points[end_point] = net + scope = os.path.dirname(net.name) + scopes[scope] = end_point + if final_endpoint is not None and end_point == final_endpoint: + break + + # Add all tensors that end with 'output' to + # endpoints + for t in net.graph.get_operations(): + scope = os.path.dirname(t.name) + bn = os.path.basename(t.name) + if scope in scopes and t.name.endswith('output'): + end_points[scopes[scope] + '/' + bn] = t.outputs[0] + return net, end_points + + +@contextlib.contextmanager +def _scope_all(scope, default_scope=None): + with tf.variable_scope(scope, default_name=default_scope) as s,\ + tf.name_scope(s.original_name_scope): + yield s + + +@slim.add_arg_scope +def mobilenet(inputs, + num_classes=1001, + prediction_fn=slim.softmax, + reuse=None, + scope='Mobilenet', + base_only=False, + **mobilenet_args): + """Mobilenet model for classification, supports both V1 and V2. + + Note: default mode is inference, use mobilenet.training_scope to create + training network. + + + Args: + inputs: a tensor of shape [batch_size, height, width, channels]. + num_classes: number of predicted classes. If 0 or None, the logits layer + is omitted and the input features to the logits layer (before dropout) + are returned instead. + prediction_fn: a function to get predictions out of logits + (default softmax). + reuse: whether or not the network and its variables should be reused. To be + able to reuse 'scope' must be given. + scope: Optional variable_scope. + base_only: if True will only create the base of the network (no pooling + and no logits). + **mobilenet_args: passed to mobilenet_base verbatim. + - conv_defs: list of conv defs + - multiplier: Float multiplier for the depth (number of channels) + for all convolution ops. The value must be greater than zero. Typical + usage will be to set this value in (0, 1) to reduce the number of + parameters or computation cost of the model. + - output_stride: will ensure that the last layer has at most total stride. + If the architecture calls for more stride than that provided + (e.g. output_stride=16, but the architecture has 5 stride=2 operators), + it will replace output_stride with fractional convolutions using Atrous + Convolutions. + + Returns: + logits: the pre-softmax activations, a tensor of size + [batch_size, num_classes] + end_points: a dictionary from components of the network to the corresponding + activation tensor. + + Raises: + ValueError: Input rank is invalid. + """ + is_training = mobilenet_args.get('is_training', False) + input_shape = inputs.get_shape().as_list() + if len(input_shape) != 4: + raise ValueError('Expected rank 4 input, was: %d' % len(input_shape)) + + with tf.variable_scope(scope, 'Mobilenet', reuse=reuse) as scope: + inputs = tf.identity(inputs, 'input') + net, end_points = mobilenet_base(inputs, scope=scope, **mobilenet_args) + if base_only: + return net, end_points + + net = tf.identity(net, name='embedding') + + with tf.variable_scope('Logits'): + net = global_pool(net) + end_points['global_pool'] = net + if not num_classes: + return net, end_points + net = slim.dropout(net, scope='Dropout', is_training=is_training) + # 1 x 1 x num_classes + # Note: legacy scope name. + logits = slim.conv2d( + net, + num_classes, [1, 1], + activation_fn=None, + normalizer_fn=None, + biases_initializer=tf.zeros_initializer(), + scope='Conv2d_1c_1x1') + + logits = tf.squeeze(logits, [1, 2]) + + logits = tf.identity(logits, name='output') + end_points['Logits'] = logits + if prediction_fn: + end_points['Predictions'] = prediction_fn(logits, 'Predictions') + return logits, end_points + + +def global_pool(input_tensor, pool_op=tf.nn.avg_pool): + """Applies avg pool to produce 1x1 output. + + NOTE: This function is funcitonally equivalenet to reduce_mean, but it has + baked in average pool which has better support across hardware. + + Args: + input_tensor: input tensor + pool_op: pooling op (avg pool is default) + Returns: + a tensor batch_size x 1 x 1 x depth. + """ + shape = input_tensor.get_shape().as_list() + if shape[1] is None or shape[2] is None: + kernel_size = tf.convert_to_tensor( + [1, tf.shape(input_tensor)[1], + tf.shape(input_tensor)[2], 1]) + else: + kernel_size = [1, shape[1], shape[2], 1] + output = pool_op( + input_tensor, ksize=kernel_size, strides=[1, 1, 1, 1], padding='VALID') + # Recover output shape, for unknown shape. + output.set_shape([None, 1, 1, None]) + return output + + +def training_scope(is_training=True, + weight_decay=0.00004, + stddev=0.09, + dropout_keep_prob=0.8, + bn_decay=0.997): + """Defines Mobilenet training scope. + + Usage: + with tf.contrib.slim.arg_scope(mobilenet.training_scope()): + logits, endpoints = mobilenet_v2.mobilenet(input_tensor) + + # the network created will be trainble with dropout/batch norm + # initialized appropriately. + Args: + is_training: if set to False this will ensure that all customizations are + set to non-training mode. This might be helpful for code that is reused + across both training/evaluation, but most of the time training_scope with + value False is not needed. + + weight_decay: The weight decay to use for regularizing the model. + stddev: Standard deviation for initialization, if negative uses xavier. + dropout_keep_prob: dropout keep probability + bn_decay: decay for the batch norm moving averages. + + Returns: + An argument scope to use via arg_scope. + """ + # Note: do not introduce parameters that would change the inference + # model here (for example whether to use bias), modify conv_def instead. + batch_norm_params = { + 'is_training': is_training, + 'decay': bn_decay, + } + + if stddev < 0: + weight_intitializer = slim.initializers.xavier_initializer() + else: + weight_intitializer = tf.truncated_normal_initializer(stddev=stddev) + + # Set weight_decay for weights in Conv and FC layers. + with slim.arg_scope( + [slim.conv2d, slim.fully_connected, slim.separable_conv2d], + weights_initializer=weight_intitializer, + normalizer_fn=slim.batch_norm), \ + slim.arg_scope([mobilenet_base, mobilenet], is_training=is_training),\ + slim.arg_scope([slim.batch_norm], **batch_norm_params), \ + slim.arg_scope([slim.dropout], is_training=is_training, + keep_prob=dropout_keep_prob), \ + slim.arg_scope([slim.conv2d], \ + weights_regularizer=slim.l2_regularizer(weight_decay)), \ + slim.arg_scope([slim.separable_conv2d], weights_regularizer=None) as s: + return s diff --git a/libs/networks/mobilenet/mobilenet_v2.py b/libs/networks/mobilenet/mobilenet_v2.py new file mode 100644 index 0000000..84300cd --- /dev/null +++ b/libs/networks/mobilenet/mobilenet_v2.py @@ -0,0 +1,188 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Implementation of Mobilenet V2. + +Architecture: https://arxiv.org/abs/1801.04381 + +The base model gives 72.2% accuracy on ImageNet, with 300MMadds, +3.4 M parameters. +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import copy + +import tensorflow as tf + +from libs.networks.mobilenet import conv_blocks as ops +from libs.networks.mobilenet import mobilenet as lib + +slim = tf.contrib.slim +op = lib.op + +expand_input = ops.expand_input_by_factor + +# pyformat: disable +# Architecture: https://arxiv.org/abs/1801.04381 +V2_DEF = dict( + defaults={ + # Note: these parameters of batch norm affect the architecture + # that's why they are here and not in training_scope. + (slim.batch_norm,): {'center': True, 'scale': True}, + (slim.conv2d, slim.fully_connected, slim.separable_conv2d): { + 'normalizer_fn': slim.batch_norm, 'activation_fn': tf.nn.relu6 + }, + (ops.expanded_conv,): { + 'expansion_size': expand_input(6), + 'split_expansion': 1, + 'normalizer_fn': slim.batch_norm, + 'residual': True + }, + (slim.conv2d, slim.separable_conv2d): {'padding': 'SAME'} + }, + spec=[ + op(slim.conv2d, stride=2, num_outputs=32, kernel_size=[3, 3]), + op(ops.expanded_conv, + expansion_size=expand_input(1, divisible_by=1), + num_outputs=16), + op(ops.expanded_conv, stride=2, num_outputs=24), + op(ops.expanded_conv, stride=1, num_outputs=24), + op(ops.expanded_conv, stride=2, num_outputs=32), + op(ops.expanded_conv, stride=1, num_outputs=32), + op(ops.expanded_conv, stride=1, num_outputs=32), + op(ops.expanded_conv, stride=2, num_outputs=64), + op(ops.expanded_conv, stride=1, num_outputs=64), + op(ops.expanded_conv, stride=1, num_outputs=64), + op(ops.expanded_conv, stride=1, num_outputs=64), + op(ops.expanded_conv, stride=1, num_outputs=96), + op(ops.expanded_conv, stride=1, num_outputs=96), + op(ops.expanded_conv, stride=1, num_outputs=96), + op(ops.expanded_conv, stride=2, num_outputs=160), + op(ops.expanded_conv, stride=1, num_outputs=160), + op(ops.expanded_conv, stride=1, num_outputs=160), + op(ops.expanded_conv, stride=1, num_outputs=320), + op(slim.conv2d, stride=1, kernel_size=[1, 1], num_outputs=1280) + ], +) +# pyformat: enable + + +@slim.add_arg_scope +def mobilenet(input_tensor, + num_classes=1001, + depth_multiplier=1.0, + scope='MobilenetV2', + conv_defs=None, + finegrain_classification_mode=False, + min_depth=None, + divisible_by=None, + **kwargs): + """Creates mobilenet V2 network. + + Inference mode is created by default. To create training use training_scope + below. + + with tf.contrib.slim.arg_scope(mobilenet_v2.training_scope()): + logits, endpoints = mobilenet_v2.mobilenet(input_tensor) + + Args: + input_tensor: The input tensor + num_classes: number of classes + depth_multiplier: The multiplier applied to scale number of + channels in each layer. Note: this is called depth multiplier in the + paper but the name is kept for consistency with slim's model builder. + scope: Scope of the operator + conv_defs: Allows to override default conv def. + finegrain_classification_mode: When set to True, the model + will keep the last layer large even for small multipliers. Following + https://arxiv.org/abs/1801.04381 + suggests that it improves performance for ImageNet-type of problems. + *Note* ignored if final_endpoint makes the builder exit earlier. + min_depth: If provided, will ensure that all layers will have that + many channels after application of depth multiplier. + divisible_by: If provided will ensure that all layers # channels + will be divisible by this number. + **kwargs: passed directly to mobilenet.mobilenet: + prediciton_fn- what prediction function to use. + reuse-: whether to reuse variables (if reuse set to true, scope + must be given). + Returns: + logits/endpoints pair + + Raises: + ValueError: On invalid arguments + """ + if conv_defs is None: + conv_defs = V2_DEF + if 'multiplier' in kwargs: + raise ValueError('mobilenetv2 doesn\'t support generic ' + 'multiplier parameter use "depth_multiplier" instead.') + if finegrain_classification_mode: + conv_defs = copy.deepcopy(conv_defs) + if depth_multiplier < 1: + conv_defs['spec'][-1].params['num_outputs'] /= depth_multiplier + + depth_args = {} + # NB: do not set depth_args unless they are provided to avoid overriding + # whatever default depth_multiplier might have thanks to arg_scope. + if min_depth is not None: + depth_args['min_depth'] = min_depth + if divisible_by is not None: + depth_args['divisible_by'] = divisible_by + + with slim.arg_scope((lib.depth_multiplier,), **depth_args): + return lib.mobilenet( + input_tensor, + num_classes=num_classes, + conv_defs=conv_defs, + scope=scope, + multiplier=depth_multiplier, + **kwargs) + + +@slim.add_arg_scope +def mobilenet_base(input_tensor, depth_multiplier=1.0, **kwargs): + """Creates base of the mobilenet (no pooling and no logits) .""" + return mobilenet(input_tensor, + depth_multiplier=depth_multiplier, + base_only=True, **kwargs) + + +def training_scope(**kwargs): + """Defines MobilenetV2 training scope. + + Usage: + with tf.contrib.slim.arg_scope(mobilenet_v2.training_scope()): + logits, endpoints = mobilenet_v2.mobilenet(input_tensor) + + with slim. + + Args: + **kwargs: Passed to mobilenet.training_scope. The following parameters + are supported: + weight_decay- The weight decay to use for regularizing the model. + stddev- Standard deviation for initialization, if negative uses xavier. + dropout_keep_prob- dropout keep probability + bn_decay- decay for the batch norm moving averages. + + Returns: + An `arg_scope` to use for the mobilenet v2 model. + """ + return lib.training_scope(**kwargs) + + +__all__ = ['training_scope', 'mobilenet_base', 'mobilenet', 'V2_DEF'] diff --git a/libs/networks/mobilenet/mobilenet_v2_test.py b/libs/networks/mobilenet/mobilenet_v2_test.py new file mode 100644 index 0000000..40e48fa --- /dev/null +++ b/libs/networks/mobilenet/mobilenet_v2_test.py @@ -0,0 +1,176 @@ +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for mobilenet_v2.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +import copy +import tensorflow as tf +from nets.mobilenet import conv_blocks as ops +from nets.mobilenet import mobilenet +from nets.mobilenet import mobilenet_v2 + + +slim = tf.contrib.slim + + +def find_ops(optype): + """Find ops of a given type in graphdef or a graph. + + Args: + optype: operation type (e.g. Conv2D) + Returns: + List of operations. + """ + gd = tf.get_default_graph() + return [var for var in gd.get_operations() if var.type == optype] + + +class MobilenetV2Test(tf.test.TestCase): + + def setUp(self): + tf.reset_default_graph() + + def testCreation(self): + spec = dict(mobilenet_v2.V2_DEF) + _, ep = mobilenet.mobilenet( + tf.placeholder(tf.float32, (10, 224, 224, 16)), conv_defs=spec) + num_convs = len(find_ops('Conv2D')) + + # This is mostly a sanity test. No deep reason for these particular + # constants. + # + # All but first 2 and last one have two convolutions, and there is one + # extra conv that is not in the spec. (logits) + self.assertEqual(num_convs, len(spec['spec']) * 2 - 2) + # Check that depthwise are exposed. + for i in range(2, 17): + self.assertIn('layer_%d/depthwise_output' % i, ep) + + def testCreationNoClasses(self): + spec = copy.deepcopy(mobilenet_v2.V2_DEF) + net, ep = mobilenet.mobilenet( + tf.placeholder(tf.float32, (10, 224, 224, 16)), conv_defs=spec, + num_classes=None) + self.assertIs(net, ep['global_pool']) + + def testImageSizes(self): + for input_size, output_size in [(224, 7), (192, 6), (160, 5), + (128, 4), (96, 3)]: + tf.reset_default_graph() + _, ep = mobilenet_v2.mobilenet( + tf.placeholder(tf.float32, (10, input_size, input_size, 3))) + + self.assertEqual(ep['layer_18/output'].get_shape().as_list()[1:3], + [output_size] * 2) + + def testWithSplits(self): + spec = copy.deepcopy(mobilenet_v2.V2_DEF) + spec['overrides'] = { + (ops.expanded_conv,): dict(split_expansion=2), + } + _, _ = mobilenet.mobilenet( + tf.placeholder(tf.float32, (10, 224, 224, 16)), conv_defs=spec) + num_convs = len(find_ops('Conv2D')) + # All but 3 op has 3 conv operatore, the remainign 3 have one + # and there is one unaccounted. + self.assertEqual(num_convs, len(spec['spec']) * 3 - 5) + + def testWithOutputStride8(self): + out, _ = mobilenet.mobilenet_base( + tf.placeholder(tf.float32, (10, 224, 224, 16)), + conv_defs=mobilenet_v2.V2_DEF, + output_stride=8, + scope='MobilenetV2') + self.assertEqual(out.get_shape().as_list()[1:3], [28, 28]) + + def testDivisibleBy(self): + tf.reset_default_graph() + mobilenet_v2.mobilenet( + tf.placeholder(tf.float32, (10, 224, 224, 16)), + conv_defs=mobilenet_v2.V2_DEF, + divisible_by=16, + min_depth=32) + s = [op.outputs[0].get_shape().as_list()[-1] for op in find_ops('Conv2D')] + s = set(s) + self.assertSameElements([32, 64, 96, 160, 192, 320, 384, 576, 960, 1280, + 1001], s) + + def testDivisibleByWithArgScope(self): + tf.reset_default_graph() + # Verifies that depth_multiplier arg scope actually works + # if no default min_depth is provided. + with slim.arg_scope((mobilenet.depth_multiplier,), min_depth=32): + mobilenet_v2.mobilenet( + tf.placeholder(tf.float32, (10, 224, 224, 2)), + conv_defs=mobilenet_v2.V2_DEF, depth_multiplier=0.1) + s = [op.outputs[0].get_shape().as_list()[-1] for op in find_ops('Conv2D')] + s = set(s) + self.assertSameElements(s, [32, 192, 128, 1001]) + + def testFineGrained(self): + tf.reset_default_graph() + # Verifies that depth_multiplier arg scope actually works + # if no default min_depth is provided. + + mobilenet_v2.mobilenet( + tf.placeholder(tf.float32, (10, 224, 224, 2)), + conv_defs=mobilenet_v2.V2_DEF, depth_multiplier=0.01, + finegrain_classification_mode=True) + s = [op.outputs[0].get_shape().as_list()[-1] for op in find_ops('Conv2D')] + s = set(s) + # All convolutions will be 8->48, except for the last one. + self.assertSameElements(s, [8, 48, 1001, 1280]) + + def testMobilenetBase(self): + tf.reset_default_graph() + # Verifies that mobilenet_base returns pre-pooling layer. + with slim.arg_scope((mobilenet.depth_multiplier,), min_depth=32): + net, _ = mobilenet_v2.mobilenet_base( + tf.placeholder(tf.float32, (10, 224, 224, 16)), + conv_defs=mobilenet_v2.V2_DEF, depth_multiplier=0.1) + self.assertEqual(net.get_shape().as_list(), [10, 7, 7, 128]) + + def testWithOutputStride16(self): + tf.reset_default_graph() + out, _ = mobilenet.mobilenet_base( + tf.placeholder(tf.float32, (10, 224, 224, 16)), + conv_defs=mobilenet_v2.V2_DEF, + output_stride=16) + self.assertEqual(out.get_shape().as_list()[1:3], [14, 14]) + + def testWithOutputStride8AndExplicitPadding(self): + tf.reset_default_graph() + out, _ = mobilenet.mobilenet_base( + tf.placeholder(tf.float32, (10, 224, 224, 16)), + conv_defs=mobilenet_v2.V2_DEF, + output_stride=8, + use_explicit_padding=True, + scope='MobilenetV2') + self.assertEqual(out.get_shape().as_list()[1:3], [28, 28]) + + def testWithOutputStride16AndExplicitPadding(self): + tf.reset_default_graph() + out, _ = mobilenet.mobilenet_base( + tf.placeholder(tf.float32, (10, 224, 224, 16)), + conv_defs=mobilenet_v2.V2_DEF, + output_stride=16, + use_explicit_padding=True) + self.assertEqual(out.get_shape().as_list()[1:3], [14, 14]) + + +if __name__ == '__main__': + tf.test.main() diff --git a/libs/networks/mobilenet_v2.py b/libs/networks/mobilenet_v2.py new file mode 100644 index 0000000..eced27c --- /dev/null +++ b/libs/networks/mobilenet_v2.py @@ -0,0 +1,126 @@ +# -*- coding: utf-8 -*- + +from __future__ import absolute_import, print_function, division +import tensorflow.contrib.slim as slim +import tensorflow as tf + +from libs.networks.mobilenet import mobilenet_v2 +from libs.networks.mobilenet.mobilenet import training_scope +from libs.networks.mobilenet.mobilenet_v2 import op +from libs.networks.mobilenet.mobilenet_v2 import ops +expand_input = ops.expand_input_by_factor + +V2_BASE_DEF = dict( + defaults={ + # Note: these parameters of batch norm affect the architecture + # that's why they are here and not in training_scope. + (slim.batch_norm,): {'center': True, 'scale': True}, + (slim.conv2d, slim.fully_connected, slim.separable_conv2d): { + 'normalizer_fn': slim.batch_norm, 'activation_fn': tf.nn.relu6 + }, + (ops.expanded_conv,): { + 'expansion_size': expand_input(6), + 'split_expansion': 1, + 'normalizer_fn': slim.batch_norm, + 'residual': True + }, + (slim.conv2d, slim.separable_conv2d): {'padding': 'SAME'} + }, + spec=[ + op(slim.conv2d, stride=2, num_outputs=32, kernel_size=[3, 3]), + op(ops.expanded_conv, + expansion_size=expand_input(1, divisible_by=1), + num_outputs=16, scope='expanded_conv'), + op(ops.expanded_conv, stride=2, num_outputs=24, scope='expanded_conv_1'), + op(ops.expanded_conv, stride=1, num_outputs=24, scope='expanded_conv_2'), + op(ops.expanded_conv, stride=2, num_outputs=32, scope='expanded_conv_3'), + op(ops.expanded_conv, stride=1, num_outputs=32, scope='expanded_conv_4'), + op(ops.expanded_conv, stride=1, num_outputs=32, scope='expanded_conv_5'), + op(ops.expanded_conv, stride=2, num_outputs=64, scope='expanded_conv_6'), + op(ops.expanded_conv, stride=1, num_outputs=64, scope='expanded_conv_7'), + op(ops.expanded_conv, stride=1, num_outputs=64, scope='expanded_conv_8'), + op(ops.expanded_conv, stride=1, num_outputs=64, scope='expanded_conv_9'), + op(ops.expanded_conv, stride=1, num_outputs=96, scope='expanded_conv_10'), + op(ops.expanded_conv, stride=1, num_outputs=96, scope='expanded_conv_11'), + op(ops.expanded_conv, stride=1, num_outputs=96, scope='expanded_conv_12') + ], +) + + +V2_HEAD_DEF = dict( + defaults={ + # Note: these parameters of batch norm affect the architecture + # that's why they are here and not in training_scope. + (slim.batch_norm,): {'center': True, 'scale': True}, + (slim.conv2d, slim.fully_connected, slim.separable_conv2d): { + 'normalizer_fn': slim.batch_norm, 'activation_fn': tf.nn.relu6 + }, + (ops.expanded_conv,): { + 'expansion_size': expand_input(6), + 'split_expansion': 1, + 'normalizer_fn': slim.batch_norm, + 'residual': True + }, + (slim.conv2d, slim.separable_conv2d): {'padding': 'SAME'} + }, + spec=[ + op(ops.expanded_conv, stride=2, num_outputs=160, scope='expanded_conv_13'), + op(ops.expanded_conv, stride=1, num_outputs=160, scope='expanded_conv_14'), + op(ops.expanded_conv, stride=1, num_outputs=160, scope='expanded_conv_15'), + op(ops.expanded_conv, stride=1, num_outputs=320, scope='expanded_conv_16'), + op(slim.conv2d, stride=1, kernel_size=[1, 1], num_outputs=1280, scope='Conv_1') + ], +) +def mobilenetv2_scope(is_training=True, + trainable=True, + weight_decay=0.00004, + stddev=0.09, + dropout_keep_prob=0.8, + bn_decay=0.997): + """Defines Mobilenet training scope. + In default. We do not use BN + + ReWrite the scope. + """ + batch_norm_params = { + 'is_training': False, + 'trainable': False, + 'decay': bn_decay, + } + with slim.arg_scope(training_scope(is_training=is_training, weight_decay=weight_decay)): + with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.separable_conv2d], + trainable=trainable): + with slim.arg_scope([slim.batch_norm], **batch_norm_params) as sc: + return sc + + + +def mobilenetv2_base(img_batch, is_training=True): + + with slim.arg_scope(mobilenetv2_scope(is_training=is_training, trainable=True)): + + feature_to_crop, endpoints = mobilenet_v2.mobilenet_base(input_tensor=img_batch, + num_classes=None, + is_training=False, + depth_multiplier=1.0, + scope='MobilenetV2', + conv_defs=V2_BASE_DEF, + finegrain_classification_mode=False) + + # feature_to_crop = tf.Print(feature_to_crop, [tf.shape(feature_to_crop)], summarize=10, message='rpn_shape') + return feature_to_crop + + +def mobilenetv2_head(inputs, is_training=True): + with slim.arg_scope(mobilenetv2_scope(is_training=is_training, trainable=True)): + net, _ = mobilenet_v2.mobilenet(input_tensor=inputs, + num_classes=None, + is_training=False, + depth_multiplier=1.0, + scope='MobilenetV2', + conv_defs=V2_HEAD_DEF, + finegrain_classification_mode=False) + + net = tf.squeeze(net, [1, 2]) + + return net \ No newline at end of file diff --git a/libs/networks/ops.py b/libs/networks/ops.py new file mode 100644 index 0000000..9a521f3 --- /dev/null +++ b/libs/networks/ops.py @@ -0,0 +1,42 @@ +import tensorflow as tf +import tensorflow.contrib.slim as slim + + +def norm(x, norm_type, is_train, G=32, esp=1e-5): + with tf.variable_scope('{}_norm'.format(norm_type)): + if norm_type == 'none': + output = x + elif norm_type == 'batch': + output = tf.contrib.layers.batch_norm( + x, center=True, scale=True, decay=0.999, + is_training=is_train, updates_collections=None + ) + elif norm_type == 'group': + # normalize + # tranpose: [bs, h, w, c] to [bs, c, h, w] following the paper + x = tf.transpose(x, [0, 3, 1, 2]) + N, C, H, W = x.get_shape().as_list() + G = min(G, C) + x = tf.reshape(x, [-1, G, C // G, H, W]) + mean, var = tf.nn.moments(x, [2, 3, 4], keep_dims=True) + x = (x - mean) / tf.sqrt(var + esp) + # per channel gamma and beta + gamma = tf.Variable(tf.constant(1.0, shape=[C]), dtype=tf.float32, name='gamma') + beta = tf.Variable(tf.constant(0.0, shape=[C]), dtype=tf.float32, name='beta') + gamma = tf.reshape(gamma, [1, C, 1, 1]) + beta = tf.reshape(beta, [1, C, 1, 1]) + + output = tf.reshape(x, [-1, C, H, W]) * gamma + beta + # tranpose: [bs, c, h, w, c] to [bs, h, w, c] following the paper + output = tf.transpose(output, [0, 2, 3, 1]) + else: + raise NotImplementedError + return output + + +def lrelu(x, leak=0.2, name="lrelu"): + with tf.variable_scope(name): + f1 = 0.5 * (1 + leak) + f2 = 0.5 * (1 - leak) + return f1 * x + f2 * abs(x) + diff --git a/libs/networks/resnet.py b/libs/networks/resnet.py new file mode 100644 index 0000000..d8bef03 --- /dev/null +++ b/libs/networks/resnet.py @@ -0,0 +1,235 @@ +# -*- coding: utf-8 -*- + +from __future__ import absolute_import, print_function, division + + +import tensorflow as tf +import tensorflow.contrib.slim as slim +from libs.configs import cfgs +from tensorflow.contrib.slim.nets import resnet_v1 +from tensorflow.contrib.slim.nets import resnet_utils +from tensorflow.contrib.slim.python.slim.nets.resnet_v1 import resnet_v1_block +# import tfplot as tfp + + +def resnet_arg_scope( + is_training=True, weight_decay=cfgs.WEIGHT_DECAY, batch_norm_decay=0.997, + batch_norm_epsilon=1e-5, batch_norm_scale=True): + ''' + + In Default, we do not use BN to train resnet, since batch_size is too small. + So is_training is False and trainable is False in the batch_norm params. + + ''' + batch_norm_params = { + 'is_training': False, 'decay': batch_norm_decay, + 'epsilon': batch_norm_epsilon, 'scale': batch_norm_scale, + 'trainable': False, + 'updates_collections': tf.GraphKeys.UPDATE_OPS + } + + with slim.arg_scope( + [slim.conv2d], + weights_regularizer=slim.l2_regularizer(weight_decay), + weights_initializer=slim.variance_scaling_initializer(), + trainable=is_training, + activation_fn=tf.nn.relu, + normalizer_fn=slim.batch_norm, + normalizer_params=batch_norm_params): + with slim.arg_scope([slim.batch_norm], **batch_norm_params) as arg_sc: + return arg_sc + + +def fusion_two_layer(C_i, P_j, scope): + ''' + i = j+1 + :param C_i: shape is [1, h, w, c] + :param P_j: shape is [1, h/2, w/2, 256] + :return: + P_i + ''' + with tf.variable_scope(scope): + level_name = scope.split('_')[1] + + h, w = tf.shape(C_i)[1], tf.shape(C_i)[2] + upsample_p = tf.image.resize_bilinear(P_j, + size=[h, w], + name='up_sample_'+level_name) + + reduce_dim_c = slim.conv2d(C_i, + num_outputs=256, + kernel_size=[1, 1], stride=1, + scope='reduce_dim_'+level_name) + + add_f = 0.5*upsample_p + 0.5*reduce_dim_c + + # P_i = slim.conv2d(add_f, + # num_outputs=256, kernel_size=[3, 3], stride=1, + # padding='SAME', + # scope='fusion_'+level_name) + return add_f + + +# def add_heatmap(feature_maps, name): +# ''' +# +# :param feature_maps:[B, H, W, C] +# :return: +# ''' +# +# def figure_attention(activation): +# fig, ax = tfp.subplots() +# im = ax.imshow(activation, cmap='jet') +# fig.colorbar(im) +# return fig +# +# heatmap = tf.reduce_sum(feature_maps, axis=-1) +# heatmap = tf.squeeze(heatmap, axis=0) +# tfp.summary.plot(name, figure_attention, [heatmap]) + + +def resnet_base(img_batch, scope_name, is_training=True): + ''' + this code is derived from light-head rcnn. + https://github.com/zengarden/light_head_rcnn + + It is convenient to freeze blocks. So we adapt this mode. + ''' + if scope_name == 'resnet_v1_50': + middle_num_units = 6 + elif scope_name == 'resnet_v1_101': + middle_num_units = 23 + else: + raise NotImplementedError('We only support resnet_v1_50 or resnet_v1_101. Check your network name....') + + blocks = [resnet_v1_block('block1', base_depth=64, num_units=3, stride=2), + resnet_v1_block('block2', base_depth=128, num_units=4, stride=2), + resnet_v1_block('block3', base_depth=256, num_units=middle_num_units, stride=2), + resnet_v1_block('block4', base_depth=512, num_units=3, stride=1)] + # when use fpn . stride list is [1, 2, 2] + + with slim.arg_scope(resnet_arg_scope(is_training=False)): + with tf.variable_scope(scope_name, scope_name): + # Do the first few layers manually, because 'SAME' padding can behave inconsistently + # for images of different sizes: sometimes 0, sometimes 1 + net = resnet_utils.conv2d_same( + img_batch, 64, 7, stride=2, scope='conv1') + net = tf.pad(net, [[0, 0], [1, 1], [1, 1], [0, 0]]) + net = slim.max_pool2d( + net, [3, 3], stride=2, padding='VALID', scope='pool1') + + not_freezed = [False] * cfgs.FIXED_BLOCKS + (4-cfgs.FIXED_BLOCKS)*[True] + # Fixed_Blocks can be 1~3 + + with slim.arg_scope(resnet_arg_scope(is_training=(is_training and not_freezed[0]))): + C2, end_points_C2 = resnet_v1.resnet_v1(net, + blocks[0:1], + global_pool=False, + include_root_block=False, + scope=scope_name) + + # C2 = tf.Print(C2, [tf.shape(C2)], summarize=10, message='C2_shape') + # add_heatmap(C2, name='Layer2/C2_heat') + + with slim.arg_scope(resnet_arg_scope(is_training=(is_training and not_freezed[1]))): + C3, end_points_C3 = resnet_v1.resnet_v1(C2, + blocks[1:2], + global_pool=False, + include_root_block=False, + scope=scope_name) + + # C3 = tf.Print(C3, [tf.shape(C3)], summarize=10, message='C3_shape') + # add_heatmap(C3, name='Layer3/C3_heat') + with slim.arg_scope(resnet_arg_scope(is_training=(is_training and not_freezed[2]))): + C4, end_points_C4 = resnet_v1.resnet_v1(C3, + blocks[2:3], + global_pool=False, + include_root_block=False, + scope=scope_name) + + # add_heatmap(C4, name='Layer4/C4_heat') + + # C4 = tf.Print(C4, [tf.shape(C4)], summarize=10, message='C4_shape') + with slim.arg_scope(resnet_arg_scope(is_training=is_training)): + C5, end_points_C5 = resnet_v1.resnet_v1(C4, + blocks[3:4], + global_pool=False, + include_root_block=False, + scope=scope_name) + # C5 = tf.Print(C5, [tf.shape(C5)], summarize=10, message='C5_shape') + # add_heatmap(C5, name='Layer5/C5_heat') + + feature_dict = {'C2': end_points_C2['{}/block1/unit_2/bottleneck_v1'.format(scope_name)], + 'C3': end_points_C3['{}/block2/unit_3/bottleneck_v1'.format(scope_name)], + 'C4': end_points_C4['{}/block3/unit_{}/bottleneck_v1'.format(scope_name, middle_num_units - 1)], + 'C5': end_points_C5['{}/block4/unit_3/bottleneck_v1'.format(scope_name)], + # 'C5': end_points_C5['{}/block4'.format(scope_name)], + } + + pyramid_dict = {} + with tf.variable_scope('build_pyramid'): + with slim.arg_scope([slim.conv2d], weights_regularizer=slim.l2_regularizer(cfgs.WEIGHT_DECAY), + activation_fn=None, normalizer_fn=None): + + P5 = slim.conv2d(feature_dict['C5'], + num_outputs=256, + kernel_size=[1, 1], + stride=1, scope='build_P5') + + pyramid_dict['P5'] = P5 + + for level in range(4, 1, -1): # build [P4, P3, P2] + + pyramid_dict['P%d' % level] = fusion_two_layer(C_i=feature_dict["C%d" % level], + P_j=pyramid_dict["P%d" % (level + 1)], + scope='build_P%d' % level) + for level in range(5, 1, -1): + pyramid_dict['P%d' % level] = slim.conv2d(pyramid_dict['P%d' % level], + num_outputs=256, kernel_size=[3, 3], padding="SAME", + stride=1, scope="fuse_P%d" % level) + + if "P6" in cfgs.LEVLES: + P6 = slim.avg_pool2d(pyramid_dict['P5'], kernel_size=[1, 1], stride=2, scope='build_P6') + pyramid_dict['P6'] = P6 + + # for level in range(5, 1, -1): + # add_heatmap(feature_dict['C%d' % level], name='Layer%d/C%d_heat' % (level, level)) + # add_heatmap(pyramid_dict['P%d' % level], name='Layer%d/P%d_heat' % (level, level)) + + # return [P2, P3, P4, P5, P6] + print("we are in Pyramid::-======>>>>") + print(cfgs.LEVLES) + print("base_anchor_size are: ", cfgs.BASE_ANCHOR_SIZE_LIST) + print(20 * "__") + return [pyramid_dict[level_name] for level_name in cfgs.LEVLES] + + +def restnet_head(inputs, is_training, scope_name): + ''' + + :param inputs: [minibatch_size, 7, 7, 256] + :param is_training: + :param scope_name: + :return: + ''' + + with tf.variable_scope('build_fc_layers'): + + # fc1 = slim.conv2d(inputs=inputs, + # num_outputs=1024, + # kernel_size=[7, 7], + # padding='VALID', + # scope='fc1') # shape is [minibatch_size, 1, 1, 1024] + # fc1 = tf.squeeze(fc1, [1, 2], name='squeeze_fc1') + + inputs = slim.flatten(inputs=inputs, scope='flatten_inputs') + + fc1 = slim.fully_connected(inputs, num_outputs=1024, scope='fc1') + + fc2 = slim.fully_connected(fc1, num_outputs=1024, scope='fc2') + + # fc3 = slim.fully_connected(fc2, num_outputs=1024, scope='fc3') + + # we add fc3 to increase the ability of fast-rcnn head + return fc2 + diff --git a/libs/networks/resnet_gluoncv.py b/libs/networks/resnet_gluoncv.py new file mode 100644 index 0000000..c9e4063 --- /dev/null +++ b/libs/networks/resnet_gluoncv.py @@ -0,0 +1,263 @@ +# -*- coding: utf-8 -*- +from __future__ import absolute_import, division, print_function +import tensorflow as tf +import tensorflow.contrib.slim as slim + +from libs.configs import cfgs +from libs.networks.resnet import fusion_two_layer +DATA_FORMAT = "NHWC" +DEBUG = False +debug_dict = {} +BottleNeck_NUM_DICT = { + 'resnet50_v1b': [3, 4, 6, 3], + 'resnet101_v1b': [3, 4, 23, 3], + 'resnet50_v1d': [3, 4, 6, 3], + 'resnet101_v1d': [3, 4, 23, 3] +} + +BASE_CHANNELS_DICT = { + 'resnet50_v1b': [64, 128, 256, 512], + 'resnet101_v1b': [64, 128, 256, 512], + 'resnet50_v1d': [64, 128, 256, 512], + 'resnet101_v1d': [64, 128, 256, 512] +} + + +def resnet_arg_scope(freeze_norm, is_training=True, weight_decay=0.0001, + batch_norm_decay=0.9, batch_norm_epsilon=1e-5, batch_norm_scale=True): + + batch_norm_params = { + 'is_training': False, 'decay': batch_norm_decay, + 'epsilon': batch_norm_epsilon, 'scale': batch_norm_scale, + 'trainable': False, + 'updates_collections': tf.GraphKeys.UPDATE_OPS, + 'data_format': DATA_FORMAT + } + with slim.arg_scope( + [slim.conv2d], + weights_regularizer=slim.l2_regularizer(weight_decay), + weights_initializer=slim.variance_scaling_initializer(), + trainable=is_training, + activation_fn=tf.nn.relu, + normalizer_fn=slim.batch_norm, + normalizer_params=batch_norm_params): + with slim.arg_scope([slim.batch_norm], **batch_norm_params) as arg_sc: + return arg_sc + + +def stem_7x7(net, scope="C1"): + + with tf.variable_scope(scope): + net = tf.pad(net, paddings=[[0, 0], [3, 3], [3, 3], [0, 0]]) # pad for data + net = slim.conv2d(net, num_outputs=64, kernel_size=[7, 7], stride=2, + padding="VALID", biases_initializer=None, data_format=DATA_FORMAT, + scope="conv0") + if DEBUG: + debug_dict['conv_7x7_bn_relu'] = tf.transpose(net, [0, 3, 1, 2]) # NHWC --> NCHW + net = tf.pad(net, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]) + net = slim.max_pool2d(net, kernel_size=[3, 3], stride=2, padding="VALID", data_format=DATA_FORMAT) + return net + + +def stem_stack_3x3(net, input_channel=32, scope="C1"): + with tf.variable_scope(scope): + net = tf.pad(net, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]) + net = slim.conv2d(net, num_outputs=input_channel, kernel_size=[3, 3], stride=2, + padding="VALID", biases_initializer=None, data_format=DATA_FORMAT, + scope='conv0') + net = tf.pad(net, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]) + net = slim.conv2d(net, num_outputs=input_channel, kernel_size=[3, 3], stride=1, + padding="VALID", biases_initializer=None, data_format=DATA_FORMAT, + scope='conv1') + net = tf.pad(net, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]) + net = slim.conv2d(net, num_outputs=input_channel*2, kernel_size=[3, 3], stride=1, + padding="VALID", biases_initializer=None, data_format=DATA_FORMAT, + scope='conv2') + net = tf.pad(net, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]) + net = slim.max_pool2d(net, kernel_size=[3, 3], stride=2, padding="VALID", data_format=DATA_FORMAT) + return net + + +def bottleneck_v1b(input_x, base_channel, scope, stride=1, projection=False, avg_down=True): + ''' + for bottleneck_v1b: reduce spatial dim in conv_3x3 with stride 2. + ''' + with tf.variable_scope(scope): + if DEBUG: + debug_dict[input_x.op.name] = tf.transpose(input_x, [0, 3, 1, 2]) + net = slim.conv2d(input_x, num_outputs=base_channel, kernel_size=[1, 1], stride=1, + padding="VALID", biases_initializer=None, data_format=DATA_FORMAT, + scope='conv0') + if DEBUG: + debug_dict[net.op.name] = tf.transpose(net, [0, 3, 1, 2]) + + net = tf.pad(net, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]) + if DEBUG: + debug_dict[net.op.name] = tf.transpose(net, [0, 3, 1, 2]) + + net = slim.conv2d(net, num_outputs=base_channel, kernel_size=[3, 3], stride=stride, + padding="VALID", biases_initializer=None, data_format=DATA_FORMAT, + scope='conv1') + if DEBUG: + debug_dict[net.op.name] = tf.transpose(net, [0, 3, 1, 2]) + net = slim.conv2d(net, num_outputs=base_channel * 4, kernel_size=[1, 1], stride=1, + padding="VALID", biases_initializer=None, data_format=DATA_FORMAT, + activation_fn=None, scope='conv2') + if DEBUG: + debug_dict[net.op.name] = tf.transpose(net, [0, 3, 1, 2]) + # Note that : gamma in the last conv should be init with 0. + # But we just reload params from mxnet, so don't specific batch norm initializer + if projection: + + if avg_down: # design for resnet_v1d + ''' + In GluonCV, padding is "ceil mode". Here we use "SAME" to replace it, which may cause Erros. + And the erro will grow with depth of resnet. e.g. res101 erro > res50 erro + ''' + shortcut = slim.avg_pool2d(input_x, kernel_size=[stride, stride], stride=stride, padding="SAME", + data_format=DATA_FORMAT) + if DEBUG: + debug_dict[shortcut.op.name] = tf.transpose(shortcut, [0, 3, 1, 2]) + + shortcut = slim.conv2d(shortcut, num_outputs=base_channel*4, kernel_size=[1, 1], + stride=1, padding="VALID", biases_initializer=None, data_format=DATA_FORMAT, + activation_fn=None, + scope='shortcut') + if DEBUG: + debug_dict[shortcut.op.name] = tf.transpose(shortcut, [0, 3, 1, 2]) + # shortcut should have batch norm. + else: + shortcut = slim.conv2d(input_x, num_outputs=base_channel * 4, kernel_size=[1, 1], + stride=stride, padding="VALID", biases_initializer=None, activation_fn=None, + data_format=DATA_FORMAT, + scope='shortcut') + if DEBUG: + debug_dict[shortcut.op.name] = tf.transpose(shortcut, [0, 3, 1, 2]) + else: + shortcut = tf.identity(input_x, name='shortcut/Identity') + if DEBUG: + debug_dict[shortcut.op.name] = tf.transpose(shortcut, [0, 3, 1, 2]) + + net = net + shortcut + if DEBUG: + debug_dict[net.op.name] = tf.transpose(net, [0, 3, 1, 2]) + net = tf.nn.relu(net) + if DEBUG: + debug_dict[net.op.name] = tf.transpose(net, [0, 3, 1, 2]) + return net + + +def make_block(net, base_channel, bottleneck_nums, scope, avg_down=True, spatial_downsample=False): + with tf.variable_scope(scope): + first_stride = 2 if spatial_downsample else 1 + + net = bottleneck_v1b(input_x=net, base_channel=base_channel,scope='bottleneck_0', + stride=first_stride, avg_down=avg_down, projection=True) + for i in range(1, bottleneck_nums): + net = bottleneck_v1b(input_x=net, base_channel=base_channel, scope="bottleneck_%d" % i, + stride=1, avg_down=avg_down, projection=False) + return net + + +def get_resnet_v1_b_base(input_x, freeze_norm, scope="resnet50_v1b", bottleneck_nums=[3, 4, 6, 3], base_channels=[64, 128, 256, 512], + freeze=[True, False, False, False, False], is_training=True): + + assert len(bottleneck_nums) == len(base_channels), "bottleneck num should same as base_channels size" + assert len(freeze) == len(bottleneck_nums) +1, "should satisfy:: len(freeze) == len(bottleneck_nums) + 1" + feature_dict = {} + with tf.variable_scope(scope): + with slim.arg_scope(resnet_arg_scope(is_training=(not freeze[0]) and is_training, + freeze_norm=freeze_norm)): + net = stem_7x7(net=input_x, scope="C1") + feature_dict["C1"] = net + for i in range(2, len(bottleneck_nums)+2): + spatial_downsample = False if i == 2 else True + with slim.arg_scope(resnet_arg_scope(is_training=(not freeze[i-1]) and is_training, + freeze_norm=freeze_norm)): + net = make_block(net=net, base_channel=base_channels[i-2], + bottleneck_nums=bottleneck_nums[i-2], + scope="C%d" % i, + avg_down=False, spatial_downsample=spatial_downsample) + feature_dict["C%d" % i] = net + + return net, feature_dict + + +def get_resnet_v1_d_base(input_x, freeze_norm, scope="resnet50_v1d", bottleneck_nums=[3, 4, 6, 3], base_channels=[64, 128, 256, 512], + freeze=[True, False, False, False, False], is_training=True): + + assert len(bottleneck_nums) == len(base_channels), "bottleneck num should same as base_channels size" + assert len(freeze) == len(bottleneck_nums) + 1, "should satisfy:: len(freeze) == len(bottleneck_nums) + 1" + feature_dict = {} + with tf.variable_scope(scope): + with slim.arg_scope(resnet_arg_scope(is_training=((not freeze[0]) and is_training), + freeze_norm=freeze_norm)): + net = stem_stack_3x3(net=input_x, input_channel=32, scope="C1") + feature_dict["C1"] = net + # print (net) + for i in range(2, len(bottleneck_nums)+2): + spatial_downsample = False if i == 2 else True # do not downsample in C2 + with slim.arg_scope(resnet_arg_scope(is_training=((not freeze[i-1]) and is_training), + freeze_norm=freeze_norm)): + net = make_block(net=net, base_channel=base_channels[i-2], + bottleneck_nums=bottleneck_nums[i-2], + scope="C%d" % i, + avg_down=True, spatial_downsample=spatial_downsample) + feature_dict["C%d" % i] = net + + return net, feature_dict + + +# ----------------------------------- +def resnet_base(img_batch, scope_name, is_training=True): + if scope_name.endswith('b'): + get_resnet_fn = get_resnet_v1_b_base + elif scope_name.endswith('d'): + get_resnet_fn = get_resnet_v1_d_base + else: + raise ValueError("scope Name erro....") + + _, feature_dict = get_resnet_fn(input_x=img_batch, scope=scope_name, + bottleneck_nums=BottleNeck_NUM_DICT[scope_name], + base_channels=BASE_CHANNELS_DICT[scope_name], + is_training=is_training, freeze_norm=True, + freeze=cfgs.FREEZE_BLOCKS) + + pyramid_dict = {} + with tf.variable_scope('build_pyramid'): + with slim.arg_scope([slim.conv2d], weights_regularizer=slim.l2_regularizer(cfgs.WEIGHT_DECAY), + activation_fn=None, normalizer_fn=None): + + P5 = slim.conv2d(feature_dict['C5'], + num_outputs=256, + kernel_size=[1, 1], + stride=1, scope='build_P5') + + pyramid_dict['P5'] = P5 + + for level in range(4, 1, -1): # build [P4, P3, P2] + + pyramid_dict['P%d' % level] = fusion_two_layer(C_i=feature_dict["C%d" % level], + P_j=pyramid_dict["P%d" % (level + 1)], + scope='build_P%d' % level) + for level in range(5, 1, -1): + pyramid_dict['P%d' % level] = slim.conv2d(pyramid_dict['P%d' % level], + num_outputs=256, kernel_size=[3, 3], padding="SAME", + stride=1, scope="fuse_P%d" % level) + + if "P6" in cfgs.LEVLES: + P6 = slim.avg_pool2d(pyramid_dict['P5'], kernel_size=[1, 1], stride=2, scope='build_P6') + pyramid_dict['P6'] = P6 + + # for level in range(5, 1, -1): + # add_heatmap(feature_dict['C%d' % level], name='Layer%d/C%d_heat' % (level, level)) + # add_heatmap(pyramid_dict['P%d' % level], name='Layer%d/P%d_heat' % (level, level)) + + # return [P2, P3, P4, P5, P6] + print("we are in Pyramid::-======>>>>") + print(cfgs.LEVLES) + print("base_anchor_size are: ", cfgs.BASE_ANCHOR_SIZE_LIST) + print(20 * "__") + return [pyramid_dict[level_name] for level_name in cfgs.LEVLES] + + diff --git a/libs/networks/slim_nets/__init__.py b/libs/networks/slim_nets/__init__.py new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/libs/networks/slim_nets/__init__.py @@ -0,0 +1 @@ + diff --git a/libs/networks/slim_nets/alexnet.py b/libs/networks/slim_nets/alexnet.py new file mode 100644 index 0000000..4e7e563 --- /dev/null +++ b/libs/networks/slim_nets/alexnet.py @@ -0,0 +1,125 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Contains a model definition for AlexNet. + +This work was first described in: + ImageNet Classification with Deep Convolutional Neural Networks + Alex Krizhevsky, Ilya Sutskever and Geoffrey E. Hinton + +and later refined in: + One weird trick for parallelizing convolutional neural networks + Alex Krizhevsky, 2014 + +Here we provide the implementation proposed in "One weird trick" and not +"ImageNet Classification", as per the paper, the LRN layers have been removed. + +Usage: + with slim.arg_scope(alexnet.alexnet_v2_arg_scope()): + outputs, end_points = alexnet.alexnet_v2(inputs) + +@@alexnet_v2 +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf + +slim = tf.contrib.slim +trunc_normal = lambda stddev: tf.truncated_normal_initializer(0.0, stddev) + + +def alexnet_v2_arg_scope(weight_decay=0.0005): + with slim.arg_scope([slim.conv2d, slim.fully_connected], + activation_fn=tf.nn.relu, + biases_initializer=tf.constant_initializer(0.1), + weights_regularizer=slim.l2_regularizer(weight_decay)): + with slim.arg_scope([slim.conv2d], padding='SAME'): + with slim.arg_scope([slim.max_pool2d], padding='VALID') as arg_sc: + return arg_sc + + +def alexnet_v2(inputs, + num_classes=1000, + is_training=True, + dropout_keep_prob=0.5, + spatial_squeeze=True, + scope='alexnet_v2'): + """AlexNet version 2. + + Described in: http://arxiv.org/pdf/1404.5997v2.pdf + Parameters from: + github.com/akrizhevsky/cuda-convnet2/blob/master/layers/ + layers-imagenet-1gpu.cfg + + Note: All the fully_connected layers have been transformed to conv2d layers. + To use in classification mode, resize input to 224x224. To use in fully + convolutional mode, set spatial_squeeze to false. + The LRN layers have been removed and change the initializers from + random_normal_initializer to xavier_initializer. + + Args: + inputs: a tensor of size [batch_size, height, width, channels]. + num_classes: number of predicted classes. + is_training: whether or not the model is being trained. + dropout_keep_prob: the probability that activations are kept in the dropout + layers during training. + spatial_squeeze: whether or not should squeeze the spatial dimensions of the + outputs. Useful to remove unnecessary dimensions for classification. + scope: Optional scope for the variables. + + Returns: + the last op containing the log predictions and end_points dict. + """ + with tf.variable_scope(scope, 'alexnet_v2', [inputs]) as sc: + end_points_collection = sc.name + '_end_points' + # Collect outputs for conv2d, fully_connected and max_pool2d. + with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d], + outputs_collections=[end_points_collection]): + net = slim.conv2d(inputs, 64, [11, 11], 4, padding='VALID', + scope='conv1') + net = slim.max_pool2d(net, [3, 3], 2, scope='pool1') + net = slim.conv2d(net, 192, [5, 5], scope='conv2') + net = slim.max_pool2d(net, [3, 3], 2, scope='pool2') + net = slim.conv2d(net, 384, [3, 3], scope='conv3') + net = slim.conv2d(net, 384, [3, 3], scope='conv4') + net = slim.conv2d(net, 256, [3, 3], scope='conv5') + net = slim.max_pool2d(net, [3, 3], 2, scope='pool5') + + # Use conv2d instead of fully_connected layers. + with slim.arg_scope([slim.conv2d], + weights_initializer=trunc_normal(0.005), + biases_initializer=tf.constant_initializer(0.1)): + net = slim.conv2d(net, 4096, [5, 5], padding='VALID', + scope='fc6') + net = slim.dropout(net, dropout_keep_prob, is_training=is_training, + scope='dropout6') + net = slim.conv2d(net, 4096, [1, 1], scope='fc7') + net = slim.dropout(net, dropout_keep_prob, is_training=is_training, + scope='dropout7') + net = slim.conv2d(net, num_classes, [1, 1], + activation_fn=None, + normalizer_fn=None, + biases_initializer=tf.zeros_initializer(), + scope='fc8') + + # Convert end_points_collection into a end_point dict. + end_points = slim.utils.convert_collection_to_dict(end_points_collection) + if spatial_squeeze: + net = tf.squeeze(net, [1, 2], name='fc8/squeezed') + end_points[sc.name + '/fc8'] = net + return net, end_points +alexnet_v2.default_image_size = 224 diff --git a/libs/networks/slim_nets/alexnet_test.py b/libs/networks/slim_nets/alexnet_test.py new file mode 100644 index 0000000..6fc9a05 --- /dev/null +++ b/libs/networks/slim_nets/alexnet_test.py @@ -0,0 +1,145 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for slim.slim_nets.alexnet.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf + +from nets import alexnet + +slim = tf.contrib.slim + + +class AlexnetV2Test(tf.test.TestCase): + + def testBuild(self): + batch_size = 5 + height, width = 224, 224 + num_classes = 1000 + with self.test_session(): + inputs = tf.random_uniform((batch_size, height, width, 3)) + logits, _ = alexnet.alexnet_v2(inputs, num_classes) + self.assertEquals(logits.op.name, 'alexnet_v2/fc8/squeezed') + self.assertListEqual(logits.get_shape().as_list(), + [batch_size, num_classes]) + + def testFullyConvolutional(self): + batch_size = 1 + height, width = 300, 400 + num_classes = 1000 + with self.test_session(): + inputs = tf.random_uniform((batch_size, height, width, 3)) + logits, _ = alexnet.alexnet_v2(inputs, num_classes, spatial_squeeze=False) + self.assertEquals(logits.op.name, 'alexnet_v2/fc8/BiasAdd') + self.assertListEqual(logits.get_shape().as_list(), + [batch_size, 4, 7, num_classes]) + + def testEndPoints(self): + batch_size = 5 + height, width = 224, 224 + num_classes = 1000 + with self.test_session(): + inputs = tf.random_uniform((batch_size, height, width, 3)) + _, end_points = alexnet.alexnet_v2(inputs, num_classes) + expected_names = ['alexnet_v2/conv1', + 'alexnet_v2/pool1', + 'alexnet_v2/conv2', + 'alexnet_v2/pool2', + 'alexnet_v2/conv3', + 'alexnet_v2/conv4', + 'alexnet_v2/conv5', + 'alexnet_v2/pool5', + 'alexnet_v2/fc6', + 'alexnet_v2/fc7', + 'alexnet_v2/fc8' + ] + self.assertSetEqual(set(end_points.keys()), set(expected_names)) + + def testModelVariables(self): + batch_size = 5 + height, width = 224, 224 + num_classes = 1000 + with self.test_session(): + inputs = tf.random_uniform((batch_size, height, width, 3)) + alexnet.alexnet_v2(inputs, num_classes) + expected_names = ['alexnet_v2/conv1/weights', + 'alexnet_v2/conv1/biases', + 'alexnet_v2/conv2/weights', + 'alexnet_v2/conv2/biases', + 'alexnet_v2/conv3/weights', + 'alexnet_v2/conv3/biases', + 'alexnet_v2/conv4/weights', + 'alexnet_v2/conv4/biases', + 'alexnet_v2/conv5/weights', + 'alexnet_v2/conv5/biases', + 'alexnet_v2/fc6/weights', + 'alexnet_v2/fc6/biases', + 'alexnet_v2/fc7/weights', + 'alexnet_v2/fc7/biases', + 'alexnet_v2/fc8/weights', + 'alexnet_v2/fc8/biases', + ] + model_variables = [v.op.name for v in slim.get_model_variables()] + self.assertSetEqual(set(model_variables), set(expected_names)) + + def testEvaluation(self): + batch_size = 2 + height, width = 224, 224 + num_classes = 1000 + with self.test_session(): + eval_inputs = tf.random_uniform((batch_size, height, width, 3)) + logits, _ = alexnet.alexnet_v2(eval_inputs, is_training=False) + self.assertListEqual(logits.get_shape().as_list(), + [batch_size, num_classes]) + predictions = tf.argmax(logits, 1) + self.assertListEqual(predictions.get_shape().as_list(), [batch_size]) + + def testTrainEvalWithReuse(self): + train_batch_size = 2 + eval_batch_size = 1 + train_height, train_width = 224, 224 + eval_height, eval_width = 300, 400 + num_classes = 1000 + with self.test_session(): + train_inputs = tf.random_uniform( + (train_batch_size, train_height, train_width, 3)) + logits, _ = alexnet.alexnet_v2(train_inputs) + self.assertListEqual(logits.get_shape().as_list(), + [train_batch_size, num_classes]) + tf.get_variable_scope().reuse_variables() + eval_inputs = tf.random_uniform( + (eval_batch_size, eval_height, eval_width, 3)) + logits, _ = alexnet.alexnet_v2(eval_inputs, is_training=False, + spatial_squeeze=False) + self.assertListEqual(logits.get_shape().as_list(), + [eval_batch_size, 4, 7, num_classes]) + logits = tf.reduce_mean(logits, [1, 2]) + predictions = tf.argmax(logits, 1) + self.assertEquals(predictions.get_shape().as_list(), [eval_batch_size]) + + def testForward(self): + batch_size = 1 + height, width = 224, 224 + with self.test_session() as sess: + inputs = tf.random_uniform((batch_size, height, width, 3)) + logits, _ = alexnet.alexnet_v2(inputs) + sess.run(tf.global_variables_initializer()) + output = sess.run(logits) + self.assertTrue(output.any()) + +if __name__ == '__main__': + tf.test.main() diff --git a/libs/networks/slim_nets/cifarnet.py b/libs/networks/slim_nets/cifarnet.py new file mode 100644 index 0000000..44ca0fe --- /dev/null +++ b/libs/networks/slim_nets/cifarnet.py @@ -0,0 +1,112 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Contains a variant of the CIFAR-10 model definition.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf + +slim = tf.contrib.slim + +trunc_normal = lambda stddev: tf.truncated_normal_initializer(stddev=stddev) + + +def cifarnet(images, num_classes=10, is_training=False, + dropout_keep_prob=0.5, + prediction_fn=slim.softmax, + scope='CifarNet'): + """Creates a variant of the CifarNet model. + + Note that since the output is a set of 'logits', the values fall in the + interval of (-infinity, infinity). Consequently, to convert the outputs to a + probability distribution over the characters, one will need to convert them + using the softmax function: + + logits = cifarnet.cifarnet(images, is_training=False) + probabilities = tf.nn.softmax(logits) + predictions = tf.argmax(logits, 1) + + Args: + images: A batch of `Tensors` of size [batch_size, height, width, channels]. + num_classes: the number of classes in the dataset. + is_training: specifies whether or not we're currently training the model. + This variable will determine the behaviour of the dropout layer. + dropout_keep_prob: the percentage of activation values that are retained. + prediction_fn: a function to get predictions out of logits. + scope: Optional variable_scope. + + Returns: + logits: the pre-softmax activations, a tensor of size + [batch_size, `num_classes`] + end_points: a dictionary from components of the network to the corresponding + activation. + """ + end_points = {} + + with tf.variable_scope(scope, 'CifarNet', [images, num_classes]): + net = slim.conv2d(images, 64, [5, 5], scope='conv1') + end_points['conv1'] = net + net = slim.max_pool2d(net, [2, 2], 2, scope='pool1') + end_points['pool1'] = net + net = tf.nn.lrn(net, 4, bias=1.0, alpha=0.001/9.0, beta=0.75, name='norm1') + net = slim.conv2d(net, 64, [5, 5], scope='conv2') + end_points['conv2'] = net + net = tf.nn.lrn(net, 4, bias=1.0, alpha=0.001/9.0, beta=0.75, name='norm2') + net = slim.max_pool2d(net, [2, 2], 2, scope='pool2') + end_points['pool2'] = net + net = slim.flatten(net) + end_points['Flatten'] = net + net = slim.fully_connected(net, 384, scope='fc3') + end_points['fc3'] = net + net = slim.dropout(net, dropout_keep_prob, is_training=is_training, + scope='dropout3') + net = slim.fully_connected(net, 192, scope='fc4') + end_points['fc4'] = net + logits = slim.fully_connected(net, num_classes, + biases_initializer=tf.zeros_initializer(), + weights_initializer=trunc_normal(1/192.0), + weights_regularizer=None, + activation_fn=None, + scope='logits') + + end_points['Logits'] = logits + end_points['Predictions'] = prediction_fn(logits, scope='Predictions') + + return logits, end_points +cifarnet.default_image_size = 32 + + +def cifarnet_arg_scope(weight_decay=0.004): + """Defines the default cifarnet argument scope. + + Args: + weight_decay: The weight decay to use for regularizing the model. + + Returns: + An `arg_scope` to use for the inception v3 model. + """ + with slim.arg_scope( + [slim.conv2d], + weights_initializer=tf.truncated_normal_initializer(stddev=5e-2), + activation_fn=tf.nn.relu): + with slim.arg_scope( + [slim.fully_connected], + biases_initializer=tf.constant_initializer(0.1), + weights_initializer=trunc_normal(0.04), + weights_regularizer=slim.l2_regularizer(weight_decay), + activation_fn=tf.nn.relu) as sc: + return sc diff --git a/libs/networks/slim_nets/inception.py b/libs/networks/slim_nets/inception.py new file mode 100644 index 0000000..b69cd2a --- /dev/null +++ b/libs/networks/slim_nets/inception.py @@ -0,0 +1,37 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Brings all inception models under one namespace.