From 64585037b1c5d7d63b229b4d455a5519b36b48d0 Mon Sep 17 00:00:00 2001 From: dadong Date: Fri, 22 Jul 2022 18:10:42 +0800 Subject: [PATCH] release code --- .gitignore | 10 + README.md | 85 ++ assets/rcp_arch.jpg | Bin 0 -> 165994 bytes assets/rcp_arch_compressed.jpg | Bin 0 -> 63547 bytes configs/test/rcp_test.yaml | 94 +++ configs/test/rcp_test_kitti.yaml | 91 ++ configs/train/rcp_self_ft.yaml | 97 +++ configs/train/rcp_self_pre.yaml | 99 +++ configs/train/rcp_sup_ft.yaml | 97 +++ configs/train/rcp_sup_pre.yaml | 96 +++ data/__init__.py | 0 data/exp_utils.py | 103 +++ data/loaders/KITTI_mapping.txt | 200 +++++ data/loaders/ModelNet40.py | 133 +++ data/loaders/__init__.py | 2 + data/loaders/flyingthings3d_subset.py | 152 ++++ data/loaders/kitti.py | 104 +++ data/preprocess/IO.py | 185 +++++ data/preprocess/flyingthings3d_utils.py | 33 + data/preprocess/kitti_utils.py | 62 ++ .../process_flyingthings3d_subset.py | 78 ++ data/preprocess/process_kitti.py | 81 ++ 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For technical details, please refer to: + +**RCP: Recurrent Closest Point for Scene Flow Estimation on 3D Point Clouds**
+Xiaodong Gu, Chengzhou Tang, Weihao Yuan, Zuozhuo Dai, Siyu Zhu, Ping Tan
+**[[Paper](https://arxiv.org/abs/2205.11028)]**
+ +![frames](assets/rcp_arch_compressed.jpg) + + + +## Installation ++ Install python dependencies lib: +``` +pip install -r requirements.txt +``` ++ Install PointNet2 CPP lib: +``` +cd lib/pointnet2 +python3 setup.py install +``` + +## Datasets +We follow [HPLFlowNet](https://github.com/laoreja/HPLFlowNet) preprocessing methods: + ++ FlyingThings3D: Download and unzip the "Disparity", "Disparity Occlusions", "Disparity change", "Optical flow", "Flow Occlusions" for DispNet/FlowNet2.0 dataset subsets from the [FlyingThings3D website](https://lmb.informatik.uni-freiburg.de/resources/datasets/SceneFlowDatasets.en.html) (we used the paths from [this file](https://lmb.informatik.uni-freiburg.de/data/FlyingThings3D_subset/FlyingThings3D_subset_all_download_paths.txt), now they added torrent downloads) . They will be upzipped into the same directory, RAW_DATA_PATH. Then run the following script for 3D reconstruction: +``` +python data/preprocess/process_flyingthings3d_subset.py --raw_data_path ${RAW_DATA_PATH} --save_path ${SAVE_PATH}/FlyingThings3D_subset_processed_35m --only_save_near_pts +``` + ++ KITTI: Download and unzip [KITTI Scene Flow Evaluation 2015](http://www.cvlibs.net/download.php?file=data_scene_flow.zip) to directory RAW_DATA_PATH. Run the following script for 3D reconstruction: +``` +python data/preprocess/process_kitti.py ${RAW_DATA_PATH} ${SAVE_PATH}/KITTI_processed_occ_final +``` + +## Training + ++ Fully-supervised training: +```bash +python run.py -c configs/train/rcp_sup_pre.yaml +python run.py -c configs/train/rcp_sup_ft.yaml --pre_ckpt ${pretrained_ckpt} +``` + ++ Self-supervised training: +```bash +python run.py -c configs/train/rcp_self_pre.yaml +python run.py -c configs/train/rcp_self_ft.yaml --pre_ckpt ${pretrained_ckpt} +``` + +## Evaluation + ++ Evaluate on FlyingThings3D +``` +python run.py -c configs/test/rcp_test.yaml --test_ckpt ${ft_ckpt} +``` ++ Evaluate on KITTI +``` +python run.py -c configs/test/rcp_test_kitti.yaml --test_ckpt ${ft_ckpt} +``` + +## Pretrained Models +[Dowload Link](https://virutalbuy-public.oss-cn-hangzhou.aliyuncs.com/share/rcp/rcp.ckpt) + +| Datasets | EPE3D | Acc3DS | AccDR | Outliers3D | +| :--- | :---: | :---: | :---: | :---: | +| FlyingThings3D | 0.0403 | 0.8567 | 0.9635 | 0.1976 | +| KITTI | 0.0481 | 0.8491 | 0.9448 | 0.1228 | + + +## Citation +If you find this code useful in your research, please cite: + +``` +@inproceedings{gu2022rcp, + title={RCP: Recurrent Closest Point for Point Cloud}, + author={Gu, Xiaodong and Tang, Chengzhou and Yuan, Weihao and Dai, Zuozhuo and Zhu, Siyu and Tan, Ping}, + booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, + pages={8216--8226}, + year={2022} +} +``` + +## Acknowledgements +Some code are borrowed from [Flowstep3d](https://github.com/yairkit/flowstep3d), [FLOT](https://github.com/valeoai/FLOT), [flownet3d_Pytorch](https://github.com/hyangwinter/flownet3d_pytorch) and [HPLFlowNet](https://github.com/laoreja/HPLFlowNet). +Thanks for these great projects. \ No newline at end of file diff --git a/assets/rcp_arch.jpg b/assets/rcp_arch.jpg new file mode 100644 index 0000000000000000000000000000000000000000..b8606d977669c8f0742aefa21026d749d2df0dbd GIT binary patch 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transform(src_cloud[:, :3], R, t) + src_cloud_normal = transform(src_cloud[:, 3:], R) + src_cloud = np.concatenate([src_cloud_points, src_cloud_normal], + axis=-1) + src_cloud = random_select_points(src_cloud, m=src_size) + tgt_cloud = random_select_points(tgt_cloud, m=tgt_size) + + if self.split == 'train' or self.noise: + src_cloud[:, :3] = jitter_point_cloud(src_cloud[:, :3]) + tgt_cloud[:, :3] = jitter_point_cloud(tgt_cloud[:, :3]) + tgt_cloud, src_cloud = shuffle_pc(tgt_cloud), shuffle_pc( + src_cloud) + return src_cloud, tgt_cloud, R, t + + def __getitem__(self, item): + src_cloud, tgt_cloud, R, t = self.compose(item=item, + p_keep=self.p_keep) + if not self.normal: + tgt_cloud, src_cloud = tgt_cloud[:, :3], src_cloud[:, :3] + return tgt_cloud, src_cloud, R, t + + def __len__(self): + return len(self.data) diff --git a/data/loaders/__init__.py b/data/loaders/__init__.py new file mode 100644 index 0000000..c99b4ef --- /dev/null +++ b/data/loaders/__init__.py @@ -0,0 +1,2 @@ +# from .flyingthings3d_subset import * +# from .kitti import * \ No newline at end of file diff --git a/data/loaders/flyingthings3d_subset.py b/data/loaders/flyingthings3d_subset.py new file mode 100644 index 0000000..584db4c --- /dev/null +++ b/data/loaders/flyingthings3d_subset.py @@ -0,0 +1,152 @@ +import sys, os +import os.path as osp +import numpy as np +# import open3d as o3d +import torch.utils.data as data + +__all__ = ['FlyingThings3DSubset'] + + +class FlyingThings3DSubset(data.Dataset): + """ + Args: + train (bool): If True, creates dataset from training set, otherwise creates from test set. + transform (callable): + args: + """ + def __init__(self, + train, + transform, + num_points, + data_root, + overfit_samples=None, + full=True): + self.root = osp.join(data_root, 'FlyingThings3D_subset_processed_35m') + self.train = train + self.transform = transform + self.num_points = num_points + + self.samples = self.make_dataset(full, overfit_samples) + + if len(self.samples) == 0: + raise (RuntimeError("Found 0 files in subfolders of: " + self.root + "\n")) + print(f"dataset len:{len(self.samples)}") + + def __len__(self): + return len(self.samples) + + def __getitem__(self, index): + pc1_loaded, pc2_loaded = self.pc_loader(self.samples[index]) + pc1_transformed, pc2_transformed, sf_transformed = self.transform([pc1_loaded, pc2_loaded]) + + # print(pc1_loaded.shape, pc1_transformed.shape) + # import open3d as o3d + # pcd1 = o3d.geometry.PointCloud() + # pcd1.points = o3d.utility.Vector3dVector(pc1_transformed) + + # pcd2 = o3d.geometry.PointCloud() + # pcd2.points = o3d.utility.Vector3dVector(pc2_transformed) + + # pcd3 = o3d.geometry.PointCloud() + # pcd3.points = o3d.utility.Vector3dVector(pc1_loaded) + + # pcd4 = o3d.geometry.PointCloud() + # pcd4.points = o3d.utility.Vector3dVector(pc2_loaded) + + # o3d.io.write_point_cloud("debug1.ply", pcd1) + # o3d.io.write_point_cloud("debug2.ply", pcd3) + + if pc1_transformed is None: + print('path {} get pc1 is None'.format(self.samples[index]), flush=True) + index = np.random.choice(range(self.__len__())) + return self.__getitem__(index) + + pc1_norm = pc1_transformed + pc2_norm = pc2_transformed + + # pcd1 = o3d.geometry.PointCloud() + # pcd1.points = o3d.utility.Vector3dVector(pc1_transformed) + # pcd1.colors = o3d.utility.Vector3dVector(pc1_norm) + + # pcd2 = o3d.geometry.PointCloud() + # pcd2.points = o3d.utility.Vector3dVector(pc2_transformed) + # pcd2.colors = o3d.utility.Vector3dVector(pc2_norm) + + # o3d.visualization.draw_geometries([pcd1, pcd2]) + + # print(pc1_norm.shape, pc2_norm.shape, sf_transformed.shape) + # import matplotlib.pyplot as plt + # from mpl_toolkits.mplot3d import Axes3D + # print(f"x: {pc1_norm[:, 0].min()} ~ {pc1_norm[:, 0].max()}, y: {pc1_norm[:, 1].min()} ~ {pc1_norm[:, 1].max()}, z: {pc1_norm[:, 2].min()} ~ {pc1_norm[:, 2].max()}") + # flow_norm = np.linalg.norm(sf_transformed, axis=1) + # flow_norm_sort = np.sort(flow_norm) + # idx_min = flow_norm.argmin() + # idx_max = flow_norm.argmax() + # print(f"flow norm min:{sf_transformed[idx_min]} max:{sf_transformed[idx_max]}", flow_norm_sort[0], flow_norm_sort[-1], + # flow_norm_sort[flow_norm_sort.shape[0]//2], np.mean(flow_norm)) + # fig = plt.figure(figsize=(4,4)) + # ax = fig.add_subplot(131, projection='3d') + # ax.set_box_aspect(aspect = (1,1,1)) + # ax.scatter(pc1_norm[:, 0], pc1_norm[:, 1], pc1_norm[:, 2]) + # ax = fig.add_subplot(132, projection='3d') + # ax.set_box_aspect(aspect = (1,1,1)) + # ax.scatter(pc2_norm[:, 0], pc2_norm[:, 1], pc2_norm[:, 2]) + # ax = fig.add_subplot(133, projection='3d') + # ax.set_box_aspect(aspect = (1,1,1)) + # ax.scatter(sf_transformed[:, 0], sf_transformed[:, 1], sf_transformed[:, 2]) + # plt.show() + # xx + + return pc1_transformed, pc2_transformed, pc1_norm, pc2_norm, sf_transformed, self.samples[index] + + def __repr__(self): + fmt_str = 'Dataset ' + self.__class__.__name__ + '\n' + fmt_str += ' Number of datapoints: {}\n'.format(self.__len__()) + fmt_str += ' Number of points per point cloud: {}\n'.format(self.num_points) + fmt_str += ' is training: {}\n'.format(self.train) + fmt_str += ' Root Location: {}\n'.format(self.root) + tmp = ' Transforms (if any): ' + fmt_str += '{0}{1}\n'.format(tmp, self.transform.__repr__().replace('\n', '\n' + ' ' * len(tmp))) + return fmt_str + + def make_dataset(self, full, overfit_samples): + root = osp.realpath(osp.expanduser(self.root)) + root = osp.join(root, 'train') if (self.train and overfit_samples is None) else osp.join(root, 'val') + print(root) + all_paths = os.walk(root) + useful_paths = sorted([item[0] for item in all_paths if len(item[1]) == 0]) + # try: + # if (self.train and overfit_samples is None): + # assert (len(useful_paths) == 19640) + # else: + # assert (len(useful_paths) == 3824) + # except AssertionError: + # print('len(useful_paths) =', len(useful_paths)) + # sys.exit(1) + if overfit_samples is not None: + res_paths = useful_paths[:overfit_samples] + else: + if not full: + res_paths = useful_paths[::4] + else: + res_paths = useful_paths + + return res_paths + + def pc_loader(self, path): + """ + Args: + path: path to a dir, e.g., home/xiuye/share/data/Driving_processed/35mm_focallength/scene_forwards/slow/0791 + Returns: + pc1: ndarray (N, 3) np.float32 + pc2: ndarray (N, 3) np.float32 + """ + pc1 = np.load(osp.join(path, 'pc1.npy')) + pc2 = np.load(osp.join(path, 'pc2.npy')) + # multiply -1 only for subset datasets + pc1[..., -1] *= -1 + pc2[..., -1] *= -1 + pc1[..., 0] *= -1 + pc2[..., 0] *= -1 + + return pc1, pc2 diff --git a/data/loaders/kitti.py b/data/loaders/kitti.py new file mode 100644 index 0000000..de1430f --- /dev/null +++ b/data/loaders/kitti.py @@ -0,0 +1,104 @@ +import sys, os +import os.path as osp +import numpy as np + +import torch.utils.data as data + +__all__ = ['KITTI'] + + +class KITTI(data.Dataset): + """ + Args: + train (bool): If True, creates dataset from training set, otherwise creates from test set. + transform (callable): + gen_func (callable): + args: + """ + + def __init__(self, + train, + transform, + num_points, + data_root, + remove_ground=True): + self.root = osp.join(data_root, 'KITTI_processed_occ_final') + #assert train is False + self.train = train + self.transform = transform + self.num_points = num_points + self.remove_ground = remove_ground + + self.samples = self.make_dataset() + if len(self.samples) == 0: + raise (RuntimeError("Found 0 files in subfolders of: " + self.root + "\n")) + + def __len__(self): + return len(self.samples) + + def __getitem__(self, index): + pc1_loaded, pc2_loaded = self.pc_loader(self.samples[index]) + pc1_transformed, pc2_transformed, sf_transformed = self.transform([pc1_loaded, pc2_loaded]) + if pc1_transformed is None: + print('path {} get pc1 is None'.format(self.samples[index]), flush=True) + index = np.random.choice(range(self.__len__())) + return self.__getitem__(index) + + pc1_norm = pc1_transformed + pc2_norm = pc2_transformed + return pc1_transformed, pc2_transformed, pc1_norm, pc2_norm, sf_transformed, self.samples[index] + + def __repr__(self): + fmt_str = 'Dataset ' + self.__class__.__name__ + '\n' + fmt_str += ' Number of datapoints: {}\n'.format(self.__len__()) + fmt_str += ' Number of points per point cloud: {}\n'.format(self.num_points) + fmt_str += ' is removing ground: {}\n'.format(self.remove_ground) + fmt_str += ' Root Location: {}\n'.format(self.root) + tmp = ' Transforms (if any): ' + fmt_str += '{0}{1}\n'.format(tmp, self.transform.__repr__().replace('\n', '\n' + ' ' * len(tmp))) + + return fmt_str + + def make_dataset(self): + do_mapping = True + root = osp.realpath(osp.expanduser(self.root)) + + all_paths = sorted(os.walk(root)) + useful_paths = [item[0] for item in all_paths if len(item[1]) == 0] + try: + assert (len(useful_paths) == 200) + except AssertionError: + print('assert (len(useful_paths) == 200) failed!', len(useful_paths)) + + if do_mapping: + mapping_path = osp.join(osp.dirname(__file__), 'KITTI_mapping.txt') + print('mapping_path', mapping_path) + + with open(mapping_path) as fd: + lines = fd.readlines() + lines = [line.strip() for line in lines] + useful_paths = [path for path in useful_paths if lines[int(osp.split(path)[-1])] != ''] + + res_paths = useful_paths + + return res_paths + + def pc_loader(self, path): + """ + Args: + path: + Returns: + pc1: ndarray (N, 3) np.float32 + pc2: ndarray (N, 3) np.float32 + """ + pc1 = np.load(osp.join(path, 'pc1.npy')) #.astype(np.float32) + pc2 = np.load(osp.join(path, 'pc2.npy')) #.astype(np.float32) + + if self.remove_ground: + is_ground = np.logical_and(pc1[:,1] < -1.4, pc2[:,1] < -1.4) + not_ground = np.logical_not(is_ground) + + pc1 = pc1[not_ground] + pc2 = pc2[not_ground] + + return pc1, pc2 diff --git a/data/preprocess/IO.py b/data/preprocess/IO.py new file mode 100644 index 0000000..6a7a929 --- /dev/null +++ b/data/preprocess/IO.py @@ -0,0 +1,185 @@ +import os +import re +import numpy as np +import uuid +from scipy import misc +import numpy as np +from PIL import Image +import sys + + +def read(file): + if file.endswith('.float3'): return readFloat(file) + elif file.endswith('.flo'): return readFlow(file) + elif file.endswith('.ppm'): return readImage(file) + elif file.endswith('.pgm'): return readImage(file) + elif file.endswith('.png'): return readImage(file) + elif file.endswith('.jpg'): return readImage(file) + elif file.endswith('.pfm'): return readPFM(file)[0] + else: raise Exception('don\'t know how to read %s' % file) + +def write(file, data): + if file.endswith('.float3'): return writeFloat(file, data) + elif file.endswith('.flo'): return writeFlow(file, data) + elif file.endswith('.ppm'): return writeImage(file, data) + elif file.endswith('.pgm'): return writeImage(file, data) + elif file.endswith('.png'): return writeImage(file, data) + elif file.endswith('.jpg'): return writeImage(file, data) + elif file.endswith('.pfm'): return writePFM(file, data) + else: raise Exception('don\'t know how to write %s' % file) + +def readPFM(file): + file = open(file, 'rb') + + color = None + width = None + height = None + scale = None + endian = None + + header = file.readline().rstrip() + if header.decode("ascii") == 'PF': + color = True + elif header.decode("ascii") == 'Pf': + color = False + else: + raise Exception('Not a PFM file.') + + dim_match = re.match(r'^(\d+)\s(\d+)\s$', file.readline().decode("ascii")) + if dim_match: + width, height = list(map(int, dim_match.groups())) + else: + raise Exception('Malformed PFM header.') + + scale = float(file.readline().decode("ascii").rstrip()) + if scale < 0: # little-endian + endian = '<' + scale = -scale + else: + endian = '>' # big-endian + + data = np.fromfile(file, endian + 'f') + shape = (height, width, 3) if color else (height, width) + + data = np.reshape(data, shape) + data = np.flipud(data) + return data, scale + +def writePFM(file, image, scale=1): + file = open(file, 'wb') + + color = None + + if image.dtype.name != 'float32': + raise Exception('Image dtype must be float32.') + + image = np.flipud(image) + + if len(image.shape) == 3 and image.shape[2] == 3: # color image + color = True + elif len(image.shape) == 2 or len(image.shape) == 3 and image.shape[2] == 1: # greyscale + color = False + else: + raise Exception('Image must have H x W x 3, H x W x 1 or H x W dimensions.') + + file.write('PF\n' if color else 'Pf\n'.encode()) + file.write('%d %d\n'.encode() % (image.shape[1], image.shape[0])) + + endian = image.dtype.byteorder + + if endian == '<' or endian == '=' and sys.byteorder == 'little': + scale = -scale + + file.write('%f\n'.encode() % scale) + + image.tofile(file) + +def readFlow(name): + if name.endswith('.pfm') or name.endswith('.PFM'): + return readPFM(name)[0][:,:,0:2] + + f = open(name, 'rb') + + header = f.read(4) + if header.decode("utf-8") != 'PIEH': + raise Exception('Flow file header does not contain PIEH') + + width = np.fromfile(f, np.int32, 1).squeeze() + height = np.fromfile(f, np.int32, 1).squeeze() + + flow = np.fromfile(f, np.float32, width * height * 2).reshape((height, width, 2)) + + return flow.astype(np.float32) + +def readImage(name): + if name.endswith('.pfm') or name.endswith('.PFM'): + data = readPFM(name)[0] + if len(data.shape)==3: + return data[:,:,0:3] + else: + return data + + return misc.imread(name) + +def writeImage(name, data): + if name.endswith('.pfm') or name.endswith('.PFM'): + return writePFM(name, data, 1) + + return misc.imsave(name, data) + +def writeFlow(name, flow): + f = open(name, 'wb') + f.write('PIEH'.encode('utf-8')) + np.array([flow.shape[1], flow.shape[0]], dtype=np.int32).tofile(f) + flow = flow.astype(np.float32) + flow.tofile(f) + +def readFloat(name): + f = open(name, 'rb') + + if(f.readline().decode("utf-8")) != 'float\n': + raise Exception('float file %s did not contain keyword' % name) + + dim = int(f.readline()) + + dims = [] + count = 1 + for i in range(0, dim): + d = int(f.readline()) + dims.append(d) + count *= d + + dims = list(reversed(dims)) + + data = np.fromfile(f, np.float32, count).reshape(dims) + if dim > 2: + data = np.transpose(data, (2, 1, 0)) + data = np.transpose(data, (1, 0, 2)) + + return data + +def writeFloat(name, data): + f = open(name, 'wb') + + dim=len(data.shape) + if dim>3: + raise Exception('bad float file dimension: %d' % dim) + + f.write(('float\n').encode('ascii')) + f.write(('%d\n' % dim).encode('ascii')) + + if dim == 1: + f.write(('%d\n' % data.shape[0]).encode('ascii')) + else: + f.write(('%d\n' % data.shape[1]).encode('ascii')) + f.write(('%d\n' % data.shape[0]).encode('ascii')) + for i in range(2, dim): + f.write(('%d\n' % data.shape[i]).encode('ascii')) + + data = data.astype(np.float32) + if dim==2: + data.tofile(f) + + else: + np.transpose(data, (2, 0, 1)).tofile(f) + diff --git a/data/preprocess/flyingthings3d_utils.py b/data/preprocess/flyingthings3d_utils.py new file mode 100755 index 0000000..fbdb879 --- /dev/null +++ b/data/preprocess/flyingthings3d_utils.py @@ -0,0 +1,33 @@ +import numpy as np + + +def next_pixel2pc(flow, disparity, save_path=None, f=-1050., cx=479.5, cy=269.5): + height, width = disparity.shape + + BASELINE = 1.0 + depth = -1. * f * BASELINE / disparity + + x = ((np.tile(np.arange(width, dtype=np.float32)[None, :], (height, 1)) - cx + flow[..., 0]) * -1. / disparity)[:, + :, None] + y = ((np.tile(np.arange(height, dtype=np.float32)[:, None], (1, width)) - cy + flow[..., 1]) * 1. / disparity)[:, + :, None] + pc = np.concatenate((x, y, depth[:, :, None]), axis=-1) + + if save_path is not None: + np.save(save_path, pc) + return pc + + +def pixel2pc(disparity, save_path=None, f=-1050., cx=479.5, cy=269.5): + height, width = disparity.shape + + BASELINE = 1.0 + depth = -1. * f * BASELINE / disparity + + x = ((np.tile(np.arange(width, dtype=np.float32)[None, :], (height, 1)) - cx) * -1. / disparity)[:, :, None] + y = ((np.tile(np.arange(height, dtype=np.float32)[:, None], (1, width)) - cy) * 1. / disparity)[:, :, None] + pc = np.concatenate((x, y, depth[:, :, None]), axis=-1) + + if save_path is not None: + np.save(save_path, pc) + return pc \ No newline at end of file diff --git a/data/preprocess/kitti_utils.py b/data/preprocess/kitti_utils.py new file mode 100755 index 0000000..530a2b5 --- /dev/null +++ b/data/preprocess/kitti_utils.py @@ -0,0 +1,62 @@ +import numpy as np +import png + + +def pixel2xyz(depth, P_rect, px=None, py=None): + assert P_rect[0,1] == 0 + assert P_rect[1,0] == 0 + assert P_rect[2,0] == 0 + assert P_rect[2,1] == 0 + assert P_rect[0,0] == P_rect[1,1] + focal_length_pixel = P_rect[0,0] + + height, width = depth.shape[:2] + if px is None: + px = np.tile(np.arange(width, dtype=np.float32)[None, :], (height, 1)) + if py is None: + py = np.tile(np.arange(height, dtype=np.float32)[:, None], (1, width)) + const_x = P_rect[0,2] * depth + P_rect[0,3] + const_y = P_rect[1,2] * depth + P_rect[1,3] + + x = ((px * (depth + P_rect[2,3]) - const_x) / focal_length_pixel) [:, :, None] + y = ((py * (depth + P_rect[2,3]) - const_y) / focal_length_pixel) [:, :, None] + pc = np.concatenate((x, y, depth[:, :, None]), axis=-1) + + pc[..., :2] *= -1. + return pc + + +def load_uint16PNG(fpath): + reader = png.Reader(fpath) + pngdata = reader.read() + px_array = np.vstack( map(np.uint16, pngdata[2]) ) + if pngdata[3]['planes'] == 3: + width, height = pngdata[:2] + px_array = px_array.reshape(height, width, 3) + return px_array + + +def load_disp(fpath): + # A 0 value indicates an invalid pixel (ie, no + # ground truth exists, or the estimation algorithm didn't produce an estimate + # for that pixel). + array = load_uint16PNG(fpath) + valid = array > 0 + disp = array.astype(np.float32) / 256.0 + disp[np.logical_not(valid)] = -1. + return disp, valid + + +def load_op_flow(fpath): + array = load_uint16PNG(fpath) + valid = array[..., -1] == 1 + array = array.astype(np.float32) + flow = (array[..., :-1] - 2**15) / 64. + return flow, valid + + +def disp_2_depth(disparity, valid_disp, FOCAL_LENGTH_PIXEL): + BASELINE = 0.54 + depth = FOCAL_LENGTH_PIXEL * BASELINE / (disparity + 1e-5) + depth[np.logical_not(valid_disp)] = -1. + return depth diff --git a/data/preprocess/process_flyingthings3d_subset.py b/data/preprocess/process_flyingthings3d_subset.py new file mode 100644 index 0000000..8554d2f --- /dev/null +++ b/data/preprocess/process_flyingthings3d_subset.py @@ -0,0 +1,78 @@ +import sys +import os +import os.path as osp +from multiprocessing import Pool +import argparse + +import IO +from flyingthings3d_utils import * + +parser = argparse.ArgumentParser() +parser.add_argument('--raw_data_path', type=str, help="path to the raw data") +parser.add_argument('--save_path', type=str, help="save path") +parser.add_argument('--only_save_near_pts', dest='save_near', action='store_true', + help='only save near points to save disk space') + +args = parser.parse_args() +root_path = args.raw_data_path +save_path = args.save_path +# root_path = osp.abspath('/root/share/data/FlyingThings3D_subset/FlyingThings3D_subset/') +# truenas_path = osp.abspath('/root/share/data/FlyingThings3D_subset_processed_full') +# m2t_path = osp.abspath('/m2t/FlyingThings3D_subset_processed_full') + +splits = ['train', 'val'] + + +def process_one_file(params): + try: + train_val, fname = params + + save_folder_path = osp.join(save_path, train_val, fname) + os.makedirs(save_folder_path, exist_ok=True) + + disp1 = IO.read(osp.join(root_path, train_val, 'disparity', 'left', fname + '.pfm')) + disp1_occ = IO.read(osp.join(root_path, train_val, 'disparity_occlusions', 'left', fname + '.png')) + disp1_change = IO.read( + osp.join(root_path, train_val, 'disparity_change', 'left', 'into_future', fname + '.pfm')) + flow = IO.read(osp.join(root_path, train_val, 'flow', 'left', 'into_future', fname + '.flo')) + flow_occ = IO.read(osp.join(root_path, train_val, 'flow_occlusions', 'left', 'into_future', fname + '.png')) + + pc1 = pixel2pc(disp1) + pc2 = next_pixel2pc(flow, disp1 + disp1_change) + + if pc1[..., -1].max() > 0 or pc2[..., -1].max() > 0: + print('z > 0', train_val, fname, pc1[..., -1].max(), pc1[..., -1].min(), pc2[..., -1].max(), + pc2[..., -1].min()) + + valid_mask = np.logical_and(disp1_occ == 0, flow_occ == 0) + + pc1 = pc1[valid_mask] + pc2 = pc2[valid_mask] + + if not args.save_near: + np.save(osp.join(save_folder_path, 'pc1.npy'), pc1) + np.save(osp.join(save_folder_path, 'pc2.npy'), pc2) + else: + near_mask = np.logical_and(pc1[..., -1] > -35., pc2[..., -1] > -35.) + np.save(osp.join(save_folder_path, 'pc1.npy'), pc1[near_mask]) + np.save(osp.join(save_folder_path, 'pc2.npy'), pc2[near_mask]) + + except Exception as ex: + print('error in addressing params', params, 'see exception:') + print(ex) + sys.stdout.flush() + return + + +if __name__ == '__main__': + param_list = [] + for train_val in splits: + tmp_path = osp.join(root_path, train_val, 'disparity_change', 'left', 'into_future') + param_list.extend([(train_val, item.split('.')[0]) for item in os.listdir(tmp_path)]) + + pool = Pool(4) + pool.map(process_one_file, param_list) + pool.close() + pool.join() + + print('Finish all!') diff --git a/data/preprocess/process_kitti.py b/data/preprocess/process_kitti.py new file mode 100644 index 0000000..29a6eff --- /dev/null +++ b/data/preprocess/process_kitti.py @@ -0,0 +1,81 @@ +import os +import sys +import os.path as osp +from multiprocessing import Pool + +from kitti_utils import * + +calib_root = './utils/calib_cam_to_cam/' +data_root = sys.argv[1] +disp1_root = osp.join(data_root, 'training/disp_occ_0') +disp2_root = osp.join(data_root, 'training/disp_occ_1') +op_flow_root = osp.join(data_root, 'training/flow_occ') + +save_path = sys.argv[2] + + +def process_one_frame(idx): + sidx = '{:06d}'.format(idx) + + calib_path = osp.join(calib_root, sidx + '.txt') + with open(calib_path) as fd: + lines = fd.readlines() + assert len([line for line in lines if line.startswith('P_rect_02')]) == 1 + P_rect_left = \ + np.array([float(item) for item in + [line for line in lines if line.startswith('P_rect_02')][0].split()[1:]], + dtype=np.float32).reshape(3, 4) + + assert P_rect_left[0, 0] == P_rect_left[1, 1] + focal_length_pixel = P_rect_left[0, 0] + + disp1_path = osp.join(disp1_root, sidx + '_10.png') + disp1, valid_disp1 = load_disp(disp1_path) + depth1 = disp_2_depth(disp1, valid_disp1, focal_length_pixel) + pc1 = pixel2xyz(depth1, P_rect_left) + + disp2_path = osp.join(disp2_root, sidx + '_10.png') + disp2, valid_disp2 = load_disp(disp2_path) + depth2 = disp_2_depth(disp2, valid_disp2, focal_length_pixel) + + valid_disp = np.logical_and(valid_disp1, valid_disp2) + + op_flow, valid_op_flow = load_op_flow(osp.join(op_flow_root, '{:06d}_10.png'.format(idx))) + vertical = op_flow[..., 1] + horizontal = op_flow[..., 0] + height, width = op_flow.shape[:2] + + px2 = np.zeros((height, width), dtype=np.float32) + py2 = np.zeros((height, width), dtype=np.float32) + + for i in range(height): + for j in range(width): + if valid_op_flow[i, j] and valid_disp[i, j]: + try: + dx = horizontal[i, j] + dy = vertical[i, j] + except: + print('error, i,j:', i, j, 'hor and ver:', horizontal[i, j], vertical[i, j]) + continue + + px2[i, j] = j + dx + py2[i, j] = i + dy + + pc2 = pixel2xyz(depth2, P_rect_left, px=px2, py=py2) + + final_mask = np.logical_and(valid_disp, valid_op_flow) + + valid_pc1 = pc1[final_mask] + valid_pc2 = pc2[final_mask] + + truenas_path = osp.join(save_path, '{:06d}'.format(idx)) + os.makedirs(truenas_path, exist_ok=True) + np.save(osp.join(truenas_path, 'pc1.npy'), valid_pc1) + np.save(osp.join(truenas_path, 'pc2.npy'), valid_pc2) + + +pool = Pool(4) +indices = range(200) +pool.map(process_one_frame, indices) +pool.close() +pool.join() diff --git a/data/preprocess/python_pfm.py b/data/preprocess/python_pfm.py new file mode 100755 index 0000000..17ceadf --- /dev/null +++ b/data/preprocess/python_pfm.py @@ -0,0 +1,72 @@ +import re +import numpy as np +import sys + + +def readPFM(file): + file = open(file, 'rb') + + color = None + width = None + height = None + scale = None + endian = None + + header = file.readline().rstrip() + if header == 'PF': + color = True + elif header == 'Pf': + color = False + else: + raise Exception('Not a PFM file.') + + dim_match = re.match(r'^(\d+)\s(\d+)\s$', file.readline()) + if dim_match: + width, height = map(int, dim_match.groups()) + else: + raise Exception('Malformed PFM header.') + + scale = float(file.readline().rstrip()) + if scale < 0: # little-endian + endian = '<' + scale = -scale + else: + endian = '>' # big-endian + + data = np.fromfile(file, endian + 'f') + shape = (height, width, 3) if color else (height, width) + + data = np.reshape(data, shape) + data = np.flipud(data) + return data, scale + + +def writePFM(file, image, scale=1): + file = open(file, 'wb') + + color = None + + if image.dtype.name != 'float32': + raise Exception('Image dtype must be float32.') + + image = np.flipud(image) + + if len(image.shape) == 3 and image.shape[2] == 3: # color image + color = True + elif len(image.shape) == 2 or len(image.shape) == 3 and image.shape[2] == 1: # greyscale + color = False + else: + raise Exception('Image must have H x W x 3, H x W x 1 or H x W dimensions.') + + file.write('PF\n' if color else 'Pf\n') + file.write('%d %d\n' % (image.shape[1], image.shape[0])) + + endian = image.dtype.byteorder + + if endian == '<' or endian == '=' and sys.byteorder == 'little': + scale = -scale + + file.write('%f\n' % scale) + + image.tofile(file) + diff --git a/data/transforms/__init__.py b/data/transforms/__init__.py new file mode 100644 index 0000000..7986cdd --- /dev/null +++ b/data/transforms/__init__.py @@ -0,0 +1 @@ +from .transforms import * diff --git a/data/transforms/functional.py b/data/transforms/functional.py new file mode 100644 index 0000000..3e82dca --- /dev/null +++ b/data/transforms/functional.py @@ -0,0 +1,32 @@ +""" +Borrowed from HPLFlowNet +Date: May 2020 + +@inproceedings{HPLFlowNet, + title={HPLFlowNet: Hierarchical Permutohedral Lattice FlowNet for +Scene Flow Estimation on Large-scale Point Clouds}, + author={Gu, Xiuye and Wang, Yijie and Wu, Chongruo and Lee, Yong Jae and Wang, Panqu}, + booktitle={Computer Vision and Pattern Recognition (CVPR), 2019 IEEE International Conference on}, + year={2019} +} +""" + +import torch + +def to_tensor(array): + """Convert a 2D `numpy.ndarray`` to tensor, do transpose first. + + See ``ToTensor`` for more details. + + Args: + array (numpy.ndarray): Image to be converted to tensor. + + Returns: + Tensor: Converted image. + """ + assert len(array.shape) == 2 + array = array.transpose((1, 0)) + + return torch.from_numpy(array) + + diff --git a/data/transforms/transforms.py b/data/transforms/transforms.py new file mode 100644 index 0000000..828d686 --- /dev/null +++ b/data/transforms/transforms.py @@ -0,0 +1,313 @@ +""" +Borrowed from HPLFlowNet +Date: May 2020 + +@inproceedings{HPLFlowNet, + title={HPLFlowNet: Hierarchical Permutohedral Lattice FlowNet for +Scene Flow Estimation on Large-scale Point Clouds}, + author={Gu, Xiuye and Wang, Yijie and Wu, Chongruo and Lee, Yong Jae and Wang, Panqu}, + booktitle={Computer Vision and Pattern Recognition (CVPR), 2019 IEEE International Conference on}, + year={2019} +} +""" + +import os, sys +import os.path as osp +from collections import defaultdict +import numbers +import math +import numpy as np +import traceback +import time + +import torch + +from . import functional as F + +# ---------- BASIC operations ---------- +class Compose(object): + """Composes several transforms together. + + Args: + transforms (list of ``Transform`` objects): list of transforms to compose. + + Example: + >>> transforms.Compose([ + >>> transforms.CenterCrop(10), + >>> transforms.ToTensor(), + >>> ]) + """ + + def __init__(self, transforms): + self.transforms = transforms + + def __call__(self, data): + for t in self.transforms: + data = t(data) + return data + + def __repr__(self): + format_string = self.__class__.__name__ + '(' + for t in self.transforms: + format_string += '\n' + format_string += ' {0}'.format(t) + format_string += '\n)' + return format_string + + +class ToTensor(object): + def __call__(self, pic): + if not isinstance(pic, np.ndarray): + return pic + else: + return F.to_tensor(pic) + + def __repr__(self): + return self.__class__.__name__ + '()' + + +def do_pc2_aug(pc2_args, pc2): + # pc2, order: rotation, shift, jitter + # rotation + if pc2_args['deg_max']: + angle2 = pc2_args['degree_range'] + else: + angle2 = np.random.uniform(-pc2_args['degree_range'], + pc2_args['degree_range']) + cosval2 = np.cos(angle2) + sinval2 = np.sin(angle2) + matrix2 = np.array([[cosval2, 0, sinval2], + [0, 1, 0], + [-sinval2, 0, cosval2]], dtype=pc2.dtype) + # shift + shifts2 = np.random.uniform(-pc2_args['shift_range'], + pc2_args['shift_range'], + (1, 3)).astype(np.float32) + + pc2[:, :3] = pc2[:, :3].dot(matrix2.T) + shifts2 + + if 'xy_shift_range' in pc2_args and pc2_args['xy_shift_range'] is not None: + xy_shifts2 = np.random.uniform(-pc2_args['xy_shift_range'], + pc2_args['xy_shift_range'], + (1, 2)).astype(np.float32) + pc2[:, 0] = pc2[:, 0] + xy_shifts2[0, 0] + pc2[:, 2] = pc2[:, 2] + xy_shifts2[0, 1] + + return pc2 + + +def do_pc_jitter(pc, jitter_sigma, jitter_clip): + jitter2 = np.clip(jitter_sigma * np.random.randn(pc.shape[0], 3), + -jitter_clip, jitter_clip).astype(np.float32) + pc[:, :3] += jitter2 + return pc + + +class Traverse: + def __init__(self, neighborhood_size, d): + self.neighborhood_size = neighborhood_size + self.d = d + + def go(self, start_key, hash_table_list): + walking_keys = np.empty((self.d + 1, self.d + 1), dtype=np.long) + self.walk_cuboid(start_key, 0, False, walking_keys, hash_table_list) + + def walk_cuboid(self, start_key, d, has_zero, walking_keys, hash_table_list): + if d <= self.d: + walking_keys[d] = start_key.copy() + + range_end = self.neighborhood_size + 1 if (has_zero or (d < self.d)) else 1 + for i in range(range_end): + self.walk_cuboid(walking_keys[d], d + 1, has_zero or (i == 0), walking_keys, hash_table_list) + walking_keys[d] = advance_in_dimension(self.d + 1, 1, d, walking_keys[d]) + else: + hash_table_list.append(start_key.copy()) + + +# ---------- MAIN operations ---------- +class ProcessData(object): + def __init__(self, data_process_args, aug_pc2_args, num_points, allow_less_points): + self.DEPTH_THRESHOLD = data_process_args['DEPTH_THRESHOLD'] + self.no_corr = data_process_args['NO_CORR'] + self.pc2_args = aug_pc2_args + self.num_points = num_points + self.allow_less_points = allow_less_points + + def __call__(self, data): + pc1, pc2 = data + if pc1 is None: + return None, None, None, + + if self.pc2_args is not None: + pc2 = do_pc2_aug(self.pc2_args, pc2) + + sf = pc2[:, :3] - pc1[:, :3] + + if self.DEPTH_THRESHOLD > 0: + near_mask = np.logical_and(pc1[:, 2] < self.DEPTH_THRESHOLD, pc2[:, 2] < self.DEPTH_THRESHOLD) + else: + near_mask = np.ones(pc1.shape[0], dtype=np.bool) + indices = np.where(near_mask)[0] + if len(indices) == 0: + print('indices = np.where(mask)[0], len(indices) == 0') + return None, None, None + sampled_indices1 = None + sampled_indices2 = None + + if self.num_points > 0: + try: + sampled_indices1 = np.random.choice(indices, size=self.num_points, replace=False, p=None) + if self.no_corr: + sampled_indices2 = np.random.choice(indices, size=self.num_points, replace=False, p=None) + else: + sampled_indices2 = sampled_indices1 + except ValueError: + ''' + if not self.allow_less_points: + print('Cannot sample {} points'.format(self.num_points)) + return None, None, None + else: + sampled_indices1 = indices + sampled_indices2 = indices + ''' + if not self.allow_less_points: + #replicate some points + sampled_indices1 = np.random.choice(indices, size=self.num_points, replace=True, p=None) + if self.no_corr: + sampled_indices2 = np.random.choice(indices, size=self.num_points, replace=True, p=None) + else: + sampled_indices2 = sampled_indices1 + else: + sampled_indices1 = indices + sampled_indices2 = indices + else: + sampled_indices1 = indices + sampled_indices2 = indices + + pc1 = pc1[sampled_indices1] + sf = sf[sampled_indices1] + pc2 = pc2[sampled_indices2] + if self.pc2_args is not None: + pc2 = do_pc_jitter(pc2, self.pc2_args['jitter_sigma'], self.pc2_args['jitter_clip']) + return pc1, pc2, sf + + def __repr__(self): + format_string = self.__class__.__name__ + '\n(data_process_args: \n' + format_string += '\tDEPTH_THRESHOLD: {}\n'.format(self.DEPTH_THRESHOLD) + format_string += '\tNO_CORR: {}\n'.format(self.no_corr) + format_string += '\tallow_less_points: {}\n'.format(self.allow_less_points) + format_string += '\tnum_points: {}\n'.format(self.num_points) + format_string += ')' + return format_string + + +class Augmentation(object): + def __init__(self, aug_together_args, aug_pc2_args, data_process_args, num_points, allow_less_points=False): + self.together_args = aug_together_args + self.pc2_args = aug_pc2_args + self.DEPTH_THRESHOLD = data_process_args['DEPTH_THRESHOLD'] + self.no_corr = data_process_args['NO_CORR'] + self.num_points = num_points + self.allow_less_points = allow_less_points + + def __call__(self, data): + pc1, pc2 = data + if pc1 is None: + return None, None, None + + # together, order: scale, rotation, shift, jitter + # scale + scale = np.diag(np.random.uniform(self.together_args['scale_low'], + self.together_args['scale_high'], + 3).astype(np.float32)) + # rotation + angle = np.random.uniform(-self.together_args['degree_range'], + self.together_args['degree_range']) + cosval = np.cos(angle) + sinval = np.sin(angle) + rot_matrix = np.array([[cosval, 0, sinval], + [0, 1, 0], + [-sinval, 0, cosval]], dtype=np.float32) + matrix = scale.dot(rot_matrix.T) + + # shift + shifts = np.random.uniform(-self.together_args['shift_range'], + self.together_args['shift_range'], + (1, 3)).astype(np.float32) + + # jitter + jitter = np.clip(self.together_args['jitter_sigma'] * np.random.randn(pc1.shape[0], 3), + -self.together_args['jitter_clip'], + self.together_args['jitter_clip']).astype(np.float32) + bias = shifts + jitter + + pc1[:, :3] = pc1[:, :3].dot(matrix) + bias + pc2[:, :3] = pc2[:, :3].dot(matrix) + bias + + pc2 = do_pc2_aug(self.pc2_args, pc2) + + sf = pc2[:, :3] - pc1[:, :3] + + if not self.no_corr: + pc2 = do_pc_jitter(pc2, self.pc2_args['jitter_sigma'], self.pc2_args['jitter_clip']) + + if self.DEPTH_THRESHOLD > 0: + near_mask = np.logical_and(pc1[:, 2] < self.DEPTH_THRESHOLD, pc2[:, 2] < self.DEPTH_THRESHOLD) + else: + near_mask = np.ones(pc1.shape[0], dtype=np.bool) + + indices = np.where(near_mask)[0] + if len(indices) == 0: + print('indices = np.where(mask)[0], len(indices) == 0') + return None, None, None + + if self.num_points > 0: + try: + sampled_indices1 = np.random.choice(indices, size=self.num_points, replace=False, p=None) + if self.no_corr: + sampled_indices2 = np.random.choice(indices, size=self.num_points, replace=False, p=None) + else: + sampled_indices2 = sampled_indices1 + except ValueError: + ''' + if not self.allow_less_points: + print('Cannot sample {} points'.format(self.num_points)) + return None, None, None + else: + sampled_indices1 = indices + sampled_indices2 = indices + ''' + if not self.allow_less_points: + #replicate some points + sampled_indices1 = np.random.choice(indices, size=self.num_points, replace=True, p=None) + if self.no_corr: + sampled_indices2 = np.random.choice(indices, size=self.num_points, replace=True, p=None) + else: + sampled_indices2 = sampled_indices1 + else: + sampled_indices1 = indices + sampled_indices2 = indices + else: + sampled_indices1 = indices + sampled_indices2 = indices + + pc1 = pc1[sampled_indices1] + pc2 = pc2[sampled_indices2] + sf = sf[sampled_indices1] + + return pc1, pc2, sf + + def __repr__(self): + format_string = self.__class__.