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +# pylint: disable=unused-import +from nets.inception_resnet_v2 import inception_resnet_v2 +from nets.inception_resnet_v2 import inception_resnet_v2_arg_scope +from nets.inception_resnet_v2 import inception_resnet_v2_base +from nets.inception_v1 import inception_v1 +from nets.inception_v1 import inception_v1_arg_scope +from nets.inception_v1 import inception_v1_base +from nets.inception_v2 import inception_v2 +from nets.inception_v2 import inception_v2_arg_scope +from nets.inception_v2 import inception_v2_base +from nets.inception_v3 import inception_v3 +from nets.inception_v3 import inception_v3_arg_scope +from nets.inception_v3 import inception_v3_base +from nets.inception_v4 import inception_v4 +from nets.inception_v4 import inception_v4_arg_scope +from nets.inception_v4 import inception_v4_base +# pylint: enable=unused-import diff --git a/libs/networks/slim_nets/inception_resnet_v2.py b/libs/networks/slim_nets/inception_resnet_v2.py new file mode 100644 index 0000000..ddd9392 --- /dev/null +++ b/libs/networks/slim_nets/inception_resnet_v2.py @@ -0,0 +1,359 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Contains the definition of the Inception Resnet V2 architecture. + +As described in http://arxiv.org/abs/1602.07261. + + Inception-v4, Inception-ResNet and the Impact of Residual Connections + on Learning + Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + + +import tensorflow as tf + +slim = tf.contrib.slim + + +def block35(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None): + """Builds the 35x35 resnet block.""" + with tf.variable_scope(scope, 'Block35', [net], reuse=reuse): + with tf.variable_scope('Branch_0'): + tower_conv = slim.conv2d(net, 32, 1, scope='Conv2d_1x1') + with tf.variable_scope('Branch_1'): + tower_conv1_0 = slim.conv2d(net, 32, 1, scope='Conv2d_0a_1x1') + tower_conv1_1 = slim.conv2d(tower_conv1_0, 32, 3, scope='Conv2d_0b_3x3') + with tf.variable_scope('Branch_2'): + tower_conv2_0 = slim.conv2d(net, 32, 1, scope='Conv2d_0a_1x1') + tower_conv2_1 = slim.conv2d(tower_conv2_0, 48, 3, scope='Conv2d_0b_3x3') + tower_conv2_2 = slim.conv2d(tower_conv2_1, 64, 3, scope='Conv2d_0c_3x3') + mixed = tf.concat(axis=3, values=[tower_conv, tower_conv1_1, tower_conv2_2]) + up = slim.conv2d(mixed, net.get_shape()[3], 1, normalizer_fn=None, + activation_fn=None, scope='Conv2d_1x1') + net += scale * up + if activation_fn: + net = activation_fn(net) + return net + + +def block17(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None): + """Builds the 17x17 resnet block.""" + with tf.variable_scope(scope, 'Block17', [net], reuse=reuse): + with tf.variable_scope('Branch_0'): + tower_conv = slim.conv2d(net, 192, 1, scope='Conv2d_1x1') + with tf.variable_scope('Branch_1'): + tower_conv1_0 = slim.conv2d(net, 128, 1, scope='Conv2d_0a_1x1') + tower_conv1_1 = slim.conv2d(tower_conv1_0, 160, [1, 7], + scope='Conv2d_0b_1x7') + tower_conv1_2 = slim.conv2d(tower_conv1_1, 192, [7, 1], + scope='Conv2d_0c_7x1') + mixed = tf.concat(axis=3, values=[tower_conv, tower_conv1_2]) + up = slim.conv2d(mixed, net.get_shape()[3], 1, normalizer_fn=None, + activation_fn=None, scope='Conv2d_1x1') + net += scale * up + if activation_fn: + net = activation_fn(net) + return net + + +def block8(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None): + """Builds the 8x8 resnet block.""" + with tf.variable_scope(scope, 'Block8', [net], reuse=reuse): + with tf.variable_scope('Branch_0'): + tower_conv = slim.conv2d(net, 192, 1, scope='Conv2d_1x1') + with tf.variable_scope('Branch_1'): + tower_conv1_0 = slim.conv2d(net, 192, 1, scope='Conv2d_0a_1x1') + tower_conv1_1 = slim.conv2d(tower_conv1_0, 224, [1, 3], + scope='Conv2d_0b_1x3') + tower_conv1_2 = slim.conv2d(tower_conv1_1, 256, [3, 1], + scope='Conv2d_0c_3x1') + mixed = tf.concat(axis=3, values=[tower_conv, tower_conv1_2]) + up = slim.conv2d(mixed, net.get_shape()[3], 1, normalizer_fn=None, + activation_fn=None, scope='Conv2d_1x1') + net += scale * up + if activation_fn: + net = activation_fn(net) + return net + + +def inception_resnet_v2_base(inputs, + final_endpoint='Conv2d_7b_1x1', + output_stride=16, + align_feature_maps=False, + scope=None): + """Inception model from http://arxiv.org/abs/1602.07261. + + Constructs an Inception Resnet v2 network from inputs to the given final + endpoint. This method can construct the network up to the final inception + block Conv2d_7b_1x1. + + Args: + inputs: a tensor of size [batch_size, height, width, channels]. + final_endpoint: specifies the endpoint to construct the network up to. It + can be one of ['Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3', + 'MaxPool_3a_3x3', 'Conv2d_3b_1x1', 'Conv2d_4a_3x3', 'MaxPool_5a_3x3', + 'Mixed_5b', 'Mixed_6a', 'PreAuxLogits', 'Mixed_7a', 'Conv2d_7b_1x1'] + output_stride: A scalar that specifies the requested ratio of input to + output spatial resolution. Only supports 8 and 16. + align_feature_maps: When true, changes all the VALID paddings in the network + to SAME padding so that the feature maps are aligned. + scope: Optional variable_scope. + + Returns: + tensor_out: output tensor corresponding to the final_endpoint. + end_points: a set of activations for external use, for example summaries or + losses. + + Raises: + ValueError: if final_endpoint is not set to one of the predefined values, + or if the output_stride is not 8 or 16, or if the output_stride is 8 and + we request an end point after 'PreAuxLogits'. + """ + if output_stride != 8 and output_stride != 16: + raise ValueError('output_stride must be 8 or 16.') + + padding = 'SAME' if align_feature_maps else 'VALID' + + end_points = {} + + def add_and_check_final(name, net): + end_points[name] = net + return name == final_endpoint + + with tf.variable_scope(scope, 'InceptionResnetV2', [inputs]): + with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d], + stride=1, padding='SAME'): + # 149 x 149 x 32 + net = slim.conv2d(inputs, 32, 3, stride=2, padding=padding, + scope='Conv2d_1a_3x3') + if add_and_check_final('Conv2d_1a_3x3', net): return net, end_points + + # 147 x 147 x 32 + net = slim.conv2d(net, 32, 3, padding=padding, + scope='Conv2d_2a_3x3') + if add_and_check_final('Conv2d_2a_3x3', net): return net, end_points + # 147 x 147 x 64 + net = slim.conv2d(net, 64, 3, scope='Conv2d_2b_3x3') + if add_and_check_final('Conv2d_2b_3x3', net): return net, end_points + # 73 x 73 x 64 + net = slim.max_pool2d(net, 3, stride=2, padding=padding, + scope='MaxPool_3a_3x3') + if add_and_check_final('MaxPool_3a_3x3', net): return net, end_points + # 73 x 73 x 80 + net = slim.conv2d(net, 80, 1, padding=padding, + scope='Conv2d_3b_1x1') + if add_and_check_final('Conv2d_3b_1x1', net): return net, end_points + # 71 x 71 x 192 + net = slim.conv2d(net, 192, 3, padding=padding, + scope='Conv2d_4a_3x3') + if add_and_check_final('Conv2d_4a_3x3', net): return net, end_points + # 35 x 35 x 192 + net = slim.max_pool2d(net, 3, stride=2, padding=padding, + scope='MaxPool_5a_3x3') + if add_and_check_final('MaxPool_5a_3x3', net): return net, end_points + + # 35 x 35 x 320 + with tf.variable_scope('Mixed_5b'): + with tf.variable_scope('Branch_0'): + tower_conv = slim.conv2d(net, 96, 1, scope='Conv2d_1x1') + with tf.variable_scope('Branch_1'): + tower_conv1_0 = slim.conv2d(net, 48, 1, scope='Conv2d_0a_1x1') + tower_conv1_1 = slim.conv2d(tower_conv1_0, 64, 5, + scope='Conv2d_0b_5x5') + with tf.variable_scope('Branch_2'): + tower_conv2_0 = slim.conv2d(net, 64, 1, scope='Conv2d_0a_1x1') + tower_conv2_1 = slim.conv2d(tower_conv2_0, 96, 3, + scope='Conv2d_0b_3x3') + tower_conv2_2 = slim.conv2d(tower_conv2_1, 96, 3, + scope='Conv2d_0c_3x3') + with tf.variable_scope('Branch_3'): + tower_pool = slim.avg_pool2d(net, 3, stride=1, padding='SAME', + scope='AvgPool_0a_3x3') + tower_pool_1 = slim.conv2d(tower_pool, 64, 1, + scope='Conv2d_0b_1x1') + net = tf.concat( + [tower_conv, tower_conv1_1, tower_conv2_2, tower_pool_1], 3) + + if add_and_check_final('Mixed_5b', net): return net, end_points + # TODO(alemi): Register intermediate endpoints + net = slim.repeat(net, 10, block35, scale=0.17) + + # 17 x 17 x 1088 if output_stride == 8, + # 33 x 33 x 1088 if output_stride == 16 + use_atrous = output_stride == 8 + + with tf.variable_scope('Mixed_6a'): + with tf.variable_scope('Branch_0'): + tower_conv = slim.conv2d(net, 384, 3, stride=1 if use_atrous else 2, + padding=padding, + scope='Conv2d_1a_3x3') + with tf.variable_scope('Branch_1'): + tower_conv1_0 = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1') + tower_conv1_1 = slim.conv2d(tower_conv1_0, 256, 3, + scope='Conv2d_0b_3x3') + tower_conv1_2 = slim.conv2d(tower_conv1_1, 384, 3, + stride=1 if use_atrous else 2, + padding=padding, + scope='Conv2d_1a_3x3') + with tf.variable_scope('Branch_2'): + tower_pool = slim.max_pool2d(net, 3, stride=1 if use_atrous else 2, + padding=padding, + scope='MaxPool_1a_3x3') + net = tf.concat([tower_conv, tower_conv1_2, tower_pool], 3) + + if add_and_check_final('Mixed_6a', net): return net, end_points + + # TODO(alemi): register intermediate endpoints + with slim.arg_scope([slim.conv2d], rate=2 if use_atrous else 1): + net = slim.repeat(net, 20, block17, scale=0.10) + if add_and_check_final('PreAuxLogits', net): return net, end_points + + if output_stride == 8: + # TODO(gpapan): Properly support output_stride for the rest of the net. + raise ValueError('output_stride==8 is only supported up to the ' + 'PreAuxlogits end_point for now.') + + # 8 x 8 x 2080 + with tf.variable_scope('Mixed_7a'): + with tf.variable_scope('Branch_0'): + tower_conv = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1') + tower_conv_1 = slim.conv2d(tower_conv, 384, 3, stride=2, + padding=padding, + scope='Conv2d_1a_3x3') + with tf.variable_scope('Branch_1'): + tower_conv1 = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1') + tower_conv1_1 = slim.conv2d(tower_conv1, 288, 3, stride=2, + padding=padding, + scope='Conv2d_1a_3x3') + with tf.variable_scope('Branch_2'): + tower_conv2 = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1') + tower_conv2_1 = slim.conv2d(tower_conv2, 288, 3, + scope='Conv2d_0b_3x3') + tower_conv2_2 = slim.conv2d(tower_conv2_1, 320, 3, stride=2, + padding=padding, + scope='Conv2d_1a_3x3') + with tf.variable_scope('Branch_3'): + tower_pool = slim.max_pool2d(net, 3, stride=2, + padding=padding, + scope='MaxPool_1a_3x3') + net = tf.concat( + [tower_conv_1, tower_conv1_1, tower_conv2_2, tower_pool], 3) + + if add_and_check_final('Mixed_7a', net): return net, end_points + + # TODO(alemi): register intermediate endpoints + net = slim.repeat(net, 9, block8, scale=0.20) + net = block8(net, activation_fn=None) + + # 8 x 8 x 1536 + net = slim.conv2d(net, 1536, 1, scope='Conv2d_7b_1x1') + if add_and_check_final('Conv2d_7b_1x1', net): return net, end_points + + raise ValueError('final_endpoint (%s) not recognized', final_endpoint) + + +def inception_resnet_v2(inputs, num_classes=1001, is_training=True, + dropout_keep_prob=0.8,#0.8 + reuse=None, + scope='InceptionResnetV2', + create_aux_logits=True): + """Creates the Inception Resnet V2 model. + + Args: + inputs: a 4-D tensor of size [batch_size, height, width, 3]. + num_classes: number of predicted classes. + is_training: whether is training or not. + dropout_keep_prob: float, the fraction to keep before final layer. + reuse: whether or not the network and its variables should be reused. To be + able to reuse 'scope' must be given. + scope: Optional variable_scope. + create_aux_logits: Whether to include the auxilliary logits. + + Returns: + logits: the logits outputs of the model. + end_points: the set of end_points from the inception model. + """ + end_points = {} + + with tf.variable_scope(scope, 'InceptionResnetV2', [inputs, num_classes], + reuse=reuse) as scope: + with slim.arg_scope([slim.batch_norm, slim.dropout], + is_training=is_training): + + net, end_points = inception_resnet_v2_base(inputs, scope=scope) + + if create_aux_logits: + with tf.variable_scope('AuxLogits'): + aux = end_points['PreAuxLogits'] + aux = slim.avg_pool2d(aux, 5, stride=3, padding='VALID', + scope='Conv2d_1a_3x3') + aux = slim.conv2d(aux, 128, 1, scope='Conv2d_1b_1x1') + aux = slim.conv2d(aux, 768, aux.get_shape()[1:3], + padding='VALID', scope='Conv2d_2a_5x5') + aux = slim.flatten(aux) + aux = slim.fully_connected(aux, num_classes, activation_fn=None, + scope='Logits') + end_points['AuxLogits'] = aux + + with tf.variable_scope('Logits'): + net = slim.avg_pool2d(net, net.get_shape()[1:3], padding='VALID', + scope='AvgPool_1a_8x8') + net = slim.flatten(net) + + net = slim.dropout(net, dropout_keep_prob, is_training=is_training, + scope='Dropout') + + end_points['PreLogitsFlatten'] = net + # end_points['yjr_feature'] = tf.squeeze(net, axis=0) + + logits = slim.fully_connected(net, num_classes, activation_fn=None, + scope='Logits') + end_points['Logits'] = logits + end_points['Predictions'] = tf.nn.softmax(logits, name='Predictions') + + return logits, end_points +inception_resnet_v2.default_image_size = 299 + + +def inception_resnet_v2_arg_scope(weight_decay=0.00004, + batch_norm_decay=0.9997, + batch_norm_epsilon=0.001): + """Yields the scope with the default parameters for inception_resnet_v2. + + Args: + weight_decay: the weight decay for weights variables. + batch_norm_decay: decay for the moving average of batch_norm momentums. + batch_norm_epsilon: small float added to variance to avoid dividing by zero. + + Returns: + a arg_scope with the parameters needed for inception_resnet_v2. + """ + # Set weight_decay for weights in conv2d and fully_connected layers. + with slim.arg_scope([slim.conv2d, slim.fully_connected], + weights_regularizer=slim.l2_regularizer(weight_decay), + biases_regularizer=slim.l2_regularizer(weight_decay)): + + batch_norm_params = { + 'decay': batch_norm_decay, + 'epsilon': batch_norm_epsilon, + } + # Set activation_fn and parameters for batch_norm. + with slim.arg_scope([slim.conv2d], activation_fn=tf.nn.relu, + normalizer_fn=slim.batch_norm, + normalizer_params=batch_norm_params) as scope: + return scope diff --git a/libs/networks/slim_nets/inception_resnet_v2_test.py b/libs/networks/slim_nets/inception_resnet_v2_test.py new file mode 100644 index 0000000..c369ed9 --- /dev/null +++ b/libs/networks/slim_nets/inception_resnet_v2_test.py @@ -0,0 +1,265 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for slim.inception_resnet_v2.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf + +from nets import inception + + +class InceptionTest(tf.test.TestCase): + + def testBuildLogits(self): + batch_size = 5 + height, width = 299, 299 + num_classes = 1000 + with self.test_session(): + inputs = tf.random_uniform((batch_size, height, width, 3)) + logits, endpoints = inception.inception_resnet_v2(inputs, num_classes) + self.assertTrue('AuxLogits' in endpoints) + auxlogits = endpoints['AuxLogits'] + self.assertTrue( + auxlogits.op.name.startswith('InceptionResnetV2/AuxLogits')) + self.assertListEqual(auxlogits.get_shape().as_list(), + [batch_size, num_classes]) + self.assertTrue(logits.op.name.startswith('InceptionResnetV2/Logits')) + self.assertListEqual(logits.get_shape().as_list(), + [batch_size, num_classes]) + + def testBuildWithoutAuxLogits(self): + batch_size = 5 + height, width = 299, 299 + num_classes = 1000 + with self.test_session(): + inputs = tf.random_uniform((batch_size, height, width, 3)) + logits, endpoints = inception.inception_resnet_v2(inputs, num_classes, + create_aux_logits=False) + self.assertTrue('AuxLogits' not in endpoints) + self.assertTrue(logits.op.name.startswith('InceptionResnetV2/Logits')) + self.assertListEqual(logits.get_shape().as_list(), + [batch_size, num_classes]) + + def testBuildEndPoints(self): + batch_size = 5 + height, width = 299, 299 + num_classes = 1000 + with self.test_session(): + inputs = tf.random_uniform((batch_size, height, width, 3)) + _, end_points = inception.inception_resnet_v2(inputs, num_classes) + self.assertTrue('Logits' in end_points) + logits = end_points['Logits'] + self.assertListEqual(logits.get_shape().as_list(), + [batch_size, num_classes]) + self.assertTrue('AuxLogits' in end_points) + aux_logits = end_points['AuxLogits'] + self.assertListEqual(aux_logits.get_shape().as_list(), + [batch_size, num_classes]) + pre_pool = end_points['Conv2d_7b_1x1'] + self.assertListEqual(pre_pool.get_shape().as_list(), + [batch_size, 8, 8, 1536]) + + def testBuildBaseNetwork(self): + batch_size = 5 + height, width = 299, 299 + + inputs = tf.random_uniform((batch_size, height, width, 3)) + net, end_points = inception.inception_resnet_v2_base(inputs) + self.assertTrue(net.op.name.startswith('InceptionResnetV2/Conv2d_7b_1x1')) + self.assertListEqual(net.get_shape().as_list(), + [batch_size, 8, 8, 1536]) + expected_endpoints = ['Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3', + 'MaxPool_3a_3x3', 'Conv2d_3b_1x1', 'Conv2d_4a_3x3', + 'MaxPool_5a_3x3', 'Mixed_5b', 'Mixed_6a', + 'PreAuxLogits', 'Mixed_7a', 'Conv2d_7b_1x1'] + self.assertItemsEqual(end_points.keys(), expected_endpoints) + + def testBuildOnlyUptoFinalEndpoint(self): + batch_size = 5 + height, width = 299, 299 + endpoints = ['Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3', + 'MaxPool_3a_3x3', 'Conv2d_3b_1x1', 'Conv2d_4a_3x3', + 'MaxPool_5a_3x3', 'Mixed_5b', 'Mixed_6a', + 'PreAuxLogits', 'Mixed_7a', 'Conv2d_7b_1x1'] + for index, endpoint in enumerate(endpoints): + with tf.Graph().as_default(): + inputs = tf.random_uniform((batch_size, height, width, 3)) + out_tensor, end_points = inception.inception_resnet_v2_base( + inputs, final_endpoint=endpoint) + if endpoint != 'PreAuxLogits': + self.assertTrue(out_tensor.op.name.startswith( + 'InceptionResnetV2/' + endpoint)) + self.assertItemsEqual(endpoints[:index+1], end_points) + + def testBuildAndCheckAllEndPointsUptoPreAuxLogits(self): + batch_size = 5 + height, width = 299, 299 + + inputs = tf.random_uniform((batch_size, height, width, 3)) + _, end_points = inception.inception_resnet_v2_base( + inputs, final_endpoint='PreAuxLogits') + endpoints_shapes = {'Conv2d_1a_3x3': [5, 149, 149, 32], + 'Conv2d_2a_3x3': [5, 147, 147, 32], + 'Conv2d_2b_3x3': [5, 147, 147, 64], + 'MaxPool_3a_3x3': [5, 73, 73, 64], + 'Conv2d_3b_1x1': [5, 73, 73, 80], + 'Conv2d_4a_3x3': [5, 71, 71, 192], + 'MaxPool_5a_3x3': [5, 35, 35, 192], + 'Mixed_5b': [5, 35, 35, 320], + 'Mixed_6a': [5, 17, 17, 1088], + 'PreAuxLogits': [5, 17, 17, 1088] + } + + self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys()) + for endpoint_name in endpoints_shapes: + expected_shape = endpoints_shapes[endpoint_name] + self.assertTrue(endpoint_name in end_points) + self.assertListEqual(end_points[endpoint_name].get_shape().as_list(), + expected_shape) + + def testBuildAndCheckAllEndPointsUptoPreAuxLogitsWithAlignedFeatureMaps(self): + batch_size = 5 + height, width = 299, 299 + + inputs = tf.random_uniform((batch_size, height, width, 3)) + _, end_points = inception.inception_resnet_v2_base( + inputs, final_endpoint='PreAuxLogits', align_feature_maps=True) + endpoints_shapes = {'Conv2d_1a_3x3': [5, 150, 150, 32], + 'Conv2d_2a_3x3': [5, 150, 150, 32], + 'Conv2d_2b_3x3': [5, 150, 150, 64], + 'MaxPool_3a_3x3': [5, 75, 75, 64], + 'Conv2d_3b_1x1': [5, 75, 75, 80], + 'Conv2d_4a_3x3': [5, 75, 75, 192], + 'MaxPool_5a_3x3': [5, 38, 38, 192], + 'Mixed_5b': [5, 38, 38, 320], + 'Mixed_6a': [5, 19, 19, 1088], + 'PreAuxLogits': [5, 19, 19, 1088] + } + + self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys()) + for endpoint_name in endpoints_shapes: + expected_shape = endpoints_shapes[endpoint_name] + self.assertTrue(endpoint_name in end_points) + self.assertListEqual(end_points[endpoint_name].get_shape().as_list(), + expected_shape) + + def testBuildAndCheckAllEndPointsUptoPreAuxLogitsWithOutputStrideEight(self): + batch_size = 5 + height, width = 299, 299 + + inputs = tf.random_uniform((batch_size, height, width, 3)) + _, end_points = inception.inception_resnet_v2_base( + inputs, final_endpoint='PreAuxLogits', output_stride=8) + endpoints_shapes = {'Conv2d_1a_3x3': [5, 149, 149, 32], + 'Conv2d_2a_3x3': [5, 147, 147, 32], + 'Conv2d_2b_3x3': [5, 147, 147, 64], + 'MaxPool_3a_3x3': [5, 73, 73, 64], + 'Conv2d_3b_1x1': [5, 73, 73, 80], + 'Conv2d_4a_3x3': [5, 71, 71, 192], + 'MaxPool_5a_3x3': [5, 35, 35, 192], + 'Mixed_5b': [5, 35, 35, 320], + 'Mixed_6a': [5, 33, 33, 1088], + 'PreAuxLogits': [5, 33, 33, 1088] + } + + self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys()) + for endpoint_name in endpoints_shapes: + expected_shape = endpoints_shapes[endpoint_name] + self.assertTrue(endpoint_name in end_points) + self.assertListEqual(end_points[endpoint_name].get_shape().as_list(), + expected_shape) + + def testVariablesSetDevice(self): + batch_size = 5 + height, width = 299, 299 + num_classes = 1000 + with self.test_session(): + inputs = tf.random_uniform((batch_size, height, width, 3)) + # Force all Variables to reside on the device. + with tf.variable_scope('on_cpu'), tf.device('/cpu:0'): + inception.inception_resnet_v2(inputs, num_classes) + with tf.variable_scope('on_gpu'), tf.device('/gpu:0'): + inception.inception_resnet_v2(inputs, num_classes) + for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='on_cpu'): + self.assertDeviceEqual(v.device, '/cpu:0') + for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='on_gpu'): + self.assertDeviceEqual(v.device, '/gpu:0') + + def testHalfSizeImages(self): + batch_size = 5 + height, width = 150, 150 + num_classes = 1000 + with self.test_session(): + inputs = tf.random_uniform((batch_size, height, width, 3)) + logits, end_points = inception.inception_resnet_v2(inputs, num_classes) + self.assertTrue(logits.op.name.startswith('InceptionResnetV2/Logits')) + self.assertListEqual(logits.get_shape().as_list(), + [batch_size, num_classes]) + pre_pool = end_points['Conv2d_7b_1x1'] + self.assertListEqual(pre_pool.get_shape().as_list(), + [batch_size, 3, 3, 1536]) + + def testUnknownBatchSize(self): + batch_size = 1 + height, width = 299, 299 + num_classes = 1000 + with self.test_session() as sess: + inputs = tf.placeholder(tf.float32, (None, height, width, 3)) + logits, _ = inception.inception_resnet_v2(inputs, num_classes) + self.assertTrue(logits.op.name.startswith('InceptionResnetV2/Logits')) + self.assertListEqual(logits.get_shape().as_list(), + [None, num_classes]) + images = tf.random_uniform((batch_size, height, width, 3)) + sess.run(tf.global_variables_initializer()) + output = sess.run(logits, {inputs: images.eval()}) + self.assertEquals(output.shape, (batch_size, num_classes)) + + def testEvaluation(self): + batch_size = 2 + height, width = 299, 299 + num_classes = 1000 + with self.test_session() as sess: + eval_inputs = tf.random_uniform((batch_size, height, width, 3)) + logits, _ = inception.inception_resnet_v2(eval_inputs, + num_classes, + is_training=False) + predictions = tf.argmax(logits, 1) + sess.run(tf.global_variables_initializer()) + output = sess.run(predictions) + self.assertEquals(output.shape, (batch_size,)) + + def testTrainEvalWithReuse(self): + train_batch_size = 5 + eval_batch_size = 2 + height, width = 150, 150 + num_classes = 1000 + with self.test_session() as sess: + train_inputs = tf.random_uniform((train_batch_size, height, width, 3)) + inception.inception_resnet_v2(train_inputs, num_classes) + eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3)) + logits, _ = inception.inception_resnet_v2(eval_inputs, + num_classes, + is_training=False, + reuse=True) + predictions = tf.argmax(logits, 1) + sess.run(tf.global_variables_initializer()) + output = sess.run(predictions) + self.assertEquals(output.shape, (eval_batch_size,)) + + +if __name__ == '__main__': + tf.test.main() diff --git a/libs/networks/slim_nets/inception_utils.py b/libs/networks/slim_nets/inception_utils.py new file mode 100644 index 0000000..66ee41f --- /dev/null +++ b/libs/networks/slim_nets/inception_utils.py @@ -0,0 +1,71 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Contains common code shared by all inception models. + +Usage of arg scope: + with slim.arg_scope(inception_arg_scope()): + logits, end_points = inception.inception_v3(images, num_classes, + is_training=is_training) + +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf + +slim = tf.contrib.slim + + +def inception_arg_scope(weight_decay=0.00004, + use_batch_norm=True, + batch_norm_decay=0.9997, + batch_norm_epsilon=0.001): + """Defines the default arg scope for inception models. + + Args: + weight_decay: The weight decay to use for regularizing the model. + use_batch_norm: "If `True`, batch_norm is applied after each convolution. + batch_norm_decay: Decay for batch norm moving average. + batch_norm_epsilon: Small float added to variance to avoid dividing by zero + in batch norm. + + Returns: + An `arg_scope` to use for the inception models. + """ + batch_norm_params = { + # Decay for the moving averages. + 'decay': batch_norm_decay, + # epsilon to prevent 0s in variance. + 'epsilon': batch_norm_epsilon, + # collection containing update_ops. + 'updates_collections': tf.GraphKeys.UPDATE_OPS, + } + if use_batch_norm: + normalizer_fn = slim.batch_norm + normalizer_params = batch_norm_params + else: + normalizer_fn = None + normalizer_params = {} + # Set weight_decay for weights in Conv and FC layers. + with slim.arg_scope([slim.conv2d, slim.fully_connected], + weights_regularizer=slim.l2_regularizer(weight_decay)): + with slim.arg_scope( + [slim.conv2d], + weights_initializer=slim.variance_scaling_initializer(), + activation_fn=tf.nn.relu, + normalizer_fn=normalizer_fn, + normalizer_params=normalizer_params) as sc: + return sc diff --git a/libs/networks/slim_nets/inception_v1.py b/libs/networks/slim_nets/inception_v1.py new file mode 100644 index 0000000..4207c2a --- /dev/null +++ b/libs/networks/slim_nets/inception_v1.py @@ -0,0 +1,305 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Contains the definition for inception v1 classification network.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf + +from nets import inception_utils + +slim = tf.contrib.slim +trunc_normal = lambda stddev: tf.truncated_normal_initializer(0.0, stddev) + + +def inception_v1_base(inputs, + final_endpoint='Mixed_5c', + scope='InceptionV1'): + """Defines the Inception V1 base architecture. + + This architecture is defined in: + Going deeper with convolutions + Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, + Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich. + http://arxiv.org/pdf/1409.4842v1.pdf. + + Args: + inputs: a tensor of size [batch_size, height, width, channels]. + final_endpoint: specifies the endpoint to construct the network up to. It + can be one of ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1', + 'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c', + 'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d', 'Mixed_4e', + 'Mixed_4f', 'MaxPool_5a_2x2', 'Mixed_5b', 'Mixed_5c'] + scope: Optional variable_scope. + + Returns: + A dictionary from components of the network to the corresponding activation. + + Raises: + ValueError: if final_endpoint is not set to one of the predefined values. + """ + end_points = {} + with tf.variable_scope(scope, 'InceptionV1', [inputs]): + with slim.arg_scope( + [slim.conv2d, slim.fully_connected], + weights_initializer=trunc_normal(0.01)): + with slim.arg_scope([slim.conv2d, slim.max_pool2d], + stride=1, padding='SAME'): + end_point = 'Conv2d_1a_7x7' + net = slim.conv2d(inputs, 64, [7, 7], stride=2, scope=end_point) + end_points[end_point] = net + if final_endpoint == end_point: return net, end_points + end_point = 'MaxPool_2a_3x3' + net = slim.max_pool2d(net, [3, 3], stride=2, scope=end_point) + end_points[end_point] = net + if final_endpoint == end_point: return net, end_points + end_point = 'Conv2d_2b_1x1' + net = slim.conv2d(net, 64, [1, 1], scope=end_point) + end_points[end_point] = net + if final_endpoint == end_point: return net, end_points + end_point = 'Conv2d_2c_3x3' + net = slim.conv2d(net, 192, [3, 3], scope=end_point) + end_points[end_point] = net + if final_endpoint == end_point: return net, end_points + end_point = 'MaxPool_3a_3x3' + net = slim.max_pool2d(net, [3, 3], stride=2, scope=end_point) + end_points[end_point] = net + if final_endpoint == end_point: return net, end_points + + end_point = 'Mixed_3b' + with tf.variable_scope(end_point): + with tf.variable_scope('Branch_0'): + branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1') + with tf.variable_scope('Branch_1'): + branch_1 = slim.conv2d(net, 96, [1, 1], scope='Conv2d_0a_1x1') + branch_1 = slim.conv2d(branch_1, 128, [3, 3], scope='Conv2d_0b_3x3') + with tf.variable_scope('Branch_2'): + branch_2 = slim.conv2d(net, 16, [1, 1], scope='Conv2d_0a_1x1') + branch_2 = slim.conv2d(branch_2, 32, [3, 3], scope='Conv2d_0b_3x3') + with tf.variable_scope('Branch_3'): + branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3') + branch_3 = slim.conv2d(branch_3, 32, [1, 1], scope='Conv2d_0b_1x1') + net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) + end_points[end_point] = net + if final_endpoint == end_point: return net, end_points + + end_point = 'Mixed_3c' + with tf.variable_scope(end_point): + with tf.variable_scope('Branch_0'): + branch_0 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1') + with tf.variable_scope('Branch_1'): + branch_1 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1') + branch_1 = slim.conv2d(branch_1, 192, [3, 3], scope='Conv2d_0b_3x3') + with tf.variable_scope('Branch_2'): + branch_2 = slim.conv2d(net, 32, [1, 1], scope='Conv2d_0a_1x1') + branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0b_3x3') + with tf.variable_scope('Branch_3'): + branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3') + branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1') + net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) + end_points[end_point] = net + if final_endpoint == end_point: return net, end_points + + end_point = 'MaxPool_4a_3x3' + net = slim.max_pool2d(net, [3, 3], stride=2, scope=end_point) + end_points[end_point] = net + if final_endpoint == end_point: return net, end_points + + end_point = 'Mixed_4b' + with tf.variable_scope(end_point): + with tf.variable_scope('Branch_0'): + branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1') + with tf.variable_scope('Branch_1'): + branch_1 = slim.conv2d(net, 96, [1, 1], scope='Conv2d_0a_1x1') + branch_1 = slim.conv2d(branch_1, 208, [3, 3], scope='Conv2d_0b_3x3') + with tf.variable_scope('Branch_2'): + branch_2 = slim.conv2d(net, 16, [1, 1], scope='Conv2d_0a_1x1') + branch_2 = slim.conv2d(branch_2, 48, [3, 3], scope='Conv2d_0b_3x3') + with tf.variable_scope('Branch_3'): + branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3') + branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1') + net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) + end_points[end_point] = net + if final_endpoint == end_point: return net, end_points + + end_point = 'Mixed_4c' + with tf.variable_scope(end_point): + with tf.variable_scope('Branch_0'): + branch_0 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1') + with tf.variable_scope('Branch_1'): + branch_1 = slim.conv2d(net, 112, [1, 1], scope='Conv2d_0a_1x1') + branch_1 = slim.conv2d(branch_1, 224, [3, 3], scope='Conv2d_0b_3x3') + with tf.variable_scope('Branch_2'): + branch_2 = slim.conv2d(net, 24, [1, 1], scope='Conv2d_0a_1x1') + branch_2 = slim.conv2d(branch_2, 64, [3, 3], scope='Conv2d_0b_3x3') + with tf.variable_scope('Branch_3'): + branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3') + branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1') + net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) + end_points[end_point] = net + if final_endpoint == end_point: return net, end_points + + end_point = 'Mixed_4d' + with tf.variable_scope(end_point): + with tf.variable_scope('Branch_0'): + branch_0 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1') + with tf.variable_scope('Branch_1'): + branch_1 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1') + branch_1 = slim.conv2d(branch_1, 256, [3, 3], scope='Conv2d_0b_3x3') + with tf.variable_scope('Branch_2'): + branch_2 = slim.conv2d(net, 24, [1, 1], scope='Conv2d_0a_1x1') + branch_2 = slim.conv2d(branch_2, 64, [3, 3], scope='Conv2d_0b_3x3') + with tf.variable_scope('Branch_3'): + branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3') + branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1') + net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) + end_points[end_point] = net + if final_endpoint == end_point: return net, end_points + + end_point = 'Mixed_4e' + with tf.variable_scope(end_point): + with tf.variable_scope('Branch_0'): + branch_0 = slim.conv2d(net, 112, [1, 1], scope='Conv2d_0a_1x1') + with tf.variable_scope('Branch_1'): + branch_1 = slim.conv2d(net, 144, [1, 1], scope='Conv2d_0a_1x1') + branch_1 = slim.conv2d(branch_1, 288, [3, 3], scope='Conv2d_0b_3x3') + with tf.variable_scope('Branch_2'): + branch_2 = slim.conv2d(net, 32, [1, 1], scope='Conv2d_0a_1x1') + branch_2 = slim.conv2d(branch_2, 64, [3, 3], scope='Conv2d_0b_3x3') + with tf.variable_scope('Branch_3'): + branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3') + branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1') + net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) + end_points[end_point] = net + if final_endpoint == end_point: return net, end_points + + end_point = 'Mixed_4f' + with tf.variable_scope(end_point): + with tf.variable_scope('Branch_0'): + branch_0 = slim.conv2d(net, 256, [1, 1], scope='Conv2d_0a_1x1') + with tf.variable_scope('Branch_1'): + branch_1 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1') + branch_1 = slim.conv2d(branch_1, 320, [3, 3], scope='Conv2d_0b_3x3') + with tf.variable_scope('Branch_2'): + branch_2 = slim.conv2d(net, 32, [1, 1], scope='Conv2d_0a_1x1') + branch_2 = slim.conv2d(branch_2, 128, [3, 3], scope='Conv2d_0b_3x3') + with tf.variable_scope('Branch_3'): + branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3') + branch_3 = slim.conv2d(branch_3, 128, [1, 1], scope='Conv2d_0b_1x1') + net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) + end_points[end_point] = net + if final_endpoint == end_point: return net, end_points + + end_point = 'MaxPool_5a_2x2' + net = slim.max_pool2d(net, [2, 2], stride=2, scope=end_point) + end_points[end_point] = net + if final_endpoint == end_point: return net, end_points + + end_point = 'Mixed_5b' + with tf.variable_scope(end_point): + with tf.variable_scope('Branch_0'): + branch_0 = slim.conv2d(net, 256, [1, 1], scope='Conv2d_0a_1x1') + with tf.variable_scope('Branch_1'): + branch_1 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1') + branch_1 = slim.conv2d(branch_1, 320, [3, 3], scope='Conv2d_0b_3x3') + with tf.variable_scope('Branch_2'): + branch_2 = slim.conv2d(net, 32, [1, 1], scope='Conv2d_0a_1x1') + branch_2 = slim.conv2d(branch_2, 128, [3, 3], scope='Conv2d_0a_3x3') + with tf.variable_scope('Branch_3'): + branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3') + branch_3 = slim.conv2d(branch_3, 128, [1, 1], scope='Conv2d_0b_1x1') + net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) + end_points[end_point] = net + if final_endpoint == end_point: return net, end_points + + end_point = 'Mixed_5c' + with tf.variable_scope(end_point): + with tf.variable_scope('Branch_0'): + branch_0 = slim.conv2d(net, 384, [1, 1], scope='Conv2d_0a_1x1') + with tf.variable_scope('Branch_1'): + branch_1 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1') + branch_1 = slim.conv2d(branch_1, 384, [3, 3], scope='Conv2d_0b_3x3') + with tf.variable_scope('Branch_2'): + branch_2 = slim.conv2d(net, 48, [1, 1], scope='Conv2d_0a_1x1') + branch_2 = slim.conv2d(branch_2, 128, [3, 3], scope='Conv2d_0b_3x3') + with tf.variable_scope('Branch_3'): + branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3') + branch_3 = slim.conv2d(branch_3, 128, [1, 1], scope='Conv2d_0b_1x1') + net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) + end_points[end_point] = net + if final_endpoint == end_point: return net, end_points + raise ValueError('Unknown final endpoint %s' % final_endpoint) + + +def inception_v1(inputs, + num_classes=1000, + is_training=True, + dropout_keep_prob=0.8, + prediction_fn=slim.softmax, + spatial_squeeze=True, + reuse=None, + scope='InceptionV1'): + """Defines the Inception V1 architecture. + + This architecture is defined in: + + Going deeper with convolutions + Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, + Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich. + http://arxiv.org/pdf/1409.4842v1.pdf. + + The default image size used to train this network is 224x224. + + Args: + inputs: a tensor of size [batch_size, height, width, channels]. + num_classes: number of predicted classes. + is_training: whether is training or not. + dropout_keep_prob: the percentage of activation values that are retained. + prediction_fn: a function to get predictions out of logits. + spatial_squeeze: if True, logits is of shape [B, C], if false logits is + of shape [B, 1, 1, C], where B is batch_size and C is number of classes. + reuse: whether or not the network and its variables should be reused. To be + able to reuse 'scope' must be given. + scope: Optional variable_scope. + + Returns: + logits: the pre-softmax activations, a tensor of size + [batch_size, num_classes] + end_points: a dictionary from components of the network to the corresponding + activation. + """ + # Final pooling and prediction + with tf.variable_scope(scope, 'InceptionV1', [inputs, num_classes], + reuse=reuse) as scope: + with slim.arg_scope([slim.batch_norm, slim.dropout], + is_training=is_training): + net, end_points = inception_v1_base(inputs, scope=scope) + with tf.variable_scope('Logits'): + net = slim.avg_pool2d(net, [7, 7], stride=1, scope='AvgPool_0a_7x7') + net = slim.dropout(net, + dropout_keep_prob, scope='Dropout_0b') + logits = slim.conv2d(net, num_classes, [1, 1], activation_fn=None, + normalizer_fn=None, scope='Conv2d_0c_1x1') + if spatial_squeeze: + logits = tf.squeeze(logits, [1, 2], name='SpatialSqueeze') + + end_points['Logits'] = logits + end_points['Predictions'] = prediction_fn(logits, scope='Predictions') + return logits, end_points +inception_v1.default_image_size = 224 + +inception_v1_arg_scope = inception_utils.inception_arg_scope diff --git a/libs/networks/slim_nets/inception_v1_test.py b/libs/networks/slim_nets/inception_v1_test.py new file mode 100644 index 0000000..11eb14e --- /dev/null +++ b/libs/networks/slim_nets/inception_v1_test.py @@ -0,0 +1,210 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for slim_nets.inception_v1.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np +import tensorflow as tf + +from nets import inception + +slim = tf.contrib.slim + + +class InceptionV1Test(tf.test.TestCase): + + def testBuildClassificationNetwork(self): + batch_size = 5 + height, width = 224, 224 + num_classes = 1000 + + inputs = tf.random_uniform((batch_size, height, width, 3)) + logits, end_points = inception.inception_v1(inputs, num_classes) + self.assertTrue(logits.op.name.startswith('InceptionV1/Logits')) + self.assertListEqual(logits.get_shape().as_list(), + [batch_size, num_classes]) + self.assertTrue('Predictions' in end_points) + self.assertListEqual(end_points['Predictions'].get_shape().as_list(), + [batch_size, num_classes]) + + def testBuildBaseNetwork(self): + batch_size = 5 + height, width = 224, 224 + + inputs = tf.random_uniform((batch_size, height, width, 3)) + mixed_6c, end_points = inception.inception_v1_base(inputs) + self.assertTrue(mixed_6c.op.name.startswith('InceptionV1/Mixed_5c')) + self.assertListEqual(mixed_6c.get_shape().as_list(), + [batch_size, 7, 7, 1024]) + expected_endpoints = ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1', + 'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', + 'Mixed_3c', 'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c', + 'Mixed_4d', 'Mixed_4e', 'Mixed_4f', 'MaxPool_5a_2x2', + 'Mixed_5b', 'Mixed_5c'] + self.assertItemsEqual(end_points.keys(), expected_endpoints) + + def testBuildOnlyUptoFinalEndpoint(self): + batch_size = 5 + height, width = 224, 224 + endpoints = ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1', + 'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c', + 'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d', + 'Mixed_4e', 'Mixed_4f', 'MaxPool_5a_2x2', 'Mixed_5b', + 'Mixed_5c'] + for index, endpoint in enumerate(endpoints): + with tf.Graph().as_default(): + inputs = tf.random_uniform((batch_size, height, width, 3)) + out_tensor, end_points = inception.inception_v1_base( + inputs, final_endpoint=endpoint) + self.assertTrue(out_tensor.op.name.startswith( + 'InceptionV1/' + endpoint)) + self.assertItemsEqual(endpoints[:index+1], end_points) + + def testBuildAndCheckAllEndPointsUptoMixed5c(self): + batch_size = 5 + height, width = 224, 224 + + inputs = tf.random_uniform((batch_size, height, width, 3)) + _, end_points = inception.inception_v1_base(inputs, + final_endpoint='Mixed_5c') + endpoints_shapes = {'Conv2d_1a_7x7': [5, 112, 112, 64], + 'MaxPool_2a_3x3': [5, 56, 56, 64], + 'Conv2d_2b_1x1': [5, 56, 56, 64], + 'Conv2d_2c_3x3': [5, 56, 56, 192], + 'MaxPool_3a_3x3': [5, 28, 28, 192], + 'Mixed_3b': [5, 28, 28, 256], + 'Mixed_3c': [5, 28, 28, 480], + 'MaxPool_4a_3x3': [5, 14, 14, 480], + 'Mixed_4b': [5, 14, 14, 512], + 'Mixed_4c': [5, 14, 14, 512], + 'Mixed_4d': [5, 14, 14, 512], + 'Mixed_4e': [5, 14, 14, 528], + 'Mixed_4f': [5, 14, 14, 832], + 'MaxPool_5a_2x2': [5, 7, 7, 832], + 'Mixed_5b': [5, 7, 7, 832], + 'Mixed_5c': [5, 7, 7, 1024]} + + self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys()) + for endpoint_name in endpoints_shapes: + expected_shape = endpoints_shapes[endpoint_name] + self.assertTrue(endpoint_name in end_points) + self.assertListEqual(end_points[endpoint_name].get_shape().as_list(), + expected_shape) + + def testModelHasExpectedNumberOfParameters(self): + batch_size = 5 + height, width = 224, 224 + inputs = tf.random_uniform((batch_size, height, width, 3)) + with slim.arg_scope(inception.inception_v1_arg_scope()): + inception.inception_v1_base(inputs) + total_params, _ = slim.model_analyzer.analyze_vars( + slim.get_model_variables()) + self.assertAlmostEqual(5607184, total_params) + + def testHalfSizeImages(self): + batch_size = 5 + height, width = 112, 112 + + inputs = tf.random_uniform((batch_size, height, width, 3)) + mixed_5c, _ = inception.inception_v1_base(inputs) + self.assertTrue(mixed_5c.op.name.startswith('InceptionV1/Mixed_5c')) + self.assertListEqual(mixed_5c.get_shape().as_list(), + [batch_size, 4, 4, 1024]) + + def testUnknownImageShape(self): + tf.reset_default_graph() + batch_size = 2 + height, width = 224, 224 + num_classes = 1000 + input_np = np.random.uniform(0, 1, (batch_size, height, width, 3)) + with self.test_session() as sess: + inputs = tf.placeholder(tf.float32, shape=(batch_size, None, None, 3)) + logits, end_points = inception.inception_v1(inputs, num_classes) + self.assertTrue(logits.op.name.startswith('InceptionV1/Logits')) + self.assertListEqual(logits.get_shape().as_list(), + [batch_size, num_classes]) + pre_pool = end_points['Mixed_5c'] + feed_dict = {inputs: input_np} + tf.global_variables_initializer().run() + pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict) + self.assertListEqual(list(pre_pool_out.shape), [batch_size, 7, 7, 1024]) + + def testUnknowBatchSize(self): + batch_size = 1 + height, width = 224, 224 + num_classes = 1000 + + inputs = tf.placeholder(tf.float32, (None, height, width, 3)) + logits, _ = inception.inception_v1(inputs, num_classes) + self.assertTrue(logits.op.name.startswith('InceptionV1/Logits')) + self.assertListEqual(logits.get_shape().as_list(), + [None, num_classes]) + images = tf.random_uniform((batch_size, height, width, 3)) + + with self.test_session() as sess: + sess.run(tf.global_variables_initializer()) + output = sess.run(logits, {inputs: images.eval()}) + self.assertEquals(output.shape, (batch_size, num_classes)) + + def testEvaluation(self): + batch_size = 2 + height, width = 224, 224 + num_classes = 1000 + + eval_inputs = tf.random_uniform((batch_size, height, width, 3)) + logits, _ = inception.inception_v1(eval_inputs, num_classes, + is_training=False) + predictions = tf.argmax(logits, 1) + + with self.test_session() as sess: + sess.run(tf.global_variables_initializer()) + output = sess.run(predictions) + self.assertEquals(output.shape, (batch_size,)) + + def testTrainEvalWithReuse(self): + train_batch_size = 5 + eval_batch_size = 2 + height, width = 224, 224 + num_classes = 1000 + + train_inputs = tf.random_uniform((train_batch_size, height, width, 3)) + inception.inception_v1(train_inputs, num_classes) + eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3)) + logits, _ = inception.inception_v1(eval_inputs, num_classes, reuse=True) + predictions = tf.argmax(logits, 1) + + with self.test_session() as sess: + sess.run(tf.global_variables_initializer()) + output = sess.run(predictions) + self.assertEquals(output.shape, (eval_batch_size,)) + + def testLogitsNotSqueezed(self): + num_classes = 25 + images = tf.random_uniform([1, 224, 224, 3]) + logits, _ = inception.inception_v1(images, + num_classes=num_classes, + spatial_squeeze=False) + + with self.test_session() as sess: + tf.global_variables_initializer().run() + logits_out = sess.run(logits) + self.assertListEqual(list(logits_out.shape), [1, 1, 1, num_classes]) + + +if __name__ == '__main__': + tf.test.main() diff --git a/libs/networks/slim_nets/inception_v2.py b/libs/networks/slim_nets/inception_v2.py new file mode 100644 index 0000000..2651f71 --- /dev/null +++ b/libs/networks/slim_nets/inception_v2.py @@ -0,0 +1,520 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Contains the definition for inception v2 classification network.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf + +from nets import inception_utils + +slim = tf.contrib.slim +trunc_normal = lambda stddev: tf.truncated_normal_initializer(0.0, stddev) + + +def inception_v2_base(inputs, + final_endpoint='Mixed_5c', + min_depth=16, + depth_multiplier=1.0, + scope=None): + """Inception v2 (6a2). + + Constructs an Inception v2 network from inputs to the given final endpoint. + This method can construct the network up to the layer inception(5b) as + described in http://arxiv.org/abs/1502.03167. + + Args: + inputs: a tensor of shape [batch_size, height, width, channels]. + final_endpoint: specifies the endpoint to construct the network up to. It + can be one of ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1', + 'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c', 'Mixed_4a', + 'Mixed_4b', 'Mixed_4c', 'Mixed_4d', 'Mixed_4e', 'Mixed_5a', 'Mixed_5b', + 'Mixed_5c']. + min_depth: Minimum depth value (number of channels) for all convolution ops. + Enforced when depth_multiplier < 1, and not an active constraint when + depth_multiplier >= 1. + depth_multiplier: Float multiplier for the depth (number of channels) + for all convolution ops. The value must be greater than zero. Typical + usage will be to set this value in (0, 1) to reduce the number of + parameters or computation cost of the model. + scope: Optional variable_scope. + + Returns: + tensor_out: output tensor corresponding to the final_endpoint. + end_points: a set of activations for external use, for example summaries or + losses. + + Raises: + ValueError: if final_endpoint is not set to one of the predefined values, + or depth_multiplier <= 0 + """ + + # end_points will collect relevant activations for external use, for example + # summaries or losses. + end_points = {} + + # Used to find thinned depths for each layer. + if depth_multiplier <= 0: + raise ValueError('depth_multiplier is not greater than zero.') + depth = lambda d: max(int(d * depth_multiplier), min_depth) + + with tf.variable_scope(scope, 'InceptionV2', [inputs]): + with slim.arg_scope( + [slim.conv2d, slim.max_pool2d, slim.avg_pool2d, slim.separable_conv2d], + stride=1, padding='SAME'): + + # Note that sizes in the comments below assume an input spatial size of + # 224x224, however, the inputs can be of any size greater 32x32. + + # 224 x 224 x 3 + end_point = 'Conv2d_1a_7x7' + # depthwise_multiplier here is different from depth_multiplier. + # depthwise_multiplier determines the output channels of the initial + # depthwise conv (see docs for tf.nn.separable_conv2d), while + # depth_multiplier controls the # channels of the subsequent 1x1 + # convolution. Must have + # in_channels * depthwise_multipler <= out_channels + # so that the separable convolution is not overparameterized. + depthwise_multiplier = min(int(depth(64) / 3), 8) + net = slim.separable_conv2d( + inputs, depth(64), [7, 7], depth_multiplier=depthwise_multiplier, + stride=2, weights_initializer=trunc_normal(1.0), + scope=end_point) + end_points[end_point] = net + if end_point == final_endpoint: return net, end_points + # 112 x 112 x 64 + end_point = 'MaxPool_2a_3x3' + net = slim.max_pool2d(net, [3, 3], scope=end_point, stride=2) + end_points[end_point] = net + if end_point == final_endpoint: return net, end_points + # 56 x 56 x 64 + end_point = 'Conv2d_2b_1x1' + net = slim.conv2d(net, depth(64), [1, 1], scope=end_point, + weights_initializer=trunc_normal(0.1)) + end_points[end_point] = net + if end_point == final_endpoint: return net, end_points + # 56 x 56 x 64 + end_point = 'Conv2d_2c_3x3' + net = slim.conv2d(net, depth(192), [3, 3], scope=end_point) + end_points[end_point] = net + if end_point == final_endpoint: return net, end_points + # 56 x 56 x 192 + end_point = 'MaxPool_3a_3x3' + net = slim.max_pool2d(net, [3, 3], scope=end_point, stride=2) + end_points[end_point] = net + if end_point == final_endpoint: return net, end_points + # 28 x 28 x 192 + # Inception module. + end_point = 'Mixed_3b' + with tf.variable_scope(end_point): + with tf.variable_scope('Branch_0'): + branch_0 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1') + with tf.variable_scope('Branch_1'): + branch_1 = slim.conv2d( + net, depth(64), [1, 1], + weights_initializer=trunc_normal(0.09), + scope='Conv2d_0a_1x1') + branch_1 = slim.conv2d(branch_1, depth(64), [3, 3], + scope='Conv2d_0b_3x3') + with tf.variable_scope('Branch_2'): + branch_2 = slim.conv2d( + net, depth(64), [1, 1], + weights_initializer=trunc_normal(0.09), + scope='Conv2d_0a_1x1') + branch_2 = slim.conv2d(branch_2, depth(96), [3, 3], + scope='Conv2d_0b_3x3') + branch_2 = slim.conv2d(branch_2, depth(96), [3, 3], + scope='Conv2d_0c_3x3') + with tf.variable_scope('Branch_3'): + branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3') + branch_3 = slim.conv2d( + branch_3, depth(32), [1, 1], + weights_initializer=trunc_normal(0.1), + scope='Conv2d_0b_1x1') + net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) + end_points[end_point] = net + if end_point == final_endpoint: return net, end_points + # 28 x 28 x 256 + end_point = 'Mixed_3c' + with tf.variable_scope(end_point): + with tf.variable_scope('Branch_0'): + branch_0 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1') + with tf.variable_scope('Branch_1'): + branch_1 = slim.conv2d( + net, depth(64), [1, 1], + weights_initializer=trunc_normal(0.09), + scope='Conv2d_0a_1x1') + branch_1 = slim.conv2d(branch_1, depth(96), [3, 3], + scope='Conv2d_0b_3x3') + with tf.variable_scope('Branch_2'): + branch_2 = slim.conv2d( + net, depth(64), [1, 1], + weights_initializer=trunc_normal(0.09), + scope='Conv2d_0a_1x1') + branch_2 = slim.conv2d(branch_2, depth(96), [3, 3], + scope='Conv2d_0b_3x3') + branch_2 = slim.conv2d(branch_2, depth(96), [3, 3], + scope='Conv2d_0c_3x3') + with tf.variable_scope('Branch_3'): + branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3') + branch_3 = slim.conv2d( + branch_3, depth(64), [1, 1], + weights_initializer=trunc_normal(0.1), + scope='Conv2d_0b_1x1') + net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) + end_points[end_point] = net + if end_point == final_endpoint: return net, end_points + # 28 x 28 x 320 + end_point = 'Mixed_4a' + with tf.variable_scope(end_point): + with tf.variable_scope('Branch_0'): + branch_0 = slim.conv2d( + net, depth(128), [1, 1], + weights_initializer=trunc_normal(0.09), + scope='Conv2d_0a_1x1') + branch_0 = slim.conv2d(branch_0, depth(160), [3, 3], stride=2, + scope='Conv2d_1a_3x3') + with tf.variable_scope('Branch_1'): + branch_1 = slim.conv2d( + net, depth(64), [1, 1], + weights_initializer=trunc_normal(0.09), + scope='Conv2d_0a_1x1') + branch_1 = slim.conv2d( + branch_1, depth(96), [3, 3], scope='Conv2d_0b_3x3') + branch_1 = slim.conv2d( + branch_1, depth(96), [3, 3], stride=2, scope='Conv2d_1a_3x3') + with tf.variable_scope('Branch_2'): + branch_2 = slim.max_pool2d( + net, [3, 3], stride=2, scope='MaxPool_1a_3x3') + net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2]) + end_points[end_point] = net + if end_point == final_endpoint: return net, end_points + # 14 x 14 x 576 + end_point = 'Mixed_4b' + with tf.variable_scope(end_point): + with tf.variable_scope('Branch_0'): + branch_0 = slim.conv2d(net, depth(224), [1, 1], scope='Conv2d_0a_1x1') + with tf.variable_scope('Branch_1'): + branch_1 = slim.conv2d( + net, depth(64), [1, 1], + weights_initializer=trunc_normal(0.09), + scope='Conv2d_0a_1x1') + branch_1 = slim.conv2d( + branch_1, depth(96), [3, 3], scope='Conv2d_0b_3x3') + with tf.variable_scope('Branch_2'): + branch_2 = slim.conv2d( + net, depth(96), [1, 1], + weights_initializer=trunc_normal(0.09), + scope='Conv2d_0a_1x1') + branch_2 = slim.conv2d(branch_2, depth(128), [3, 3], + scope='Conv2d_0b_3x3') + branch_2 = slim.conv2d(branch_2, depth(128), [3, 3], + scope='Conv2d_0c_3x3') + with tf.variable_scope('Branch_3'): + branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3') + branch_3 = slim.conv2d( + branch_3, depth(128), [1, 1], + weights_initializer=trunc_normal(0.1), + scope='Conv2d_0b_1x1') + net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) + end_points[end_point] = net + if end_point == final_endpoint: return net, end_points + # 14 x 14 x 576 + end_point = 'Mixed_4c' + with tf.variable_scope(end_point): + with tf.variable_scope('Branch_0'): + branch_0 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1') + with tf.variable_scope('Branch_1'): + branch_1 = slim.conv2d( + net, depth(96), [1, 1], + weights_initializer=trunc_normal(0.09), + scope='Conv2d_0a_1x1') + branch_1 = slim.conv2d(branch_1, depth(128), [3, 3], + scope='Conv2d_0b_3x3') + with tf.variable_scope('Branch_2'): + branch_2 = slim.conv2d( + net, depth(96), [1, 1], + weights_initializer=trunc_normal(0.09), + scope='Conv2d_0a_1x1') + branch_2 = slim.conv2d(branch_2, depth(128), [3, 3], + scope='Conv2d_0b_3x3') + branch_2 = slim.conv2d(branch_2, depth(128), [3, 3], + scope='Conv2d_0c_3x3') + with tf.variable_scope('Branch_3'): + branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3') + branch_3 = slim.conv2d( + branch_3, depth(128), [1, 1], + weights_initializer=trunc_normal(0.1), + scope='Conv2d_0b_1x1') + net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) + end_points[end_point] = net + if end_point == final_endpoint: return net, end_points + # 14 x 14 x 576 + end_point = 'Mixed_4d' + with tf.variable_scope(end_point): + with tf.variable_scope('Branch_0'): + branch_0 = slim.conv2d(net, depth(160), [1, 1], scope='Conv2d_0a_1x1') + with tf.variable_scope('Branch_1'): + branch_1 = slim.conv2d( + net, depth(128), [1, 1], + weights_initializer=trunc_normal(0.09), + scope='Conv2d_0a_1x1') + branch_1 = slim.conv2d(branch_1, depth(160), [3, 3], + scope='Conv2d_0b_3x3') + with tf.variable_scope('Branch_2'): + branch_2 = slim.conv2d( + net, depth(128), [1, 1], + weights_initializer=trunc_normal(0.09), + scope='Conv2d_0a_1x1') + branch_2 = slim.conv2d(branch_2, depth(160), [3, 3], + scope='Conv2d_0b_3x3') + branch_2 = slim.conv2d(branch_2, depth(160), [3, 3], + scope='Conv2d_0c_3x3') + with tf.variable_scope('Branch_3'): + branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3') + branch_3 = slim.conv2d( + branch_3, depth(96), [1, 1], + weights_initializer=trunc_normal(0.1), + scope='Conv2d_0b_1x1') + net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) + end_points[end_point] = net + if end_point == final_endpoint: return net, end_points + + # 14 x 14 x 576 + end_point = 'Mixed_4e' + with tf.variable_scope(end_point): + with tf.variable_scope('Branch_0'): + branch_0 = slim.conv2d(net, depth(96), [1, 1], scope='Conv2d_0a_1x1') + with tf.variable_scope('Branch_1'): + branch_1 = slim.conv2d( + net, depth(128), [1, 1], + weights_initializer=trunc_normal(0.09), + scope='Conv2d_0a_1x1') + branch_1 = slim.conv2d(branch_1, depth(192), [3, 3], + scope='Conv2d_0b_3x3') + with tf.variable_scope('Branch_2'): + branch_2 = slim.conv2d( + net, depth(160), [1, 1], + weights_initializer=trunc_normal(0.09), + scope='Conv2d_0a_1x1') + branch_2 = slim.conv2d(branch_2, depth(192), [3, 3], + scope='Conv2d_0b_3x3') + branch_2 = slim.conv2d(branch_2, depth(192), [3, 3], + scope='Conv2d_0c_3x3') + with tf.variable_scope('Branch_3'): + branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3') + branch_3 = slim.conv2d( + branch_3, depth(96), [1, 1], + weights_initializer=trunc_normal(0.1), + scope='Conv2d_0b_1x1') + net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) + end_points[end_point] = net + if end_point == final_endpoint: return net, end_points + # 14 x 14 x 576 + end_point = 'Mixed_5a' + with tf.variable_scope(end_point): + with tf.variable_scope('Branch_0'): + branch_0 = slim.conv2d( + net, depth(128), [1, 1], + weights_initializer=trunc_normal(0.09), + scope='Conv2d_0a_1x1') + branch_0 = slim.conv2d(branch_0, depth(192), [3, 3], stride=2, + scope='Conv2d_1a_3x3') + with tf.variable_scope('Branch_1'): + branch_1 = slim.conv2d( + net, depth(192), [1, 1], + weights_initializer=trunc_normal(0.09), + scope='Conv2d_0a_1x1') + branch_1 = slim.conv2d(branch_1, depth(256), [3, 3], + scope='Conv2d_0b_3x3') + branch_1 = slim.conv2d(branch_1, depth(256), [3, 3], stride=2, + scope='Conv2d_1a_3x3') + with tf.variable_scope('Branch_2'): + branch_2 = slim.max_pool2d(net, [3, 3], stride=2, + scope='MaxPool_1a_3x3') + net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2]) + end_points[end_point] = net + if end_point == final_endpoint: return net, end_points + # 7 x 7 x 1024 + end_point = 'Mixed_5b' + with tf.variable_scope(end_point): + with tf.variable_scope('Branch_0'): + branch_0 = slim.conv2d(net, depth(352), [1, 1], scope='Conv2d_0a_1x1') + with tf.variable_scope('Branch_1'): + branch_1 = slim.conv2d( + net, depth(192), [1, 1], + weights_initializer=trunc_normal(0.09), + scope='Conv2d_0a_1x1') + branch_1 = slim.conv2d(branch_1, depth(320), [3, 3], + scope='Conv2d_0b_3x3') + with tf.variable_scope('Branch_2'): + branch_2 = slim.conv2d( + net, depth(160), [1, 1], + weights_initializer=trunc_normal(0.09), + scope='Conv2d_0a_1x1') + branch_2 = slim.conv2d(branch_2, depth(224), [3, 3], + scope='Conv2d_0b_3x3') + branch_2 = slim.conv2d(branch_2, depth(224), [3, 3], + scope='Conv2d_0c_3x3') + with tf.variable_scope('Branch_3'): + branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3') + branch_3 = slim.conv2d( + branch_3, depth(128), [1, 1], + weights_initializer=trunc_normal(0.1), + scope='Conv2d_0b_1x1') + net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) + end_points[end_point] = net + if end_point == final_endpoint: return net, end_points + + # 7 x 7 x 1024 + end_point = 'Mixed_5c' + with tf.variable_scope(end_point): + with tf.variable_scope('Branch_0'): + branch_0 = slim.conv2d(net, depth(352), [1, 1], scope='Conv2d_0a_1x1') + with tf.variable_scope('Branch_1'): + branch_1 = slim.conv2d( + net, depth(192), [1, 1], + weights_initializer=trunc_normal(0.09), + scope='Conv2d_0a_1x1') + branch_1 = slim.conv2d(branch_1, depth(320), [3, 3], + scope='Conv2d_0b_3x3') + with tf.variable_scope('Branch_2'): + branch_2 = slim.conv2d( + net, depth(192), [1, 1], + weights_initializer=trunc_normal(0.09), + scope='Conv2d_0a_1x1') + branch_2 = slim.conv2d(branch_2, depth(224), [3, 3], + scope='Conv2d_0b_3x3') + branch_2 = slim.conv2d(branch_2, depth(224), [3, 3], + scope='Conv2d_0c_3x3') + with tf.variable_scope('Branch_3'): + branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3') + branch_3 = slim.conv2d( + branch_3, depth(128), [1, 1], + weights_initializer=trunc_normal(0.1), + scope='Conv2d_0b_1x1') + net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) + end_points[end_point] = net + if end_point == final_endpoint: return net, end_points + raise ValueError('Unknown final endpoint %s' % final_endpoint) + + +def inception_v2(inputs, + num_classes=1000, + is_training=True, + dropout_keep_prob=0.8, + min_depth=16, + depth_multiplier=1.0, + prediction_fn=slim.softmax, + spatial_squeeze=True, + reuse=None, + scope='InceptionV2'): + """Inception v2 model for classification. + + Constructs an Inception v2 network for classification as described in + http://arxiv.org/abs/1502.03167. + + The default image size used to train this network is 224x224. + + Args: + inputs: a tensor of shape [batch_size, height, width, channels]. + num_classes: number of predicted classes. + is_training: whether is training or not. + dropout_keep_prob: the percentage of activation values that are retained. + min_depth: Minimum depth value (number of channels) for all convolution ops. + Enforced when depth_multiplier < 1, and not an active constraint when + depth_multiplier >= 1. + depth_multiplier: Float multiplier for the depth (number of channels) + for all convolution ops. The value must be greater than zero. Typical + usage will be to set this value in (0, 1) to reduce the number of + parameters or computation cost of the model. + prediction_fn: a function to get predictions out of logits. + spatial_squeeze: if True, logits is of shape [B, C], if false logits is + of shape [B, 1, 1, C], where B is batch_size and C is number of classes. + reuse: whether or not the network and its variables should be reused. To be + able to reuse 'scope' must be given. + scope: Optional variable_scope. + + Returns: + logits: the pre-softmax activations, a tensor of size + [batch_size, num_classes] + end_points: a dictionary from components of the network to the corresponding + activation. + + Raises: + ValueError: if final_endpoint is not set to one of the predefined values, + or depth_multiplier <= 0 + """ + if depth_multiplier <= 0: + raise ValueError('depth_multiplier is not greater than zero.') + + # Final pooling and prediction + with tf.variable_scope(scope, 'InceptionV2', [inputs, num_classes], + reuse=reuse) as scope: + with slim.arg_scope([slim.batch_norm, slim.dropout], + is_training=is_training): + net, end_points = inception_v2_base( + inputs, scope=scope, min_depth=min_depth, + depth_multiplier=depth_multiplier) + with tf.variable_scope('Logits'): + kernel_size = _reduced_kernel_size_for_small_input(net, [7, 7]) + net = slim.avg_pool2d(net, kernel_size, padding='VALID', + scope='AvgPool_1a_{}x{}'.format(*kernel_size)) + # 1 x 1 x 1024 + net = slim.dropout(net, keep_prob=dropout_keep_prob, scope='Dropout_1b') + logits = slim.conv2d(net, num_classes, [1, 1], activation_fn=None, + normalizer_fn=None, scope='Conv2d_1c_1x1') + if spatial_squeeze: + logits = tf.squeeze(logits, [1, 2], name='SpatialSqueeze') + end_points['Logits'] = logits + end_points['Predictions'] = prediction_fn(logits, scope='Predictions') + return logits, end_points +inception_v2.default_image_size = 224 + + +def _reduced_kernel_size_for_small_input(input_tensor, kernel_size): + """Define kernel size which is automatically reduced for small input. + + If the shape of the input images is unknown at graph construction time this + function assumes that the input images are is large enough. + + Args: + input_tensor: input tensor of size [batch_size, height, width, channels]. + kernel_size: desired kernel size of length 2: [kernel_height, kernel_width] + + Returns: + a tensor with the kernel size. + + TODO(jrru): Make this function work with unknown shapes. Theoretically, this + can be done with the code below. Problems are two-fold: (1) If the shape was + known, it will be lost. (2) inception.slim.ops._two_element_tuple cannot + handle tensors that define the kernel size. + shape = tf.shape(input_tensor) + return = tf.pack([tf.minimum(shape[1], kernel_size[0]), + tf.minimum(shape[2], kernel_size[1])]) + + """ + shape = input_tensor.get_shape().as_list() + if shape[1] is None or shape[2] is None: + kernel_size_out = kernel_size + else: + kernel_size_out = [min(shape[1], kernel_size[0]), + min(shape[2], kernel_size[1])] + return kernel_size_out + + +inception_v2_arg_scope = inception_utils.inception_arg_scope diff --git a/libs/networks/slim_nets/inception_v2_test.py b/libs/networks/slim_nets/inception_v2_test.py new file mode 100644 index 0000000..397aa50 --- /dev/null +++ b/libs/networks/slim_nets/inception_v2_test.py @@ -0,0 +1,262 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for slim_nets.inception_v2.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np +import tensorflow as tf + +from nets import inception + +slim = tf.contrib.slim + + +class InceptionV2Test(tf.test.TestCase): + + def testBuildClassificationNetwork(self): + batch_size = 5 + height, width = 224, 224 + num_classes = 1000 + + inputs = tf.random_uniform((batch_size, height, width, 3)) + logits, end_points = inception.inception_v2(inputs, num_classes) + self.assertTrue(logits.op.name.startswith('InceptionV2/Logits')) + self.assertListEqual(logits.get_shape().as_list(), + [batch_size, num_classes]) + self.assertTrue('Predictions' in end_points) + self.assertListEqual(end_points['Predictions'].get_shape().as_list(), + [batch_size, num_classes]) + + def testBuildBaseNetwork(self): + batch_size = 5 + height, width = 224, 224 + + inputs = tf.random_uniform((batch_size, height, width, 3)) + mixed_5c, end_points = inception.inception_v2_base(inputs) + self.assertTrue(mixed_5c.op.name.startswith('InceptionV2/Mixed_5c')) + self.assertListEqual(mixed_5c.get_shape().as_list(), + [batch_size, 7, 7, 1024]) + expected_endpoints = ['Mixed_3b', 'Mixed_3c', 'Mixed_4a', 'Mixed_4b', + 'Mixed_4c', 'Mixed_4d', 'Mixed_4e', 'Mixed_5a', + 'Mixed_5b', 'Mixed_5c', 'Conv2d_1a_7x7', + 'MaxPool_2a_3x3', 'Conv2d_2b_1x1', 'Conv2d_2c_3x3', + 'MaxPool_3a_3x3'] + self.assertItemsEqual(end_points.keys(), expected_endpoints) + + def testBuildOnlyUptoFinalEndpoint(self): + batch_size = 5 + height, width = 224, 224 + endpoints = ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1', + 'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c', + 'Mixed_4a', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d', 'Mixed_4e', + 'Mixed_5a', 'Mixed_5b', 'Mixed_5c'] + for index, endpoint in enumerate(endpoints): + with tf.Graph().as_default(): + inputs = tf.random_uniform((batch_size, height, width, 3)) + out_tensor, end_points = inception.inception_v2_base( + inputs, final_endpoint=endpoint) + self.assertTrue(out_tensor.op.name.startswith( + 'InceptionV2/' + endpoint)) + self.assertItemsEqual(endpoints[:index+1], end_points) + + def testBuildAndCheckAllEndPointsUptoMixed5c(self): + batch_size = 5 + height, width = 224, 224 + + inputs = tf.random_uniform((batch_size, height, width, 3)) + _, end_points = inception.inception_v2_base(inputs, + final_endpoint='Mixed_5c') + endpoints_shapes = {'Mixed_3b': [batch_size, 28, 28, 256], + 'Mixed_3c': [batch_size, 28, 28, 320], + 'Mixed_4a': [batch_size, 14, 14, 576], + 'Mixed_4b': [batch_size, 14, 14, 576], + 'Mixed_4c': [batch_size, 14, 14, 576], + 'Mixed_4d': [batch_size, 14, 14, 576], + 'Mixed_4e': [batch_size, 14, 14, 576], + 'Mixed_5a': [batch_size, 7, 7, 1024], + 'Mixed_5b': [batch_size, 7, 7, 1024], + 'Mixed_5c': [batch_size, 7, 7, 1024], + 'Conv2d_1a_7x7': [batch_size, 112, 112, 64], + 'MaxPool_2a_3x3': [batch_size, 56, 56, 64], + 'Conv2d_2b_1x1': [batch_size, 56, 56, 64], + 'Conv2d_2c_3x3': [batch_size, 56, 56, 192], + 'MaxPool_3a_3x3': [batch_size, 28, 28, 192]} + self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys()) + for endpoint_name in endpoints_shapes: + expected_shape = endpoints_shapes[endpoint_name] + self.assertTrue(endpoint_name in end_points) + self.assertListEqual(end_points[endpoint_name].get_shape().as_list(), + expected_shape) + + def testModelHasExpectedNumberOfParameters(self): + batch_size = 5 + height, width = 224, 224 + inputs = tf.random_uniform((batch_size, height, width, 3)) + with slim.arg_scope(inception.inception_v2_arg_scope()): + inception.inception_v2_base(inputs) + total_params, _ = slim.model_analyzer.analyze_vars( + slim.get_model_variables()) + self.assertAlmostEqual(10173112, total_params) + + def testBuildEndPointsWithDepthMultiplierLessThanOne(self): + batch_size = 5 + height, width = 224, 224 + num_classes = 1000 + + inputs = tf.random_uniform((batch_size, height, width, 3)) + _, end_points = inception.inception_v2(inputs, num_classes) + + endpoint_keys = [key for key in end_points.keys() + if key.startswith('Mixed') or key.startswith('Conv')] + + _, end_points_with_multiplier = inception.inception_v2( + inputs, num_classes, scope='depth_multiplied_net', + depth_multiplier=0.5) + + for key in endpoint_keys: + original_depth = end_points[key].get_shape().as_list()[3] + new_depth = end_points_with_multiplier[key].get_shape().as_list()[3] + self.assertEqual(0.5 * original_depth, new_depth) + + def testBuildEndPointsWithDepthMultiplierGreaterThanOne(self): + batch_size = 5 + height, width = 224, 224 + num_classes = 1000 + + inputs = tf.random_uniform((batch_size, height, width, 3)) + _, end_points = inception.inception_v2(inputs, num_classes) + + endpoint_keys = [key for key in end_points.keys() + if key.startswith('Mixed') or key.startswith('Conv')] + + _, end_points_with_multiplier = inception.inception_v2( + inputs, num_classes, scope='depth_multiplied_net', + depth_multiplier=2.0) + + for key in endpoint_keys: + original_depth = end_points[key].get_shape().as_list()[3] + new_depth = end_points_with_multiplier[key].get_shape().as_list()[3] + self.assertEqual(2.0 * original_depth, new_depth) + + def testRaiseValueErrorWithInvalidDepthMultiplier(self): + batch_size = 5 + height, width = 224, 224 + num_classes = 1000 + + inputs = tf.random_uniform((batch_size, height, width, 3)) + with self.assertRaises(ValueError): + _ = inception.inception_v2(inputs, num_classes, depth_multiplier=-0.1) + with self.assertRaises(ValueError): + _ = inception.inception_v2(inputs, num_classes, depth_multiplier=0.0) + + def testHalfSizeImages(self): + batch_size = 5 + height, width = 112, 112 + num_classes = 1000 + + inputs = tf.random_uniform((batch_size, height, width, 3)) + logits, end_points = inception.inception_v2(inputs, num_classes) + self.assertTrue(logits.op.name.startswith('InceptionV2/Logits')) + self.assertListEqual(logits.get_shape().as_list(), + [batch_size, num_classes]) + pre_pool = end_points['Mixed_5c'] + self.assertListEqual(pre_pool.get_shape().as_list(), + [batch_size, 4, 4, 1024]) + + def testUnknownImageShape(self): + tf.reset_default_graph() + batch_size = 2 + height, width = 224, 224 + num_classes = 1000 + input_np = np.random.uniform(0, 1, (batch_size, height, width, 3)) + with self.test_session() as sess: + inputs = tf.placeholder(tf.float32, shape=(batch_size, None, None, 3)) + logits, end_points = inception.inception_v2(inputs, num_classes) + self.assertTrue(logits.op.name.startswith('InceptionV2/Logits')) + self.assertListEqual(logits.get_shape().as_list(), + [batch_size, num_classes]) + pre_pool = end_points['Mixed_5c'] + feed_dict = {inputs: input_np} + tf.global_variables_initializer().run() + pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict) + self.assertListEqual(list(pre_pool_out.shape), [batch_size, 7, 7, 1024]) + + def testUnknowBatchSize(self): + batch_size = 1 + height, width = 224, 224 + num_classes = 1000 + + inputs = tf.placeholder(tf.float32, (None, height, width, 3)) + logits, _ = inception.inception_v2(inputs, num_classes) + self.assertTrue(logits.op.name.startswith('InceptionV2/Logits')) + self.assertListEqual(logits.get_shape().as_list(), + [None, num_classes]) + images = tf.random_uniform((batch_size, height, width, 3)) + + with self.test_session() as sess: + sess.run(tf.global_variables_initializer()) + output = sess.run(logits, {inputs: images.eval()}) + self.assertEquals(output.shape, (batch_size, num_classes)) + + def testEvaluation(self): + batch_size = 2 + height, width = 224, 224 + num_classes = 1000 + + eval_inputs = tf.random_uniform((batch_size, height, width, 3)) + logits, _ = inception.inception_v2(eval_inputs, num_classes, + is_training=False) + predictions = tf.argmax(logits, 1) + + with self.test_session() as sess: + sess.run(tf.global_variables_initializer()) + output = sess.run(predictions) + self.assertEquals(output.shape, (batch_size,)) + + def testTrainEvalWithReuse(self): + train_batch_size = 5 + eval_batch_size = 2 + height, width = 150, 150 + num_classes = 1000 + + train_inputs = tf.random_uniform((train_batch_size, height, width, 3)) + inception.inception_v2(train_inputs, num_classes) + eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3)) + logits, _ = inception.inception_v2(eval_inputs, num_classes, reuse=True) + predictions = tf.argmax(logits, 1) + + with self.test_session() as sess: + sess.run(tf.global_variables_initializer()) + output = sess.run(predictions) + self.assertEquals(output.shape, (eval_batch_size,)) + + def testLogitsNotSqueezed(self): + num_classes = 25 + images = tf.random_uniform([1, 224, 224, 3]) + logits, _ = inception.inception_v2(images, + num_classes=num_classes, + spatial_squeeze=False) + + with self.test_session() as sess: + tf.global_variables_initializer().run() + logits_out = sess.run(logits) + self.assertListEqual(list(logits_out.shape), [1, 1, 1, num_classes]) + + +if __name__ == '__main__': + tf.test.main() diff --git a/libs/networks/slim_nets/inception_v3.py b/libs/networks/slim_nets/inception_v3.py new file mode 100644 index 0000000..d64bcfd --- /dev/null +++ b/libs/networks/slim_nets/inception_v3.py @@ -0,0 +1,560 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Contains the definition for inception v3 classification network.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf + +from nets import inception_utils + +slim = tf.contrib.slim +trunc_normal = lambda stddev: tf.truncated_normal_initializer(0.0, stddev) + + +def inception_v3_base(inputs, + final_endpoint='Mixed_7c', + min_depth=16, + depth_multiplier=1.0, + scope=None): + """Inception model from http://arxiv.org/abs/1512.00567. + + Constructs an Inception v3 network from inputs to the given final endpoint. + This method can construct the network up to the final inception block + Mixed_7c. + + Note that the names of the layers in the paper do not correspond to the names + of the endpoints registered by this function although they build the same + network. + + Here is a mapping from the old_names to the new names: + Old name | New name + ======================================= + conv0 | Conv2d_1a_3x3 + conv1 | Conv2d_2a_3x3 + conv2 | Conv2d_2b_3x3 + pool1 | MaxPool_3a_3x3 + conv3 | Conv2d_3b_1x1 + conv4 | Conv2d_4a_3x3 + pool2 | MaxPool_5a_3x3 + mixed_35x35x256a | Mixed_5b + mixed_35x35x288a | Mixed_5c + mixed_35x35x288b | Mixed_5d + mixed_17x17x768a | Mixed_6a + mixed_17x17x768b | Mixed_6b + mixed_17x17x768c | Mixed_6c + mixed_17x17x768d | Mixed_6d + mixed_17x17x768e | Mixed_6e + mixed_8x8x1280a | Mixed_7a + mixed_8x8x2048a | Mixed_7b + mixed_8x8x2048b | Mixed_7c + + Args: + inputs: a tensor of size [batch_size, height, width, channels]. + final_endpoint: specifies the endpoint to construct the network up to. It + can be one of ['Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3', + 'MaxPool_3a_3x3', 'Conv2d_3b_1x1', 'Conv2d_4a_3x3', 'MaxPool_5a_3x3', + 'Mixed_5b', 'Mixed_5c', 'Mixed_5d', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c', + 'Mixed_6d', 'Mixed_6e', 'Mixed_7a', 'Mixed_7b', 'Mixed_7c']. + min_depth: Minimum depth value (number of channels) for all convolution ops. + Enforced when depth_multiplier < 1, and not an active constraint when + depth_multiplier >= 1. + depth_multiplier: Float multiplier for the depth (number of channels) + for all convolution ops. The value must be greater than zero. Typical + usage will be to set this value in (0, 1) to reduce the number of + parameters or computation cost of the model. + scope: Optional variable_scope. + + Returns: + tensor_out: output tensor corresponding to the final_endpoint. + end_points: a set of activations for external use, for example summaries or + losses. + + Raises: + ValueError: if final_endpoint is not set to one of the predefined values, + or depth_multiplier <= 0 + """ + # end_points will collect relevant activations for external use, for example + # summaries or losses. + end_points = {} + + if depth_multiplier <= 0: + raise ValueError('depth_multiplier is not greater than zero.') + depth = lambda d: max(int(d * depth_multiplier), min_depth) + + with tf.variable_scope(scope, 'InceptionV3', [inputs]): + with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d], + stride=1, padding='VALID'): + # 299 x 299 x 3 + end_point = 'Conv2d_1a_3x3' + net = slim.conv2d(inputs, depth(32), [3, 3], stride=2, scope=end_point) + end_points[end_point] = net + if end_point == final_endpoint: return net, end_points + # 149 x 149 x 32 + end_point = 'Conv2d_2a_3x3' + net = slim.conv2d(net, depth(32), [3, 3], scope=end_point) + end_points[end_point] = net + if end_point == final_endpoint: return net, end_points + # 147 x 147 x 32 + end_point = 'Conv2d_2b_3x3' + net = slim.conv2d(net, depth(64), [3, 3], padding='SAME', scope=end_point) + end_points[end_point] = net + if end_point == final_endpoint: return net, end_points + # 147 x 147 x 64 + end_point = 'MaxPool_3a_3x3' + net = slim.max_pool2d(net, [3, 3], stride=2, scope=end_point) + end_points[end_point] = net + if end_point == final_endpoint: return net, end_points + # 73 x 73 x 64 + end_point = 'Conv2d_3b_1x1' + net = slim.conv2d(net, depth(80), [1, 1], scope=end_point) + end_points[end_point] = net + if end_point == final_endpoint: return net, end_points + # 73 x 73 x 80. + end_point = 'Conv2d_4a_3x3' + net = slim.conv2d(net, depth(192), [3, 3], scope=end_point) + end_points[end_point] = net + if end_point == final_endpoint: return net, end_points + # 71 x 71 x 192. + end_point = 'MaxPool_5a_3x3' + net = slim.max_pool2d(net, [3, 3], stride=2, scope=end_point) + end_points[end_point] = net + if end_point == final_endpoint: return net, end_points + # 35 x 35 x 192. + + # Inception blocks + with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d], + stride=1, padding='SAME'): + # mixed: 35 x 35 x 256. + end_point = 'Mixed_5b' + with tf.variable_scope(end_point): + with tf.variable_scope('Branch_0'): + branch_0 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1') + with tf.variable_scope('Branch_1'): + branch_1 = slim.conv2d(net, depth(48), [1, 1], scope='Conv2d_0a_1x1') + branch_1 = slim.conv2d(branch_1, depth(64), [5, 5], + scope='Conv2d_0b_5x5') + with tf.variable_scope('Branch_2'): + branch_2 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1') + branch_2 = slim.conv2d(branch_2, depth(96), [3, 3], + scope='Conv2d_0b_3x3') + branch_2 = slim.conv2d(branch_2, depth(96), [3, 3], + scope='Conv2d_0c_3x3') + with tf.variable_scope('Branch_3'): + branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3') + branch_3 = slim.conv2d(branch_3, depth(32), [1, 1], + scope='Conv2d_0b_1x1') + net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) + end_points[end_point] = net + if end_point == final_endpoint: return net, end_points + + # mixed_1: 35 x 35 x 288. + end_point = 'Mixed_5c' + with tf.variable_scope(end_point): + with tf.variable_scope('Branch_0'): + branch_0 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1') + with tf.variable_scope('Branch_1'): + branch_1 = slim.conv2d(net, depth(48), [1, 1], scope='Conv2d_0b_1x1') + branch_1 = slim.conv2d(branch_1, depth(64), [5, 5], + scope='Conv_1_0c_5x5') + with tf.variable_scope('Branch_2'): + branch_2 = slim.conv2d(net, depth(64), [1, 1], + scope='Conv2d_0a_1x1') + branch_2 = slim.conv2d(branch_2, depth(96), [3, 3], + scope='Conv2d_0b_3x3') + branch_2 = slim.conv2d(branch_2, depth(96), [3, 3], + scope='Conv2d_0c_3x3') + with tf.variable_scope('Branch_3'): + branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3') + branch_3 = slim.conv2d(branch_3, depth(64), [1, 1], + scope='Conv2d_0b_1x1') + net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) + end_points[end_point] = net + if end_point == final_endpoint: return net, end_points + + # mixed_2: 35 x 35 x 288. + end_point = 'Mixed_5d' + with tf.variable_scope(end_point): + with tf.variable_scope('Branch_0'): + branch_0 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1') + with tf.variable_scope('Branch_1'): + branch_1 = slim.conv2d(net, depth(48), [1, 1], scope='Conv2d_0a_1x1') + branch_1 = slim.conv2d(branch_1, depth(64), [5, 5], + scope='Conv2d_0b_5x5') + with tf.variable_scope('Branch_2'): + branch_2 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1') + branch_2 = slim.conv2d(branch_2, depth(96), [3, 3], + scope='Conv2d_0b_3x3') + branch_2 = slim.conv2d(branch_2, depth(96), [3, 3], + scope='Conv2d_0c_3x3') + with tf.variable_scope('Branch_3'): + branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3') + branch_3 = slim.conv2d(branch_3, depth(64), [1, 1], + scope='Conv2d_0b_1x1') + net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) + end_points[end_point] = net + if end_point == final_endpoint: return net, end_points + + # mixed_3: 17 x 17 x 768. + end_point = 'Mixed_6a' + with tf.variable_scope(end_point): + with tf.variable_scope('Branch_0'): + branch_0 = slim.conv2d(net, depth(384), [3, 3], stride=2, + padding='VALID', scope='Conv2d_1a_1x1') + with tf.variable_scope('Branch_1'): + branch_1 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1') + branch_1 = slim.conv2d(branch_1, depth(96), [3, 3], + scope='Conv2d_0b_3x3') + branch_1 = slim.conv2d(branch_1, depth(96), [3, 3], stride=2, + padding='VALID', scope='Conv2d_1a_1x1') + with tf.variable_scope('Branch_2'): + branch_2 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID', + scope='MaxPool_1a_3x3') + net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2]) + end_points[end_point] = net + if end_point == final_endpoint: return net, end_points + + # mixed4: 17 x 17 x 768. + end_point = 'Mixed_6b' + with tf.variable_scope(end_point): + with tf.variable_scope('Branch_0'): + branch_0 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1') + with tf.variable_scope('Branch_1'): + branch_1 = slim.conv2d(net, depth(128), [1, 1], scope='Conv2d_0a_1x1') + branch_1 = slim.conv2d(branch_1, depth(128), [1, 7], + scope='Conv2d_0b_1x7') + branch_1 = slim.conv2d(branch_1, depth(192), [7, 1], + scope='Conv2d_0c_7x1') + with tf.variable_scope('Branch_2'): + branch_2 = slim.conv2d(net, depth(128), [1, 1], scope='Conv2d_0a_1x1') + branch_2 = slim.conv2d(branch_2, depth(128), [7, 1], + scope='Conv2d_0b_7x1') + branch_2 = slim.conv2d(branch_2, depth(128), [1, 7], + scope='Conv2d_0c_1x7') + branch_2 = slim.conv2d(branch_2, depth(128), [7, 1], + scope='Conv2d_0d_7x1') + branch_2 = slim.conv2d(branch_2, depth(192), [1, 7], + scope='Conv2d_0e_1x7') + with tf.variable_scope('Branch_3'): + branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3') + branch_3 = slim.conv2d(branch_3, depth(192), [1, 1], + scope='Conv2d_0b_1x1') + net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) + end_points[end_point] = net + if end_point == final_endpoint: return net, end_points + + # mixed_5: 17 x 17 x 768. + end_point = 'Mixed_6c' + with tf.variable_scope(end_point): + with tf.variable_scope('Branch_0'): + branch_0 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1') + with tf.variable_scope('Branch_1'): + branch_1 = slim.conv2d(net, depth(160), [1, 1], scope='Conv2d_0a_1x1') + branch_1 = slim.conv2d(branch_1, depth(160), [1, 7], + scope='Conv2d_0b_1x7') + branch_1 = slim.conv2d(branch_1, depth(192), [7, 1], + scope='Conv2d_0c_7x1') + with tf.variable_scope('Branch_2'): + branch_2 = slim.conv2d(net, depth(160), [1, 1], scope='Conv2d_0a_1x1') + branch_2 = slim.conv2d(branch_2, depth(160), [7, 1], + scope='Conv2d_0b_7x1') + branch_2 = slim.conv2d(branch_2, depth(160), [1, 7], + scope='Conv2d_0c_1x7') + branch_2 = slim.conv2d(branch_2, depth(160), [7, 1], + scope='Conv2d_0d_7x1') + branch_2 = slim.conv2d(branch_2, depth(192), [1, 7], + scope='Conv2d_0e_1x7') + with tf.variable_scope('Branch_3'): + branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3') + branch_3 = slim.conv2d(branch_3, depth(192), [1, 1], + scope='Conv2d_0b_1x1') + net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) + end_points[end_point] = net + if end_point == final_endpoint: return net, end_points + # mixed_6: 17 x 17 x 768. + end_point = 'Mixed_6d' + with tf.variable_scope(end_point): + with tf.variable_scope('Branch_0'): + branch_0 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1') + with tf.variable_scope('Branch_1'): + branch_1 = slim.conv2d(net, depth(160), [1, 1], scope='Conv2d_0a_1x1') + branch_1 = slim.conv2d(branch_1, depth(160), [1, 7], + scope='Conv2d_0b_1x7') + branch_1 = slim.conv2d(branch_1, depth(192), [7, 1], + scope='Conv2d_0c_7x1') + with tf.variable_scope('Branch_2'): + branch_2 = slim.conv2d(net, depth(160), [1, 1], scope='Conv2d_0a_1x1') + branch_2 = slim.conv2d(branch_2, depth(160), [7, 1], + scope='Conv2d_0b_7x1') + branch_2 = slim.conv2d(branch_2, depth(160), [1, 7], + scope='Conv2d_0c_1x7') + branch_2 = slim.conv2d(branch_2, depth(160), [7, 1], + scope='Conv2d_0d_7x1') + branch_2 = slim.conv2d(branch_2, depth(192), [1, 7], + scope='Conv2d_0e_1x7') + with tf.variable_scope('Branch_3'): + branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3') + branch_3 = slim.conv2d(branch_3, depth(192), [1, 1], + scope='Conv2d_0b_1x1') + net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) + end_points[end_point] = net + if end_point == final_endpoint: return net, end_points + + # mixed_7: 17 x 17 x 768. + end_point = 'Mixed_6e' + with tf.variable_scope(end_point): + with tf.variable_scope('Branch_0'): + branch_0 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1') + with tf.variable_scope('Branch_1'): + branch_1 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1') + branch_1 = slim.conv2d(branch_1, depth(192), [1, 7], + scope='Conv2d_0b_1x7') + branch_1 = slim.conv2d(branch_1, depth(192), [7, 1], + scope='Conv2d_0c_7x1') + with tf.variable_scope('Branch_2'): + branch_2 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1') + branch_2 = slim.conv2d(branch_2, depth(192), [7, 1], + scope='Conv2d_0b_7x1') + branch_2 = slim.conv2d(branch_2, depth(192), [1, 7], + scope='Conv2d_0c_1x7') + branch_2 = slim.conv2d(branch_2, depth(192), [7, 1], + scope='Conv2d_0d_7x1') + branch_2 = slim.conv2d(branch_2, depth(192), [1, 7], + scope='Conv2d_0e_1x7') + with tf.variable_scope('Branch_3'): + branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3') + branch_3 = slim.conv2d(branch_3, depth(192), [1, 1], + scope='Conv2d_0b_1x1') + net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) + end_points[end_point] = net + if end_point == final_endpoint: return net, end_points + + # mixed_8: 8 x 8 x 1280. + end_point = 'Mixed_7a' + with tf.variable_scope(end_point): + with tf.variable_scope('Branch_0'): + branch_0 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1') + branch_0 = slim.conv2d(branch_0, depth(320), [3, 3], stride=2, + padding='VALID', scope='Conv2d_1a_3x3') + with tf.variable_scope('Branch_1'): + branch_1 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1') + branch_1 = slim.conv2d(branch_1, depth(192), [1, 7], + scope='Conv2d_0b_1x7') + branch_1 = slim.conv2d(branch_1, depth(192), [7, 1], + scope='Conv2d_0c_7x1') + branch_1 = slim.conv2d(branch_1, depth(192), [3, 3], stride=2, + padding='VALID', scope='Conv2d_1a_3x3') + with tf.variable_scope('Branch_2'): + branch_2 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID', + scope='MaxPool_1a_3x3') + net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2]) + end_points[end_point] = net + if end_point == final_endpoint: return net, end_points + # mixed_9: 8 x 8 x 2048. + end_point = 'Mixed_7b' + with tf.variable_scope(end_point): + with tf.variable_scope('Branch_0'): + branch_0 = slim.conv2d(net, depth(320), [1, 1], scope='Conv2d_0a_1x1') + with tf.variable_scope('Branch_1'): + branch_1 = slim.conv2d(net, depth(384), [1, 1], scope='Conv2d_0a_1x1') + branch_1 = tf.concat(axis=3, values=[ + slim.conv2d(branch_1, depth(384), [1, 3], scope='Conv2d_0b_1x3'), + slim.conv2d(branch_1, depth(384), [3, 1], scope='Conv2d_0b_3x1')]) + with tf.variable_scope('Branch_2'): + branch_2 = slim.conv2d(net, depth(448), [1, 1], scope='Conv2d_0a_1x1') + branch_2 = slim.conv2d( + branch_2, depth(384), [3, 3], scope='Conv2d_0b_3x3') + branch_2 = tf.concat(axis=3, values=[ + slim.conv2d(branch_2, depth(384), [1, 3], scope='Conv2d_0c_1x3'), + slim.conv2d(branch_2, depth(384), [3, 1], scope='Conv2d_0d_3x1')]) + with tf.variable_scope('Branch_3'): + branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3') + branch_3 = slim.conv2d( + branch_3, depth(192), [1, 1], scope='Conv2d_0b_1x1') + net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) + end_points[end_point] = net + if end_point == final_endpoint: return net, end_points + + # mixed_10: 8 x 8 x 2048. + end_point = 'Mixed_7c' + with tf.variable_scope(end_point): + with tf.variable_scope('Branch_0'): + branch_0 = slim.conv2d(net, depth(320), [1, 1], scope='Conv2d_0a_1x1') + with tf.variable_scope('Branch_1'): + branch_1 = slim.conv2d(net, depth(384), [1, 1], scope='Conv2d_0a_1x1') + branch_1 = tf.concat(axis=3, values=[ + slim.conv2d(branch_1, depth(384), [1, 3], scope='Conv2d_0b_1x3'), + slim.conv2d(branch_1, depth(384), [3, 1], scope='Conv2d_0c_3x1')]) + with tf.variable_scope('Branch_2'): + branch_2 = slim.conv2d(net, depth(448), [1, 1], scope='Conv2d_0a_1x1') + branch_2 = slim.conv2d( + branch_2, depth(384), [3, 3], scope='Conv2d_0b_3x3') + branch_2 = tf.concat(axis=3, values=[ + slim.conv2d(branch_2, depth(384), [1, 3], scope='Conv2d_0c_1x3'), + slim.conv2d(branch_2, depth(384), [3, 1], scope='Conv2d_0d_3x1')]) + with tf.variable_scope('Branch_3'): + branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3') + branch_3 = slim.conv2d( + branch_3, depth(192), [1, 1], scope='Conv2d_0b_1x1') + net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) + end_points[end_point] = net + if end_point == final_endpoint: return net, end_points + raise ValueError('Unknown final endpoint %s' % final_endpoint) + + +def inception_v3(inputs, + num_classes=1000, + is_training=True, + dropout_keep_prob=0.8, + min_depth=16, + depth_multiplier=1.0, + prediction_fn=slim.softmax, + spatial_squeeze=True, + reuse=None, + scope='InceptionV3'): + """Inception model from http://arxiv.org/abs/1512.00567. + + "Rethinking the Inception Architecture for Computer Vision" + + Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, + Zbigniew Wojna. + + With the default arguments this method constructs the exact model defined in + the paper. However, one can experiment with variations of the inception_v3 + network by changing arguments dropout_keep_prob, min_depth and + depth_multiplier. + + The default image size used to train this network is 299x299. + + Args: + inputs: a tensor of size [batch_size, height, width, channels]. + num_classes: number of predicted classes. + is_training: whether is training or not. + dropout_keep_prob: the percentage of activation values that are retained. + min_depth: Minimum depth value (number of channels) for all convolution ops. + Enforced when depth_multiplier < 1, and not an active constraint when + depth_multiplier >= 1. + depth_multiplier: Float multiplier for the depth (number of channels) + for all convolution ops. The value must be greater than zero. Typical + usage will be to set this value in (0, 1) to reduce the number of + parameters or computation cost of the model. + prediction_fn: a function to get predictions out of logits. + spatial_squeeze: if True, logits is of shape [B, C], if false logits is + of shape [B, 1, 1, C], where B is batch_size and C is number of classes. + reuse: whether or not the network and its variables should be reused. To be + able to reuse 'scope' must be given. + scope: Optional variable_scope. + + Returns: + logits: the pre-softmax activations, a tensor of size + [batch_size, num_classes] + end_points: a dictionary from components of the network to the corresponding + activation. + + Raises: + ValueError: if 'depth_multiplier' is less than or equal to zero. + """ + if depth_multiplier <= 0: + raise ValueError('depth_multiplier is not greater than zero.') + depth = lambda d: max(int(d * depth_multiplier), min_depth) + + with tf.variable_scope(scope, 'InceptionV3', [inputs, num_classes], + reuse=reuse) as scope: + with slim.arg_scope([slim.batch_norm, slim.dropout], + is_training=is_training): + net, end_points = inception_v3_base( + inputs, scope=scope, min_depth=min_depth, + depth_multiplier=depth_multiplier) + + # Auxiliary Head logits + with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d], + stride=1, padding='SAME'): + aux_logits = end_points['Mixed_6e'] + with tf.variable_scope('AuxLogits'): + aux_logits = slim.avg_pool2d( + aux_logits, [5, 5], stride=3, padding='VALID', + scope='AvgPool_1a_5x5') + aux_logits = slim.conv2d(aux_logits, depth(128), [1, 1], + scope='Conv2d_1b_1x1') + + # Shape of feature map before the final layer. + kernel_size = _reduced_kernel_size_for_small_input( + aux_logits, [5, 5]) + aux_logits = slim.conv2d( + aux_logits, depth(768), kernel_size, + weights_initializer=trunc_normal(0.01), + padding='VALID', scope='Conv2d_2a_{}x{}'.format(*kernel_size)) + aux_logits = slim.conv2d( + aux_logits, num_classes, [1, 1], activation_fn=None, + normalizer_fn=None, weights_initializer=trunc_normal(0.001), + scope='Conv2d_2b_1x1') + if spatial_squeeze: + aux_logits = tf.squeeze(aux_logits, [1, 2], name='SpatialSqueeze') + end_points['AuxLogits'] = aux_logits + + # Final pooling and prediction + with tf.variable_scope('Logits'): + kernel_size = _reduced_kernel_size_for_small_input(net, [8, 8]) + net = slim.avg_pool2d(net, kernel_size, padding='VALID', + scope='AvgPool_1a_{}x{}'.format(*kernel_size)) + # 1 x 1 x 2048 + net = slim.dropout(net, keep_prob=dropout_keep_prob, scope='Dropout_1b') + end_points['PreLogits'] = net + # 2048 + logits = slim.conv2d(net, num_classes, [1, 1], activation_fn=None, + normalizer_fn=None, scope='Conv2d_1c_1x1') + if spatial_squeeze: + logits = tf.squeeze(logits, [1, 2], name='SpatialSqueeze') + # 1000 + end_points['Logits'] = logits + end_points['Predictions'] = prediction_fn(logits, scope='Predictions') + return logits, end_points +inception_v3.default_image_size = 299 + + +def _reduced_kernel_size_for_small_input(input_tensor, kernel_size): + """Define kernel size which is automatically reduced for small input. + + If the shape of the input images is unknown at graph construction time this + function assumes that the input images are is large enough. + + Args: + input_tensor: input tensor of size [batch_size, height, width, channels]. + kernel_size: desired kernel size of length 2: [kernel_height, kernel_width] + + Returns: + a tensor with the kernel size. + + TODO(jrru): Make this function work with unknown shapes. Theoretically, this + can be done with the code below. Problems are two-fold: (1) If the shape was + known, it will be lost. (2) inception.slim.ops._two_element_tuple cannot + handle tensors that define the kernel size. + shape = tf.shape(input_tensor) + return = tf.pack([tf.minimum(shape[1], kernel_size[0]), + tf.minimum(shape[2], kernel_size[1])]) + + """ + shape = input_tensor.get_shape().as_list() + if shape[1] is None or shape[2] is None: + kernel_size_out = kernel_size + else: + kernel_size_out = [min(shape[1], kernel_size[0]), + min(shape[2], kernel_size[1])] + return kernel_size_out + + +inception_v3_arg_scope = inception_utils.inception_arg_scope diff --git a/libs/networks/slim_nets/inception_v3_test.py b/libs/networks/slim_nets/inception_v3_test.py new file mode 100644 index 0000000..a6f3c95 --- /dev/null +++ b/libs/networks/slim_nets/inception_v3_test.py @@ -0,0 +1,292 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for slim_nets.inception_v1.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np +import tensorflow as tf + +from nets import inception + +slim = tf.contrib.slim + + +class InceptionV3Test(tf.test.TestCase): + + def testBuildClassificationNetwork(self): + batch_size = 5 + height, width = 299, 299 + num_classes = 1000 + + inputs = tf.random_uniform((batch_size, height, width, 3)) + logits, end_points = inception.inception_v3(inputs, num_classes) + self.assertTrue(logits.op.name.startswith('InceptionV3/Logits')) + self.assertListEqual(logits.get_shape().as_list(), + [batch_size, num_classes]) + self.assertTrue('Predictions' in end_points) + self.assertListEqual(end_points['Predictions'].get_shape().as_list(), + [batch_size, num_classes]) + + def testBuildBaseNetwork(self): + batch_size = 5 + height, width = 299, 299 + + inputs = tf.random_uniform((batch_size, height, width, 3)) + final_endpoint, end_points = inception.inception_v3_base(inputs) + self.assertTrue(final_endpoint.op.name.startswith( + 'InceptionV3/Mixed_7c')) + self.assertListEqual(final_endpoint.get_shape().as_list(), + [batch_size, 8, 8, 2048]) + expected_endpoints = ['Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3', + 'MaxPool_3a_3x3', 'Conv2d_3b_1x1', 'Conv2d_4a_3x3', + 'MaxPool_5a_3x3', 'Mixed_5b', 'Mixed_5c', 'Mixed_5d', + 'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d', + 'Mixed_6e', 'Mixed_7a', 'Mixed_7b', 'Mixed_7c'] + self.assertItemsEqual(end_points.keys(), expected_endpoints) + + def testBuildOnlyUptoFinalEndpoint(self): + batch_size = 5 + height, width = 299, 299 + endpoints = ['Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3', + 'MaxPool_3a_3x3', 'Conv2d_3b_1x1', 'Conv2d_4a_3x3', + 'MaxPool_5a_3x3', 'Mixed_5b', 'Mixed_5c', 'Mixed_5d', + 'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d', + 'Mixed_6e', 'Mixed_7a', 'Mixed_7b', 'Mixed_7c'] + + for index, endpoint in enumerate(endpoints): + with tf.Graph().as_default(): + inputs = tf.random_uniform((batch_size, height, width, 3)) + out_tensor, end_points = inception.inception_v3_base( + inputs, final_endpoint=endpoint) + self.assertTrue(out_tensor.op.name.startswith( + 'InceptionV3/' + endpoint)) + self.assertItemsEqual(endpoints[:index+1], end_points) + + def testBuildAndCheckAllEndPointsUptoMixed7c(self): + batch_size = 5 + height, width = 299, 299 + + inputs = tf.random_uniform((batch_size, height, width, 3)) + _, end_points = inception.inception_v3_base( + inputs, final_endpoint='Mixed_7c') + endpoints_shapes = {'Conv2d_1a_3x3': [batch_size, 149, 149, 32], + 'Conv2d_2a_3x3': [batch_size, 147, 147, 32], + 'Conv2d_2b_3x3': [batch_size, 147, 147, 64], + 'MaxPool_3a_3x3': [batch_size, 73, 73, 64], + 'Conv2d_3b_1x1': [batch_size, 73, 73, 80], + 'Conv2d_4a_3x3': [batch_size, 71, 71, 192], + 'MaxPool_5a_3x3': [batch_size, 35, 35, 192], + 'Mixed_5b': [batch_size, 35, 35, 256], + 'Mixed_5c': [batch_size, 35, 35, 288], + 'Mixed_5d': [batch_size, 35, 35, 288], + 'Mixed_6a': [batch_size, 17, 17, 768], + 'Mixed_6b': [batch_size, 17, 17, 768], + 'Mixed_6c': [batch_size, 17, 17, 768], + 'Mixed_6d': [batch_size, 17, 17, 768], + 'Mixed_6e': [batch_size, 17, 17, 768], + 'Mixed_7a': [batch_size, 8, 8, 1280], + 'Mixed_7b': [batch_size, 8, 8, 2048], + 'Mixed_7c': [batch_size, 8, 8, 2048]} + self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys()) + for endpoint_name in endpoints_shapes: + expected_shape = endpoints_shapes[endpoint_name] + self.assertTrue(endpoint_name in end_points) + self.assertListEqual(end_points[endpoint_name].get_shape().as_list(), + expected_shape) + + def testModelHasExpectedNumberOfParameters(self): + batch_size = 5 + height, width = 299, 299 + inputs = tf.random_uniform((batch_size, height, width, 3)) + with slim.arg_scope(inception.inception_v3_arg_scope()): + inception.inception_v3_base(inputs) + total_params, _ = slim.model_analyzer.analyze_vars( + slim.get_model_variables()) + self.assertAlmostEqual(21802784, total_params) + + def testBuildEndPoints(self): + batch_size = 5 + height, width = 299, 299 + num_classes = 1000 + + inputs = tf.random_uniform((batch_size, height, width, 3)) + _, end_points = inception.inception_v3(inputs, num_classes) + self.assertTrue('Logits' in end_points) + logits = end_points['Logits'] + self.assertListEqual(logits.get_shape().as_list(), + [batch_size, num_classes]) + self.assertTrue('AuxLogits' in end_points) + aux_logits = end_points['AuxLogits'] + self.assertListEqual(aux_logits.get_shape().as_list(), + [batch_size, num_classes]) + self.assertTrue('Mixed_7c' in end_points) + pre_pool = end_points['Mixed_7c'] + self.assertListEqual(pre_pool.get_shape().as_list(), + [batch_size, 8, 8, 2048]) + self.assertTrue('PreLogits' in end_points) + pre_logits = end_points['PreLogits'] + self.assertListEqual(pre_logits.get_shape().as_list(), + [batch_size, 1, 1, 2048]) + + def testBuildEndPointsWithDepthMultiplierLessThanOne(self): + batch_size = 5 + height, width = 299, 299 + num_classes = 1000 + + inputs = tf.random_uniform((batch_size, height, width, 3)) + _, end_points = inception.inception_v3(inputs, num_classes) + + endpoint_keys = [key for key in end_points.keys() + if key.startswith('Mixed') or key.startswith('Conv')] + + _, end_points_with_multiplier = inception.inception_v3( + inputs, num_classes, scope='depth_multiplied_net', + depth_multiplier=0.5) + + for key in endpoint_keys: + original_depth = end_points[key].get_shape().as_list()[3] + new_depth = end_points_with_multiplier[key].get_shape().as_list()[3] + self.assertEqual(0.5 * original_depth, new_depth) + + def testBuildEndPointsWithDepthMultiplierGreaterThanOne(self): + batch_size = 5 + height, width = 299, 299 + num_classes = 1000 + + inputs = tf.random_uniform((batch_size, height, width, 3)) + _, end_points = inception.inception_v3(inputs, num_classes) + + endpoint_keys = [key for key in end_points.keys() + if key.startswith('Mixed') or key.startswith('Conv')] + + _, end_points_with_multiplier = inception.inception_v3( + inputs, num_classes, scope='depth_multiplied_net', + depth_multiplier=2.0) + + for key in endpoint_keys: + original_depth = end_points[key].get_shape().as_list()[3] + new_depth = end_points_with_multiplier[key].get_shape().as_list()[3] + self.assertEqual(2.0 * original_depth, new_depth) + + def testRaiseValueErrorWithInvalidDepthMultiplier(self): + batch_size = 5 + height, width = 299, 299 + num_classes = 1000 + + inputs = tf.random_uniform((batch_size, height, width, 3)) + with self.assertRaises(ValueError): + _ = inception.inception_v3(inputs, num_classes, depth_multiplier=-0.1) + with self.assertRaises(ValueError): + _ = inception.inception_v3(inputs, num_classes, depth_multiplier=0.0) + + def testHalfSizeImages(self): + batch_size = 5 + height, width = 150, 150 + num_classes = 1000 + + inputs = tf.random_uniform((batch_size, height, width, 3)) + logits, end_points = inception.inception_v3(inputs, num_classes) + self.assertTrue(logits.op.name.startswith('InceptionV3/Logits')) + self.assertListEqual(logits.get_shape().as_list(), + [batch_size, num_classes]) + pre_pool = end_points['Mixed_7c'] + self.assertListEqual(pre_pool.get_shape().as_list(), + [batch_size, 3, 3, 2048]) + + def testUnknownImageShape(self): + tf.reset_default_graph() + batch_size = 2 + height, width = 299, 299 + num_classes = 1000 + input_np = np.random.uniform(0, 1, (batch_size, height, width, 3)) + with self.test_session() as sess: + inputs = tf.placeholder(tf.float32, shape=(batch_size, None, None, 3)) + logits, end_points = inception.inception_v3(inputs, num_classes) + self.assertListEqual(logits.get_shape().as_list(), + [batch_size, num_classes]) + pre_pool = end_points['Mixed_7c'] + feed_dict = {inputs: input_np} + tf.global_variables_initializer().run() + pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict) + self.assertListEqual(list(pre_pool_out.shape), [batch_size, 8, 8, 2048]) + + def testUnknowBatchSize(self): + batch_size = 1 + height, width = 299, 299 + num_classes = 1000 + + inputs = tf.placeholder(tf.float32, (None, height, width, 3)) + logits, _ = inception.inception_v3(inputs, num_classes) + self.assertTrue(logits.op.name.startswith('InceptionV3/Logits')) + self.assertListEqual(logits.get_shape().as_list(), + [None, num_classes]) + images = tf.random_uniform((batch_size, height, width, 3)) + + with self.test_session() as sess: + sess.run(tf.global_variables_initializer()) + output = sess.run(logits, {inputs: images.eval()}) + self.assertEquals(output.shape, (batch_size, num_classes)) + + def testEvaluation(self): + batch_size = 2 + height, width = 299, 299 + num_classes = 1000 + + eval_inputs = tf.random_uniform((batch_size, height, width, 3)) + logits, _ = inception.inception_v3(eval_inputs, num_classes, + is_training=False) + predictions = tf.argmax(logits, 1) + + with self.test_session() as sess: + sess.run(tf.global_variables_initializer()) + output = sess.run(predictions) + self.assertEquals(output.shape, (batch_size,)) + + def testTrainEvalWithReuse(self): + train_batch_size = 5 + eval_batch_size = 2 + height, width = 150, 150 + num_classes = 1000 + + train_inputs = tf.random_uniform((train_batch_size, height, width, 3)) + inception.inception_v3(train_inputs, num_classes) + eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3)) + logits, _ = inception.inception_v3(eval_inputs, num_classes, + is_training=False, reuse=True) + predictions = tf.argmax(logits, 1) + + with self.test_session() as sess: + sess.run(tf.global_variables_initializer()) + output = sess.run(predictions) + self.assertEquals(output.shape, (eval_batch_size,)) + + def testLogitsNotSqueezed(self): + num_classes = 25 + images = tf.random_uniform([1, 299, 299, 3]) + logits, _ = inception.inception_v3(images, + num_classes=num_classes, + spatial_squeeze=False) + + with self.test_session() as sess: + tf.global_variables_initializer().run() + logits_out = sess.run(logits) + self.assertListEqual(list(logits_out.shape), [1, 1, 1, num_classes]) + + +if __name__ == '__main__': + tf.test.main() diff --git a/libs/networks/slim_nets/inception_v4.py b/libs/networks/slim_nets/inception_v4.py new file mode 100644 index 0000000..b4f07ea --- /dev/null +++ b/libs/networks/slim_nets/inception_v4.py @@ -0,0 +1,323 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Contains the definition of the Inception V4 architecture. + +As described in http://arxiv.org/abs/1602.07261. + + Inception-v4, Inception-ResNet and the Impact of Residual Connections + on Learning + Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf + +from nets import inception_utils + +slim = tf.contrib.slim + + +def block_inception_a(inputs, scope=None, reuse=None): + """Builds Inception-A block for Inception v4 network.""" + # By default use stride=1 and SAME padding + with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d], + stride=1, padding='SAME'): + with tf.variable_scope(scope, 'BlockInceptionA', [inputs], reuse=reuse): + with tf.variable_scope('Branch_0'): + branch_0 = slim.conv2d(inputs, 96, [1, 1], scope='Conv2d_0a_1x1') + with tf.variable_scope('Branch_1'): + branch_1 = slim.conv2d(inputs, 64, [1, 1], scope='Conv2d_0a_1x1') + branch_1 = slim.conv2d(branch_1, 96, [3, 3], scope='Conv2d_0b_3x3') + with tf.variable_scope('Branch_2'): + branch_2 = slim.conv2d(inputs, 64, [1, 1], scope='Conv2d_0a_1x1') + branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0b_3x3') + branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0c_3x3') + with tf.variable_scope('Branch_3'): + branch_3 = slim.avg_pool2d(inputs, [3, 3], scope='AvgPool_0a_3x3') + branch_3 = slim.conv2d(branch_3, 96, [1, 1], scope='Conv2d_0b_1x1') + return tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) + + +def block_reduction_a(inputs, scope=None, reuse=None): + """Builds Reduction-A block for Inception v4 network.""" + # By default use stride=1 and SAME padding + with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d], + stride=1, padding='SAME'): + with tf.variable_scope(scope, 'BlockReductionA', [inputs], reuse=reuse): + with tf.variable_scope('Branch_0'): + branch_0 = slim.conv2d(inputs, 384, [3, 3], stride=2, padding='VALID', + scope='Conv2d_1a_3x3') + with tf.variable_scope('Branch_1'): + branch_1 = slim.conv2d(inputs, 192, [1, 1], scope='Conv2d_0a_1x1') + branch_1 = slim.conv2d(branch_1, 224, [3, 3], scope='Conv2d_0b_3x3') + branch_1 = slim.conv2d(branch_1, 256, [3, 3], stride=2, + padding='VALID', scope='Conv2d_1a_3x3') + with tf.variable_scope('Branch_2'): + branch_2 = slim.max_pool2d(inputs, [3, 3], stride=2, padding='VALID', + scope='MaxPool_1a_3x3') + return tf.concat(axis=3, values=[branch_0, branch_1, branch_2]) + + +def block_inception_b(inputs, scope=None, reuse=None): + """Builds Inception-B block for Inception v4 network.""" + # By default use stride=1 and SAME padding + with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d], + stride=1, padding='SAME'): + with tf.variable_scope(scope, 'BlockInceptionB', [inputs], reuse=reuse): + with tf.variable_scope('Branch_0'): + branch_0 = slim.conv2d(inputs, 384, [1, 1], scope='Conv2d_0a_1x1') + with tf.variable_scope('Branch_1'): + branch_1 = slim.conv2d(inputs, 192, [1, 1], scope='Conv2d_0a_1x1') + branch_1 = slim.conv2d(branch_1, 224, [1, 7], scope='Conv2d_0b_1x7') + branch_1 = slim.conv2d(branch_1, 256, [7, 1], scope='Conv2d_0c_7x1') + with tf.variable_scope('Branch_2'): + branch_2 = slim.conv2d(inputs, 192, [1, 1], scope='Conv2d_0a_1x1') + branch_2 = slim.conv2d(branch_2, 192, [7, 1], scope='Conv2d_0b_7x1') + branch_2 = slim.conv2d(branch_2, 224, [1, 7], scope='Conv2d_0c_1x7') + branch_2 = slim.conv2d(branch_2, 224, [7, 1], scope='Conv2d_0d_7x1') + branch_2 = slim.conv2d(branch_2, 256, [1, 7], scope='Conv2d_0e_1x7') + with tf.variable_scope('Branch_3'): + branch_3 = slim.avg_pool2d(inputs, [3, 3], scope='AvgPool_0a_3x3') + branch_3 = slim.conv2d(branch_3, 128, [1, 1], scope='Conv2d_0b_1x1') + return tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) + + +def block_reduction_b(inputs, scope=None, reuse=None): + """Builds Reduction-B block for Inception v4 network.""" + # By default use stride=1 and SAME padding + with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d], + stride=1, padding='SAME'): + with tf.variable_scope(scope, 'BlockReductionB', [inputs], reuse=reuse): + with tf.variable_scope('Branch_0'): + branch_0 = slim.conv2d(inputs, 192, [1, 1], scope='Conv2d_0a_1x1') + branch_0 = slim.conv2d(branch_0, 192, [3, 3], stride=2, + padding='VALID', scope='Conv2d_1a_3x3') + with tf.variable_scope('Branch_1'): + branch_1 = slim.conv2d(inputs, 256, [1, 1], scope='Conv2d_0a_1x1') + branch_1 = slim.conv2d(branch_1, 256, [1, 7], scope='Conv2d_0b_1x7') + branch_1 = slim.conv2d(branch_1, 320, [7, 1], scope='Conv2d_0c_7x1') + branch_1 = slim.conv2d(branch_1, 320, [3, 3], stride=2, + padding='VALID', scope='Conv2d_1a_3x3') + with tf.variable_scope('Branch_2'): + branch_2 = slim.max_pool2d(inputs, [3, 3], stride=2, padding='VALID', + scope='MaxPool_1a_3x3') + return tf.concat(axis=3, values=[branch_0, branch_1, branch_2]) + + +def block_inception_c(inputs, scope=None, reuse=None): + """Builds Inception-C block for Inception v4 network.""" + # By default use stride=1 and SAME padding + with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d], + stride=1, padding='SAME'): + with tf.variable_scope(scope, 'BlockInceptionC', [inputs], reuse=reuse): + with tf.variable_scope('Branch_0'): + branch_0 = slim.conv2d(inputs, 256, [1, 1], scope='Conv2d_0a_1x1') + with tf.variable_scope('Branch_1'): + branch_1 = slim.conv2d(inputs, 384, [1, 1], scope='Conv2d_0a_1x1') + branch_1 = tf.concat(axis=3, values=[ + slim.conv2d(branch_1, 256, [1, 3], scope='Conv2d_0b_1x3'), + slim.conv2d(branch_1, 256, [3, 1], scope='Conv2d_0c_3x1')]) + with tf.variable_scope('Branch_2'): + branch_2 = slim.conv2d(inputs, 384, [1, 1], scope='Conv2d_0a_1x1') + branch_2 = slim.conv2d(branch_2, 448, [3, 1], scope='Conv2d_0b_3x1') + branch_2 = slim.conv2d(branch_2, 512, [1, 3], scope='Conv2d_0c_1x3') + branch_2 = tf.concat(axis=3, values=[ + slim.conv2d(branch_2, 256, [1, 3], scope='Conv2d_0d_1x3'), + slim.conv2d(branch_2, 256, [3, 1], scope='Conv2d_0e_3x1')]) + with tf.variable_scope('Branch_3'): + branch_3 = slim.avg_pool2d(inputs, [3, 3], scope='AvgPool_0a_3x3') + branch_3 = slim.conv2d(branch_3, 256, [1, 1], scope='Conv2d_0b_1x1') + return tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) + + +def inception_v4_base(inputs, final_endpoint='Mixed_7d', scope=None): + """Creates the Inception V4 network up to the given final endpoint. + + Args: + inputs: a 4-D tensor of size [batch_size, height, width, 3]. + final_endpoint: specifies the endpoint to construct the network up to. + It can be one of [ 'Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3', + 'Mixed_3a', 'Mixed_4a', 'Mixed_5a', 'Mixed_5b', 'Mixed_5c', 'Mixed_5d', + 'Mixed_5e', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d', 'Mixed_6e', + 'Mixed_6f', 'Mixed_6g', 'Mixed_6h', 'Mixed_7a', 'Mixed_7b', 'Mixed_7c', + 'Mixed_7d'] + scope: Optional variable_scope. + + Returns: + logits: the logits outputs of the model. + end_points: the set of end_points from the inception model. + + Raises: + ValueError: if final_endpoint is not set to one of the predefined values, + """ + end_points = {} + + def add_and_check_final(name, net): + end_points[name] = net + return name == final_endpoint + + with tf.variable_scope(scope, 'InceptionV4', [inputs]): + with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d], + stride=1, padding='SAME'): + # 299 x 299 x 3 + net = slim.conv2d(inputs, 32, [3, 3], stride=2, + padding='VALID', scope='Conv2d_1a_3x3') + if add_and_check_final('Conv2d_1a_3x3', net): return net, end_points + # 149 x 149 x 32 + net = slim.conv2d(net, 32, [3, 3], padding='VALID', + scope='Conv2d_2a_3x3') + if add_and_check_final('Conv2d_2a_3x3', net): return net, end_points + # 147 x 147 x 32 + net = slim.conv2d(net, 64, [3, 3], scope='Conv2d_2b_3x3') + if add_and_check_final('Conv2d_2b_3x3', net): return net, end_points + # 147 x 147 x 64 + with tf.variable_scope('Mixed_3a'): + with tf.variable_scope('Branch_0'): + branch_0 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID', + scope='MaxPool_0a_3x3') + with tf.variable_scope('Branch_1'): + branch_1 = slim.conv2d(net, 96, [3, 3], stride=2, padding='VALID', + scope='Conv2d_0a_3x3') + net = tf.concat(axis=3, values=[branch_0, branch_1]) + if add_and_check_final('Mixed_3a', net): return net, end_points + + # 73 x 73 x 160 + with tf.variable_scope('Mixed_4a'): + with tf.variable_scope('Branch_0'): + branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1') + branch_0 = slim.conv2d(branch_0, 96, [3, 3], padding='VALID', + scope='Conv2d_1a_3x3') + with tf.variable_scope('Branch_1'): + branch_1 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1') + branch_1 = slim.conv2d(branch_1, 64, [1, 7], scope='Conv2d_0b_1x7') + branch_1 = slim.conv2d(branch_1, 64, [7, 1], scope='Conv2d_0c_7x1') + branch_1 = slim.conv2d(branch_1, 96, [3, 3], padding='VALID', + scope='Conv2d_1a_3x3') + net = tf.concat(axis=3, values=[branch_0, branch_1]) + if add_and_check_final('Mixed_4a', net): return net, end_points + + # 71 x 71 x 192 + with tf.variable_scope('Mixed_5a'): + with tf.variable_scope('Branch_0'): + branch_0 = slim.conv2d(net, 192, [3, 3], stride=2, padding='VALID', + scope='Conv2d_1a_3x3') + with tf.variable_scope('Branch_1'): + branch_1 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID', + scope='MaxPool_1a_3x3') + net = tf.concat(axis=3, values=[branch_0, branch_1]) + if add_and_check_final('Mixed_5a', net): return net, end_points + + # 35 x 35 x 384 + # 4 x Inception-A blocks + for idx in range(4): + block_scope = 'Mixed_5' + chr(ord('b') + idx) + net = block_inception_a(net, block_scope) + if add_and_check_final(block_scope, net): return net, end_points + + # 35 x 35 x 384 + # Reduction-A block + net = block_reduction_a(net, 'Mixed_6a') + if add_and_check_final('Mixed_6a', net): return net, end_points + + # 17 x 17 x 1024 + # 7 x Inception-B blocks + for idx in range(7): + block_scope = 'Mixed_6' + chr(ord('b') + idx) + net = block_inception_b(net, block_scope) + if add_and_check_final(block_scope, net): return net, end_points + + # 17 x 17 x 1024 + # Reduction-B block + net = block_reduction_b(net, 'Mixed_7a') + if add_and_check_final('Mixed_7a', net): return net, end_points + + # 8 x 8 x 1536 + # 3 x Inception-C blocks + for idx in range(3): + block_scope = 'Mixed_7' + chr(ord('b') + idx) + net = block_inception_c(net, block_scope) + if add_and_check_final(block_scope, net): return net, end_points + raise ValueError('Unknown final endpoint %s' % final_endpoint) + + +def inception_v4(inputs, num_classes=1001, is_training=True, + dropout_keep_prob=0.8, + reuse=None, + scope='InceptionV4', + create_aux_logits=True): + """Creates the Inception V4 model. + + Args: + inputs: a 4-D tensor of size [batch_size, height, width, 3]. + num_classes: number of predicted classes. + is_training: whether is training or not. + dropout_keep_prob: float, the fraction to keep before final layer. + reuse: whether or not the network and its variables should be reused. To be + able to reuse 'scope' must be given. + scope: Optional variable_scope. + create_aux_logits: Whether to include the auxiliary logits. + + Returns: + logits: the logits outputs of the model. + end_points: the set of end_points from the inception model. + """ + end_points = {} + with tf.variable_scope(scope, 'InceptionV4', [inputs], reuse=reuse) as scope: + with slim.arg_scope([slim.batch_norm, slim.dropout], + is_training=is_training): + net, end_points = inception_v4_base(inputs, scope=scope) + + with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d], + stride=1, padding='SAME'): + # Auxiliary Head logits + if create_aux_logits: + with tf.variable_scope('AuxLogits'): + # 17 x 17 x 1024 + aux_logits = end_points['Mixed_6h'] + aux_logits = slim.avg_pool2d(aux_logits, [5, 5], stride=3, + padding='VALID', + scope='AvgPool_1a_5x5') + aux_logits = slim.conv2d(aux_logits, 128, [1, 1], + scope='Conv2d_1b_1x1') + aux_logits = slim.conv2d(aux_logits, 768, + aux_logits.get_shape()[1:3], + padding='VALID', scope='Conv2d_2a') + aux_logits = slim.flatten(aux_logits) + aux_logits = slim.fully_connected(aux_logits, num_classes, + activation_fn=None, + scope='Aux_logits') + end_points['AuxLogits'] = aux_logits + + # Final pooling and prediction + with tf.variable_scope('Logits'): + # 8 x 8 x 1536 + net = slim.avg_pool2d(net, net.get_shape()[1:3], padding='VALID', + scope='AvgPool_1a') + # 1 x 1 x 1536 + net = slim.dropout(net, dropout_keep_prob, scope='Dropout_1b') + net = slim.flatten(net, scope='PreLogitsFlatten') + end_points['PreLogitsFlatten'] = net + # 1536 + logits = slim.fully_connected(net, num_classes, activation_fn=None, + scope='Logits') + end_points['Logits'] = logits + end_points['Predictions'] = tf.nn.softmax(logits, name='Predictions') + return logits, end_points +inception_v4.default_image_size = 299 + + +inception_v4_arg_scope = inception_utils.inception_arg_scope diff --git a/libs/networks/slim_nets/inception_v4_test.py b/libs/networks/slim_nets/inception_v4_test.py new file mode 100644 index 0000000..11cffb6 --- /dev/null +++ b/libs/networks/slim_nets/inception_v4_test.py @@ -0,0 +1,216 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for slim.inception_v4.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf + +from nets import inception + + +class InceptionTest(tf.test.TestCase): + + def testBuildLogits(self): + batch_size = 5 + height, width = 299, 299 + num_classes = 1000 + inputs = tf.random_uniform((batch_size, height, width, 3)) + logits, end_points = inception.inception_v4(inputs, num_classes) + auxlogits = end_points['AuxLogits'] + predictions = end_points['Predictions'] + self.assertTrue(auxlogits.op.name.startswith('InceptionV4/AuxLogits')) + self.assertListEqual(auxlogits.get_shape().as_list(), + [batch_size, num_classes]) + self.assertTrue(logits.op.name.startswith('InceptionV4/Logits')) + self.assertListEqual(logits.get_shape().as_list(), + [batch_size, num_classes]) + self.assertTrue(predictions.op.name.startswith( + 'InceptionV4/Logits/Predictions')) + self.assertListEqual(predictions.get_shape().as_list(), + [batch_size, num_classes]) + + def testBuildWithoutAuxLogits(self): + batch_size = 5 + height, width = 299, 299 + num_classes = 1000 + inputs = tf.random_uniform((batch_size, height, width, 3)) + logits, endpoints = inception.inception_v4(inputs, num_classes, + create_aux_logits=False) + self.assertFalse('AuxLogits' in endpoints) + self.assertTrue(logits.op.name.startswith('InceptionV4/Logits')) + self.assertListEqual(logits.get_shape().as_list(), + [batch_size, num_classes]) + + def testAllEndPointsShapes(self): + batch_size = 5 + height, width = 299, 299 + num_classes = 1000 + inputs = tf.random_uniform((batch_size, height, width, 3)) + _, end_points = inception.inception_v4(inputs, num_classes) + endpoints_shapes = {'Conv2d_1a_3x3': [batch_size, 149, 149, 32], + 'Conv2d_2a_3x3': [batch_size, 147, 147, 32], + 'Conv2d_2b_3x3': [batch_size, 147, 147, 64], + 'Mixed_3a': [batch_size, 73, 73, 160], + 'Mixed_4a': [batch_size, 71, 71, 192], + 'Mixed_5a': [batch_size, 35, 35, 384], + # 4 x Inception-A blocks + 'Mixed_5b': [batch_size, 35, 35, 384], + 'Mixed_5c': [batch_size, 35, 35, 384], + 'Mixed_5d': [batch_size, 35, 35, 384], + 'Mixed_5e': [batch_size, 35, 35, 384], + # Reduction-A block + 'Mixed_6a': [batch_size, 17, 17, 1024], + # 7 x Inception-B blocks + 'Mixed_6b': [batch_size, 17, 17, 1024], + 'Mixed_6c': [batch_size, 17, 17, 1024], + 'Mixed_6d': [batch_size, 17, 17, 1024], + 'Mixed_6e': [batch_size, 17, 17, 1024], + 'Mixed_6f': [batch_size, 17, 17, 1024], + 'Mixed_6g': [batch_size, 17, 17, 1024], + 'Mixed_6h': [batch_size, 17, 17, 1024], + # Reduction-A block + 'Mixed_7a': [batch_size, 8, 8, 1536], + # 3 x Inception-C blocks + 'Mixed_7b': [batch_size, 8, 8, 1536], + 'Mixed_7c': [batch_size, 8, 8, 1536], + 'Mixed_7d': [batch_size, 8, 8, 1536], + # Logits and predictions + 'AuxLogits': [batch_size, num_classes], + 'PreLogitsFlatten': [batch_size, 1536], + 'Logits': [batch_size, num_classes], + 'Predictions': [batch_size, num_classes]} + self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys()) + for endpoint_name in endpoints_shapes: + expected_shape = endpoints_shapes[endpoint_name] + self.assertTrue(endpoint_name in end_points) + self.assertListEqual(end_points[endpoint_name].get_shape().as_list(), + expected_shape) + + def testBuildBaseNetwork(self): + batch_size = 5 + height, width = 299, 299 + inputs = tf.random_uniform((batch_size, height, width, 3)) + net, end_points = inception.inception_v4_base(inputs) + self.assertTrue(net.op.name.startswith( + 'InceptionV4/Mixed_7d')) + self.assertListEqual(net.get_shape().as_list(), [batch_size, 8, 8, 1536]) + expected_endpoints = [ + 'Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3', 'Mixed_3a', + 'Mixed_4a', 'Mixed_5a', 'Mixed_5b', 'Mixed_5c', 'Mixed_5d', + 'Mixed_5e', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d', + 'Mixed_6e', 'Mixed_6f', 'Mixed_6g', 'Mixed_6h', 'Mixed_7a', + 'Mixed_7b', 'Mixed_7c', 'Mixed_7d'] + self.assertItemsEqual(end_points.keys(), expected_endpoints) + for name, op in end_points.iteritems(): + self.assertTrue(op.name.startswith('InceptionV4/' + name)) + + def testBuildOnlyUpToFinalEndpoint(self): + batch_size = 5 + height, width = 299, 299 + all_endpoints = [ + 'Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3', 'Mixed_3a', + 'Mixed_4a', 'Mixed_5a', 'Mixed_5b', 'Mixed_5c', 'Mixed_5d', + 'Mixed_5e', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d', + 'Mixed_6e', 'Mixed_6f', 'Mixed_6g', 'Mixed_6h', 'Mixed_7a', + 'Mixed_7b', 'Mixed_7c', 'Mixed_7d'] + for index, endpoint in enumerate(all_endpoints): + with tf.Graph().as_default(): + inputs = tf.random_uniform((batch_size, height, width, 3)) + out_tensor, end_points = inception.inception_v4_base( + inputs, final_endpoint=endpoint) + self.assertTrue(out_tensor.op.name.startswith( + 'InceptionV4/' + endpoint)) + self.assertItemsEqual(all_endpoints[:index+1], end_points) + + def testVariablesSetDevice(self): + batch_size = 5 + height, width = 299, 299 + num_classes = 1000 + inputs = tf.random_uniform((batch_size, height, width, 3)) + # Force all Variables to reside on the device. + with tf.variable_scope('on_cpu'), tf.device('/cpu:0'): + inception.inception_v4(inputs, num_classes) + with tf.variable_scope('on_gpu'), tf.device('/gpu:0'): + inception.inception_v4(inputs, num_classes) + for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='on_cpu'): + self.assertDeviceEqual(v.device, '/cpu:0') + for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='on_gpu'): + self.assertDeviceEqual(v.device, '/gpu:0') + + def testHalfSizeImages(self): + batch_size = 5 + height, width = 150, 150 + num_classes = 1000 + inputs = tf.random_uniform((batch_size, height, width, 3)) + logits, end_points = inception.inception_v4(inputs, num_classes) + self.assertTrue(logits.op.name.startswith('InceptionV4/Logits')) + self.assertListEqual(logits.get_shape().as_list(), + [batch_size, num_classes]) + pre_pool = end_points['Mixed_7d'] + self.assertListEqual(pre_pool.get_shape().as_list(), + [batch_size, 3, 3, 1536]) + + def testUnknownBatchSize(self): + batch_size = 1 + height, width = 299, 299 + num_classes = 1000 + with self.test_session() as sess: + inputs = tf.placeholder(tf.float32, (None, height, width, 3)) + logits, _ = inception.inception_v4(inputs, num_classes) + self.assertTrue(logits.op.name.startswith('InceptionV4/Logits')) + self.assertListEqual(logits.get_shape().as_list(), + [None, num_classes]) + images = tf.random_uniform((batch_size, height, width, 3)) + sess.run(tf.global_variables_initializer()) + output = sess.run(logits, {inputs: images.eval()}) + self.assertEquals(output.shape, (batch_size, num_classes)) + + def testEvaluation(self): + batch_size = 2 + height, width = 299, 299 + num_classes = 1000 + with self.test_session() as sess: + eval_inputs = tf.random_uniform((batch_size, height, width, 3)) + logits, _ = inception.inception_v4(eval_inputs, + num_classes, + is_training=False) + predictions = tf.argmax(logits, 1) + sess.run(tf.global_variables_initializer()) + output = sess.run(predictions) + self.assertEquals(output.shape, (batch_size,)) + + def testTrainEvalWithReuse(self): + train_batch_size = 5 + eval_batch_size = 2 + height, width = 150, 150 + num_classes = 1000 + with self.test_session() as sess: + train_inputs = tf.random_uniform((train_batch_size, height, width, 3)) + inception.inception_v4(train_inputs, num_classes) + eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3)) + logits, _ = inception.inception_v4(eval_inputs, + num_classes, + is_training=False, + reuse=True) + predictions = tf.argmax(logits, 1) + sess.run(tf.global_variables_initializer()) + output = sess.run(predictions) + self.assertEquals(output.shape, (eval_batch_size,)) + + +if __name__ == '__main__': + tf.test.main() diff --git a/libs/networks/slim_nets/lenet.py b/libs/networks/slim_nets/lenet.py new file mode 100644 index 0000000..789d2bd --- /dev/null +++ b/libs/networks/slim_nets/lenet.py @@ -0,0 +1,93 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Contains a variant of the LeNet model definition.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf + +slim = tf.contrib.slim + + +def lenet(images, num_classes=10, is_training=False, + dropout_keep_prob=0.5, + prediction_fn=slim.softmax, + scope='LeNet'): + """Creates a variant of the LeNet model. + + Note that since the output is a set of 'logits', the values fall in the + interval of (-infinity, infinity). Consequently, to convert the outputs to a + probability distribution over the characters, one will need to convert them + using the softmax function: + + logits = lenet.lenet(images, is_training=False) + probabilities = tf.nn.softmax(logits) + predictions = tf.argmax(logits, 1) + + Args: + images: A batch of `Tensors` of size [batch_size, height, width, channels]. + num_classes: the number of classes in the dataset. + is_training: specifies whether or not we're currently training the model. + This variable will determine the behaviour of the dropout layer. + dropout_keep_prob: the percentage of activation values that are retained. + prediction_fn: a function to get predictions out of logits. + scope: Optional variable_scope. + + Returns: + logits: the pre-softmax activations, a tensor of size + [batch_size, `num_classes`] + end_points: a dictionary from components of the network to the corresponding + activation. + """ + end_points = {} + + with tf.variable_scope(scope, 'LeNet', [images, num_classes]): + net = slim.conv2d(images, 32, [5, 5], scope='conv1') + net = slim.max_pool2d(net, [2, 2], 2, scope='pool1') + net = slim.conv2d(net, 64, [5, 5], scope='conv2') + net = slim.max_pool2d(net, [2, 2], 2, scope='pool2') + net = slim.flatten(net) + end_points['Flatten'] = net + + net = slim.fully_connected(net, 1024, scope='fc3') + net = slim.dropout(net, dropout_keep_prob, is_training=is_training, + scope='dropout3') + logits = slim.fully_connected(net, num_classes, activation_fn=None, + scope='fc4') + + end_points['Logits'] = logits + end_points['Predictions'] = prediction_fn(logits, scope='Predictions') + + return logits, end_points +lenet.default_image_size = 28 + + +def lenet_arg_scope(weight_decay=0.0): + """Defines the default lenet argument scope. + + Args: + weight_decay: The weight decay to use for regularizing the model. + + Returns: + An `arg_scope` to use for the inception v3 model. + """ + with slim.arg_scope( + [slim.conv2d, slim.fully_connected], + weights_regularizer=slim.l2_regularizer(weight_decay), + weights_initializer=tf.truncated_normal_initializer(stddev=0.1), + activation_fn=tf.nn.relu) as sc: + return sc diff --git a/libs/networks/slim_nets/mobilenet_v1.md b/libs/networks/slim_nets/mobilenet_v1.md new file mode 100644 index 0000000..342f305 --- /dev/null +++ b/libs/networks/slim_nets/mobilenet_v1.md @@ -0,0 +1,47 @@ +# MobileNet_v1 + +[MobileNets](https://arxiv.org/abs/1704.04861) are small, low-latency, low-power models parameterized to meet the resource constraints of a variety of use cases. They can be built upon for classification, detection, embeddings and segmentation similar to how other popular large scale models, such as Inception, are used. MobileNets can be run efficiently on mobile devices with [TensorFlow Mobile](https://www.tensorflow.org/mobile/). + +MobileNets trade off between latency, size and accuracy while comparing favorably with popular models from the literature. + +![alt text](mobilenet_v1.png "MobileNet Graph") + +# Pre-trained Models + +Choose the right MobileNet model to fit your latency and size budget. The size of the network in memory and on disk is proportional to the number of parameters. The latency and power usage of the network scales with the number of Multiply-Accumulates (MACs) which measures the number of fused Multiplication and Addition operations. These MobileNet models have been trained on the +[ILSVRC-2012-CLS](http://www.image-net.org/challenges/LSVRC/2012/) +image classification dataset. Accuracies were computed by evaluating using a single image crop. + +Model Checkpoint | Million MACs | Million Parameters | Top-1 Accuracy| Top-5 Accuracy | +:----:|:------------:|:----------:|:-------:|:-------:| +[MobileNet_v1_1.0_224](http://download.tensorflow.org/models/mobilenet_v1_1.0_224_2017_06_14.tar.gz)|569|4.24|70.7|89.5| +[MobileNet_v1_1.0_192](http://download.tensorflow.org/models/mobilenet_v1_1.0_192_2017_06_14.tar.gz)|418|4.24|69.3|88.9| +[MobileNet_v1_1.0_160](http://download.tensorflow.org/models/mobilenet_v1_1.0_160_2017_06_14.tar.gz)|291|4.24|67.2|87.5| +[MobileNet_v1_1.0_128](http://download.tensorflow.org/models/mobilenet_v1_1.0_128_2017_06_14.tar.gz)|186|4.24|64.1|85.3| +[MobileNet_v1_0.75_224](http://download.tensorflow.org/models/mobilenet_v1_0.75_224_2017_06_14.tar.gz)|317|2.59|68.4|88.2| +[MobileNet_v1_0.75_192](http://download.tensorflow.org/models/mobilenet_v1_0.75_192_2017_06_14.tar.gz)|233|2.59|67.4|87.3| +[MobileNet_v1_0.75_160](http://download.tensorflow.org/models/mobilenet_v1_0.75_160_2017_06_14.tar.gz)|162|2.59|65.2|86.1| +[MobileNet_v1_0.75_128](http://download.tensorflow.org/models/mobilenet_v1_0.75_128_2017_06_14.tar.gz)|104|2.59|61.8|83.6| +[MobileNet_v1_0.50_224](http://download.tensorflow.org/models/mobilenet_v1_0.50_224_2017_06_14.tar.gz)|150|1.34|64.0|85.4| +[MobileNet_v1_0.50_192](http://download.tensorflow.org/models/mobilenet_v1_0.50_192_2017_06_14.tar.gz)|110|1.34|62.1|84.0| +[MobileNet_v1_0.50_160](http://download.tensorflow.org/models/mobilenet_v1_0.50_160_2017_06_14.tar.gz)|77|1.34|59.9|82.5| +[MobileNet_v1_0.50_128](http://download.tensorflow.org/models/mobilenet_v1_0.50_128_2017_06_14.tar.gz)|49|1.34|56.2|79.6| +[MobileNet_v1_0.25_224](http://download.tensorflow.org/models/mobilenet_v1_0.25_224_2017_06_14.tar.gz)|41|0.47|50.6|75.0| +[MobileNet_v1_0.25_192](http://download.tensorflow.org/models/mobilenet_v1_0.25_192_2017_06_14.tar.gz)|34|0.47|49.0|73.6| +[MobileNet_v1_0.25_160](http://download.tensorflow.org/models/mobilenet_v1_0.25_160_2017_06_14.tar.gz)|21|0.47|46.0|70.7| +[MobileNet_v1_0.25_128](http://download.tensorflow.org/models/mobilenet_v1_0.25_128_2017_06_14.tar.gz)|14|0.47|41.3|66.2| + + +Here is an example of how to download the MobileNet_v1_1.0_224 checkpoint: + +```shell +$ CHECKPOINT_DIR=/tmp/checkpoints +$ mkdir ${CHECKPOINT_DIR} +$ wget http://download.tensorflow.org/models/mobilenet_v1_1.0_224_2017_06_14.tar.gz +$ tar -xvf mobilenet_v1_1.0_224_2017_06_14.tar.gz +$ mv mobilenet_v1_1.0_224.ckpt.