__name__ + '\n(together_args: \n' + for key in sorted(self.together_args.keys()): + format_string += '\t{:10s} {}\n'.format(key, self.together_args[key]) + format_string += '\npc2_args: \n' + for key in sorted(self.pc2_args.keys()): + format_string += '\t{:10s} {}\n'.format(key, self.pc2_args[key]) + format_string += '\ndata_process_args: \n' + format_string += '\tDEPTH_THRESHOLD: {}\n'.format(self.DEPTH_THRESHOLD) + format_string += '\tNO_CORR: {}\n'.format(self.no_corr) + format_string += '\tallow_less_points: {}\n'.format(self.allow_less_points) + format_string += '\tnum_points: {}\n'.format(self.num_points) + format_string += ')' + return format_string diff --git a/data/utils/pr_utils.py b/data/utils/pr_utils.py new file mode 100644 index 0000000..23d0f80 --- /dev/null +++ b/data/utils/pr_utils.py @@ -0,0 +1,162 @@ +import math +import numpy as np + +def random_select_points(pc, m): + if m < 0: + idx = np.arange(pc.shape[0]) + np.random.shuffle(idx) + return pc[idx, :] + n = pc.shape[0] + replace = False if n >= m else True + idx = np.random.choice(n, size=(m, ), replace=replace) + return pc[idx, :] + + +def generate_rotation_x_matrix(theta): + mat = np.eye(3, dtype=np.float32) + mat[1, 1] = math.cos(theta) + mat[1, 2] = -math.sin(theta) + mat[2, 1] = math.sin(theta) + mat[2, 2] = math.cos(theta) + return mat + + +def generate_rotation_y_matrix(theta): + mat = np.eye(3, dtype=np.float32) + mat[0, 0] = math.cos(theta) + mat[0, 2] = math.sin(theta) + mat[2, 0] = -math.sin(theta) + mat[2, 2] = math.cos(theta) + return mat + + +def generate_rotation_z_matrix(theta): + mat = np.eye(3, dtype=np.float32) + mat[0, 0] = math.cos(theta) + mat[0, 1] = -math.sin(theta) + mat[1, 0] = math.sin(theta) + mat[1, 1] = math.cos(theta) + return mat + + +def generate_random_rotation_matrix(angle1=-45, angle2=45): + thetax = np.random.uniform() * np.pi * angle2 / 180.0 + thetay = np.random.uniform() * np.pi * angle2 / 180.0 + thetaz = np.random.uniform() * np.pi * angle2 / 180.0 + matx = generate_rotation_x_matrix(thetax) + maty = generate_rotation_y_matrix(thetay) + matz = generate_rotation_z_matrix(thetaz) + return np.dot(matx, np.dot(maty, matz)) + + +def generate_random_tranlation_vector(range1=-0.5, range2=0.5): + tranlation_vector = np.random.uniform(range1, range2, size=(3, )).astype(np.float32) + return tranlation_vector + + +def transform(pc, R, t=None): + pc = np.dot(pc, R.T) + if t is not None: + pc = pc + t + return pc + + +# The transformation between unit quaternion and rotation matrix is referenced to +# https://zhuanlan.zhihu.com/p/45404840 + +def quat2mat(quat): + w, x, y, z = quat + R = np.zeros((3, 3), dtype=np.float32) + R[0][0] = 1 - 2*y*y - 2*z*z + R[0][1] = 2*x*y - 2*z*w + R[0][2] = 2*x*z + 2*y*w + R[1][0] = 2*x*y + 2*z*w + R[1][1] = 1 - 2*x*x - 2*z*z + R[1][2] = 2*y*z - 2*x*w + R[2][0] = 2*x*z - 2*y*w + R[2][1] = 2*y*z + 2*x*w + R[2][2] = 1 - 2*x*x - 2*y*y + return R + + +def mat2quat(mat): + w = math.sqrt(mat[0, 0] + mat[1, 1] + mat[2, 2] + 1 + 1e-8) / 2 + x = (mat[2, 1] - mat[1, 2]) / (4 * w + 1e-8) + y = (mat[0, 2] - mat[2, 0]) / (4 * w + 1e-8) + z = (mat[1, 0] - mat[0, 1]) / (4 * w + 1e-8) + return w, x, y, z + + +def jitter_point_cloud(pc, sigma=0.01, clip=0.05): + N, C = pc.shape + assert(clip > 0) + #jittered_data = np.clip(sigma * np.random.randn(N, C), -1*clip, clip).astype(np.float32) + jittered_data = np.clip( + np.random.normal(0.0, scale=sigma, size=(N, 3)), + -1 * clip, clip).astype(np.float32) + jittered_data += pc + return jittered_data + + +def shift_point_cloud(pc, shift_range=0.1): + N, C = pc.shape + shifts = np.random.uniform(-shift_range, shift_range, (1, C)).astype(np.float32) + pc += shifts + return pc + + +def random_scale_point_cloud(pc, scale_low=0.8, scale_high=1.25): + scale = np.random.uniform(scale_low, scale_high, 1) + pc *= scale + return pc + + + +def uniform_2_sphere(num: int = None): + """Uniform sampling on a 2-sphere + + Source: https://gist.github.com/andrewbolster/10274979 + + Args: + num: Number of vectors to sample (or None if single) + + Returns: + Random Vector (np.ndarray) of size (num, 3) with norm 1. + If num is None returned value will have size (3,) + + """ + if num is not None: + phi = np.random.uniform(0.0, 2 * np.pi, num) + cos_theta = np.random.uniform(-1.0, 1.0, num) + else: + phi = np.random.uniform(0.0, 2 * np.pi) + cos_theta = np.random.uniform(-1.0, 1.0) + + theta = np.arccos(cos_theta) + x = np.sin(theta) * np.cos(phi) + y = np.sin(theta) * np.sin(phi) + z = np.cos(theta) + return np.stack((x, y, z), axis=-1) + + +def random_crop(pc, p_keep): + rand_xyz = uniform_2_sphere() + centroid = np.mean(pc[:, :3], axis=0) + pc_centered = pc[:, :3] - centroid + + dist_from_plane = np.dot(pc_centered, rand_xyz) + mask = dist_from_plane > np.percentile(dist_from_plane, (1.0 - p_keep) * 100) + return pc[mask, :] + + +def shuffle_pc(pc): + return np.random.permutation(pc) + + +def flip_pc(pc, r=0.5): + if np.random.random() > r: + pc[:, 1] = -1 * pc[:, 1] + return pc + + + diff --git a/lib/pointnet2/pointnet2_modules.py b/lib/pointnet2/pointnet2_modules.py new file mode 100644 index 0000000..5f125ce --- /dev/null +++ b/lib/pointnet2/pointnet2_modules.py @@ -0,0 +1,160 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +from . import pointnet2_utils +from . import pytorch_utils as pt_utils +from typing import List + + +class _PointnetSAModuleBase(nn.Module): + + def __init__(self): + super().__init__() + self.npoint = None + self.groupers = None + self.mlps = None + self.pool_method = 'max_pool' + + def forward(self, xyz: torch.Tensor, features: torch.Tensor = None, new_xyz=None) -> (torch.Tensor, torch.Tensor): + """ + :param xyz: (B, N, 3) tensor of the xyz coordinates of the features + :param features: (B, N, C) tensor of the descriptors of the the features + :param new_xyz: + :return: + new_xyz: (B, npoint, 3) tensor of the new features' xyz + new_features: (B, npoint, \sum_k(mlps[k][-1])) tensor of the new_features descriptors + """ + new_features_list = [] + + xyz_flipped = xyz.transpose(1, 2).contiguous() + if new_xyz is None: + new_xyz = pointnet2_utils.gather_operation( + xyz_flipped, + pointnet2_utils.furthest_point_sample(xyz, self.npoint) + ).transpose(1, 2).contiguous() if self.npoint is not None else None + + for i in range(len(self.groupers)): + new_features = self.groupers[i](xyz, new_xyz, features) # (B, C, npoint, nsample) + + new_features = self.mlps[i](new_features) # (B, mlp[-1], npoint, nsample) + if self.pool_method == 'max_pool': + new_features = F.max_pool2d( + new_features, kernel_size=[1, new_features.size(3)] + ) # (B, mlp[-1], npoint, 1) + elif self.pool_method == 'avg_pool': + new_features = F.avg_pool2d( + new_features, kernel_size=[1, new_features.size(3)] + ) # (B, mlp[-1], npoint, 1) + else: + raise NotImplementedError + + new_features = new_features.squeeze(-1) # (B, mlp[-1], npoint) + new_features_list.append(new_features) + + return new_xyz, torch.cat(new_features_list, dim=1) + + +class PointnetSAModuleMSG(_PointnetSAModuleBase): + """Pointnet set abstraction layer with multiscale grouping""" + + def __init__(self, *, npoint: int, radii: List[float], nsamples: List[int], mlps: List[List[int]], bn: bool = True, + use_xyz: bool = True, pool_method='max_pool', instance_norm=False): + """ + :param npoint: int + :param radii: list of float, list of radii to group with + :param nsamples: list of int, number of samples in each ball query + :param mlps: list of list of int, spec of the pointnet before the global pooling for each scale + :param bn: whether to use batchnorm + :param use_xyz: + :param pool_method: max_pool / avg_pool + :param instance_norm: whether to use instance_norm + """ + super().__init__() + + assert len(radii) == len(nsamples) == len(mlps) + + self.npoint = npoint + self.groupers = nn.ModuleList() + self.mlps = nn.ModuleList() + for i in range(len(radii)): + radius = radii[i] + nsample = nsamples[i] + self.groupers.append( + pointnet2_utils.QueryAndGroup(radius, nsample, use_xyz=use_xyz) + if npoint is not None else pointnet2_utils.GroupAll(use_xyz) + ) + mlp_spec = mlps[i] + if use_xyz: + mlp_spec[0] += 3 + + self.mlps.append(pt_utils.SharedMLP(mlp_spec, bn=bn, instance_norm=instance_norm)) + self.pool_method = pool_method + + +class PointnetSAModule(PointnetSAModuleMSG): + """Pointnet set abstraction layer""" + + def __init__(self, *, mlp: List[int], npoint: int = None, radius: float = None, nsample: int = None, + bn: bool = True, use_xyz: bool = True, pool_method='max_pool', instance_norm=False): + """ + :param mlp: list of int, spec of the pointnet before the global max_pool + :param npoint: int, number of features + :param radius: float, radius of ball + :param nsample: int, number of samples in the ball query + :param bn: whether to use batchnorm + :param use_xyz: + :param pool_method: max_pool / avg_pool + :param instance_norm: whether to use instance_norm + """ + super().__init__( + mlps=[mlp], npoint=npoint, radii=[radius], nsamples=[nsample], bn=bn, use_xyz=use_xyz, + pool_method=pool_method, instance_norm=instance_norm + ) + + +class PointnetFPModule(nn.Module): + r"""Propigates the features of one set to another""" + + def __init__(self, *, mlp: List[int], bn: bool = True): + """ + :param mlp: list of int + :param bn: whether to use batchnorm + """ + super().__init__() + self.mlp = pt_utils.SharedMLP(mlp, bn=bn) + + def forward( + self, unknown: torch.Tensor, known: torch.Tensor, unknow_feats: torch.Tensor, known_feats: torch.Tensor + ) -> torch.Tensor: + """ + :param unknown: (B, n, 3) tensor of the xyz positions of the unknown features + :param known: (B, m, 3) tensor of the xyz positions of the known features + :param unknow_feats: (B, C1, n) tensor of the features to be propigated to + :param known_feats: (B, C2, m) tensor of features to be propigated + :return: + new_features: (B, mlp[-1], n) tensor of the features of the unknown features + """ + if known is not None: + dist, idx = pointnet2_utils.three_nn(unknown, known) + dist_recip = 1.0 / (dist + 1e-8) + norm = torch.sum(dist_recip, dim=2, keepdim=True) + weight = dist_recip / norm + + interpolated_feats = pointnet2_utils.three_interpolate(known_feats, idx, weight) + else: + interpolated_feats = known_feats.expand(*known_feats.size()[0:2], unknown.size(1)) + + if unknow_feats is not None: + new_features = torch.cat([interpolated_feats, unknow_feats], dim=1) # (B, C2 + C1, n) + else: + new_features = interpolated_feats + + new_features = new_features.unsqueeze(-1) + new_features = self.mlp(new_features) + + return new_features.squeeze(-1) + + +if __name__ == "__main__": + pass diff --git a/lib/pointnet2/pointnet2_utils.py b/lib/pointnet2/pointnet2_utils.py new file mode 100644 index 0000000..13c7609 --- /dev/null +++ b/lib/pointnet2/pointnet2_utils.py @@ -0,0 +1,323 @@ +import torch +from torch.autograd import Variable +from torch.autograd import Function +import torch.nn as nn +from typing import Tuple + +import pointnet2_cuda as pointnet2 + + +class FurthestPointSampling(Function): + @staticmethod + def forward(ctx, xyz: torch.Tensor, npoint: int) -> torch.Tensor: + """ + Uses iterative furthest point sampling to select a set of npoint features that have the largest + minimum distance + :param ctx: + :param xyz: (B, N, 3) where N > npoint + :param npoint: int, number of features in the sampled set + :return: + output: (B, npoint) tensor containing the set + """ + assert xyz.is_contiguous() + + B, N, _ = xyz.size() + output = torch.cuda.IntTensor(B, npoint) + temp = torch.cuda.FloatTensor(B, N).fill_(1e10) + + pointnet2.furthest_point_sampling_wrapper(B, N, npoint, xyz, temp, output) + return output + + @staticmethod + def backward(xyz, a=None): + return None, None + + +furthest_point_sample = FurthestPointSampling.apply + + +class GatherOperation(Function): + + @staticmethod + def forward(ctx, features: torch.Tensor, idx: torch.Tensor) -> torch.Tensor: + """ + :param ctx: + :param features: (B, C, N) + :param idx: (B, npoint) index tensor of the features to gather + :return: + output: (B, C, npoint) + """ + assert features.is_contiguous() + assert idx.is_contiguous() + + B, npoint = idx.size() + _, C, N = features.size() + output = torch.cuda.FloatTensor(B, C, npoint) + + pointnet2.gather_points_wrapper(B, C, N, npoint, features, idx, output) + + ctx.for_backwards = (idx, C, N) + return output + + @staticmethod + def backward(ctx, grad_out): + idx, C, N = ctx.for_backwards + B, npoint = idx.size() + + grad_features = Variable(torch.cuda.FloatTensor(B, C, N).zero_()) + grad_out_data = grad_out.data.contiguous() + pointnet2.gather_points_grad_wrapper(B, C, N, npoint, grad_out_data, idx, grad_features.data) + return grad_features, None + + +gather_operation = GatherOperation.apply + +class KNN(Function): + + @staticmethod + def forward(ctx, k: int, unknown: torch.Tensor, known: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Find the three nearest neighbors of unknown in known + :param ctx: + :param unknown: (B, N, 3) + :param known: (B, M, 3) + :return: + dist: (B, N, k) l2 distance to the three nearest neighbors + idx: (B, N, k) index of 3 nearest neighbors + """ + assert unknown.is_contiguous() + assert known.is_contiguous() + + B, N, _ = unknown.size() + m = known.size(1) + dist2 = torch.cuda.FloatTensor(B, N, k) + idx = torch.cuda.IntTensor(B, N, k) + + pointnet2.knn_wrapper(B, N, m, k, unknown, known, dist2, idx) + return torch.sqrt(dist2), idx + + @staticmethod + def backward(ctx, a=None, b=None): + return None, None, None +knn = KNN.apply + +class ThreeNN(Function): + + @staticmethod + def forward(ctx, unknown: torch.Tensor, known: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Find the three nearest neighbors of unknown in known + :param ctx: + :param unknown: (B, N, 3) + :param known: (B, M, 3) + :return: + dist: (B, N, 3) l2 distance to the three nearest neighbors + idx: (B, N, 3) index of 3 nearest neighbors + """ + assert unknown.is_contiguous() + assert known.is_contiguous() + + B, N, _ = unknown.size() + m = known.size(1) + dist2 = torch.cuda.FloatTensor(B, N, 3) + idx = torch.cuda.IntTensor(B, N, 3) + + pointnet2.three_nn_wrapper(B, N, m, unknown, known, dist2, idx) + return torch.sqrt(dist2), idx + + @staticmethod + def backward(ctx, a=None, b=None): + return None, None + + +three_nn = ThreeNN.apply + + +class ThreeInterpolate(Function): + + @staticmethod + def forward(ctx, features: torch.Tensor, idx: torch.Tensor, weight: torch.Tensor) -> torch.Tensor: + """ + Performs weight linear interpolation on 3 features + :param ctx: + :param features: (B, C, M) Features descriptors to be interpolated from + :param idx: (B, n, 3) three nearest neighbors of the target features in features + :param weight: (B, n, 3) weights + :return: + output: (B, C, N) tensor of the interpolated features + """ + assert features.is_contiguous() + assert idx.is_contiguous() + assert weight.is_contiguous() + + B, c, m = features.size() + n = idx.size(1) + ctx.three_interpolate_for_backward = (idx, weight, m) + output = torch.cuda.FloatTensor(B, c, n) + + pointnet2.three_interpolate_wrapper(B, c, m, n, features, idx, weight, output) + return output + + @staticmethod + def backward(ctx, grad_out: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ + :param ctx: + :param grad_out: (B, C, N) tensor with gradients of outputs + :return: + grad_features: (B, C, M) tensor with gradients of features + None: + None: + """ + idx, weight, m = ctx.three_interpolate_for_backward + B, c, n = grad_out.size() + + grad_features = Variable(torch.cuda.FloatTensor(B, c, m).zero_()) + grad_out_data = grad_out.data.contiguous() + + pointnet2.three_interpolate_grad_wrapper(B, c, n, m, grad_out_data, idx, weight, grad_features.data) + return grad_features, None, None + + +three_interpolate = ThreeInterpolate.apply + + +class GroupingOperation(Function): + + @staticmethod + def forward(ctx, features: torch.Tensor, idx: torch.Tensor) -> torch.Tensor: + """ + :param ctx: + :param features: (B, C, N) tensor of features to group + :param idx: (B, npoint, nsample) tensor containing the indicies of features to group with + :return: + output: (B, C, npoint, nsample) tensor + """ + assert features.is_contiguous() + assert idx.is_contiguous() + idx = idx.int() + B, nfeatures, nsample = idx.size() + _, C, N = features.size() + output = torch.cuda.FloatTensor(B, C, nfeatures, nsample) + + pointnet2.group_points_wrapper(B, C, N, nfeatures, nsample, features, idx, output) + + ctx.for_backwards = (idx, N) + return output + + @staticmethod + def backward(ctx, grad_out: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: + """ + :param ctx: + :param grad_out: (B, C, npoint, nsample) tensor of the gradients of the output from forward + :return: + grad_features: (B, C, N) gradient of the features + """ + idx, N = ctx.for_backwards + + B, C, npoint, nsample = grad_out.size() + grad_features = Variable(torch.cuda.FloatTensor(B, C, N).zero_()) + + grad_out_data = grad_out.data.contiguous() + pointnet2.group_points_grad_wrapper(B, C, N, npoint, nsample, grad_out_data, idx, grad_features.data) + return grad_features, None + + +grouping_operation = GroupingOperation.apply + + +class BallQuery(Function): + + @staticmethod + def forward(ctx, radius: float, nsample: int, xyz: torch.Tensor, new_xyz: torch.Tensor) -> torch.Tensor: + """ + :param ctx: + :param radius: float, radius of the balls + :param nsample: int, maximum number of features in the balls + :param xyz: (B, N, 3) xyz coordinates of the features + :param new_xyz: (B, npoint, 3) centers of the ball query + :return: + idx: (B, npoint, nsample) tensor with the indicies of the features that form the query balls + """ + assert new_xyz.is_contiguous() + assert xyz.is_contiguous() + + B, N, _ = xyz.size() + npoint = new_xyz.size(1) + idx = torch.cuda.IntTensor(B, npoint, nsample).zero_() + + pointnet2.ball_query_wrapper(B, N, npoint, radius, nsample, new_xyz, xyz, idx) + return idx + + @staticmethod + def backward(ctx, a=None): + return None, None, None, None + + +ball_query = BallQuery.apply + + +class QueryAndGroup(nn.Module): + def __init__(self, radius: float, nsample: int, use_xyz: bool = True): + """ + :param radius: float, radius of ball + :param nsample: int, maximum number of features to gather in the ball + :param use_xyz: + """ + super().__init__() + self.radius, self.nsample, self.use_xyz = radius, nsample, use_xyz + + def forward(self, xyz: torch.Tensor, new_xyz: torch.Tensor, features: torch.Tensor = None) -> Tuple[torch.Tensor]: + """ + :param xyz: (B, N, 3) xyz coordinates of the features + :param new_xyz: (B, npoint, 3) centroids + :param features: (B, C, N) descriptors of the features + :return: + new_features: (B, 3 + C, npoint, nsample) + """ + # idx = ball_query(self.radius, self.nsample, xyz, new_xyz) + B, N, C = new_xyz.shape + dist, idx = knn(self.nsample, new_xyz, xyz) + if self.radius is not None: + tmp_idx = idx[:, :, 0].unsqueeze(2).repeat(1, 1, self.nsample).to(idx.device) + idx[dist > self.radius] = tmp_idx[dist > self.radius] + xyz_trans = xyz.transpose(1, 2).contiguous() + grouped_xyz = grouping_operation(xyz_trans, idx) # (B, 3, npoint, nsample) + grouped_xyz -= new_xyz.transpose(1, 2).unsqueeze(-1) + + if features is not None: + grouped_features = grouping_operation(features, idx) + if self.use_xyz: + new_features = torch.cat([grouped_xyz, grouped_features], dim=1) # (B, C + 3, npoint, nsample) + else: + new_features = grouped_features + else: + assert self.use_xyz, "Cannot have not features and not use xyz as a feature!" + new_features = grouped_xyz + + return new_features, grouped_xyz + + +class GroupAll(nn.Module): + def __init__(self, use_xyz: bool = True): + super().__init__() + self.use_xyz = use_xyz + + def forward(self, xyz: torch.Tensor, new_xyz: torch.Tensor, features: torch.Tensor = None): + """ + :param xyz: (B, N, 3) xyz coordinates of the features + :param new_xyz: ignored + :param features: (B, C, N) descriptors of the features + :return: + new_features: (B, C + 3, 1, N) + """ + grouped_xyz = xyz.transpose(1, 2).unsqueeze(2) + if features is not None: + grouped_features = features.unsqueeze(2) + if self.use_xyz: + new_features = torch.cat([grouped_xyz, grouped_features], dim=1) # (B, 3 + C, 1, N) + else: + new_features = grouped_features + else: + new_features = grouped_xyz + + return new_features, grouped_xyz diff --git a/lib/pointnet2/pytorch_utils.py b/lib/pointnet2/pytorch_utils.py new file mode 100644 index 0000000..09cb7bc --- /dev/null +++ b/lib/pointnet2/pytorch_utils.py @@ -0,0 +1,236 @@ +import torch.nn as nn +from typing import List, Tuple + + +class SharedMLP(nn.Sequential): + + def __init__( + self, + args: List[int], + *, + bn: bool = False, + activation=nn.ReLU(inplace=True), + preact: bool = False, + first: bool = False, + name: str = "", + instance_norm: bool = False, + ): + super().__init__() + + for i in range(len(args) - 1): + self.add_module( + name + 'layer{}'.format(i), + Conv2d( + args[i], + args[i + 1], + bn=(not first or not preact or (i != 0)) and bn, + activation=activation + if (not first or not preact or (i != 0)) else None, + preact=preact, + instance_norm=instance_norm + ) + ) + + +class _ConvBase(nn.Sequential): + + def __init__( + self, + in_size, + out_size, + kernel_size, + stride, + padding, + activation, + bn, + init, + conv=None, + batch_norm=None, + bias=True, + preact=False, + name="", + instance_norm=False, + instance_norm_func=None + ): + super().__init__() + + bias = bias and (not bn) + conv_unit = conv( + in_size, + out_size, + kernel_size=kernel_size, + stride=stride, + padding=padding, + bias=bias + ) + init(conv_unit.weight) + if bias: + nn.init.constant_(conv_unit.bias, 0) + + if bn: + if not preact: + bn_unit = batch_norm(out_size) + else: + bn_unit = batch_norm(in_size) + if instance_norm: + if not preact: + in_unit = instance_norm_func(out_size, affine=False, track_running_stats=False) + else: + in_unit = instance_norm_func(in_size, affine=False, track_running_stats=False) + + if preact: + if bn: + self.add_module(name + 'bn', bn_unit) + + if activation is not None: + self.add_module(name + 'activation', activation) + + if not bn and instance_norm: + self.add_module(name + 'in', in_unit) + + self.add_module(name + 'conv', conv_unit) + + if not preact: + if bn: + self.add_module(name + 'bn', bn_unit) + + if activation is not None: + self.add_module(name + 'activation', activation) + + if not bn and instance_norm: + self.add_module(name + 'in', in_unit) + + +class _BNBase(nn.Sequential): + + def __init__(self, in_size, batch_norm=None, name=""): + super().__init__() + self.add_module(name + "bn", batch_norm(in_size)) + + nn.init.constant_(self[0].weight, 1.0) + nn.init.constant_(self[0].bias, 0) + + +class BatchNorm1d(_BNBase): + + def __init__(self, in_size: int, *, name: str = ""): + super().__init__(in_size, batch_norm=nn.BatchNorm1d, name=name) + + +class BatchNorm2d(_BNBase): + + def __init__(self, in_size: int, name: str = ""): + super().__init__(in_size, batch_norm=nn.BatchNorm2d, name=name) + + +class Conv1d(_ConvBase): + + def __init__( + self, + in_size: int, + out_size: int, + *, + kernel_size: int = 1, + stride: int = 1, + padding: int = 0, + activation=nn.ReLU(inplace=True), + bn: bool = False, + init=nn.init.kaiming_normal_, + bias: bool = True, + preact: bool = False, + name: str = "", + instance_norm=False + ): + super().__init__( + in_size, + out_size, + kernel_size, + stride, + padding, + activation, + bn, + init, + conv=nn.Conv1d, + batch_norm=BatchNorm1d, + bias=bias, + preact=preact, + name=name, + instance_norm=instance_norm, + instance_norm_func=nn.InstanceNorm1d + ) + + +class Conv2d(_ConvBase): + + def __init__( + self, + in_size: int, + out_size: int, + *, + kernel_size: Tuple[int, int] = (1, 1), + stride: Tuple[int, int] = (1, 1), + padding: Tuple[int, int] = (0, 0), + activation=nn.ReLU(inplace=True), + bn: bool = False, + init=nn.init.kaiming_normal_, + bias: bool = True, + preact: bool = False, + name: str = "", + instance_norm=False + ): + super().__init__( + in_size, + out_size, + kernel_size, + stride, + padding, + activation, + bn, + init, + conv=nn.Conv2d, + batch_norm=BatchNorm2d, + bias=bias, + preact=preact, + name=name, + instance_norm=instance_norm, + instance_norm_func=nn.InstanceNorm2d + ) + + +class FC(nn.Sequential): + + def __init__( + self, + in_size: int, + out_size: int, + *, + activation=nn.ReLU(inplace=True), + bn: bool = False, + init=None, + preact: bool = False, + name: str = "" + ): + super().__init__() + + fc = nn.Linear(in_size, out_size, bias=not bn) + if init is not None: + init(fc.weight) + if not bn: + nn.init.constant(fc.bias, 0) + + if preact: + if bn: + self.add_module(name + 'bn', BatchNorm1d(in_size)) + + if activation is not None: + self.add_module(name + 'activation', activation) + + self.add_module(name + 'fc', fc) + + if not preact: + if bn: + self.add_module(name + 'bn', BatchNorm1d(out_size)) + + if activation is not None: + self.add_module(name + 'activation', activation) + diff --git a/lib/pointnet2/setup.py b/lib/pointnet2/setup.py new file mode 100644 index 0000000..99e59e3 --- /dev/null +++ b/lib/pointnet2/setup.py @@ -0,0 +1,23 @@ +from setuptools import setup +from torch.utils.cpp_extension import BuildExtension, CUDAExtension + +setup( + name='pointnet2', + ext_modules=[ + CUDAExtension('pointnet2_cuda', [ + 'src/pointnet2_api.cpp', + + 'src/ball_query.cpp', + 'src/ball_query_gpu.cu', + 'src/group_points.cpp', + 'src/group_points_gpu.cu', + 'src/interpolate.cpp', + 'src/interpolate_gpu.cu', + 'src/sampling.cpp', + 'src/sampling_gpu.cu', + ], + extra_compile_args={'cxx': ['-g'], + 'nvcc': ['-O2']}) + ], + cmdclass={'build_ext': BuildExtension} +) diff --git a/lib/pointnet2/src/ball_query.cpp b/lib/pointnet2/src/ball_query.cpp new file mode 100644 index 0000000..f6eb2f8 --- /dev/null +++ b/lib/pointnet2/src/ball_query.cpp @@ -0,0 +1,28 @@ +#include +#include +#include +#include +#include +#include "ball_query_gpu.h" + +extern THCState *state; + +//#define CHECK_CUDA(x) AT_CHECK(x.type().is_cuda(), #x, " must be a CUDAtensor ") +//#define CHECK_CONTIGUOUS(x) AT_CHECK(x.is_contiguous(), #x, " must be contiguous ") +#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x, " must be a CUDAtensor ") +#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x, " must be contiguous ") +#define CHECK_INPUT(x) CHECK_CUDA(x);CHECK_CONTIGUOUS(x) + +int ball_query_wrapper_fast(int b, int n, int m, float radius, int nsample, + at::Tensor new_xyz_tensor, at::Tensor xyz_tensor, at::Tensor idx_tensor) { + CHECK_INPUT(new_xyz_tensor); + CHECK_INPUT(xyz_tensor); + const float *new_xyz = new_xyz_tensor.data(); + const float *xyz = xyz_tensor.data(); + int *idx = idx_tensor.data(); + +// cudaStream_t stream = THCState_getCurrentStream(state); + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + ball_query_kernel_launcher_fast(b, n, m, radius, nsample, new_xyz, xyz, idx, stream); + return 1; +} \ No newline at end of file diff --git a/lib/pointnet2/src/ball_query_gpu.cu b/lib/pointnet2/src/ball_query_gpu.cu new file mode 100644 index 0000000..f8840aa --- /dev/null +++ b/lib/pointnet2/src/ball_query_gpu.cu @@ -0,0 +1,67 @@ +#include +#include +#include + +#include "ball_query_gpu.h" +#include "cuda_utils.h" + + +__global__ void ball_query_kernel_fast(int b, int n, int m, float radius, int nsample, + const float *__restrict__ new_xyz, const float *__restrict__ xyz, int *__restrict__ idx) { + // new_xyz: (B, M, 3) + // xyz: (B, N, 3) + // output: + // idx: (B, M, nsample) + int bs_idx = blockIdx.y; + int pt_idx = blockIdx.x * blockDim.x + threadIdx.x; + if (bs_idx >= b || pt_idx >= m) return; + + new_xyz += bs_idx * m * 3 + pt_idx * 3; + xyz += bs_idx * n * 3; + idx += bs_idx * m * nsample + pt_idx * nsample; + + float radius2 = radius * radius; + float new_x = new_xyz[0]; + float new_y = new_xyz[1]; + float new_z = new_xyz[2]; + + int cnt = 0; + for (int k = 0; k < n; ++k) { + float x = xyz[k * 3 + 0]; + float y = xyz[k * 3 + 1]; + float z = xyz[k * 3 + 2]; + float d2 = (new_x - x) * (new_x - x) + (new_y - y) * (new_y - y) + (new_z - z) * (new_z - z); + if (d2 < radius2){ + if (cnt == 0){ + for (int l = 0; l < nsample; ++l) { + idx[l] = k; + } + } + idx[cnt] = k; + ++cnt; + if (cnt >= nsample) break; + } + } +} + + +void ball_query_kernel_launcher_fast(int b, int n, int m, float radius, int nsample, \ + const float *new_xyz, const float *xyz, int *idx, cudaStream_t stream) { + // new_xyz: (B, M, 3) + // xyz: (B, N, 3) + // output: + // idx: (B, M, nsample) + + cudaError_t err; + + dim3 blocks(DIVUP(m, THREADS_PER_BLOCK), b); // blockIdx.x(col), blockIdx.y(row) + dim3 threads(THREADS_PER_BLOCK); + + ball_query_kernel_fast<<>>(b, n, m, radius, nsample, new_xyz, xyz, idx); + // cudaDeviceSynchronize(); // for using printf in kernel function + err = cudaGetLastError(); + if (cudaSuccess != err) { + fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err)); + exit(-1); + } +} \ No newline at end of file diff --git a/lib/pointnet2/src/ball_query_gpu.h b/lib/pointnet2/src/ball_query_gpu.h new file mode 100644 index 0000000..ffc831a --- /dev/null +++ b/lib/pointnet2/src/ball_query_gpu.h @@ -0,0 +1,15 @@ +#ifndef _BALL_QUERY_GPU_H +#define _BALL_QUERY_GPU_H + +#include +#include +#include +#include + +int ball_query_wrapper_fast(int b, int n, int m, float radius, int nsample, + at::Tensor new_xyz_tensor, at::Tensor xyz_tensor, at::Tensor idx_tensor); + +void ball_query_kernel_launcher_fast(int b, int n, int m, float radius, int nsample, + const float *xyz, const float *new_xyz, int *idx, cudaStream_t stream); + +#endif diff --git a/lib/pointnet2/src/cuda_utils.h b/lib/pointnet2/src/cuda_utils.h new file mode 100644 index 0000000..7fe2796 --- /dev/null +++ b/lib/pointnet2/src/cuda_utils.h @@ -0,0 +1,15 @@ +#ifndef _CUDA_UTILS_H +#define _CUDA_UTILS_H + +#include + +#define TOTAL_THREADS 1024 +#define THREADS_PER_BLOCK 256 +#define DIVUP(m,n) ((m) / (n) + ((m) % (n) > 0)) + +inline int opt_n_threads(int work_size) { + const int pow_2 = std::log(static_cast(work_size)) / std::log(2.0); + + return max(min(1 << pow_2, TOTAL_THREADS), 1); +} +#endif diff --git a/lib/pointnet2/src/group_points.cpp b/lib/pointnet2/src/group_points.cpp new file mode 100644 index 0000000..29122b5 --- /dev/null +++ b/lib/pointnet2/src/group_points.cpp @@ -0,0 +1,37 @@ +#include +#include +#include +#include +#include +#include "group_points_gpu.h" + +extern THCState *state; + + +int group_points_grad_wrapper_fast(int b, int c, int n, int npoints, int nsample, + at::Tensor grad_out_tensor, at::Tensor idx_tensor, at::Tensor grad_points_tensor) { + + float *grad_points = grad_points_tensor.data(); + const int *idx = idx_tensor.data(); + const float *grad_out = grad_out_tensor.data(); + +// cudaStream_t stream = THCState_getCurrentStream(state); + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + group_points_grad_kernel_launcher_fast(b, c, n, npoints, nsample, grad_out, idx, grad_points, stream); + return 1; +} + + +int group_points_wrapper_fast(int b, int c, int n, int npoints, int nsample, + at::Tensor points_tensor, at::Tensor idx_tensor, at::Tensor out_tensor) { + + const float *points = points_tensor.data(); + const int *idx = idx_tensor.data(); + float *out = out_tensor.data(); + +// cudaStream_t stream = THCState_getCurrentStream(state); + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + group_points_kernel_launcher_fast(b, c, n, npoints, nsample, points, idx, out, stream); + return 1; +} \ No newline at end of file diff --git a/lib/pointnet2/src/group_points_gpu.cu b/lib/pointnet2/src/group_points_gpu.cu new file mode 100644 index 0000000..c015a81 --- /dev/null +++ b/lib/pointnet2/src/group_points_gpu.cu @@ -0,0 +1,86 @@ +#include +#include + +#include "cuda_utils.h" +#include "group_points_gpu.h" + + +__global__ void group_points_grad_kernel_fast(int b, int c, int n, int npoints, int nsample, + const float *__restrict__ grad_out, const int *__restrict__ idx, float *__restrict__ grad_points) { + // grad_out: (B, C, npoints, nsample) + // idx: (B, npoints, nsample) + // output: + // grad_points: (B, C, N) + int bs_idx = blockIdx.z; + int c_idx = blockIdx.y; + int index = blockIdx.x * blockDim.x + threadIdx.x; + int pt_idx = index / nsample; + if (bs_idx >= b || c_idx >= c || pt_idx >= npoints) return; + + int sample_idx = index % nsample; + grad_out += bs_idx * c * npoints * nsample + c_idx * npoints * nsample + pt_idx * nsample + sample_idx; + idx += bs_idx * npoints * nsample + pt_idx * nsample + sample_idx; + + atomicAdd(grad_points + bs_idx * c * n + c_idx * n + idx[0] , grad_out[0]); +} + +void group_points_grad_kernel_launcher_fast(int b, int c, int n, int npoints, int nsample, + const float *grad_out, const int *idx, float *grad_points, cudaStream_t stream) { + // grad_out: (B, C, npoints, nsample) + // idx: (B, npoints, nsample) + // output: + // grad_points: (B, C, N) + cudaError_t err; + dim3 blocks(DIVUP(npoints * nsample, THREADS_PER_BLOCK), c, b); // blockIdx.x(col), blockIdx.y(row) + dim3 threads(THREADS_PER_BLOCK); + + group_points_grad_kernel_fast<<>>(b, c, n, npoints, nsample, grad_out, idx, grad_points); + + err = cudaGetLastError(); + if (cudaSuccess != err) { + fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err)); + exit(-1); + } +} + + +__global__ void group_points_kernel_fast(int b, int c, int n, int npoints, int nsample, + const float *__restrict__ points, const int *__restrict__ idx, float *__restrict__ out) { + // points: (B, C, N) + // idx: (B, npoints, nsample) + // output: + // out: (B, C, npoints, nsample) + int bs_idx = blockIdx.z; + int c_idx = blockIdx.y; + int index = blockIdx.x * blockDim.x + threadIdx.x; + int pt_idx = index / nsample; + if (bs_idx >= b || c_idx >= c || pt_idx >= npoints) return; + + int sample_idx = index % nsample; + + idx += bs_idx * npoints * nsample + pt_idx * nsample + sample_idx; + int in_idx = bs_idx * c * n + c_idx * n + idx[0]; + int out_idx = bs_idx * c * npoints * nsample + c_idx * npoints * nsample + pt_idx * nsample + sample_idx; + + out[out_idx] = points[in_idx]; +} + + +void group_points_kernel_launcher_fast(int b, int c, int n, int npoints, int nsample, + const float *points, const int *idx, float *out, cudaStream_t stream) { + // points: (B, C, N) + // idx: (B, npoints, nsample) + // output: + // out: (B, C, npoints, nsample) + cudaError_t err; + dim3 blocks(DIVUP(npoints * nsample, THREADS_PER_BLOCK), c, b); // blockIdx.x(col), blockIdx.y(row) + dim3 threads(THREADS_PER_BLOCK); + + group_points_kernel_fast<<>>(b, c, n, npoints, nsample, points, idx, out); + // cudaDeviceSynchronize(); // for using printf in kernel function + err = cudaGetLastError(); + if (cudaSuccess != err) { + fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err)); + exit(-1); + } +} diff --git a/lib/pointnet2/src/group_points_gpu.h b/lib/pointnet2/src/group_points_gpu.h new file mode 100644 index 0000000..76c73ca --- /dev/null +++ b/lib/pointnet2/src/group_points_gpu.h @@ -0,0 +1,22 @@ +#ifndef _GROUP_POINTS_GPU_H +#define _GROUP_POINTS_GPU_H + +#include +#include +#include +#include + + +int group_points_wrapper_fast(int b, int c, int n, int npoints, int nsample, + at::Tensor points_tensor, at::Tensor idx_tensor, at::Tensor out_tensor); + +void group_points_kernel_launcher_fast(int b, int c, int n, int npoints, int nsample, + const float *points, const int *idx, float *out, cudaStream_t stream); + +int group_points_grad_wrapper_fast(int b, int c, int n, int npoints, int nsample, + at::Tensor grad_out_tensor, at::Tensor idx_tensor, at::Tensor grad_points_tensor); + +void group_points_grad_kernel_launcher_fast(int b, int c, int n, int npoints, int nsample, + const float *grad_out, const int *idx, float *grad_points, cudaStream_t stream); + +#endif diff --git a/lib/pointnet2/src/interpolate.cpp b/lib/pointnet2/src/interpolate.cpp new file mode 100644 index 0000000..82db08f --- /dev/null +++ b/lib/pointnet2/src/interpolate.cpp @@ -0,0 +1,69 @@ +#include +#include +#include +#include +#include +#include +#include +#include +#include "interpolate_gpu.h" + +extern THCState *state; + + +void three_nn_wrapper_fast(int b, int n, int m, at::Tensor unknown_tensor, + at::Tensor known_tensor, at::Tensor dist2_tensor, at::Tensor idx_tensor) { + const float *unknown = unknown_tensor.data(); + const float *known = known_tensor.data(); + float *dist2 = dist2_tensor.data(); + int *idx = idx_tensor.data(); + +// cudaStream_t stream = THCState_getCurrentStream(state); + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + three_nn_kernel_launcher_fast(b, n, m, unknown, known, dist2, idx, stream); +} + +void knn_wrapper_fast(int b, int n, int m, int k, at::Tensor unknown_tensor, + at::Tensor known_tensor, at::Tensor dist2_tensor, at::Tensor idx_tensor) { + const float *unknown = unknown_tensor.data(); + const float *known = known_tensor.data(); + float *dist2 = dist2_tensor.data(); + int *idx = idx_tensor.data(); + +// cudaStream_t stream = THCState_getCurrentStream(state); + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + knn_kernel_launcher_fast(b, n, m, k, unknown, known, dist2, idx, stream); +} + + +void three_interpolate_wrapper_fast(int b, int c, int m, int n, + at::Tensor points_tensor, + at::Tensor idx_tensor, + at::Tensor weight_tensor, + at::Tensor out_tensor) { + + const float *points = points_tensor.data(); + const float *weight = weight_tensor.data(); + float *out = out_tensor.data(); + const int *idx = idx_tensor.data(); + +// cudaStream_t stream = THCState_getCurrentStream(state); + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + three_interpolate_kernel_launcher_fast(b, c, m, n, points, idx, weight, out, stream); +} + +void three_interpolate_grad_wrapper_fast(int b, int c, int n, int m, + at::Tensor grad_out_tensor, + at::Tensor idx_tensor, + at::Tensor weight_tensor, + at::Tensor grad_points_tensor) { + + const float *grad_out = grad_out_tensor.data(); + const float *weight = weight_tensor.data(); + float *grad_points = grad_points_tensor.data(); + const int *idx = idx_tensor.data(); + +// cudaStream_t stream = THCState_getCurrentStream(state); + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + three_interpolate_grad_kernel_launcher_fast(b, c, n, m, grad_out, idx, weight, grad_points, stream); +} \ No newline at end of file diff --git a/lib/pointnet2/src/interpolate_gpu.cu b/lib/pointnet2/src/interpolate_gpu.cu new file mode 100644 index 0000000..2bcac2e --- /dev/null +++ b/lib/pointnet2/src/interpolate_gpu.cu @@ -0,0 +1,233 @@ +#include +#include +#include + +#include "cuda_utils.h" +#include "interpolate_gpu.h" + + +__global__ void knn_kernel_fast(int b, int n, int m, int k, const float *__restrict__ unknown, + const float *__restrict__ known, float *__restrict__ dist2, int *__restrict__ idx) { + // unknown: (B, N, 3) + // known: (B, M, 3) + // output: + // dist2: (B, N, k) + // idx: (B, N, k) + + int bs_idx = blockIdx.y; + int pt_idx = blockIdx.x * blockDim.x + threadIdx.x; + if (bs_idx >= b || pt_idx >= n) return; + + unknown += bs_idx * n * 3 + pt_idx * 3; + known += bs_idx * m * 3; + dist2 += bs_idx * n * k + pt_idx * k; + idx += bs_idx * n * k + pt_idx * k; + + float ux = unknown[0]; + float uy = unknown[1]; + float uz = unknown[2]; + + double best[200]; + int besti[200]; + for(int i = 0; i < k; i++){ + best[i] = 1e40; + besti[i] = 0; + } + for (int i = 0; i < m; ++i) { + float x = known[i * 3 + 0]; + float y = known[i * 3 + 1]; + float z = known[i * 3 + 2]; + float d = (ux - x) * (ux - x) + (uy - y) * (uy - y) + (uz - z) * (uz - z); + for(int j = 0; j < k; j++){ + if(d < best[j]){ + for(int l = k - 1; l > j; l--){ + best[l] = best[l - 1]; + besti[l] = besti[l - 1]; + } + best[j] = d; + besti[j] = i; + break; + } + } + } + for(int i = 0; i < k; i++){ + idx[i] = besti[i]; + dist2[i] = best[i]; + } +} + + +void knn_kernel_launcher_fast(int b, int n, int m, int k, const float *unknown, + const float *known, float *dist2, int *idx, cudaStream_t stream) { + // unknown: (B, N, 3) + // known: (B, M, 3) + // output: + // dist2: (B, N, k) + // idx: (B, N, k) + + cudaError_t err; + dim3 blocks(DIVUP(n, THREADS_PER_BLOCK), b); // blockIdx.x(col), blockIdx.y(row) + dim3 threads(THREADS_PER_BLOCK); + + knn_kernel_fast<<>>(b, n, m, k, unknown, known, dist2, idx); + + err = cudaGetLastError(); + if (cudaSuccess != err) { + fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err)); + exit(-1); + } +} + +__global__ void three_nn_kernel_fast(int b, int n, int m, const float *__restrict__ unknown, + const float *__restrict__ known, float *__restrict__ dist2, int *__restrict__ idx) { + // unknown: (B, N, 3) + // known: (B, M, 3) + // output: + // dist2: (B, N, 3) + // idx: (B, N, 3) + + int bs_idx = blockIdx.y; + int pt_idx = blockIdx.x * blockDim.x + threadIdx.x; + if (bs_idx >= b || pt_idx >= n) return; + + unknown += bs_idx * n * 3 + pt_idx * 3; + known += bs_idx * m * 3; + dist2 += bs_idx * n * 3 + pt_idx * 3; + idx += bs_idx * n * 3 + pt_idx * 3; + + float ux = unknown[0]; + float uy = unknown[1]; + float uz = unknown[2]; + + double best1 = 1e40, best2 = 1e40, best3 = 1e40; + int besti1 = 0, besti2 = 0, besti3 = 0; + for (int k = 0; k < m; ++k) { + float x = known[k * 3 + 0]; + float y = known[k * 3 + 1]; + float z = known[k * 3 + 2]; + float d = (ux - x) * (ux - x) + (uy - y) * (uy - y) + (uz - z) * (uz - z); + if (d < best1) { + best3 = best2; besti3 = besti2; + best2 = best1; besti2 = besti1; + best1 = d; besti1 = k; + } + else if (d < best2) { + best3 = best2; besti3 = besti2; + best2 = d; besti2 = k; + } + else if (d < best3) { + best3 = d; besti3 = k; + } + } + dist2[0] = best1; dist2[1] = best2; dist2[2] = best3; + idx[0] = besti1; idx[1] = besti2; idx[2] = besti3; +} + + +void three_nn_kernel_launcher_fast(int b, int n, int m, const float *unknown, + const float *known, float *dist2, int *idx, cudaStream_t stream) { + // unknown: (B, N, 3) + // known: (B, M, 3) + // output: + // dist2: (B, N, 3) + // idx: (B, N, 3) + + cudaError_t err; + dim3 blocks(DIVUP(n, THREADS_PER_BLOCK), b); // blockIdx.x(col), blockIdx.y(row) + dim3 threads(THREADS_PER_BLOCK); + + three_nn_kernel_fast<<>>(b, n, m, unknown, known, dist2, idx); + + err = cudaGetLastError(); + if (cudaSuccess != err) { + fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err)); + exit(-1); + } +} + + +__global__ void three_interpolate_kernel_fast(int b, int c, int m, int n, const float *__restrict__ points, + const int *__restrict__ idx, const float *__restrict__ weight, float *__restrict__ out) { + // points: (B, C, M) + // idx: (B, N, 3) + // weight: (B, N, 3) + // output: + // out: (B, C, N) + + int bs_idx = blockIdx.z; + int c_idx = blockIdx.y; + int pt_idx = blockIdx.x * blockDim.x + threadIdx.x; + + if (bs_idx >= b || c_idx >= c || pt_idx >= n) return; + + weight += bs_idx * n * 3 + pt_idx * 3; + points += bs_idx * c * m + c_idx * m; + idx += bs_idx * n * 3 + pt_idx * 3; + out += bs_idx * c * n + c_idx * n; + + out[pt_idx] = weight[0] * points[idx[0]] + weight[1] * points[idx[1]] + weight[2] * points[idx[2]]; +} + +void three_interpolate_kernel_launcher_fast(int b, int c, int m, int n, + const float *points, const int *idx, const float *weight, float *out, cudaStream_t stream) { + // points: (B, C, M) + // idx: (B, N, 3) + // weight: (B, N, 3) + // output: + // out: (B, C, N) + + cudaError_t err; + dim3 blocks(DIVUP(n, THREADS_PER_BLOCK), c, b); // blockIdx.x(col), blockIdx.y(row) + dim3 threads(THREADS_PER_BLOCK); + three_interpolate_kernel_fast<<>>(b, c, m, n, points, idx, weight, out); + + err = cudaGetLastError(); + if (cudaSuccess != err) { + fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err)); + exit(-1); + } +} + + +__global__ void three_interpolate_grad_kernel_fast(int b, int c, int n, int m, const float *__restrict__ grad_out, + const int *__restrict__ idx, const float *__restrict__ weight, float *__restrict__ grad_points) { + // grad_out: (B, C, N) + // weight: (B, N, 3) + // output: + // grad_points: (B, C, M) + + int bs_idx = blockIdx.z; + int c_idx = blockIdx.y; + int pt_idx = blockIdx.x * blockDim.x + threadIdx.x; + + if (bs_idx >= b || c_idx >= c || pt_idx >= n) return; + + grad_out += bs_idx * c * n + c_idx * n + pt_idx; + weight += bs_idx * n * 3 + pt_idx * 3; + grad_points += bs_idx * c * m + c_idx * m; + idx += bs_idx * n * 3 + pt_idx * 3; + + + atomicAdd(grad_points + idx[0], grad_out[0] * weight[0]); + atomicAdd(grad_points + idx[1], grad_out[0] * weight[1]); + atomicAdd(grad_points + idx[2], grad_out[0] * weight[2]); +} + +void three_interpolate_grad_kernel_launcher_fast(int b, int c, int n, int m, const float *grad_out, + const int *idx, const float *weight, float *grad_points, cudaStream_t stream) { + // grad_out: (B, C, N) + // weight: (B, N, 3) + // output: + // grad_points: (B, C, M) + + cudaError_t err; + dim3 blocks(DIVUP(n, THREADS_PER_BLOCK), c, b); // blockIdx.x(col), blockIdx.y(row) + dim3 threads(THREADS_PER_BLOCK); + three_interpolate_grad_kernel_fast<<>>(b, c, n, m, grad_out, idx, weight, grad_points); + + err = cudaGetLastError(); + if (cudaSuccess != err) { + fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err)); + exit(-1); + } +} \ No newline at end of file diff --git a/lib/pointnet2/src/interpolate_gpu.h b/lib/pointnet2/src/interpolate_gpu.h new file mode 100644 index 0000000..5ceb3ad --- /dev/null +++ b/lib/pointnet2/src/interpolate_gpu.h @@ -0,0 +1,36 @@ +#ifndef _INTERPOLATE_GPU_H +#define _INTERPOLATE_GPU_H + +#include +#include +#include +#include + + +void three_nn_wrapper_fast(int b, int n, int m, at::Tensor unknown_tensor, + at::Tensor known_tensor, at::Tensor dist2_tensor, at::Tensor idx_tensor); + +void three_nn_kernel_launcher_fast(int b, int n, int m, const float *unknown, + const float *known, float *dist2, int *idx, cudaStream_t stream); + +void knn_wrapper_fast(int b, int n, int m, int k, at::Tensor unknown_tensor, + at::Tensor known_tensor, at::Tensor dist2_tensor, at::Tensor idx_tensor); + +void knn_kernel_launcher_fast(int b, int n, int m, int k, const float *unknown, + const float *known, float *dist2, int *idx, cudaStream_t stream); + + +void three_interpolate_wrapper_fast(int b, int c, int m, int n, at::Tensor points_tensor, + at::Tensor idx_tensor, at::Tensor weight_tensor, at::Tensor out_tensor); + +void three_interpolate_kernel_launcher_fast(int b, int c, int m, int n, + const float *points, const int *idx, const float *weight, float *out, cudaStream_t stream); + + +void three_interpolate_grad_wrapper_fast(int b, int c, int n, int m, at::Tensor grad_out_tensor, + at::Tensor idx_tensor, at::Tensor weight_tensor, at::Tensor grad_points_tensor); + +void three_interpolate_grad_kernel_launcher_fast(int b, int c, int n, int m, const float *grad_out, + const int *idx, const float *weight, float *grad_points, cudaStream_t stream); + +#endif diff --git a/lib/pointnet2/src/pointnet2_api.cpp b/lib/pointnet2/src/pointnet2_api.cpp new file mode 100644 index 0000000..def9a3c --- /dev/null +++ b/lib/pointnet2/src/pointnet2_api.cpp @@ -0,0 +1,25 @@ +#include +#include + +#include "ball_query_gpu.h" +#include "group_points_gpu.h" +#include "sampling_gpu.h" +#include "interpolate_gpu.h" + + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("ball_query_wrapper", &ball_query_wrapper_fast, "ball_query_wrapper_fast"); + + m.def("group_points_wrapper", &group_points_wrapper_fast, "group_points_wrapper_fast"); + m.def("group_points_grad_wrapper", &group_points_grad_wrapper_fast, "group_points_grad_wrapper_fast"); + + m.def("gather_points_wrapper", &gather_points_wrapper_fast, "gather_points_wrapper_fast"); + m.def("gather_points_grad_wrapper", &gather_points_grad_wrapper_fast, "gather_points_grad_wrapper_fast"); + + m.def("furthest_point_sampling_wrapper", &furthest_point_sampling_wrapper, "furthest_point_sampling_wrapper"); + + m.def("knn_wrapper", &knn_wrapper_fast, "knn_wrapper_fast"); + m.def("three_nn_wrapper", &three_nn_wrapper_fast, "three_nn_wrapper_fast"); + m.def("three_interpolate_wrapper", &three_interpolate_wrapper_fast, "three_interpolate_wrapper_fast"); + m.def("three_interpolate_grad_wrapper", &three_interpolate_grad_wrapper_fast, "three_interpolate_grad_wrapper_fast"); +} diff --git a/lib/pointnet2/src/sampling.cpp b/lib/pointnet2/src/sampling.cpp new file mode 100644 index 0000000..0aa9996 --- /dev/null +++ b/lib/pointnet2/src/sampling.cpp @@ -0,0 +1,49 @@ +#include +#include +#include +#include + +#include "sampling_gpu.h" + +extern THCState *state; + + +int gather_points_wrapper_fast(int b, int c, int n, int npoints, + at::Tensor points_tensor, at::Tensor idx_tensor, at::Tensor out_tensor){ + const float *points = points_tensor.data(); + const int *idx = idx_tensor.data(); + float *out = out_tensor.data(); + +// cudaStream_t stream = THCState_getCurrentStream(state); + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + gather_points_kernel_launcher_fast(b, c, n, npoints, points, idx, out, stream); + return 1; +} + + +int gather_points_grad_wrapper_fast(int b, int c, int n, int npoints, + at::Tensor grad_out_tensor, at::Tensor idx_tensor, at::Tensor grad_points_tensor) { + + const float *grad_out = grad_out_tensor.data(); + const int *idx = idx_tensor.data(); + float *grad_points = grad_points_tensor.data(); + +// cudaStream_t stream = THCState_getCurrentStream(state); + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + gather_points_grad_kernel_launcher_fast(b, c, n, npoints, grad_out, idx, grad_points, stream); + return 1; +} + + +int furthest_point_sampling_wrapper(int b, int n, int m, + at::Tensor points_tensor, at::Tensor temp_tensor, at::Tensor idx_tensor) { + + const float *points = points_tensor.data(); + float *temp = temp_tensor.data(); + int *idx = idx_tensor.data(); + +// cudaStream_t stream = THCState_getCurrentStream(state); + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + furthest_point_sampling_kernel_launcher(b, n, m, points, temp, idx, stream); + return 1; +} diff --git a/lib/pointnet2/src/sampling_gpu.cu b/lib/pointnet2/src/sampling_gpu.cu new file mode 100644 index 0000000..9e49a60 --- /dev/null +++ b/lib/pointnet2/src/sampling_gpu.cu @@ -0,0 +1,253 @@ +#include +#include + +#include "cuda_utils.h" +#include "sampling_gpu.h" + + +__global__ void gather_points_kernel_fast(int b, int c, int n, int m, + const float *__restrict__ points, const int *__restrict__ idx, float *__restrict__ out) { + // points: (B, C, N) + // idx: (B, M) + // output: + // out: (B, C, M) + + int bs_idx = blockIdx.z; + int c_idx = blockIdx.y; + int pt_idx = blockIdx.x * blockDim.x + threadIdx.x; + if (bs_idx >= b || c_idx >= c || pt_idx >= m) return; + + out += bs_idx * c * m + c_idx * m + pt_idx; + idx += bs_idx * m + pt_idx; + points += bs_idx * c * n + c_idx * n; + out[0] = points[idx[0]]; +} + +void gather_points_kernel_launcher_fast(int b, int c, int n, int npoints, + const float *points, const int *idx, float *out, cudaStream_t stream) { + // points: (B, C, N) + // idx: (B, npoints) + // output: + // out: (B, C, npoints) + + cudaError_t err; + dim3 blocks(DIVUP(npoints, THREADS_PER_BLOCK), c, b); // blockIdx.x(col), blockIdx.y(row) + dim3 threads(THREADS_PER_BLOCK); + + gather_points_kernel_fast<<>>(b, c, n, npoints, points, idx, out); + + err = cudaGetLastError(); + if (cudaSuccess != err) { + fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err)); + exit(-1); + } +} + +__global__ void gather_points_grad_kernel_fast(int b, int c, int n, int m, const float *__restrict__ grad_out, + const int *__restrict__ idx, float *__restrict__ grad_points) { + // grad_out: (B, C, M) + // idx: (B, M) + // output: + // grad_points: (B, C, N) + + int bs_idx = blockIdx.z; + int c_idx = blockIdx.y; + int pt_idx = blockIdx.x * blockDim.x + threadIdx.x; + if (bs_idx >= b || c_idx >= c || pt_idx >= m) return; + + grad_out += bs_idx * c * m + c_idx * m + pt_idx; + idx += bs_idx * m + pt_idx; + grad_points += bs_idx * c * n + c_idx * n; + + atomicAdd(grad_points + idx[0], grad_out[0]); +} + +void gather_points_grad_kernel_launcher_fast(int b, int c, int n, int npoints, + const float *grad_out, const int *idx, float *grad_points, cudaStream_t stream) { + // grad_out: (B, C, npoints) + // idx: (B, npoints) + // output: + // grad_points: (B, C, N) + + cudaError_t err; + dim3 blocks(DIVUP(npoints, THREADS_PER_BLOCK), c, b); // blockIdx.x(col), blockIdx.y(row) + dim3 threads(THREADS_PER_BLOCK); + + gather_points_grad_kernel_fast<<>>(b, c, n, npoints, grad_out, idx, grad_points); + + err = cudaGetLastError(); + if (cudaSuccess != err) { + fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err)); + exit(-1); + } +} + + +__device__ void __update(float *__restrict__ dists, int *__restrict__ dists_i, int idx1, int idx2){ + const float v1 = dists[idx1], v2 = dists[idx2]; + const int i1 = dists_i[idx1], i2 = dists_i[idx2]; + dists[idx1] = max(v1, v2); + dists_i[idx1] = v2 > v1 ? i2 : i1; +} + +template +__global__ void furthest_point_sampling_kernel(int b, int n, int m, + const float *__restrict__ dataset, float *__restrict__ temp, int *__restrict__ idxs) { + // dataset: (B, N, 3) + // tmp: (B, N) + // output: + // idx: (B, M) + + if (m <= 0) return; + __shared__ float dists[block_size]; + __shared__ int dists_i[block_size]; + + int batch_index = blockIdx.x; + dataset += batch_index * n * 3; + temp += batch_index * n; + idxs += batch_index * m; + + int tid = threadIdx.x; + const int stride = block_size; + + int old = 0; + if (threadIdx.x == 0) + idxs[0] = old; + + __syncthreads(); + for (int j = 1; j < m; j++) { + int besti = 0; + float best = -1; + float x1 = dataset[old * 3 + 0]; + float y1 = dataset[old * 3 + 1]; + float z1 = dataset[old * 3 + 2]; + for (int k = tid; k < n; k += stride) { + float x2, y2, z2; + x2 = dataset[k * 3 + 0]; + y2 = dataset[k * 3 + 1]; + z2 = dataset[k * 3 + 2]; + // float mag = (x2 * x2) + (y2 * y2) + (z2 * z2); + // if (mag <= 1e-3) + // continue; + + float d = (x2 - x1) * (x2 - x1) + (y2 - y1) * (y2 - y1) + (z2 - z1) * (z2 - z1); + float d2 = min(d, temp[k]); + temp[k] = d2; + besti = d2 > best ? k : besti; + best = d2 > best ? d2 : best; + } + dists[tid] = best; + dists_i[tid] = besti; + __syncthreads(); + + if (block_size >= 1024) { + if (tid < 512) { + __update(dists, dists_i, tid, tid + 512); + } + __syncthreads(); + } + + if (block_size >= 512) { + if (tid < 256) { + __update(dists, dists_i, tid, tid + 256); + } + __syncthreads(); + } + if (block_size >= 256) { + if (tid < 128) { + __update(dists, dists_i, tid, tid + 128); + } + __syncthreads(); + } + if (block_size >= 128) { + if (tid < 64) { + __update(dists, dists_i, tid, tid + 64); + } + __syncthreads(); + } + if (block_size >= 64) { + if (tid < 32) { + __update(dists, dists_i, tid, tid + 32); + } + __syncthreads(); + } + if (block_size >= 32) { + if (tid < 16) { + __update(dists, dists_i, tid, tid + 16); + } + __syncthreads(); + } + if (block_size >= 16) { + if (tid < 8) { + __update(dists, dists_i, tid, tid + 8); + } + __syncthreads(); + } + if (block_size >= 8) { + if (tid < 4) { + __update(dists, dists_i, tid, tid + 4); + } + __syncthreads(); + } + if (block_size >= 4) { + if (tid < 2) { + __update(dists, dists_i, tid, tid + 2); + } + __syncthreads(); + } + if (block_size >= 2) { + if (tid < 1) { + __update(dists, dists_i, tid, tid + 1); + } + __syncthreads(); + } + + old = dists_i[0]; + if (tid == 0) + idxs[j] = old; + } +} + +void furthest_point_sampling_kernel_launcher(int b, int n, int m, + const float *dataset, float *temp, int *idxs, cudaStream_t stream) { + // dataset: (B, N, 3) + // tmp: (B, N) + // output: + // idx: (B, M) + + cudaError_t err; + unsigned int n_threads = opt_n_threads(n); + + switch (n_threads) { + case 1024: + furthest_point_sampling_kernel<1024><<>>(b, n, m, dataset, temp, idxs); break; + case 512: + furthest_point_sampling_kernel<512><<>>(b, n, m, dataset, temp, idxs); break; + case 256: + furthest_point_sampling_kernel<256><<>>(b, n, m, dataset, temp, idxs); break; + case 128: + furthest_point_sampling_kernel<128><<>>(b, n, m, dataset, temp, idxs); break; + case 64: + furthest_point_sampling_kernel<64><<>>(b, n, m, dataset, temp, idxs); break; + case 32: + furthest_point_sampling_kernel<32><<>>(b, n, m, dataset, temp, idxs); break; + case 16: + furthest_point_sampling_kernel<16><<>>(b, n, m, dataset, temp, idxs); break; + case 8: + furthest_point_sampling_kernel<8><<>>(b, n, m, dataset, temp, idxs); break; + case 4: + furthest_point_sampling_kernel<4><<>>(b, n, m, dataset, temp, idxs); break; + case 2: + furthest_point_sampling_kernel<2><<>>(b, n, m, dataset, temp, idxs); break; + case 1: + furthest_point_sampling_kernel<1><<>>(b, n, m, dataset, temp, idxs); break; + default: + furthest_point_sampling_kernel<512><<>>(b, n, m, dataset, temp, idxs); + } + + err = cudaGetLastError(); + if (cudaSuccess != err) { + fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err)); + exit(-1); + } +} diff --git a/lib/pointnet2/src/sampling_gpu.h b/lib/pointnet2/src/sampling_gpu.h new file mode 100644 index 0000000..6200c59 --- /dev/null +++ b/lib/pointnet2/src/sampling_gpu.h @@ -0,0 +1,29 @@ +#ifndef _SAMPLING_GPU_H +#define _SAMPLING_GPU_H + +#include +#include +#include + + +int gather_points_wrapper_fast(int b, int c, int n, int npoints, + at::Tensor points_tensor, at::Tensor idx_tensor, at::Tensor out_tensor); + +void gather_points_kernel_launcher_fast(int b, int c, int n, int npoints, + const float *points, const int *idx, float *out, cudaStream_t stream); + + +int gather_points_grad_wrapper_fast(int b, int c, int n, int npoints, + at::Tensor grad_out_tensor, at::Tensor idx_tensor, at::Tensor grad_points_tensor); + +void gather_points_grad_kernel_launcher_fast(int b, int c, int n, int npoints, + const float *grad_out, const int *idx, float *grad_points, cudaStream_t stream); + + +int furthest_point_sampling_wrapper(int b, int n, int m, + at::Tensor points_tensor, at::Tensor temp_tensor, at::Tensor idx_tensor); + +void furthest_point_sampling_kernel_launcher(int b, int n, int m, + const float *dataset, float *temp, int *idxs, cudaStream_t stream); + +#endif diff --git a/losses/__init__.py b/losses/__init__.py new file mode 100644 index 0000000..6e2b4a5 --- /dev/null +++ b/losses/__init__.py @@ -0,0 +1,11 @@ +from losses.supervised_losses import SupervisedL1Loss, SupervisedL2Loss, SupervisedL1RegLoss, SupervisedL1RegLossV1 +from losses.unsupervised_losses import UnSupervisedL1Loss +from losses.common_losses import get_loss_weights + +losses_dict = { + 'sv_l1': SupervisedL1Loss, + 'sv_l2': SupervisedL2Loss, + 'sv_l1_reg': SupervisedL1RegLoss, + 'sv_l1_reg_v1': SupervisedL1RegLossV1, + 'unsup_l1': UnSupervisedL1Loss, + } \ No newline at end of file diff --git a/losses/common_losses.py b/losses/common_losses.py new file mode 100644 index 0000000..91be43a --- /dev/null +++ b/losses/common_losses.py @@ -0,0 +1,237 @@ +import torch +from torch.nn import Module, MSELoss, L1Loss +from lib.pointnet2 import pointnet2_utils as pointutils + +RADIUS = 2.5 + +def get_loss_weights(weights, seq_len, gamma): + + if weights is None: + return [1.0 / seq_len] * seq_len + + elif len(weights) == 1: + return [weights] * seq_len + + elif len(weights) == 2: + w_init, w_base = weights[0], weights[1] + weights_new = [w_init, w_base] + for i in range(2, seq_len): + w = w_base * gamma ** (i - 1) + weights_new.append(w) + + return [1.0 * e / sum(weights_new) for e in weights_new] + + elif len(weights) == seq_len: + return weights + + else: + raise NotImplementedError + +class KnnLoss(Module): + def __init__(self, k, radius, loss_norm, **kwargs): + super(KnnLoss, self).__init__() + self.k = k + self.radius = radius + self.loss_norm = loss_norm + + def forward(self, pc_source: torch.Tensor, pred_flow: torch.Tensor) -> torch.Tensor: + flow = pred_flow.permute(0, 2, 1).contiguous() + dist, idx = pointutils.knn(self.k, pc_source, pc_source) + tmp_idx = idx[:, :, 0].unsqueeze(2).repeat(1, 1, self.k).to(idx.device) + idx[dist > self.radius] = tmp_idx[dist > self.radius] + nn_flow = pointutils.grouping_operation(flow, idx.detach()) + loss = (flow.unsqueeze(3) - nn_flow).norm(p=self.loss_norm, dim=1).mean( dim=-1) + return loss.mean() + + +class BallQLoss(Module): + def __init__(self, k, radius, loss_norm, **kwargs): + super(BallQLoss, self).__init__() + self.k = k + self.radius = radius + self.loss_norm = loss_norm + + def forward(self, pc_source: torch.Tensor, pred_flow: torch.Tensor) -> torch.Tensor: + flow = pred_flow.permute(0, 2, 1).contiguous() + idx = pointutils.ball_query(self.radius, self.k, pc_source, pc_source) + nn_flow = pointutils.grouping_operation(flow, idx.detach()) # retrieve flow of nn + loss = (flow.unsqueeze(3) - nn_flow).norm(p=self.loss_norm, dim=1).mean( dim=-1) + return loss.mean() + + +class SmoothnessLoss(Module): + def __init__(self, w_knn, w_ball_q, knn_loss_params, ball_q_loss_params, **kwargs): + super(SmoothnessLoss, self).__init__() + self.knn_loss = KnnLoss(**knn_loss_params) + self.ball_q_loss = BallQLoss(**ball_q_loss_params) + self.w_knn = w_knn + self.w_ball_q = w_ball_q + + def forward(self, pc_source: torch.Tensor, pred_flow: torch.Tensor) -> torch.Tensor: + loss = (self.w_knn * self.knn_loss(pc_source, pred_flow)) + (self.w_ball_q * self.ball_q_loss(pc_source, pred_flow)) + return loss + + +class ChamferLoss(Module): + def __init__(self, k, loss_norm, **kwargs): + super(ChamferLoss, self).__init__() + self.k = k + self.loss_norm = loss_norm + + def forward(self, pc_source: torch.Tensor, pc_target: torch.Tensor, pred_flow: torch.Tensor) -> torch.Tensor: + pc_target = pc_target.contiguous() + pc_target_t = pc_target.permute(0, 2, 1).contiguous() + pc_pred = (pc_source + pred_flow).contiguous() + pc_pred_t = pc_pred.permute(0, 2, 1).contiguous() + + _, idx = pointutils.knn(self.k, pc_pred, pc_target) + nn1 = pointutils.grouping_operation(pc_target_t, idx.detach()) + dist1 = (pc_pred_t.unsqueeze(3) - nn1).norm(p=self.loss_norm, dim=1).mean( dim=-1) # nn flow consistency + _, idx = pointutils.knn(self.k, pc_target, pc_pred) + nn2 = pointutils.grouping_operation(pc_pred_t, idx.detach()) + dist2 = (pc_target_t.unsqueeze(3) - nn2).norm(p=self.loss_norm, dim=1).mean( dim=-1) # nn flow consistency + ch_dist = (dist1 + dist2) + return ch_dist.mean() + +def square_distance(src, dst): + """ + Calculate Euclid distance between each two points. + + src^T * dst = xn * xm + yn * ym + zn * zmï¼› + sum(src^2, dim=-1) = xn*xn + yn*yn + zn*zn; + sum(dst^2, dim=-1) = xm*xm + ym*ym + zm*zm; + dist = (xn - xm)^2 + (yn - ym)^2 + (zn - zm)^2 + = sum(src**2,dim=-1)+sum(dst**2,dim=-1)-2*src^T*dst + + Input: + src: source points, [B, N, C] + dst: target points, [B, M, C] + Output: + dist: per-point square distance, [B, N, M] + """ + B, N, _ = src.shape + _, M, _ = dst.shape + dist = -2 * torch.matmul(src, dst.permute(0, 2, 1)) + dist += torch.sum(src ** 2, -1).view(B, N, 1) + dist += torch.sum(dst ** 2, -1).view(B, 1, M) + return dist + +def knn_point(nsample, xyz, new_xyz): + """ + Input: + nsample: max sample number in local region + xyz: all points, [B, N, C] + new_xyz: query points, [B, S, C] + Return: + group_idx: grouped points index, [B, S, nsample] + """ + sqrdists = square_distance(new_xyz, xyz) + _, group_idx = torch.topk(sqrdists, nsample, dim = -1, largest=False, sorted=False) + return group_idx + + +def index_points_group(points, knn_idx): + """ + Input: + points: input points data, [B, N, C] + knn_idx: sample index data, [B, N, K] + Return: + new_points:, indexed points data, [B, N, K, C] + """ + points_flipped = points.permute(0, 2, 1).contiguous() + new_points = pointutils.grouping_operation(points_flipped, knn_idx.int()).permute(0, 2, 3, 1) + + return new_points + + +def curvatureWarp(pc, warped_pc, nsample=10, radius=RADIUS): + # pc: B 3 N + assert pc.shape[1] == 3 + warped_pc = warped_pc.permute(0, 2, 1) + pc = pc.permute(0, 2, 1) + + dist, kidx = pointutils.knn(nsample, pc.contiguous(), pc.contiguous()) #(B, N, 10) + + if radius is not None: + tmp_idx = kidx[:, :, 0].unsqueeze(2).repeat(1, 1, nsample).to(kidx.device) + kidx[dist > radius] = tmp_idx[dist > radius] + + grouped_pc = index_points_group(warped_pc, kidx) + pc_curvature = torch.sum(grouped_pc - warped_pc.unsqueeze(2), dim = 2) / 9.0 + return pc_curvature # B N 3 + + +def curvature(pc, nsample=10, radius=RADIUS): + # pc: B 3 N + assert pc.shape[1] == 3 + pc = pc.permute(0, 2, 1) + + dist, kidx = pointutils.knn(nsample, pc.contiguous(), pc.contiguous()) #(B, N, 10) + + if radius is not None: + tmp_idx = kidx[:, :, 0].unsqueeze(2).repeat(1, 1, nsample).to(kidx.device) + kidx[dist > radius] = tmp_idx[dist > radius] + + # sqrdist = square_distance(pc, pc) + # _, kidx = torch.topk(sqrdist, 10, dim = -1, largest=False, sorted=False) + + grouped_pc = index_points_group(pc, kidx) # B N 10 3 + pc_curvature = torch.sum(grouped_pc - pc.unsqueeze(2), dim = 2) / 9.0 + return pc_curvature # B N 3 + + +def interpolateCurvature(pc1, pc2, pc2_curvature, nsample=5, radius=RADIUS): + ''' + pc1: B 3 N + pc2: B 3 M + pc2_curvature: B 3 M + ''' + assert pc1.shape[1] == 3 + assert pc2.shape[1] == 3 + + B, _, N = pc1.shape + pc1 = pc1.permute(0, 2, 1) + pc2 = pc2.permute(0, 2, 1) + pc2_curvature = pc2_curvature + + dist, kidx = pointutils.knn(nsample, pc1.contiguous(), pc2.contiguous()) #(B, N, 10) + + if radius is not None: + tmp_idx = kidx[:, :, 0].unsqueeze(2).repeat(1, 1, nsample).to(kidx.device) + kidx[dist > radius] = tmp_idx[dist > radius] + + grouped_pc2_curvature = index_points_group(pc2_curvature, kidx) # B N 5 3 + norm = torch.sum(1.0 / (dist + 1e-8), dim = 2, keepdim = True) + weight = (1.0 / (dist + 1e-8)) / norm + + inter_pc2_curvature = torch.sum(weight.view(B, N, 5, 1) * grouped_pc2_curvature, dim = 2) + return inter_pc2_curvature + + +class CurvatureLoss(Module): + def __init__(self, **kwargs): + super(CurvatureLoss, self).__init__() + + def forward(self, pc_source: torch.Tensor, pc_target: torch.Tensor, pred_flow: torch.Tensor) -> torch.Tensor: + """ + pc_source: [B, N, 3] + """ + pc_source = pc_source.permute(0, 2, 1).contiguous() + pc_target = pc_target.permute(0, 2, 1).contiguous() + pred_flow = pred_flow.permute(0, 2, 1).contiguous() + + cur_pc1_warp = pc_source + pred_flow + + cur_pc2_curvature = curvature(pc_target) + moved_pc1_curvature = curvatureWarp(pc_source, cur_pc1_warp) + #curvature + inter_pc2_curvature = interpolateCurvature(cur_pc1_warp, pc_target, cur_pc2_curvature) + curvatureLoss = torch.sum((inter_pc2_curvature - moved_pc1_curvature) ** 2, dim = 2).sum(dim = 1).mean() + + return curvatureLoss + +if __name__ == "__main__": + xyz1 = torch.arange(300).reshape(1, 3, 100).float().cuda() + cur1 = curvature(xyz1) + print(xyz1.shape) + print(cur1) \ No newline at end of file diff --git a/losses/supervised_losses.py b/losses/supervised_losses.py new file mode 100644 index 0000000..5bc6e70 --- /dev/null +++ b/losses/supervised_losses.py @@ -0,0 +1,78 @@ +import torch +from torch.nn import Module, MSELoss, L1Loss +from .common_losses import CurvatureLoss, SmoothnessLoss, curvature, get_loss_weights + + +class SupervisedL1Loss(Module): + def __init__(self, **kwargs): + super(SupervisedL1Loss, self).