* ${CHECKPOINT_DIR} +$ rm mobilenet_v1_1.0_224_2017_06_14.tar.gz +``` +More information on integrating MobileNets into your project can be found at the [TF-Slim Image Classification Library](https://github.com/tensorflow/models/blob/master/slim/README.md). + +To get started running models on-device go to [TensorFlow Mobile](https://www.tensorflow.org/mobile/). diff --git a/libs/networks/slim_nets/mobilenet_v1.png 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The value must be greater than zero. Typical + usage will be to set this value in (0, 1) to reduce the number of + parameters or computation cost of the model. + conv_defs: A list of ConvDef namedtuples specifying the net architecture. + output_stride: An integer that specifies the requested ratio of input to + output spatial resolution. If not None, then we invoke atrous convolution + if necessary to prevent the network from reducing the spatial resolution + of the activation maps. Allowed values are 8 (accurate fully convolutional + mode), 16 (fast fully convolutional mode), 32 (classification mode). + scope: Optional variable_scope. + + Returns: + tensor_out: output tensor corresponding to the final_endpoint. + end_points: a set of activations for external use, for example summaries or + losses. + + Raises: + ValueError: if final_endpoint is not set to one of the predefined values, + or depth_multiplier <= 0, or the target output_stride is not + allowed. + """ + depth = lambda d: max(int(d * depth_multiplier), min_depth) + end_points = {} + + # Used to find thinned depths for each layer. + if depth_multiplier <= 0: + raise ValueError('depth_multiplier is not greater than zero.') + + if conv_defs is None: + conv_defs = _CONV_DEFS + + if output_stride is not None and output_stride not in [8, 16, 32]: + raise ValueError('Only allowed output_stride values are 8, 16, 32.') + + with tf.variable_scope(scope, 'MobilenetV1', [inputs]): + with slim.arg_scope([slim.conv2d, slim.separable_conv2d], padding='SAME'): + # The current_stride variable keeps track of the output stride of the + # activations, i.e., the running product of convolution strides up to the + # current network layer. This allows us to invoke atrous convolution + # whenever applying the next convolution would result in the activations + # having output stride larger than the target output_stride. + current_stride = 1 + + # The atrous convolution rate parameter. + rate = 1 + + net = inputs + for i, conv_def in enumerate(conv_defs): + end_point_base = 'Conv2d_%d' % i + + if output_stride is not None and current_stride == output_stride: + # If we have reached the target output_stride, then we need to employ + # atrous convolution with stride=1 and multiply the atrous rate by the + # current unit's stride for use in subsequent layers. + layer_stride = 1 + layer_rate = rate + rate *= conv_def.stride + else: + layer_stride = conv_def.stride + layer_rate = 1 + current_stride *= conv_def.stride + + if isinstance(conv_def, Conv): + end_point = end_point_base + net = slim.conv2d(net, depth(conv_def.depth), conv_def.kernel, + stride=conv_def.stride, + normalizer_fn=slim.batch_norm, + scope=end_point) + end_points[end_point] = net + if end_point == final_endpoint: + return net, end_points + + elif isinstance(conv_def, DepthSepConv): + end_point = end_point_base + '_depthwise' + + # By passing filters=None + # separable_conv2d produces only a depthwise convolution layer + net = slim.separable_conv2d(net, None, conv_def.kernel, + depth_multiplier=1, + stride=layer_stride, + rate=layer_rate, + normalizer_fn=slim.batch_norm, + scope=end_point) + + end_points[end_point] = net + if end_point == final_endpoint: + return net, end_points + + end_point = end_point_base + '_pointwise' + + net = slim.conv2d(net, depth(conv_def.depth), [1, 1], + stride=1, + normalizer_fn=slim.batch_norm, + scope=end_point) + + end_points[end_point] = net + if end_point == final_endpoint: + return net, end_points + else: + raise ValueError('Unknown convolution type %s for layer %d' + % (conv_def.ltype, i)) + raise ValueError('Unknown final endpoint %s' % final_endpoint) + + +def mobilenet_v1(inputs, + num_classes=1000, + dropout_keep_prob=0.999, + is_training=True, + min_depth=8, + depth_multiplier=1.0, + conv_defs=None, + prediction_fn=tf.contrib.layers.softmax, + spatial_squeeze=True, + reuse=None, + scope='MobilenetV1'): + """Mobilenet v1 model for classification. + + Args: + inputs: a tensor of shape [batch_size, height, width, channels]. + num_classes: number of predicted classes. + dropout_keep_prob: the percentage of activation values that are retained. + is_training: whether is training or not. + min_depth: Minimum depth value (number of channels) for all convolution ops. + Enforced when depth_multiplier < 1, and not an active constraint when + depth_multiplier >= 1. + depth_multiplier: Float multiplier for the depth (number of channels) + for all convolution ops. The value must be greater than zero. Typical + usage will be to set this value in (0, 1) to reduce the number of + parameters or computation cost of the model. + conv_defs: A list of ConvDef namedtuples specifying the net architecture. + prediction_fn: a function to get predictions out of logits. + spatial_squeeze: if True, logits is of shape is [B, C], if false logits is + of shape [B, 1, 1, C], where B is batch_size and C is number of classes. + reuse: whether or not the network and its variables should be reused. To be + able to reuse 'scope' must be given. + scope: Optional variable_scope. + + Returns: + logits: the pre-softmax activations, a tensor of size + [batch_size, num_classes] + end_points: a dictionary from components of the network to the corresponding + activation. + + Raises: + ValueError: Input rank is invalid. + """ + input_shape = inputs.get_shape().as_list() + if len(input_shape) != 4: + raise ValueError('Invalid input tensor rank, expected 4, was: %d' % + len(input_shape)) + + with tf.variable_scope(scope, 'MobilenetV1', [inputs, num_classes], + reuse=reuse) as scope: + with slim.arg_scope([slim.batch_norm, slim.dropout], + is_training=is_training): + net, end_points = mobilenet_v1_base(inputs, scope=scope, + min_depth=min_depth, + depth_multiplier=depth_multiplier, + conv_defs=conv_defs) + with tf.variable_scope('Logits'): + kernel_size = _reduced_kernel_size_for_small_input(net, [7, 7]) + net = slim.avg_pool2d(net, kernel_size, padding='VALID', + scope='AvgPool_1a') + end_points['AvgPool_1a'] = net + # 1 x 1 x 1024 + net = slim.dropout(net, keep_prob=dropout_keep_prob, scope='Dropout_1b') + logits = slim.conv2d(net, num_classes, [1, 1], activation_fn=None, + normalizer_fn=None, scope='Conv2d_1c_1x1') + if spatial_squeeze: + logits = tf.squeeze(logits, [1, 2], name='SpatialSqueeze') + end_points['Logits'] = logits + if prediction_fn: + end_points['Predictions'] = prediction_fn(logits, scope='Predictions') + return logits, end_points + +mobilenet_v1.default_image_size = 224 + + +def _reduced_kernel_size_for_small_input(input_tensor, kernel_size): + """Define kernel size which is automatically reduced for small input. + + If the shape of the input images is unknown at graph construction time this + function assumes that the input images are large enough. + + Args: + input_tensor: input tensor of size [batch_size, height, width, channels]. + kernel_size: desired kernel size of length 2: [kernel_height, kernel_width] + + Returns: + a tensor with the kernel size. + """ + shape = input_tensor.get_shape().as_list() + if shape[1] is None or shape[2] is None: + kernel_size_out = kernel_size + else: + kernel_size_out = [min(shape[1], kernel_size[0]), + min(shape[2], kernel_size[1])] + return kernel_size_out + + +def mobilenet_v1_arg_scope(is_training=True, + weight_decay=0.00004, + stddev=0.09, + regularize_depthwise=False): + """Defines the default MobilenetV1 arg scope. + + Args: + is_training: Whether or not we're training the model. + weight_decay: The weight decay to use for regularizing the model. + stddev: The standard deviation of the trunctated normal weight initializer. + regularize_depthwise: Whether or not apply regularization on depthwise. + + Returns: + An `arg_scope` to use for the mobilenet v1 model. + """ + batch_norm_params = { + 'is_training': is_training, + 'center': True, + 'scale': True, + 'decay': 0.9997, + 'epsilon': 0.001, + } + + # Set weight_decay for weights in Conv and DepthSepConv layers. + weights_init = tf.truncated_normal_initializer(stddev=stddev) + regularizer = tf.contrib.layers.l2_regularizer(weight_decay) + if regularize_depthwise: + depthwise_regularizer = regularizer + else: + depthwise_regularizer = None + with slim.arg_scope([slim.conv2d, slim.separable_conv2d], + weights_initializer=weights_init, + activation_fn=tf.nn.relu6, normalizer_fn=slim.batch_norm): + with slim.arg_scope([slim.batch_norm], **batch_norm_params): + with slim.arg_scope([slim.conv2d], weights_regularizer=regularizer): + with slim.arg_scope([slim.separable_conv2d], + weights_regularizer=depthwise_regularizer) as sc: + return sc diff --git a/libs/networks/slim_nets/mobilenet_v1_test.py b/libs/networks/slim_nets/mobilenet_v1_test.py new file mode 100644 index 0000000..44e6644 --- /dev/null +++ b/libs/networks/slim_nets/mobilenet_v1_test.py @@ -0,0 +1,450 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================= +"""Tests for MobileNet v1.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np +import tensorflow as tf + +from nets import mobilenet_v1 + +slim = tf.contrib.slim + + +class MobilenetV1Test(tf.test.TestCase): + + def testBuildClassificationNetwork(self): + batch_size = 5 + height, width = 224, 224 + num_classes = 1000 + + inputs = tf.random_uniform((batch_size, height, width, 3)) + logits, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes) + self.assertTrue(logits.op.name.startswith('MobilenetV1/Logits')) + self.assertListEqual(logits.get_shape().as_list(), + [batch_size, num_classes]) + self.assertTrue('Predictions' in end_points) + self.assertListEqual(end_points['Predictions'].get_shape().as_list(), + [batch_size, num_classes]) + + def testBuildBaseNetwork(self): + batch_size = 5 + height, width = 224, 224 + + inputs = tf.random_uniform((batch_size, height, width, 3)) + net, end_points = mobilenet_v1.mobilenet_v1_base(inputs) + self.assertTrue(net.op.name.startswith('MobilenetV1/Conv2d_13')) + self.assertListEqual(net.get_shape().as_list(), + [batch_size, 7, 7, 1024]) + expected_endpoints = ['Conv2d_0', + 'Conv2d_1_depthwise', 'Conv2d_1_pointwise', + 'Conv2d_2_depthwise', 'Conv2d_2_pointwise', + 'Conv2d_3_depthwise', 'Conv2d_3_pointwise', + 'Conv2d_4_depthwise', 'Conv2d_4_pointwise', + 'Conv2d_5_depthwise', 'Conv2d_5_pointwise', + 'Conv2d_6_depthwise', 'Conv2d_6_pointwise', + 'Conv2d_7_depthwise', 'Conv2d_7_pointwise', + 'Conv2d_8_depthwise', 'Conv2d_8_pointwise', + 'Conv2d_9_depthwise', 'Conv2d_9_pointwise', + 'Conv2d_10_depthwise', 'Conv2d_10_pointwise', + 'Conv2d_11_depthwise', 'Conv2d_11_pointwise', + 'Conv2d_12_depthwise', 'Conv2d_12_pointwise', + 'Conv2d_13_depthwise', 'Conv2d_13_pointwise'] + self.assertItemsEqual(end_points.keys(), expected_endpoints) + + def testBuildOnlyUptoFinalEndpoint(self): + batch_size = 5 + height, width = 224, 224 + endpoints = ['Conv2d_0', + 'Conv2d_1_depthwise', 'Conv2d_1_pointwise', + 'Conv2d_2_depthwise', 'Conv2d_2_pointwise', + 'Conv2d_3_depthwise', 'Conv2d_3_pointwise', + 'Conv2d_4_depthwise', 'Conv2d_4_pointwise', + 'Conv2d_5_depthwise', 'Conv2d_5_pointwise', + 'Conv2d_6_depthwise', 'Conv2d_6_pointwise', + 'Conv2d_7_depthwise', 'Conv2d_7_pointwise', + 'Conv2d_8_depthwise', 'Conv2d_8_pointwise', + 'Conv2d_9_depthwise', 'Conv2d_9_pointwise', + 'Conv2d_10_depthwise', 'Conv2d_10_pointwise', + 'Conv2d_11_depthwise', 'Conv2d_11_pointwise', + 'Conv2d_12_depthwise', 'Conv2d_12_pointwise', + 'Conv2d_13_depthwise', 'Conv2d_13_pointwise'] + for index, endpoint in enumerate(endpoints): + with tf.Graph().as_default(): + inputs = tf.random_uniform((batch_size, height, width, 3)) + out_tensor, end_points = mobilenet_v1.mobilenet_v1_base( + inputs, final_endpoint=endpoint) + self.assertTrue(out_tensor.op.name.startswith( + 'MobilenetV1/' + endpoint)) + self.assertItemsEqual(endpoints[:index+1], end_points) + + def testBuildCustomNetworkUsingConvDefs(self): + batch_size = 5 + height, width = 224, 224 + conv_defs = [ + mobilenet_v1.Conv(kernel=[3, 3], stride=2, depth=32), + mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=64), + mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=128), + mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=512) + ] + + inputs = tf.random_uniform((batch_size, height, width, 3)) + net, end_points = mobilenet_v1.mobilenet_v1_base( + inputs, final_endpoint='Conv2d_3_pointwise', conv_defs=conv_defs) + self.assertTrue(net.op.name.startswith('MobilenetV1/Conv2d_3')) + self.assertListEqual(net.get_shape().as_list(), + [batch_size, 56, 56, 512]) + expected_endpoints = ['Conv2d_0', + 'Conv2d_1_depthwise', 'Conv2d_1_pointwise', + 'Conv2d_2_depthwise', 'Conv2d_2_pointwise', + 'Conv2d_3_depthwise', 'Conv2d_3_pointwise'] + self.assertItemsEqual(end_points.keys(), expected_endpoints) + + def testBuildAndCheckAllEndPointsUptoConv2d_13(self): + batch_size = 5 + height, width = 224, 224 + + inputs = tf.random_uniform((batch_size, height, width, 3)) + with slim.arg_scope([slim.conv2d, slim.separable_conv2d], + normalizer_fn=slim.batch_norm): + _, end_points = mobilenet_v1.mobilenet_v1_base( + inputs, final_endpoint='Conv2d_13_pointwise') + endpoints_shapes = {'Conv2d_0': [batch_size, 112, 112, 32], + 'Conv2d_1_depthwise': [batch_size, 112, 112, 32], + 'Conv2d_1_pointwise': [batch_size, 112, 112, 64], + 'Conv2d_2_depthwise': [batch_size, 56, 56, 64], + 'Conv2d_2_pointwise': [batch_size, 56, 56, 128], + 'Conv2d_3_depthwise': [batch_size, 56, 56, 128], + 'Conv2d_3_pointwise': [batch_size, 56, 56, 128], + 'Conv2d_4_depthwise': [batch_size, 28, 28, 128], + 'Conv2d_4_pointwise': [batch_size, 28, 28, 256], + 'Conv2d_5_depthwise': [batch_size, 28, 28, 256], + 'Conv2d_5_pointwise': [batch_size, 28, 28, 256], + 'Conv2d_6_depthwise': [batch_size, 14, 14, 256], + 'Conv2d_6_pointwise': [batch_size, 14, 14, 512], + 'Conv2d_7_depthwise': [batch_size, 14, 14, 512], + 'Conv2d_7_pointwise': [batch_size, 14, 14, 512], + 'Conv2d_8_depthwise': [batch_size, 14, 14, 512], + 'Conv2d_8_pointwise': [batch_size, 14, 14, 512], + 'Conv2d_9_depthwise': [batch_size, 14, 14, 512], + 'Conv2d_9_pointwise': [batch_size, 14, 14, 512], + 'Conv2d_10_depthwise': [batch_size, 14, 14, 512], + 'Conv2d_10_pointwise': [batch_size, 14, 14, 512], + 'Conv2d_11_depthwise': [batch_size, 14, 14, 512], + 'Conv2d_11_pointwise': [batch_size, 14, 14, 512], + 'Conv2d_12_depthwise': [batch_size, 7, 7, 512], + 'Conv2d_12_pointwise': [batch_size, 7, 7, 1024], + 'Conv2d_13_depthwise': [batch_size, 7, 7, 1024], + 'Conv2d_13_pointwise': [batch_size, 7, 7, 1024]} + self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys()) + for endpoint_name, expected_shape in endpoints_shapes.iteritems(): + self.assertTrue(endpoint_name in end_points) + self.assertListEqual(end_points[endpoint_name].get_shape().as_list(), + expected_shape) + + def testOutputStride16BuildAndCheckAllEndPointsUptoConv2d_13(self): + batch_size = 5 + height, width = 224, 224 + output_stride = 16 + + inputs = tf.random_uniform((batch_size, height, width, 3)) + with slim.arg_scope([slim.conv2d, slim.separable_conv2d], + normalizer_fn=slim.batch_norm): + _, end_points = mobilenet_v1.mobilenet_v1_base( + inputs, output_stride=output_stride, + final_endpoint='Conv2d_13_pointwise') + endpoints_shapes = {'Conv2d_0': [batch_size, 112, 112, 32], + 'Conv2d_1_depthwise': [batch_size, 112, 112, 32], + 'Conv2d_1_pointwise': [batch_size, 112, 112, 64], + 'Conv2d_2_depthwise': [batch_size, 56, 56, 64], + 'Conv2d_2_pointwise': [batch_size, 56, 56, 128], + 'Conv2d_3_depthwise': [batch_size, 56, 56, 128], + 'Conv2d_3_pointwise': [batch_size, 56, 56, 128], + 'Conv2d_4_depthwise': [batch_size, 28, 28, 128], + 'Conv2d_4_pointwise': [batch_size, 28, 28, 256], + 'Conv2d_5_depthwise': [batch_size, 28, 28, 256], + 'Conv2d_5_pointwise': [batch_size, 28, 28, 256], + 'Conv2d_6_depthwise': [batch_size, 14, 14, 256], + 'Conv2d_6_pointwise': [batch_size, 14, 14, 512], + 'Conv2d_7_depthwise': [batch_size, 14, 14, 512], + 'Conv2d_7_pointwise': [batch_size, 14, 14, 512], + 'Conv2d_8_depthwise': [batch_size, 14, 14, 512], + 'Conv2d_8_pointwise': [batch_size, 14, 14, 512], + 'Conv2d_9_depthwise': [batch_size, 14, 14, 512], + 'Conv2d_9_pointwise': [batch_size, 14, 14, 512], + 'Conv2d_10_depthwise': [batch_size, 14, 14, 512], + 'Conv2d_10_pointwise': [batch_size, 14, 14, 512], + 'Conv2d_11_depthwise': [batch_size, 14, 14, 512], + 'Conv2d_11_pointwise': [batch_size, 14, 14, 512], + 'Conv2d_12_depthwise': [batch_size, 14, 14, 512], + 'Conv2d_12_pointwise': [batch_size, 14, 14, 1024], + 'Conv2d_13_depthwise': [batch_size, 14, 14, 1024], + 'Conv2d_13_pointwise': [batch_size, 14, 14, 1024]} + self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys()) + for endpoint_name, expected_shape in endpoints_shapes.iteritems(): + self.assertTrue(endpoint_name in end_points) + self.assertListEqual(end_points[endpoint_name].get_shape().as_list(), + expected_shape) + + def testOutputStride8BuildAndCheckAllEndPointsUptoConv2d_13(self): + batch_size = 5 + height, width = 224, 224 + output_stride = 8 + + inputs = tf.random_uniform((batch_size, height, width, 3)) + with slim.arg_scope([slim.conv2d, slim.separable_conv2d], + normalizer_fn=slim.batch_norm): + _, end_points = mobilenet_v1.mobilenet_v1_base( + inputs, output_stride=output_stride, + final_endpoint='Conv2d_13_pointwise') + endpoints_shapes = {'Conv2d_0': [batch_size, 112, 112, 32], + 'Conv2d_1_depthwise': [batch_size, 112, 112, 32], + 'Conv2d_1_pointwise': [batch_size, 112, 112, 64], + 'Conv2d_2_depthwise': [batch_size, 56, 56, 64], + 'Conv2d_2_pointwise': [batch_size, 56, 56, 128], + 'Conv2d_3_depthwise': [batch_size, 56, 56, 128], + 'Conv2d_3_pointwise': [batch_size, 56, 56, 128], + 'Conv2d_4_depthwise': [batch_size, 28, 28, 128], + 'Conv2d_4_pointwise': [batch_size, 28, 28, 256], + 'Conv2d_5_depthwise': [batch_size, 28, 28, 256], + 'Conv2d_5_pointwise': [batch_size, 28, 28, 256], + 'Conv2d_6_depthwise': [batch_size, 28, 28, 256], + 'Conv2d_6_pointwise': [batch_size, 28, 28, 512], + 'Conv2d_7_depthwise': [batch_size, 28, 28, 512], + 'Conv2d_7_pointwise': [batch_size, 28, 28, 512], + 'Conv2d_8_depthwise': [batch_size, 28, 28, 512], + 'Conv2d_8_pointwise': [batch_size, 28, 28, 512], + 'Conv2d_9_depthwise': [batch_size, 28, 28, 512], + 'Conv2d_9_pointwise': [batch_size, 28, 28, 512], + 'Conv2d_10_depthwise': [batch_size, 28, 28, 512], + 'Conv2d_10_pointwise': [batch_size, 28, 28, 512], + 'Conv2d_11_depthwise': [batch_size, 28, 28, 512], + 'Conv2d_11_pointwise': [batch_size, 28, 28, 512], + 'Conv2d_12_depthwise': [batch_size, 28, 28, 512], + 'Conv2d_12_pointwise': [batch_size, 28, 28, 1024], + 'Conv2d_13_depthwise': [batch_size, 28, 28, 1024], + 'Conv2d_13_pointwise': [batch_size, 28, 28, 1024]} + self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys()) + for endpoint_name, expected_shape in endpoints_shapes.iteritems(): + self.assertTrue(endpoint_name in end_points) + self.assertListEqual(end_points[endpoint_name].get_shape().as_list(), + expected_shape) + + def testBuildAndCheckAllEndPointsApproximateFaceNet(self): + batch_size = 5 + height, width = 128, 128 + + inputs = tf.random_uniform((batch_size, height, width, 3)) + with slim.arg_scope([slim.conv2d, slim.separable_conv2d], + normalizer_fn=slim.batch_norm): + _, end_points = mobilenet_v1.mobilenet_v1_base( + inputs, final_endpoint='Conv2d_13_pointwise', depth_multiplier=0.75) + # For the Conv2d_0 layer FaceNet has depth=16 + endpoints_shapes = {'Conv2d_0': [batch_size, 64, 64, 24], + 'Conv2d_1_depthwise': [batch_size, 64, 64, 24], + 'Conv2d_1_pointwise': [batch_size, 64, 64, 48], + 'Conv2d_2_depthwise': [batch_size, 32, 32, 48], + 'Conv2d_2_pointwise': [batch_size, 32, 32, 96], + 'Conv2d_3_depthwise': [batch_size, 32, 32, 96], + 'Conv2d_3_pointwise': [batch_size, 32, 32, 96], + 'Conv2d_4_depthwise': [batch_size, 16, 16, 96], + 'Conv2d_4_pointwise': [batch_size, 16, 16, 192], + 'Conv2d_5_depthwise': [batch_size, 16, 16, 192], + 'Conv2d_5_pointwise': [batch_size, 16, 16, 192], + 'Conv2d_6_depthwise': [batch_size, 8, 8, 192], + 'Conv2d_6_pointwise': [batch_size, 8, 8, 384], + 'Conv2d_7_depthwise': [batch_size, 8, 8, 384], + 'Conv2d_7_pointwise': [batch_size, 8, 8, 384], + 'Conv2d_8_depthwise': [batch_size, 8, 8, 384], + 'Conv2d_8_pointwise': [batch_size, 8, 8, 384], + 'Conv2d_9_depthwise': [batch_size, 8, 8, 384], + 'Conv2d_9_pointwise': [batch_size, 8, 8, 384], + 'Conv2d_10_depthwise': [batch_size, 8, 8, 384], + 'Conv2d_10_pointwise': [batch_size, 8, 8, 384], + 'Conv2d_11_depthwise': [batch_size, 8, 8, 384], + 'Conv2d_11_pointwise': [batch_size, 8, 8, 384], + 'Conv2d_12_depthwise': [batch_size, 4, 4, 384], + 'Conv2d_12_pointwise': [batch_size, 4, 4, 768], + 'Conv2d_13_depthwise': [batch_size, 4, 4, 768], + 'Conv2d_13_pointwise': [batch_size, 4, 4, 768]} + self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys()) + for endpoint_name, expected_shape in endpoints_shapes.iteritems(): + self.assertTrue(endpoint_name in end_points) + self.assertListEqual(end_points[endpoint_name].get_shape().as_list(), + expected_shape) + + def testModelHasExpectedNumberOfParameters(self): + batch_size = 5 + height, width = 224, 224 + inputs = tf.random_uniform((batch_size, height, width, 3)) + with slim.arg_scope([slim.conv2d, slim.separable_conv2d], + normalizer_fn=slim.batch_norm): + mobilenet_v1.mobilenet_v1_base(inputs) + total_params, _ = slim.model_analyzer.analyze_vars( + slim.get_model_variables()) + self.assertAlmostEqual(3217920L, total_params) + + def testBuildEndPointsWithDepthMultiplierLessThanOne(self): + batch_size = 5 + height, width = 224, 224 + num_classes = 1000 + + inputs = tf.random_uniform((batch_size, height, width, 3)) + _, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes) + + endpoint_keys = [key for key in end_points.keys() if key.startswith('Conv')] + + _, end_points_with_multiplier = mobilenet_v1.mobilenet_v1( + inputs, num_classes, scope='depth_multiplied_net', + depth_multiplier=0.5) + + for key in endpoint_keys: + original_depth = end_points[key].get_shape().as_list()[3] + new_depth = end_points_with_multiplier[key].get_shape().as_list()[3] + self.assertEqual(0.5 * original_depth, new_depth) + + def testBuildEndPointsWithDepthMultiplierGreaterThanOne(self): + batch_size = 5 + height, width = 224, 224 + num_classes = 1000 + + inputs = tf.random_uniform((batch_size, height, width, 3)) + _, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes) + + endpoint_keys = [key for key in end_points.keys() + if key.startswith('Mixed') or key.startswith('Conv')] + + _, end_points_with_multiplier = mobilenet_v1.mobilenet_v1( + inputs, num_classes, scope='depth_multiplied_net', + depth_multiplier=2.0) + + for key in endpoint_keys: + original_depth = end_points[key].get_shape().as_list()[3] + new_depth = end_points_with_multiplier[key].get_shape().as_list()[3] + self.assertEqual(2.0 * original_depth, new_depth) + + def testRaiseValueErrorWithInvalidDepthMultiplier(self): + batch_size = 5 + height, width = 224, 224 + num_classes = 1000 + + inputs = tf.random_uniform((batch_size, height, width, 3)) + with self.assertRaises(ValueError): + _ = mobilenet_v1.mobilenet_v1( + inputs, num_classes, depth_multiplier=-0.1) + with self.assertRaises(ValueError): + _ = mobilenet_v1.mobilenet_v1( + inputs, num_classes, depth_multiplier=0.0) + + def testHalfSizeImages(self): + batch_size = 5 + height, width = 112, 112 + num_classes = 1000 + + inputs = tf.random_uniform((batch_size, height, width, 3)) + logits, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes) + self.assertTrue(logits.op.name.startswith('MobilenetV1/Logits')) + self.assertListEqual(logits.get_shape().as_list(), + [batch_size, num_classes]) + pre_pool = end_points['Conv2d_13_pointwise'] + self.assertListEqual(pre_pool.get_shape().as_list(), + [batch_size, 4, 4, 1024]) + + def testUnknownImageShape(self): + tf.reset_default_graph() + batch_size = 2 + height, width = 224, 224 + num_classes = 1000 + input_np = np.random.uniform(0, 1, (batch_size, height, width, 3)) + with self.test_session() as sess: + inputs = tf.placeholder(tf.float32, shape=(batch_size, None, None, 3)) + logits, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes) + self.assertTrue(logits.op.name.startswith('MobilenetV1/Logits')) + self.assertListEqual(logits.get_shape().as_list(), + [batch_size, num_classes]) + pre_pool = end_points['Conv2d_13_pointwise'] + feed_dict = {inputs: input_np} + tf.global_variables_initializer().run() + pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict) + self.assertListEqual(list(pre_pool_out.shape), [batch_size, 7, 7, 1024]) + + def testUnknowBatchSize(self): + batch_size = 1 + height, width = 224, 224 + num_classes = 1000 + + inputs = tf.placeholder(tf.float32, (None, height, width, 3)) + logits, _ = mobilenet_v1.mobilenet_v1(inputs, num_classes) + self.assertTrue(logits.op.name.startswith('MobilenetV1/Logits')) + self.assertListEqual(logits.get_shape().as_list(), + [None, num_classes]) + images = tf.random_uniform((batch_size, height, width, 3)) + + with self.test_session() as sess: + sess.run(tf.global_variables_initializer()) + output = sess.run(logits, {inputs: images.eval()}) + self.assertEquals(output.shape, (batch_size, num_classes)) + + def testEvaluation(self): + batch_size = 2 + height, width = 224, 224 + num_classes = 1000 + + eval_inputs = tf.random_uniform((batch_size, height, width, 3)) + logits, _ = mobilenet_v1.mobilenet_v1(eval_inputs, num_classes, + is_training=False) + predictions = tf.argmax(logits, 1) + + with self.test_session() as sess: + sess.run(tf.global_variables_initializer()) + output = sess.run(predictions) + self.assertEquals(output.shape, (batch_size,)) + + def testTrainEvalWithReuse(self): + train_batch_size = 5 + eval_batch_size = 2 + height, width = 150, 150 + num_classes = 1000 + + train_inputs = tf.random_uniform((train_batch_size, height, width, 3)) + mobilenet_v1.mobilenet_v1(train_inputs, num_classes) + eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3)) + logits, _ = mobilenet_v1.mobilenet_v1(eval_inputs, num_classes, + reuse=True) + predictions = tf.argmax(logits, 1) + + with self.test_session() as sess: + sess.run(tf.global_variables_initializer()) + output = sess.run(predictions) + self.assertEquals(output.shape, (eval_batch_size,)) + + def testLogitsNotSqueezed(self): + num_classes = 25 + images = tf.random_uniform([1, 224, 224, 3]) + logits, _ = mobilenet_v1.mobilenet_v1(images, + num_classes=num_classes, + spatial_squeeze=False) + + with self.test_session() as sess: + tf.global_variables_initializer().run() + logits_out = sess.run(logits) + self.assertListEqual(list(logits_out.shape), [1, 1, 1, num_classes]) + + +if __name__ == '__main__': + tf.test.main() diff --git a/libs/networks/slim_nets/nets_factory.py b/libs/networks/slim_nets/nets_factory.py new file mode 100644 index 0000000..7c04161 --- /dev/null +++ b/libs/networks/slim_nets/nets_factory.py @@ -0,0 +1,112 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Contains a factory for building various models.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +import functools + +import tensorflow as tf + +from nets import alexnet +from nets import cifarnet +from nets import inception +from nets import lenet +from nets import mobilenet_v1 +from nets import overfeat +from nets import resnet_v1 +from nets import resnet_v2 +from nets import vgg + +slim = tf.contrib.slim + +networks_map = {'alexnet_v2': alexnet.alexnet_v2, + 'cifarnet': cifarnet.cifarnet, + 'overfeat': overfeat.overfeat, + 'vgg_a': vgg.vgg_a, + 'vgg_16': vgg.vgg_16, + 'vgg_19': vgg.vgg_19, + 'inception_v1': inception.inception_v1, + 'inception_v2': inception.inception_v2, + 'inception_v3': inception.inception_v3, + 'inception_v4': inception.inception_v4, + 'inception_resnet_v2': inception.inception_resnet_v2, + 'lenet': lenet.lenet, + 'resnet_v1_50': resnet_v1.resnet_v1_50, + 'resnet_v1_101': resnet_v1.resnet_v1_101, + 'resnet_v1_152': resnet_v1.resnet_v1_152, + 'resnet_v1_200': resnet_v1.resnet_v1_200, + 'resnet_v2_50': resnet_v2.resnet_v2_50, + 'resnet_v2_101': resnet_v2.resnet_v2_101, + 'resnet_v2_152': resnet_v2.resnet_v2_152, + 'resnet_v2_200': resnet_v2.resnet_v2_200, + 'mobilenet_v1': mobilenet_v1.mobilenet_v1, + } + +arg_scopes_map = {'alexnet_v2': alexnet.alexnet_v2_arg_scope, + 'cifarnet': cifarnet.cifarnet_arg_scope, + 'overfeat': overfeat.overfeat_arg_scope, + 'vgg_a': vgg.vgg_arg_scope, + 'vgg_16': vgg.vgg_arg_scope, + 'vgg_19': vgg.vgg_arg_scope, + 'inception_v1': inception.inception_v3_arg_scope, + 'inception_v2': inception.inception_v3_arg_scope, + 'inception_v3': inception.inception_v3_arg_scope, + 'inception_v4': inception.inception_v4_arg_scope, + 'inception_resnet_v2': + inception.inception_resnet_v2_arg_scope, + 'lenet': lenet.lenet_arg_scope, + 'resnet_v1_50': resnet_v1.resnet_arg_scope, + 'resnet_v1_101': resnet_v1.resnet_arg_scope, + 'resnet_v1_152': resnet_v1.resnet_arg_scope, + 'resnet_v1_200': resnet_v1.resnet_arg_scope, + 'resnet_v2_50': resnet_v2.resnet_arg_scope, + 'resnet_v2_101': resnet_v2.resnet_arg_scope, + 'resnet_v2_152': resnet_v2.resnet_arg_scope, + 'resnet_v2_200': resnet_v2.resnet_arg_scope, + 'mobilenet_v1': mobilenet_v1.mobilenet_v1_arg_scope, + } + + +def get_network_fn(name, num_classes, weight_decay=0.0, is_training=False): + """Returns a network_fn such as `logits, end_points = network_fn(images)`. + + Args: + name: The name of the network. + num_classes: The number of classes to use for classification. + weight_decay: The l2 coefficient for the model weights. + is_training: `True` if the model is being used for training and `False` + otherwise. + + Returns: + network_fn: A function that applies the model to a batch of images. It has + the following signature: + logits, end_points = network_fn(images) + Raises: + ValueError: If network `name` is not recognized. + """ + if name not in networks_map: + raise ValueError('Name of network unknown %s' % name) + arg_scope = arg_scopes_map[name](weight_decay=weight_decay) + func = networks_map[name] + @functools.wraps(func) + def network_fn(images): + with slim.arg_scope(arg_scope): + return func(images, num_classes, is_training=is_training) + if hasattr(func, 'default_image_size'): + network_fn.default_image_size = func.default_image_size + + return network_fn diff --git a/libs/networks/slim_nets/nets_factory_test.py b/libs/networks/slim_nets/nets_factory_test.py new file mode 100644 index 0000000..b4ab1f8 --- /dev/null +++ b/libs/networks/slim_nets/nets_factory_test.py @@ -0,0 +1,61 @@ +# Copyright 2016 Google Inc. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +"""Tests for slim.inception.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf + +from nets import nets_factory + +slim = tf.contrib.slim + + +class NetworksTest(tf.test.TestCase): + + def testGetNetworkFn(self): + batch_size = 5 + num_classes = 1000 + for net in nets_factory.networks_map: + with self.test_session(): + net_fn = nets_factory.get_network_fn(net, num_classes) + # Most networks use 224 as their default_image_size + image_size = getattr(net_fn, 'default_image_size', 224) + inputs = tf.random_uniform((batch_size, image_size, image_size, 3)) + logits, end_points = net_fn(inputs) + self.assertTrue(isinstance(logits, tf.Tensor)) + self.assertTrue(isinstance(end_points, dict)) + self.assertEqual(logits.get_shape().as_list()[0], batch_size) + self.assertEqual(logits.get_shape().as_list()[-1], num_classes) + + def testGetNetworkFnArgScope(self): + batch_size = 5 + num_classes = 10 + net = 'cifarnet' + with self.test_session(use_gpu=True): + net_fn = nets_factory.get_network_fn(net, num_classes) + image_size = getattr(net_fn, 'default_image_size', 224) + with slim.arg_scope([slim.model_variable, slim.variable], + device='/CPU:0'): + inputs = tf.random_uniform((batch_size, image_size, image_size, 3)) + net_fn(inputs) + weights = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, 'CifarNet/conv1')[0] + self.assertDeviceEqual('/CPU:0', weights.device) + +if __name__ == '__main__': + tf.test.main() diff --git a/libs/networks/slim_nets/overfeat.py b/libs/networks/slim_nets/overfeat.py new file mode 100644 index 0000000..64a5425 --- /dev/null +++ b/libs/networks/slim_nets/overfeat.py @@ -0,0 +1,118 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Contains the model definition for the OverFeat network. + +The definition for the network was obtained from: + OverFeat: Integrated Recognition, Localization and Detection using + Convolutional Networks + Pierre Sermanet, David Eigen, Xiang Zhang, Michael Mathieu, Rob Fergus and + Yann LeCun, 2014 + http://arxiv.org/abs/1312.6229 + +Usage: + with slim.arg_scope(overfeat.overfeat_arg_scope()): + outputs, end_points = overfeat.overfeat(inputs) + +@@overfeat +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf + +slim = tf.contrib.slim +trunc_normal = lambda stddev: tf.truncated_normal_initializer(0.0, stddev) + + +def overfeat_arg_scope(weight_decay=0.0005): + with slim.arg_scope([slim.conv2d, slim.fully_connected], + activation_fn=tf.nn.relu, + weights_regularizer=slim.l2_regularizer(weight_decay), + biases_initializer=tf.zeros_initializer()): + with slim.arg_scope([slim.conv2d], padding='SAME'): + with slim.arg_scope([slim.max_pool2d], padding='VALID') as arg_sc: + return arg_sc + + +def overfeat(inputs, + num_classes=1000, + is_training=True, + dropout_keep_prob=0.5, + spatial_squeeze=True, + scope='overfeat'): + """Contains the model definition for the OverFeat network. + + The definition for the network was obtained from: + OverFeat: Integrated Recognition, Localization and Detection using + Convolutional Networks + Pierre Sermanet, David Eigen, Xiang Zhang, Michael Mathieu, Rob Fergus and + Yann LeCun, 2014 + http://arxiv.org/abs/1312.6229 + + Note: All the fully_connected layers have been transformed to conv2d layers. + To use in classification mode, resize input to 231x231. To use in fully + convolutional mode, set spatial_squeeze to false. + + Args: + inputs: a tensor of size [batch_size, height, width, channels]. + num_classes: number of predicted classes. + is_training: whether or not the model is being trained. + dropout_keep_prob: the probability that activations are kept in the dropout + layers during training. + spatial_squeeze: whether or not should squeeze the spatial dimensions of the + outputs. Useful to remove unnecessary dimensions for classification. + scope: Optional scope for the variables. + + Returns: + the last op containing the log predictions and end_points dict. + + """ + with tf.variable_scope(scope, 'overfeat', [inputs]) as sc: + end_points_collection = sc.name + '_end_points' + # Collect outputs for conv2d, fully_connected and max_pool2d + with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d], + outputs_collections=end_points_collection): + net = slim.conv2d(inputs, 64, [11, 11], 4, padding='VALID', + scope='conv1') + net = slim.max_pool2d(net, [2, 2], scope='pool1') + net = slim.conv2d(net, 256, [5, 5], padding='VALID', scope='conv2') + net = slim.max_pool2d(net, [2, 2], scope='pool2') + net = slim.conv2d(net, 512, [3, 3], scope='conv3') + net = slim.conv2d(net, 1024, [3, 3], scope='conv4') + net = slim.conv2d(net, 1024, [3, 3], scope='conv5') + net = slim.max_pool2d(net, [2, 2], scope='pool5') + with slim.arg_scope([slim.conv2d], + weights_initializer=trunc_normal(0.005), + biases_initializer=tf.constant_initializer(0.1)): + # Use conv2d instead of fully_connected layers. + net = slim.conv2d(net, 3072, [6, 6], padding='VALID', scope='fc6') + net = slim.dropout(net, dropout_keep_prob, is_training=is_training, + scope='dropout6') + net = slim.conv2d(net, 4096, [1, 1], scope='fc7') + net = slim.dropout(net, dropout_keep_prob, is_training=is_training, + scope='dropout7') + net = slim.conv2d(net, num_classes, [1, 1], + activation_fn=None, + normalizer_fn=None, + biases_initializer=tf.zeros_initializer(), + scope='fc8') + # Convert end_points_collection into a end_point dict. + end_points = slim.utils.convert_collection_to_dict(end_points_collection) + if spatial_squeeze: + net = tf.squeeze(net, [1, 2], name='fc8/squeezed') + end_points[sc.name + '/fc8'] = net + return net, end_points +overfeat.default_image_size = 231 diff --git a/libs/networks/slim_nets/overfeat_test.py b/libs/networks/slim_nets/overfeat_test.py new file mode 100644 index 0000000..446f9ac --- /dev/null +++ b/libs/networks/slim_nets/overfeat_test.py @@ -0,0 +1,145 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for slim.slim_nets.overfeat.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf + +from nets import overfeat + +slim = tf.contrib.slim + + +class OverFeatTest(tf.test.TestCase): + + def testBuild(self): + batch_size = 5 + height, width = 231, 231 + num_classes = 1000 + with self.test_session(): + inputs = tf.random_uniform((batch_size, height, width, 3)) + logits, _ = overfeat.overfeat(inputs, num_classes) + self.assertEquals(logits.op.name, 'overfeat/fc8/squeezed') + self.assertListEqual(logits.get_shape().as_list(), + [batch_size, num_classes]) + + def testFullyConvolutional(self): + batch_size = 1 + height, width = 281, 281 + num_classes = 1000 + with self.test_session(): + inputs = tf.random_uniform((batch_size, height, width, 3)) + logits, _ = overfeat.overfeat(inputs, num_classes, spatial_squeeze=False) + self.assertEquals(logits.op.name, 'overfeat/fc8/BiasAdd') + self.assertListEqual(logits.get_shape().as_list(), + [batch_size, 2, 2, num_classes]) + + def testEndPoints(self): + batch_size = 5 + height, width = 231, 231 + num_classes = 1000 + with self.test_session(): + inputs = tf.random_uniform((batch_size, height, width, 3)) + _, end_points = overfeat.overfeat(inputs, num_classes) + expected_names = ['overfeat/conv1', + 'overfeat/pool1', + 'overfeat/conv2', + 'overfeat/pool2', + 'overfeat/conv3', + 'overfeat/conv4', + 'overfeat/conv5', + 'overfeat/pool5', + 'overfeat/fc6', + 'overfeat/fc7', + 'overfeat/fc8' + ] + self.assertSetEqual(set(end_points.keys()), set(expected_names)) + + def testModelVariables(self): + batch_size = 5 + height, width = 231, 231 + num_classes = 1000 + with self.test_session(): + inputs = tf.random_uniform((batch_size, height, width, 3)) + overfeat.overfeat(inputs, num_classes) + expected_names = ['overfeat/conv1/weights', + 'overfeat/conv1/biases', + 'overfeat/conv2/weights', + 'overfeat/conv2/biases', + 'overfeat/conv3/weights', + 'overfeat/conv3/biases', + 'overfeat/conv4/weights', + 'overfeat/conv4/biases', + 'overfeat/conv5/weights', + 'overfeat/conv5/biases', + 'overfeat/fc6/weights', + 'overfeat/fc6/biases', + 'overfeat/fc7/weights', + 'overfeat/fc7/biases', + 'overfeat/fc8/weights', + 'overfeat/fc8/biases', + ] + model_variables = [v.op.name for v in slim.get_model_variables()] + self.assertSetEqual(set(model_variables), set(expected_names)) + + def testEvaluation(self): + batch_size = 2 + height, width = 231, 231 + num_classes = 1000 + with self.test_session(): + eval_inputs = tf.random_uniform((batch_size, height, width, 3)) + logits, _ = overfeat.overfeat(eval_inputs, is_training=False) + self.assertListEqual(logits.get_shape().as_list(), + [batch_size, num_classes]) + predictions = tf.argmax(logits, 1) + self.assertListEqual(predictions.get_shape().as_list(), [batch_size]) + + def testTrainEvalWithReuse(self): + train_batch_size = 2 + eval_batch_size = 1 + train_height, train_width = 231, 231 + eval_height, eval_width = 281, 281 + num_classes = 1000 + with self.test_session(): + train_inputs = tf.random_uniform( + (train_batch_size, train_height, train_width, 3)) + logits, _ = overfeat.overfeat(train_inputs) + self.assertListEqual(logits.get_shape().as_list(), + [train_batch_size, num_classes]) + tf.get_variable_scope().reuse_variables() + eval_inputs = tf.random_uniform( + (eval_batch_size, eval_height, eval_width, 3)) + logits, _ = overfeat.overfeat(eval_inputs, is_training=False, + spatial_squeeze=False) + self.assertListEqual(logits.get_shape().as_list(), + [eval_batch_size, 2, 2, num_classes]) + logits = tf.reduce_mean(logits, [1, 2]) + predictions = tf.argmax(logits, 1) + self.assertEquals(predictions.get_shape().as_list(), [eval_batch_size]) + + def testForward(self): + batch_size = 1 + height, width = 231, 231 + with self.test_session() as sess: + inputs = tf.random_uniform((batch_size, height, width, 3)) + logits, _ = overfeat.overfeat(inputs) + sess.run(tf.global_variables_initializer()) + output = sess.run(logits) + self.assertTrue(output.any()) + +if __name__ == '__main__': + tf.test.main() diff --git a/libs/networks/slim_nets/resnet_utils.py b/libs/networks/slim_nets/resnet_utils.py new file mode 100644 index 0000000..bb674ea --- /dev/null +++ b/libs/networks/slim_nets/resnet_utils.py @@ -0,0 +1,244 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Contains building blocks for various versions of Residual Networks. + +Residual networks (ResNets) were proposed in: + Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun + Deep Residual Learning for Image Recognition. arXiv:1512.03385, 2015 + +More variants were introduced in: + Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun + Identity Mappings in Deep Residual Networks. arXiv: 1603.05027, 2016 + +We can obtain different ResNet variants by changing the network depth, width, +and form of residual unit. This module implements the infrastructure for +building them. Concrete ResNet units and full ResNet networks are implemented in +the accompanying resnet_v1.py and resnet_v2.py modules. + +Compared to https://github.com/KaimingHe/deep-residual-networks, in the current +implementation we subsample the output activations in the last residual unit of +each block, instead of subsampling the input activations in the first residual +unit of each block. The two implementations give identical results but our +implementation is more memory efficient. +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import collections +import tensorflow as tf + +slim = tf.contrib.slim + + +class Block(collections.namedtuple('Block', ['scope', 'unit_fn', 'args'])): + """A named tuple describing a ResNet block. + + Its parts are: + scope: The scope of the `Block`. + unit_fn: The ResNet unit function which takes as input a `Tensor` and + returns another `Tensor` with the output of the ResNet unit. + args: A list of length equal to the number of units in the `Block`. The list + contains one (depth, depth_bottleneck, stride) tuple for each unit in the + block to serve as argument to unit_fn. + """ + + +def subsample(inputs, factor, scope=None): + """Subsamples the input along the spatial dimensions. + + Args: + inputs: A `Tensor` of size [batch, height_in, width_in, channels]. + factor: The subsampling factor. + scope: Optional variable_scope. + + Returns: + output: A `Tensor` of size [batch, height_out, width_out, channels] with the + input, either intact (if factor == 1) or subsampled (if factor > 1). + """ + if factor == 1: + return inputs + else: + return slim.max_pool2d(inputs, [1, 1], stride=factor, scope=scope) + + +def conv2d_same(inputs, num_outputs, kernel_size, stride, rate=1, scope=None): + """Strided 2-D convolution with 'SAME' padding. + + When stride > 1, then we do explicit zero-padding, followed by conv2d with + 'VALID' padding. + + Note that + + net = conv2d_same(inputs, num_outputs, 3, stride=stride) + + is equivalent to + + net = slim.conv2d(inputs, num_outputs, 3, stride=1, padding='SAME') + net = subsample(net, factor=stride) + + whereas + + net = slim.conv2d(inputs, num_outputs, 3, stride=stride, padding='SAME') + + is different when the input's height or width is even, which is why we add the + current function. For more details, see ResnetUtilsTest.testConv2DSameEven(). + + Args: + inputs: A 4-D tensor of size [batch, height_in, width_in, channels]. + num_outputs: An integer, the number of output filters. + kernel_size: An int with the kernel_size of the filters. + stride: An integer, the output stride. + rate: An integer, rate for atrous convolution. + scope: Scope. + + Returns: + output: A 4-D tensor of size [batch, height_out, width_out, channels] with + the convolution output. + """ + if stride == 1: + return slim.conv2d(inputs, num_outputs, kernel_size, stride=1, rate=rate, + padding='SAME', scope=scope) + else: + kernel_size_effective = kernel_size + (kernel_size - 1) * (rate - 1) + pad_total = kernel_size_effective - 1 + pad_beg = pad_total // 2 + pad_end = pad_total - pad_beg + inputs = tf.pad(inputs, + [[0, 0], [pad_beg, pad_end], [pad_beg, pad_end], [0, 0]]) + return slim.conv2d(inputs, num_outputs, kernel_size, stride=stride, + rate=rate, padding='VALID', scope=scope) + + +@slim.add_arg_scope +def stack_blocks_dense(net, blocks, output_stride=None, + outputs_collections=None): + """Stacks ResNet `Blocks` and controls output feature density. + + First, this function creates scopes for the ResNet in the form of + 'block_name/unit_1', 'block_name/unit_2', etc. + + Second, this function allows the user to explicitly control the ResNet + output_stride, which is the ratio of the input to output spatial resolution. + This is useful for dense prediction tasks such as semantic segmentation or + object detection. + + Most ResNets consist of 4 ResNet blocks and subsample the activations by a + factor of 2 when transitioning between consecutive ResNet blocks. This results + to a nominal ResNet output_stride equal to 8. If we set the output_stride to + half the nominal network stride (e.g., output_stride=4), then we compute + responses twice. + + Control of the output feature density is implemented by atrous convolution. + + Args: + net: A `Tensor` of size [batch, height, width, channels]. + blocks: A list of length equal to the number of ResNet `Blocks`. Each + element is a ResNet `Block` object describing the units in the `Block`. + output_stride: If `None`, then the output will be computed at the nominal + network stride. If output_stride is not `None`, it specifies the requested + ratio of input to output spatial resolution, which needs to be equal to + the product of unit strides from the start up to some level of the ResNet. + For example, if the ResNet employs units with strides 1, 2, 1, 3, 4, 1, + then valid values for the output_stride are 1, 2, 6, 24 or None (which + is equivalent to output_stride=24). + outputs_collections: Collection to add the ResNet block outputs. + + Returns: + net: Output tensor with stride equal to the specified output_stride. + + Raises: + ValueError: If the target output_stride is not valid. + """ + # The current_stride variable keeps track of the effective stride of the + # activations. This allows us to invoke atrous convolution whenever applying + # the next residual unit would result in the activations having stride larger + # than the target output_stride. + current_stride = 1 + + # The atrous convolution rate parameter. + rate = 1 + + for block in blocks: + with tf.variable_scope(block.scope, 'block', [net]) as sc: + for i, unit in enumerate(block.args): + if output_stride is not None and current_stride > output_stride: + raise ValueError('The target output_stride cannot be reached.') + + with tf.variable_scope('unit_%d' % (i + 1), values=[net]): + # If we have reached the target output_stride, then we need to employ + # atrous convolution with stride=1 and multiply the atrous rate by the + # current unit's stride for use in subsequent layers. + if output_stride is not None and current_stride == output_stride: + net = block.unit_fn(net, rate=rate, **dict(unit, stride=1)) + rate *= unit.get('stride', 1) + + else: + net = block.unit_fn(net, rate=1, **unit) + current_stride *= unit.get('stride', 1) + net = slim.utils.collect_named_outputs(outputs_collections, sc.name, net) + + if output_stride is not None and current_stride != output_stride: + raise ValueError('The target output_stride cannot be reached.') + + return net + + +def resnet_arg_scope(weight_decay=0.0001, + batch_norm_decay=0.997, #0.997 + batch_norm_epsilon=1e-5, + batch_norm_scale=True): + """Defines the default ResNet arg scope. + + TODO(gpapan): The batch-normalization related default values above are + appropriate for use in conjunction with the reference ResNet models + released at https://github.com/KaimingHe/deep-residual-networks. When + training ResNets from scratch, they might need to be tuned. + + Args: + weight_decay: The weight decay to use for regularizing the model. + batch_norm_decay: The moving average decay when estimating layer activation + statistics in batch normalization. + batch_norm_epsilon: Small constant to prevent division by zero when + normalizing activations by their variance in batch normalization. + batch_norm_scale: If True, uses an explicit `gamma` multiplier to scale the + activations in the batch normalization layer. + + Returns: + An `arg_scope` to use for the resnet models. + """ + batch_norm_params = { + 'decay': batch_norm_decay, + 'epsilon': batch_norm_epsilon, + 'scale': batch_norm_scale, + 'updates_collections': tf.GraphKeys.UPDATE_OPS, + } + + with slim.arg_scope( + [slim.conv2d], + weights_regularizer=slim.l2_regularizer(weight_decay), + weights_initializer=slim.variance_scaling_initializer(), + activation_fn=tf.nn.relu, + normalizer_fn=slim.batch_norm, + normalizer_params=batch_norm_params): + with slim.arg_scope([slim.batch_norm], **batch_norm_params): + # The following implies padding='SAME' for pool1, which makes feature + # alignment easier for dense prediction tasks. This is also used in + # https://github.com/facebook/fb.resnet.torch. However the accompanying + # code of 'Deep Residual Learning for Image Recognition' uses + # padding='VALID' for pool1. You can switch to that choice by setting + # slim.arg_scope([slim.max_pool2d], padding='VALID'). + with slim.arg_scope([slim.max_pool2d], padding='SAME') as arg_sc: + return arg_sc diff --git a/libs/networks/slim_nets/resnet_v1.py b/libs/networks/slim_nets/resnet_v1.py new file mode 100644 index 0000000..81f2a83 --- /dev/null +++ b/libs/networks/slim_nets/resnet_v1.py @@ -0,0 +1,329 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Contains definitions for the original form of Residual Networks. + +The 'v1' residual networks (ResNets) implemented in this module were proposed +by: +[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun + Deep Residual Learning for Image Recognition. arXiv:1512.03385 + +Other variants were introduced in: +[2] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun + Identity Mappings in Deep Residual Networks. arXiv: 1603.05027 + +The networks defined in this module utilize the bottleneck building block of +[1] with projection shortcuts only for increasing depths. They employ batch +normalization *after* every weight layer. This is the architecture used by +MSRA in the Imagenet and MSCOCO 2016 competition models ResNet-101 and +ResNet-152. See [2; Fig. 1a] for a comparison between the current 'v1' +architecture and the alternative 'v2' architecture of [2] which uses batch +normalization *before* every weight layer in the so-called full pre-activation +units. + +Typical use: + + from tensorflow.contrib.slim.slim_nets import resnet_v1 + +ResNet-101 for image classification into 1000 classes: + + # inputs has shape [batch, 224, 224, 3] + with slim.arg_scope(resnet_v1.resnet_arg_scope()): + net, end_points = resnet_v1.resnet_v1_101(inputs, 1000, is_training=False) + +ResNet-101 for semantic segmentation into 21 classes: + + # inputs has shape [batch, 513, 513, 3] + with slim.arg_scope(resnet_v1.resnet_arg_scope()): + net, end_points = resnet_v1.resnet_v1_101(inputs, + 21, + is_training=False, + global_pool=False, + output_stride=16) +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf + +from libs.networks.slim_nets import resnet_utils + + +resnet_arg_scope = resnet_utils.resnet_arg_scope +slim = tf.contrib.slim + + +@slim.add_arg_scope +def bottleneck(inputs, depth, depth_bottleneck, stride, rate=1, + outputs_collections=None, scope=None): + """Bottleneck residual unit variant with BN after convolutions. + + This is the original residual unit proposed in [1]. See Fig. 1(a) of [2] for + its definition. Note that we use here the bottleneck variant which has an + extra bottleneck layer. + + When putting together two consecutive ResNet blocks that use this unit, one + should use stride = 2 in the last unit of the first block. + + Args: + inputs: A tensor of size [batch, height, width, channels]. + depth: The depth of the ResNet unit output. + depth_bottleneck: The depth of the bottleneck layers. + stride: The ResNet unit's stride. Determines the amount of downsampling of + the units output compared to its input. + rate: An integer, rate for atrous convolution. + outputs_collections: Collection to add the ResNet unit output. + scope: Optional variable_scope. + + Returns: + The ResNet unit's output. + """ + with tf.variable_scope(scope, 'bottleneck_v1', [inputs]) as sc: + depth_in = slim.utils.last_dimension(inputs.get_shape(), min_rank=4) + if depth == depth_in: + shortcut = resnet_utils.subsample(inputs, stride, 'shortcut') + else: + shortcut = slim.conv2d(inputs, depth, [1, 1], stride=stride, + activation_fn=None, scope='shortcut') + + residual = slim.conv2d(inputs, depth_bottleneck, [1, 1], stride=1, + scope='conv1') + residual = resnet_utils.conv2d_same(residual, depth_bottleneck, 3, stride, + rate=rate, scope='conv2') + residual = slim.conv2d(residual, depth, [1, 1], stride=1, + activation_fn=None, scope='conv3') + + output = tf.nn.relu(shortcut + residual) + + return slim.utils.collect_named_outputs(outputs_collections, + sc.original_name_scope, + output) + + +def resnet_v1(inputs, + blocks, + num_classes=None, + is_training=True, + global_pool=True, + output_stride=None, + include_root_block=True, + spatial_squeeze=False, + reuse=None, + scope=None): + """Generator for v1 ResNet models. + + This function generates a family of ResNet v1 models. See the resnet_v1_*() + methods for specific model instantiations, obtained by selecting different + block instantiations that produce ResNets of various depths. + + Training for image classification on Imagenet is usually done with [224, 224] + inputs, resulting in [7, 7] feature maps at the output of the last ResNet + block for the ResNets defined in [1] that have nominal stride equal to 32. + However, for dense prediction tasks we advise that one uses inputs with + spatial dimensions that are multiples of 32 plus 1, e.g., [321, 321]. In + this case the feature maps at the ResNet output will have spatial shape + [(height - 1) / output_stride + 1, (width - 1) / output_stride + 1] + and corners exactly aligned with the input image corners, which greatly + facilitates alignment of the features to the image. Using as input [225, 225] + images results in [8, 8] feature maps at the output of the last ResNet block. + + For dense prediction tasks, the ResNet needs to run in fully-convolutional + (FCN) mode and global_pool needs to be set to False. The ResNets in [1, 2] all + have nominal stride equal to 32 and a good choice in FCN mode is to use + output_stride=16 in order to increase the density of the computed features at + small computational and memory overhead, cf. http://arxiv.org/abs/1606.00915. + + Args: + inputs: A tensor of size [batch, height_in, width_in, channels]. + blocks: A list of length equal to the number of ResNet blocks. Each element + is a resnet_utils.Block object describing the units in the block. + num_classes: Number of predicted classes for classification tasks. If None + we return the features before the logit layer. + is_training: whether is training or not. + global_pool: If True, we perform global average pooling before computing the + logits. Set to True for image classification, False for dense prediction. + output_stride: If None, then the output will be computed at the nominal + network stride. If output_stride is not None, it specifies the requested + ratio of input to output spatial resolution. + include_root_block: If True, include the initial convolution followed by + max-pooling, if False excludes it. + spatial_squeeze: if True, logits is of shape [B, C], if false logits is + of shape [B, 1, 1, C], where B is batch_size and C is number of classes. + reuse: whether or not the network and its variables should be reused. To be + able to reuse 'scope' must be given. + scope: Optional variable_scope. + + Returns: + net: A rank-4 tensor of size [batch, height_out, width_out, channels_out]. + If global_pool is False, then height_out and width_out are reduced by a + factor of output_stride compared to the respective height_in and width_in, + else both height_out and width_out equal one. If num_classes is None, then + net is the output of the last ResNet block, potentially after global + average pooling. If num_classes is not None, net contains the pre-softmax + activations. + end_points: A dictionary from components of the network to the corresponding + activation. + + Raises: + ValueError: If the target output_stride is not valid. + """ + with tf.variable_scope(scope, 'resnet_v1', [inputs], reuse=reuse) as sc: + end_points_collection = sc.name + '_end_points' + with slim.arg_scope([slim.conv2d, bottleneck, + resnet_utils.stack_blocks_dense], + outputs_collections=end_points_collection): + with slim.arg_scope([slim.batch_norm], is_training=is_training): + net = inputs + if include_root_block: + if output_stride is not None: + if output_stride % 4 != 0: + raise ValueError('The output_stride needs to be a multiple of 4.') + output_stride /= 4 + net = resnet_utils.conv2d_same(net, 64, 7, stride=2, scope='conv1') + net = slim.max_pool2d(net, [3, 3], stride=2, scope='pool1') + net = resnet_utils.stack_blocks_dense(net, blocks, output_stride) + if global_pool: + # Global average pooling. + net = tf.reduce_mean(net, [1, 2], name='pool5', keep_dims=True) + # yjr_feature = tf.squeeze(net, [0, 1, 2]) + if num_classes is not None: + net = slim.conv2d(net, num_classes, [1, 1], activation_fn=None, + normalizer_fn=None, scope='logits') + if spatial_squeeze: + logits = tf.squeeze(net, [1, 2], name='SpatialSqueeze') + else: + logits = net + # Convert end_points_collection into a dictionary of end_points. + end_points = slim.utils.convert_collection_to_dict( + end_points_collection) + if num_classes is not None: + end_points['predictions'] = slim.softmax(logits, scope='predictions') + + ### + # end_points['yjr_feature'] = yjr_feature + return logits, end_points +resnet_v1.default_image_size = 224 + + +def resnet_v1_block(scope, base_depth, num_units, stride): + """Helper function for creating a resnet_v1 bottleneck block. + + Args: + scope: The scope of the block. + base_depth: The depth of the bottleneck layer for each unit. + num_units: The number of units in the block. + stride: The stride of the block, implemented as a stride in the last unit. + All other units have stride=1. + + Returns: + A resnet_v1 bottleneck block. + """ + return resnet_utils.Block(scope, bottleneck, [{ + 'depth': base_depth * 4, + 'depth_bottleneck': base_depth, + 'stride': 1 + }] * (num_units - 1) + [{ + 'depth': base_depth * 4, + 'depth_bottleneck': base_depth, + 'stride': stride + }]) + + +def resnet_v1_50(inputs, + num_classes=None, + is_training=True, + global_pool=True, + output_stride=None, + spatial_squeeze=True, + reuse=None, + scope='resnet_v1_50'): + """ResNet-50 model of [1]. See resnet_v1() for arg and return description.""" + blocks = [ + resnet_v1_block('block1', base_depth=64, num_units=3, stride=2), + resnet_v1_block('block2', base_depth=128, num_units=4, stride=2), + resnet_v1_block('block3', base_depth=256, num_units=6, stride=2), + resnet_v1_block('block4', base_depth=512, num_units=3, stride=1), + ] + return resnet_v1(inputs, blocks, num_classes, is_training, + global_pool=global_pool, output_stride=output_stride, + include_root_block=True, spatial_squeeze=spatial_squeeze, + reuse=reuse, scope=scope) +resnet_v1_50.default_image_size = resnet_v1.default_image_size + + +def resnet_v1_101(inputs, + num_classes=None, + is_training=True, + global_pool=True, + output_stride=None, + spatial_squeeze=True, + reuse=None, + scope='resnet_v1_101'): + """ResNet-101 model of [1]. See resnet_v1() for arg and return description.""" + blocks = [ + resnet_v1_block('block1', base_depth=64, num_units=3, stride=2), + resnet_v1_block('block2', base_depth=128, num_units=4, stride=2), + resnet_v1_block('block3', base_depth=256, num_units=23, stride=2), + resnet_v1_block('block4', base_depth=512, num_units=3, stride=1), + ] + return resnet_v1(inputs, blocks, num_classes, is_training, + global_pool=global_pool, output_stride=output_stride, + include_root_block=True, spatial_squeeze=spatial_squeeze, + reuse=reuse, scope=scope) +resnet_v1_101.default_image_size = resnet_v1.default_image_size + + +def resnet_v1_152(inputs, + num_classes=None, + is_training=True, + global_pool=True, + output_stride=None, + spatial_squeeze=True, + reuse=None, + scope='resnet_v1_152'): + """ResNet-152 model of [1]. See resnet_v1() for arg and return description.""" + blocks = [ + resnet_v1_block('block1', base_depth=64, num_units=3, stride=2), + resnet_v1_block('block2', base_depth=128, num_units=8, stride=2), + resnet_v1_block('block3', base_depth=256, num_units=36, stride=2), + resnet_v1_block('block4', base_depth=512, num_units=3, stride=1), + ] + return resnet_v1(inputs, blocks, num_classes, is_training, + global_pool=global_pool, output_stride=output_stride, + include_root_block=True, spatial_squeeze=spatial_squeeze, + reuse=reuse, scope=scope) +resnet_v1_152.default_image_size = resnet_v1.default_image_size + + +def resnet_v1_200(inputs, + num_classes=None, + is_training=True, + global_pool=True, + output_stride=None, + spatial_squeeze=True, + reuse=None, + scope='resnet_v1_200'): + """ResNet-200 model of [2]. See resnet_v1() for arg and return description.""" + blocks = [ + resnet_v1_block('block1', base_depth=64, num_units=3, stride=2), + resnet_v1_block('block2', base_depth=128, num_units=24, stride=2), + resnet_v1_block('block3', base_depth=256, num_units=36, stride=2), + resnet_v1_block('block4', base_depth=512, num_units=3, stride=1), + ] + return resnet_v1(inputs, blocks, num_classes, is_training, + global_pool=global_pool, output_stride=output_stride, + include_root_block=True, spatial_squeeze=spatial_squeeze, + reuse=reuse, scope=scope) +resnet_v1_200.default_image_size = resnet_v1.default_image_size diff --git a/libs/networks/slim_nets/resnet_v1_test.py b/libs/networks/slim_nets/resnet_v1_test.py new file mode 100644 index 0000000..5b27a18 --- /dev/null +++ b/libs/networks/slim_nets/resnet_v1_test.py @@ -0,0 +1,440 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for slim.slim_nets.resnet_v1.