__init__() + self.l1_loss = L1Loss() + + def forward(self, pc_source: torch.Tensor, pc_target: torch.Tensor, pred_flow: torch.Tensor, gt_flow: torch.Tensor) -> torch.Tensor: + return self.l1_loss(pred_flow, gt_flow) + + +class SupervisedL2Loss(Module): + def __init__(self, **kwargs): + super(SupervisedL2Loss, self).__init__() + self.l2_loss = MSELoss() + + def forward(self, pc_source: torch.Tensor, pc_target: torch.Tensor, pred_flow: torch.Tensor, gt_flow: torch.Tensor) -> torch.Tensor: + return self.l2_loss(pred_flow, gt_flow) + + +class SupervisedL1RegLoss(Module): + def __init__(self, w_data, w_smoothness, smoothness_loss_params, **kwargs): + super(SupervisedL1RegLoss, self).__init__() + self.data_loss = L1Loss() + self.smoothness_loss = SmoothnessLoss(**smoothness_loss_params) + self.w_data = w_data + self.w_smoothness = w_smoothness + + def forward(self, pc_source: torch.Tensor, pc_target: torch.Tensor, pred_flow: torch.Tensor, gt_flow: torch.Tensor, seq_len=0, i=0) -> torch.Tensor: + if len(self.w_data) == 1: + w_data = self.w_data[0] + w_smoothness = self.w_smoothness[0] + else: + assert seq_len > 0 + w_data = get_loss_weights(self.w_data, seq_len, i)[i] + w_smoothness = get_loss_weights(self.w_smoothness, seq_len, i)[i] + + loss = (w_data * self.data_loss(pred_flow, gt_flow)) + (w_smoothness * self.smoothness_loss(pc_source, pred_flow)) + return loss + + +class SupervisedL1RegLossV1(Module): + def __init__(self, w_data, w_smoothness, smoothness_loss_params, **kwargs): + super(SupervisedL1RegLossV1, self).__init__() + self.data_loss = L1Loss() + self.smoothness_loss = SmoothnessLoss(**smoothness_loss_params) + + curvature_loss_params = kwargs.get("curvature_loss_params", {}) + w_curvature = kwargs.get("w_curvature", [0.1]) + print("CurvatureLoss", curvature_loss_params, w_curvature) + self.curvature_loss = CurvatureLoss(**curvature_loss_params) + + self.w_data = w_data + self.w_smoothness = w_smoothness + self.w_curvature = w_curvature + + def forward(self, pc_source: torch.Tensor, pc_target: torch.Tensor, pred_flow: torch.Tensor, gt_flow: torch.Tensor, seq_len=0, i=0) -> torch.Tensor: + """ + pc_source: [B, N, 3] + """ + if len(self.w_data) == 1: + w_data = self.w_data[0] + w_smoothness = self.w_smoothness[0] + w_curvature = self.w_curvature[0] + else: + assert seq_len > 0 + w_data = get_loss_weights(self.w_data, seq_len, i)[i] + w_smoothness = get_loss_weights(self.w_smoothness, seq_len, i)[i] + w_curvature = get_loss_weights(self.w_curvature, seq_len, i)[i] + + loss = (w_data * self.data_loss(pred_flow, gt_flow)) + (w_smoothness * self.smoothness_loss(pc_source, pred_flow)) + \ + (w_curvature / 200 * self.curvature_loss(pc_source, pc_target, pred_flow)) + + return loss + \ No newline at end of file diff --git a/losses/unsupervised_losses.py b/losses/unsupervised_losses.py new file mode 100644 index 0000000..4f7c42c --- /dev/null +++ b/losses/unsupervised_losses.py @@ -0,0 +1,22 @@ +import torch +from torch.nn import Module, MSELoss, L1Loss +from .common_losses import SmoothnessLoss, ChamferLoss + + +class UnSupervisedL1Loss(Module): + def __init__(self, w_data, w_smoothness, smoothness_loss_params, chamfer_loss_params, **kwargs): + super(UnSupervisedL1Loss, self).__init__() + self.data_loss = ChamferLoss(**chamfer_loss_params) + self.smoothness_loss = SmoothnessLoss(**smoothness_loss_params) + self.w_data = w_data + self.w_smoothness = w_smoothness + + def forward(self, pc_source: torch.Tensor, pc_target: torch.Tensor, pred_flow: torch.Tensor, gt_flow: torch.Tensor, seq_len=None, i=0) -> torch.Tensor: + if len(self.w_data) == 1: + w_data = self.w_data[0] + w_smoothness = self.w_smoothness[0] + else: + w_data = self.w_data[i] + w_smoothness = self.w_smoothness[i] + loss = (w_data * self.data_loss(pc_source, pc_target, pred_flow)) + (w_smoothness * self.smoothness_loss(pc_source, pred_flow)) + return loss \ No newline at end of file diff --git a/models/SF_RCP.py b/models/SF_RCP.py new file mode 100644 index 0000000..35cb677 --- /dev/null +++ b/models/SF_RCP.py @@ -0,0 +1,377 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from models.common import PointNetSetAbstraction, PointNetFeaturePropogation, PointWiseOptimLayer, Sinkhorn + +class FeatureMatching(nn.Module): + def __init__(self, npoint, use_instance_norm, supporth_th, feature_norm, max_iter): + super(FeatureMatching, self).__init__() + self.support_th = supporth_th ** 2 # 10m + self.feature_norm = feature_norm + self.max_iter = max_iter + # Mass regularisation + self.gamma = torch.nn.Parameter(torch.zeros(1)) + # Entropic regularisation + self.epsilon = torch.nn.Parameter(torch.zeros(1)) + self.sinkhorn = Sinkhorn() + + self.extract_glob = FeatureExtractionGlobal(npoint, use_instance_norm) + + # upsample flow + self.fp0 = PointNetFeaturePropogation(in_channel=3, mlp=[]) + self.sa1 = PointNetSetAbstraction(npoint=int(npoint / 16), radius=None, nsample=16, in_channel=3, mlp=[32, 32, 64], + group_all=False, use_instance_norm=use_instance_norm) + self.fp1 = PointNetFeaturePropogation(in_channel=64, mlp=[]) + self.sa2 = PointNetSetAbstraction(npoint=int(npoint / 8), radius=None, nsample=16, in_channel=64, mlp=[64, 64, 128], + group_all=False, use_instance_norm=use_instance_norm) + self.fp2 = PointNetFeaturePropogation(in_channel=128, mlp=[]) + + self.flow_regressor = FlowRegressor(npoint, use_instance_norm) + self.flow_up_sample = PointNetFeaturePropogation(in_channel=3, mlp=[]) + + + def upsample_flow(self, pc1_l, pc1_l_glob, flow_inp): + """ + flow_inp: [B, N, 3] + return: [B, 3, N] + """ + + flow_inp = flow_inp.permute(0, 2, 1).contiguous() #[B, 3, N] + + flow_feat = self.fp0(pc1_l_glob["s16"], pc1_l_glob["s32"], None, flow_inp) + _, corr_feats_l2 = self.sa1(pc1_l_glob["s16"], flow_feat) + + flow_feat = self.fp1(pc1_l_glob["s8"], pc1_l_glob["s16"], None, corr_feats_l2) + _, flow_feat = self.sa2(pc1_l_glob["s8"], flow_feat) + + flow_feat = self.fp2(pc1_l["s4"], pc1_l_glob["s8"], None, flow_feat) + + flow, flow_lr = self.flow_regressor(pc1_l, flow_feat) + + flow_up, flow_lr_up = self.flow_up_sample(pc1_l["s1"], pc1_l_glob["s32"], None, flow_inp), \ + self.flow_up_sample(pc1_l["s4"], pc1_l_glob["s32"], None, flow_inp) + + flow, flow_lr = flow + flow_up, flow_lr + flow_lr_up + + return flow, flow_lr + + + def calc_feats_corr(self, pcloud1, pcloud2, feature1, feature2, norm): + """ + pcloud1, pcloud2: [B, N, 3] + feature1, feature2: [B, N, C] + """ + if norm: + feature1 = feature1 / torch.sqrt(torch.sum(feature1 ** 2, -1, keepdim=True) + 1e-6) + feature2 = feature2 / torch.sqrt(torch.sum(feature2 ** 2, -1, keepdim=True) + 1e-6) + corr_mat = torch.bmm(feature1, feature2.transpose(1, 2)) # [B, N1, N2] + else: + corr_mat = torch.bmm(feature1, feature2.transpose(1, 2)) / feature1.shape[2]**.5 #[B, N1, N2] + + if self.support_th is not None: + distance_matrix = torch.sum(pcloud1 ** 2, -1, keepdim=True) #[B, N1, 1] + distance_matrix = distance_matrix + torch.sum( + pcloud2 ** 2, -1, keepdim=True + ).transpose(1, 2) #[B, N1, N2] + distance_matrix = distance_matrix - 2 * torch.bmm(pcloud1, pcloud2.transpose(1, 2)) #[B, N1, N2] + support = (distance_matrix < self.support_th).float() #[B, N1, N2] + else: + support = torch.ones_like(corr_mat) + return corr_mat, support + + + def calc_corr_mat(self, pcloud1, pcloud2, feature1, feature2): + """ + pcloud1, pcloud2: [B, N, 3] + feature1, feature2: [B, N, C] + corr_mat: [B, N1, N2] + """ + epsilon = torch.exp(self.epsilon) + 0.03 + corr_mat, support = self.calc_feats_corr(pcloud1, pcloud2, feature1, feature2, norm=self.feature_norm) + C = 1.0 - corr_mat + corr_mat = torch.exp(-C / epsilon) * support + return corr_mat + + def get_flow_init(self, pcloud1, pcloud2, feats1, feats2): + """ + pcloud1, pcloud2: [B, 3, N] + feats1, feats2: [B, C, N] + """ + + corr_mat = self.calc_corr_mat(pcloud1.permute(0, 2, 1), pcloud2.permute(0, 2, 1), + feats1.permute(0, 2, 1), feats2.permute(0, 2, 1)) + + corr_mat = self.sinkhorn(corr_mat, torch.exp(self.epsilon) + 0.03, self.gamma, self.max_iter) + + row_sum = corr_mat.sum(-1, keepdim=True) #[B, N1, 1] + flow_init = (corr_mat @ pcloud2.permute(0, 2, 1).contiguous()) / (row_sum + 1e-6) - pcloud1.permute(0, 2, 1).contiguous() #[B, N1, 3] + + return flow_init + + + def forward(self, pc1_l, pc2_l, feats1, feats2): + """ + pc1_l, pc2_l: dict([B, 3, N]) + feats1, feats2: [B, C, N] + """ + pc1_l_glob, feats1_glob = self.extract_glob(pc1_l["s4"], feats1) + pc2_l_glob, feats2_glob = self.extract_glob(pc2_l["s4"], feats2) + + flow_init_s32 = self.get_flow_init(pc1_l_glob["s32"], pc2_l_glob["s32"], feats1_glob, feats2_glob) + + flow_init, flow_init_s4 = self.upsample_flow(pc1_l, pc1_l_glob, flow_init_s32) + + return flow_init, flow_init_s4 + + +class FlowRegressor(nn.Module): + def __init__(self, npoint, use_instance_norm, input_dim=128, nsample=32): + super(FlowRegressor, self).__init__() + self.sa1 = PointNetSetAbstraction(npoint=int(npoint / 4), radius=None, nsample=nsample, in_channel=input_dim, mlp=[input_dim, input_dim], + group_all=False, use_instance_norm=use_instance_norm) + self.sa2 = PointNetSetAbstraction(npoint=int(npoint / 4), radius=None, nsample=nsample, in_channel=input_dim, mlp=[input_dim, input_dim], + group_all=False, use_instance_norm=use_instance_norm) + + self.fc = nn.Sequential(nn.Linear(input_dim, input_dim), nn.ReLU(inplace=True), + nn.Linear(input_dim, 3)) + + self.up_sample = PointNetFeaturePropogation(in_channel=3, mlp=[]) + + def forward(self, pc1_l, feats): + """ + pc1_l: dict([B, 3, N]) + feats: [B, C, N] + return: [B, 3, N] + """ + _, x = self.sa1(pc1_l["s4"], feats) + _, x = self.sa2(pc1_l["s4"], x) + x = x.permute(0, 2, 1).contiguous() #[B, N, C] + x = self.fc(x) + flow_lr = x.permute(0, 2, 1).contiguous() #[B, 3, N] + + flow = self.up_sample(pc1_l["s1"], pc1_l["s4"], None, flow_lr) #[B, 3, N] + + return flow, flow_lr + + +class FeatureExtractionGlobal(nn.Module): + def __init__(self, npoint, use_instance_norm): + super(FeatureExtractionGlobal, self).__init__() + self.sa1 = PointNetSetAbstraction(npoint=int(npoint / 8), radius=None, nsample=32, in_channel=64, mlp=[128, 128, 128], + group_all=False, use_instance_norm=use_instance_norm) + self.sa2 = PointNetSetAbstraction(npoint=int(npoint / 16), radius=None, nsample=24, in_channel=128, mlp=[128, 128, 128], + group_all=False, use_instance_norm=use_instance_norm) + self.sa3 = PointNetSetAbstraction(npoint=int(npoint / 32), radius=None, nsample=16, in_channel=128, mlp=[256, 256, 256], + group_all=False, use_instance_norm=use_instance_norm) + + def forward(self, pc, feature): + pc_l1, feat_l1 = self.sa1(pc, feature) + pc_l2, feat_l2 = self.sa2(pc_l1, feat_l1) + pc_l3, feat_l3 = self.sa3(pc_l2, feat_l2) + + pc_l = dict(s8=pc_l1, s16=pc_l2, s32=pc_l3) + return pc_l, feat_l3 + + +class FeatureExtraction(nn.Module): + def __init__(self, npoint, use_instance_norm): + super(FeatureExtraction, self).__init__() + self.sa1 = PointNetSetAbstraction(npoint=int(npoint / 2), radius=None, nsample=32, in_channel=3, mlp=[32, 32, 32], + group_all=False, return_fps=True, use_instance_norm=use_instance_norm) + self.sa2 = PointNetSetAbstraction(npoint=int(npoint / 4), radius=None, nsample=32, in_channel=32, mlp=[64, 64, 64], + group_all=False, return_fps=True, use_instance_norm=use_instance_norm) + + def forward(self, pc, feature, fps_idx=None): + """ + pc: [B, 3, N] + feature: [B, 3, N] + """ + fps_idx1 = fps_idx["s2"] if fps_idx is not None else None + pc_l1, feat_l1, fps_idx1 = self.sa1(pc, feature, fps_idx=fps_idx1) + fps_idx2 = fps_idx["s4"] if fps_idx is not None else None + pc_l2, feat_l2, fps_idx2 = self.sa2(pc_l1, feat_l1, fps_idx=fps_idx2) + pc_l = dict(s1=pc, s2=pc_l1, s4=pc_l2) + fps_idx = dict(s2=fps_idx1, s4=fps_idx2) + return pc_l, feat_l2, fps_idx + + +class HiddenInitNet(nn.Module): + def __init__(self, npoint, use_instance_norm): + super(HiddenInitNet, self).__init__() + self.sa1 = PointNetSetAbstraction(npoint=int(npoint / 4), radius=None, nsample=8, in_channel=64, mlp=[128, 128, 128], + group_all=False, use_instance_norm=use_instance_norm) + self.sa2 = PointNetSetAbstraction(npoint=int(npoint / 4), radius=None, nsample=8, in_channel=128, mlp=[128], + group_all=False, use_act=False, use_instance_norm=use_instance_norm) + + def forward(self, pc, feature): + _, feat_l1 = self.sa1(pc, feature) + _, feat_l2 = self.sa2(pc, feat_l1) + + h_init = torch.tanh(feat_l2) + return h_init + + +class GRUReg(nn.Module): + def __init__(self, npoint, hidden_dim, input_dim, use_instance_norm): + super().__init__() + in_ch = hidden_dim + input_dim + + self.flow_proj = nn.ModuleList( + [PointNetSetAbstraction(npoint=int(npoint / 4), radius=None, nsample=16, in_channel=3, mlp=[32, 32, 32], + group_all=False, use_instance_norm=use_instance_norm), + PointNetSetAbstraction(npoint=int(npoint / 4), radius=None, nsample=8, in_channel=32, mlp=[16, 16, 16], + group_all=False, use_instance_norm=use_instance_norm)]) + + self.hidden_init_net = HiddenInitNet(npoint, use_instance_norm) + + self.gru_layers = nn.ModuleList([ + PointNetSetAbstraction(npoint=int(npoint / 4), radius=None, nsample=4, in_channel=in_ch, + mlp=[hidden_dim], group_all=False, use_act=False, use_instance_norm=use_instance_norm), + PointNetSetAbstraction(npoint=int(npoint / 4), radius=None, nsample=4, in_channel=in_ch, + mlp=[hidden_dim], group_all=False, use_act=False, use_instance_norm=use_instance_norm), + PointNetSetAbstraction(npoint=int(npoint / 4), radius=None, nsample=4, in_channel=in_ch, + mlp=[hidden_dim], group_all=False, use_act=False, use_instance_norm=use_instance_norm)]) + + + def gru(self, h, gru_inp, pc): + hx = torch.cat([h, gru_inp], dim=1) + z = torch.sigmoid(self.gru_layers[0](pc, hx)[1]) + r = torch.sigmoid(self.gru_layers[1](pc, hx)[1]) + q = torch.tanh(self.gru_layers[2](pc, torch.cat([r * h, gru_inp], dim=1))[1]) + h = (1 - z) * h + z * q + return h + + def get_gru_input(self, feats1_new, cost, flow, pc): + flow_feats = flow + for flow_conv in self.flow_proj: + _, flow_feats = flow_conv(pc, flow_feats) + + gru_inp = torch.cat([feats1_new, cost, flow_feats, flow], dim=1) # [64, 128, 16, 3] + + return gru_inp + + def forward(self, h, feats1_new, cost, flow_lr, pc1_l): + gru_inp = self.get_gru_input(feats1_new, cost, flow_lr, pc=pc1_l["s4"]) + + h = self.gru(h, gru_inp, pc1_l["s4"]) + return h + +class SF_RCP(nn.Module): + def __init__(self, npoint=8192, use_instance_norm=False, **kwargs): + super().__init__() + self.radius = kwargs.get("radius", 3.5) + self.nsample = kwargs.get("nsample", 6) + self.radius_min = kwargs.get("radius_min", 3.5) + self.nsample_min = kwargs.get("nsample_min", 6) + self.use_curvature = kwargs.get("use_curvature", True) + self.flow_ratio = kwargs.get("flow_ratio", 0.1) + self.init_max_iter = kwargs.get("init_max_iter", 0) + self.init_feature_norm = kwargs.get("init_feature_norm", True) + self.support_th = kwargs.get("support_th", 10) + + self.feature_extraction = FeatureExtraction(npoint, use_instance_norm) + self.feature_matching = FeatureMatching(npoint, use_instance_norm, + supporth_th=self.support_th, + feature_norm=self.init_feature_norm, + max_iter=self.init_max_iter) + + self.pointwise_optim_layer = PointWiseOptimLayer( + nsample=self.nsample, + radius=self.radius, + in_channel=64, + mlp=[128, 128, 128], + use_curvature=self.use_curvature) + + self.gru = GRUReg(npoint, hidden_dim=128, input_dim=128+64+16+3, use_instance_norm=use_instance_norm) + + self.flow_regressor = FlowRegressor(npoint, use_instance_norm) + + + def initialization(self, pc1_l, pc2_l, feats1, feats2): + """ + pc1: [B, 3, N] + pc2: [B, 3, N] + feature1: [B, 3, N] + feature2: [B, 3, N] + """ + flow, flow_lr = self.feature_matching(pc1_l, pc2_l, feats1, feats2) + + return flow, flow_lr + + + def pointwise_optimization(self, pc1_l_new, pc2_l, feats1_new, feats2, pc1_l, flow_lr, iter): + _, cost, score, pos2_grouped = self.pointwise_optim_layer( + pc1_l_new["s4"], + pc2_l["s4"], + feats1_new, + feats2, + nsample=max(self.nsample_min, self.nsample // (2**iter)), + radius=max(self.radius_min, self.radius / (2**iter)), + pos1_raw=pc1_l["s4"], + return_score=True) + + # pc1_new_l_loc: [B, 3, N, S] + # pos2_grouped: [B, C, N, S] + delta_flow_tmp = ((pos2_grouped - pc1_l_new["s4"].unsqueeze(-1)) * score.mean(dim=1, keepdim=True)).sum(dim=-1) #[B, 3, N] + flow_lr = flow_lr + self.flow_ratio * delta_flow_tmp + + return flow_lr, cost + + def update_pos(self, pc, pc_lr, flow, flow_lr): + pc = pc + flow + pc_lr = pc_lr + flow_lr + return pc, pc_lr + + + def forward(self, pc1, pc2, feature1, feature2, iters=1): + """ + pc1: [B, N, 3] + pc2: [B, N, 3] + feature1: [B, N, 3] + feature2: [B, N, 3] + """ + # prepare + flow_predictions = [] + pc1 = pc1.permute(0, 2, 1).contiguous() # B 3 N + pc2 = pc2.permute(0, 2, 1).contiguous() # B 3 N + feature1 = feature1.permute(0, 2, 1).contiguous() # B 3 N + feature2 = feature2.permute(0, 2, 1).contiguous() # B 3 N + + # feature extraction + pc1_l, feats1, fps_idx1 = self.feature_extraction(pc1, feature1) + pc2_l, feats2, _ = self.feature_extraction(pc2, feature2) + + # initialization, flow_lr_init(flow_low_resolution) + flow_init, flow_lr_init = self.initialization(pc1_l, pc2_l, feats1, feats2) + flow_predictions.append(flow_init.permute(0, 2, 1)) + + # gru init hidden state + h = self.gru.hidden_init_net(pc1_l["s4"], feats1) + + # update position + pc1_lr_raw = pc1_l["s4"] + pc1_new, pc1_lr_new = self.update_pos(pc1, pc1_lr_raw, flow_init, flow_lr_init) + + # iterative optim + for iter in range(iters - 1): + pc1_new = pc1_new.detach() + pc1_lr_new = pc1_lr_new.detach() + flow_lr = pc1_lr_new - pc1_lr_raw + + pc1_l_new, feats1_new, _ = self.feature_extraction(pc1_new, pc1_new, fps_idx1) + + # pointwise optimization to get udpated flow_lr and cost + flow_lr_update, cost = self.pointwise_optimization(pc1_l_new, pc2_l, feats1_new, feats2, pc1_l, flow_lr, iter) + flow_lr = flow_lr_update + + # gru regularization + h = self.gru(h, feats1_new, cost, flow_lr, pc1_l) + # pred flow_lr + delta_flow, delta_flow_lr = self.flow_regressor(pc1_l, h) + + pc1_new, pc1_lr_new = self.update_pos(pc1_new, pc1_lr_new, delta_flow, delta_flow_lr) + + flow = pc1_new - pc1 + flow_predictions.append(flow.permute(0, 2, 1)) + + return flow_predictions diff --git a/models/__init__.py b/models/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/models/common.py b/models/common.py new file mode 100644 index 0000000..5d45eea --- /dev/null +++ b/models/common.py @@ -0,0 +1,340 @@ +import math +import torch +import torch.nn as nn +import torch.nn.functional as F +from lib.pointnet2 import pointnet2_utils as pointutils +from losses.common_losses import curvature, curvatureWarp +from utils.geometry import estimate_pointcloud_normals_from_two_pcds + + +class PointNetSetAbstractionRatio(nn.Module): + def __init__(self, ratio, radius, nsample, in_channel, mlp, group_all, + return_fps=False, use_xyz=True, use_act=True, act=F.relu, mean_aggr=False, use_instance_norm=False): + super(PointNetSetAbstractionRatio, self).__init__() + self.ratio = ratio + self.radius = radius + self.nsample = nsample + self.group_all = group_all + self.use_xyz = use_xyz + self.use_act = use_act + self.mean_aggr = mean_aggr + self.act = act + self.mlp_convs = nn.ModuleList() + self.mlp_bns = nn.ModuleList() + last_channel = (in_channel + 3) if use_xyz else in_channel + for out_channel in mlp: + self.mlp_convs.append(nn.Conv2d(last_channel, out_channel, 1, bias=False)) + if use_instance_norm: + self.mlp_bns.append(nn.InstanceNorm2d(out_channel, affine=True)) + else: + self.mlp_bns.append(nn.BatchNorm2d(out_channel)) + + last_channel = out_channel + + if group_all: + self.queryandgroup = pointutils.GroupAll(self.use_xyz) + else: + self.queryandgroup = pointutils.QueryAndGroup(radius, nsample, self.use_xyz) + self.return_fps = return_fps + + def forward(self, xyz, points, fps_idx=None): + """ + Input: + xyz: input points position data, [B, C, N] + points: input points data, [B, D, N] + Return: + new_xyz: sampled points position data, [B, C, S] + new_points: sample points feature data, [B, D', S] + """ + device = xyz.device + B, C, N = xyz.shape + npoint = int(N * self.ratio) + + xyz = xyz.contiguous() + xyz_t = xyz.permute(0, 2, 1).contiguous() + + if (self.group_all == False) and (npoint != -1): + if fps_idx == None: + fps_idx = pointutils.furthest_point_sample(xyz_t, npoint) # [B, N] + new_xyz = pointutils.gather_operation(xyz, fps_idx) # [B, C, N] + else: + new_xyz = xyz + new_points, _ = self.queryandgroup(xyz_t, new_xyz.transpose(2, 1).contiguous(), points) # [B, 3+C, N, S] + + # new_xyz: sampled points position data, [B, C, npoint] + # new_points: sampled points data, [B, C+D, npoint, nsample] + for i, conv in enumerate(self.mlp_convs): + if self.use_act: + bn = self.mlp_bns[i] + new_points = self.act(bn(conv(new_points))) + else: + new_points = conv(new_points) + + if self.mean_aggr: + new_points = torch.mean(new_points, -1) + else: + new_points = torch.max(new_points, -1)[0] + + if self.return_fps: + return new_xyz, new_points, fps_idx + else: + return new_xyz, new_points + + +class PointNetSetAbstraction(nn.Module): + def __init__(self, npoint, radius, nsample, in_channel, mlp, group_all, + return_fps=False, use_xyz=True, use_act=True, act=F.relu, mean_aggr=False, use_instance_norm=False): + super(PointNetSetAbstraction, self).