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np +import tensorflow as tf + +from nets import resnet_utils +from nets import resnet_v1 + +slim = tf.contrib.slim + + +def create_test_input(batch_size, height, width, channels): + """Create test input tensor. + + Args: + batch_size: The number of images per batch or `None` if unknown. + height: The height of each image or `None` if unknown. + width: The width of each image or `None` if unknown. + channels: The number of channels per image or `None` if unknown. + + Returns: + Either a placeholder `Tensor` of dimension + [batch_size, height, width, channels] if any of the inputs are `None` or a + constant `Tensor` with the mesh grid values along the spatial dimensions. + """ + if None in [batch_size, height, width, channels]: + return tf.placeholder(tf.float32, (batch_size, height, width, channels)) + else: + return tf.to_float( + np.tile( + np.reshape( + np.reshape(np.arange(height), [height, 1]) + + np.reshape(np.arange(width), [1, width]), + [1, height, width, 1]), + [batch_size, 1, 1, channels])) + + +class ResnetUtilsTest(tf.test.TestCase): + + def testSubsampleThreeByThree(self): + x = tf.reshape(tf.to_float(tf.range(9)), [1, 3, 3, 1]) + x = resnet_utils.subsample(x, 2) + expected = tf.reshape(tf.constant([0, 2, 6, 8]), [1, 2, 2, 1]) + with self.test_session(): + self.assertAllClose(x.eval(), expected.eval()) + + def testSubsampleFourByFour(self): + x = tf.reshape(tf.to_float(tf.range(16)), [1, 4, 4, 1]) + x = resnet_utils.subsample(x, 2) + expected = tf.reshape(tf.constant([0, 2, 8, 10]), [1, 2, 2, 1]) + with self.test_session(): + self.assertAllClose(x.eval(), expected.eval()) + + def testConv2DSameEven(self): + n, n2 = 4, 2 + + # Input image. + x = create_test_input(1, n, n, 1) + + # Convolution kernel. + w = create_test_input(1, 3, 3, 1) + w = tf.reshape(w, [3, 3, 1, 1]) + + tf.get_variable('Conv/weights', initializer=w) + tf.get_variable('Conv/biases', initializer=tf.zeros([1])) + tf.get_variable_scope().reuse_variables() + + y1 = slim.conv2d(x, 1, [3, 3], stride=1, scope='Conv') + y1_expected = tf.to_float([[14, 28, 43, 26], + [28, 48, 66, 37], + [43, 66, 84, 46], + [26, 37, 46, 22]]) + y1_expected = tf.reshape(y1_expected, [1, n, n, 1]) + + y2 = resnet_utils.subsample(y1, 2) + y2_expected = tf.to_float([[14, 43], + [43, 84]]) + y2_expected = tf.reshape(y2_expected, [1, n2, n2, 1]) + + y3 = resnet_utils.conv2d_same(x, 1, 3, stride=2, scope='Conv') + y3_expected = y2_expected + + y4 = slim.conv2d(x, 1, [3, 3], stride=2, scope='Conv') + y4_expected = tf.to_float([[48, 37], + [37, 22]]) + y4_expected = tf.reshape(y4_expected, [1, n2, n2, 1]) + + with self.test_session() as sess: + sess.run(tf.global_variables_initializer()) + self.assertAllClose(y1.eval(), y1_expected.eval()) + self.assertAllClose(y2.eval(), y2_expected.eval()) + self.assertAllClose(y3.eval(), y3_expected.eval()) + self.assertAllClose(y4.eval(), y4_expected.eval()) + + def testConv2DSameOdd(self): + n, n2 = 5, 3 + + # Input image. + x = create_test_input(1, n, n, 1) + + # Convolution kernel. + w = create_test_input(1, 3, 3, 1) + w = tf.reshape(w, [3, 3, 1, 1]) + + tf.get_variable('Conv/weights', initializer=w) + tf.get_variable('Conv/biases', initializer=tf.zeros([1])) + tf.get_variable_scope().reuse_variables() + + y1 = slim.conv2d(x, 1, [3, 3], stride=1, scope='Conv') + y1_expected = tf.to_float([[14, 28, 43, 58, 34], + [28, 48, 66, 84, 46], + [43, 66, 84, 102, 55], + [58, 84, 102, 120, 64], + [34, 46, 55, 64, 30]]) + y1_expected = tf.reshape(y1_expected, [1, n, n, 1]) + + y2 = resnet_utils.subsample(y1, 2) + y2_expected = tf.to_float([[14, 43, 34], + [43, 84, 55], + [34, 55, 30]]) + y2_expected = tf.reshape(y2_expected, [1, n2, n2, 1]) + + y3 = resnet_utils.conv2d_same(x, 1, 3, stride=2, scope='Conv') + y3_expected = y2_expected + + y4 = slim.conv2d(x, 1, [3, 3], stride=2, scope='Conv') + y4_expected = y2_expected + + with self.test_session() as sess: + sess.run(tf.global_variables_initializer()) + self.assertAllClose(y1.eval(), y1_expected.eval()) + self.assertAllClose(y2.eval(), y2_expected.eval()) + self.assertAllClose(y3.eval(), y3_expected.eval()) + self.assertAllClose(y4.eval(), y4_expected.eval()) + + def _resnet_plain(self, inputs, blocks, output_stride=None, scope=None): + """A plain ResNet without extra layers before or after the ResNet blocks.""" + with tf.variable_scope(scope, values=[inputs]): + with slim.arg_scope([slim.conv2d], outputs_collections='end_points'): + net = resnet_utils.stack_blocks_dense(inputs, blocks, output_stride) + end_points = slim.utils.convert_collection_to_dict('end_points') + return net, end_points + + def testEndPointsV1(self): + """Test the end points of a tiny v1 bottleneck network.""" + blocks = [ + resnet_v1.resnet_v1_block( + 'block1', base_depth=1, num_units=2, stride=2), + resnet_v1.resnet_v1_block( + 'block2', base_depth=2, num_units=2, stride=1), + ] + inputs = create_test_input(2, 32, 16, 3) + with slim.arg_scope(resnet_utils.resnet_arg_scope()): + _, end_points = self._resnet_plain(inputs, blocks, scope='tiny') + expected = [ + 'tiny/block1/unit_1/bottleneck_v1/shortcut', + 'tiny/block1/unit_1/bottleneck_v1/conv1', + 'tiny/block1/unit_1/bottleneck_v1/conv2', + 'tiny/block1/unit_1/bottleneck_v1/conv3', + 'tiny/block1/unit_2/bottleneck_v1/conv1', + 'tiny/block1/unit_2/bottleneck_v1/conv2', + 'tiny/block1/unit_2/bottleneck_v1/conv3', + 'tiny/block2/unit_1/bottleneck_v1/shortcut', + 'tiny/block2/unit_1/bottleneck_v1/conv1', + 'tiny/block2/unit_1/bottleneck_v1/conv2', + 'tiny/block2/unit_1/bottleneck_v1/conv3', + 'tiny/block2/unit_2/bottleneck_v1/conv1', + 'tiny/block2/unit_2/bottleneck_v1/conv2', + 'tiny/block2/unit_2/bottleneck_v1/conv3'] + self.assertItemsEqual(expected, end_points) + + def _stack_blocks_nondense(self, net, blocks): + """A simplified ResNet Block stacker without output stride control.""" + for block in blocks: + with tf.variable_scope(block.scope, 'block', [net]): + for i, unit in enumerate(block.args): + with tf.variable_scope('unit_%d' % (i + 1), values=[net]): + net = block.unit_fn(net, rate=1, **unit) + return net + + def testAtrousValuesBottleneck(self): + """Verify the values of dense feature extraction by atrous convolution. + + Make sure that dense feature extraction by stack_blocks_dense() followed by + subsampling gives identical results to feature extraction at the nominal + network output stride using the simple self._stack_blocks_nondense() above. + """ + block = resnet_v1.resnet_v1_block + blocks = [ + block('block1', base_depth=1, num_units=2, stride=2), + block('block2', base_depth=2, num_units=2, stride=2), + block('block3', base_depth=4, num_units=2, stride=2), + block('block4', base_depth=8, num_units=2, stride=1), + ] + nominal_stride = 8 + + # Test both odd and even input dimensions. + height = 30 + width = 31 + with slim.arg_scope(resnet_utils.resnet_arg_scope()): + with slim.arg_scope([slim.batch_norm], is_training=False): + for output_stride in [1, 2, 4, 8, None]: + with tf.Graph().as_default(): + with self.test_session() as sess: + tf.set_random_seed(0) + inputs = create_test_input(1, height, width, 3) + # Dense feature extraction followed by subsampling. + output = resnet_utils.stack_blocks_dense(inputs, + blocks, + output_stride) + if output_stride is None: + factor = 1 + else: + factor = nominal_stride // output_stride + + output = resnet_utils.subsample(output, factor) + # Make the two networks use the same weights. + tf.get_variable_scope().reuse_variables() + # Feature extraction at the nominal network rate. + expected = self._stack_blocks_nondense(inputs, blocks) + sess.run(tf.global_variables_initializer()) + output, expected = sess.run([output, expected]) + self.assertAllClose(output, expected, atol=1e-4, rtol=1e-4) + + +class ResnetCompleteNetworkTest(tf.test.TestCase): + """Tests with complete small ResNet v1 networks.""" + + def _resnet_small(self, + inputs, + num_classes=None, + is_training=True, + global_pool=True, + output_stride=None, + include_root_block=True, + reuse=None, + scope='resnet_v1_small'): + """A shallow and thin ResNet v1 for faster tests.""" + block = resnet_v1.resnet_v1_block + blocks = [ + block('block1', base_depth=1, num_units=3, stride=2), + block('block2', base_depth=2, num_units=3, stride=2), + block('block3', base_depth=4, num_units=3, stride=2), + block('block4', base_depth=8, num_units=2, stride=1), + ] + return resnet_v1.resnet_v1(inputs, blocks, num_classes, + is_training=is_training, + global_pool=global_pool, + output_stride=output_stride, + include_root_block=include_root_block, + reuse=reuse, + scope=scope) + + def testClassificationEndPoints(self): + global_pool = True + num_classes = 10 + inputs = create_test_input(2, 224, 224, 3) + with slim.arg_scope(resnet_utils.resnet_arg_scope()): + logits, end_points = self._resnet_small(inputs, num_classes, + global_pool=global_pool, + scope='resnet') + self.assertTrue(logits.op.name.startswith('resnet/logits')) + self.assertListEqual(logits.get_shape().as_list(), [2, 1, 1, num_classes]) + self.assertTrue('predictions' in end_points) + self.assertListEqual(end_points['predictions'].get_shape().as_list(), + [2, 1, 1, num_classes]) + + def testClassificationShapes(self): + global_pool = True + num_classes = 10 + inputs = create_test_input(2, 224, 224, 3) + with slim.arg_scope(resnet_utils.resnet_arg_scope()): + _, end_points = self._resnet_small(inputs, num_classes, + global_pool=global_pool, + scope='resnet') + endpoint_to_shape = { + 'resnet/block1': [2, 28, 28, 4], + 'resnet/block2': [2, 14, 14, 8], + 'resnet/block3': [2, 7, 7, 16], + 'resnet/block4': [2, 7, 7, 32]} + for endpoint in endpoint_to_shape: + shape = endpoint_to_shape[endpoint] + self.assertListEqual(end_points[endpoint].get_shape().as_list(), shape) + + def testFullyConvolutionalEndpointShapes(self): + global_pool = False + num_classes = 10 + inputs = create_test_input(2, 321, 321, 3) + with slim.arg_scope(resnet_utils.resnet_arg_scope()): + _, end_points = self._resnet_small(inputs, num_classes, + global_pool=global_pool, + scope='resnet') + endpoint_to_shape = { + 'resnet/block1': [2, 41, 41, 4], + 'resnet/block2': [2, 21, 21, 8], + 'resnet/block3': [2, 11, 11, 16], + 'resnet/block4': [2, 11, 11, 32]} + for endpoint in endpoint_to_shape: + shape = endpoint_to_shape[endpoint] + self.assertListEqual(end_points[endpoint].get_shape().as_list(), shape) + + def testRootlessFullyConvolutionalEndpointShapes(self): + global_pool = False + num_classes = 10 + inputs = create_test_input(2, 128, 128, 3) + with slim.arg_scope(resnet_utils.resnet_arg_scope()): + _, end_points = self._resnet_small(inputs, num_classes, + global_pool=global_pool, + include_root_block=False, + scope='resnet') + endpoint_to_shape = { + 'resnet/block1': [2, 64, 64, 4], + 'resnet/block2': [2, 32, 32, 8], + 'resnet/block3': [2, 16, 16, 16], + 'resnet/block4': [2, 16, 16, 32]} + for endpoint in endpoint_to_shape: + shape = endpoint_to_shape[endpoint] + self.assertListEqual(end_points[endpoint].get_shape().as_list(), shape) + + def testAtrousFullyConvolutionalEndpointShapes(self): + global_pool = False + num_classes = 10 + output_stride = 8 + inputs = create_test_input(2, 321, 321, 3) + with slim.arg_scope(resnet_utils.resnet_arg_scope()): + _, end_points = self._resnet_small(inputs, + num_classes, + global_pool=global_pool, + output_stride=output_stride, + scope='resnet') + endpoint_to_shape = { + 'resnet/block1': [2, 41, 41, 4], + 'resnet/block2': [2, 41, 41, 8], + 'resnet/block3': [2, 41, 41, 16], + 'resnet/block4': [2, 41, 41, 32]} + for endpoint in endpoint_to_shape: + shape = endpoint_to_shape[endpoint] + self.assertListEqual(end_points[endpoint].get_shape().as_list(), shape) + + def testAtrousFullyConvolutionalValues(self): + """Verify dense feature extraction with atrous convolution.""" + nominal_stride = 32 + for output_stride in [4, 8, 16, 32, None]: + with slim.arg_scope(resnet_utils.resnet_arg_scope()): + with tf.Graph().as_default(): + with self.test_session() as sess: + tf.set_random_seed(0) + inputs = create_test_input(2, 81, 81, 3) + # Dense feature extraction followed by subsampling. + output, _ = self._resnet_small(inputs, None, is_training=False, + global_pool=False, + output_stride=output_stride) + if output_stride is None: + factor = 1 + else: + factor = nominal_stride // output_stride + output = resnet_utils.subsample(output, factor) + # Make the two networks use the same weights. + tf.get_variable_scope().reuse_variables() + # Feature extraction at the nominal network rate. + expected, _ = self._resnet_small(inputs, None, is_training=False, + global_pool=False) + sess.run(tf.global_variables_initializer()) + self.assertAllClose(output.eval(), expected.eval(), + atol=1e-4, rtol=1e-4) + + def testUnknownBatchSize(self): + batch = 2 + height, width = 65, 65 + global_pool = True + num_classes = 10 + inputs = create_test_input(None, height, width, 3) + with slim.arg_scope(resnet_utils.resnet_arg_scope()): + logits, _ = self._resnet_small(inputs, num_classes, + global_pool=global_pool, + scope='resnet') + self.assertTrue(logits.op.name.startswith('resnet/logits')) + self.assertListEqual(logits.get_shape().as_list(), + [None, 1, 1, num_classes]) + images = create_test_input(batch, height, width, 3) + with self.test_session() as sess: + sess.run(tf.global_variables_initializer()) + output = sess.run(logits, {inputs: images.eval()}) + self.assertEqual(output.shape, (batch, 1, 1, num_classes)) + + def testFullyConvolutionalUnknownHeightWidth(self): + batch = 2 + height, width = 65, 65 + global_pool = False + inputs = create_test_input(batch, None, None, 3) + with slim.arg_scope(resnet_utils.resnet_arg_scope()): + output, _ = self._resnet_small(inputs, None, global_pool=global_pool) + self.assertListEqual(output.get_shape().as_list(), + [batch, None, None, 32]) + images = create_test_input(batch, height, width, 3) + with self.test_session() as sess: + sess.run(tf.global_variables_initializer()) + output = sess.run(output, {inputs: images.eval()}) + self.assertEqual(output.shape, (batch, 3, 3, 32)) + + def testAtrousFullyConvolutionalUnknownHeightWidth(self): + batch = 2 + height, width = 65, 65 + global_pool = False + output_stride = 8 + inputs = create_test_input(batch, None, None, 3) + with slim.arg_scope(resnet_utils.resnet_arg_scope()): + output, _ = self._resnet_small(inputs, + None, + global_pool=global_pool, + output_stride=output_stride) + self.assertListEqual(output.get_shape().as_list(), + [batch, None, None, 32]) + images = create_test_input(batch, height, width, 3) + with self.test_session() as sess: + sess.run(tf.global_variables_initializer()) + output = sess.run(output, {inputs: images.eval()}) + self.assertEqual(output.shape, (batch, 9, 9, 32)) + + +if __name__ == '__main__': + tf.test.main() diff --git a/libs/networks/slim_nets/resnet_v2.py b/libs/networks/slim_nets/resnet_v2.py new file mode 100644 index 0000000..7617701 --- /dev/null +++ b/libs/networks/slim_nets/resnet_v2.py @@ -0,0 +1,333 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Contains definitions for the preactivation form of Residual Networks. + +Residual networks (ResNets) were originally proposed in: +[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun + Deep Residual Learning for Image Recognition. arXiv:1512.03385 + +The full preactivation 'v2' ResNet variant implemented in this module was +introduced by: +[2] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun + Identity Mappings in Deep Residual Networks. arXiv: 1603.05027 + +The key difference of the full preactivation 'v2' variant compared to the +'v1' variant in [1] is the use of batch normalization before every weight layer. + +Typical use: + + from tensorflow.contrib.slim.slim_nets import resnet_v2 + +ResNet-101 for image classification into 1000 classes: + + # inputs has shape [batch, 224, 224, 3] + with slim.arg_scope(resnet_v2.resnet_arg_scope()): + net, end_points = resnet_v2.resnet_v2_101(inputs, 1000, is_training=False) + +ResNet-101 for semantic segmentation into 21 classes: + + # inputs has shape [batch, 513, 513, 3] + with slim.arg_scope(resnet_v2.resnet_arg_scope(is_training)): + net, end_points = resnet_v2.resnet_v2_101(inputs, + 21, + is_training=False, + global_pool=False, + output_stride=16) +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf + +from nets import resnet_utils + +slim = tf.contrib.slim +resnet_arg_scope = resnet_utils.resnet_arg_scope + + +@slim.add_arg_scope +def bottleneck(inputs, depth, depth_bottleneck, stride, rate=1, + outputs_collections=None, scope=None): + """Bottleneck residual unit variant with BN before convolutions. + + This is the full preactivation residual unit variant proposed in [2]. See + Fig. 1(b) of [2] for its definition. Note that we use here the bottleneck + variant which has an extra bottleneck layer. + + When putting together two consecutive ResNet blocks that use this unit, one + should use stride = 2 in the last unit of the first block. + + Args: + inputs: A tensor of size [batch, height, width, channels]. + depth: The depth of the ResNet unit output. + depth_bottleneck: The depth of the bottleneck layers. + stride: The ResNet unit's stride. Determines the amount of downsampling of + the units output compared to its input. + rate: An integer, rate for atrous convolution. + outputs_collections: Collection to add the ResNet unit output. + scope: Optional variable_scope. + + Returns: + The ResNet unit's output. + """ + with tf.variable_scope(scope, 'bottleneck_v2', [inputs]) as sc: + depth_in = slim.utils.last_dimension(inputs.get_shape(), min_rank=4) + preact = slim.batch_norm(inputs, activation_fn=tf.nn.relu, scope='preact') + if depth == depth_in: + shortcut = resnet_utils.subsample(inputs, stride, 'shortcut') + else: + shortcut = slim.conv2d(preact, depth, [1, 1], stride=stride, + normalizer_fn=None, activation_fn=None, + scope='shortcut') + + residual = slim.conv2d(preact, depth_bottleneck, [1, 1], stride=1, + scope='conv1') + residual = resnet_utils.conv2d_same(residual, depth_bottleneck, 3, stride, + rate=rate, scope='conv2') + residual = slim.conv2d(residual, depth, [1, 1], stride=1, + normalizer_fn=None, activation_fn=None, + scope='conv3') + + output = shortcut + residual + + return slim.utils.collect_named_outputs(outputs_collections, + sc.original_name_scope, + output) + + +def resnet_v2(inputs, + blocks, + num_classes=None, + is_training=True, + global_pool=True, + output_stride=None, + include_root_block=True, + spatial_squeeze=False, + reuse=None, + scope=None): + """Generator for v2 (preactivation) ResNet models. + + This function generates a family of ResNet v2 models. See the resnet_v2_*() + methods for specific model instantiations, obtained by selecting different + block instantiations that produce ResNets of various depths. + + Training for image classification on Imagenet is usually done with [224, 224] + inputs, resulting in [7, 7] feature maps at the output of the last ResNet + block for the ResNets defined in [1] that have nominal stride equal to 32. + However, for dense prediction tasks we advise that one uses inputs with + spatial dimensions that are multiples of 32 plus 1, e.g., [321, 321]. In + this case the feature maps at the ResNet output will have spatial shape + [(height - 1) / output_stride + 1, (width - 1) / output_stride + 1] + and corners exactly aligned with the input image corners, which greatly + facilitates alignment of the features to the image. Using as input [225, 225] + images results in [8, 8] feature maps at the output of the last ResNet block. + + For dense prediction tasks, the ResNet needs to run in fully-convolutional + (FCN) mode and global_pool needs to be set to False. The ResNets in [1, 2] all + have nominal stride equal to 32 and a good choice in FCN mode is to use + output_stride=16 in order to increase the density of the computed features at + small computational and memory overhead, cf. http://arxiv.org/abs/1606.00915. + + Args: + inputs: A tensor of size [batch, height_in, width_in, channels]. + blocks: A list of length equal to the number of ResNet blocks. Each element + is a resnet_utils.Block object describing the units in the block. + num_classes: Number of predicted classes for classification tasks. If None + we return the features before the logit layer. + is_training: whether is training or not. + global_pool: If True, we perform global average pooling before computing the + logits. Set to True for image classification, False for dense prediction. + output_stride: If None, then the output will be computed at the nominal + network stride. If output_stride is not None, it specifies the requested + ratio of input to output spatial resolution. + include_root_block: If True, include the initial convolution followed by + max-pooling, if False excludes it. If excluded, `inputs` should be the + results of an activation-less convolution. + spatial_squeeze: if True, logits is of shape [B, C], if false logits is + of shape [B, 1, 1, C], where B is batch_size and C is number of classes. + reuse: whether or not the network and its variables should be reused. To be + able to reuse 'scope' must be given. + scope: Optional variable_scope. + + + Returns: + net: A rank-4 tensor of size [batch, height_out, width_out, channels_out]. + If global_pool is False, then height_out and width_out are reduced by a + factor of output_stride compared to the respective height_in and width_in, + else both height_out and width_out equal one. If num_classes is None, then + net is the output of the last ResNet block, potentially after global + average pooling. If num_classes is not None, net contains the pre-softmax + activations. + end_points: A dictionary from components of the network to the corresponding + activation. + + Raises: + ValueError: If the target output_stride is not valid. + """ + with tf.variable_scope(scope, 'resnet_v2', [inputs], reuse=reuse) as sc: + end_points_collection = sc.name + '_end_points' + with slim.arg_scope([slim.conv2d, bottleneck, + resnet_utils.stack_blocks_dense], + outputs_collections=end_points_collection): + with slim.arg_scope([slim.batch_norm], is_training=is_training): + net = inputs + if include_root_block: + if output_stride is not None: + if output_stride % 4 != 0: + raise ValueError('The output_stride needs to be a multiple of 4.') + output_stride /= 4 + # We do not include batch normalization or activation functions in + # conv1 because the first ResNet unit will perform these. Cf. + # Appendix of [2]. + with slim.arg_scope([slim.conv2d], + activation_fn=None, normalizer_fn=None): + net = resnet_utils.conv2d_same(net, 64, 7, stride=2, scope='conv1') + net = slim.max_pool2d(net, [3, 3], stride=2, scope='pool1') + net = resnet_utils.stack_blocks_dense(net, blocks, output_stride) + # This is needed because the pre-activation variant does not have batch + # normalization or activation functions in the residual unit output. See + # Appendix of [2]. + net = slim.batch_norm(net, activation_fn=tf.nn.relu, scope='postnorm') + if global_pool: + # Global average pooling. + net = tf.reduce_mean(net, [1, 2], name='pool5', keep_dims=True) + if num_classes is not None: + net = slim.conv2d(net, num_classes, [1, 1], activation_fn=None, + normalizer_fn=None, scope='logits') + if spatial_squeeze: + logits = tf.squeeze(net, [1, 2], name='SpatialSqueeze') + else: + logits = net + # Convert end_points_collection into a dictionary of end_points. + end_points = slim.utils.convert_collection_to_dict( + end_points_collection) + if num_classes is not None: + end_points['predictions'] = slim.softmax(logits, scope='predictions') + return logits, end_points +resnet_v2.default_image_size = 224 + + +def resnet_v2_block(scope, base_depth, num_units, stride): + """Helper function for creating a resnet_v2 bottleneck block. + + Args: + scope: The scope of the block. + base_depth: The depth of the bottleneck layer for each unit. + num_units: The number of units in the block. + stride: The stride of the block, implemented as a stride in the last unit. + All other units have stride=1. + + Returns: + A resnet_v2 bottleneck block. + """ + return resnet_utils.Block(scope, bottleneck, [{ + 'depth': base_depth * 4, + 'depth_bottleneck': base_depth, + 'stride': 1 + }] * (num_units - 1) + [{ + 'depth': base_depth * 4, + 'depth_bottleneck': base_depth, + 'stride': stride + }]) +resnet_v2.default_image_size = 224 + + +def resnet_v2_50(inputs, + num_classes=None, + is_training=True, + global_pool=True, + output_stride=None, + spatial_squeeze=False, + reuse=None, + scope='resnet_v2_50'): + """ResNet-50 model of [1]. See resnet_v2() for arg and return description.""" + blocks = [ + resnet_v2_block('block1', base_depth=64, num_units=3, stride=2), + resnet_v2_block('block2', base_depth=128, num_units=4, stride=2), + resnet_v2_block('block3', base_depth=256, num_units=6, stride=2), + resnet_v2_block('block4', base_depth=512, num_units=3, stride=1), + ] + return resnet_v2(inputs, blocks, num_classes, is_training=is_training, + global_pool=global_pool, output_stride=output_stride, + include_root_block=True, spatial_squeeze=spatial_squeeze, + reuse=reuse, scope=scope) +resnet_v2_50.default_image_size = resnet_v2.default_image_size + + +def resnet_v2_101(inputs, + num_classes=None, + is_training=True, + global_pool=True, + output_stride=None, + spatial_squeeze=False, + reuse=None, + scope='resnet_v2_101'): + """ResNet-101 model of [1]. See resnet_v2() for arg and return description.""" + blocks = [ + resnet_v2_block('block1', base_depth=64, num_units=3, stride=2), + resnet_v2_block('block2', base_depth=128, num_units=4, stride=2), + resnet_v2_block('block3', base_depth=256, num_units=23, stride=2), + resnet_v2_block('block4', base_depth=512, num_units=3, stride=1), + ] + return resnet_v2(inputs, blocks, num_classes, is_training=is_training, + global_pool=global_pool, output_stride=output_stride, + include_root_block=True, spatial_squeeze=spatial_squeeze, + reuse=reuse, scope=scope) +resnet_v2_101.default_image_size = resnet_v2.default_image_size + + +def resnet_v2_152(inputs, + num_classes=None, + is_training=True, + global_pool=True, + output_stride=None, + spatial_squeeze=False, + reuse=None, + scope='resnet_v2_152'): + """ResNet-152 model of [1]. See resnet_v2() for arg and return description.""" + blocks = [ + resnet_v2_block('block1', base_depth=64, num_units=3, stride=2), + resnet_v2_block('block2', base_depth=128, num_units=8, stride=2), + resnet_v2_block('block3', base_depth=256, num_units=36, stride=2), + resnet_v2_block('block4', base_depth=512, num_units=3, stride=1), + ] + return resnet_v2(inputs, blocks, num_classes, is_training=is_training, + global_pool=global_pool, output_stride=output_stride, + include_root_block=True, spatial_squeeze=spatial_squeeze, + reuse=reuse, scope=scope) +resnet_v2_152.default_image_size = resnet_v2.default_image_size + + +def resnet_v2_200(inputs, + num_classes=None, + is_training=True, + global_pool=True, + output_stride=None, + spatial_squeeze=False, + reuse=None, + scope='resnet_v2_200'): + """ResNet-200 model of [2]. See resnet_v2() for arg and return description.""" + blocks = [ + resnet_v2_block('block1', base_depth=64, num_units=3, stride=2), + resnet_v2_block('block2', base_depth=128, num_units=24, stride=2), + resnet_v2_block('block3', base_depth=256, num_units=36, stride=2), + resnet_v2_block('block4', base_depth=512, num_units=3, stride=1), + ] + return resnet_v2(inputs, blocks, num_classes, is_training=is_training, + global_pool=global_pool, output_stride=output_stride, + include_root_block=True, spatial_squeeze=spatial_squeeze, + reuse=reuse, scope=scope) +resnet_v2_200.default_image_size = resnet_v2.default_image_size diff --git a/libs/networks/slim_nets/resnet_v2_test.py b/libs/networks/slim_nets/resnet_v2_test.py new file mode 100644 index 0000000..c181664 --- /dev/null +++ b/libs/networks/slim_nets/resnet_v2_test.py @@ -0,0 +1,443 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for slim.slim_nets.resnet_v2.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np +import tensorflow as tf + +from nets import resnet_utils +from nets import resnet_v2 + +slim = tf.contrib.slim + + +def create_test_input(batch_size, height, width, channels): + """Create test input tensor. + + Args: + batch_size: The number of images per batch or `None` if unknown. + height: The height of each image or `None` if unknown. + width: The width of each image or `None` if unknown. + channels: The number of channels per image or `None` if unknown. + + Returns: + Either a placeholder `Tensor` of dimension + [batch_size, height, width, channels] if any of the inputs are `None` or a + constant `Tensor` with the mesh grid values along the spatial dimensions. + """ + if None in [batch_size, height, width, channels]: + return tf.placeholder(tf.float32, (batch_size, height, width, channels)) + else: + return tf.to_float( + np.tile( + np.reshape( + np.reshape(np.arange(height), [height, 1]) + + np.reshape(np.arange(width), [1, width]), + [1, height, width, 1]), + [batch_size, 1, 1, channels])) + + +class ResnetUtilsTest(tf.test.TestCase): + + def testSubsampleThreeByThree(self): + x = tf.reshape(tf.to_float(tf.range(9)), [1, 3, 3, 1]) + x = resnet_utils.subsample(x, 2) + expected = tf.reshape(tf.constant([0, 2, 6, 8]), [1, 2, 2, 1]) + with self.test_session(): + self.assertAllClose(x.eval(), expected.eval()) + + def testSubsampleFourByFour(self): + x = tf.reshape(tf.to_float(tf.range(16)), [1, 4, 4, 1]) + x = resnet_utils.subsample(x, 2) + expected = tf.reshape(tf.constant([0, 2, 8, 10]), [1, 2, 2, 1]) + with self.test_session(): + self.assertAllClose(x.eval(), expected.eval()) + + def testConv2DSameEven(self): + n, n2 = 4, 2 + + # Input image. + x = create_test_input(1, n, n, 1) + + # Convolution kernel. + w = create_test_input(1, 3, 3, 1) + w = tf.reshape(w, [3, 3, 1, 1]) + + tf.get_variable('Conv/weights', initializer=w) + tf.get_variable('Conv/biases', initializer=tf.zeros([1])) + tf.get_variable_scope().reuse_variables() + + y1 = slim.conv2d(x, 1, [3, 3], stride=1, scope='Conv') + y1_expected = tf.to_float([[14, 28, 43, 26], + [28, 48, 66, 37], + [43, 66, 84, 46], + [26, 37, 46, 22]]) + y1_expected = tf.reshape(y1_expected, [1, n, n, 1]) + + y2 = resnet_utils.subsample(y1, 2) + y2_expected = tf.to_float([[14, 43], + [43, 84]]) + y2_expected = tf.reshape(y2_expected, [1, n2, n2, 1]) + + y3 = resnet_utils.conv2d_same(x, 1, 3, stride=2, scope='Conv') + y3_expected = y2_expected + + y4 = slim.conv2d(x, 1, [3, 3], stride=2, scope='Conv') + y4_expected = tf.to_float([[48, 37], + [37, 22]]) + y4_expected = tf.reshape(y4_expected, [1, n2, n2, 1]) + + with self.test_session() as sess: + sess.run(tf.global_variables_initializer()) + self.assertAllClose(y1.eval(), y1_expected.eval()) + self.assertAllClose(y2.eval(), y2_expected.eval()) + self.assertAllClose(y3.eval(), y3_expected.eval()) + self.assertAllClose(y4.eval(), y4_expected.eval()) + + def testConv2DSameOdd(self): + n, n2 = 5, 3 + + # Input image. + x = create_test_input(1, n, n, 1) + + # Convolution kernel. + w = create_test_input(1, 3, 3, 1) + w = tf.reshape(w, [3, 3, 1, 1]) + + tf.get_variable('Conv/weights', initializer=w) + tf.get_variable('Conv/biases', initializer=tf.zeros([1])) + tf.get_variable_scope().reuse_variables() + + y1 = slim.conv2d(x, 1, [3, 3], stride=1, scope='Conv') + y1_expected = tf.to_float([[14, 28, 43, 58, 34], + [28, 48, 66, 84, 46], + [43, 66, 84, 102, 55], + [58, 84, 102, 120, 64], + [34, 46, 55, 64, 30]]) + y1_expected = tf.reshape(y1_expected, [1, n, n, 1]) + + y2 = resnet_utils.subsample(y1, 2) + y2_expected = tf.to_float([[14, 43, 34], + [43, 84, 55], + [34, 55, 30]]) + y2_expected = tf.reshape(y2_expected, [1, n2, n2, 1]) + + y3 = resnet_utils.conv2d_same(x, 1, 3, stride=2, scope='Conv') + y3_expected = y2_expected + + y4 = slim.conv2d(x, 1, [3, 3], stride=2, scope='Conv') + y4_expected = y2_expected + + with self.test_session() as sess: + sess.run(tf.global_variables_initializer()) + self.assertAllClose(y1.eval(), y1_expected.eval()) + self.assertAllClose(y2.eval(), y2_expected.eval()) + self.assertAllClose(y3.eval(), y3_expected.eval()) + self.assertAllClose(y4.eval(), y4_expected.eval()) + + def _resnet_plain(self, inputs, blocks, output_stride=None, scope=None): + """A plain ResNet without extra layers before or after the ResNet blocks.""" + with tf.variable_scope(scope, values=[inputs]): + with slim.arg_scope([slim.conv2d], outputs_collections='end_points'): + net = resnet_utils.stack_blocks_dense(inputs, blocks, output_stride) + end_points = slim.utils.convert_collection_to_dict('end_points') + return net, end_points + + def testEndPointsV2(self): + """Test the end points of a tiny v2 bottleneck network.""" + blocks = [ + resnet_v2.resnet_v2_block( + 'block1', base_depth=1, num_units=2, stride=2), + resnet_v2.resnet_v2_block( + 'block2', base_depth=2, num_units=2, stride=1), + ] + inputs = create_test_input(2, 32, 16, 3) + with slim.arg_scope(resnet_utils.resnet_arg_scope()): + _, end_points = self._resnet_plain(inputs, blocks, scope='tiny') + expected = [ + 'tiny/block1/unit_1/bottleneck_v2/shortcut', + 'tiny/block1/unit_1/bottleneck_v2/conv1', + 'tiny/block1/unit_1/bottleneck_v2/conv2', + 'tiny/block1/unit_1/bottleneck_v2/conv3', + 'tiny/block1/unit_2/bottleneck_v2/conv1', + 'tiny/block1/unit_2/bottleneck_v2/conv2', + 'tiny/block1/unit_2/bottleneck_v2/conv3', + 'tiny/block2/unit_1/bottleneck_v2/shortcut', + 'tiny/block2/unit_1/bottleneck_v2/conv1', + 'tiny/block2/unit_1/bottleneck_v2/conv2', + 'tiny/block2/unit_1/bottleneck_v2/conv3', + 'tiny/block2/unit_2/bottleneck_v2/conv1', + 'tiny/block2/unit_2/bottleneck_v2/conv2', + 'tiny/block2/unit_2/bottleneck_v2/conv3'] + self.assertItemsEqual(expected, end_points) + + def _stack_blocks_nondense(self, net, blocks): + """A simplified ResNet Block stacker without output stride control.""" + for block in blocks: + with tf.variable_scope(block.scope, 'block', [net]): + for i, unit in enumerate(block.args): + with tf.variable_scope('unit_%d' % (i + 1), values=[net]): + net = block.unit_fn(net, rate=1, **unit) + return net + + def testAtrousValuesBottleneck(self): + """Verify the values of dense feature extraction by atrous convolution. + + Make sure that dense feature extraction by stack_blocks_dense() followed by + subsampling gives identical results to feature extraction at the nominal + network output stride using the simple self._stack_blocks_nondense() above. + """ + block = resnet_v2.resnet_v2_block + blocks = [ + block('block1', base_depth=1, num_units=2, stride=2), + block('block2', base_depth=2, num_units=2, stride=2), + block('block3', base_depth=4, num_units=2, stride=2), + block('block4', base_depth=8, num_units=2, stride=1), + ] + nominal_stride = 8 + + # Test both odd and even input dimensions. + height = 30 + width = 31 + with slim.arg_scope(resnet_utils.resnet_arg_scope()): + with slim.arg_scope([slim.batch_norm], is_training=False): + for output_stride in [1, 2, 4, 8, None]: + with tf.Graph().as_default(): + with self.test_session() as sess: + tf.set_random_seed(0) + inputs = create_test_input(1, height, width, 3) + # Dense feature extraction followed by subsampling. + output = resnet_utils.stack_blocks_dense(inputs, + blocks, + output_stride) + if output_stride is None: + factor = 1 + else: + factor = nominal_stride // output_stride + + output = resnet_utils.subsample(output, factor) + # Make the two networks use the same weights. + tf.get_variable_scope().reuse_variables() + # Feature extraction at the nominal network rate. + expected = self._stack_blocks_nondense(inputs, blocks) + sess.run(tf.global_variables_initializer()) + output, expected = sess.run([output, expected]) + self.assertAllClose(output, expected, atol=1e-4, rtol=1e-4) + + +class ResnetCompleteNetworkTest(tf.test.TestCase): + """Tests with complete small ResNet v2 networks.""" + + def _resnet_small(self, + inputs, + num_classes=None, + is_training=True, + global_pool=True, + output_stride=None, + include_root_block=True, + reuse=None, + scope='resnet_v2_small'): + """A shallow and thin ResNet v2 for faster tests.""" + block = resnet_v2.resnet_v2_block + blocks = [ + block('block1', base_depth=1, num_units=3, stride=2), + block('block2', base_depth=2, num_units=3, stride=2), + block('block3', base_depth=4, num_units=3, stride=2), + block('block4', base_depth=8, num_units=2, stride=1), + ] + return resnet_v2.resnet_v2(inputs, blocks, num_classes, + is_training=is_training, + global_pool=global_pool, + output_stride=output_stride, + include_root_block=include_root_block, + reuse=reuse, + scope=scope) + + def testClassificationEndPoints(self): + global_pool = True + num_classes = 10 + inputs = create_test_input(2, 224, 224, 3) + with slim.arg_scope(resnet_utils.resnet_arg_scope()): + logits, end_points = self._resnet_small(inputs, num_classes, + global_pool=global_pool, + scope='resnet') + self.assertTrue(logits.op.name.startswith('resnet/logits')) + self.assertListEqual(logits.get_shape().as_list(), [2, 1, 1, num_classes]) + self.assertTrue('predictions' in end_points) + self.assertListEqual(end_points['predictions'].get_shape().as_list(), + [2, 1, 1, num_classes]) + + def testClassificationShapes(self): + global_pool = True + num_classes = 10 + inputs = create_test_input(2, 224, 224, 3) + with slim.arg_scope(resnet_utils.resnet_arg_scope()): + _, end_points = self._resnet_small(inputs, num_classes, + global_pool=global_pool, + scope='resnet') + endpoint_to_shape = { + 'resnet/block1': [2, 28, 28, 4], + 'resnet/block2': [2, 14, 14, 8], + 'resnet/block3': [2, 7, 7, 16], + 'resnet/block4': [2, 7, 7, 32]} + for endpoint in endpoint_to_shape: + shape = endpoint_to_shape[endpoint] + self.assertListEqual(end_points[endpoint].get_shape().as_list(), shape) + + def testFullyConvolutionalEndpointShapes(self): + global_pool = False + num_classes = 10 + inputs = create_test_input(2, 321, 321, 3) + with slim.arg_scope(resnet_utils.resnet_arg_scope()): + _, end_points = self._resnet_small(inputs, num_classes, + global_pool=global_pool, + scope='resnet') + endpoint_to_shape = { + 'resnet/block1': [2, 41, 41, 4], + 'resnet/block2': [2, 21, 21, 8], + 'resnet/block3': [2, 11, 11, 16], + 'resnet/block4': [2, 11, 11, 32]} + for endpoint in endpoint_to_shape: + shape = endpoint_to_shape[endpoint] + self.assertListEqual(end_points[endpoint].get_shape().as_list(), shape) + + def testRootlessFullyConvolutionalEndpointShapes(self): + global_pool = False + num_classes = 10 + inputs = create_test_input(2, 128, 128, 3) + with slim.arg_scope(resnet_utils.resnet_arg_scope()): + _, end_points = self._resnet_small(inputs, num_classes, + global_pool=global_pool, + include_root_block=False, + scope='resnet') + endpoint_to_shape = { + 'resnet/block1': [2, 64, 64, 4], + 'resnet/block2': [2, 32, 32, 8], + 'resnet/block3': [2, 16, 16, 16], + 'resnet/block4': [2, 16, 16, 32]} + for endpoint in endpoint_to_shape: + shape = endpoint_to_shape[endpoint] + self.assertListEqual(end_points[endpoint].get_shape().as_list(), shape) + + def testAtrousFullyConvolutionalEndpointShapes(self): + global_pool = False + num_classes = 10 + output_stride = 8 + inputs = create_test_input(2, 321, 321, 3) + with slim.arg_scope(resnet_utils.resnet_arg_scope()): + _, end_points = self._resnet_small(inputs, + num_classes, + global_pool=global_pool, + output_stride=output_stride, + scope='resnet') + endpoint_to_shape = { + 'resnet/block1': [2, 41, 41, 4], + 'resnet/block2': [2, 41, 41, 8], + 'resnet/block3': [2, 41, 41, 16], + 'resnet/block4': [2, 41, 41, 32]} + for endpoint in endpoint_to_shape: + shape = endpoint_to_shape[endpoint] + self.assertListEqual(end_points[endpoint].get_shape().as_list(), shape) + + def testAtrousFullyConvolutionalValues(self): + """Verify dense feature extraction with atrous convolution.""" + nominal_stride = 32 + for output_stride in [4, 8, 16, 32, None]: + with slim.arg_scope(resnet_utils.resnet_arg_scope()): + with tf.Graph().as_default(): + with self.test_session() as sess: + tf.set_random_seed(0) + inputs = create_test_input(2, 81, 81, 3) + # Dense feature extraction followed by subsampling. + output, _ = self._resnet_small(inputs, None, + is_training=False, + global_pool=False, + output_stride=output_stride) + if output_stride is None: + factor = 1 + else: + factor = nominal_stride // output_stride + output = resnet_utils.subsample(output, factor) + # Make the two networks use the same weights. + tf.get_variable_scope().reuse_variables() + # Feature extraction at the nominal network rate. + expected, _ = self._resnet_small(inputs, None, + is_training=False, + global_pool=False) + sess.run(tf.global_variables_initializer()) + self.assertAllClose(output.eval(), expected.eval(), + atol=1e-4, rtol=1e-4) + + def testUnknownBatchSize(self): + batch = 2 + height, width = 65, 65 + global_pool = True + num_classes = 10 + inputs = create_test_input(None, height, width, 3) + with slim.arg_scope(resnet_utils.resnet_arg_scope()): + logits, _ = self._resnet_small(inputs, num_classes, + global_pool=global_pool, + scope='resnet') + self.assertTrue(logits.op.name.startswith('resnet/logits')) + self.assertListEqual(logits.get_shape().as_list(), + [None, 1, 1, num_classes]) + images = create_test_input(batch, height, width, 3) + with self.test_session() as sess: + sess.run(tf.global_variables_initializer()) + output = sess.run(logits, {inputs: images.eval()}) + self.assertEqual(output.shape, (batch, 1, 1, num_classes)) + + def testFullyConvolutionalUnknownHeightWidth(self): + batch = 2 + height, width = 65, 65 + global_pool = False + inputs = create_test_input(batch, None, None, 3) + with slim.arg_scope(resnet_utils.resnet_arg_scope()): + output, _ = self._resnet_small(inputs, None, + global_pool=global_pool) + self.assertListEqual(output.get_shape().as_list(), + [batch, None, None, 32]) + images = create_test_input(batch, height, width, 3) + with self.test_session() as sess: + sess.run(tf.global_variables_initializer()) + output = sess.run(output, {inputs: images.eval()}) + self.assertEqual(output.shape, (batch, 3, 3, 32)) + + def testAtrousFullyConvolutionalUnknownHeightWidth(self): + batch = 2 + height, width = 65, 65 + global_pool = False + output_stride = 8 + inputs = create_test_input(batch, None, None, 3) + with slim.arg_scope(resnet_utils.resnet_arg_scope()): + output, _ = self._resnet_small(inputs, + None, + global_pool=global_pool, + output_stride=output_stride) + self.assertListEqual(output.get_shape().as_list(), + [batch, None, None, 32]) + images = create_test_input(batch, height, width, 3) + with self.test_session() as sess: + sess.run(tf.global_variables_initializer()) + output = sess.run(output, {inputs: images.eval()}) + self.assertEqual(output.shape, (batch, 9, 9, 32)) + + +if __name__ == '__main__': + tf.test.main() diff --git a/libs/networks/slim_nets/vgg.py b/libs/networks/slim_nets/vgg.py new file mode 100644 index 0000000..4c74c5a --- /dev/null +++ b/libs/networks/slim_nets/vgg.py @@ -0,0 +1,265 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Contains model definitions for versions of the Oxford VGG network. + +These model definitions were introduced in the following technical report: + + Very Deep Convolutional Networks For Large-Scale Image Recognition + Karen Simonyan and Andrew Zisserman + arXiv technical report, 2015 + PDF: http://arxiv.org/pdf/1409.1556.pdf + ILSVRC 2014 Slides: http://www.robots.ox.ac.uk/~karen/pdf/ILSVRC_2014.pdf + CC-BY-4.0 + +More information can be obtained from the VGG website: +www.robots.ox.ac.uk/~vgg/research/very_deep/ + +Usage: + with slim.arg_scope(vgg.vgg_arg_scope()): + outputs, end_points = vgg.vgg_a(inputs) + + with slim.arg_scope(vgg.vgg_arg_scope()): + outputs, end_points = vgg.vgg_16(inputs) + +@@vgg_a +@@vgg_16 +@@vgg_19 +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf + +slim = tf.contrib.slim + + +def vgg_arg_scope(weight_decay=0.0005): + """Defines the VGG arg scope. + + Args: + weight_decay: The l2 regularization coefficient. + + Returns: + An arg_scope. + """ + with slim.arg_scope([slim.conv2d, slim.fully_connected], + activation_fn=tf.nn.relu, + weights_regularizer=slim.l2_regularizer(weight_decay), + biases_initializer=tf.zeros_initializer()): + with slim.arg_scope([slim.conv2d], padding='SAME') as arg_sc: + return arg_sc + + +def vgg_a(inputs, + num_classes=1000, + is_training=True, + dropout_keep_prob=0.5, + spatial_squeeze=True, + scope='vgg_a', + fc_conv_padding='VALID'): + """Oxford Net VGG 11-Layers version A Example. + + Note: All the fully_connected layers have been transformed to conv2d layers. + To use in classification mode, resize input to 224x224. + + Args: + inputs: a tensor of size [batch_size, height, width, channels]. + num_classes: number of predicted classes. + is_training: whether or not the model is being trained. + dropout_keep_prob: the probability that activations are kept in the dropout + layers during training. + spatial_squeeze: whether or not should squeeze the spatial dimensions of the + outputs. Useful to remove unnecessary dimensions for classification. + scope: Optional scope for the variables. + fc_conv_padding: the type of padding to use for the fully connected layer + that is implemented as a convolutional layer. Use 'SAME' padding if you + are applying the network in a fully convolutional manner and want to + get a prediction map downsampled by a factor of 32 as an output. Otherwise, + the output prediction map will be (input / 32) - 6 in case of 'VALID' padding. + + Returns: + the last op containing the log predictions and end_points dict. + """ + with tf.variable_scope(scope, 'vgg_a', [inputs]) as sc: + end_points_collection = sc.name + '_end_points' + # Collect outputs for conv2d, fully_connected and max_pool2d. + with slim.arg_scope([slim.conv2d, slim.max_pool2d], + outputs_collections=end_points_collection): + net = slim.repeat(inputs, 1, slim.conv2d, 64, [3, 3], scope='conv1') + net = slim.max_pool2d(net, [2, 2], scope='pool1') + net = slim.repeat(net, 1, slim.conv2d, 128, [3, 3], scope='conv2') + net = slim.max_pool2d(net, [2, 2], scope='pool2') + net = slim.repeat(net, 2, slim.conv2d, 256, [3, 3], scope='conv3') + net = slim.max_pool2d(net, [2, 2], scope='pool3') + net = slim.repeat(net, 2, slim.conv2d, 512, [3, 3], scope='conv4') + net = slim.max_pool2d(net, [2, 2], scope='pool4') + net = slim.repeat(net, 2, slim.conv2d, 512, [3, 3], scope='conv5') + net = slim.max_pool2d(net, [2, 2], scope='pool5') + # Use conv2d instead of fully_connected layers. + net = slim.conv2d(net, 4096, [7, 7], padding=fc_conv_padding, scope='fc6') + net = slim.dropout(net, dropout_keep_prob, is_training=is_training, + scope='dropout6') + net = slim.conv2d(net, 4096, [1, 1], scope='fc7') + net = slim.dropout(net, dropout_keep_prob, is_training=is_training, + scope='dropout7') + net = slim.conv2d(net, num_classes, [1, 1], + activation_fn=None, + normalizer_fn=None, + scope='fc8') + # Convert end_points_collection into a end_point dict. + end_points = slim.utils.convert_collection_to_dict(end_points_collection) + if spatial_squeeze: + net = tf.squeeze(net, [1, 2], name='fc8/squeezed') + end_points[sc.name + '/fc8'] = net + return net, end_points +vgg_a.default_image_size = 224 + + +def vgg_16(inputs, + num_classes=1000, + is_training=True, + dropout_keep_prob=0.5, + spatial_squeeze=True, + scope='vgg_16', + fc_conv_padding='VALID'): + """Oxford Net VGG 16-Layers version D Example. + + Note: All the fully_connected layers have been transformed to conv2d layers. + To use in classification mode, resize input to 224x224. + + Args: + inputs: a tensor of size [batch_size, height, width, channels]. + num_classes: number of predicted classes. + is_training: whether or not the model is being trained. + dropout_keep_prob: the probability that activations are kept in the dropout + layers during training. + spatial_squeeze: whether or not should squeeze the spatial dimensions of the + outputs. Useful to remove unnecessary dimensions for classification. + scope: Optional scope for the variables. + fc_conv_padding: the type of padding to use for the fully connected layer + that is implemented as a convolutional layer. Use 'SAME' padding if you + are applying the network in a fully convolutional manner and want to + get a prediction map downsampled by a factor of 32 as an output. Otherwise, + the output prediction map will be (input / 32) - 6 in case of 'VALID' padding. + + Returns: + the last op containing the log predictions and end_points dict. + """ + with tf.variable_scope(scope, 'vgg_16', [inputs]) as sc: + end_points_collection = sc.name + '_end_points' + # Collect outputs for conv2d, fully_connected and max_pool2d. + with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d], + outputs_collections=end_points_collection): + net = slim.repeat(inputs, 2, slim.conv2d, 64, [3, 3], scope='conv1') + net = slim.max_pool2d(net, [2, 2], scope='pool1') + net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], scope='conv2') + net = slim.max_pool2d(net, [2, 2], scope='pool2') + net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3], scope='conv3') + net = slim.max_pool2d(net, [2, 2], scope='pool3') + net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv4') + net = slim.max_pool2d(net, [2, 2], scope='pool4') + net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv5') + net = slim.max_pool2d(net, [2, 2], scope='pool5') + # Use conv2d instead of fully_connected layers. + net = slim.conv2d(net, 4096, [7, 7], padding=fc_conv_padding, scope='fc6') + net = slim.dropout(net, dropout_keep_prob, is_training=is_training, + scope='dropout6') + net = slim.conv2d(net, 4096, [1, 1], scope='fc7') + net = slim.dropout(net, dropout_keep_prob, is_training=is_training, + scope='dropout7') + # yjr_feature = tf.squeeze(net) + net = slim.conv2d(net, num_classes, [1, 1], + activation_fn=None, + normalizer_fn=None, + scope='fc8') + # Convert end_points_collection into a end_point dict. + end_points = slim.utils.convert_collection_to_dict(end_points_collection) + if spatial_squeeze: + net = tf.squeeze(net, [1, 2], name='fc8/squeezed') + end_points[sc.name + '/fc8'] = net + # end_points['yjr_feature'] = yjr_feature + end_points['predictions'] = slim.softmax(net, scope='predictions') + return net, end_points +vgg_16.default_image_size = 224 + + +def vgg_19(inputs, + num_classes=1000, + is_training=True, + dropout_keep_prob=0.5, + spatial_squeeze=True, + scope='vgg_19', + fc_conv_padding='VALID'): + """Oxford Net VGG 19-Layers version E Example. + + Note: All the fully_connected layers have been transformed to conv2d layers. + To use in classification mode, resize input to 224x224. + + Args: + inputs: a tensor of size [batch_size, height, width, channels]. + num_classes: number of predicted classes. + is_training: whether or not the model is being trained. + dropout_keep_prob: the probability that activations are kept in the dropout + layers during training. + spatial_squeeze: whether or not should squeeze the spatial dimensions of the + outputs. Useful to remove unnecessary dimensions for classification. + scope: Optional scope for the variables. + fc_conv_padding: the type of padding to use for the fully connected layer + that is implemented as a convolutional layer. Use 'SAME' padding if you + are applying the network in a fully convolutional manner and want to + get a prediction map downsampled by a factor of 32 as an output. Otherwise, + the output prediction map will be (input / 32) - 6 in case of 'VALID' padding. + + Returns: + the last op containing the log predictions and end_points dict. + """ + with tf.variable_scope(scope, 'vgg_19', [inputs]) as sc: + end_points_collection = sc.name + '_end_points' + # Collect outputs for conv2d, fully_connected and max_pool2d. + with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d], + outputs_collections=end_points_collection): + net = slim.repeat(inputs, 2, slim.conv2d, 64, [3, 3], scope='conv1') + net = slim.max_pool2d(net, [2, 2], scope='pool1') + net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], scope='conv2') + net = slim.max_pool2d(net, [2, 2], scope='pool2') + net = slim.repeat(net, 4, slim.conv2d, 256, [3, 3], scope='conv3') + net = slim.max_pool2d(net, [2, 2], scope='pool3') + net = slim.repeat(net, 4, slim.conv2d, 512, [3, 3], scope='conv4') + net = slim.max_pool2d(net, [2, 2], scope='pool4') + net = slim.repeat(net, 4, slim.conv2d, 512, [3, 3], scope='conv5') + net = slim.max_pool2d(net, [2, 2], scope='pool5') + # Use conv2d instead of fully_connected layers. + net = slim.conv2d(net, 4096, [7, 7], padding=fc_conv_padding, scope='fc6') + net = slim.dropout(net, dropout_keep_prob, is_training=is_training, + scope='dropout6') + net = slim.conv2d(net, 4096, [1, 1], scope='fc7') + net = slim.dropout(net, dropout_keep_prob, is_training=is_training, + scope='dropout7') + net = slim.conv2d(net, num_classes, [1, 1], + activation_fn=None, + normalizer_fn=None, + scope='fc8') + # Convert end_points_collection into a end_point dict. + end_points = slim.utils.convert_collection_to_dict(end_points_collection) + if spatial_squeeze: + net = tf.squeeze(net, [1, 2], name='fc8/squeezed') + end_points[sc.name + '/fc8'] = net + return net, end_points +vgg_19.default_image_size = 224 + +# Alias +vgg_d = vgg_16 +vgg_e = vgg_19 diff --git a/libs/networks/slim_nets/vgg_test.py b/libs/networks/slim_nets/vgg_test.py new file mode 100644 index 0000000..8e383b3 --- /dev/null +++ b/libs/networks/slim_nets/vgg_test.py @@ -0,0 +1,455 @@ +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for slim.slim_nets.vgg.