__init__() + self.npoint = npoint + self.radius = radius + self.nsample = nsample + self.group_all = group_all + self.use_xyz = use_xyz + self.use_act = use_act + self.mean_aggr = mean_aggr + self.act = act + self.mlp_convs = nn.ModuleList() + self.mlp_bns = nn.ModuleList() + last_channel = (in_channel + 3) if use_xyz else in_channel + for out_channel in mlp: + self.mlp_convs.append(nn.Conv2d(last_channel, out_channel, 1, bias=False)) + if use_instance_norm: + self.mlp_bns.append(nn.InstanceNorm2d(out_channel, affine=True)) + else: + self.mlp_bns.append(nn.BatchNorm2d(out_channel)) + + last_channel = out_channel + + if group_all: + self.queryandgroup = pointutils.GroupAll(self.use_xyz) + else: + self.queryandgroup = pointutils.QueryAndGroup(radius, nsample, self.use_xyz) + self.return_fps = return_fps + + def forward(self, xyz, points, fps_idx=None): + """ + Input: + xyz: input points position data, [B, C, N] + points: input points data, [B, D, N] + Return: + new_xyz: sampled points position data, [B, S, C] + new_points: sample points feature data, [B, S, D'] + """ + device = xyz.device + B, C, N = xyz.shape + xyz = xyz.contiguous() + xyz_t = xyz.permute(0, 2, 1).contiguous() + + if (self.group_all == False) and (self.npoint != -1): + if fps_idx == None: + fps_idx = pointutils.furthest_point_sample(xyz_t, self.npoint) # [B, N] + new_xyz = pointutils.gather_operation(xyz, fps_idx) # [B, C, N] + else: + new_xyz = xyz + new_points, _ = self.queryandgroup(xyz_t, new_xyz.transpose(2, 1).contiguous(), points) # [B, 3+C, N, S] + + # new_xyz: sampled points position data, [B, C, npoint] + # new_points: sampled points data, [B, C+D, npoint, nsample] + for i, conv in enumerate(self.mlp_convs): + if self.use_act: + bn = self.mlp_bns[i] + new_points = self.act(bn(conv(new_points))) + else: + new_points = conv(new_points) + + if self.mean_aggr: + new_points = torch.mean(new_points, -1) + else: + new_points = torch.max(new_points, -1)[0] + + if self.return_fps: + return new_xyz, new_points, fps_idx + else: + return new_xyz, new_points + +class PointNetFeaturePropogation(nn.Module): + def __init__(self, in_channel, mlp, learn_mask=False, nsample=3): + super(PointNetFeaturePropogation, self).__init__() + self.mlp_convs = nn.ModuleList() + self.mlp_bns = nn.ModuleList() + self.apply_mlp = mlp is not None + last_channel = in_channel + self.nsample = nsample + if self.apply_mlp: + for out_channel in mlp: + self.mlp_convs.append(nn.Conv1d(last_channel, out_channel, 1)) + self.mlp_bns.append(nn.BatchNorm1d(out_channel)) + last_channel = out_channel + + if learn_mask: + self.queryandgroup = pointutils.QueryAndGroup(None, 9, use_xyz=True) + last_channel = (128 + 3) + for out_channel in [32, 1]: + self.mlp_convs.append(nn.Conv2d(last_channel, out_channel, 1, bias=False)) + self.mlp_bns.append(nn.BatchNorm2d(out_channel)) + last_channel = out_channel + + def forward(self, pos1, pos2, feature1, feature2, hidden=None): + """ + Input: + pos1: input points position data, [B, C, N] + pos2: sampled input points position data, [B, C, S] + feature1: input points data, [B, D, N] + feature2: input points data, [B, D, S] + Return: + feat_new: upsampled points data, [B, D', N] + """ + pos1_t = pos1.permute(0, 2, 1).contiguous() + pos2_t = pos2.permute(0, 2, 1).contiguous() + B, C, N = pos1.shape + + if hidden is None: + if self.nsample == 3: + dists, idx = pointutils.three_nn(pos1_t, pos2_t) + else: + dists, idx = pointutils.knn(self.nsample, pos1_t, pos2_t) + dists[dists < 1e-10] = 1e-10 + weight = 1.0 / dists + weight = weight / torch.sum(weight, -1, keepdim=True) # [B,N,3] + interpolated_feat = torch.sum(pointutils.grouping_operation(feature2, idx) * weight.view(B, 1, N, self.nsample), + dim=-1) # [B,C,N,3] + else: + dist, idx = pointutils.knn(9, pos1_t, pos2_t) + + new_feat, _ = self.queryandgroup(pos2_t, pos1_t, hidden) # [B, 3+C, N, 9] + + for i, conv in enumerate(self.mlp_convs): + new_feat = conv(new_feat) + weight = torch.softmax(new_feat, dim=-1) #[B, 1, N, 9] + interpolated_feat = torch.sum(pointutils.grouping_operation(feature2, idx) * weight, + dim=-1) # [B, C, N] + + if feature1 is not None: + feat_new = torch.cat([interpolated_feat, feature1], 1) + else: + feat_new = interpolated_feat + + if self.apply_mlp: + for i, conv in enumerate(self.mlp_convs): + bn = self.mlp_bns[i] + feat_new = F.relu(bn(conv(feat_new))) + return feat_new + +class Sinkhorn(nn.Module): + def __init__(self): + super().__init__() + + def forward(self, corr, epsilon, gamma, max_iter): + # Early return if no iteration + if max_iter == 0: + return corr + + # Init. of Sinkhorn algorithm + power = gamma / (gamma + epsilon) + a = ( + torch.ones( + (corr.shape[0], corr.shape[1], 1), device=corr.device, dtype=corr.dtype + ) + / corr.shape[1] + ) + prob1 = ( + torch.ones( + (corr.shape[0], corr.shape[1], 1), device=corr.device, dtype=corr.dtype + ) + / corr.shape[1] + ) + prob2 = ( + torch.ones( + (corr.shape[0], corr.shape[2], 1), device=corr.device, dtype=corr.dtype + ) + / corr.shape[2] + ) + + # Sinkhorn algorithm + for _ in range(max_iter): + # Update b + KTa = torch.bmm(corr.transpose(1, 2), a) + b = torch.pow(prob2 / (KTa + 1e-8), power) + # Update a + Kb = torch.bmm(corr, b) + a = torch.pow(prob1 / (Kb + 1e-8), power) + + # Transportation map + T = torch.mul(torch.mul(a, corr), b.transpose(1, 2)) + + return T + + +class PointWiseOptimLayer(nn.Module): + def __init__(self, nsample, radius, in_channel, mlp, use_curvature=True): + super().__init__() + self.nsample = nsample + self.radius = radius + self.use_curvature = use_curvature + + self.pos_embed = nn.Sequential(nn.Conv1d(3, 32, 1), nn.ReLU(inplace=True), nn.Conv1d(32, 64, 1)) + + self.qk_net = nn.Sequential(nn.Conv1d(in_channel + 64, in_channel + 64, 1), nn.ReLU(inplace=True), + nn.Conv1d(in_channel + 64, in_channel + 64, 1)) + if self.use_curvature: + self.curvate_net = nn.Sequential(nn.Conv1d(3, 32, 1), nn.ReLU(inplace=True), + nn.Conv1d(32, 32, 1)) + self.mlp_conv = nn.Conv1d(in_channel + 64 + 32, mlp[-1], 1, bias=True) + else: + self.mlp_conv = nn.Conv1d(in_channel + 64, mlp[-1], 1, bias=True) + + def forward(self, pos1, pos2, feature1, feature2, nsample, radius=None, pos1_raw=None, return_score=False): + """ + Input: + pos1: (batch_size, 3, npoint) + pos2: (batch_size, 3, npoint) + feature1: (batch_size, channel, npoint) + feature2: (batch_size, channel, npoint) + Output: + pos1: (batch_size, 3, npoint) + cost: (batch_size, channel, npoint) + """ + pos1_t = pos1.permute(0, 2, 1).contiguous() + pos2_t = pos2.permute(0, 2, 1).contiguous() + self.nsample = nsample + self.radius = radius + + dist, idx = pointutils.knn(self.nsample, pos1_t, pos2_t) #[B, N, K] + if self.radius is not None: + tmp_idx = idx[:, :, 0].unsqueeze(2).repeat(1, 1, self.nsample).to(idx.device) + idx[dist > self.radius] = tmp_idx[dist > self.radius] + + pos1_embed_norm = self.pos_embed(pos1) + pos2_embed_norm = self.pos_embed(pos2) # [B, C1, N] + + feat1_w_pos = torch.cat([feature1, pos1_embed_norm], dim=1) + feat2_w_pos = torch.cat([feature2, pos2_embed_norm], dim=1) # [B, C1+C2, N] + + feat1_w_pos = self.qk_net(feat1_w_pos) + feat2_w_pos = self.qk_net(feat2_w_pos) # [B, C1+C2, N] + + feat2_grouped = pointutils.grouping_operation(feat2_w_pos, idx) # [B, C1+C2, N, S] + + score = torch.softmax(feat1_w_pos.unsqueeze(-1) * feat2_grouped * 1./ math.sqrt(feat1_w_pos.shape[1]), dim=-1) # [B, C1+C2, N, S] + cost = (score * (feat1_w_pos.unsqueeze(-1) - feat2_grouped) ** 2).sum(dim=-1) # [B, C1+C2, N] + + if self.use_curvature: + curvate1_raw = curvature(pos1_raw).permute(0, 2, 1) #[B, 3, N] + curvate1 = curvature(pos1).permute(0, 2, 1) #[B, 3, N] + curvate_cost = self.curvate_net(curvate1_raw) - self.curvate_net(curvate1) + curvate_cost = curvate_cost ** 2 + cost = self.mlp_conv(torch.cat([cost, curvate_cost], dim=1)) # [B, C, N] + else: + cost = self.mlp_conv(cost) # [B, C, N] + + if return_score: + pos2_grouped = pointutils.grouping_operation(pos2, idx) # [B, 3, N, S] + # [B, N, K] + index = (dist > self.radius).sum(dim=2, keepdim=True).float() > (dist.shape[2] - 0.1) #[B, N, 1] + index = index.unsqueeze(1).repeat(1, score.shape[1], 1, dist.shape[2]) #[B, N, K] + score_tmp = score.clone() + score_tmp[index] = 0.0 + score = score_tmp + return pos1, cost, score, pos2_grouped + else: + return pos1, cost \ No newline at end of file diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..2eb4bd2 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,4 @@ +pytorch-lightning==1.5.3 +torch==1.7.1 +numpy==1.20.3 +scipy==1.7.3 \ No newline at end of file diff --git a/run.py b/run.py new file mode 100644 index 0000000..818c71a --- /dev/null +++ b/run.py @@ -0,0 +1,120 @@ +import json +import sys +import os +import yaml +import argparse +import datetime +import os.path as osp +import importlib +import torch +from pytorch_lightning import Trainer +from pytorch_lightning.loggers import TensorBoardLogger +from pytorch_lightning.callbacks import ModelCheckpoint +import pytorch_lightning as pl +from utils.utils import Unbuffered + + +def main(config): + # mdkir save dir + if config['train']: + time_str = str(datetime.datetime.now().strftime("%Y-%m-%d_%H-%M")) + exp_ckpt_dir = osp.join(config["logging_params"]['ckpt']['save_dir'], time_str) + else: + exp_ckpt_dir = osp.join(config["logging_params"]['ckpt']['save_dir'], 'test') + exp_logger_dir = os.path.join(exp_ckpt_dir, "log") + os.makedirs(exp_ckpt_dir, exist_ok=True) + os.makedirs(exp_logger_dir, exist_ok=True) + + # save print info to txt + log_file = os.path.join(exp_logger_dir, "log.txt") + sys.stdout = Unbuffered(sys.stdout, log_file) + print(f"shell command:{sys.argv}") + # print(json.dumps(config, indent=4)) + + # Create model and experiment instance + model_version = config['model_params']['model_name'] + full_path = "{}.{}".format("models", model_version) + if importlib.util.find_spec(full_path): + model = getattr(importlib.import_module(full_path), model_version) + else: + raise ValueError('Unknown class {}'.format(model_version)) + model = model(**config['model_params']) + + if config["task"] == "sceneflow": + from scene_flow import SceneFlowModel + experiment = SceneFlowModel(model, config) + else: + raise NotImplementedError + + if 'pre_trained_weights_checkpoint' in config['exp_params'].keys(): + print(f"Loading pre-trained model: {config['exp_params']['pre_trained_weights_checkpoint']}") + checkpoint = torch.load(config['exp_params']['pre_trained_weights_checkpoint'], map_location=lambda storage, loc: storage) + experiment.load_state_dict(checkpoint['state_dict']) + + + # Create Logger + if config["logging_params"]["log"]: + tensorboard_logger = TensorBoardLogger(save_dir=exp_logger_dir, name="default") + + # Create a trainer instance + # use trainer_params to set num_nodes and gpus + monitor = config["logging_params"]["ckpt"]["monitor"] + ckpt_callback = ModelCheckpoint( + dirpath=exp_ckpt_dir, + monitor=monitor, + filename="{epoch:03d}-{" + monitor + ":.4f}", + save_last=True, + save_top_k=config["logging_params"]["ckpt"]["top_k"], + save_on_train_epoch_end=True, + mode="min", + ) + + trainer = Trainer(logger=tensorboard_logger if config["logging_params"]["log"] else None, + callbacks=[ckpt_callback], + **config['trainer_params']) + + if config['train']: + print('Start Training!') + trainer.fit(experiment, ckpt_path=config['trainer_params'].get("resume_from_checkpoint", None)) + else: + print('Start Testing') + trainer.test(experiment, ckpt_path=config['trainer_params']["resume_from_checkpoint"]) + + +if __name__ == '__main__': + # Load config file from input arguments + parser = argparse.ArgumentParser(description='Generic runner') + parser.add_argument('--config', '-c', + dest='filename', + metavar='FILE', + help='Path to .yaml config file for the experiment', + default='configs/test/flowstep3d_self.yaml') + parser.add_argument('--prefix', '-p', + default='default') + parser.add_argument("--seed", default=12) + parser.add_argument("--pre_ckpt", type=str, default=None) + parser.add_argument("--test_ckpt", type=str, default=None) + parser.add_argument("--gpus", type=int, default=None, help="the number of gpus") + parser.add_argument("--batch_size", type=int, default=None) + + args = parser.parse_args() + with open(args.filename, 'r') as f: + try: + config = yaml.safe_load(f) + except yaml.YAMLError as exc: + print(exc) + + pl.utilities.seed.seed_everything(seed=args.seed) + + # Run + config["logging_params"]["ckpt"]["save_dir"] = os.path.join(config["logging_params"]["ckpt"]["save_dir"], args.prefix) + if args.pre_ckpt is not None: + config["exp_params"]["pre_trained_weights_checkpoint"] = args.pre_ckpt + if args.test_ckpt is not None: + config["trainer_params"]["resume_from_checkpoint"] = args.test_ckpt + if args.gpus is not None: + config["trainer_params"]["gpus"] = args.gpus + if args.batch_size is not None: + config["exp_params"]["batch_size"] = args.batch_size + main(config) + sys.stdout.close() diff --git a/scene_flow.py b/scene_flow.py new file mode 100644 index 0000000..b418fc0 --- /dev/null +++ b/scene_flow.py @@ -0,0 +1,189 @@ +from typing import List, Union +import torch +from torch.utils.data import DataLoader +import pytorch_lightning as pl +from losses.common_losses import curvature, curvatureWarp + +from utils.metrics import SceneFlowMetrics +from utils.utils import get_num_workers +from data.exp_utils import get_datasets +from losses import * +from lib.pointnet2 import pointnet2_utils as pointutils + + +class SceneFlowModel(pl.LightningModule): + + def __init__(self, model, cfg): + """ + Initializes an experiment. + + Arguments: + model : a torch.nn.Module object, model to be tested + hparams : a dictionary of hyper parameters + """ + + super(SceneFlowModel, self).__init__() + self.model = model + self.save_hyperparameters() + + self.loss = losses_dict[cfg["exp_params"]['loss']['loss_type']](**cfg["exp_params"]['loss']) + + self.train_metrics = SceneFlowMetrics(split='train', loss_params=cfg["exp_params"]['loss'], reduce_op='mean') + self.val_metrics = SceneFlowMetrics(split='val', loss_params=cfg["exp_params"]['loss'], reduce_op='mean') + + def forward(self, pos1, pos2, feat1, feat2, iters): + """ + A forward call + """ + return self.model(pos1, pos2, feat1, feat2, iters) + + def sequence_loss(self, pos1, pos2, flows_pred, flow_gt): + if 'loss_iters_w' in self.hparams["cfg"]["exp_params"]: + assert (len(self.hparams["cfg"]["exp_params"]['loss_iters_w']) == len(flows_pred)) or (len(self.hparams["cfg"]["exp_params"]['loss_iters_w']) == 2) + if len(self.hparams["cfg"]["exp_params"]['loss_iters_w']) != 2: + w_new = self.hparams["cfg"]["exp_params"]['loss_iters_w'] + else: + gamma = self.hparams["cfg"]["exp_params"]['loss_gamma'] + w_new = get_loss_weights(self.hparams["cfg"]["exp_params"]['loss_iters_w'], seq_len=len(flows_pred), gamma=gamma) + + loss = torch.zeros(1).cuda() + for i, w in enumerate(w_new): + loss += w * self.loss(pos1, pos2, flows_pred[i], flow_gt, len(flows_pred), i) + else: + loss = self.loss(pos1, pos2, flows_pred[-1], flow_gt) + return loss + + def training_step(self, batch, batch_idx): + """ + Executes a single training step + """ + pos1, pos2, feat1, feat2, flow_gt, fnames = batch + flows_pred = self(pos1, pos2, feat1, feat2, self.hparams["cfg"]["exp_params"]['train_iters']) + loss = self.sequence_loss(pos1, pos2, flows_pred, flow_gt) + metrics = self.train_metrics(pos1, pos2, flows_pred, flow_gt) + + i_last = self.hparams["cfg"]["exp_params"]['train_iters'] - 1 + train_epe = metrics[f'train_epe3d_i#{i_last}'] + + # self.log('train_loss', loss, sync_dist=False, prog_bar=False, on_step=False, on_epoch=True, logger=True) + self.log('train_epe', train_epe, sync_dist=False, prog_bar=True, on_step=False, on_epoch=True, logger=True) + self.log_dict(metrics, sync_dist=False, prog_bar=False, on_step=False, on_epoch=True, logger=True) # No need to sync_dist since metrics are already synced + + return {"loss": loss, "metrics": metrics} + + + def _test_val_step(self, batch, batch_idx, split): + pos1, pos2, feat1, feat2, flow_gt, fnames = batch #(B, N, 3) + flows_pred = self(pos1, pos2, feat1, feat2, self.hparams["cfg"]["exp_params"][f'{split}_iters']) + loss = self.sequence_loss(pos1, pos2, flows_pred, flow_gt) + metrics = self.val_metrics(pos1, pos2, flows_pred, flow_gt) + + i_last = self.hparams["cfg"]["exp_params"][f'{split}_iters'] - 1 + val_epe = metrics[f'val_epe3d_i#{i_last}'] + metrics_last = {k: v for k, v in metrics.items() if f"i#{i_last}" in k} + + self.log('val_epe', val_epe, prog_bar=True, sync_dist=True, on_step=False, on_epoch=True, logger=True) + # self.log_dict(metrics, prog_bar=False, sync_dist=True, on_step=False, on_epoch=True, logger=True) # No need to sync_dist since metrics are already synced + self.log_dict(metrics_last, prog_bar=False, sync_dist=True, on_step=False, on_epoch=True, logger=True) # No need to sync_dist since metrics are already synced + + return {"val_epe": metrics[f'val_epe3d_i#{i_last}'], "val_loss": loss} + + def _test_val_epoch_end(self, outputs): + val_epe = torch.stack([e['val_epe'] for e in outputs], dim=0).mean(0) + # val_loss = torch.stack([e['val_loss'] for e in outputs], dim=0).mean(0) + if len(self.trainer.lr_schedulers) > 0: + print(f"**********val_epe: {val_epe}, lr:{self.trainer.lr_schedulers[0]['scheduler'].get_last_lr()}") + else: + print(f"**********val_epe: {val_epe}") + self.log("val_epe_epoch", value=val_epe, prog_bar=True, sync_dist=True, on_step=False, on_epoch=True, logger=True) + # self.log("val_loss_epoch", value=val_loss, prog_bar=True, sync_dist=True, on_step=False, on_epoch=True, logger=True) + return None + + def test_epoch_end(self, outputs): + return self._test_val_epoch_end(outputs) + + def validation_epoch_end(self, outputs): + return self._test_val_epoch_end(outputs) + + def validation_step(self, batch, batch_idx): + """ + Executes a single validation step. + """ + return self._test_val_step(batch, batch_idx, 'val') + + def test_step(self, batch, batch_idx): + """ + Executes a single test step. + """ + return self._test_val_step(batch, batch_idx, 'test') + + + def configure_optimizers(self): + optimizer = getattr(torch.optim, self.hparams["cfg"]["exp_params"]["optimizer"]["type"]) + # Create optimizer with parameters + params = self.hparams["cfg"]["exp_params"]["optimizer"] + params.pop("type", None) + optimizer = optimizer(self.parameters(), **params) + + # Load and initialize scheduler + scheduler = getattr(torch.optim.lr_scheduler, self.hparams["cfg"]["exp_params"]["scheduler"]["type"]) + params = self.hparams["cfg"]["exp_params"]["scheduler"] + params.pop("type", None) + scheduler = scheduler(optimizer, **params) + + return {'optimizer': optimizer, + 'lr_scheduler': scheduler} + + def train_dataloader(self): + """ + Returns a train set Dataloader object + """ + return self._dataloader(split_type='train') + + def val_dataloader(self): + """ + Returns a validation set Dataloader object + """ + return self._dataloader(split_type='val') + + def test_dataloader(self): + """ + Returns a validation set Dataloader object + """ + return self._dataloader(split_type='test') + + + def setup(self, stage: str): + """ + Load datasets + """ + train_dataset, val_dataset, test_dataset = get_datasets(task="sceneflow", data_params=self.hparams["cfg"]["exp_params"]['data']) + self.train_dataset = train_dataset + self.val_dataset = val_dataset + self.test_dataset = test_dataset + + def _dataloader(self, split_type: str): + """ + Arguments: + split_type : (str) shouild be one of ['train', 'val', 'test'] + Return: + A DataLoader object of the correspondig split + """ + + split_dict = { + 'train': self.train_dataset, + 'val': self.val_dataset, + 'test': self.test_dataset + } + is_train = (split_type == 'train') + num_workers = get_num_workers(self.hparams["cfg"]["exp_params"]['num_workers']) + if split_dict[split_type] is None: + loader = None + else: + loader = DataLoader(split_dict[split_type], + batch_size=self.hparams["cfg"]["exp_params"]['batch_size'], + num_workers=num_workers, + pin_memory=True, + shuffle=True if is_train else False) + return loader + diff --git a/scripts/download_models.sh b/scripts/download_models.sh new file mode 100644 index 0000000..bb4d1d2 --- /dev/null +++ b/scripts/download_models.sh @@ -0,0 +1,15 @@ +#!