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf + +from nets import vgg + +slim = tf.contrib.slim + + +class VGGATest(tf.test.TestCase): + + def testBuild(self): + batch_size = 5 + height, width = 224, 224 + num_classes = 1000 + with self.test_session(): + inputs = tf.random_uniform((batch_size, height, width, 3)) + logits, _ = vgg.vgg_a(inputs, num_classes) + self.assertEquals(logits.op.name, 'vgg_a/fc8/squeezed') + self.assertListEqual(logits.get_shape().as_list(), + [batch_size, num_classes]) + + def testFullyConvolutional(self): + batch_size = 1 + height, width = 256, 256 + num_classes = 1000 + with self.test_session(): + inputs = tf.random_uniform((batch_size, height, width, 3)) + logits, _ = vgg.vgg_a(inputs, num_classes, spatial_squeeze=False) + self.assertEquals(logits.op.name, 'vgg_a/fc8/BiasAdd') + self.assertListEqual(logits.get_shape().as_list(), + [batch_size, 2, 2, num_classes]) + + def testEndPoints(self): + batch_size = 5 + height, width = 224, 224 + num_classes = 1000 + with self.test_session(): + inputs = tf.random_uniform((batch_size, height, width, 3)) + _, end_points = vgg.vgg_a(inputs, num_classes) + expected_names = ['vgg_a/conv1/conv1_1', + 'vgg_a/pool1', + 'vgg_a/conv2/conv2_1', + 'vgg_a/pool2', + 'vgg_a/conv3/conv3_1', + 'vgg_a/conv3/conv3_2', + 'vgg_a/pool3', + 'vgg_a/conv4/conv4_1', + 'vgg_a/conv4/conv4_2', + 'vgg_a/pool4', + 'vgg_a/conv5/conv5_1', + 'vgg_a/conv5/conv5_2', + 'vgg_a/pool5', + 'vgg_a/fc6', + 'vgg_a/fc7', + 'vgg_a/fc8' + ] + self.assertSetEqual(set(end_points.keys()), set(expected_names)) + + def testModelVariables(self): + batch_size = 5 + height, width = 224, 224 + num_classes = 1000 + with self.test_session(): + inputs = tf.random_uniform((batch_size, height, width, 3)) + vgg.vgg_a(inputs, num_classes) + expected_names = ['vgg_a/conv1/conv1_1/weights', + 'vgg_a/conv1/conv1_1/biases', + 'vgg_a/conv2/conv2_1/weights', + 'vgg_a/conv2/conv2_1/biases', + 'vgg_a/conv3/conv3_1/weights', + 'vgg_a/conv3/conv3_1/biases', + 'vgg_a/conv3/conv3_2/weights', + 'vgg_a/conv3/conv3_2/biases', + 'vgg_a/conv4/conv4_1/weights', + 'vgg_a/conv4/conv4_1/biases', + 'vgg_a/conv4/conv4_2/weights', + 'vgg_a/conv4/conv4_2/biases', + 'vgg_a/conv5/conv5_1/weights', + 'vgg_a/conv5/conv5_1/biases', + 'vgg_a/conv5/conv5_2/weights', + 'vgg_a/conv5/conv5_2/biases', + 'vgg_a/fc6/weights', + 'vgg_a/fc6/biases', + 'vgg_a/fc7/weights', + 'vgg_a/fc7/biases', + 'vgg_a/fc8/weights', + 'vgg_a/fc8/biases', + ] + model_variables = [v.op.name for v in slim.get_model_variables()] + self.assertSetEqual(set(model_variables), set(expected_names)) + + def testEvaluation(self): + batch_size = 2 + height, width = 224, 224 + num_classes = 1000 + with self.test_session(): + eval_inputs = tf.random_uniform((batch_size, height, width, 3)) + logits, _ = vgg.vgg_a(eval_inputs, is_training=False) + self.assertListEqual(logits.get_shape().as_list(), + [batch_size, num_classes]) + predictions = tf.argmax(logits, 1) + self.assertListEqual(predictions.get_shape().as_list(), [batch_size]) + + def testTrainEvalWithReuse(self): + train_batch_size = 2 + eval_batch_size = 1 + train_height, train_width = 224, 224 + eval_height, eval_width = 256, 256 + num_classes = 1000 + with self.test_session(): + train_inputs = tf.random_uniform( + (train_batch_size, train_height, train_width, 3)) + logits, _ = vgg.vgg_a(train_inputs) + self.assertListEqual(logits.get_shape().as_list(), + [train_batch_size, num_classes]) + tf.get_variable_scope().reuse_variables() + eval_inputs = tf.random_uniform( + (eval_batch_size, eval_height, eval_width, 3)) + logits, _ = vgg.vgg_a(eval_inputs, is_training=False, + spatial_squeeze=False) + self.assertListEqual(logits.get_shape().as_list(), + [eval_batch_size, 2, 2, num_classes]) + logits = tf.reduce_mean(logits, [1, 2]) + predictions = tf.argmax(logits, 1) + self.assertEquals(predictions.get_shape().as_list(), [eval_batch_size]) + + def testForward(self): + batch_size = 1 + height, width = 224, 224 + with self.test_session() as sess: + inputs = tf.random_uniform((batch_size, height, width, 3)) + logits, _ = vgg.vgg_a(inputs) + sess.run(tf.global_variables_initializer()) + output = sess.run(logits) + self.assertTrue(output.any()) + + +class VGG16Test(tf.test.TestCase): + + def testBuild(self): + batch_size = 5 + height, width = 224, 224 + num_classes = 1000 + with self.test_session(): + inputs = tf.random_uniform((batch_size, height, width, 3)) + logits, _ = vgg.vgg_16(inputs, num_classes) + self.assertEquals(logits.op.name, 'vgg_16/fc8/squeezed') + self.assertListEqual(logits.get_shape().as_list(), + [batch_size, num_classes]) + + def testFullyConvolutional(self): + batch_size = 1 + height, width = 256, 256 + num_classes = 1000 + with self.test_session(): + inputs = tf.random_uniform((batch_size, height, width, 3)) + logits, _ = vgg.vgg_16(inputs, num_classes, spatial_squeeze=False) + self.assertEquals(logits.op.name, 'vgg_16/fc8/BiasAdd') + self.assertListEqual(logits.get_shape().as_list(), + [batch_size, 2, 2, num_classes]) + + def testEndPoints(self): + batch_size = 5 + height, width = 224, 224 + num_classes = 1000 + with self.test_session(): + inputs = tf.random_uniform((batch_size, height, width, 3)) + _, end_points = vgg.vgg_16(inputs, num_classes) + expected_names = ['vgg_16/conv1/conv1_1', + 'vgg_16/conv1/conv1_2', + 'vgg_16/pool1', + 'vgg_16/conv2/conv2_1', + 'vgg_16/conv2/conv2_2', + 'vgg_16/pool2', + 'vgg_16/conv3/conv3_1', + 'vgg_16/conv3/conv3_2', + 'vgg_16/conv3/conv3_3', + 'vgg_16/pool3', + 'vgg_16/conv4/conv4_1', + 'vgg_16/conv4/conv4_2', + 'vgg_16/conv4/conv4_3', + 'vgg_16/pool4', + 'vgg_16/conv5/conv5_1', + 'vgg_16/conv5/conv5_2', + 'vgg_16/conv5/conv5_3', + 'vgg_16/pool5', + 'vgg_16/fc6', + 'vgg_16/fc7', + 'vgg_16/fc8' + ] + self.assertSetEqual(set(end_points.keys()), set(expected_names)) + + def testModelVariables(self): + batch_size = 5 + height, width = 224, 224 + num_classes = 1000 + with self.test_session(): + inputs = tf.random_uniform((batch_size, height, width, 3)) + vgg.vgg_16(inputs, num_classes) + expected_names = ['vgg_16/conv1/conv1_1/weights', + 'vgg_16/conv1/conv1_1/biases', + 'vgg_16/conv1/conv1_2/weights', + 'vgg_16/conv1/conv1_2/biases', + 'vgg_16/conv2/conv2_1/weights', + 'vgg_16/conv2/conv2_1/biases', + 'vgg_16/conv2/conv2_2/weights', + 'vgg_16/conv2/conv2_2/biases', + 'vgg_16/conv3/conv3_1/weights', + 'vgg_16/conv3/conv3_1/biases', + 'vgg_16/conv3/conv3_2/weights', + 'vgg_16/conv3/conv3_2/biases', + 'vgg_16/conv3/conv3_3/weights', + 'vgg_16/conv3/conv3_3/biases', + 'vgg_16/conv4/conv4_1/weights', + 'vgg_16/conv4/conv4_1/biases', + 'vgg_16/conv4/conv4_2/weights', + 'vgg_16/conv4/conv4_2/biases', + 'vgg_16/conv4/conv4_3/weights', + 'vgg_16/conv4/conv4_3/biases', + 'vgg_16/conv5/conv5_1/weights', + 'vgg_16/conv5/conv5_1/biases', + 'vgg_16/conv5/conv5_2/weights', + 'vgg_16/conv5/conv5_2/biases', + 'vgg_16/conv5/conv5_3/weights', + 'vgg_16/conv5/conv5_3/biases', + 'vgg_16/fc6/weights', + 'vgg_16/fc6/biases', + 'vgg_16/fc7/weights', + 'vgg_16/fc7/biases', + 'vgg_16/fc8/weights', + 'vgg_16/fc8/biases', + ] + model_variables = [v.op.name for v in slim.get_model_variables()] + self.assertSetEqual(set(model_variables), set(expected_names)) + + def testEvaluation(self): + batch_size = 2 + height, width = 224, 224 + num_classes = 1000 + with self.test_session(): + eval_inputs = tf.random_uniform((batch_size, height, width, 3)) + logits, _ = vgg.vgg_16(eval_inputs, is_training=False) + self.assertListEqual(logits.get_shape().as_list(), + [batch_size, num_classes]) + predictions = tf.argmax(logits, 1) + self.assertListEqual(predictions.get_shape().as_list(), [batch_size]) + + def testTrainEvalWithReuse(self): + train_batch_size = 2 + eval_batch_size = 1 + train_height, train_width = 224, 224 + eval_height, eval_width = 256, 256 + num_classes = 1000 + with self.test_session(): + train_inputs = tf.random_uniform( + (train_batch_size, train_height, train_width, 3)) + logits, _ = vgg.vgg_16(train_inputs) + self.assertListEqual(logits.get_shape().as_list(), + [train_batch_size, num_classes]) + tf.get_variable_scope().reuse_variables() + eval_inputs = tf.random_uniform( + (eval_batch_size, eval_height, eval_width, 3)) + logits, _ = vgg.vgg_16(eval_inputs, is_training=False, + spatial_squeeze=False) + self.assertListEqual(logits.get_shape().as_list(), + [eval_batch_size, 2, 2, num_classes]) + logits = tf.reduce_mean(logits, [1, 2]) + predictions = tf.argmax(logits, 1) + self.assertEquals(predictions.get_shape().as_list(), [eval_batch_size]) + + def testForward(self): + batch_size = 1 + height, width = 224, 224 + with self.test_session() as sess: + inputs = tf.random_uniform((batch_size, height, width, 3)) + logits, _ = vgg.vgg_16(inputs) + sess.run(tf.global_variables_initializer()) + output = sess.run(logits) + self.assertTrue(output.any()) + + +class VGG19Test(tf.test.TestCase): + + def testBuild(self): + batch_size = 5 + height, width = 224, 224 + num_classes = 1000 + with self.test_session(): + inputs = tf.random_uniform((batch_size, height, width, 3)) + logits, _ = vgg.vgg_19(inputs, num_classes) + self.assertEquals(logits.op.name, 'vgg_19/fc8/squeezed') + self.assertListEqual(logits.get_shape().as_list(), + [batch_size, num_classes]) + + def testFullyConvolutional(self): + batch_size = 1 + height, width = 256, 256 + num_classes = 1000 + with self.test_session(): + inputs = tf.random_uniform((batch_size, height, width, 3)) + logits, _ = vgg.vgg_19(inputs, num_classes, spatial_squeeze=False) + self.assertEquals(logits.op.name, 'vgg_19/fc8/BiasAdd') + self.assertListEqual(logits.get_shape().as_list(), + [batch_size, 2, 2, num_classes]) + + def testEndPoints(self): + batch_size = 5 + height, width = 224, 224 + num_classes = 1000 + with self.test_session(): + inputs = tf.random_uniform((batch_size, height, width, 3)) + _, end_points = vgg.vgg_19(inputs, num_classes) + expected_names = [ + 'vgg_19/conv1/conv1_1', + 'vgg_19/conv1/conv1_2', + 'vgg_19/pool1', + 'vgg_19/conv2/conv2_1', + 'vgg_19/conv2/conv2_2', + 'vgg_19/pool2', + 'vgg_19/conv3/conv3_1', + 'vgg_19/conv3/conv3_2', + 'vgg_19/conv3/conv3_3', + 'vgg_19/conv3/conv3_4', + 'vgg_19/pool3', + 'vgg_19/conv4/conv4_1', + 'vgg_19/conv4/conv4_2', + 'vgg_19/conv4/conv4_3', + 'vgg_19/conv4/conv4_4', + 'vgg_19/pool4', + 'vgg_19/conv5/conv5_1', + 'vgg_19/conv5/conv5_2', + 'vgg_19/conv5/conv5_3', + 'vgg_19/conv5/conv5_4', + 'vgg_19/pool5', + 'vgg_19/fc6', + 'vgg_19/fc7', + 'vgg_19/fc8' + ] + self.assertSetEqual(set(end_points.keys()), set(expected_names)) + + def testModelVariables(self): + batch_size = 5 + height, width = 224, 224 + num_classes = 1000 + with self.test_session(): + inputs = tf.random_uniform((batch_size, height, width, 3)) + vgg.vgg_19(inputs, num_classes) + expected_names = [ + 'vgg_19/conv1/conv1_1/weights', + 'vgg_19/conv1/conv1_1/biases', + 'vgg_19/conv1/conv1_2/weights', + 'vgg_19/conv1/conv1_2/biases', + 'vgg_19/conv2/conv2_1/weights', + 'vgg_19/conv2/conv2_1/biases', + 'vgg_19/conv2/conv2_2/weights', + 'vgg_19/conv2/conv2_2/biases', + 'vgg_19/conv3/conv3_1/weights', + 'vgg_19/conv3/conv3_1/biases', + 'vgg_19/conv3/conv3_2/weights', + 'vgg_19/conv3/conv3_2/biases', + 'vgg_19/conv3/conv3_3/weights', + 'vgg_19/conv3/conv3_3/biases', + 'vgg_19/conv3/conv3_4/weights', + 'vgg_19/conv3/conv3_4/biases', + 'vgg_19/conv4/conv4_1/weights', + 'vgg_19/conv4/conv4_1/biases', + 'vgg_19/conv4/conv4_2/weights', + 'vgg_19/conv4/conv4_2/biases', + 'vgg_19/conv4/conv4_3/weights', + 'vgg_19/conv4/conv4_3/biases', + 'vgg_19/conv4/conv4_4/weights', + 'vgg_19/conv4/conv4_4/biases', + 'vgg_19/conv5/conv5_1/weights', + 'vgg_19/conv5/conv5_1/biases', + 'vgg_19/conv5/conv5_2/weights', + 'vgg_19/conv5/conv5_2/biases', + 'vgg_19/conv5/conv5_3/weights', + 'vgg_19/conv5/conv5_3/biases', + 'vgg_19/conv5/conv5_4/weights', + 'vgg_19/conv5/conv5_4/biases', + 'vgg_19/fc6/weights', + 'vgg_19/fc6/biases', + 'vgg_19/fc7/weights', + 'vgg_19/fc7/biases', + 'vgg_19/fc8/weights', + 'vgg_19/fc8/biases', + ] + model_variables = [v.op.name for v in slim.get_model_variables()] + self.assertSetEqual(set(model_variables), set(expected_names)) + + def testEvaluation(self): + batch_size = 2 + height, width = 224, 224 + num_classes = 1000 + with self.test_session(): + eval_inputs = tf.random_uniform((batch_size, height, width, 3)) + logits, _ = vgg.vgg_19(eval_inputs, is_training=False) + self.assertListEqual(logits.get_shape().as_list(), + [batch_size, num_classes]) + predictions = tf.argmax(logits, 1) + self.assertListEqual(predictions.get_shape().as_list(), [batch_size]) + + def testTrainEvalWithReuse(self): + train_batch_size = 2 + eval_batch_size = 1 + train_height, train_width = 224, 224 + eval_height, eval_width = 256, 256 + num_classes = 1000 + with self.test_session(): + train_inputs = tf.random_uniform( + (train_batch_size, train_height, train_width, 3)) + logits, _ = vgg.vgg_19(train_inputs) + self.assertListEqual(logits.get_shape().as_list(), + [train_batch_size, num_classes]) + tf.get_variable_scope().reuse_variables() + eval_inputs = tf.random_uniform( + (eval_batch_size, eval_height, eval_width, 3)) + logits, _ = vgg.vgg_19(eval_inputs, is_training=False, + spatial_squeeze=False) + self.assertListEqual(logits.get_shape().as_list(), + [eval_batch_size, 2, 2, num_classes]) + logits = tf.reduce_mean(logits, [1, 2]) + predictions = tf.argmax(logits, 1) + self.assertEquals(predictions.get_shape().as_list(), [eval_batch_size]) + + def testForward(self): + batch_size = 1 + height, width = 224, 224 + with self.test_session() as sess: + inputs = tf.random_uniform((batch_size, height, width, 3)) + logits, _ = vgg.vgg_19(inputs) + sess.run(tf.global_variables_initializer()) + output = sess.run(logits) + self.assertTrue(output.any()) + +if __name__ == '__main__': + tf.test.main() diff --git a/libs/setup.py b/libs/setup.py new file mode 100644 index 0000000..e7e4b4e --- /dev/null +++ b/libs/setup.py @@ -0,0 +1,225 @@ +# -------------------------------------------------------- +# Fast R-CNN +# Copyright (c) 2015 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ross Girshick +# -------------------------------------------------------- + +import os +from os.path import join as pjoin +from setuptools import setup +from distutils.extension import Extension +from Cython.Distutils import build_ext +import subprocess +import numpy as np + + +def find_in_path(name, path): + "Find a file in a search path" + # Adapted fom + # http://code.activestate.com/recipes/52224-find-a-file-given-a-search-path/ + for dir in path.split(os.pathsep): + binpath = pjoin(dir, name) + if os.path.exists(binpath): + return os.path.abspath(binpath) + return None + + +def locate_cuda(): + """Locate the CUDA environment on the system + + Returns a dict with keys 'home', 'nvcc', 'include', and 'lib64' + and values giving the absolute path to each directory. + + Starts by looking for the CUDAHOME env variable. If not found, everything + is based on finding 'nvcc' in the PATH. + """ + + # first check if the CUDAHOME env variable is in use + if 'CUDAHOME' in os.environ: + home = os.environ['CUDAHOME'] + nvcc = pjoin(home, 'bin', 'nvcc') + else: + # otherwise, search the PATH for NVCC + default_path = pjoin(os.sep, 'usr', 'local', 'cuda', 'bin') + nvcc = find_in_path('nvcc', os.environ['PATH'] + os.pathsep + default_path) + if nvcc is None: + raise EnvironmentError('The nvcc binary could not be ' + 'located in your $PATH. Either add it to your path, or set $CUDAHOME') + home = os.path.dirname(os.path.dirname(nvcc)) + + cudaconfig = {'home':home, 'nvcc':nvcc, + 'include': pjoin(home, 'include'), + 'lib64': pjoin(home, 'lib64')} + for k, v in cudaconfig.iteritems(): + if not os.path.exists(v): + raise EnvironmentError('The CUDA %s path could not be located in %s' % (k, v)) + + return cudaconfig +CUDA = locate_cuda() + + +# Obtain the numpy include directory. This logic works across numpy versions. +try: + numpy_include = np.get_include() +except AttributeError: + numpy_include = np.get_numpy_include() + + +def customize_compiler_for_nvcc(self): + """inject deep into distutils to customize how the dispatch + to gcc/nvcc works. + + If you subclass UnixCCompiler, it's not trivial to get your subclass + injected in, and still have the right customizations (i.e. + distutils.sysconfig.customize_compiler) run on it. So instead of going + the OO route, I have this. Note, it's kindof like a wierd functional + subclassing going on.""" + + # tell the compiler it can processes .cu + self.src_extensions.append('.cu') + + # save references to the default compiler_so and _comple methods + default_compiler_so = self.compiler_so + super = self._compile + + # now redefine the _compile method. This gets executed for each + # object but distutils doesn't have the ability to change compilers + # based on source extension: we add it. + def _compile(obj, src, ext, cc_args, extra_postargs, pp_opts): + if os.path.splitext(src)[1] == '.cu': + # use the cuda for .cu files + self.set_executable('compiler_so', CUDA['nvcc']) + # use only a subset of the extra_postargs, which are 1-1 translated + # from the extra_compile_args in the Extension class + postargs = extra_postargs['nvcc'] + else: + postargs = extra_postargs['gcc'] + + super(obj, src, ext, cc_args, postargs, pp_opts) + # reset the default compiler_so, which we might have changed for cuda + self.compiler_so = default_compiler_so + + # inject our redefined _compile method into the class + self._compile = _compile + + +# run the customize_compiler +class custom_build_ext(build_ext): + def build_extensions(self): + customize_compiler_for_nvcc(self.compiler) + build_ext.build_extensions(self) + + +ext_modules = [ + # Extension( + # "utils.cython_bbox", + # ["utils/bbox.pyx"], + # extra_compile_args={'gcc': ["-Wno-cpp", "-Wno-unused-function"]}, + # include_dirs = [numpy_include] + # ), + # Extension( + # "nms.cpu_nms", + # ["nms/cpu_nms.pyx"], + # extra_compile_args={'gcc': ["-Wno-cpp", "-Wno-unused-function"]}, + # include_dirs = [numpy_include] + # ), + # + # Extension( + # "rotation.rotate_cython_nms", + # ["rotation/rotate_cython_nms.pyx"], + # extra_compile_args={'gcc': ["-Wno-cpp", "-Wno-unused-function"]}, + # include_dirs = [numpy_include] + # ), + # + # Extension( + # "rotation.rotate_circle_nms", + # ["rotation/rotate_circle_nms.pyx"], + # extra_compile_args={'gcc': ["-Wno-cpp", "-Wno-unused-function"]}, + # include_dirs = [numpy_include] + # ), + # + # Extension('nms.gpu_nms', + # ['nms/nms_kernel.cu', 'nms/gpu_nms.pyx'], + # library_dirs=[CUDA['lib64']], + # libraries=['cudart'], + # language='c++', + # runtime_library_dirs=[CUDA['lib64']], + # # this syntax is specific to this build system + # # we're only going to use certain compiler args with nvcc and not with + # # gcc the implementation of this trick is in customize_compiler() below + # extra_compile_args={'gcc': ["-Wno-unused-function"], + # 'nvcc': ['-arch=sm_35', + # '--ptxas-options=-v', + # '-c', + # '--compiler-options', + # "'-fPIC'"]}, + # include_dirs = [numpy_include, CUDA['include']] + # ), + # Extension('rotation.rotate_gpu_nms', + # ['rotation/rotate_nms_kernel.cu', 'rotation/rotate_gpu_nms.pyx'], + # library_dirs=[CUDA['lib64']], + # libraries=['cudart'], + # language='c++', + # runtime_library_dirs=[CUDA['lib64']], + # # this syntax is specific to this build system + # # we're only going to use certain compiler args with nvcc anrbd not with + # # gcc the implementation of this trick is in customize_compiler() below + # extra_compile_args={'gcc': ["-Wno-unused-function"], + # 'nvcc': ['-arch=sm_35', + # '--ptxas-options=-v', + # '-c', + # '--compiler-options', + # "'-fPIC'"]}, + # include_dirs = [numpy_include, CUDA['include']] + # ), + Extension('rotation.rbbox_overlaps', + ['rotation/rbbox_overlaps_kernel.cu', 'rotation/rbbox_overlaps.pyx'], + library_dirs=[CUDA['lib64']], + libraries=['cudart'], + language='c++', + runtime_library_dirs=[CUDA['lib64']], + # this syntax is specific to this build system + # we're only going to use certain compiler args with nvcc and not with + # gcc the implementation of this trick is in customize_compiler() below + extra_compile_args={'gcc': ["-Wno-unused-function"], + 'nvcc': ['-arch=sm_35', + '--ptxas-options=-v', + '-c', + '--compiler-options', + "'-fPIC'"]}, + include_dirs = [numpy_include, CUDA['include']] + ), + # Extension('rotation.rotate_polygon_nms', + # ['rotation/rotate_polygon_nms_kernel.cu', 'rotation/rotate_polygon_nms.pyx'], + # library_dirs=[CUDA['lib64']], + # libraries=['cudart'], + # language='c++', + # runtime_library_dirs=[CUDA['lib64']], + # # this syntax is specific to this build system + # # we're only going to use certain compiler args with nvcc and not with + # # gcc the implementation of this trick is in customize_compiler() below + # extra_compile_args={'gcc': ["-Wno-unused-function"], + # 'nvcc': ['-arch=sm_35', + # '--ptxas-options=-v', + # '-c', + # '--compiler-options', + # "'-fPIC'"]}, + # include_dirs = [numpy_include, CUDA['include']] + # ), + # + # Extension( + # 'pycocotools._mask', + # sources=['pycocotools/maskApi.c', 'pycocotools/_mask.pyx'], + # include_dirs = [numpy_include, 'pycocotools'], + # extra_compile_args={ + # 'gcc': ['-Wno-cpp', '-Wno-unused-function', '-std=c99']}, + # ), +] + +setup( + name='fast_rcnn', + ext_modules=ext_modules, + # inject our custom trigger + cmdclass={'build_ext': custom_build_ext}, +) diff --git a/libs/val_libs/__init__.py b/libs/val_libs/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/libs/val_libs/voc_eval.py b/libs/val_libs/voc_eval.py new file mode 100644 index 0000000..38f72ad --- /dev/null +++ b/libs/val_libs/voc_eval.py @@ -0,0 +1,266 @@ +# -------------------------------------------------------- +# Fast/er R-CNN +# Licensed under The MIT License [see LICENSE for details] +# Written by Bharath Hariharan +# -------------------------------------------------------- +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import xml.etree.ElementTree as ET +import os +import pickle +import numpy as np + +from libs.label_name_dict.label_dict import NAME_LABEL_MAP +from libs.configs import cfgs +from help_utils.tools import * + +def write_voc_results_file(all_boxes, test_imgid_list, det_save_dir): + ''' + + :param all_boxes: is a list. each item reprensent the detections of a img. + the detections is a array. shape is [-1, 6]. [category, score, xmin, ymin, xmax, ymax] + Note that: if none detections in this img. that the detetions is : [] + + :param test_imgid_list: + :param det_save_path: + :return: + ''' + for cls, cls_id in NAME_LABEL_MAP.items(): + if cls == 'back_ground': + continue + print("Writing {} VOC resutls file".format(cls)) + + mkdir(det_save_dir) + det_save_path = os.path.join(det_save_dir, "det_"+cls+".txt") + with open(det_save_path, 'wt') as f: + for index, img_name in enumerate(test_imgid_list): + this_img_detections = all_boxes[index] + + this_cls_detections = this_img_detections[this_img_detections[:, 0]==cls_id] + if this_cls_detections.shape[0] == 0: + continue # this cls has none detections in this img + for a_det in this_cls_detections: + f.write('{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'. + format(img_name, a_det[1], + a_det[2], a_det[3], + a_det[4], a_det[5])) # that is [img_name, score, xmin, ymin, xmax, ymax] + + +def parse_rec(filename): + """ Parse a PASCAL VOC xml file """ + tree = ET.parse(filename) + objects = [] + for obj in tree.findall('object'): + obj_struct = {} + obj_struct['name'] = obj.find('name').text + obj_struct['pose'] = obj.find('pose').text + obj_struct['truncated'] = int(obj.find('truncated').text) + obj_struct['difficult'] = int(obj.find('difficult').text) + # obj_struct['difficult'] = int(0) + bbox = obj.find('bndbox') + obj_struct['bbox'] = [int(bbox.find('xmin').text), + int(bbox.find('ymin').text), + int(bbox.find('xmax').text), + int(bbox.find('ymax').text)] + objects.append(obj_struct) + + return objects + + +def voc_ap(rec, prec, use_07_metric=False): + """ ap = voc_ap(rec, prec, [use_07_metric]) + Compute VOC AP given precision and recall. + If use_07_metric is true, uses the + VOC 07 11 point method (default:False). + """ + if use_07_metric: + # 11 point metric + ap = 0. + for t in np.arange(0., 1.1, 0.1): + if np.sum(rec >= t) == 0: + p = 0 + else: + p = np.max(prec[rec >= t]) + ap = ap + p / 11. + else: + # correct AP calculation + # first append sentinel values at the end + mrec = np.concatenate(([0.], rec, [1.])) + mpre = np.concatenate(([0.], prec, [0.])) + + # compute the precision envelope + for i in range(mpre.size - 1, 0, -1): + mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i]) + + # to calculate area under PR curve, look for points + # where X axis (recall) changes value + i = np.where(mrec[1:] != mrec[:-1])[0] + + # and sum (\Delta recall) * prec + ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) + return ap + + +def voc_eval(detpath, annopath, test_imgid_list, cls_name, ovthresh=0.5, + use_07_metric=False, use_diff=False): + ''' + + :param detpath: + :param annopath: + :param test_imgid_list: it 's a list that contains the img_name of test_imgs + :param cls_name: + :param ovthresh: + :param use_07_metric: + :param use_diff: + :return: + ''' + # 1. parse xml to get gtboxes + + # read list of images + imagenames = test_imgid_list + + recs = {} + for i, imagename in enumerate(imagenames): + recs[imagename] = parse_rec(os.path.join(annopath, imagename+'.xml')) + # if i % 100 == 0: + # print('Reading annotation for {:d}/{:d}'.format( + # i + 1, len(imagenames))) + + # 2. get gtboxes for this class. + class_recs = {} + num_pos = 0 + # if cls_name == 'person': + # print ("aaa") + for imagename in imagenames: + R = [obj for obj in recs[imagename] if obj['name'] == cls_name] + bbox = np.array([x['bbox'] for x in R]) + if use_diff: + difficult = np.array([False for x in R]).astype(np.bool) + else: + difficult = np.array([x['difficult'] for x in R]).astype(np.bool) + det = [False] * len(R) + num_pos = num_pos + sum(~difficult) # ignored the diffcult boxes + class_recs[imagename] = {'bbox': bbox, + 'difficult': difficult, + 'det': det} # det means that gtboxes has already been detected + + # 3. read the detection file + detfile = os.path.join(detpath, "det_"+cls_name+".txt") + with open(detfile, 'r') as f: + lines = f.readlines() + + # for a line. that is [img_name, confidence, xmin, ymin, xmax, ymax] + splitlines = [x.strip().split(' ') for x in lines] # a list that include a list + image_ids = [x[0] for x in splitlines] # img_id is img_name + confidence = np.array([float(x[1]) for x in splitlines]) + BB = np.array([[float(z) for z in x[2:]] for x in splitlines]) + + nd = len(image_ids) # num of detections. That, a line is a det_box. + tp = np.zeros(nd) + fp = np.zeros(nd) + + if BB.shape[0] > 0: + # sort by confidence + sorted_ind = np.argsort(-confidence) + sorted_scores = np.sort(-confidence) + BB = BB[sorted_ind, :] + image_ids = [image_ids[x] for x in sorted_ind] #reorder the img_name + + # go down dets and mark TPs and FPs + for d in range(nd): + R = class_recs[image_ids[d]] # img_id is img_name + bb = BB[d, :].astype(float) + ovmax = -np.inf + BBGT = R['bbox'].astype(float) + + if BBGT.size > 0: + # compute overlaps + # intersection + ixmin = np.maximum(BBGT[:, 0], bb[0]) + iymin = np.maximum(BBGT[:, 1], bb[1]) + ixmax = np.minimum(BBGT[:, 2], bb[2]) + iymax = np.minimum(BBGT[:, 3], bb[3]) + iw = np.maximum(ixmax - ixmin + 1., 0.) + ih = np.maximum(iymax - iymin + 1., 0.) + inters = iw * ih + + # union + uni = ((bb[2] - bb[0] + 1.) * (bb[3] - bb[1] + 1.) + + (BBGT[:, 2] - BBGT[:, 0] + 1.) * + (BBGT[:, 3] - BBGT[:, 1] + 1.) - inters) + + overlaps = inters / uni + ovmax = np.max(overlaps) + jmax = np.argmax(overlaps) + + if ovmax > ovthresh: + if not R['difficult'][jmax]: + if not R['det'][jmax]: + tp[d] = 1. + R['det'][jmax] = 1 + else: + fp[d] = 1. + else: + fp[d] = 1. + + # 4. get recall, precison and AP + fp = np.cumsum(fp) + tp = np.cumsum(tp) + rec = tp / float(num_pos) + # avoid divide by zero in case the first detection matches a difficult + # ground truth + prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps) + ap = voc_ap(rec, prec, use_07_metric=cfgs.USE_07_METRIC) + + return rec, prec, ap + + +def do_python_eval(test_imgid_list, test_annotation_path): + AP_list = [] + # import matplotlib.pyplot as plt + # import matplotlib.colors as colors + # color_list = colors.cnames.keys()[::6] + + for cls, index in NAME_LABEL_MAP.items(): + if cls == 'back_ground': + continue + recall, precision, AP = voc_eval(detpath=os.path.join(cfgs.EVALUATE_DIR, cfgs.VERSION), + test_imgid_list=test_imgid_list, + cls_name=cls, + annopath=test_annotation_path, + ovthresh=cfgs.EVAL_THRESHOLD, + use_07_metric=cfgs.USE_07_METRIC) + AP_list += [AP] + print("cls : {}|| Recall: {} || Precison: {}|| AP: {}".format(cls, recall[-1], precision[-1], AP)) + # plt.plot(recall, precision, label=cls, color=color_list[index]) + # plt.legend(loc='upper right') + print(10*"__") + # plt.show() + # plt.savefig(cfgs.VERSION+'.jpg') + print("mAP is : {}".format(np.mean(AP_list))) + + +def voc_evaluate_detections(all_boxes, test_annotation_path, test_imgid_list): + ''' + + :param all_boxes: is a list. each item reprensent the detections of a img. + + The detections is a array. shape is [-1, 6]. [category, score, xmin, ymin, xmax, ymax] + Note that: if none detections in this img. that the detetions is : [] + :return: + ''' + test_imgid_list = [item.split('.')[0] for item in test_imgid_list] + + write_voc_results_file(all_boxes, test_imgid_list=test_imgid_list, + det_save_dir=os.path.join(cfgs.EVALUATE_DIR, cfgs.VERSION)) + do_python_eval(test_imgid_list, test_annotation_path=test_annotation_path) + + + + + + + + diff --git a/output/trained_weights/README.md b/output/trained_weights/README.md new file mode 100644 index 0000000..8f910a3 --- /dev/null +++ b/output/trained_weights/README.md @@ -0,0 +1 @@ +Please download [trained model](https://github.com/DetectionTeamUCAS/Models/tree/master/FPN_Tensorflow) by this project, then put it here. \ No newline at end of file diff --git a/scalars.png b/scalars.png new file mode 100644 index 0000000000000000000000000000000000000000..24e19cb88be9d455f6a8a8feef53237e53f9d72a GIT binary patch literal 131550 zcmb@tRajeXw*?y9DFupKp;(au#oZ}Rpm=e2cXx*tEiS>G;$FNs!Ci{G2A7ll@qho` zH|OF!&sjGiAuH=$^PMv07;{A^DM(?U5}^VB01W9b;>rL3;wJzAP7E0q`iT}ZF+B7I z&Pi1ID>5?j(uU$X^ev&Ygod+g_`XDo4%huS6UbB;Q0}w>(=TLai zP-rKxSF^&;~0d)HaJ#{Gx zE%*NS;$T=B=9UhRx9dKKW&`|_rIf70)!)Fy_VcHwUK>Bg8}KXdo<8}toUI3~3Gy6} z*IVCS^|Yox3Oy~VXA{@YGcowS$dU(o@6>$qc|PxkWP9B9(2{$f?E`ZsIyR=>2vl#4 z7{&3=3PyHM7OuQGt>eFGd?P6E5&0MS@afYK{^>Wv{RfMCzFG(B|60ISeEBqkK-YI= z(K?q-PPGJSIP!t~c_&O329W)H-wCrZwRV4L*U8@`>RV~ob8^Z*p%2qdn~gK;eV^U2 zYK+uvmQDQe>B-LbvZuiRbWYFbYQ$#kaW!)W;D5Ftcns>=6@0kM&Ul)@R}Y?Av(V>` z{MYtbkxgr!`}qY9vSJ>U;ZwNa;^4090kVgEO(7QcgHMSm%e4<#FSlK0ZU#L9Z20KW$$l21(@_pdd1_vJ|DP?+^Psjrfv6ba?GCDkqTz9^{;YTSnguRCTNogJ zaOvrJIMHOgof;S9Mx7@N$EW=jr$(X>#1u$f_#5xwZs_RVf#I#0?YqkSNzv+ z%ED}Bq+H9>JT_YwIzR8i=PFq4t~-{02KK(Em_awLcQ*QJi@^p z|KHG+`$V*Dv$C>PZndx`z=tHpj_*#4^SU-2$*D+5m(w+;rt`D?*-V#a$q)tdMYnku zX(^5nrH8SWot{-$GBLDClh&9|Cs|vWa<67kPu&*>iLOqqw(KAte%)-jITn~EZ3zyd zOcQ8}iFkEf=CVf+a23?*dL;BRd9ri-FrKm6*q%GK?>x(KD)3fxuNSmLoS{%uYcP=e znf99f)O-JNgLH}f*uA9>I5)2}*YqHMv!NreP&BnLaj_SnR9xS;P}g1n-mjju5!!(5ihvvf(?TbN9JA8v}6|GaI z04$l+)7+_+H|M#aRyVSh)x~3%`cwXPEA=ui9|k+JgbCFx$JPGQ5ihpm>)Dm>|1MC6 zgV|JGz)H+%YF}V9MxLoPuBA=as^>Zsr`7Fb)h*A#?=UM7l}<`52q*Vs7H2Gz$;H3k z!R`U9GYpV2QVDuD+d^YjyhIFpwJM$W8P1>NArsg4#lF>PE*6oY+=b6|CX;)aRAy<2 z74pH^CGB!aBgsS2bIP=G*ot9w@O6LL^>pOehrG<^Z09O_Xcyzi7B}FP8V}9g4o_%K zH!?9s?;}W2Fb4_*X=qJ}-L2b@K__nOgbmw1Jxm=Kt0JSk|kYWZa zG&Z#QXZbM6zwt(AfUMK0^KDS`g!w4TK?GqdRs|C$8Au@dA%vEyo&N&o7?sYp~-QCUc8ZgBn)`-%d-40uSYT@m?KmG~N3Nyu=F~z?&p+etbDger zI!Te1&lb{z4I_}Oyj)Vz`W|^-?SWz{rP6QaPu-oXuD>0cFV=uI#CuNgie}!CdtW^X z(6+_yWVWUqkhoxAELt;jr(GX;)e#=^-SFLztt_nzI9$~^cQ00Ztvwe`wQ67AFe$X1 zU~;rvd$^sf%x`5D+@;f!kBZS=)5M%20;(7U{Y;rj`@7dcdrYlyj+j$-CM<&7+g

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eval_json): + eval_gt = COCO(eval_json) + + eval_dt = eval_gt.loadRes(detected_json) + cocoEval = COCOeval(eval_gt, eval_dt, iouType='bbox') + + # cocoEval.params.imgIds = eval_gt.getImgIds() + cocoEval.evaluate() + cocoEval.accumulate() + cocoEval.summarize() + + +detected_json = '/home/yangxue/isilon/yangxue/code/ADAS/output/yangxue/fpn/fpn.res50.coco.roialign.2x.detectron.new.concat/eval_dump/epoch-2.coco' +eval_gt = '/unsullied/sharefs/_research_detection/GeneralDetection/COCO/data/MSCOCO/instances_minival2014.json' +cocoval(detected_json, eval_gt) \ No newline at end of file diff --git a/tools/demo.py b/tools/demo.py new file mode 100644 index 0000000..4eb19c2 --- /dev/null +++ b/tools/demo.py @@ -0,0 +1,300 @@ +# -*- coding:utf-8 -*- + +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import os, sys + +sys.path.append("../") +import cv2 +import numpy as np +from timeit import default_timer as timer +import argparse +import tensorflow as tf + +from data.io.image_preprocess import short_side_resize_for_inference_data +from libs.networks import build_whole_network +from help_utils.tools import * +from libs.box_utils import draw_box_in_img +from libs.label_name_dict.label_dict import * +from help_utils import tools +from libs.box_utils import nms + + +def get_file_paths_recursive(folder=None, file_ext=None): + """ Get the absolute path of all files in given folder recursively + :param folder: + :param file_ext: + :return: + """ + file_list = [] + if folder is None: + return file_list + + for dir_path, dir_names, file_names in os.walk(folder): + for file_name in file_names: + if file_ext is None: + file_list.append(os.path.join(dir_path, file_name)) + continue + if file_name.endswith(file_ext): + file_list.append(os.path.join(dir_path, file_name)) + return file_list + + +def inference(det_net, file_paths, des_folder, h_len, w_len, h_overlap, w_overlap, save_res=False): + + if save_res: + assert cfgs.SHOW_SCORE_THRSHOLD >= 0.5, \ + 'please set score threshold (example: SHOW_SCORE_THRSHOLD = 0.5) in cfgs.py' + + else: + assert cfgs.SHOW_SCORE_THRSHOLD <= 0.005, \ + 'please set score threshold (example: SHOW_SCORE_THRSHOLD = 0.00) in cfgs.py' + + # 1. preprocess img + img_plac = tf.placeholder(dtype=tf.uint8, shape=[None, None, 3]) + img_batch = tf.cast(img_plac, tf.float32) + if cfgs.NET_NAME in ['resnet101_v1d']: + img_batch = (img_batch / 255 - tf.constant(cfgs.PIXEL_MEAN_)) / tf.constant(cfgs.PIXEL_STD) + else: + img_batch = img_batch - tf.constant(cfgs.PIXEL_MEAN) + + img_batch = tf.expand_dims(img_batch, axis=0) + img_batch = short_side_resize_for_inference_data(img_tensor=img_batch, + target_shortside_len=cfgs.IMG_SHORT_SIDE_LEN, + is_resize=False) + + det_boxes_h, det_scores_h, det_category_h = det_net.build_whole_detection_network(input_img_batch=img_batch, + gtboxes_batch=None) + + init_op = tf.group( + tf.global_variables_initializer(), + tf.local_variables_initializer() + ) + + restorer, restore_ckpt = det_net.get_restorer() + + config = tf.ConfigProto() + config.gpu_options.allow_growth = True + + with tf.Session(config=config) as sess: + sess.run(init_op) + if not restorer is None: + restorer.restore(sess, restore_ckpt) + print('restore model') + + if not os.path.exists('./tmp.txt'): + fw = open('./tmp.txt', 'w') + fw.close() + + fr = open('./tmp.txt', 'r') + pass_img = fr.readlines() + fr.close() + + for count, img_path in enumerate(file_paths): + fw = open('./tmp.txt', 'a+') + if img_path + '\n' in pass_img: + continue + start = timer() + img = cv2.imread(img_path) + + box_res = [] + label_res = [] + score_res = [] + + imgH = img.shape[0] + imgW = img.shape[1] + + if imgH < h_len: + temp = np.zeros([h_len, imgW, 3], np.float32) + temp[0:imgH, :, :] = img + img = temp + imgH = h_len + + if imgW < w_len: + temp = np.zeros([imgH, w_len, 3], np.float32) + temp[:, 0:imgW, :] = img + img = temp + imgW = w_len + + for hh in range(0, imgH, h_len - h_overlap): + if imgH - hh - 1 < h_len: + hh_ = imgH - h_len + else: + hh_ = hh + for ww in range(0, imgW, w_len - w_overlap): + if imgW - ww - 1 < w_len: + ww_ = imgW - w_len + else: + ww_ = ww + src_img = img[hh_:(hh_ + h_len), ww_:(ww_ + w_len), :] + + det_boxes_h_, det_scores_h_, det_category_h_ = \ + sess.run( + [det_boxes_h, det_scores_h, det_category_h], + feed_dict={img_plac: src_img[:, :, ::-1]} + ) + + if len(det_boxes_h_) > 0: + for ii in range(len(det_boxes_h_)): + box = det_boxes_h_[ii] + box[0] = box[0] + ww_ + box[1] = box[1] + hh_ + box[2] = box[2] + ww_ + box[3] = box[3] + hh_ + box_res.append(box) + label_res.append(det_category_h_[ii]) + score_res.append(det_scores_h_[ii]) + + box_res = np.array(box_res) + label_res = np.array(label_res) + score_res = np.array(score_res) + + box_res_, label_res_, score_res_ = [], [], [] + + h_threshold = {'roundabout': 0.35, 'tennis-court': 0.35, 'swimming-pool': 0.4, 'storage-tank': 0.3, + 'soccer-ball-field': 0.3, 'small-vehicle': 0.4, 'ship': 0.35, 'plane': 0.35, + 'large-vehicle': 0.4, 'helicopter': 0.4, 'harbor': 0.3, 'ground-track-field': 0.4, + 'bridge': 0.3, 'basketball-court': 0.4, 'baseball-diamond': 0.3} + + for sub_class in range(1, cfgs.CLASS_NUM + 1): + index = np.where(label_res == sub_class)[0] + if len(index) == 0: + continue + tmp_boxes_h = box_res[index] + tmp_label_h = label_res[index] + tmp_score_h = score_res[index] + + tmp_boxes_h = np.array(tmp_boxes_h) + tmp = np.zeros([tmp_boxes_h.shape[0], tmp_boxes_h.shape[1] + 1]) + tmp[:, 0:-1] = tmp_boxes_h + tmp[:, -1] = np.array(tmp_score_h) + + inx = nms.py_cpu_nms(dets=np.array(tmp, np.float32), + thresh=h_threshold[LABEl_NAME_MAP[sub_class]], + max_output_size=500) + + box_res_.extend(np.array(tmp_boxes_h)[inx]) + score_res_.extend(np.array(tmp_score_h)[inx]) + label_res_.extend(np.array(tmp_label_h)[inx]) + + time_elapsed = timer() - start + + if save_res: + + scores = np.array(score_res_) + labels = np.array(label_res_) + boxes = np.array(box_res_) + valid_show = scores > cfgs.SHOW_SCORE_THRSHOLD + scores = scores[valid_show] + boxes = boxes[valid_show] + labels = labels[valid_show] + + det_detections_h = draw_box_in_img.draw_boxes_with_label_and_scores(np.array(img, np.float32), + boxes=np.array(boxes), + labels=np.array(labels), + scores=np.array(scores), + in_graph=False) + + save_dir = os.path.join(des_folder, cfgs.VERSION) + tools.mkdir(save_dir) + cv2.imwrite(save_dir + '/' + img_path.split('/')[-1].split('.')[0] + '_h.jpg', + det_detections_h) + + view_bar('{} cost {}s'.format(img_path.split('/')[-1].split('.')[0], + time_elapsed), count + 1, len(file_paths)) + + else: + # eval txt + CLASS_DOTA = NAME_LABEL_MAP.keys() + + # Task2 + write_handle_h = {} + txt_dir_h = os.path.join('txt_output', cfgs.VERSION + '_h') + tools.mkdir(txt_dir_h) + for sub_class in CLASS_DOTA: + if sub_class == 'back_ground': + continue + write_handle_h[sub_class] = open(os.path.join(txt_dir_h, 'Task2_%s.txt' % sub_class), 'a+') + + for i, hbox in enumerate(box_res_): + command = '%s %.3f %.1f %.1f %.1f %.1f\n' % (img_path.split('/')[-1].split('.')[0], + score_res_[i], + hbox[0], hbox[1], hbox[2], hbox[3]) + write_handle_h[LABEl_NAME_MAP[label_res_[i]]].write(command) + + for sub_class in CLASS_DOTA: + if sub_class == 'back_ground': + continue + write_handle_h[sub_class].close() + + view_bar('%s cost %.3fs' % (img_path.split('/')[-1].split('.')[0], + time_elapsed), count + 1, len(file_paths)) + fw.write('{}\n'.format(img_path)) + fw.close() + os.remove('./tmp.txt') + + +def parse_args(): + """ + Parse input arguments + """ + parser = argparse.ArgumentParser(description='Train a Fast R-CNN network') + parser.add_argument('--src_folder', dest='src_folder', + help='images path', + default=None, type=str) + parser.add_argument('--des_folder', dest='des_folder', + help='output path', + default=None, type=str) + parser.add_argument('--h_len', dest='h_len', + help='image height', + default=800, type=int) + parser.add_argument('--w_len', dest='w_len', + help='image width', + default=800, type=int) + parser.add_argument('--h_overlap', dest='h_overlap', + help='height overlap', + default=200, type=int) + parser.add_argument('--w_overlap', dest='w_overlap', + help='width overlap', + default=200, type=int) + parser.add_argument('--image_ext', dest='image_ext', + help='image format', + default='.png', type=str) + parser.add_argument('--save_res', dest='save_res', + help='save results', + default=True, type=bool) + parser.add_argument('--gpu', dest='gpu', + help='gpu index', + default='0', type=str) + + if len(sys.argv) == 1: + parser.print_help() + sys.exit(1) + + args = parser.parse_args() + return args + + +if __name__ == "__main__": + # args = parse_args() + # print('Called with args:') + # print(args) + + # os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu + os.environ["CUDA_VISIBLE_DEVICES"] = '0' + + # file_paths = get_file_paths_recursive(args.src_folder, args.image_ext) + # + # det_net = build_whole_network.DetectionNetwork(base_network_name=cfgs.NET_NAME, + # is_training=False) + # + # inference(det_net, file_paths, args.des_folder, args.h_len, args.w_len, + # args.h_overlap, args.w_overlap, args.save_res) + + file_paths = get_file_paths_recursive('/unsullied/sharefs/yangxue/isilon/yangxue/data/DOTA/test/images', '.png') + det_net = build_whole_network.DetectionNetwork(base_network_name=cfgs.NET_NAME, + is_training=False) + inference(det_net, file_paths, './demos/', 800, 800, + 200, 200, False) diff --git a/tools/eval.py b/tools/eval.py new file mode 100644 index 0000000..02c679b --- /dev/null +++ b/tools/eval.py @@ -0,0 +1,211 @@ +# -*- coding:utf-8 -*- + +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import os, sys +import tensorflow as tf +import time +import cv2 +import pickle +import numpy as np +sys.path.append("../") + +from data.io.image_preprocess import short_side_resize_for_inference_data +from libs.configs import cfgs +from libs.networks import build_whole_network +from libs.val_libs import voc_eval +from libs.box_utils import draw_box_in_img +import argparse +from help_utils import tools + + +def eval_with_plac(det_net, real_test_imgname_list, img_root, draw_imgs=False): + + # 1. preprocess img + img_plac = tf.placeholder(dtype=tf.uint8, shape=[None, None, 3]) # is RGB. not BGR + img_batch = tf.cast(img_plac, tf.float32) + + img_batch = short_side_resize_for_inference_data(img_tensor=img_batch, + target_shortside_len=cfgs.IMG_SHORT_SIDE_LEN, + length_limitation=cfgs.IMG_MAX_LENGTH) + if cfgs.NET_NAME in ['resnet101_v1d', 'resnet50_v1d']: + img_batch = (img_batch / 255 - tf.constant(cfgs.PIXEL_MEAN_)) / tf.constant(cfgs.PIXEL_STD) + else: + img_batch = img_batch - tf.constant(cfgs.PIXEL_MEAN) + + # img_batch = (img_batch - tf.constant(cfgs.PIXEL_MEAN)) / (tf.constant(cfgs.PIXEL_STD)*255) + img_batch = tf.expand_dims(img_batch, axis=0) + + detection_boxes, detection_scores, detection_category = det_net.build_whole_detection_network( + input_img_batch=img_batch, + gtboxes_batch=None) + + init_op = tf.group( + tf.global_variables_initializer(), + tf.local_variables_initializer() + ) + + restorer, restore_ckpt = det_net.get_restorer() + + config = tf.ConfigProto() + config.gpu_options.allow_growth = True + + with tf.Session(config=config) as sess: + sess.run(init_op) + if not restorer is None: + restorer.restore(sess, restore_ckpt) + print('restore model') + + all_boxes = [] + for i, a_img_name in enumerate(real_test_imgname_list): + + raw_img = cv2.imread(os.path.join(img_root, a_img_name)) + raw_h, raw_w = raw_img.shape[0], raw_img.shape[1] + + start = time.time() + resized_img, detected_boxes, detected_scores, detected_categories = \ + sess.run( + [img_batch, detection_boxes, detection_scores, detection_category], + feed_dict={img_plac: raw_img[:, :, ::-1]} # cv is BGR. But need RGB + ) + end = time.time() + # print("{} cost time : {} ".format(img_name, (end - start))) + if draw_imgs: + show_indices = detected_scores >= cfgs.SHOW_SCORE_THRSHOLD + show_scores = detected_scores[show_indices] + show_boxes = detected_boxes[show_indices] + show_categories = detected_categories[show_indices] + + draw_img = np.squeeze(resized_img, 0) + if cfgs.NET_NAME in ['resnet101_v1d', 'resnet50_v1d']: + draw_img = (draw_img * np.array(cfgs.PIXEL_STD) + np.array(cfgs.PIXEL_MEAN_)) * 255 + else: + draw_img = draw_img + np.array(cfgs.PIXEL_MEAN) + + # draw_img = draw_img * (np.array(cfgs.PIXEL_STD)*255) + np.array(cfgs.PIXEL_MEAN) + + final_detections = draw_box_in_img.draw_boxes_with_label_and_scores(draw_img, + boxes=show_boxes, + labels=show_categories, + scores=show_scores, + in_graph=False) + if not os.path.exists(cfgs.TEST_SAVE_PATH): + os.makedirs(cfgs.TEST_SAVE_PATH) + + cv2.imwrite(cfgs.TEST_SAVE_PATH + '/' + a_img_name + '.jpg', + final_detections[:, :, ::-1]) + + xmin, ymin, xmax, ymax = detected_boxes[:, 0], detected_boxes[:, 1], \ + detected_boxes[:, 2], detected_boxes[:, 3] + + resized_h, resized_w = resized_img.shape[1], resized_img.shape[2] + + xmin = xmin * raw_w / resized_w + xmax = xmax * raw_w / resized_w + + ymin = ymin * raw_h / resized_h + ymax = ymax * raw_h / resized_h + + boxes = np.transpose(np.stack([xmin, ymin, xmax, ymax])) + dets = np.hstack((detected_categories.reshape(-1, 1), + detected_scores.reshape(-1, 1), + boxes)) + all_boxes.append(dets) + + tools.view_bar('%s image cost %.3fs' % (a_img_name, (end - start)), i + 1, len(real_test_imgname_list)) + + save_dir = os.path.join(cfgs.EVALUATE_DIR, cfgs.VERSION) + if not os.path.exists(save_dir): + os.makedirs(save_dir) + fw1 = open(os.path.join(save_dir, 'detections.pkl'), 'wb') + pickle.dump(all_boxes, fw1) + return all_boxes + + +def eval(num_imgs, eval_dir, annotation_dir, showbox): + + # with open('/home/yjr/DataSet/VOC/VOC_test/VOC2007/ImageSets/Main/aeroplane_test.txt') as f: + # all_lines = f.readlines() + # test_imgname_list = [a_line.split()[0].strip() for a_line in all_lines] + + test_imgname_list = [item for item in os.listdir(eval_dir) + if item.endswith(('.jpg', 'jpeg', '.png', '.tif', '.tiff'))] + if num_imgs == np.inf: + real_test_imgname_list = test_imgname_list + else: + real_test_imgname_list = test_imgname_list[: num_imgs] + + faster_rcnn = build_whole_network.DetectionNetwork(base_network_name=cfgs.NET_NAME, + is_training=False) + all_boxes = eval_with_plac(det_net=faster_rcnn, real_test_imgname_list=real_test_imgname_list, + img_root=eval_dir, + draw_imgs=showbox) + + save_dir = os.path.join(cfgs.EVALUATE_DIR, cfgs.VERSION) + if not os.path.exists(save_dir): + os.makedirs(save_dir) + with open(os.path.join(save_dir, 'detections.pkl'), 'rb') as f: + all_boxes = pickle.load(f) + + print(len(all_boxes)) + + voc_eval.voc_evaluate_detections(all_boxes=all_boxes, + test_annotation_path=annotation_dir, + test_imgid_list=real_test_imgname_list) + + +def parse_args(): + + # /unsullied/sharefs/yangxue/isilon/yangxue/data/BDD100K/BDD100K_VOC/bdd100k_val + # /unsullied/sharefs/yangxue/isilon/yangxue/data/VOC2007/test/VOCdevkit/VOC2007 + + parser = argparse.ArgumentParser('evaluate the result with Pascal2007 stdand') + + parser.add_argument('--eval_imgs', dest='eval_imgs', + help='evaluate imgs dir ', + default='/unsullied/sharefs/yangxue/isilon/yangxue/data/VOC2007/VOCdevkit/VOC2007/JPEGImages', type=str) + parser.add_argument('--annotation_dir', dest='test_annotation_dir', + help='the dir save annotations', + default='/unsullied/sharefs/yangxue/isilon/yangxue/data/VOC2007/VOCdevkit/VOC2007/Annotations', type=str) + parser.add_argument('--showbox', dest='showbox', + help='whether show detecion results when evaluation', + default=True, type=bool) + parser.add_argument('--GPU', dest='GPU', + help='gpu id', + default='0', type=str) + parser.add_argument('--eval_num', dest='eval_num', + help='the num of eval imgs', + default=np.inf, type=int) + args = parser.parse_args() + return args + + +if __name__ == '__main__': + + args = parse_args() + print(20*"--") + print(args) + print(20*"--") + os.environ["CUDA_VISIBLE_DEVICES"] = args.GPU + eval(np.inf, # use np.inf to test all the imgs. use 10 to test 10 imgs. + eval_dir=args.eval_imgs, + annotation_dir=args.test_annotation_dir, + showbox=args.showbox) + + + + + + + + + + + + + + + + diff --git a/tools/eval_bdd.py b/tools/eval_bdd.py new file mode 100644 index 0000000..7deb4ba --- /dev/null +++ b/tools/eval_bdd.py @@ -0,0 +1,211 @@ +# -*- coding:utf-8 -*- + +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import os, sys +import tensorflow as tf +import time +import cv2 +import pickle +import numpy as np +sys.path.append("../") + +from data.io.image_preprocess import short_side_resize_for_inference_data +from libs.configs import cfgs +from libs.networks import build_whole_network +from libs.val_libs import voc_eval +from libs.box_utils import draw_box_in_img +import argparse +from help_utils import tools + + +def eval_with_plac(det_net, real_test_imgname_list, img_root, draw_imgs=False): + + # 1. preprocess img + img_plac = tf.placeholder(dtype=tf.uint8, shape=[None, None, 3]) # is RGB. not BGR + img_batch = tf.cast(img_plac, tf.float32) + + img_batch = short_side_resize_for_inference_data(img_tensor=img_batch, + target_shortside_len=cfgs.IMG_SHORT_SIDE_LEN, + length_limitation=cfgs.IMG_MAX_LENGTH) + # if cfgs.NET_NAME in ['resnet101_v1d', 'resnet50_v1d']: + # img_batch = (img_batch / 255 - tf.constant(cfgs.PIXEL_MEAN_)) / tf.constant(cfgs.PIXEL_STD) + # else: + # img_batch = img_batch - tf.constant(cfgs.PIXEL_MEAN) + + img_batch = (img_batch - tf.constant(cfgs.PIXEL_MEAN)) / (tf.constant(cfgs.PIXEL_STD)*255) + img_batch = tf.expand_dims(img_batch, axis=0) + + detection_boxes, detection_scores, detection_category = det_net.build_whole_detection_network( + input_img_batch=img_batch, + gtboxes_batch=None) + + init_op = tf.group( + tf.global_variables_initializer(), + tf.local_variables_initializer() + ) + + restorer, restore_ckpt = det_net.get_restorer() + + config = tf.ConfigProto() + config.gpu_options.allow_growth = True + + with tf.Session(config=config) as sess: + sess.run(init_op) + if not restorer is None: + restorer.restore(sess, restore_ckpt) + print('restore model') + + all_boxes = [] + for i, a_img_name in enumerate(real_test_imgname_list): + + raw_img = cv2.imread(os.path.join(img_root, a_img_name)) + raw_h, raw_w = raw_img.shape[0], raw_img.shape[1] + + start = time.time() + resized_img, detected_boxes, detected_scores, detected_categories = \ + sess.run( + [img_batch, detection_boxes, detection_scores, detection_category], + feed_dict={img_plac: raw_img[:, :, ::-1]} # cv is BGR. But need RGB + ) + end = time.time() + # print("{} cost time : {} ".format(img_name, (end - start))) + if draw_imgs: + show_indices = detected_scores >= cfgs.SHOW_SCORE_THRSHOLD + show_scores = detected_scores[show_indices] + show_boxes = detected_boxes[show_indices] + show_categories = detected_categories[show_indices] + + draw_img = np.squeeze(resized_img, 0) + # if cfgs.NET_NAME in ['resnet101_v1d', 'resnet50_v1d']: + # draw_img = (draw_img * np.array(cfgs.PIXEL_STD) + np.array(cfgs.PIXEL_MEAN_)) * 255 + # else: + # draw_img = draw_img + np.array(cfgs.PIXEL_MEAN) + + draw_img = draw_img * (np.array(cfgs.PIXEL_STD)*255) + np.array(cfgs.PIXEL_MEAN) + + final_detections = draw_box_in_img.draw_boxes_with_label_and_scores(draw_img, + boxes=show_boxes, + labels=show_categories, + scores=show_scores, + in_graph=False) + if not os.path.exists(cfgs.TEST_SAVE_PATH): + os.makedirs(cfgs.TEST_SAVE_PATH) + + cv2.imwrite(cfgs.TEST_SAVE_PATH + '/' + a_img_name + '.jpg', + final_detections[:, :, ::-1]) + + xmin, ymin, xmax, ymax = detected_boxes[:, 0], detected_boxes[:, 1], \ + detected_boxes[:, 2], detected_boxes[:, 3] + + resized_h, resized_w = resized_img.shape[1], resized_img.shape[2] + + xmin = xmin * raw_w / resized_w + xmax = xmax * raw_w / resized_w + + ymin = ymin * raw_h / resized_h + ymax = ymax * raw_h / resized_h + + boxes = np.transpose(np.stack([xmin, ymin, xmax, ymax])) + dets = np.hstack((detected_categories.reshape(-1, 1), + detected_scores.reshape(-1, 1), + boxes)) + all_boxes.append(dets) + + tools.view_bar('{} image cost {}s'.format(a_img_name, (end - start)), i + 1, len(real_test_imgname_list)) + + save_dir = os.path.join(cfgs.EVALUATE_DIR, cfgs.VERSION) + if not os.path.exists(save_dir): + os.makedirs(save_dir) + fw1 = open(os.path.join(save_dir, 'detections.pkl'), 'wb') + pickle.dump(all_boxes, fw1) + return all_boxes + + +def eval(num_imgs, eval_dir, annotation_dir, showbox): + + # with open('/home/yjr/DataSet/VOC/VOC_test/VOC2007/ImageSets/Main/aeroplane_test.txt') as f: + # all_lines = f.readlines() + # test_imgname_list = [a_line.split()[0].strip() for a_line in all_lines] + + test_imgname_list = [item for item in os.listdir(eval_dir) + if item.endswith(('.jpg', 'jpeg', '.png', '.tif', '.tiff'))] + if num_imgs == np.inf: + real_test_imgname_list = test_imgname_list + else: + real_test_imgname_list = test_imgname_list[: num_imgs] + + faster_rcnn = build_whole_network.DetectionNetwork(base_network_name=cfgs.NET_NAME, + is_training=False) + all_boxes = eval_with_plac(det_net=faster_rcnn, real_test_imgname_list=real_test_imgname_list, + img_root=eval_dir, + draw_imgs=showbox) + + save_dir = os.path.join(cfgs.EVALUATE_DIR, cfgs.VERSION) + if not os.path.exists(save_dir): + os.makedirs(save_dir) + with open(os.path.join(save_dir, 'detections.pkl'), 'rb') as f: + all_boxes = pickle.load(f) + + print(len(all_boxes)) + + voc_eval.voc_evaluate_detections(all_boxes=all_boxes, + test_annotation_path=annotation_dir, + test_imgid_list=real_test_imgname_list) + + +def parse_args(): + + # /unsullied/sharefs/yangxue/isilon/yangxue/data/BDD100K/BDD100K_VOC/bdd100k_val + # /unsullied/sharefs/yangxue/isilon/yangxue/data/VOC2007/test/VOCdevkit/VOC2007 + + parser = argparse.ArgumentParser('evaluate the result with Pascal2007 stdand') + + parser.add_argument('--eval_imgs', dest='eval_imgs', + help='evaluate imgs dir ', + default='/unsullied/sharefs/yangxue/isilon/yangxue/data/BDD100K/BDD100K_VOC/bdd100k_val/val', type=str) + parser.add_argument('--annotation_dir', dest='test_annotation_dir', + help='the dir save annotations', + default='/unsullied/sharefs/yangxue/isilon/yangxue/data/BDD100K/BDD100K_VOC/bdd100k_val/Annotations', type=str) + parser.add_argument('--showbox', dest='showbox', + help='whether show detecion results when evaluation', + default=True, type=bool) + parser.add_argument('--GPU', dest='GPU', + help='gpu id', + default='0', type=str) + parser.add_argument('--eval_num', dest='eval_num', + help='the num of eval imgs', + default=np.inf, type=int) + args = parser.parse_args() + return args + + +if __name__ == '__main__': + + args = parse_args() + print(20*"--") + print(args) + print(20*"--") + os.environ["CUDA_VISIBLE_DEVICES"] = args.GPU + eval(np.inf, # use np.inf to test all the imgs. use 10 to test 10 imgs. + eval_dir=args.eval_imgs, + annotation_dir=args.test_annotation_dir, + showbox=args.showbox) + + + + + + + + + + + + + + + + diff --git a/tools/eval_coco.py b/tools/eval_coco.py new file mode 100644 index 0000000..a45170c --- /dev/null +++ b/tools/eval_coco.py @@ -0,0 +1,231 @@ +# -*- coding:utf-8 -*- + +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import os, sys +import tensorflow as tf +import time +import cv2 +import pickle +import numpy as np +import json +sys.path.append("../") + +from data.io.image_preprocess import short_side_resize_for_inference_data +from libs.networks import build_whole_network +from libs.box_utils import draw_box_in_img +import argparse +from help_utils import tools +from libs.label_name_dict.label_dict import * + +from data.lib_coco.PythonAPI.pycocotools.coco import COCO +from data.lib_coco.PythonAPI.pycocotools.cocoeval import COCOeval + + +def cocoval(detected_json, eval_json): + eval_gt = COCO(eval_json) + + eval_dt = eval_gt.loadRes(detected_json) + cocoEval = COCOeval(eval_gt, eval_dt, iouType='bbox') + + # cocoEval.params.imgIds = eval_gt.getImgIds() + cocoEval.evaluate() + cocoEval.accumulate() + cocoEval.summarize() + + +def eval_coco(det_net, real_test_img_list, draw_imgs=False): + + # 1. preprocess img + img_plac = tf.placeholder(dtype=tf.uint8, shape=[None, None, 3]) # is RGB. not BGR + img_batch = tf.cast(img_plac, tf.float32) + + img_batch = short_side_resize_for_inference_data(img_tensor=img_batch, + target_shortside_len=cfgs.IMG_SHORT_SIDE_LEN, + length_limitation=cfgs.IMG_MAX_LENGTH, + is_resize=True) + if cfgs.NET_NAME in ['resnet101_v1d', 'resnet50_v1d']: + img_batch = (img_batch / 255 - tf.constant(cfgs.PIXEL_MEAN_)) / tf.constant(cfgs.PIXEL_STD) + else: + img_batch = img_batch - tf.constant(cfgs.PIXEL_MEAN) + + # img_batch = (img_batch - tf.constant(cfgs.PIXEL_MEAN)) / (tf.constant(cfgs.PIXEL_STD)*255) + img_batch = tf.expand_dims(img_batch, axis=0) + + detection_boxes, detection_scores, detection_category = det_net.build_whole_detection_network( + input_img_batch=img_batch, + gtboxes_batch=None) + + init_op = tf.group( + tf.global_variables_initializer(), + tf.local_variables_initializer() + ) + + restorer, restore_ckpt = det_net.get_restorer() + + config = tf.ConfigProto() + config.gpu_options.allow_growth = True + + with tf.Session(config=config) as sess: + sess.run(init_op) + if not restorer is None: + restorer.restore(sess, restore_ckpt) + print('restore model') + + save_path = os.path.join('./eval_coco', cfgs.VERSION) + tools.mkdir(save_path) + fw_json_dt = open(os.path.join(save_path, 'coco_minival.json'), 'w') + coco_det = [] + for i, a_img in enumerate(real_test_img_list): + + record = json.loads(a_img) + + img_path = os.path.join('/data/COCO/val2017', record['fpath'].split('_')[-1]) + + raw_img = cv2.imread(img_path) + # raw_img = cv2.imread(record['fpath']) + raw_h, raw_w = raw_img.shape[0], raw_img.shape[1] + + start = time.time() + resized_img, detected_boxes, detected_scores, detected_categories = \ + sess.run( + [img_batch, detection_boxes, detection_scores, detection_category], + feed_dict={img_plac: raw_img[:, :, ::-1]} # cv is BGR. But need RGB + ) + end = time.time() + + eval_indices = detected_scores >= 0.01 + detected_scores = detected_scores[eval_indices] + detected_boxes = detected_boxes[eval_indices] + detected_categories = detected_categories[eval_indices] + + # print("{} cost time : {} ".format(img_name, (end - start))) + if draw_imgs: + show_indices = detected_scores >= cfgs.SHOW_SCORE_THRSHOLD + show_scores = detected_scores[show_indices] + show_boxes = detected_boxes[show_indices] + show_categories = detected_categories[show_indices] + + draw_img = np.squeeze(resized_img, 0) + if cfgs.NET_NAME in ['resnet101_v1d', 'resnet50_v1d']: + draw_img = (draw_img * np.array(cfgs.PIXEL_STD) + np.array(cfgs.PIXEL_MEAN_)) * 255 + else: + draw_img = draw_img + np.array(cfgs.PIXEL_MEAN) + + # draw_img = draw_img * (np.array(cfgs.PIXEL_STD)*255) + np.array(cfgs.PIXEL_MEAN) + + final_detections = draw_box_in_img.draw_boxes_with_label_and_scores(draw_img, + boxes=show_boxes, + labels=show_categories, + scores=show_scores, + in_graph=False) + if not os.path.exists(cfgs.TEST_SAVE_PATH): + os.makedirs(cfgs.TEST_SAVE_PATH) + + cv2.imwrite(cfgs.TEST_SAVE_PATH + '/' + record['ID'], + final_detections[:, :, ::-1]) + + xmin, ymin, xmax, ymax = detected_boxes[:, 0], detected_boxes[:, 1], \ + detected_boxes[:, 2], detected_boxes[:, 3] + + resized_h, resized_w = resized_img.shape[1], resized_img.shape[2] + + xmin = xmin * raw_w / resized_w + xmax = xmax * raw_w / resized_w + + ymin = ymin * raw_h / resized_h + ymax = ymax * raw_h / resized_h + + boxes = np.transpose(np.stack([xmin, ymin, xmax-xmin, ymax-ymin])) + + # cost much time + for j, box in enumerate(boxes): + coco_det.append({'bbox': [float(box[0]), float(box[1]), float(box[2]), float(box[3])], + 'score': float(detected_scores[j]), 'image_id': int(record['ID'].split('.jpg')[0].split('_000000')[-1]), + 'category_id': int(classes_originID[LABEl_NAME_MAP[detected_categories[j]]])}) + + tools.view_bar('%s image cost %.3fs' % (record['ID'], (end - start)), i + 1, len(real_test_img_list)) + + json.dump(coco_det, fw_json_dt) + fw_json_dt.close() + return os.path.join(save_path, 'coco_minival.json') + + +def eval(num_imgs, eval_data, eval_gt, showbox): + + with open(eval_data) as f: + test_img_list = f.readlines() + + if num_imgs == np.inf: + real_test_img_list = test_img_list + else: + real_test_img_list = test_img_list[: num_imgs] + + faster_rcnn = build_whole_network.DetectionNetwork(base_network_name=cfgs.NET_NAME, + is_training=False) + detected_json = eval_coco(det_net=faster_rcnn, real_test_img_list=real_test_img_list, draw_imgs=showbox) + + # save_path = os.path.join('./eval_coco', cfgs.VERSION) + # detected_json = os.path.join(save_path, 'coco_res.json') + cocoval(detected_json, eval_gt) + + +def parse_args(): + + parser = argparse.ArgumentParser('evaluate the result with Pascal2007 stdand') + + parser.add_argument('--eval_data', dest='eval_data', + help='evaluate imgs dir ', + default='/data/COCO/coco_minival2014.odgt', type=str) + parser.add_argument('--eval_gt', dest='eval_gt', + help='eval gt', + default='/data/COCO/instances_minival2014.json', + type=str) + parser.add_argument('--showbox', dest='showbox', + help='whether show detecion results when evaluation', + default=True, type=bool) + parser.add_argument('--GPU', dest='GPU', + help='gpu id', + default='0', type=str) + parser.add_argument('--eval_num', dest='eval_num', + help='the num of eval imgs', + default=np.inf, type=int) + args = parser.parse_args() + return args + + +if __name__ == '__main__': + + args = parse_args() + print(20*"--") + print(args) + print(20*"--") + os.environ["CUDA_VISIBLE_DEVICES"] = args.GPU + eval(np.inf, # use np.inf to test all the imgs. use 10 to test 10 imgs. + eval_data=args.eval_data, + eval_gt=args.eval_gt, + showbox=args.showbox) + + # os.environ["CUDA_VISIBLE_DEVICES"] = '0' + # eval(np.inf, # use np.inf to test all the imgs. use 10 to test 10 imgs. + # eval_data='/data/COCO/coco_minival2014.odgt', + # eval_gt='/data/COCO/instances_minival2014.json', + # showbox=False) + + + + + + + + + + + + + + + + diff --git a/tools/eval_coco_pyramid.py b/tools/eval_coco_pyramid.py new file mode 100644 index 0000000..0338733 --- /dev/null +++ b/tools/eval_coco_pyramid.py @@ -0,0 +1,270 @@ +# -*- coding:utf-8 -*- + +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import os, sys +import tensorflow as tf +import time +import cv2 +import pickle +import numpy as np +import json +sys.path.append("../") + +from data.io.image_preprocess import short_side_resize_for_inference_data +from libs.configs import cfgs +from libs.networks import build_whole_network +from libs.val_libs import voc_eval +from libs.box_utils import draw_box_in_img +import argparse +from help_utils import tools +from libs.label_name_dict.label_dict import * +from libs.box_utils import nms + +from data.lib_coco.PythonAPI.pycocotools.coco import COCO +from data.lib_coco.PythonAPI.pycocotools.cocoeval import COCOeval + + +def cocoval(detected_json, eval_json): + eval_gt = COCO(eval_json) + + eval_dt = eval_gt.loadRes(detected_json) + cocoEval = COCOeval(eval_gt, eval_dt, iouType='bbox') + + # cocoEval.params.imgIds = eval_gt.getImgIds() + cocoEval.evaluate() + cocoEval.accumulate() + cocoEval.summarize() + + +def eval_coco(det_net, real_test_img_list, draw_imgs=False): + + # 1. preprocess img + img_plac = tf.placeholder(dtype=tf.uint8, shape=[None, None, 3]) # is RGB. not BGR + img_batch = tf.cast(img_plac, tf.float32) + + img_batch = short_side_resize_for_inference_data(img_tensor=img_batch, + target_shortside_len=cfgs.IMG_SHORT_SIDE_LEN, + length_limitation=cfgs.IMG_MAX_LENGTH, + is_resize=False) + if cfgs.NET_NAME in ['resnet101_v1d', 'resnet50_v1d']: + img_batch = (img_batch / 255 - tf.constant(cfgs.PIXEL_MEAN_)) / tf.constant(cfgs.PIXEL_STD) + else: + img_batch = img_batch - tf.constant(cfgs.PIXEL_MEAN) + + # img_batch = (img_batch - tf.constant(cfgs.PIXEL_MEAN)) / (tf.constant(cfgs.PIXEL_STD)*255) + img_batch = tf.expand_dims(img_batch, axis=0) + + detection_boxes, detection_scores, detection_category = det_net.build_whole_detection_network( + input_img_batch=img_batch, + gtboxes_batch=None) + + init_op = tf.group( + tf.global_variables_initializer(), + tf.local_variables_initializer() + ) + + restorer, restore_ckpt = det_net.get_restorer() + + config = tf.ConfigProto() + config.gpu_options.allow_growth = True + + with tf.Session(config=config) as sess: + sess.run(init_op) + if not restorer is None: + restorer.restore(sess, restore_ckpt) + print('restore model') + + save_path = os.path.join('./eval_coco', cfgs.VERSION) + tools.mkdir(save_path) + fw_json_dt = open(os.path.join(save_path, 'coco_minival_ms.json'), 'w') + coco_det = [] + for i, a_img in enumerate(real_test_img_list): + + record = json.loads(a_img) + raw_img = cv2.imread(record['fpath']) + raw_h, raw_w = raw_img.shape[0], raw_img.shape[1] + + start = time.time() + + detected_scores_, detected_boxes_, detected_categories_ = [], [], [] + + for ss in [600, 800, 1000, 1200]: # cfgs.IMG_SHORT_SIDE_LEN: + img_resize = cv2.resize(raw_img, (ss, ss)) + + resized_img, tmp_detected_boxes, tmp_detected_scores, tmp_detected_categories = \ + sess.run( + [img_batch, detection_boxes, detection_scores, detection_category], + feed_dict={img_plac: img_resize[:, :, ::-1]} # cv is BGR. But need RGB + ) + + eval_indices = tmp_detected_scores >= 0.01 + tmp_detected_scores = tmp_detected_scores[eval_indices] + tmp_detected_boxes = tmp_detected_boxes[eval_indices] + tmp_detected_categories = tmp_detected_categories[eval_indices] + + xmin, ymin, xmax, ymax = tmp_detected_boxes[:, 0], tmp_detected_boxes[:, 1], \ + tmp_detected_boxes[:, 2], tmp_detected_boxes[:, 3] + + resized_h, resized_w = resized_img.shape[1], resized_img.shape[2] + + xmin = xmin * raw_w / resized_w + xmax = xmax * raw_w / resized_w + + ymin = ymin * raw_h / resized_h + ymax = ymax * raw_h / resized_h + + resize_boxes = np.transpose(np.stack([xmin, ymin, xmax, ymax])) + + detected_scores_.append(tmp_detected_scores) + detected_boxes_.append(resize_boxes) + detected_categories_.append(tmp_detected_categories) + + detected_scores_ = np.concatenate(detected_scores_) + detected_boxes_ = np.concatenate(detected_boxes_) + detected_categories_ = np.concatenate(detected_categories_) + + detected_scores, detected_boxes, detected_categories = [], [], [] + + for sub_class in range(1, cfgs.CLASS_NUM + 1): + index = np.where(detected_categories_ == sub_class)[0] + if len(index) == 0: + continue + tmp_boxes_h = detected_boxes_[index] + tmp_label_h = detected_categories_[index] + tmp_score_h = detected_scores_[index] + + tmp_boxes_h = np.array(tmp_boxes_h) + tmp = np.zeros([tmp_boxes_h.shape[0], tmp_boxes_h.shape[1] + 1]) + tmp[:, 0:-1] = tmp_boxes_h + tmp[:, -1] = np.array(tmp_score_h) + + inx = nms.py_cpu_nms(dets=np.array(tmp, np.float32), + thresh=cfgs.FAST_RCNN_NMS_IOU_THRESHOLD, + max_output_size=500) + + detected_boxes.extend(np.array(tmp_boxes_h)[inx]) + detected_scores.extend(np.array(tmp_score_h)[inx]) + detected_categories.extend(np.array(tmp_label_h)[inx]) + + detected_scores = np.array(detected_scores) + detected_boxes = np.array(detected_boxes) + detected_categories = np.array(detected_categories) + + # print("{} cost time : {} ".format(img_name, (end - start))) + if draw_imgs: + show_indices = detected_scores >= cfgs.SHOW_SCORE_THRSHOLD + show_scores = detected_scores[show_indices] + show_boxes = detected_boxes[show_indices] + show_categories = detected_categories[show_indices] + + # if cfgs.NET_NAME in ['resnet101_v1d', 'resnet50_v1d']: + # draw_img = (raw_img * np.array(cfgs.PIXEL_STD) + np.array(cfgs.PIXEL_MEAN_)) * 255 + # else: + # draw_img = raw_img + np.array(cfgs.PIXEL_MEAN) + + # draw_img = draw_img * (np.array(cfgs.PIXEL_STD)*255) + np.array(cfgs.PIXEL_MEAN) + + raw_img = np.array(raw_img, np.float32) + final_detections = draw_box_in_img.draw_boxes_with_label_and_scores(raw_img, + boxes=show_boxes, + labels=show_categories, + scores=show_scores, + in_graph=False) + if not os.path.exists(cfgs.TEST_SAVE_PATH): + os.makedirs(cfgs.TEST_SAVE_PATH) + + cv2.imwrite(cfgs.TEST_SAVE_PATH + '/' + record['ID'], + final_detections) + + # cost much time + for j, box in enumerate(detected_boxes): + coco_det.append({'bbox': [float(box[0]), float(box[1]), float(box[2]-box[0]), float(box[3]-box[1])], + 'score': float(detected_scores[j]), 'image_id': int(record['ID'].split('.jpg')[0].split('_000000')[-1]), + 'category_id': int(classes_originID[LABEl_NAME_MAP[detected_categories[j]]])}) + end = time.time() + tools.view_bar('%s image cost %.3fs' % (record['ID'], (end - start)), i + 1, len(real_test_img_list)) + + json.dump(coco_det, fw_json_dt) + fw_json_dt.close() + return os.path.join(save_path, 'coco_minival_ms.json') + + +def eval(num_imgs, eval_data, eval_gt, showbox): + + with open(eval_data) as f: + test_img_list = f.readlines() + + if num_imgs == np.inf: + real_test_img_list = test_img_list + else: + real_test_img_list = test_img_list[: num_imgs] + + faster_rcnn = build_whole_network.DetectionNetwork(base_network_name=cfgs.NET_NAME, + is_training=False) + detected_json = eval_coco(det_net=faster_rcnn, real_test_img_list=real_test_img_list, draw_imgs=showbox) + + # save_path = os.path.join('./eval_coco', cfgs.VERSION) + # detected_json = os.path.join(save_path, 'coco_res.json') + cocoval(detected_json, eval_gt) + + +def parse_args(): + + parser = argparse.ArgumentParser('evaluate the result with Pascal2007 stdand') + + parser.add_argument('--eval_data', dest='eval_data', + help='evaluate imgs dir ', + default='/unsullied/sharefs/_research_detection/GeneralDetection/COCO/data/MSCOCO/odformat/coco_minival2014.odgt', type=str) + parser.add_argument('--eval_gt', dest='eval_gt', + help='eval gt', + default='/unsullied/sharefs/_research_detection/GeneralDetection/COCO/data/MSCOCO/instances_minival2014.json', + type=str) + parser.add_argument('--showbox', dest='showbox', + help='whether show detecion results when evaluation', + default=True, type=bool) + parser.add_argument('--GPU', dest='GPU', + help='gpu id', + default='0', type=str) + parser.add_argument('--eval_num', dest='eval_num', + help='the num of eval imgs', + default=np.inf, type=int) + args = parser.parse_args() + return args + + +if __name__ == '__main__': + + # args = parse_args() + # print(20*"--") + # print(args) + # print(20*"--") + # os.environ["CUDA_VISIBLE_DEVICES"] = args.GPU + # eval(np.inf, # use np.inf to test all the imgs. use 10 to test 10 imgs. + # eval_data=args.eval_data, + # eval_gt=args.eval_gt, + # showbox=args.showbox) + + os.environ["CUDA_VISIBLE_DEVICES"] = '0' + eval(np.inf, # use np.inf to test all the imgs. use 10 to test 10 imgs. + eval_data='/unsullied/sharefs/_research_detection/GeneralDetection/COCO/data/MSCOCO/odformat/coco_minival2014.odgt', + eval_gt='/unsullied/sharefs/_research_detection/GeneralDetection/COCO/data/MSCOCO/instances_minival2014.json', + showbox=False) + + + + + + + + + + + + + + + + diff --git a/tools/eval_voc2012.py b/tools/eval_voc2012.py new file mode 100644 index 0000000..7b66e54 --- /dev/null +++ b/tools/eval_voc2012.py @@ -0,0 +1,203 @@ +# -*- coding:utf-8 -*- + +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import os, sys +import tensorflow as tf +import time +import cv2 +import pickle +import numpy as np +sys.path.append("../") + +from data.io.image_preprocess import short_side_resize_for_inference_data +from libs.configs import cfgs +from libs.networks import build_whole_network +from libs.val_libs import voc_eval +from libs.box_utils import draw_box_in_img +import argparse +from help_utils import tools +from libs.label_name_dict.label_dict import * + + +def eval_with_plac(det_net, real_test_imgname_list, img_root, draw_imgs=False): + + # 1. preprocess img + img_plac = tf.placeholder(dtype=tf.uint8, shape=[None, None, 3]) # is RGB. not BGR + img_batch = tf.cast(img_plac, tf.float32) + + img_batch = short_side_resize_for_inference_data(img_tensor=img_batch, + target_shortside_len=cfgs.IMG_SHORT_SIDE_LEN, + length_limitation=cfgs.IMG_MAX_LENGTH) + if cfgs.NET_NAME in ['resnet101_v1d', 'resnet50_v1d']: + img_batch = (img_batch / 255 - tf.constant(cfgs.PIXEL_MEAN_)) / tf.constant(cfgs.PIXEL_STD) + else: + img_batch = img_batch - tf.constant(cfgs.PIXEL_MEAN) + + # img_batch = (img_batch - tf.constant(cfgs.PIXEL_MEAN)) / (tf.constant(cfgs.PIXEL_STD)*255) + img_batch = tf.expand_dims(img_batch, axis=0) + + detection_boxes, detection_scores, detection_category = det_net.build_whole_detection_network( + input_img_batch=img_batch, + gtboxes_batch=None) + + init_op = tf.group( + tf.global_variables_initializer(), + tf.local_variables_initializer() + ) + + restorer, restore_ckpt = det_net.get_restorer() + + config = tf.ConfigProto() + config.gpu_options.allow_growth = True + + with tf.Session(config=config) as sess: + sess.run(init_op) + if not restorer is None: + restorer.restore(sess, restore_ckpt) + print('restore model') + + for i, a_img_name in enumerate(real_test_imgname_list): + + raw_img = cv2.imread(os.path.join(img_root, a_img_name)) + raw_h, raw_w = raw_img.shape[0], raw_img.shape[1] + + start = time.time() + resized_img, detected_boxes, detected_scores, detected_categories = \ + sess.run( + [img_batch, detection_boxes, detection_scores, detection_category], + feed_dict={img_plac: raw_img[:, :, ::-1]} # cv is BGR. But need RGB + ) + end = time.time() + # print("{} cost time : {} ".format(img_name, (end - start))) + if draw_imgs: + show_indices = detected_scores >= cfgs.SHOW_SCORE_THRSHOLD + show_scores = detected_scores[show_indices] + show_boxes = detected_boxes[show_indices] + show_categories = detected_categories[show_indices] + + draw_img = np.squeeze(resized_img, 0) + if cfgs.NET_NAME in ['resnet101_v1d', 'resnet50_v1d']: + draw_img = (draw_img * np.array(cfgs.PIXEL_STD) + np.array(cfgs.PIXEL_MEAN_)) * 255 + else: + draw_img = draw_img + np.array(cfgs.PIXEL_MEAN) + + # draw_img = draw_img * (np.array(cfgs.PIXEL_STD)*255) + np.array(cfgs.PIXEL_MEAN) + + final_detections = draw_box_in_img.draw_boxes_with_label_and_scores(draw_img, + boxes=show_boxes, + labels=show_categories, + scores=show_scores, + in_graph=False) + if not os.path.exists(cfgs.TEST_SAVE_PATH): + os.makedirs(cfgs.TEST_SAVE_PATH) + + cv2.imwrite(cfgs.TEST_SAVE_PATH + '/' + a_img_name, + final_detections[:, :, ::-1]) + + xmin, ymin, xmax, ymax = detected_boxes[:, 0], detected_boxes[:, 1], \ + detected_boxes[:, 2], detected_boxes[:, 3] + + resized_h, resized_w = resized_img.shape[1], resized_img.shape[2] + + xmin = xmin * raw_w / resized_w + xmax = xmax * raw_w / resized_w + + ymin = ymin * raw_h / resized_h + ymax = ymax * raw_h / resized_h + + boxes = np.transpose(np.stack([xmin, ymin, xmax, ymax])) + dets = np.hstack((detected_categories.reshape(-1, 1), + detected_scores.reshape(-1, 1), + boxes)) + # all_boxes.append(dets) + + # eval txt + CLASS_VOC = NAME_LABEL_MAP.keys() + + write_handle = {} + txt_dir = os.path.join('voc2012_eval', cfgs.VERSION, 'results', 'VOC2012', 'Main') + tools.mkdir(txt_dir) + for sub_class in CLASS_VOC: + if sub_class == 'back_ground': + continue + write_handle[sub_class] = open(os.path.join(txt_dir, 'comp3_det_test_%s.txt' % sub_class), 'a+') + + for det in dets: + command = '%s %.6f %.6f %.6f %.6f %.6f\n' % (a_img_name.split('/')[-1].split('.')[0], + det[1], + det[2], det[3], det[4], det[5]) + write_handle[LABEl_NAME_MAP[det[0]]].write(command) + + for sub_class in CLASS_VOC: + if sub_class == 'back_ground': + continue + write_handle[sub_class].close() + + tools.view_bar('%s image cost %.3fs' % (a_img_name, (end - start)), i + 1, len(real_test_imgname_list)) + + +def eval(num_imgs, eval_dir, showbox): + + test_imgname_list = [item for item in os.listdir(eval_dir) + if item.endswith(('.jpg', 'jpeg', '.png', '.tif', '.tiff'))] + if num_imgs == np.inf: + real_test_imgname_list = test_imgname_list + else: + real_test_imgname_list = test_imgname_list[: num_imgs] + + faster_rcnn = build_whole_network.DetectionNetwork(base_network_name=cfgs.NET_NAME, + is_training=False) + eval_with_plac(det_net=faster_rcnn, real_test_imgname_list=real_test_imgname_list, + img_root=eval_dir, + draw_imgs=showbox) + + +def parse_args(): + + parser = argparse.ArgumentParser('evaluate the result with Pascal2007 stdand') + + parser.add_argument('--eval_imgs', dest='eval_imgs', + help='evaluate imgs dir ', + default='/unsullied/sharefs/yangxue/isilon/yangxue/data/VOC2007/test/VOCdevkit/VOC2007/JPEGImages', type=str) + parser.add_argument('--showbox', dest='showbox', + help='whether show detecion results when evaluation', + default=True, type=bool) + parser.add_argument('--GPU', dest='GPU', + help='gpu id', + default='0', type=str) + parser.add_argument('--eval_num', dest='eval_num', + help='the num of eval imgs', + default=np.inf, type=int) + args = parser.parse_args() + return args + + +if __name__ == '__main__': + + args = parse_args() + print(20*"--") + print(args) + print(20*"--") + os.environ["CUDA_VISIBLE_DEVICES"] = args.GPU + eval(np.inf, # use np.inf to test all the imgs. use 10 to test 10 imgs. + eval_dir=args.eval_imgs, + showbox=args.showbox) + + + + + + + + + + + + + + + + diff --git a/tools/inference.py b/tools/inference.py new file mode 100644 index 0000000..f964e48 --- /dev/null +++ b/tools/inference.py @@ -0,0 +1,152 @@ +# -*- coding:utf-8 -*- + +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import os, sys +import tensorflow as tf +import time +import cv2 +import argparse +import numpy as np +sys.path.append("../") + +from data.io.image_preprocess import short_side_resize_for_inference_data +from libs.configs import cfgs +from libs.networks import build_whole_network +from libs.box_utils import draw_box_in_img +from help_utils import tools + + +def detect(det_net, inference_save_path, real_test_imgname_list): + + # 1. preprocess img + img_plac = tf.placeholder(dtype=tf.uint8, shape=[None, None, 3]) # is RGB. not GBR + img_batch = tf.cast(img_plac, tf.float32) + img_batch = short_side_resize_for_inference_data(img_tensor=img_batch, + target_shortside_len=cfgs.IMG_SHORT_SIDE_LEN, + length_limitation=cfgs.IMG_MAX_LENGTH) + + if cfgs.NET_NAME in ['resnet101_v1d', 'resnet50_v1d']: + img_batch = (img_batch / 255 - tf.constant(cfgs.PIXEL_MEAN_)) / tf.constant(cfgs.PIXEL_STD) + else: + img_batch = img_batch - tf.constant(cfgs.PIXEL_MEAN) + img_batch = tf.expand_dims(img_batch, axis=0) # [1, None, None, 3] + + detection_boxes, detection_scores, detection_category = det_net.build_whole_detection_network( + input_img_batch=img_batch, + gtboxes_batch=None) + + init_op = tf.group( + tf.global_variables_initializer(), + tf.local_variables_initializer() + ) + + restorer, restore_ckpt = det_net.get_restorer() + + config = tf.ConfigProto() + config.gpu_options.allow_growth = True + + with tf.Session(config=config) as sess: + sess.run(init_op) + if not restorer is None: + restorer.restore(sess, restore_ckpt) + print('restore model') + + for i, a_img_name in enumerate(real_test_imgname_list): + + raw_img = cv2.imread(a_img_name) + start = time.time() + resized_img, detected_boxes, detected_scores, detected_categories = \ + sess.run( + [img_batch, detection_boxes, detection_scores, detection_category], + feed_dict={img_plac: raw_img[:, :, ::-1]} # cv is BGR. But need RGB + ) + end = time.time() + # print("{} cost time : {} ".format(img_name, (end - start))) + + show_indices = detected_scores >= cfgs.SHOW_SCORE_THRSHOLD + show_scores = detected_scores[show_indices] + show_boxes = detected_boxes[show_indices] + show_categories = detected_categories[show_indices] + + draw_img = np.squeeze(resized_img, 0) + + if cfgs.NET_NAME in ['resnet101_v1d', 'resnet50_v1d']: + draw_img = (draw_img * np.array(cfgs.PIXEL_STD) + np.array(cfgs.PIXEL_MEAN_)) * 255 + else: + draw_img = draw_img + np.array(cfgs.PIXEL_MEAN) + final_detections = draw_box_in_img.draw_boxes_with_label_and_scores(draw_img, + boxes=show_boxes, + labels=show_categories, + scores=show_scores, + in_graph=False) + nake_name = a_img_name.split('/')[-1] + # print (inference_save_path + '/' + nake_name) + cv2.imwrite(inference_save_path + '/' + nake_name, + final_detections[:, :, ::-1]) + + tools.view_bar('{} image cost {}s'.format(a_img_name, (end - start)), i + 1, len(real_test_imgname_list)) + + +def inference(test_dir, inference_save_path): + + test_imgname_list = [os.path.join(test_dir, img_name) for img_name in os.listdir(test_dir) + if img_name.endswith(('.jpg', '.png', '.jpeg', '.tif', '.tiff'))] + assert len(test_imgname_list) != 0, 'test_dir has no imgs there.' \ + ' Note that, we only support img format of (.jpg, .png, and .tiff) ' + + faster_rcnn = build_whole_network.DetectionNetwork(base_network_name=cfgs.NET_NAME, + is_training=False) + detect(det_net=faster_rcnn, inference_save_path=inference_save_path, real_test_imgname_list=test_imgname_list) + + +def parse_args(): + """ + Parse input arguments + """ + parser = argparse.ArgumentParser(description='TestImgs...U need provide the test dir') + parser.add_argument('--data_dir', dest='data_dir', + help='data path', + default='demos', type=str) + parser.add_argument('--save_dir', dest='save_dir', + help='demo imgs to save', + default='inference_results', type=str) + parser.add_argument('--GPU', dest='GPU', + help='gpu id ', + default='0', type=str) + + if len(sys.argv) == 1: + parser.print_help() + sys.exit(1) + + args = parser.parse_args() + + return args + + +if __name__ == '__main__': + + args = parse_args() + print('Called with args:') + print(args) + os.environ["CUDA_VISIBLE_DEVICES"] = args.GPU + inference(args.data_dir, + inference_save_path=args.save_dir) + + + + + + + + + + + + + + + + diff --git a/tools/inference_for_coco.py b/tools/inference_for_coco.py new file mode 100644 index 0000000..9ada2cb --- /dev/null +++ b/tools/inference_for_coco.py @@ -0,0 +1,186 @@ +# -*- coding:utf-8 -*- + +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import os, sys +import tensorflow as tf +import time +import cv2 +import pickle +import numpy as np +sys.path.append("../") +sys.path.insert(0, '/home/yjr/PycharmProjects/Faster-RCNN_TF/data/lib_coco/PythonAPI') +from data.io.image_preprocess import short_side_resize_for_inference_data +from libs.configs import cfgs +from libs.networks import build_whole_network +from libs.val_libs import voc_eval +from libs.box_utils import draw_box_in_img +from libs.label_name_dict.coco_dict import LABEL_NAME_MAP, classes_originID +from help_utils import tools +from data.lib_coco.PythonAPI.pycocotools.coco import COCO +import json + +os.environ["CUDA_VISIBLE_DEVICES"] = cfgs.GPU_GROUP + + +def eval_with_plac(det_net, imgId_list, coco, out_json_root, draw_imgs=False): + + # 1. preprocess img + img_plac = tf.placeholder(dtype=tf.uint8, shape=[None, None, 3]) # is RGB. not GBR + img_batch = tf.cast(img_plac, tf.float32) + + img_batch = short_side_resize_for_inference_data(img_tensor=img_batch, + target_shortside_len=cfgs.IMG_SHORT_SIDE_LEN, + length_limitation=cfgs.IMG_MAX_LENGTH) + img_batch = img_batch - tf.constant(cfgs.PIXEL_MEAN) + img_batch = tf.expand_dims(img_batch, axis=0) + + detection_boxes, detection_scores, detection_category = det_net.build_whole_detection_network( + input_img_batch=img_batch, + gtboxes_batch=None) + + init_op = tf.group( + tf.global_variables_initializer(), + tf.local_variables_initializer() + ) + + restorer, restore_ckpt = det_net.get_restorer() + + config = tf.ConfigProto() + config.gpu_options.allow_growth = True + + # coco_test_results = [] + + with tf.Session(config=config) as sess: + sess.run(init_op) + if not restorer is None: + restorer.restore(sess, restore_ckpt) + print('restore model') + + for i, imgid in enumerate(imgId_list): + imgname = coco.loadImgs(ids=[imgid])[0]['file_name'] + raw_img = cv2.imread(os.path.join("/home/yjr/DataSet/COCO/2017/test2017", imgname)) + + raw_h, raw_w = raw_img.shape[0], raw_img.shape[1] + start = time.time() + resized_img, detected_boxes, detected_scores, detected_categories = \ + sess.run( + [img_batch, detection_boxes, detection_scores, detection_category], + feed_dict={img_plac: raw_img[:, :, ::-1]} # cv is BGR. But need RGB + ) + end = time.time() + + if draw_imgs: + show_indices = detected_scores >= cfgs.SHOW_SCORE_THRSHOLD + show_scores = detected_scores[show_indices] + show_boxes = detected_boxes[show_indices] + show_categories = detected_categories[show_indices] + + draw_img = np.squeeze(resized_img, 0) + if cfgs.NET_NAME in ['resnet101_v1d', 'resnet50_v1d']: + draw_img = (draw_img * np.array(cfgs.PIXEL_STD) + np.array(cfgs.PIXEL_MEAN_)) * 255 + else: + draw_img = draw_img + np.array(cfgs.PIXEL_MEAN) + final_detections = draw_box_in_img.draw_boxes_with_label_and_scores(draw_img, + boxes=show_boxes, + labels=show_categories, + scores=show_scores, + in_graph=False) + cv2.imwrite(cfgs.TEST_SAVE_PATH + '/' + str(imgid) + '.jpg', + final_detections[:, :, ::-1]) + + xmin, ymin, xmax, ymax = detected_boxes[:, 0], detected_boxes[:, 1], \ + detected_boxes[:, 2], detected_boxes[:, 3] + + resized_h, resized_w = resized_img.shape[1], resized_img.shape[2] + + xmin = xmin * raw_w / resized_w + xmax = xmax * raw_w / resized_w + + ymin = ymin * raw_h / resized_h + ymax = ymax * raw_h / resized_h + + boxes = np.transpose(np.stack([xmin, ymin, xmax-xmin, ymax-ymin])) + + dets = np.hstack((detected_categories.reshape(-1, 1), + detected_scores.reshape(-1, 1), + boxes)) + + a_img_detect_result = [] + for a_det in dets: + label, score, bbox = a_det[0], a_det[1], a_det[2:] + cat_id = classes_originID[LABEL_NAME_MAP[label]] + if score<0.00001: + continue + det_object = {"image_id": imgid, + "category_id": cat_id, + "bbox": bbox.tolist(), + "score": float(score)} + # print (det_object) + a_img_detect_result.append(det_object) + f = open(os.path.join(out_json_root, 'each_img', str(imgid)+'.json'), 'w') + json.dump(a_img_detect_result, f) # , indent=4 + f.close() + del a_img_detect_result + del dets + del boxes + del resized_img + del raw_img + tools.view_bar('{} image cost {}s'.format(imgid, (end - start)), i + 1, len(imgId_list)) + + +def eval(num_imgs): + + + # annotation_path = '/home/yjr/DataSet/COCO/2017/test_annotations/image_info_test2017.json' + annotation_path = '/home/yjr/DataSet/COCO/2017/test_annotations/image_info_test-dev2017.json' + # annotation_path = '/home/yjr/DataSet/COCO/2017/annotations/instances_train2017.json' + print("load coco .... it will cost about 17s..") + coco = COCO(annotation_path) + + imgId_list = coco.getImgIds() + + if num_imgs !=np.inf: + imgId_list = imgId_list[: num_imgs] + + faster_rcnn = build_whole_network.DetectionNetwork(base_network_name=cfgs.NET_NAME, + is_training=False) + save_dir = os.path.join(cfgs.EVALUATE_DIR, cfgs.VERSION) + eval_with_plac(det_net=faster_rcnn, coco=coco, imgId_list=imgId_list, out_json_root=save_dir, + draw_imgs=True) + print("each img over**************") + + final_detections = [] + with open(os.path.join(save_dir, 'coco2017test_results.json'), 'w') as wf: + for imgid in imgId_list: + f = open(os.path.join(save_dir, 'each_img', str(imgid)+'.json')) + tmp_list = json.load(f) + # print (type(tmp_list)) + final_detections.extend(tmp_list) + del tmp_list + f.close() + json.dump(final_detections, wf) + + +if __name__ == '__main__': + + eval(np.inf) + + + + + + + + + + + + + + + + + diff --git a/tools/multi_gpu_train.py b/tools/multi_gpu_train.py new file mode 100644 index 0000000..831ae40 --- /dev/null +++ b/tools/multi_gpu_train.py @@ -0,0 +1,372 @@ +# -*- coding:utf-8 -*- + +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import tensorflow as tf +import tensorflow.contrib.slim as slim +import os, sys +import numpy as np +import time +sys.path.append("../") + +from libs.configs import cfgs +from libs.networks import build_whole_network +from data.io.read_tfrecord_multi_gpu import next_batch +from libs.box_utils import show_box_in_tensor +from help_utils import tools + +os.environ["CUDA_VISIBLE_DEVICES"] = cfgs.GPU_GROUP + + +def average_gradients(tower_grads): + """Calculate the average gradient for each shared variable across all towers. + Note that this function provides a synchronization point across all towers. + Args: + tower_grads: List of lists of (gradient, variable) tuples. The outer list + is over individual gradients. The inner list is over the gradient + calculation for each tower. + Returns: + List of pairs of (gradient, variable) where the gradient has been averaged + across all towers. + """ + average_grads = [] + for grad_and_vars in zip(*tower_grads): + # Note that each grad_and_vars looks like the following: + # ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN)) + grads = [] + for g, _ in grad_and_vars: + # Add 0 dimension to the gradients to represent the tower. + expanded_g = tf.expand_dims(g, 0) + + # Append on a 'tower' dimension which we will average over below. + grads.append(expanded_g) + + # Average over the 'tower' dimension. + grad = tf.concat(axis=0, values=grads) + grad = tf.reduce_mean(grad, 0) + + # Keep in mind that the Variables are redundant because they are shared + # across towers. So .. we will just return the first tower's pointer to + # the Variable. + v = grad_and_vars[0][1] + grad_and_var = (grad, v) + average_grads.append(grad_and_var) + return average_grads + + +def sum_gradients(tower_grads): + """Calculate the average gradient for each shared variable across all towers. + Note that this function provides a synchronization point across all towers. + Args: + tower_grads: List of lists of (gradient, variable) tuples. The outer list + is over individual gradients. The inner list is over the gradient + calculation for each tower. + Returns: + List of pairs of (gradient, variable) where the gradient has been averaged + across all towers. + """ + sum_grads = [] + for grad_and_vars in zip(*tower_grads): + # Note that each grad_and_vars looks like the following: + # ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN)) + grads = [] + for g, _ in grad_and_vars: + # Add 0 dimension to the gradients to represent the tower. + expanded_g = tf.expand_dims(g, 0) + + # Append on a 'tower' dimension which we will average over below. + grads.append(expanded_g) + + # Average over the 'tower' dimension. + grad = tf.concat(axis=0, values=grads) + grad = tf.reduce_sum(grad, 0) + + # Keep in mind that the Variables are redundant because they are shared + # across towers. So .. we will just return the first tower's pointer to + # the Variable. + v = grad_and_vars[0][1] + grad_and_var = (grad, v) + sum_grads.append(grad_and_var) + return sum_grads + + +def get_gtboxes_and_label(gtboxes_and_label, num_objects): + return gtboxes_and_label[:int(num_objects), :] + + +def warmup_lr(init_lr, global_step, warmup_step, num_gpu): + def warmup(end_lr, global_step, warmup_step): + start_lr = end_lr * 0.1 + global_step = tf.cast(global_step, tf.float32) + return start_lr + (end_lr - start_lr) * global_step / warmup_step + + def decay(start_lr, global_step, num_gpu): + lr = tf.train.piecewise_constant(global_step, + boundaries=[np.int64(cfgs.DECAY_STEP[0] // num_gpu), + np.int64(cfgs.DECAY_STEP[1] // num_gpu), + np.int64(cfgs.DECAY_STEP[2] // num_gpu)], + values=[start_lr, start_lr / 10., start_lr / 100., start_lr / 1000.]) + return lr + + return tf.cond(tf.less_equal(global_step, warmup_step), + true_fn=lambda: warmup(init_lr, global_step, warmup_step), + false_fn=lambda: decay(init_lr, global_step, num_gpu)) + + +def train(): + + with tf.Graph().as_default(), tf.device('/cpu:0'): + + num_gpu = len(cfgs.GPU_GROUP.strip().split(',')) + global_step = slim.get_or_create_global_step() + + lr = warmup_lr(cfgs.LR, global_step, cfgs.WARM_SETP, num_gpu) + tf.summary.scalar('lr', lr) + + optimizer = tf.train.MomentumOptimizer(lr, momentum=cfgs.MOMENTUM) + faster_rcnn = build_whole_network.DetectionNetwork(base_network_name=cfgs.NET_NAME, + is_training=True) + + with tf.name_scope('get_batch'): + img_name_batch, img_batch, gtboxes_and_label_batch, num_objects_batch, img_h_batch, img_w_batch = \ + next_batch(dataset_name=cfgs.DATASET_NAME, # 'pascal', 'coco' + batch_size=cfgs.BATCH_SIZE * num_gpu, + shortside_len=cfgs.IMG_SHORT_SIDE_LEN, + is_training=True) + + # data processing + inputs_list = [] + for i in range(num_gpu): + # img_name = img_name_batch[i] + img = tf.expand_dims(img_batch[i], axis=0) + if cfgs.NET_NAME in ['resnet101_v1d', 'resnet50_v1d']: + img = img / tf.constant([cfgs.PIXEL_STD]) + + gtboxes_and_label = tf.cast(tf.reshape(gtboxes_and_label_batch[i], [-1, 5]), tf.float32) + num_objects = num_objects_batch[i] + num_objects = tf.cast(tf.reshape(num_objects, [-1, ]), tf.float32) + + img_h = img_h_batch[i] + img_w = img_w_batch[i] + # img_h = tf.cast(tf.reshape(img_h, [-1, ]), tf.float32) + # img_w = tf.cast(tf.reshape(img_w, [-1, ]), tf.float32) + + inputs_list.append([img, gtboxes_and_label, num_objects, img_h, img_w]) + + # put_op_list = [] + # get_op_list = [] + # for i in range(num_gpu): + # with tf.device("/GPU:%s" % i): + # area = tf.contrib.staging.StagingArea( + # dtypes=[tf.float32, tf.float32, tf.float32]) + # put_op_list.append(area.put(inputs_list[i])) + # get_op_list.append(area.get()) + + tower_grads = [] + biases_regularizer = tf.no_regularizer + weights_regularizer = tf.contrib.layers.l2_regularizer(cfgs.WEIGHT_DECAY) + + total_loss_dict = { + 'rpn_cls_loss': tf.constant(0., tf.float32), + 'rpn_loc_loss': tf.constant(0., tf.float32), + 'fastrcnn_cls_loss': tf.constant(0., tf.float32), + 'fastrcnn_loc_loss': tf.constant(0., tf.float32), + 'total_losses': tf.constant(0., tf.float32), + + } + + with tf.variable_scope(tf.get_variable_scope()): + for i in range(num_gpu): + with tf.device('/gpu:%d' % i): + with tf.name_scope('tower_%d' % i): + with slim.arg_scope( + [slim.model_variable, slim.variable], + device='/device:CPU:0'): + with slim.arg_scope([slim.conv2d, slim.conv2d_in_plane, + slim.conv2d_transpose, slim.separable_conv2d, slim.fully_connected], + weights_regularizer=weights_regularizer, + biases_regularizer=biases_regularizer, + biases_initializer=tf.constant_initializer(0.0)): + + gtboxes_and_label = tf.py_func(get_gtboxes_and_label, + inp=[inputs_list[i][1], inputs_list[i][2]], + Tout=tf.float32) + gtboxes_and_label = tf.reshape(gtboxes_and_label, [-1, 5]) + + img = inputs_list[i][0] + img_shape = inputs_list[i][-2:] + img = tf.image.crop_to_bounding_box(image=img, + offset_height=0, + offset_width=0, + target_height=tf.cast(img_shape[0], tf.int32), + target_width=tf.cast(img_shape[1], tf.int32)) + + outputs = faster_rcnn.build_whole_detection_network(input_img_batch=img, + gtboxes_batch=gtboxes_and_label) + gtboxes_in_img = show_box_in_tensor.draw_boxes_with_categories(img_batch=img, + boxes=gtboxes_and_label[ + :, :-1], + labels=gtboxes_and_label[ + :, -1]) + tf.summary.image('Compare/gtboxes_gpu:%d' % i, gtboxes_in_img) + + if cfgs.ADD_BOX_IN_TENSORBOARD: + detections_in_img = show_box_in_tensor.draw_boxes_with_categories_and_scores( + img_batch=img, + boxes=outputs[0], + scores=outputs[1], + labels=outputs[2]) + tf.summary.image('Compare/final_detection_gpu:%d' % i, detections_in_img) + + loss_dict = outputs[-1] + + total_losses = 0.0 + for k in loss_dict.keys(): + total_losses += loss_dict[k] + total_loss_dict[k] += loss_dict[k] / num_gpu + + total_losses = total_losses / num_gpu + total_loss_dict['total_losses'] += total_losses + + if i == num_gpu - 1: + regularization_losses = tf.get_collection( + tf.GraphKeys.REGULARIZATION_LOSSES) + # weight_decay_loss = tf.add_n(slim.losses.get_regularization_losses()) + total_losses = total_losses + tf.add_n(regularization_losses) + + tf.get_variable_scope().reuse_variables() + grads = optimizer.compute_gradients(total_losses) + tower_grads.append(grads) + + for k in total_loss_dict.keys(): + tf.summary.scalar('{}/{}'.format(k.split('_')[0], k), total_loss_dict[k]) + + if len(tower_grads) > 1: + grads = sum_gradients(tower_grads) + else: + grads = tower_grads[0] + + # final_gvs = [] + # with tf.variable_scope('Gradient_Mult'): + # for grad, var in grads: + # scale = 1. + # # if '/biases:' in var.name: + # # scale *= 2. + # if 'conv_new' in var.name: + # scale *= 3. + # if not np.allclose(scale, 1.0): + # grad = tf.multiply(grad, scale) + # final_gvs.append((grad, var)) + + apply_gradient_op = optimizer.apply_gradients(grads, global_step=global_step) + + variable_averages = tf.train.ExponentialMovingAverage(0.9999, global_step) + variables_averages_op = variable_averages.apply(tf.trainable_variables()) + + train_op = tf.group(apply_gradient_op, variables_averages_op) + # train_op = optimizer.apply_gradients(final_gvs, global_step=global_step) + summary_op = tf.summary.merge_all() + + restorer, restore_ckpt = faster_rcnn.get_restorer() + saver = tf.train.Saver(max_to_keep=10) + + init_op = tf.group( + tf.global_variables_initializer(), + tf.local_variables_initializer() + ) + + tfconfig = tf.ConfigProto( + allow_soft_placement=True, log_device_placement=False) + tfconfig.gpu_options.allow_growth = True + with tf.Session(config=tfconfig) as sess: + sess.run(init_op) + + # sess.run(tf.initialize_all_variables()) + coord = tf.train.Coordinator() + threads = tf.train.start_queue_runners(coord=coord, sess=sess) + + summary_path = os.path.join(cfgs.SUMMARY_PATH, cfgs.VERSION) + tools.mkdir(summary_path) + summary_writer = tf.summary.FileWriter(summary_path, graph=sess.graph) + + if not restorer is None: + restorer.restore(sess, restore_ckpt) + print('restore model') + + for step in range(cfgs.MAX_ITERATION // num_gpu): + training_time = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())) + + # start = time.time() + # + # _, global_stepnp, total_loss_dict_ = \ + # sess.run([train_op, global_step, total_loss_dict]) + # + # end = time.time() + # + # print('**' * 20) + # print("""%s: global_step%d current_step%d""" + # % (training_time, global_stepnp * num_gpu, step * num_gpu)) + # print("""per_cost_time:%.3fs""" + # % ((end - start) / num_gpu)) + # loss_str = '' + # for k in total_loss_dict_.keys(): + # loss_str += '%s:%.3f\n' % (k, total_loss_dict_[k]) + # print(loss_str) + + if step % cfgs.SHOW_TRAIN_INFO_INTE != 0 and step % cfgs.SMRY_ITER != 0: + _, global_stepnp = sess.run([train_op, global_step]) + + else: + if step % cfgs.SHOW_TRAIN_INFO_INTE == 0 and step % cfgs.SMRY_ITER != 0: + start = time.time() + + _, global_stepnp, total_loss_dict_ = \ + sess.run([train_op, global_step, total_loss_dict]) + + end = time.time() + + print('***'*20) + print("""%s: global_step:%d current_step:%d""" + % (training_time, (global_stepnp-1)*num_gpu, step*num_gpu)) + print("""per_cost_time:%.3fs""" + % ((end - start) / num_gpu)) + loss_str = '' + for k in total_loss_dict_.keys(): + loss_str += '%s:%.3f\n' % (k, total_loss_dict_[k]) + print(loss_str) + + else: + if step % cfgs.SMRY_ITER == 0: + _, global_stepnp, summary_str = sess.run([train_op, global_step, summary_op]) + summary_writer.add_summary(summary_str, (global_stepnp-1)*num_gpu) + summary_writer.flush() + + if (step > 0 and step % (cfgs.SAVE_WEIGHTS_INTE // num_gpu) == 0) or (step >= cfgs.MAX_ITERATION // num_gpu - 1): + + save_dir = os.path.join(cfgs.TRAINED_CKPT, cfgs.VERSION) + if not os.path.exists(save_dir): + os.mkdir(save_dir) + + save_ckpt = os.path.join(save_dir, 'coco_' + str((global_stepnp-1)*num_gpu) + 'model.ckpt') + saver.save(sess, save_ckpt) + print(' weights had been saved') + + coord.request_stop() + coord.join(threads) + + +if __name__ == '__main__': + + train() + + + + + + + + + + diff --git a/tools/multi_gpu_train_aug.py b/tools/multi_gpu_train_aug.py new file mode 100644 index 0000000..cc84ab6 --- /dev/null +++ b/tools/multi_gpu_train_aug.py @@ -0,0 +1,349 @@ +# -*- coding:utf-8 -*- + +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import tensorflow as tf +import tensorflow.contrib.slim as slim +import os, sys +import numpy as np +import time +sys.path.append("../") + +from libs.configs import cfgs +# from libs.networks import build_whole_network2 +from libs.networks import build_whole_network +from data.io.read_tfrecord_multi_gpu_aug import next_batch +from libs.box_utils import show_box_in_tensor +from help_utils import tools + +os.environ["CUDA_VISIBLE_DEVICES"] = cfgs.GPU_GROUP + + +def average_gradients(tower_grads): + """Calculate the average gradient for each shared variable across all towers. + Note that this function provides a synchronization point across all towers. + Args: + tower_grads: List of lists of (gradient, variable) tuples. The outer list + is over individual gradients. The inner list is over the gradient + calculation for each tower. + Returns: + List of pairs of (gradient, variable) where the gradient has been averaged + across all towers. + """ + average_grads = [] + for grad_and_vars in zip(*tower_grads): + # Note that each grad_and_vars looks like the following: + # ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN)) + grads = [] + for g, _ in grad_and_vars: + # Add 0 dimension to the gradients to represent the tower. + expanded_g = tf.expand_dims(g, 0) + + # Append on a 'tower' dimension which we will average over below. + grads.append(expanded_g) + + # Average over the 'tower' dimension. + grad = tf.concat(axis=0, values=grads) + grad = tf.reduce_mean(grad, 0) + + # Keep in mind that the Variables are redundant because they are shared + # across towers. So .. we will just return the first tower's pointer to + # the Variable. + v = grad_and_vars[0][1] + grad_and_var = (grad, v) + average_grads.append(grad_and_var) + return average_grads + + +def sum_gradients(tower_grads): + """Calculate the average gradient for each shared variable across all towers. + Note that this function provides a synchronization point across all towers. + Args: + tower_grads: List of lists of (gradient, variable) tuples. The outer list + is over individual gradients. The inner list is over the gradient + calculation for each tower. + Returns: + List of pairs of (gradient, variable) where the gradient has been averaged + across all towers. + """ + sum_grads = [] + for grad_and_vars in zip(*tower_grads): + # Note that each grad_and_vars looks like the following: + # ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN)) + grads = [] + for g, _ in grad_and_vars: + # Add 0 dimension to the gradients to represent the tower. + expanded_g = tf.expand_dims(g, 0) + + # Append on a 'tower' dimension which we will average over below. + grads.append(expanded_g) + + # Average over the 'tower' dimension. + grad = tf.concat(axis=0, values=grads) + grad = tf.reduce_sum(grad, 0) + + # Keep in mind that the Variables are redundant because they are shared + # across towers. So .. we will just return the first tower's pointer to + # the Variable. + v = grad_and_vars[0][1] + grad_and_var = (grad, v) + sum_grads.append(grad_and_var) + return sum_grads + + +def get_gtboxes_and_label(gtboxes_and_label, num_objects): + return gtboxes_and_label[:int(num_objects), :] + + +def train(): + + with tf.Graph().as_default(), tf.device('/cpu:0'): + + num_gpu = len(cfgs.GPU_GROUP.strip().split(',')) + global_step = slim.get_or_create_global_step() + lr = tf.train.piecewise_constant(global_step, + boundaries=[np.int64(cfgs.DECAY_STEP[0] // num_gpu), + np.int64(cfgs.DECAY_STEP[1] // num_gpu), + np.int64(cfgs.DECAY_STEP[2] // num_gpu)], + values=[cfgs.LR, cfgs.LR / 10., cfgs.LR / 100., cfgs.LR / 1000.]) + tf.summary.scalar('lr', lr) + + optimizer = tf.train.MomentumOptimizer(lr, momentum=cfgs.MOMENTUM) + faster_rcnn = build_whole_network.DetectionNetwork(base_network_name=cfgs.NET_NAME, + is_training=True) + + with tf.name_scope('get_batch'): + img_name_batch, img_batch, gtboxes_and_label_batch, num_objects_batch, img_h_batch, img_w_batch = \ + next_batch(dataset_name=cfgs.DATASET_NAME, # 'pascal', 'coco' + batch_size=cfgs.BATCH_SIZE * num_gpu, + shortside_len=cfgs.IMG_SHORT_SIDE_LEN, + is_training=True) + if cfgs.NET_NAME in ['resnet101_v1d', 'resnet50_v1d']: + img_batch = img_batch / tf.constant([cfgs.PIXEL_STD]) + + # data processing + inputs_list = [] + for i in range(num_gpu): + # img_name = img_name_batch[i] + img_ex = tf.expand_dims(img_batch[i], axis=0) + # if cfgs.NET_NAME in ['resnet101_v1d', 'resnet50_v1d']: + # img = img / tf.constant([cfgs.PIXEL_STD]) + + gtboxes_and_label_pad = tf.cast(tf.reshape(gtboxes_and_label_batch[i], [-1, 5]), tf.float32) + num_objects = num_objects_batch[i] + num_objects = tf.cast(tf.reshape(num_objects, [-1, ]), tf.float32) + + img_h = img_h_batch[i] + img_w = img_w_batch[i] + # img_h = tf.cast(tf.reshape(img_h, [-1, ]), tf.float32) + # img_w = tf.cast(tf.reshape(img_w, [-1, ]), tf.float32) + + inputs_list.append([img_ex, gtboxes_and_label_pad, num_objects, img_h, img_w]) + + # put_op_list = [] + # get_op_list = [] + # for i in range(num_gpu): + # with tf.device("/GPU:%s" % i): + # area = tf.contrib.staging.StagingArea( + # dtypes=[tf.float32, tf.float32, tf.float32]) + # put_op_list.append(area.put(inputs_list[i])) + # get_op_list.append(area.get()) + + tower_grads = [] + biases_regularizer = tf.no_regularizer + weights_regularizer = tf.contrib.layers.l2_regularizer(cfgs.WEIGHT_DECAY) + + total_loss_dict = { + 'rpn_cls_loss': tf.constant(0., tf.float32), + 'rpn_loc_loss': tf.constant(0., tf.float32), + 'fastrcnn_cls_loss': tf.constant(0., tf.float32), + 'fastrcnn_loc_loss': tf.constant(0., tf.float32), + 'total_losses': tf.constant(0., tf.float32), + + } + if cfgs.USE_ATTENTION: + total_loss_dict['mask_loss'] = tf.constant(0., tf.float32) + + with tf.variable_scope(tf.get_variable_scope()): + for i in range(num_gpu): + with tf.device('/gpu:%d' % i): + with tf.name_scope('tower_%d' % i): + with slim.arg_scope( + [slim.model_variable, slim.variable], + device='/device:CPU:0'): + with slim.arg_scope([slim.conv2d, slim.conv2d_in_plane, + slim.conv2d_transpose, slim.separable_conv2d, slim.fully_connected], + weights_regularizer=weights_regularizer, + biases_regularizer=biases_regularizer, + biases_initializer=tf.constant_initializer(0.0)): + + gtboxes_and_label = tf.py_func(get_gtboxes_and_label, + inp=[inputs_list[i][1], inputs_list[i][2]], + Tout=tf.float32) + gtboxes_and_label = tf.reshape(gtboxes_and_label, [-1, 5]) + + img = inputs_list[i][0] + img_shape = inputs_list[i][-2:] + img = tf.image.crop_to_bounding_box(image=img, + offset_height=0, + offset_width=0, + target_height=tf.cast(img_shape[0], tf.int32), + target_width=tf.cast(img_shape[1], tf.int32)) + + outputs = faster_rcnn.build_whole_detection_network(input_img_batch=img, + gtboxes_batch=gtboxes_and_label) + gtboxes_in_img = show_box_in_tensor.draw_boxes_with_categories(img_batch=img, + boxes=gtboxes_and_label[ + :, :-1], + labels=gtboxes_and_label[ + :, -1]) + tf.summary.image('Compare/gtboxes_gpu:%d' % i, gtboxes_in_img) + + if cfgs.ADD_BOX_IN_TENSORBOARD: + detections_in_img = show_box_in_tensor.draw_boxes_with_categories_and_scores( + img_batch=img, + boxes=outputs[0], + scores=outputs[1], + labels=outputs[2]) + tf.summary.image('Compare/final_detection_gpu:%d' % i, detections_in_img) + + loss_dict = outputs[-1] + + total_losses = 0.0 + for k in loss_dict.keys(): + total_losses += loss_dict[k] + total_loss_dict[k] += loss_dict[k] / num_gpu + + total_losses = total_losses / num_gpu + total_loss_dict['total_losses'] += total_losses + + if i == num_gpu - 1: + regularization_losses = tf.get_collection( + tf.GraphKeys.REGULARIZATION_LOSSES) + # weight_decay_loss = tf.add_n(slim.losses.get_regularization_losses()) + total_losses = total_losses + tf.add_n(regularization_losses) + + tf.get_variable_scope().reuse_variables() + grads = optimizer.compute_gradients(total_losses) + tower_grads.append(grads) + + for k in total_loss_dict.keys(): + tf.summary.scalar('{}/{}'.format(k.split('_')[0], k), total_loss_dict[k]) + + if len(tower_grads) > 1: + grads = sum_gradients(tower_grads) + else: + grads = tower_grads[0] + + # final_gvs = [] + # with tf.variable_scope('Gradient_Mult'): + # for grad, var in grads: + # scale = 1. + # # if '/biases:' in var.name: + # # scale *= 2. + # if 'conv_new' in var.name: + # scale *= 3. + # if not np.allclose(scale, 1.0): + # grad = tf.multiply(grad, scale) + # final_gvs.append((grad, var)) + + apply_gradient_op = optimizer.apply_gradients(grads, global_step=global_step) + + variable_averages = tf.train.ExponentialMovingAverage(0.9999, global_step) + variables_averages_op = variable_averages.apply(tf.trainable_variables()) + + train_op = tf.group(apply_gradient_op, variables_averages_op) + # train_op = optimizer.apply_gradients(final_gvs, global_step=global_step) + summary_op = tf.summary.merge_all() + + restorer, restore_ckpt = faster_rcnn.get_restorer() + saver = tf.train.Saver(max_to_keep=10) + + init_op = tf.group( + tf.global_variables_initializer(), + tf.local_variables_initializer() + ) + + tfconfig = tf.ConfigProto( + allow_soft_placement=True, log_device_placement=False) + tfconfig.gpu_options.allow_growth = True + with tf.Session(config=tfconfig) as sess: + sess.run(init_op) + + # sess.run(tf.initialize_all_variables()) + coord = tf.train.Coordinator() + threads = tf.train.start_queue_runners(coord=coord, sess=sess) + + summary_path = os.path.join(cfgs.SUMMARY_PATH, cfgs.VERSION) + tools.mkdir(summary_path) + summary_writer = tf.summary.FileWriter(summary_path, graph=sess.graph) + + if not restorer is None: + restorer.restore(sess, restore_ckpt) + print('restore model') + + for step in range(cfgs.MAX_ITERATION // num_gpu): + training_time = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())) + + # inputs_list_ = sess.run(inputs_list) + # import cv2 + # for kk in range(4): + # cv2.imwrite('./img{}.jpg'.format(kk), inputs_list_[kk][0][0]) + + if step % cfgs.SHOW_TRAIN_INFO_INTE != 0 and step % cfgs.SMRY_ITER != 0: + _, global_stepnp = sess.run([train_op, global_step]) + + else: + if step % cfgs.SHOW_TRAIN_INFO_INTE == 0 and step % cfgs.SMRY_ITER != 0: + start = time.time() + + _, global_stepnp, total_loss_dict_ = \ + sess.run([train_op, global_step, total_loss_dict]) + + end = time.time() + + print('***'*20) + print("""%s: global_step:%d current_step:%d""" + % (training_time, (global_stepnp-1)*num_gpu, step*num_gpu)) + print("""per_cost_time:%.3fs""" + % ((end - start) / num_gpu)) + loss_str = '' + for k in total_loss_dict_.keys(): + loss_str += '%s:%.3f\n' % (k, total_loss_dict_[k]) + print(loss_str) + + else: + if step % cfgs.SMRY_ITER == 0: + _, global_stepnp, summary_str = sess.run([train_op, global_step, summary_op]) + summary_writer.add_summary(summary_str, (global_stepnp-1)*num_gpu) + summary_writer.flush() + + if (step > 0 and step % (cfgs.SAVE_WEIGHTS_INTE // num_gpu) == 0) or (step >= cfgs.MAX_ITERATION // num_gpu - 1): + + save_dir = os.path.join(cfgs.TRAINED_CKPT, cfgs.VERSION) + if not os.path.exists(save_dir): + os.mkdir(save_dir) + + save_ckpt = os.path.join(save_dir, 'coco_' + str((global_stepnp-1)*num_gpu) + 'model.ckpt') + saver.save(sess, save_ckpt) + print(' weights had been saved') + + coord.request_stop() + coord.join(threads) + + +if __name__ == '__main__': + + train() + + + + + + + + + + diff --git a/tools/multi_gpu_train_cascade.py b/tools/multi_gpu_train_cascade.py new file mode 100644 index 0000000..9e716eb --- /dev/null +++ b/tools/multi_gpu_train_cascade.py @@ -0,0 +1,372 @@ +# -*- coding:utf-8 -*- + +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import tensorflow as tf +import tensorflow.contrib.slim as slim +import os, sys +import numpy as np +import time +sys.path.append("../") + +from libs.configs import cfgs +from libs.networks import build_whole_network_cascade +from data.io.read_tfrecord_multi_gpu import next_batch +from libs.box_utils import show_box_in_tensor +from help_utils import tools + +os.environ["CUDA_VISIBLE_DEVICES"] = cfgs.GPU_GROUP + + +def average_gradients(tower_grads): + """Calculate the average gradient for each shared variable across all towers. + Note that this function provides a synchronization point across all towers. + Args: + tower_grads: List of lists of (gradient, variable) tuples. The outer list + is over individual gradients. The inner list is over the gradient + calculation for each tower. + Returns: + List of pairs of (gradient, variable) where the gradient has been averaged + across all towers. + """ + average_grads = [] + for grad_and_vars in zip(*tower_grads): + # Note that each grad_and_vars looks like the following: + # ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN)) + grads = [] + for g, _ in grad_and_vars: + # Add 0 dimension to the gradients to represent the tower. + expanded_g = tf.expand_dims(g, 0) + + # Append on a 'tower' dimension which we will average over below. + grads.append(expanded_g) + + # Average over the 'tower' dimension. + grad = tf.concat(axis=0, values=grads) + grad = tf.reduce_mean(grad, 0) + + # Keep in mind that the Variables are redundant because they are shared + # across towers. So .. we will just return the first tower's pointer to + # the Variable. + v = grad_and_vars[0][1] + grad_and_var = (grad, v) + average_grads.append(grad_and_var) + return average_grads + + +def sum_gradients(tower_grads): + """Calculate the average gradient for each shared variable across all towers. + Note that this function provides a synchronization point across all towers. + Args: + tower_grads: List of lists of (gradient, variable) tuples. The outer list + is over individual gradients. The inner list is over the gradient + calculation for each tower. + Returns: + List of pairs of (gradient, variable) where the gradient has been averaged + across all towers. + """ + sum_grads = [] + for grad_and_vars in zip(*tower_grads): + # Note that each grad_and_vars looks like the following: + # ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN)) + grads = [] + for g, _ in grad_and_vars: + # Add 0 dimension to the gradients to represent the tower. + expanded_g = tf.expand_dims(g, 0) + + # Append on a 'tower' dimension which we will average over below. + grads.append(expanded_g) + + # Average over the 'tower' dimension. + grad = tf.concat(axis=0, values=grads) + grad = tf.reduce_sum(grad, 0) + + # Keep in mind that the Variables are redundant because they are shared + # across towers. So .. we will just return the first tower's pointer to + # the Variable. + v = grad_and_vars[0][1] + grad_and_var = (grad, v) + sum_grads.append(grad_and_var) + return sum_grads + + +def get_gtboxes_and_label(gtboxes_and_label, num_objects): + return gtboxes_and_label[:int(num_objects), :] + + +def warmup_lr(init_lr, global_step, warmup_step, num_gpu): + def warmup(end_lr, global_step, warmup_step): + start_lr = end_lr * 0.1 + global_step = tf.cast(global_step, tf.float32) + return start_lr + (end_lr - start_lr) * global_step / warmup_step + + def decay(start_lr, global_step, num_gpu): + lr = tf.train.piecewise_constant(global_step, + boundaries=[np.int64(cfgs.DECAY_STEP[0] // num_gpu), + np.int64(cfgs.DECAY_STEP[1] // num_gpu), + np.int64(cfgs.DECAY_STEP[2] // num_gpu)], + values=[start_lr, start_lr / 10., start_lr / 100., start_lr / 1000.]) + return lr + + return tf.cond(tf.less_equal(global_step, warmup_step), + true_fn=lambda: warmup(init_lr, global_step, warmup_step), + false_fn=lambda: decay(init_lr, global_step, num_gpu)) + + +def train(): + + with tf.Graph().as_default(), tf.device('/cpu:0'): + + num_gpu = len(cfgs.GPU_GROUP.strip().split(',')) + global_step = slim.get_or_create_global_step() + + lr = warmup_lr(cfgs.LR, global_step, cfgs.WARM_SETP, num_gpu) + tf.summary.scalar('lr', lr) + + optimizer = tf.train.MomentumOptimizer(lr, momentum=cfgs.MOMENTUM) + faster_rcnn = build_whole_network_cascade.DetectionNetwork(base_network_name=cfgs.NET_NAME, + is_training=True) + + with tf.name_scope('get_batch'): + img_name_batch, img_batch, gtboxes_and_label_batch, num_objects_batch, img_h_batch, img_w_batch = \ + next_batch(dataset_name=cfgs.DATASET_NAME, # 'pascal', 'coco' + batch_size=cfgs.BATCH_SIZE * num_gpu, + shortside_len=cfgs.IMG_SHORT_SIDE_LEN, + is_training=True) + + # data processing + inputs_list = [] + for i in range(num_gpu): + # img_name = img_name_batch[i] + img = tf.expand_dims(img_batch[i], axis=0) + if cfgs.NET_NAME in ['resnet101_v1d', 'resnet50_v1d']: + img = img / tf.constant([cfgs.PIXEL_STD]) + + gtboxes_and_label = tf.cast(tf.reshape(gtboxes_and_label_batch[i], [-1, 5]), tf.float32) + num_objects = num_objects_batch[i] + num_objects = tf.cast(tf.reshape(num_objects, [-1, ]), tf.float32) + + img_h = img_h_batch[i] + img_w = img_w_batch[i] + # img_h = tf.cast(tf.reshape(img_h, [-1, ]), tf.float32) + # img_w = tf.cast(tf.reshape(img_w, [-1, ]), tf.float32) + + inputs_list.append([img, gtboxes_and_label, num_objects, img_h, img_w]) + + # put_op_list = [] + # get_op_list = [] + # for i in range(num_gpu): + # with tf.device("/GPU:%s" % i): + # area = tf.contrib.staging.StagingArea( + # dtypes=[tf.float32, tf.float32, tf.float32]) + # put_op_list.append(area.put(inputs_list[i])) + # get_op_list.append(area.get()) + + tower_grads = [] + biases_regularizer = tf.no_regularizer + weights_regularizer = tf.contrib.layers.l2_regularizer(cfgs.WEIGHT_DECAY) + + total_loss_dict = { + 'rpn_cls_loss': tf.constant(0., tf.float32), + 'rpn_loc_loss': tf.constant(0., tf.float32), + 'fastrcnn_cls_loss': tf.constant(0., tf.float32), + 'fastrcnn_loc_loss': tf.constant(0., tf.float32), + 'total_losses': tf.constant(0., tf.float32), + + } + + with tf.variable_scope(tf.get_variable_scope()): + for i in range(num_gpu): + with tf.device('/gpu:%d' % i): + with tf.name_scope('tower_%d' % i): + with slim.arg_scope( + [slim.model_variable, slim.variable], + device='/device:CPU:0'): + with slim.arg_scope([slim.conv2d, slim.conv2d_in_plane, + slim.conv2d_transpose, slim.separable_conv2d, slim.fully_connected], + weights_regularizer=weights_regularizer, + biases_regularizer=biases_regularizer, + biases_initializer=tf.constant_initializer(0.0)): + + gtboxes_and_label = tf.py_func(get_gtboxes_and_label, + inp=[inputs_list[i][1], inputs_list[i][2]], + Tout=tf.float32) + gtboxes_and_label = tf.reshape(gtboxes_and_label, [-1, 5]) + + img = inputs_list[i][0] + img_shape = inputs_list[i][-2:] + img = tf.image.crop_to_bounding_box(image=img, + offset_height=0, + offset_width=0, + target_height=tf.cast(img_shape[0], tf.int32), + target_width=tf.cast(img_shape[1], tf.int32)) + + outputs = faster_rcnn.build_whole_detection_network(input_img_batch=img, + gtboxes_batch=gtboxes_and_label) + gtboxes_in_img = show_box_in_tensor.draw_boxes_with_categories(img_batch=img, + boxes=gtboxes_and_label[ + :, :-1], + labels=gtboxes_and_label[ + :, -1]) + tf.summary.image('Compare/gtboxes_gpu:%d' % i, gtboxes_in_img) + + if cfgs.ADD_BOX_IN_TENSORBOARD: + detections_in_img = show_box_in_tensor.draw_boxes_with_categories_and_scores( + img_batch=img, + boxes=outputs[0], + scores=outputs[1], + labels=outputs[2]) + tf.summary.image('Compare/final_detection_gpu:%d' % i, detections_in_img) + + loss_dict = outputs[-1] + + total_losses = 0.0 + for k in loss_dict.keys(): + total_losses += loss_dict[k] + total_loss_dict[k] += loss_dict[k] / num_gpu + + total_losses = total_losses / num_gpu + total_loss_dict['total_losses'] += total_losses + + if i == num_gpu - 1: + regularization_losses = tf.get_collection( + tf.GraphKeys.REGULARIZATION_LOSSES) + # weight_decay_loss = tf.add_n(slim.losses.get_regularization_losses()) + total_losses = total_losses + tf.add_n(regularization_losses) + + tf.get_variable_scope().reuse_variables() + grads = optimizer.compute_gradients(total_losses) + tower_grads.append(grads) + + for k in total_loss_dict.keys(): + tf.summary.scalar('{}/{}'.format(k.split('_')[0], k), total_loss_dict[k]) + + if len(tower_grads) > 1: + grads = sum_gradients(tower_grads) + else: + grads = tower_grads[0] + + # final_gvs = [] + # with tf.variable_scope('Gradient_Mult'): + # for grad, var in grads: + # scale = 1. + # # if '/biases:' in var.name: + # # scale *= 2. + # if 'conv_new' in var.name: + # scale *= 3. + # if not np.allclose(scale, 1.0): + # grad = tf.multiply(grad, scale) + # final_gvs.append((grad, var)) + + apply_gradient_op = optimizer.apply_gradients(grads, global_step=global_step) + + variable_averages = tf.train.ExponentialMovingAverage(0.9999, global_step) + variables_averages_op = variable_averages.apply(tf.trainable_variables()) + + train_op = tf.group(apply_gradient_op, variables_averages_op) + # train_op = optimizer.apply_gradients(final_gvs, global_step=global_step) + summary_op = tf.summary.merge_all() + + restorer, restore_ckpt = faster_rcnn.get_restorer() + saver = tf.train.Saver(max_to_keep=10) + + init_op = tf.group( + tf.global_variables_initializer(), + tf.local_variables_initializer() + ) + + tfconfig = tf.ConfigProto( + allow_soft_placement=True, log_device_placement=False) + tfconfig.gpu_options.allow_growth = True + with tf.Session(config=tfconfig) as sess: + sess.run(init_op) + + # sess.run(tf.initialize_all_variables()) + coord = tf.train.Coordinator() + threads = tf.train.start_queue_runners(coord=coord, sess=sess) + + summary_path = os.path.join(cfgs.SUMMARY_PATH, cfgs.VERSION) + tools.mkdir(summary_path) + summary_writer = tf.summary.FileWriter(summary_path, graph=sess.graph) + + if not restorer is None: + restorer.restore(sess, restore_ckpt) + print('restore model') + + for step in range(cfgs.MAX_ITERATION // num_gpu): + training_time = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())) + + # start = time.time() + # + # _, global_stepnp, total_loss_dict_ = \ + # sess.run([train_op, global_step, total_loss_dict]) + # + # end = time.time() + # + # print('**' * 20) + # print("""%s: global_step%d current_step%d""" + # % (training_time, global_stepnp * num_gpu, step * num_gpu)) + # print("""per_cost_time:%.3fs""" + # % ((end - start) / num_gpu)) + # loss_str = '' + # for k in total_loss_dict_.keys(): + # loss_str += '%s:%.3f\n' % (k, total_loss_dict_[k]) + # print(loss_str) + + if step % cfgs.SHOW_TRAIN_INFO_INTE != 0 and step % cfgs.SMRY_ITER != 0: + _, global_stepnp = sess.run([train_op, global_step]) + + else: + if step % cfgs.SHOW_TRAIN_INFO_INTE == 0 and step % cfgs.SMRY_ITER != 0: + start = time.time() + + _, global_stepnp, total_loss_dict_ = \ + sess.run([train_op, global_step, total_loss_dict]) + + end = time.time() + + print('***'*20) + print("""%s: global_step:%d current_step:%d""" + % (training_time, (global_stepnp-1)*num_gpu, step*num_gpu)) + print("""per_cost_time:%.3fs""" + % ((end - start) / num_gpu)) + loss_str = '' + for k in total_loss_dict_.keys(): + loss_str += '%s:%.3f\n' % (k, total_loss_dict_[k]) + print(loss_str) + + else: + if step % cfgs.SMRY_ITER == 0: + _, global_stepnp, summary_str = sess.run([train_op, global_step, summary_op]) + summary_writer.add_summary(summary_str, (global_stepnp-1)*num_gpu) + summary_writer.flush() + + if (step > 0 and step % (cfgs.SAVE_WEIGHTS_INTE // num_gpu) == 0) or (step >= cfgs.MAX_ITERATION // num_gpu - 1): + + save_dir = os.path.join(cfgs.TRAINED_CKPT, cfgs.VERSION) + if not os.path.exists(save_dir): + os.mkdir(save_dir) + + save_ckpt = os.path.join(save_dir, 'coco_' + str((global_stepnp-1)*num_gpu) + 'model.ckpt') + saver.save(sess, save_ckpt) + print(' weights had been saved') + + coord.request_stop() + coord.join(threads) + + +if __name__ == '__main__': + + train() + + + + + + + + + + diff --git a/tools/multi_gpu_train_warmup_cosine.py b/tools/multi_gpu_train_warmup_cosine.py new file mode 100644 index 0000000..9ce043e --- /dev/null +++ b/tools/multi_gpu_train_warmup_cosine.py @@ -0,0 +1,380 @@ +# -*- coding:utf-8 -*- + +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import tensorflow as tf +import tensorflow.contrib.slim as slim +import os, sys +import numpy as np +import time +sys.path.append("../") + +from libs.configs import cfgs +# from libs.networks import build_whole_network2 +from libs.networks import build_whole_network +from data.io.read_tfrecord_multi_gpu import next_batch +from libs.box_utils import show_box_in_tensor +from help_utils import tools + +os.environ["CUDA_VISIBLE_DEVICES"] = cfgs.GPU_GROUP + + +def warmup_and_cosine_lr(init_lr, global_step, warmup_step, decay_steps, alpha=1e-6): + def warmup_lr(init_lr, global_step, warmup_step): + global_step = tf.cast(global_step, tf.float32) + return 1e-6 + (init_lr - 1e-6) * global_step / warmup_step + + def cosine_lr(init_lr, global_step, decay_steps, alpha=0.0): + return tf.train.cosine_decay(learning_rate=init_lr, + global_step=global_step - warmup_step, + decay_steps=decay_steps - warmup_step, + alpha=alpha) + + return tf.cond(tf.less_equal(global_step, warmup_step), + true_fn=lambda: warmup_lr(init_lr, global_step, warmup_step), + false_fn=lambda: cosine_lr(init_lr, global_step, decay_steps, alpha)) + + +def average_gradients(tower_grads): + """Calculate the average gradient for each shared variable across all towers. + Note that this function provides a synchronization point across all towers. + Args: + tower_grads: List of lists of (gradient, variable) tuples. The outer list + is over individual gradients. The inner list is over the gradient + calculation for each tower. + Returns: + List of pairs of (gradient, variable) where the gradient has been averaged + across all towers. + """ + average_grads = [] + for grad_and_vars in zip(*tower_grads): + # Note that each grad_and_vars looks like the following: + # ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN)) + grads = [] + for g, _ in grad_and_vars: + # Add 0 dimension to the gradients to represent the tower. + expanded_g = tf.expand_dims(g, 0) + + # Append on a 'tower' dimension which we will average over below. + grads.append(expanded_g) + + # Average over the 'tower' dimension. + grad = tf.concat(axis=0, values=grads) + grad = tf.reduce_mean(grad, 0) + + # Keep in mind that the Variables are redundant because they are shared + # across towers. So .. we will just return the first tower's pointer to + # the Variable. + v = grad_and_vars[0][1] + grad_and_var = (grad, v) + average_grads.append(grad_and_var) + return average_grads + + +def sum_gradients(tower_grads): + """Calculate the average gradient for each shared variable across all towers. + Note that this function provides a synchronization point across all towers. + Args: + tower_grads: List of lists of (gradient, variable) tuples. The outer list + is over individual gradients. The inner list is over the gradient + calculation for each tower. + Returns: + List of pairs of (gradient, variable) where the gradient has been averaged + across all towers. + """ + sum_grads = [] + for grad_and_vars in zip(*tower_grads): + # Note that each grad_and_vars looks like the following: + # ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN)) + grads = [] + for g, _ in grad_and_vars: + # Add 0 dimension to the gradients to represent the tower. + expanded_g = tf.expand_dims(g, 0) + + # Append on a 'tower' dimension which we will average over below. + grads.append(expanded_g) + + # Average over the 'tower' dimension. + grad = tf.concat(axis=0, values=grads) + grad = tf.reduce_sum(grad, 0) + + # Keep in mind that the Variables are redundant because they are shared + # across towers. So .. we will just return the first tower's pointer to + # the Variable. + v = grad_and_vars[0][1] + grad_and_var = (grad, v) + sum_grads.append(grad_and_var) + return sum_grads + + +def get_gtboxes_and_label(gtboxes_and_label, num_objects): + return gtboxes_and_label[:int(num_objects), :] + + +def train(): + + with tf.Graph().as_default(), tf.device('/cpu:0'): + + num_gpu = len(cfgs.GPU_GROUP.strip().split(',')) + global_step = slim.get_or_create_global_step() + # lr = tf.train.piecewise_constant(global_step, + # boundaries=[np.int64(cfgs.DECAY_STEP[0] // num_gpu), + # np.int64(cfgs.DECAY_STEP[1] // num_gpu), + # np.int64(cfgs.DECAY_STEP[2] // num_gpu)], + # values=[cfgs.LR, cfgs.LR / 10., cfgs.LR / 100., cfgs.LR / 1000.]) + + lr = warmup_and_cosine_lr(init_lr=cfgs.LR, + global_step=global_step, + warmup_step=5000, decay_steps=cfgs.MAX_ITERATION, alpha=1e-6) + tf.summary.scalar('lr', lr) + + optimizer = tf.train.MomentumOptimizer(lr, momentum=cfgs.MOMENTUM) + faster_rcnn = build_whole_network.DetectionNetwork(base_network_name=cfgs.NET_NAME, + is_training=True) + + with tf.name_scope('get_batch'): + img_name_batch, img_batch, gtboxes_and_label_batch, num_objects_batch, img_h_batch, img_w_batch = \ + next_batch(dataset_name=cfgs.DATASET_NAME, # 'pascal', 'coco' + batch_size=cfgs.BATCH_SIZE * num_gpu, + shortside_len=cfgs.IMG_SHORT_SIDE_LEN, + is_training=True) + # gtboxes_and_label = tf.reshape(gtboxes_and_label_batch, [-1, 5]) + # if cfgs.NET_NAME in ['resnet101_v1d', 'resnet50_v1d']: + # img_batch = img_batch / tf.constant([cfgs.PIXEL_STD]) + + # data processing + inputs_list = [] + for i in range(num_gpu): + # img_name = img_name_batch[i] + img = tf.expand_dims(img_batch[i], axis=0) + if cfgs.NET_NAME in ['resnet101_v1d', 'resnet50_v1d']: + img = img / tf.constant([cfgs.PIXEL_STD]) + + gtboxes_and_label = tf.cast(tf.reshape(gtboxes_and_label_batch[i], [-1, 5]), tf.float32) + num_objects = num_objects_batch[i] + num_objects = tf.cast(tf.reshape(num_objects, [-1, ]), tf.float32) + + img_h = img_h_batch[i] + img_w = img_w_batch[i] + # img_h = tf.cast(tf.reshape(img_h, [-1, ]), tf.float32) + # img_w = tf.cast(tf.reshape(img_w, [-1, ]), tf.float32) + + inputs_list.append([img, gtboxes_and_label, num_objects, img_h, img_w]) + + # put_op_list = [] + # get_op_list = [] + # for i in range(num_gpu): + # with tf.device("/GPU:%s" % i): + # area = tf.contrib.staging.StagingArea( + # dtypes=[tf.float32, tf.float32, tf.float32]) + # put_op_list.append(area.put(inputs_list[i])) + # get_op_list.append(area.get()) + + tower_grads = [] + biases_regularizer = tf.no_regularizer + weights_regularizer = tf.contrib.layers.l2_regularizer(cfgs.WEIGHT_DECAY) + + total_loss_dict = { + 'rpn_cls_loss': tf.constant(0., tf.float32), + 'rpn_loc_loss': tf.constant(0., tf.float32), + 'fastrcnn_cls_loss': tf.constant(0., tf.float32), + 'fastrcnn_loc_loss': tf.constant(0., tf.float32), + 'total_losses': tf.constant(0., tf.float32), + + } + + with tf.variable_scope(tf.get_variable_scope()): + for i in range(num_gpu): + with tf.device('/gpu:%d' % i): + with tf.name_scope('tower_%d' % i): + with slim.arg_scope( + [slim.model_variable, slim.variable], + device='/device:CPU:0'): + with slim.arg_scope([slim.conv2d, slim.conv2d_in_plane, + slim.conv2d_transpose, slim.separable_conv2d, slim.fully_connected], + weights_regularizer=weights_regularizer, + biases_regularizer=biases_regularizer, + biases_initializer=tf.constant_initializer(0.0)): + + gtboxes_and_label = tf.py_func(get_gtboxes_and_label, + inp=[inputs_list[i][1], inputs_list[i][2]], + Tout=tf.float32) + gtboxes_and_label = tf.reshape(gtboxes_and_label, [-1, 5]) + + img = inputs_list[i][0] + img_shape = inputs_list[i][-2:] + img = tf.image.crop_to_bounding_box(image=img, + offset_height=0, + offset_width=0, + target_height=tf.cast(img_shape[0], tf.int32), + target_width=tf.cast(img_shape[1], tf.int32)) + + outputs = faster_rcnn.build_whole_detection_network(input_img_batch=img, + gtboxes_batch=gtboxes_and_label) + gtboxes_in_img = show_box_in_tensor.draw_boxes_with_categories(img_batch=img, + boxes=gtboxes_and_label[ + :, :-1], + labels=gtboxes_and_label[ + :, -1]) + tf.summary.image('Compare/gtboxes_gpu:%d' % i, gtboxes_in_img) + + if cfgs.ADD_BOX_IN_TENSORBOARD: + detections_in_img = show_box_in_tensor.draw_boxes_with_categories_and_scores( + img_batch=img, + boxes=outputs[0], + scores=outputs[1], + labels=outputs[2]) + tf.summary.image('Compare/final_detection_gpu:%d' % i, detections_in_img) + + loss_dict = outputs[-1] + + total_losses = 0.0 + for k in loss_dict.keys(): + total_losses += loss_dict[k] + total_loss_dict[k] += loss_dict[k] / num_gpu + + total_losses = total_losses / num_gpu + total_loss_dict['total_losses'] += total_losses + + if i == num_gpu - 1: + regularization_losses = tf.get_collection( + tf.GraphKeys.REGULARIZATION_LOSSES) + # weight_decay_loss = tf.add_n(slim.losses.get_regularization_losses()) + total_losses = total_losses + tf.add_n(regularization_losses) + + tf.get_variable_scope().reuse_variables() + grads = optimizer.compute_gradients(total_losses) + tower_grads.append(grads) + + for k in total_loss_dict.keys(): + tf.summary.scalar('{}/{}'.format(k.split('_')[0], k), total_loss_dict[k]) + + if len(tower_grads) > 1: + grads = sum_gradients(tower_grads) + else: + grads = tower_grads[0] + + # final_gvs = [] + # with tf.variable_scope('Gradient_Mult'): + # for grad, var in grads: + # scale = 1. + # # if '/biases:' in var.name: + # # scale *= 2. + # if 'conv_new' in var.name: + # scale *= 3. + # if not np.allclose(scale, 1.0): + # grad = tf.multiply(grad, scale) + # final_gvs.append((grad, var)) + + apply_gradient_op = optimizer.apply_gradients(grads, global_step=global_step) + + variable_averages = tf.train.ExponentialMovingAverage(0.9999, global_step) + variables_averages_op = variable_averages.apply(tf.trainable_variables()) + + train_op = tf.group(apply_gradient_op, variables_averages_op) + # train_op = optimizer.apply_gradients(final_gvs, global_step=global_step) + summary_op = tf.summary.merge_all() + + restorer, restore_ckpt = faster_rcnn.get_restorer() + saver = tf.train.Saver(max_to_keep=10) + + init_op = tf.group( + tf.global_variables_initializer(), + tf.local_variables_initializer() + ) + + tfconfig = tf.ConfigProto( + allow_soft_placement=True, log_device_placement=False) + tfconfig.gpu_options.allow_growth = True + with tf.Session(config=tfconfig) as sess: + sess.run(init_op) + + # sess.run(tf.initialize_all_variables()) + coord = tf.train.Coordinator() + threads = tf.train.start_queue_runners(coord=coord, sess=sess) + + summary_path = os.path.join(cfgs.SUMMARY_PATH, cfgs.VERSION) + tools.mkdir(summary_path) + summary_writer = tf.summary.FileWriter(summary_path, graph=sess.graph) + + if not restorer is None: + restorer.restore(sess, restore_ckpt) + print('restore model') + + for step in range(cfgs.MAX_ITERATION // num_gpu): + training_time = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())) + + # start = time.time() + # + # _, global_stepnp, total_loss_dict_ = \ + # sess.run([train_op, global_step, total_loss_dict]) + # + # end = time.time() + # + # print('**' * 20) + # print("""%s: global_step%d current_step%d""" + # % (training_time, global_stepnp * num_gpu, step * num_gpu)) + # print("""per_cost_time:%.3fs""" + # % ((end - start) / num_gpu)) + # loss_str = '' + # for k in total_loss_dict_.keys(): + # loss_str += '%s:%.3f\n' % (k, total_loss_dict_[k]) + # print(loss_str) + + if step % cfgs.SHOW_TRAIN_INFO_INTE != 0 and step % cfgs.SMRY_ITER != 0: + _, global_stepnp = sess.run([train_op, global_step]) + + else: + if step % cfgs.SHOW_TRAIN_INFO_INTE == 0 and step % cfgs.SMRY_ITER != 0: + start = time.time() + + _, global_stepnp, total_loss_dict_ = \ + sess.run([train_op, global_step, total_loss_dict]) + + end = time.time() + + print('***'*20) + print("""%s: global_step:%d current_step:%d""" + % (training_time, (global_stepnp-1)*num_gpu, step*num_gpu)) + print("""per_cost_time:%.3fs""" + % ((end - start) / num_gpu)) + loss_str = '' + for k in total_loss_dict_.keys(): + loss_str += '%s:%.3f\n' % (k, total_loss_dict_[k]) + print(loss_str) + + else: + if step % cfgs.SMRY_ITER == 0: + _, global_stepnp, summary_str = sess.run([train_op, global_step, summary_op]) + summary_writer.add_summary(summary_str, (global_stepnp-1)*num_gpu) + summary_writer.flush() + + if (step > 0 and step % (cfgs.SAVE_WEIGHTS_INTE // num_gpu) == 0) or (step >= cfgs.MAX_ITERATION // num_gpu - 1): + + save_dir = os.path.join(cfgs.TRAINED_CKPT, cfgs.VERSION) + if not os.path.exists(save_dir): + os.mkdir(save_dir) + + save_ckpt = os.path.join(save_dir, 'coco_' + str((global_stepnp-1)*num_gpu) + 'model.ckpt') + saver.save(sess, save_ckpt) + print(' weights had been saved') + + coord.request_stop() + coord.join(threads) + + +if __name__ == '__main__': + + train() + + + + + + + + + + diff --git a/tools/test.py b/tools/test.py new file mode 100644 index 0000000..68ee5ea --- /dev/null +++ b/tools/test.py @@ -0,0 +1,165 @@ +# -*- coding:utf-8 -*- + +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import os, sys +import tensorflow as tf +import time +import cv2 +import argparse +import numpy as np +sys.path.append("../") + +from data.io.image_preprocess import short_side_resize_for_inference_data +from libs.configs import cfgs +from libs.networks import build_whole_network +from libs.box_utils import draw_box_in_img +from help_utils import tools + + +def detect(det_net, inference_save_path, real_test_imgname_list): + + # 1. preprocess img + img_plac = tf.placeholder(dtype=tf.uint8, shape=[None, None, 3]) # is RGB. not GBR + img_batch = tf.cast(img_plac, tf.float32) + img_batch = short_side_resize_for_inference_data(img_tensor=img_batch, + target_shortside_len=cfgs.IMG_SHORT_SIDE_LEN, + length_limitation=cfgs.IMG_MAX_LENGTH) + + if cfgs.NET_NAME in ['resnet101_v1d', 'resnet50_v1d']: + img_batch = (img_batch / 255 - tf.constant(cfgs.PIXEL_MEAN_)) / tf.constant(cfgs.PIXEL_STD) + else: + img_batch = img_batch - tf.constant(cfgs.PIXEL_MEAN) + img_batch = tf.expand_dims(img_batch, axis=0) # [1, None, None, 3] + + detection_boxes, detection_scores, detection_category = det_net.build_whole_detection_network( + input_img_batch=img_batch, + gtboxes_batch=None) + + init_op = tf.group( + tf.global_variables_initializer(), + tf.local_variables_initializer() + ) + + restorer, restore_ckpt = det_net.get_restorer() + + config = tf.ConfigProto() + config.gpu_options.allow_growth = True + + with tf.Session(config=config) as sess: + sess.run(init_op) + if not restorer is None: + restorer.restore(sess, restore_ckpt) + print('restore model') + + for i, a_img_name in enumerate(real_test_imgname_list): + + raw_img = cv2.imread(a_img_name) + start = time.time() + resized_img, detected_boxes, detected_scores, detected_categories = \ + sess.run( + [img_batch, detection_boxes, detection_scores, detection_category], + feed_dict={img_plac: raw_img[:, :, ::-1]} + ) + end = time.time() + # print("{} cost time : {} ".format(img_name, (end - start))) + + raw_h, raw_w = raw_img.shape[0], raw_img.shape[1] + + xmin, ymin, xmax, ymax = detected_boxes[:, 0], detected_boxes[:, 1], \ + detected_boxes[:, 2], detected_boxes[:, 3] + + resized_h, resized_w = resized_img.shape[1], resized_img.shape[2] + + xmin = xmin * raw_w / resized_w + xmax = xmax * raw_w / resized_w + + ymin = ymin * raw_h / resized_h + ymax = ymax * raw_h / resized_h + + detected_boxes = np.transpose(np.stack([xmin, ymin, xmax, ymax])) + + show_indices = detected_scores >= cfgs.SHOW_SCORE_THRSHOLD + show_scores = detected_scores[show_indices] + show_boxes = detected_boxes[show_indices] + show_categories = detected_categories[show_indices] + + # if cfgs.NET_NAME in ['resnet101_v1d', 'resnet50_v1d']: + # raw_img = (raw_img / 255 - np.array(cfgs.PIXEL_MEAN_)) /np.array(cfgs.PIXEL_STD) + # else: + # raw_img = raw_img - np.array(cfgs.PIXEL_MEAN) + final_detections = draw_box_in_img.draw_boxes_with_label_and_scores(raw_img, + boxes=show_boxes, + labels=show_categories, + scores=show_scores, + in_graph=False) + nake_name = a_img_name.split('/')[-1] + # print (inference_save_path + '/' + nake_name) + cv2.imwrite(inference_save_path + '/' + nake_name, + final_detections[:, :, ::-1]) + + tools.view_bar('{} image cost {}s'.format(a_img_name, (end - start)), i + 1, len(real_test_imgname_list)) + + +def test(test_dir, inference_save_path): + + test_imgname_list = [os.path.join(test_dir, img_name) for img_name in os.listdir(test_dir) + if img_name.endswith(('.jpg', '.png', '.jpeg', '.tif', '.tiff'))] + assert len(test_imgname_list) != 0, 'test_dir has no imgs there.' \ + ' Note that, we only support img format of (.jpg, .png, and .tiff) ' + + faster_rcnn = build_whole_network.DetectionNetwork(base_network_name=cfgs.NET_NAME, + is_training=False) + detect(det_net=faster_rcnn, inference_save_path=inference_save_path, real_test_imgname_list=test_imgname_list) + + +def parse_args(): + """ + Parse input arguments + """ + parser = argparse.ArgumentParser(description='TestImgs...U need provide the test dir') + parser.add_argument('--data_dir', dest='data_dir', + help='data path', + default='demos', type=str) + parser.add_argument('--save_dir', dest='save_dir', + help='demo imgs to save', + default='inference_results', type=str) + parser.add_argument('--GPU', dest='GPU', + help='gpu id ', + default='0', type=str) + + if len(sys.argv) == 1: + parser.print_help() + sys.exit(1) + + args = parser.parse_args() + + return args + + +if __name__ == '__main__': + + args = parse_args() + print('Called with args:') + print(args) + os.environ["CUDA_VISIBLE_DEVICES"] = args.GPU + test(args.data_dir, + inference_save_path=args.save_dir) + + + + + + + + + + + + + + + + diff --git a/tools/test_coco.py b/tools/test_coco.py new file mode 100644 index 0000000..42e05e1 --- /dev/null +++ b/tools/test_coco.py @@ -0,0 +1,215 @@ +# -*- coding:utf-8 -*- + +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import os, sys +import tensorflow as tf +import time +import cv2 +import pickle +import numpy as np +import json +sys.path.append("../") + +from data.io.image_preprocess import short_side_resize_for_inference_data +from libs.configs import cfgs +from libs.networks import build_whole_network +from libs.val_libs import voc_eval +from libs.box_utils import draw_box_in_img +import argparse +from help_utils import tools +from libs.label_name_dict.label_dict import * + +from data.lib_coco.PythonAPI.pycocotools.coco import COCO +from data.lib_coco.PythonAPI.pycocotools.cocoeval import COCOeval + + +def test_coco(det_net, real_test_img_list, eval_data, draw_imgs=False): + + # 1. preprocess img + img_plac = tf.placeholder(dtype=tf.uint8, shape=[None, None, 3]) # is RGB. not BGR + img_batch = tf.cast(img_plac, tf.float32) + + img_batch = short_side_resize_for_inference_data(img_tensor=img_batch, + target_shortside_len=cfgs.IMG_SHORT_SIDE_LEN, + length_limitation=cfgs.IMG_MAX_LENGTH) + if cfgs.NET_NAME in ['resnet101_v1d', 'resnet50_v1d']: + img_batch = (img_batch / 255 - tf.constant(cfgs.PIXEL_MEAN_)) / tf.constant(cfgs.PIXEL_STD) + else: + img_batch = img_batch - tf.constant(cfgs.PIXEL_MEAN) + + # img_batch = (img_batch - tf.constant(cfgs.PIXEL_MEAN)) / (tf.constant(cfgs.PIXEL_STD)*255) + img_batch = tf.expand_dims(img_batch, axis=0) + + detection_boxes, detection_scores, detection_category = det_net.build_whole_detection_network( + input_img_batch=img_batch, + gtboxes_batch=None) + + init_op = tf.group( + tf.global_variables_initializer(), + tf.local_variables_initializer() + ) + + restorer, restore_ckpt = det_net.get_restorer() + + config = tf.ConfigProto() + config.gpu_options.allow_growth = True + + with tf.Session(config=config) as sess: + sess.run(init_op) + if not restorer is None: + restorer.restore(sess, restore_ckpt) + print('restore model') + + save_path = os.path.join('./eval_coco', cfgs.VERSION) + tools.mkdir(save_path) + fw_json_dt = open(os.path.join(save_path, 'coco_test-dev.json'), 'w') + coco_det = [] + for i, a_img in enumerate(real_test_img_list): + + raw_img = cv2.imread(os.path.join(eval_data, a_img['file_name'])) + raw_h, raw_w = raw_img.shape[0], raw_img.shape[1] + + start = time.time() + resized_img, detected_boxes, detected_scores, detected_categories = \ + sess.run( + [img_batch, detection_boxes, detection_scores, detection_category], + feed_dict={img_plac: raw_img[:, :, ::-1]} # cv is BGR. But need RGB + ) + end = time.time() + + eval_indices = detected_scores >= 0.01 + detected_scores = detected_scores[eval_indices] + detected_boxes = detected_boxes[eval_indices] + detected_categories = detected_categories[eval_indices] + + # print("{} cost time : {} ".format(img_name, (end - start))) + if draw_imgs: + show_indices = detected_scores >= cfgs.SHOW_SCORE_THRSHOLD + show_scores = detected_scores[show_indices] + show_boxes = detected_boxes[show_indices] + show_categories = detected_categories[show_indices] + + draw_img = np.squeeze(resized_img, 0) + if cfgs.NET_NAME in ['resnet101_v1d', 'resnet50_v1d']: + draw_img = (draw_img * np.array(cfgs.PIXEL_STD) + np.array(cfgs.PIXEL_MEAN_)) * 255 + else: + draw_img = draw_img + np.array(cfgs.PIXEL_MEAN) + + # draw_img = draw_img * (np.array(cfgs.PIXEL_STD)*255) + np.array(cfgs.PIXEL_MEAN) + + final_detections = draw_box_in_img.draw_boxes_with_label_and_scores(draw_img, + boxes=show_boxes, + labels=show_categories, + scores=show_scores, + in_graph=False) + if not os.path.exists(cfgs.TEST_SAVE_PATH): + os.makedirs(cfgs.TEST_SAVE_PATH) + + cv2.imwrite(cfgs.TEST_SAVE_PATH + '/' + '{}.jpg'.format(a_img['id']), + final_detections[:, :, ::-1]) + + xmin, ymin, xmax, ymax = detected_boxes[:, 0], detected_boxes[:, 1], \ + detected_boxes[:, 2], detected_boxes[:, 3] + + resized_h, resized_w = resized_img.shape[1], resized_img.shape[2] + + xmin = xmin * raw_w / resized_w + xmax = xmax * raw_w / resized_w + + ymin = ymin * raw_h / resized_h + ymax = ymax * raw_h / resized_h + + boxes = np.transpose(np.stack([xmin, ymin, xmax-xmin, ymax-ymin])) + + # cost much time + for j, box in enumerate(boxes): + coco_det.append({'bbox': [float(box[0]), float(box[1]), float(box[2]), float(box[3])], + 'score': float(detected_scores[j]), 'image_id': a_img['id'], + 'category_id': int(classes_originID[LABEl_NAME_MAP[detected_categories[j]]])}) + + tools.view_bar('%s image cost %.3fs' % (a_img['id'], (end - start)), i + 1, len(real_test_img_list)) + + json.dump(coco_det, fw_json_dt) + fw_json_dt.close() + + +def eval(num_imgs, eval_data, json_file, showbox): + + with open(json_file) as f: + test_img_list = json.load(f)['images'] + + if num_imgs == np.inf: + real_test_img_list = test_img_list + else: + real_test_img_list = test_img_list[: num_imgs] + + faster_rcnn = build_whole_network.DetectionNetwork(base_network_name=cfgs.NET_NAME, + is_training=False) + test_coco(det_net=faster_rcnn, real_test_img_list=real_test_img_list, eval_data=eval_data, draw_imgs=showbox) + + +def parse_args(): + + parser = argparse.ArgumentParser('evaluate the result with Pascal2007 stdand') + + parser.add_argument('--eval_data', dest='eval_data', + help='evaluate imgs dir ', + default='/unsullied/sharefs/_research_detection/GeneralDetection/COCO/data/MSCOCO/odformat/coco_minival2014.odgt', type=str) + parser.add_argument('--json_file', dest='json_file', + help='test-dev json file', + default='/home/yangxue/isilon/yangxue/code/yxdet/FPN_Tensorflow/tools/image_info_test-dev2017.json', type=str) + parser.add_argument('--showbox', dest='showbox', + help='whether show detecion results when evaluation', + default=False, type=bool) + parser.add_argument('--GPU', dest='GPU', + help='gpu id', + default='0', type=str) + parser.add_argument('--eval_num', dest='eval_num', + help='the num of eval imgs', + default=np.inf, type=int) + args = parser.parse_args() + return args + + +if __name__ == '__main__': + + # args = parse_args() + # print(20*"--") + # print(args) + # print(20*"--") + # os.environ["CUDA_VISIBLE_DEVICES"] = args.GPU + # eval(np.inf, # use np.inf to test all the imgs. use 10 to test 10 imgs. + # eval_data=args.eval_data, + # eval_gt=args.eval_gt, + # showbox=args.showbox) + + os.environ["CUDA_VISIBLE_DEVICES"] = '0' + eval(np.inf, # use np.inf to test all the imgs. use 10 to test 10 imgs. + eval_data='/unsullied/sharefs/yangxue/isilon/yangxue/data/COCO/test2017', + json_file='/home/yangxue/isilon/yangxue/code/yxdet/FPN_TF_DEV/tools/image_info_test-dev2017.json', + showbox=True) + + # cocoval('./eval_coco/FPN_Res101_20190108_v1/coco_res.json', + # '/unsullied/sharefs/_research_detection/GeneralDetection/COCO/data/MSCOCO/instances_minival2014.json') + + + + + + + + + + + + + + + + + + + diff --git a/tools/test_coco_pyramid.py b/tools/test_coco_pyramid.py new file mode 100644 index 0000000..2ddc297 --- /dev/null +++ b/tools/test_coco_pyramid.py @@ -0,0 +1,267 @@ +# -*- coding:utf-8 -*- + +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import os, sys +import tensorflow as tf +import time +import cv2 +import pickle +import numpy as np +import json +sys.path.append("../") + +from data.io.image_preprocess import short_side_resize_for_inference_data +from libs.configs import cfgs +from libs.networks import build_whole_network +from libs.val_libs import voc_eval +from libs.box_utils import draw_box_in_img +import argparse +from help_utils import tools +from libs.label_name_dict.label_dict import * +from libs.box_utils import nms + +from data.lib_coco.PythonAPI.pycocotools.coco import COCO +from data.lib_coco.PythonAPI.pycocotools.cocoeval import COCOeval + + +def cocoval(detected_json, eval_json): + eval_gt = COCO(eval_json) + + eval_dt = eval_gt.loadRes(detected_json) + cocoEval = COCOeval(eval_gt, eval_dt, iouType='bbox') + + # cocoEval.params.imgIds = eval_gt.getImgIds() + cocoEval.evaluate() + cocoEval.accumulate() + cocoEval.summarize() + + +def test_coco(det_net, real_test_img_list, eval_data, draw_imgs=False): + + # 1. preprocess img + img_plac = tf.placeholder(dtype=tf.uint8, shape=[None, None, 3]) # is RGB. not BGR + img_batch = tf.cast(img_plac, tf.float32) + + img_batch = short_side_resize_for_inference_data(img_tensor=img_batch, + target_shortside_len=cfgs.IMG_SHORT_SIDE_LEN, + length_limitation=cfgs.IMG_MAX_LENGTH, + is_resize=False) + if cfgs.NET_NAME in ['resnet101_v1d', 'resnet50_v1d']: + img_batch = (img_batch / 255 - tf.constant(cfgs.PIXEL_MEAN_)) / tf.constant(cfgs.PIXEL_STD) + else: + img_batch = img_batch - tf.constant(cfgs.PIXEL_MEAN) + + # img_batch = (img_batch - tf.constant(cfgs.PIXEL_MEAN)) / (tf.constant(cfgs.PIXEL_STD)*255) + img_batch = tf.expand_dims(img_batch, axis=0) + + detection_boxes, detection_scores, detection_category = det_net.build_whole_detection_network( + input_img_batch=img_batch, + gtboxes_batch=None) + + init_op = tf.group( + tf.global_variables_initializer(), + tf.local_variables_initializer() + ) + + restorer, restore_ckpt = det_net.get_restorer() + + config = tf.ConfigProto() + config.gpu_options.allow_growth = True + + with tf.Session(config=config) as sess: + sess.run(init_op) + if not restorer is None: + restorer.restore(sess, restore_ckpt) + print('restore model') + + save_path = os.path.join('./eval_coco', cfgs.VERSION) + tools.mkdir(save_path) + fw_json_dt = open(os.path.join(save_path, 'coco_test-dev_ms.json'), 'w') + coco_det = [] + for i, a_img in enumerate(real_test_img_list): + + raw_img = cv2.imread(os.path.join(eval_data, a_img['file_name'])) + raw_h, raw_w = raw_img.shape[0], raw_img.shape[1] + + start = time.time() + + detected_scores_, detected_boxes_, detected_categories_ = [], [], [] + + for ss in [600, 800, 1000, 1200]: # cfgs.IMG_SHORT_SIDE_LEN: + img_resize = cv2.resize(raw_img, (ss, ss)) + + resized_img, tmp_detected_boxes, tmp_detected_scores, tmp_detected_categories = \ + sess.run( + [img_batch, detection_boxes, detection_scores, detection_category], + feed_dict={img_plac: img_resize[:, :, ::-1]} # cv is BGR. But need RGB + ) + + eval_indices = tmp_detected_scores >= 0.01 + tmp_detected_scores = tmp_detected_scores[eval_indices] + tmp_detected_boxes = tmp_detected_boxes[eval_indices] + tmp_detected_categories = tmp_detected_categories[eval_indices] + + xmin, ymin, xmax, ymax = tmp_detected_boxes[:, 0], tmp_detected_boxes[:, 1], \ + tmp_detected_boxes[:, 2], tmp_detected_boxes[:, 3] + + resized_h, resized_w = resized_img.shape[1], resized_img.shape[2] + + xmin = xmin * raw_w / resized_w + xmax = xmax * raw_w / resized_w + + ymin = ymin * raw_h / resized_h + ymax = ymax * raw_h / resized_h + + resize_boxes = np.transpose(np.stack([xmin, ymin, xmax, ymax])) + + detected_scores_.append(tmp_detected_scores) + detected_boxes_.append(resize_boxes) + detected_categories_.append(tmp_detected_categories) + + detected_scores_ = np.concatenate(detected_scores_) + detected_boxes_ = np.concatenate(detected_boxes_) + detected_categories_ = np.concatenate(detected_categories_) + + detected_scores, detected_boxes, detected_categories = [], [], [] + + for sub_class in range(1, cfgs.CLASS_NUM + 1): + index = np.where(detected_categories_ == sub_class)[0] + if len(index) == 0: + continue + tmp_boxes_h = detected_boxes_[index] + tmp_label_h = detected_categories_[index] + tmp_score_h = detected_scores_[index] + + tmp_boxes_h = np.array(tmp_boxes_h) + tmp = np.zeros([tmp_boxes_h.shape[0], tmp_boxes_h.shape[1] + 1]) + tmp[:, 0:-1] = tmp_boxes_h + tmp[:, -1] = np.array(tmp_score_h) + + inx = nms.py_cpu_nms(dets=np.array(tmp, np.float32), + thresh=cfgs.FAST_RCNN_NMS_IOU_THRESHOLD, + max_output_size=120) + + detected_boxes.extend(np.array(tmp_boxes_h)[inx]) + detected_scores.extend(np.array(tmp_score_h)[inx]) + detected_categories.extend(np.array(tmp_label_h)[inx]) + + detected_scores = np.array(detected_scores) + detected_boxes = np.array(detected_boxes) + detected_categories = np.array(detected_categories) + + # print("{} cost time : {} ".format(img_name, (end - start))) + if draw_imgs: + show_indices = detected_scores >= cfgs.SHOW_SCORE_THRSHOLD + show_scores = detected_scores[show_indices] + show_boxes = detected_boxes[show_indices] + show_categories = detected_categories[show_indices] + + # if cfgs.NET_NAME in ['resnet101_v1d', 'resnet50_v1d']: + # draw_img = (raw_img * np.array(cfgs.PIXEL_STD) + np.array(cfgs.PIXEL_MEAN_)) * 255 + # else: + # draw_img = raw_img + np.array(cfgs.PIXEL_MEAN) + + # draw_img = draw_img * (np.array(cfgs.PIXEL_STD)*255) + np.array(cfgs.PIXEL_MEAN) + + raw_img = np.array(raw_img, np.float32) + final_detections = draw_box_in_img.draw_boxes_with_label_and_scores(raw_img, + boxes=show_boxes, + labels=show_categories, + scores=show_scores, + in_graph=False) + if not os.path.exists(cfgs.TEST_SAVE_PATH): + os.makedirs(cfgs.TEST_SAVE_PATH) + + cv2.imwrite(cfgs.TEST_SAVE_PATH + '/' + '{}.jpg'.format(a_img['id']), + final_detections[:, :, ::-1]) + + # cost much time + for j, box in enumerate(detected_boxes): + coco_det.append({'bbox': [float(box[0]), float(box[1]), float(box[2]-box[0]), float(box[3]-box[1])], + 'score': float(detected_scores[j]), 'image_id': a_img['id'], + 'category_id': int(classes_originID[LABEl_NAME_MAP[detected_categories[j]]])}) + end = time.time() + tools.view_bar('%s image cost %.3fs' % (a_img['id'], (end - start)), i + 1, len(real_test_img_list)) + + json.dump(coco_det, fw_json_dt) + fw_json_dt.close() + + +def eval(num_imgs, eval_data, json_file, showbox): + + with open(json_file) as f: + test_img_list = json.load(f)['images'] + + if num_imgs == np.inf: + real_test_img_list = test_img_list + else: + real_test_img_list = test_img_list[: num_imgs] + + faster_rcnn = build_whole_network.DetectionNetwork(base_network_name=cfgs.NET_NAME, + is_training=False) + test_coco(det_net=faster_rcnn, real_test_img_list=real_test_img_list, eval_data=eval_data, draw_imgs=showbox) + + +def parse_args(): + + parser = argparse.ArgumentParser('evaluate the result with Pascal2007 stdand') + + parser.add_argument('--eval_data', dest='eval_data', + help='evaluate imgs dir ', + default='/unsullied/sharefs/_research_detection/GeneralDetection/COCO/data/MSCOCO/odformat/coco_minival2014.odgt', type=str) + parser.add_argument('--json_file', dest='json_file', + help='test-dev json file', + default='/home/yangxue/isilon/yangxue/code/yxdet/FPN_Tensorflow/tools/image_info_test-dev2017.json', type=str) + parser.add_argument('--showbox', dest='showbox', + help='whether show detecion results when evaluation', + default=False, type=bool) + parser.add_argument('--GPU', dest='GPU', + help='gpu id', + default='0', type=str) + parser.add_argument('--eval_num', dest='eval_num', + help='the num of eval imgs', + default=np.inf, type=int) + args = parser.parse_args() + return args + + +if __name__ == '__main__': + + # args = parse_args() + # print(20*"--") + # print(args) + # print(20*"--") + # os.environ["CUDA_VISIBLE_DEVICES"] = args.GPU + # eval(np.inf, # use np.inf to test all the imgs. use 10 to test 10 imgs. + # eval_data=args.eval_data, + # eval_gt=args.eval_gt, + # showbox=args.showbox) + + os.environ["CUDA_VISIBLE_DEVICES"] = '0' + eval(np.inf, # use np.inf to test all the imgs. use 10 to test 10 imgs. + eval_data='/unsullied/sharefs/yangxue/isilon/yangxue/data/COCO/test2017', + json_file='/home/yangxue/isilon/yangxue/code/yxdet/FPN_TF_DEV/tools/image_info_test-dev2017.json', + showbox=False) + + # cocoval('./eval_coco/FPN_Res101_20190108_v1/coco_res.json', + # '/unsullied/sharefs/_research_detection/GeneralDetection/COCO/data/MSCOCO/instances_minival2014.json') + + + + + + + + + + + + + + + + + diff --git a/tools/test_pyramid_dota.py b/tools/test_pyramid_dota.py new file mode 100644 index 0000000..e4989c9 --- /dev/null +++ b/tools/test_pyramid_dota.py @@ -0,0 +1,283 @@ +# -*- coding:utf-8 -*- + +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import os, sys + +sys.path.append("../") +import cv2 +import numpy as np +from timeit import default_timer as timer +import tensorflow as tf + +from data.io.image_preprocess import short_side_resize_for_inference_data +from libs.networks import build_whole_network +from help_utils.tools import * +from libs.box_utils import draw_box_in_img +# from libs.box_utils import coordinate_convert +from libs.label_name_dict.label_dict import LABEl_NAME_MAP, NAME_LABEL_MAP +from help_utils import tools +# from libs.box_utils import nms +from libs.box_utils.cython_utils.cython_nms import nms +from libs.configs import cfgs + + +def get_file_paths_recursive(folder=None, file_ext=None): + """ Get the absolute path of all files in given folder recursively + :param folder: + :param file_ext: + :return: + """ + file_list = [] + if folder is None: + return file_list + + # for dir_path, dir_names, file_names in os.walk(folder): + # for file_name in file_names: + # if file_ext is None: + # file_list.append(os.path.join(dir_path, file_name)) + # continue + # if file_name.endswith(file_ext): + # file_list.append(os.path.join(dir_path, file_name)) + file_list = [os.path.join(folder, f) for f in sorted(os.listdir(folder)) if f.endswith(file_ext)] + + return file_list + + +def inference(det_net, file_paths, des_folder, h_len, w_len, h_overlap, w_overlap, save_res=False): + + if save_res: + assert cfgs.SHOW_SCORE_THRSHOLD >= 0.5, \ + 'please set score threshold (example: SHOW_SCORE_THRSHOLD = 0.5) in cfgs.py' + + else: + assert cfgs.SHOW_SCORE_THRSHOLD < 0.005, \ + 'please set score threshold (example: SHOW_SCORE_THRSHOLD = 0.00) in cfgs.py' + + tmp_file = './tmp_%s.txt' % cfgs.VERSION + + # 1. preprocess img + img_plac = tf.placeholder(dtype=tf.uint8, shape=[None, None, 3]) + img_batch = tf.cast(img_plac, tf.float32) + if cfgs.NET_NAME in ['resnet101_v1d', 'resnet50_v1d']: + img_batch = (img_batch / 255 - tf.constant(cfgs.PIXEL_MEAN_)) / tf.constant(cfgs.PIXEL_STD) + else: + img_batch = img_batch - tf.constant(cfgs.PIXEL_MEAN) + + img_batch = tf.expand_dims(img_batch, axis=0) + + img_batch = short_side_resize_for_inference_data(img_tensor=img_batch, + target_shortside_len=cfgs.IMG_SHORT_SIDE_LEN[0], + is_resize=False) + + det_boxes_h, det_scores_h, det_category_h = det_net.build_whole_detection_network(input_img_batch=img_batch, + gtboxes_batch=None) + + init_op = tf.group( + tf.global_variables_initializer(), + tf.local_variables_initializer() + ) + + restorer, restore_ckpt = det_net.get_restorer() + + config = tf.ConfigProto() + config.gpu_options.allow_growth = True + with tf.Session(config=config) as sess: + sess.run(init_op) + if not restorer is None: + restorer.restore(sess, restore_ckpt) + print('restore model') + + if not os.path.exists(tmp_file): + fw = open(tmp_file, 'w') + fw.close() + + fr = open(tmp_file, 'r') + pass_img = fr.readlines() + fr.close() + + for count, img_path in enumerate(file_paths): + fw = open(tmp_file, 'a+') + if img_path + '\n' in pass_img: + continue + start = timer() + img = cv2.imread(img_path) + + box_res = [] + label_res = [] + score_res = [] + + imgH = img.shape[0] + imgW = img.shape[1] + ori_H = imgH + ori_W = imgW + # print(" ori_h, ori_w: ", imgH, imgW) + if imgH < h_len: + temp = np.zeros([h_len, imgW, 3], np.float32) + temp[0:imgH, :, :] = img + img = temp + imgH = h_len + + if imgW < w_len: + temp = np.zeros([imgH, w_len, 3], np.float32) + temp[:, 0:imgW, :] = img + img = temp + imgW = w_len + + for hh in range(0, imgH, h_len - h_overlap): + if imgH - hh - 1 < h_len: + hh_ = imgH - h_len + else: + hh_ = hh + for ww in range(0, imgW, w_len - w_overlap): + if imgW - ww - 1 < w_len: + ww_ = imgW - w_len + else: + ww_ = ww + src_img = img[hh_:(hh_ + h_len), ww_:(ww_ + w_len), :] + + for short_size in cfgs.IMG_SHORT_SIDE_LEN: + max_len = 1200 + + if h_len < w_len: + new_h, new_w = short_size, min(int(short_size*float(w_len)/h_len), max_len) + else: + new_h, new_w = min(int(short_size*float(h_len)/w_len), max_len), short_size + + img_resize = cv2.resize(src_img, (new_h, new_w)) + + det_boxes_h_, det_scores_h_, det_category_h_ = \ + sess.run( + [det_boxes_h, det_scores_h, det_category_h], + feed_dict={img_plac: img_resize[:, :, ::-1]} + ) + + valid = det_scores_h_ > 1e-4 + det_boxes_h_ = det_boxes_h_[valid] + det_scores_h_ = det_scores_h_[valid] + det_category_h_ = det_category_h_[valid] + det_boxes_h_[:, 0] = det_boxes_h_[:, 0] * w_len / new_w + det_boxes_h_[:, 1] = det_boxes_h_[:, 1] * h_len / new_h + det_boxes_h_[:, 2] = det_boxes_h_[:, 2] * w_len / new_w + det_boxes_h_[:, 3] = det_boxes_h_[:, 3] * h_len / new_h + + if len(det_boxes_h_) > 0: + for ii in range(len(det_boxes_h_)): + box = det_boxes_h_[ii] + box[0] = box[0] + ww_ + box[1] = box[1] + hh_ + box[2] = box[2] + ww_ + box[3] = box[3] + hh_ + box_res.append(box) + label_res.append(det_category_h_[ii]) + score_res.append(det_scores_h_[ii]) + + box_res = np.array(box_res) + label_res = np.array(label_res) + score_res = np.array(score_res) + + box_res_, label_res_, score_res_ = [], [], [] + + # h_threshold = {'roundabout': 0.35, 'tennis-court': 0.35, 'swimming-pool': 0.4, 'storage-tank': 0.3, + # 'soccer-ball-field': 0.3, 'small-vehicle': 0.4, 'ship': 0.35, 'plane': 0.35, + # 'large-vehicle': 0.4, 'helicopter': 0.4, 'harbor': 0.3, 'ground-track-field': 0.4, + # 'bridge': 0.3, 'basketball-court': 0.4, 'baseball-diamond': 0.3} + h_threshold = {'turntable': 0.5, 'tennis-court': 0.5, 'swimming-pool': 0.5, 'storage-tank': 0.5, + 'soccer-ball-field': 0.5, 'small-vehicle': 0.5, 'ship': 0.5, 'plane': 0.5, + 'large-vehicle': 0.5, 'helicopter': 0.5, 'harbor': 0.5, 'ground-track-field': 0.5, + 'bridge': 0.5, 'basketball-court': 0.5, 'baseball-diamond': 0.5, 'container-crane': 0.5} + + for sub_class in range(1, cfgs.CLASS_NUM + 1): + index = np.where(label_res == sub_class)[0] + if len(index) == 0: + continue + tmp_boxes_h = box_res[index] + tmp_label_h = label_res[index] + tmp_score_h = score_res[index] + + tmp_boxes_h = np.array(tmp_boxes_h) + tmp = np.zeros([tmp_boxes_h.shape[0], tmp_boxes_h.shape[1] + 1]) + tmp[:, 0:-1] = tmp_boxes_h + tmp[:, -1] = np.array(tmp_score_h) + + # inx = nms.py_cpu_nms(dets=np.array(tmp, np.float32), + # thresh=h_threshold[LABEL_NAME_MAP[sub_class]], + # max_output_size=500) + + inx = nms(np.array(tmp, np.float32), + h_threshold[LABEl_NAME_MAP[sub_class]]) + + inx = inx[:500] # max_outpus is 500 + + box_res_.extend(np.array(tmp_boxes_h)[inx]) + score_res_.extend(np.array(tmp_score_h)[inx]) + label_res_.extend(np.array(tmp_label_h)[inx]) + + time_elapsed = timer() - start + + if save_res: + scores = np.array(score_res_) + labels = np.array(label_res_) + boxes = np.array(box_res_) + valid_show = scores > cfgs.SHOW_SCORE_THRSHOLD + scores = scores[valid_show] + boxes = boxes[valid_show] + labels = labels[valid_show] + det_detections_h = draw_box_in_img.draw_boxes_with_label_and_scores(np.array(img, np.float32), + boxes=boxes, + labels=labels, + scores=scores, + in_graph=False) + det_detections_h = det_detections_h[:ori_H, :ori_W] + save_dir = os.path.join(des_folder, cfgs.VERSION) + tools.mkdir(save_dir) + cv2.imwrite(save_dir + '/' + img_path.split('/')[-1].split('.')[0] + '_h_s%d_t%f.jpg' %(h_len, cfgs.FAST_RCNN_NMS_IOU_THRESHOLD), + det_detections_h) + + view_bar('{} cost {}s'.format(img_path.split('/')[-1].split('.')[0], + time_elapsed), count + 1, len(file_paths)) + + else: + # eval txt + CLASS_DOTA = NAME_LABEL_MAP.keys() + + # Task2 + write_handle_h = {} + txt_dir_h = os.path.join('txt_output', cfgs.VERSION + '_h') + tools.mkdir(txt_dir_h) + for sub_class in CLASS_DOTA: + if sub_class == 'back_ground': + continue + write_handle_h[sub_class] = open(os.path.join(txt_dir_h, 'Task2_%s.txt' % sub_class), 'a+') + + for i, hbox in enumerate(box_res_): + command = '%s %.3f %.1f %.1f %.1f %.1f\n' % (img_path.split('/')[-1].split('.')[0], + score_res_[i], + hbox[0], hbox[1], hbox[2], hbox[3]) + write_handle_h[LABEl_NAME_MAP[label_res_[i]]].write(command) + + for sub_class in CLASS_DOTA: + if sub_class == 'back_ground': + continue + write_handle_h[sub_class].close() + + view_bar('{} cost {}s'.format(img_path.split('/')[-1].split('.')[0], + time_elapsed), count + 1, len(file_paths)) + fw.write('{}\n'.format(img_path)) + fw.close() + os.remove(tmp_file) + + +if __name__ == "__main__": + + os.environ["CUDA_VISIBLE_DEVICES"] = '0' + + file_paths = get_file_paths_recursive('/data/DOTA/test/images', '.png') + + det_net = build_whole_network.DetectionNetwork(base_network_name=cfgs.NET_NAME, + is_training=False) + inference(det_net, file_paths, './doai2019', 800, 800, + 200, 200, save_res=False) + diff --git a/tools/train.py b/tools/train.py new file mode 100644 index 0000000..fd35449 --- /dev/null +++ b/tools/train.py @@ -0,0 +1,233 @@ +# -*- coding:utf-8 -*- + +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import tensorflow as tf +import tensorflow.contrib.slim as slim +import os, sys +import numpy as np +import time +sys.path.append("../") + +from libs.configs import cfgs +# from libs.networks import build_whole_network2 +from libs.networks import build_whole_network +from data.io.read_tfrecord import next_batch +from libs.box_utils import show_box_in_tensor +from help_utils import tools + +os.environ["CUDA_VISIBLE_DEVICES"] = cfgs.GPU_GROUP + + +def warmup_and_cosine_lr(init_lr, global_step, warmup_step, decay_steps, alpha=1e-6): + def warmup_lr(init_lr, global_step, warmup_step): + global_step = tf.cast(global_step, tf.float32) + return 1e-6 + (init_lr - 1e-6) * global_step / warmup_step + + def cosine_lr(init_lr, global_step, decay_steps, alpha=0.0): + return tf.train.cosine_decay(learning_rate=init_lr, + global_step=global_step - warmup_step, + decay_steps=decay_steps - warmup_step, + alpha=alpha) + + return tf.cond(tf.less_equal(global_step, warmup_step), + true_fn=lambda: warmup_lr(init_lr, global_step, warmup_step), + false_fn=lambda: cosine_lr(init_lr, global_step, decay_steps, alpha)) + + +def train(): + + faster_rcnn = build_whole_network.DetectionNetwork(base_network_name=cfgs.NET_NAME, + is_training=True) + + with tf.name_scope('get_batch'): + img_name_batch, img_batch, gtboxes_and_label_batch, num_objects_batch = \ + next_batch(dataset_name=cfgs.DATASET_NAME, # 'pascal', 'coco' + batch_size=cfgs.BATCH_SIZE, + shortside_len=cfgs.IMG_SHORT_SIDE_LEN, + is_training=True) + gtboxes_and_label = tf.reshape(gtboxes_and_label_batch, [-1, 5]) + if cfgs.NET_NAME in ['resnet101_v1d', 'resnet50_v1d']: + img_batch = img_batch / tf.constant([cfgs.PIXEL_STD]) + + biases_regularizer = tf.no_regularizer + weights_regularizer = tf.contrib.layers.l2_regularizer(cfgs.WEIGHT_DECAY) + + # list as many types of layers as possible, even if they are not used now + with slim.arg_scope([slim.conv2d, slim.conv2d_in_plane, + slim.conv2d_transpose, slim.separable_conv2d, slim.fully_connected], + weights_regularizer=weights_regularizer, + biases_regularizer=biases_regularizer, + biases_initializer=tf.constant_initializer(0.0)): + final_bbox, final_scores, final_category, loss_dict = faster_rcnn.build_whole_detection_network( + input_img_batch=img_batch, + gtboxes_batch=gtboxes_and_label) + + # ----------------------------------------------------------------------------------------------------build loss + weight_decay_loss = tf.add_n(slim.losses.get_regularization_losses()) + rpn_location_loss = loss_dict['rpn_loc_loss'] + rpn_cls_loss = loss_dict['rpn_cls_loss'] + rpn_total_loss = rpn_location_loss + rpn_cls_loss + + fastrcnn_cls_loss = loss_dict['fastrcnn_cls_loss'] + fastrcnn_loc_loss = loss_dict['fastrcnn_loc_loss'] + fastrcnn_total_loss = fastrcnn_cls_loss + fastrcnn_loc_loss + + if cfgs.USE_ATTENTION: + mask_total_loss = loss_dict['mask_loss'] + total_loss = rpn_total_loss + fastrcnn_total_loss + weight_decay_loss + mask_total_loss + else: + total_loss = rpn_total_loss + fastrcnn_total_loss + weight_decay_loss + + # ---------------------------------------------------------------------------------------------------add summary + + tf.summary.scalar('RPN_LOSS/cls_loss', rpn_cls_loss) + tf.summary.scalar('RPN_LOSS/location_loss', rpn_location_loss) + tf.summary.scalar('RPN_LOSS/rpn_total_loss', rpn_total_loss) + + tf.summary.scalar('FAST_LOSS/fastrcnn_cls_loss', fastrcnn_cls_loss) + tf.summary.scalar('FAST_LOSS/fastrcnn_location_loss', fastrcnn_loc_loss) + tf.summary.scalar('FAST_LOSS/fastrcnn_total_loss', fastrcnn_total_loss) + + tf.summary.scalar('LOSS/total_loss', total_loss) + tf.summary.scalar('LOSS/regular_weights', weight_decay_loss) + if cfgs.USE_ATTENTION: + tf.summary.scalar('LOSS/mask_loss', mask_total_loss) + + gtboxes_in_img = show_box_in_tensor.draw_boxes_with_categories(img_batch=img_batch, + boxes=gtboxes_and_label[:, :-1], + labels=gtboxes_and_label[:, -1]) + if cfgs.ADD_BOX_IN_TENSORBOARD: + detections_in_img = show_box_in_tensor.draw_boxes_with_categories_and_scores(img_batch=img_batch, + boxes=final_bbox, + labels=final_category, + scores=final_scores) + tf.summary.image('Compare/final_detection', detections_in_img) + tf.summary.image('Compare/gtboxes', gtboxes_in_img) + + # ___________________________________________________________________________________________________add summary + + global_step = slim.get_or_create_global_step() + lr = tf.train.piecewise_constant(global_step, + boundaries=[np.int64(cfgs.DECAY_STEP[0]), np.int64(cfgs.DECAY_STEP[1]), np.int64(cfgs.DECAY_STEP[2])], + values=[cfgs.LR, cfgs.LR / 10., cfgs.LR / 100., cfgs.LR / 1000.]) + tf.summary.scalar('lr', lr) + optimizer = tf.train.MomentumOptimizer(lr, momentum=cfgs.MOMENTUM) + # optimizer = tf.train.AdamOptimizer(lr) + + # ---------------------------------------------------------------------------------------------compute gradients + gradients = faster_rcnn.get_gradients(optimizer, total_loss) + + # enlarge_gradients for bias + if cfgs.MUTILPY_BIAS_GRADIENT: + gradients = faster_rcnn.enlarge_gradients_for_bias(gradients) + + if cfgs.GRADIENT_CLIPPING_BY_NORM: + with tf.name_scope('clip_gradients'): + gradients = slim.learning.clip_gradient_norms(gradients, + cfgs.GRADIENT_CLIPPING_BY_NORM) + + # train_op + train_op = optimizer.apply_gradients(grads_and_vars=gradients, + global_step=global_step) + summary_op = tf.summary.merge_all() + init_op = tf.group( + tf.global_variables_initializer(), + tf.local_variables_initializer() + ) + + restorer, restore_ckpt = faster_rcnn.get_restorer() + saver = tf.train.Saver(max_to_keep=10) + + config = tf.ConfigProto() + config.gpu_options.allow_growth = True + + with tf.Session(config=config) as sess: + sess.run(init_op) + if not restorer is None: + restorer.restore(sess, restore_ckpt) + print('restore model') + coord = tf.train.Coordinator() + threads = tf.train.start_queue_runners(sess, coord) + + summary_path = os.path.join(cfgs.SUMMARY_PATH, cfgs.VERSION) + tools.mkdir(summary_path) + summary_writer = tf.summary.FileWriter(summary_path, graph=sess.graph) + + for step in range(cfgs.MAX_ITERATION): + training_time = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())) + + if step % cfgs.SHOW_TRAIN_INFO_INTE != 0 and step % cfgs.SMRY_ITER != 0: + _, global_stepnp = sess.run([train_op, global_step]) + + else: + if step % cfgs.SHOW_TRAIN_INFO_INTE == 0 and step % cfgs.SMRY_ITER != 0: + start = time.time() + + if cfgs.USE_ATTENTION: + _, global_stepnp, img_name, rpnLocLoss, rpnClsLoss, rpnTotalLoss, \ + fastrcnnLocLoss, fastrcnnClsLoss, fastrcnnTotalLoss, maskLoss, totalLoss = \ + sess.run( + [train_op, global_step, img_name_batch, rpn_location_loss, rpn_cls_loss, rpn_total_loss, + fastrcnn_loc_loss, fastrcnn_cls_loss, fastrcnn_total_loss, mask_total_loss, total_loss]) + else: + + _, global_stepnp, img_name, rpnLocLoss, rpnClsLoss, rpnTotalLoss, \ + fastrcnnLocLoss, fastrcnnClsLoss, fastrcnnTotalLoss, totalLoss = \ + sess.run( + [train_op, global_step, img_name_batch, rpn_location_loss, rpn_cls_loss, rpn_total_loss, + fastrcnn_loc_loss, fastrcnn_cls_loss, fastrcnn_total_loss, total_loss]) + + end = time.time() + if cfgs.USE_ATTENTION: + print(""" %s: step%d image_name:%s |\t + rpn_loc_loss:%.3f |\t rpn_cla_loss:%.3f |\t rpn_total_loss:%.3f | + fast_rcnn_loc_loss:%.3f |\t fast_rcnn_cla_loss:%.3f |\t fast_rcnn_total_loss:%.3f | + mask_loss:%.3f |\t total_loss:%.3f |\t per_cost_time:%.3fs""" + % (training_time, global_stepnp, str(img_name[0]), rpnLocLoss, rpnClsLoss, + rpnTotalLoss, fastrcnnLocLoss, fastrcnnClsLoss, fastrcnnTotalLoss, maskLoss, + totalLoss, (end - start))) + else: + + print(""" %s: step%d image_name:%s |\t + rpn_loc_loss:%.3f |\t rpn_cla_loss:%.3f |\t rpn_total_loss:%.3f | + fast_rcnn_loc_loss:%.3f |\t fast_rcnn_cla_loss:%.3f |\t fast_rcnn_total_loss:%.3f | + total_loss:%.3f |\t per_cost_time:%.3fs""" + % (training_time, global_stepnp, str(img_name[0]), rpnLocLoss, rpnClsLoss, + rpnTotalLoss, fastrcnnLocLoss, fastrcnnClsLoss, fastrcnnTotalLoss, totalLoss, + (end - start))) + else: + if step % cfgs.SMRY_ITER == 0: + _, global_stepnp, summary_str = sess.run([train_op, global_step, summary_op]) + summary_writer.add_summary(summary_str, global_stepnp) + summary_writer.flush() + + if (step > 0 and step % cfgs.SAVE_WEIGHTS_INTE == 0) or (step == cfgs.MAX_ITERATION - 1): + + save_dir = os.path.join(cfgs.TRAINED_CKPT, cfgs.VERSION) + if not os.path.exists(save_dir): + os.mkdir(save_dir) + + save_ckpt = os.path.join(save_dir, 'voc_' + str(global_stepnp) + 'model.ckpt') + saver.save(sess, save_ckpt) + print(' weights had been saved') + + coord.request_stop() + coord.join(threads) + + +if __name__ == '__main__': + + train() + + + + + + + + + + diff --git a/tools/train_for_coco.py b/tools/train_for_coco.py new file mode 100644 index 0000000..68bdaef --- /dev/null +++ b/tools/train_for_coco.py @@ -0,0 +1,265 @@ +# -*- coding:utf-8 -*- + +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import tensorflow as tf +import tensorflow.contrib.slim as slim +import os, sys +sys.path.append("../") +# sys.path.append("../data/lib_coco") +# sys.path.append('../data/lib_coco/PythonAPI/') + +import numpy as np +import time + +from libs.configs import cfgs +from libs.networks import build_whole_network +from data.io import image_preprocess +from libs.box_utils import show_box_in_tensor +from help_utils import tools +from data.io.COCO.get_coco_next_batch import next_img +# from data.lib_coco.get_coco_next_batch import next_img + + +os.environ["CUDA_VISIBLE_DEVICES"] = cfgs.GPU_GROUP + + +def preprocess_img(img_plac, gtbox_plac): + ''' + + :param img_plac: [H, W, 3] uint 8 img. In RGB. + :param gtbox_plac: shape of [-1, 5]. [xmin, ymin, xmax, ymax, label] + :return: + ''' + + img = tf.cast(img_plac, tf.float32) + + # gtboxes_and_label = tf.cast(gtbox_plac, tf.float32) + img, gtboxes_and_label = image_preprocess.short_side_resize(img_tensor=img, + gtboxes_and_label=gtbox_plac, + target_shortside_len=cfgs.IMG_SHORT_SIDE_LEN, + length_limitation=cfgs.IMG_MAX_LENGTH) + img, gtboxes_and_label = image_preprocess.random_flip_left_right(img_tensor=img, + gtboxes_and_label=gtboxes_and_label) + if cfgs.NET_NAME in ['resnet101_v1d', 'resnet50_v1d']: + img = img / 255 - tf.constant([[cfgs.PIXEL_MEAN_]]) + else: + img = img - tf.constant([[cfgs.PIXEL_MEAN]]) + img_batch = tf.expand_dims(img, axis=0) + + # gtboxes_and_label = tf.Print(gtboxes_and_label, [tf.shape(gtboxes_and_label)], message='gtbox shape') + return img_batch, gtboxes_and_label + + +def train(): + + faster_rcnn = build_whole_network.DetectionNetwork(base_network_name=cfgs.NET_NAME, + is_training=True) + + with tf.name_scope('get_batch'): + img_plac = tf.placeholder(dtype=tf.uint8, shape=[None, None, 3]) + gtbox_plac = tf.placeholder(dtype=tf.int32, shape=[None, 5]) + + img_batch, gtboxes_and_label = preprocess_img(img_plac, gtbox_plac) + if cfgs.NET_NAME in ['resnet101_v1d']: + img_batch = img_batch / tf.constant([cfgs.PIXEL_STD]) + # gtboxes_and_label = tf.reshape(gtboxes_and_label_batch, [-1, 5]) + + biases_regularizer = tf.no_regularizer + weights_regularizer = tf.contrib.layers.l2_regularizer(cfgs.WEIGHT_DECAY) + + # list as many types of layers as possible, even if they are not used now + with slim.arg_scope([slim.conv2d, slim.conv2d_in_plane, \ + slim.conv2d_transpose, slim.separable_conv2d, slim.fully_connected], + weights_regularizer=weights_regularizer, + biases_regularizer=biases_regularizer, + biases_initializer=tf.constant_initializer(0.0)): + final_bbox, final_scores, final_category, loss_dict = faster_rcnn.build_whole_detection_network( + input_img_batch=img_batch, + gtboxes_batch=gtboxes_and_label) + + # ----------------------------------------------------------------------------------------------------build loss + weight_decay_loss = tf.add_n(slim.losses.get_regularization_losses()) + rpn_location_loss = loss_dict['rpn_loc_loss'] + rpn_cls_loss = loss_dict['rpn_cls_loss'] + rpn_total_loss = rpn_location_loss + rpn_cls_loss + + fastrcnn_cls_loss = loss_dict['fastrcnn_cls_loss'] + fastrcnn_loc_loss = loss_dict['fastrcnn_loc_loss'] + fastrcnn_total_loss = fastrcnn_cls_loss + fastrcnn_loc_loss + + if cfgs.USE_ATTENTION: + mask_total_loss = loss_dict['mask_loss'] + total_loss = rpn_total_loss + fastrcnn_total_loss + weight_decay_loss + mask_total_loss + else: + total_loss = rpn_total_loss + fastrcnn_total_loss + weight_decay_loss + + # ---------------------------------------------------------------------------------------------------add summary + tf.summary.scalar('RPN_LOSS/cls_loss', rpn_cls_loss) + tf.summary.scalar('RPN_LOSS/location_loss', rpn_location_loss) + tf.summary.scalar('RPN_LOSS/rpn_total_loss', rpn_total_loss) + + tf.summary.scalar('FAST_LOSS/fastrcnn_cls_loss', fastrcnn_cls_loss) + tf.summary.scalar('FAST_LOSS/fastrcnn_location_loss', fastrcnn_loc_loss) + tf.summary.scalar('FAST_LOSS/fastrcnn_total_loss', fastrcnn_total_loss) + + tf.summary.scalar('LOSS/total_loss', total_loss) + tf.summary.scalar('LOSS/regular_weights', weight_decay_loss) + if cfgs.USE_ATTENTION: + tf.summary.scalar('LOSS/mask_loss', mask_total_loss) + + gtboxes_in_img = show_box_in_tensor.draw_boxes_with_categories(img_batch=img_batch, + boxes=gtboxes_and_label[:, :-1], + labels=gtboxes_and_label[:, -1]) + if cfgs.ADD_BOX_IN_TENSORBOARD: + detections_in_img = show_box_in_tensor.draw_boxes_with_categories_and_scores(img_batch=img_batch, + boxes=final_bbox, + labels=final_category, + scores=final_scores) + tf.summary.image('Compare/final_detection', detections_in_img) + tf.summary.image('Compare/gtboxes', gtboxes_in_img) + + # ___________________________________________________________________________________________________add summary + + global_step = slim.get_or_create_global_step() + lr = tf.train.piecewise_constant(global_step, + boundaries=[np.int64(cfgs.DECAY_STEP[0]), np.int64(cfgs.DECAY_STEP[1])], + values=[cfgs.LR, cfgs.LR / 10., cfgs.LR / 100.]) + tf.summary.scalar('lr', lr) + optimizer = tf.train.MomentumOptimizer(lr, momentum=cfgs.MOMENTUM) + + # ---------------------------------------------------------------------------------------------compute gradients + gradients = faster_rcnn.get_gradients(optimizer, total_loss) + + # enlarge_gradients for bias + if cfgs.MUTILPY_BIAS_GRADIENT: + gradients = faster_rcnn.enlarge_gradients_for_bias(gradients) + + if cfgs.GRADIENT_CLIPPING_BY_NORM: + with tf.name_scope('clip_gradients'): + gradients = slim.learning.clip_gradient_norms(gradients, + cfgs.GRADIENT_CLIPPING_BY_NORM) + + # train_op + train_op = optimizer.apply_gradients(grads_and_vars=gradients, + global_step=global_step) + summary_op = tf.summary.merge_all() + init_op = tf.group( + tf.global_variables_initializer(), + tf.local_variables_initializer() + ) + + restorer, restore_ckpt = faster_rcnn.get_restorer() + saver = tf.train.Saver(max_to_keep=5) + + config = tf.ConfigProto() + config.gpu_options.allow_growth = True + + with tf.Session(config=config) as sess: + sess.run(init_op) + if not restorer is None: + restorer.restore(sess, restore_ckpt) + print('restore model') + + summary_path = os.path.join(cfgs.SUMMARY_PATH, cfgs.VERSION) + tools.mkdir(summary_path) + summary_writer = tf.summary.FileWriter(summary_path, graph=sess.graph) + + for step in range(cfgs.MAX_ITERATION): + + img_id, img, gt_info = next_img(step=step) + + training_time = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())) + + if step % cfgs.SHOW_TRAIN_INFO_INTE != 0 and step % cfgs.SMRY_ITER != 0: + _, global_stepnp = sess.run([train_op, global_step], + feed_dict={img_plac: img, + gtbox_plac: gt_info} + ) + + else: + if step % cfgs.SHOW_TRAIN_INFO_INTE == 0 and step % cfgs.SMRY_ITER != 0: + start = time.time() + + if cfgs.USE_ATTENTION: + _, global_stepnp, rpnLocLoss, rpnClsLoss, rpnTotalLoss, \ + fastrcnnLocLoss, fastrcnnClsLoss, fastrcnnTotalLoss, maskLoss, totalLoss = \ + sess.run( + [train_op, global_step, rpn_location_loss, rpn_cls_loss, rpn_total_loss, + fastrcnn_loc_loss, fastrcnn_cls_loss, fastrcnn_total_loss, mask_total_loss, total_loss], + feed_dict={img_plac: img, + gtbox_plac: gt_info} + ) + else: + + _, global_stepnp, rpnLocLoss, rpnClsLoss, rpnTotalLoss, \ + fastrcnnLocLoss, fastrcnnClsLoss, fastrcnnTotalLoss, totalLoss = \ + sess.run( + [train_op, global_step, rpn_location_loss, rpn_cls_loss, rpn_total_loss, + fastrcnn_loc_loss, fastrcnn_cls_loss, fastrcnn_total_loss, total_loss], + feed_dict={img_plac: img, + gtbox_plac: gt_info} + ) + + end = time.time() + if cfgs.USE_ATTENTION: + print(""" {}: step{} image_name:{} |\t + rpn_loc_loss:{} |\t rpn_cla_loss:{} |\t rpn_total_loss:{} | + fast_rcnn_loc_loss:{} |\t fast_rcnn_cla_loss:{} |\t fast_rcnn_total_loss:{} | + mask_loss:{} |\t total_loss:{} |\t per_cost_time:{}s""" \ + .format(training_time, global_stepnp, str(img_id), rpnLocLoss, rpnClsLoss, + rpnTotalLoss, fastrcnnLocLoss, fastrcnnClsLoss, fastrcnnTotalLoss, maskLoss, + totalLoss, (end - start))) + else: + + print(""" {}: step{} image_name:{} |\t + rpn_loc_loss:{} |\t rpn_cla_loss:{} |\t rpn_total_loss:{} | + fast_rcnn_loc_loss:{} |\t fast_rcnn_cla_loss:{} |\t fast_rcnn_total_loss:{} | + total_loss:{} |\t per_cost_time:{}s""" \ + .format(training_time, global_stepnp, str(img_id), rpnLocLoss, rpnClsLoss, + rpnTotalLoss, fastrcnnLocLoss, fastrcnnClsLoss, fastrcnnTotalLoss, totalLoss, + (end - start))) + + else: + if step % cfgs.SMRY_ITER == 0: + _, global_stepnp, summary_str = sess.run([train_op, global_step, summary_op], + feed_dict={img_plac: img, + gtbox_plac: gt_info} + ) + summary_writer.add_summary(summary_str, global_stepnp) + summary_writer.flush() + + if (step > 0 and step % cfgs.SAVE_WEIGHTS_INTE == 0) or (step == cfgs.MAX_ITERATION - 1): + + save_dir = os.path.join(cfgs.TRAINED_CKPT, cfgs.VERSION) + if not os.path.exists(save_dir): + os.mkdir(save_dir) + + save_ckpt = os.path.join(save_dir, 'voc_' + str(global_stepnp) + 'model.ckpt') + saver.save(sess, save_ckpt) + print(' weights had been saved') + + +if __name__ == '__main__': + + train() + +# + + + + + + + + + + + + + + + + diff --git a/tools/train_with_placeholder.py b/tools/train_with_placeholder.py new file mode 100644 index 0000000..af91562 --- /dev/null +++ b/tools/train_with_placeholder.py @@ -0,0 +1,233 @@ +# -*- coding:utf-8 -*- + +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import tensorflow as tf +import tensorflow.contrib.slim as slim +import os, sys +sys.path.append("../") +# sys.path.append("../data/lib_coco") +# sys.path.append('../data/lib_coco/PythonAPI/') + +import numpy as np +import time + +from libs.configs import cfgs +from libs.networks import build_whole_network +from data.io import image_preprocess +from libs.box_utils import show_box_in_tensor +from help_utils import tools +from data.io.BDD100K.get_bdd100k_next_batch import next_img +# from data.lib_coco.get_coco_next_batch import next_img + + +os.environ["CUDA_VISIBLE_DEVICES"] = cfgs.GPU_GROUP + + +def preprocess_img(img_plac, gtbox_plac): + ''' + + :param img_plac: [H, W, 3] uint 8 img. In RGB. + :param gtbox_plac: shape of [-1, 5]. [xmin, ymin, xmax, ymax, label] + :return: + ''' + + img = tf.cast(img_plac, tf.float32) + + # gtboxes_and_label = tf.cast(gtbox_plac, tf.float32) + img, gtboxes_and_label = image_preprocess.short_side_resize(img_tensor=img, + gtboxes_and_label=gtbox_plac, + target_shortside_len=cfgs.IMG_SHORT_SIDE_LEN, + length_limitation=cfgs.IMG_MAX_LENGTH) + img, gtboxes_and_label = image_preprocess.random_flip_left_right(img_tensor=img, + gtboxes_and_label=gtboxes_and_label) + if cfgs.NET_NAME in ['resnet101_v1d', 'resnet50_v1d']: + img = img / 255 - tf.constant([[cfgs.PIXEL_MEAN_]]) + else: + img = img - tf.constant([[cfgs.PIXEL_MEAN]]) + img_batch = tf.expand_dims(img, axis=0) + + # gtboxes_and_label = tf.Print(gtboxes_and_label, [tf.shape(gtboxes_and_label)], message='gtbox shape') + return img_batch, gtboxes_and_label + + +def train(): + + faster_rcnn = build_whole_network.DetectionNetwork(base_network_name=cfgs.NET_NAME, + is_training=True) + + with tf.name_scope('get_batch'): + img_plac = tf.placeholder(dtype=tf.uint8, shape=[None, None, 3]) + gtbox_plac = tf.placeholder(dtype=tf.int32, shape=[None, 5]) + + img_batch, gtboxes_and_label = preprocess_img(img_plac, gtbox_plac) + if cfgs.NET_NAME in ['resnet101_v1d', 'resnet50_v1d']: + img_batch = img_batch / tf.constant([cfgs.PIXEL_STD]) + # gtboxes_and_label = tf.reshape(gtboxes_and_label_batch, [-1, 5]) + + biases_regularizer = tf.no_regularizer + weights_regularizer = tf.contrib.layers.l2_regularizer(cfgs.WEIGHT_DECAY) + + # list as many types of layers as possible, even if they are not used now + with slim.arg_scope([slim.conv2d, slim.conv2d_in_plane, \ + slim.conv2d_transpose, slim.separable_conv2d, slim.fully_connected], + weights_regularizer=weights_regularizer, + biases_regularizer=biases_regularizer, + biases_initializer=tf.constant_initializer(0.0)): + final_bbox, final_scores, final_category, loss_dict = faster_rcnn.build_whole_detection_network( + input_img_batch=img_batch, + gtboxes_batch=gtboxes_and_label) + + # ----------------------------------------------------------------------------------------------------build loss + weight_decay_loss = tf.add_n(slim.losses.get_regularization_losses()) + rpn_location_loss = loss_dict['rpn_loc_loss'] + rpn_cls_loss = loss_dict['rpn_cls_loss'] + rpn_total_loss = rpn_location_loss + rpn_cls_loss + + fastrcnn_cls_loss = loss_dict['fastrcnn_cls_loss'] + fastrcnn_loc_loss = loss_dict['fastrcnn_loc_loss'] + fastrcnn_total_loss = fastrcnn_cls_loss + fastrcnn_loc_loss + + total_loss = rpn_total_loss + fastrcnn_total_loss + weight_decay_loss + + # ---------------------------------------------------------------------------------------------------add summary + tf.summary.scalar('RPN_LOSS/cls_loss', rpn_cls_loss) + tf.summary.scalar('RPN_LOSS/location_loss', rpn_location_loss) + tf.summary.scalar('RPN_LOSS/rpn_total_loss', rpn_total_loss) + + tf.summary.scalar('FAST_LOSS/fastrcnn_cls_loss', fastrcnn_cls_loss) + tf.summary.scalar('FAST_LOSS/fastrcnn_location_loss', fastrcnn_loc_loss) + tf.summary.scalar('FAST_LOSS/fastrcnn_total_loss', fastrcnn_total_loss) + + tf.summary.scalar('LOSS/total_loss', total_loss) + tf.summary.scalar('LOSS/regular_weights', weight_decay_loss) + + gtboxes_in_img = show_box_in_tensor.draw_boxes_with_categories(img_batch=img_batch, + boxes=gtboxes_and_label[:, :-1], + labels=gtboxes_and_label[:, -1]) + if cfgs.ADD_BOX_IN_TENSORBOARD: + detections_in_img = show_box_in_tensor.draw_boxes_with_categories_and_scores(img_batch=img_batch, + boxes=final_bbox, + labels=final_category, + scores=final_scores) + tf.summary.image('Compare/final_detection', detections_in_img) + tf.summary.image('Compare/gtboxes', gtboxes_in_img) + + # ___________________________________________________________________________________________________add summary + + global_step = slim.get_or_create_global_step() + lr = tf.train.piecewise_constant(global_step, + boundaries=[np.int64(cfgs.DECAY_STEP[0]), np.int64(cfgs.DECAY_STEP[1])], + values=[cfgs.LR, cfgs.LR / 10., cfgs.LR / 100.]) + tf.summary.scalar('lr', lr) + optimizer = tf.train.MomentumOptimizer(lr, momentum=cfgs.MOMENTUM) + + # ---------------------------------------------------------------------------------------------compute gradients + gradients = faster_rcnn.get_gradients(optimizer, total_loss) + + # enlarge_gradients for bias + if cfgs.MUTILPY_BIAS_GRADIENT: + gradients = faster_rcnn.enlarge_gradients_for_bias(gradients) + + if cfgs.GRADIENT_CLIPPING_BY_NORM: + with tf.name_scope('clip_gradients_YJR'): + gradients = slim.learning.clip_gradient_norms(gradients, + cfgs.GRADIENT_CLIPPING_BY_NORM) + + # train_op + train_op = optimizer.apply_gradients(grads_and_vars=gradients, + global_step=global_step) + summary_op = tf.summary.merge_all() + init_op = tf.group( + tf.global_variables_initializer(), + tf.local_variables_initializer() + ) + + restorer, restore_ckpt = faster_rcnn.get_restorer() + saver = tf.train.Saver(max_to_keep=5) + + config = tf.ConfigProto() + config.gpu_options.allow_growth = True + + with tf.Session(config=config) as sess: + sess.run(init_op) + if not restorer is None: + restorer.restore(sess, restore_ckpt) + print('restore model') + + summary_path = os.path.join(cfgs.SUMMARY_PATH, cfgs.VERSION) + tools.mkdir(summary_path) + summary_writer = tf.summary.FileWriter(summary_path, graph=sess.graph) + + for step in range(cfgs.MAX_ITERATION): + + img_id, img, gt_info = next_img(step=step) + training_time = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())) + + if step % cfgs.SHOW_TRAIN_INFO_INTE != 0 and step % cfgs.SMRY_ITER != 0: + _, global_stepnp = sess.run([train_op, global_step], + feed_dict={img_plac: img, + gtbox_plac: gt_info} + ) + + else: + if step % cfgs.SHOW_TRAIN_INFO_INTE == 0 and step % cfgs.SMRY_ITER != 0: + start = time.time() + + _, global_stepnp, rpnLocLoss, rpnClsLoss, rpnTotalLoss, \ + fastrcnnLocLoss, fastrcnnClsLoss, fastrcnnTotalLoss, totalLoss = \ + sess.run( + [train_op, global_step, rpn_location_loss, rpn_cls_loss, rpn_total_loss, + fastrcnn_loc_loss, fastrcnn_cls_loss, fastrcnn_total_loss, total_loss], + feed_dict={img_plac: img, gtbox_plac: gt_info}) + end = time.time() + print(""" {}: step{} image_name:{} |\t + rpn_loc_loss:{} |\t rpn_cla_loss:{} |\t rpn_total_loss:{} | + fast_rcnn_loc_loss:{} |\t fast_rcnn_cla_loss:{} |\t fast_rcnn_total_loss:{} | + total_loss:{} |\t per_cost_time:{}s""" \ + .format(training_time, global_stepnp, str(img_id), rpnLocLoss, rpnClsLoss, + rpnTotalLoss, fastrcnnLocLoss, fastrcnnClsLoss, fastrcnnTotalLoss, totalLoss, + (end - start))) + else: + if step % cfgs.SMRY_ITER == 0: + _, global_stepnp, summary_str = sess.run([train_op, global_step, summary_op], + feed_dict={img_plac: img, + gtbox_plac: gt_info} + ) + summary_writer.add_summary(summary_str, global_stepnp) + summary_writer.flush() + + if (step > 0 and step % cfgs.SAVE_WEIGHTS_INTE == 0) or (step == cfgs.MAX_ITERATION - 1): + + save_dir = os.path.join(cfgs.TRAINED_CKPT, cfgs.VERSION) + if not os.path.exists(save_dir): + os.mkdir(save_dir) + + save_ckpt = os.path.join(save_dir, 'voc_' + str(global_stepnp) + 'model.ckpt') + saver.save(sess, save_ckpt) + print(' weights had been saved') + + +if __name__ == '__main__': + + train() + +# + + + + + + + + + + + + + + + +

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