/bin/bash + +echo "Downloading models.." +mkdir checkpoints + +# Download +fileId=1RpZVdGacp0Y4H145ekUtcssmrSOhVLqF +fileName=flowstep3d_checkpoints.tar.gz +curl -sc /tmp/cookie "https://drive.google.com/uc?export=download&id=${fileId}" > /dev/null +code="$(awk '/_warning_/ {print $NF}' /tmp/cookie)" +curl -Lb /tmp/cookie "https://drive.google.com/uc?export=download&confirm=${code}&id=${fileId}" -o ${fileName} + +# Extract +tar -xzvf ${fileName} -C checkpoints +rm ${fileName} \ No newline at end of file diff --git a/utils/__init__.py b/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/utils/calib_cam_to_cam/000000.txt b/utils/calib_cam_to_cam/000000.txt new file mode 100644 index 0000000..43471e4 --- /dev/null +++ b/utils/calib_cam_to_cam/000000.txt @@ -0,0 +1,34 @@ +calib_time: 09-Jan-2012 13:57:47 +corner_dist: 9.950000e-02 +S_00: 1.392000e+03 5.120000e+02 +K_00: 9.842439e+02 0.000000e+00 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100644 index 0000000..04a75a0 --- /dev/null +++ b/utils/calib_cam_to_cam/000001.txt @@ -0,0 +1,34 @@ +calib_time: 09-Jan-2012 13:57:47 +corner_dist: 9.950000e-02 +S_00: 1.392000e+03 5.120000e+02 +K_00: 9.842439e+02 0.000000e+00 6.900000e+02 0.000000e+00 9.808141e+02 2.331966e+02 0.000000e+00 0.000000e+00 1.000000e+00 +D_00: -3.728755e-01 2.037299e-01 2.219027e-03 1.383707e-03 -7.233722e-02 +R_00: 1.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 1.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 1.000000e+00 +T_00: 2.573699e-16 -1.059758e-16 1.614870e-16 +S_rect_00: 1.242000e+03 3.750000e+02 +R_rect_00: 9.999239e-01 9.837760e-03 -7.445048e-03 -9.869795e-03 9.999421e-01 -4.278459e-03 7.402527e-03 4.351614e-03 9.999631e-01 +P_rect_00: 7.215377e+02 0.000000e+00 6.095593e+02 0.000000e+00 0.000000e+00 7.215377e+02 1.728540e+02 0.000000e+00 0.000000e+00 0.000000e+00 1.000000e+00 0.000000e+00 +S_01: 1.392000e+03 5.120000e+02 +K_01: 9.895267e+02 0.000000e+00 7.020000e+02 0.000000e+00 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0.000000e+00 6.071928e+02 0.000000e+00 0.000000e+00 7.188560e+02 1.852157e+02 0.000000e+00 0.000000e+00 0.000000e+00 1.000000e+00 0.000000e+00 +S_01: 1.392000e+03 5.120000e+02 +K_01: 9.903522e+02 0.000000e+00 7.020000e+02 0.000000e+00 9.855674e+02 2.607319e+02 0.000000e+00 0.000000e+00 1.000000e+00 +D_01: -3.712084e-01 1.978723e-01 -3.709831e-05 -3.440494e-04 -6.724045e-02 +R_01: 9.993440e-01 1.814887e-02 -3.134011e-02 -1.842595e-02 9.997935e-01 -8.575221e-03 3.117801e-02 9.147067e-03 9.994720e-01 +T_01: -5.370000e-01 5.964270e-03 -1.274584e-02 +S_rect_01: 1.241000e+03 3.760000e+02 +R_rect_01: 9.996568e-01 -1.110284e-02 2.372712e-02 1.099810e-02 9.999292e-01 4.539964e-03 -2.377585e-02 -4.277453e-03 9.997082e-01 +P_rect_01: 7.188560e+02 0.000000e+00 6.071928e+02 -3.861448e+02 0.000000e+00 7.188560e+02 1.852157e+02 0.000000e+00 0.000000e+00 0.000000e+00 1.000000e+00 0.000000e+00 +S_02: 1.392000e+03 5.120000e+02 +K_02: 9.601149e+02 0.000000e+00 6.947923e+02 0.000000e+00 9.548911e+02 2.403547e+02 0.000000e+00 0.000000e+00 1.000000e+00 +D_02: -3.685917e-01 1.928022e-01 4.069233e-04 7.247536e-04 -6.276909e-02 +R_02: 9.999788e-01 -5.008404e-03 -4.151018e-03 4.990516e-03 9.999783e-01 -4.308488e-03 4.172506e-03 4.287682e-03 9.999821e-01 +T_02: 5.954406e-02 -7.675338e-04 3.582565e-03 +S_rect_02: 1.241000e+03 3.760000e+02 +R_rect_02: 9.999191e-01 1.228161e-02 -3.316013e-03 -1.228209e-02 9.999246e-01 -1.245511e-04 3.314233e-03 1.652686e-04 9.999945e-01 +P_rect_02: 7.188560e+02 0.000000e+00 6.071928e+02 4.538225e+01 0.000000e+00 7.188560e+02 1.852157e+02 -1.130887e-01 0.000000e+00 0.000000e+00 1.000000e+00 3.779761e-03 +S_03: 1.392000e+03 5.120000e+02 +K_03: 9.049931e+02 0.000000e+00 6.957698e+02 0.000000e+00 9.004945e+02 2.389820e+02 0.000000e+00 0.000000e+00 1.000000e+00 +D_03: -3.735725e-01 2.066816e-01 -6.133284e-04 -1.193269e-04 -7.600861e-02 +R_03: 9.995578e-01 1.656369e-02 -2.469315e-02 -1.663353e-02 9.998582e-01 -2.625576e-03 2.464616e-02 3.035149e-03 9.996916e-01 +T_03: -4.738786e-01 5.991982e-03 -3.215069e-03 +S_rect_03: 1.241000e+03 3.760000e+02 +R_rect_03: 9.998092e-01 -9.354781e-03 1.714961e-02 9.382303e-03 9.999548e-01 -1.525064e-03 -1.713457e-02 1.685675e-03 9.998518e-01 +P_rect_03: 7.188560e+02 0.000000e+00 6.071928e+02 -3.372877e+02 0.000000e+00 7.188560e+02 1.852157e+02 2.369057e+00 0.000000e+00 0.000000e+00 1.000000e+00 4.915215e-03 diff --git a/utils/calib_cam_to_cam/000199.txt b/utils/calib_cam_to_cam/000199.txt new file mode 100644 index 0000000..e599cd5 --- /dev/null +++ b/utils/calib_cam_to_cam/000199.txt @@ -0,0 +1,34 @@ +calib_time: 09-Jan-2012 14:00:15 +corner_dist: 9.950000e-02 +S_00: 1.392000e+03 5.120000e+02 +K_00: 9.799200e+02 0.000000e+00 6.900000e+02 0.000000e+00 9.741183e+02 2.486443e+02 0.000000e+00 0.000000e+00 1.000000e+00 +D_00: -3.745594e-01 2.049385e-01 1.110145e-03 1.379375e-03 -7.084798e-02 +R_00: 1.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 1.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 1.000000e+00 +T_00: -9.251859e-17 8.326673e-17 -7.401487e-17 +S_rect_00: 1.241000e+03 3.760000e+02 +R_rect_00: 9.999454e-01 7.259129e-03 -7.519551e-03 -7.292213e-03 9.999638e-01 -4.381729e-03 7.487471e-03 4.436324e-03 9.999621e-01 +P_rect_00: 7.188560e+02 0.000000e+00 6.071928e+02 0.000000e+00 0.000000e+00 7.188560e+02 1.852157e+02 0.000000e+00 0.000000e+00 0.000000e+00 1.000000e+00 0.000000e+00 +S_01: 1.392000e+03 5.120000e+02 +K_01: 9.903522e+02 0.000000e+00 7.020000e+02 0.000000e+00 9.855674e+02 2.607319e+02 0.000000e+00 0.000000e+00 1.000000e+00 +D_01: -3.712084e-01 1.978723e-01 -3.709831e-05 -3.440494e-04 -6.724045e-02 +R_01: 9.993440e-01 1.814887e-02 -3.134011e-02 -1.842595e-02 9.997935e-01 -8.575221e-03 3.117801e-02 9.147067e-03 9.994720e-01 +T_01: -5.370000e-01 5.964270e-03 -1.274584e-02 +S_rect_01: 1.241000e+03 3.760000e+02 +R_rect_01: 9.996568e-01 -1.110284e-02 2.372712e-02 1.099810e-02 9.999292e-01 4.539964e-03 -2.377585e-02 -4.277453e-03 9.997082e-01 +P_rect_01: 7.188560e+02 0.000000e+00 6.071928e+02 -3.861448e+02 0.000000e+00 7.188560e+02 1.852157e+02 0.000000e+00 0.000000e+00 0.000000e+00 1.000000e+00 0.000000e+00 +S_02: 1.392000e+03 5.120000e+02 +K_02: 9.601149e+02 0.000000e+00 6.947923e+02 0.000000e+00 9.548911e+02 2.403547e+02 0.000000e+00 0.000000e+00 1.000000e+00 +D_02: -3.685917e-01 1.928022e-01 4.069233e-04 7.247536e-04 -6.276909e-02 +R_02: 9.999788e-01 -5.008404e-03 -4.151018e-03 4.990516e-03 9.999783e-01 -4.308488e-03 4.172506e-03 4.287682e-03 9.999821e-01 +T_02: 5.954406e-02 -7.675338e-04 3.582565e-03 +S_rect_02: 1.241000e+03 3.760000e+02 +R_rect_02: 9.999191e-01 1.228161e-02 -3.316013e-03 -1.228209e-02 9.999246e-01 -1.245511e-04 3.314233e-03 1.652686e-04 9.999945e-01 +P_rect_02: 7.188560e+02 0.000000e+00 6.071928e+02 4.538225e+01 0.000000e+00 7.188560e+02 1.852157e+02 -1.130887e-01 0.000000e+00 0.000000e+00 1.000000e+00 3.779761e-03 +S_03: 1.392000e+03 5.120000e+02 +K_03: 9.049931e+02 0.000000e+00 6.957698e+02 0.000000e+00 9.004945e+02 2.389820e+02 0.000000e+00 0.000000e+00 1.000000e+00 +D_03: -3.735725e-01 2.066816e-01 -6.133284e-04 -1.193269e-04 -7.600861e-02 +R_03: 9.995578e-01 1.656369e-02 -2.469315e-02 -1.663353e-02 9.998582e-01 -2.625576e-03 2.464616e-02 3.035149e-03 9.996916e-01 +T_03: -4.738786e-01 5.991982e-03 -3.215069e-03 +S_rect_03: 1.241000e+03 3.760000e+02 +R_rect_03: 9.998092e-01 -9.354781e-03 1.714961e-02 9.382303e-03 9.999548e-01 -1.525064e-03 -1.713457e-02 1.685675e-03 9.998518e-01 +P_rect_03: 7.188560e+02 0.000000e+00 6.071928e+02 -3.372877e+02 0.000000e+00 7.188560e+02 1.852157e+02 2.369057e+00 0.000000e+00 0.000000e+00 1.000000e+00 4.915215e-03 diff --git a/utils/easydict.py b/utils/easydict.py new file mode 100644 index 0000000..868fa70 --- /dev/null +++ b/utils/easydict.py @@ -0,0 +1,100 @@ +class EasyDict(dict): + """ + Get attributes + >>> d = EasyDict({'foo':3}) + >>> d['foo'] + 3 + >>> d.foo + 3 + >>> d.bar + Traceback (most recent call last): + ... + AttributeError: 'EasyDict' object has no attribute 'bar' + >>> #Works recursively + >>> d = EasyDict({'foo':3, 'bar':{'x':1, 'y':2}}) + >>> isinstance(d.bar, dict) + True + >>> d.bar.x + 1 + >>> EasyDict({}) + {} + >>> EasyDict(d={}) + {} + >>> EasyDict(None) + {} + >>> d = {'a': 1} + >>> EasyDict(**d) + {'a': 1} + >>> d = EasyDict() + >>> d.foo = 3 + >>> d.foo + 3 + >>> d.bar = {'prop': 'value'} + >>> d.bar.prop + 'value' + >>> d + {'foo': 3, 'bar': {'prop': 'value'}} + >>> d.bar.prop = 'newer' + >>> d.bar.prop + 'newer' + >>> d = EasyDict({'foo':0, 'bar':[{'x':1, 'y':2}, {'x':3, 'y':4}]}) + >>> isinstance(d.bar, list) + True + >>> from operator import attrgetter + >>> list(map(attrgetter('x'), d.bar)) + [1, 3] + >>> list(map(attrgetter('y'), d.bar)) + [2, 4] + >>> d = EasyDict() + >>> list(d.keys()) + [] + >>> d = EasyDict(foo=3, bar=dict(x=1, y=2)) + >>> d.foo + 3 + >>> d.bar.x + 1 + >>> o = EasyDict({'clean':True}) + >>> list(o.items()) + [('clean', True)] + >>> #And like a class + >>> class Flower(EasyDict): + ... power = 1 + ... + >>> f = Flower() + >>> f.power + 1 + >>> f = Flower({'height': 12}) + >>> f.height + 12 + >>> f['power'] + 1 + >>> sorted(f.keys()) + ['height', 'power'] + """ + def __init__(self, d=None, **kwargs): + if d is None: + d = {} + if kwargs: + d.update(**kwargs) + for k, v in d.items(): + setattr(self, k, v) + # Class attributes + for k in self.__class__.__dict__.keys(): + if not (k.startswith('__') and k.endswith('__')): + setattr(self, k, getattr(self, k)) + + def __setattr__(self, name, value): + if isinstance(value, (list, tuple)): + value = [self.__class__(x) + if isinstance(x, dict) else x for x in value] + elif isinstance(value, dict) and not isinstance(value, self.__class__): + value = self.__class__(value) + super(EasyDict, self).__setattr__(name, value) + super(EasyDict, self).__setitem__(name, value) + + __setitem__ = __setattr__ + + +if __name__ == "__main__": + import doctest + doctest.testmod() diff --git a/utils/geometry.py b/utils/geometry.py new file mode 100755 index 0000000..ca35b06 --- /dev/null +++ b/utils/geometry.py @@ -0,0 +1,784 @@ +import os.path as osp +from typing import TYPE_CHECKING, Tuple, Union +import numpy as np +import torch +import torch.nn.functional as F +from pytorch3d.ops.utils import convert_pointclouds_to_tensor, get_point_covariances +from pytorch3d.ops import knn_points +if TYPE_CHECKING: + from pytorch3d.structures import Pointclouds + +from lib.pointnet2 import pointnet2_utils as pointutils + +def get_batch_2d_flow(pc1, pc2, predicted_pc2, paths): + if 'KITTI' in paths[0] or 'kitti' in paths[0]: + focallengths = [] + cxs = [] + cys = [] + constx = [] + consty = [] + constz = [] + for path in paths: + fname = osp.split(path)[-1] + calib_path = osp.join( + osp.dirname(__file__), + 'calib_cam_to_cam', + fname + '.txt') + with open(calib_path) as fd: + lines = fd.readlines() + P_rect_left = \ + np.array([float(item) for item in + [line for line in lines if line.startswith('P_rect_02')][0].split()[1:]], + dtype=np.float32).reshape(3, 4) + focallengths.append(-P_rect_left[0, 0]) + cxs.append(P_rect_left[0, 2]) + cys.append(P_rect_left[1, 2]) + constx.append(P_rect_left[0, 3]) + consty.append(P_rect_left[1, 3]) + constz.append(P_rect_left[2, 3]) + focallengths = np.array(focallengths)[:, None, None] + cxs = np.array(cxs)[:, None, None] + cys = np.array(cys)[:, None, None] + constx = np.array(constx)[:, None, None] + consty = np.array(consty)[:, None, None] + constz = np.array(constz)[:, None, None] + + px1, py1 = project_3d_to_2d(pc1, f=focallengths, cx=cxs, cy=cys, + constx=constx, consty=consty, constz=constz) + px2, py2 = project_3d_to_2d(predicted_pc2, f=focallengths, cx=cxs, cy=cys, + constx=constx, consty=consty, constz=constz) + px2_gt, py2_gt = project_3d_to_2d(pc2, f=focallengths, cx=cxs, cy=cys, + constx=constx, consty=consty, constz=constz) + else: + px1, py1 = project_3d_to_2d(pc1) + px2, py2 = project_3d_to_2d(predicted_pc2) + px2_gt, py2_gt = project_3d_to_2d(pc2) + + flow_x = px2 - px1 + flow_y = py2 - py1 + + flow_x_gt = px2_gt - px1 + flow_y_gt = py2_gt - py1 + + flow_pred = np.concatenate((flow_x[..., None], flow_y[..., None]), axis=-1) + flow_gt = np.concatenate((flow_x_gt[..., None], flow_y_gt[..., None]), axis=-1) + return flow_pred, flow_gt + + + +def project_3d_to_2d(pc, f=-1050., cx=479.5, cy=269.5, constx=0, consty=0, constz=0): + x = (pc[..., 0] * f + cx * pc[..., 2] + constx) / (pc[..., 2] + constz) + y = (pc[..., 1] * f + cy * pc[..., 2] + consty) / (pc[..., 2] + constz) + + return x, y + + +def estimate_pointcloud_normals( + pointclouds: Union[torch.Tensor, "Pointclouds"], + neighborhood_size: int = 50, + disambiguate_directions: bool = True, +) -> torch.Tensor: + """ + Estimates the normals of a batch of `pointclouds`. + + The function uses `estimate_pointcloud_local_coord_frames` to estimate + the normals. Please refer to that function for more detailed information. + + Args: + **pointclouds**: Batch of 3-dimensional points of shape + `(minibatch, num_point, 3)` or a `Pointclouds` object. + **neighborhood_size**: The size of the neighborhood used to estimate the + geometry around each point. + **disambiguate_directions**: If `True`, uses the algorithm from [1] to + ensure sign consistency of the normals of neigboring points. + + Returns: + **normals**: A tensor of normals for each input point + of shape `(minibatch, num_point, 3)`. + If `pointclouds` are of `Pointclouds` class, returns a padded tensor. + + References: + [1] Tombari, Salti, Di Stefano: Unique Signatures of Histograms for + Local Surface Description, ECCV 2010. + """ + + curvatures, local_coord_frames = estimate_pointcloud_local_coord_frames( + pointclouds, + neighborhood_size=neighborhood_size, + disambiguate_directions=disambiguate_directions, + ) + + # the normals correspond to the first vector of each local coord frame + normals = local_coord_frames[:, :, :, 0] + + return normals + + +def estimate_pointcloud_local_coord_frames( + pointclouds: Union[torch.Tensor, "Pointclouds"], + neighborhood_size: int = 50, + disambiguate_directions: bool = True, +) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Estimates the principal directions of curvature (which includes normals) + of a batch of `pointclouds`. + + The algorithm first finds `neighborhood_size` nearest neighbors for each + point of the point clouds, followed by obtaining principal vectors of + covariance matrices of each of the point neighborhoods. + The main principal vector corresponds to the normals, while the + other 2 are the direction of the highest curvature and the 2nd highest + curvature. + + Note that each principal direction is given up to a sign. Hence, + the function implements `disambiguate_directions` switch that allows + to ensure consistency of the sign of neighboring normals. The implementation + follows the sign disabiguation from SHOT descriptors [1]. + + The algorithm also returns the curvature values themselves. + These are the eigenvalues of the estimated covariance matrices + of each point neighborhood. + + Args: + **pointclouds**: Batch of 3-dimensional points of shape + `(minibatch, num_point, 3)` or a `Pointclouds` object. + **neighborhood_size**: The size of the neighborhood used to estimate the + geometry around each point. + **disambiguate_directions**: If `True`, uses the algorithm from [1] to + ensure sign consistency of the normals of neigboring points. + + Returns: + **curvatures**: The three principal curvatures of each point + of shape `(minibatch, num_point, 3)`. + If `pointclouds` are of `Pointclouds` class, returns a padded tensor. + **local_coord_frames**: The three principal directions of the curvature + around each point of shape `(minibatch, num_point, 3, 3)`. + The principal directions are stored in columns of the output. + E.g. `local_coord_frames[i, j, :, 0]` is the normal of + `j`-th point in the `i`-th pointcloud. + If `pointclouds` are of `Pointclouds` class, returns a padded tensor. + + References: + [1] Tombari, Salti, Di Stefano: Unique Signatures of Histograms for + Local Surface Description, ECCV 2010. + """ + + points_padded, num_points = convert_pointclouds_to_tensor(pointclouds) + + ba, N, dim = points_padded.shape + if dim != 3: + raise ValueError( + "The pointclouds argument has to be of shape (minibatch, N, 3)" + ) + + if (num_points <= neighborhood_size).any(): + raise ValueError( + "The neighborhood_size argument has to be" + + " >= size of each of the point clouds." + ) + + # undo global mean for stability + # TODO: replace with tutil.wmean once landed + pcl_mean = points_padded.sum(1) / num_points[:, None] + points_centered = points_padded - pcl_mean[:, None, :] + + # get the per-point covariance and nearest neighbors used to compute it + cov, knns = get_point_covariances(points_centered, num_points, neighborhood_size) + + # get the local coord frames as principal directions of + # the per-point covariance + # this is done with torch.symeig, which returns the + # eigenvectors (=principal directions) in an ascending order of their + # corresponding eigenvalues, while the smallest eigenvalue's eigenvector + # corresponds to the normal direction + # curvatures, local_coord_frames = torch.symeig(cov, eigenvectors=True) + + curvatures, local_coord_frames = torch.symeig(cov.detach().cpu(), eigenvectors=True) + curvatures, local_coord_frames = curvatures.cuda(), local_coord_frames.cuda() + + # disambiguate the directions of individual principal vectors + if disambiguate_directions: + # disambiguate normal + n = _disambiguate_vector_directions( + points_centered, knns, local_coord_frames[:, :, :, 0] + ) + # disambiguate the main curvature + z = _disambiguate_vector_directions( + points_centered, knns, local_coord_frames[:, :, :, 2] + ) + # the secondary curvature is just a cross between n and z + y = torch.cross(n, z, dim=2) + # cat to form the set of principal directions + local_coord_frames = torch.stack((n, y, z), dim=3) + + return curvatures, local_coord_frames + + +def _disambiguate_vector_directions(pcl, knns, vecs): + """ + Disambiguates normal directions according to [1]. + + References: + [1] Tombari, Salti, Di Stefano: Unique Signatures of Histograms for + Local Surface Description, ECCV 2010. + """ + # parse out K from the shape of knns + K = knns.shape[2] + # the difference between the mean of each neighborhood and + # each element of the neighborhood + df = knns - pcl[:, :, None] + # projection of the difference on the principal direction + proj = (vecs[:, :, None] * df).sum(3) + # check how many projections are positive + n_pos = (proj > 0).type_as(knns).sum(2, keepdim=True) + # flip the principal directions where number of positive correlations + flip = (n_pos < (0.5 * K)).type_as(knns) + vecs = (1.0 - 2.0 * flip) * vecs + return vecs + + + +def estimate_pointcloud_normals_from_two_pcds( + pcd1, pcd2, + neighborhood_size: int = 50, + disambiguate_directions: bool = True, + radius=None +) -> torch.Tensor: + """ + Estimates the normals of a batch of `pointclouds`. + + The function uses `estimate_pointcloud_local_coord_frames` to estimate + the normals. Please refer to that function for more detailed information. + + Args: + **pointclouds**: Batch of 3-dimensional points of shape + `(minibatch, num_point, 3)` or a `Pointclouds` object. + **neighborhood_size**: The size of the neighborhood used to estimate the + geometry around each point. + **disambiguate_directions**: If `True`, uses the algorithm from [1] to + ensure sign consistency of the normals of neigboring points. + + Returns: + **normals**: A tensor of normals for each input point + of shape `(minibatch, num_point, 3)`. + If `pointclouds` are of `Pointclouds` class, returns a padded tensor. + + References: + [1] Tombari, Salti, Di Stefano: Unique Signatures of Histograms for + Local Surface Description, ECCV 2010. + """ + + curvatures, local_coord_frames, knns, knns_idx = estimate_pointcloud_local_coord_frames_from_two_pcds( + pcd1, pcd2, + neighborhood_size=neighborhood_size, + disambiguate_directions=disambiguate_directions, + radius=radius + ) + + # # the normals correspond to the first vector of each local coord frame + # normals = local_coord_frames[:, :, :, 0] + + return local_coord_frames, knns, knns_idx + +def get_point_covariances_from_two_pcds( + pcd1, pcd2, + num_points_per_cloud: int, + neighborhood_size: int, + radius: float +) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Computes the per-point covariance matrices by of the 3D locations of + K-nearest neighbors of each point. + + Args: + **points_padded**: Input point clouds as a padded tensor + of shape `(minibatch, num_points, dim)`. + **num_points_per_cloud**: Number of points per cloud + of shape `(minibatch,)`. + **neighborhood_size**: Number of nearest neighbors for each point + used to estimate the covariance matrices. + + Returns: + **covariances**: A batch of per-point covariance matrices + of shape `(minibatch, dim, dim)`. + **k_nearest_neighbors**: A batch of `neighborhood_size` nearest + neighbors for each of the point cloud points + of shape `(minibatch, num_points, neighborhood_size, dim)`. + """ + # get K nearest neighbor idx for each point in the point cloud + # k_nearest_neighbors = knn_points( + # pcd1, + # pcd2, + # lengths1=num_points_per_cloud, + # lengths2=num_points_per_cloud, + # K=neighborhood_size, + # return_nn=True, + # ).knn + + dist, idx = pointutils.knn(neighborhood_size, pcd1, pcd2) + if radius is not None: + tmp_idx = idx[:, :, 0].unsqueeze(2).repeat(1, 1, neighborhood_size).to(idx.device) + idx[dist > radius] = tmp_idx[dist > radius] + k_nearest_neighbors = pointutils.grouping_operation(pcd2.permute(0, 2, 1).contiguous(), idx).permute(0, 2, 3, 1) # [B, N, K, 3] + + # obtain the mean of the neighborhood + pt_mean = k_nearest_neighbors.mean(2, keepdim=True) + # compute the diff of the neighborhood and the mean of the neighborhood + central_diff = k_nearest_neighbors - pt_mean + # per-nn-point covariances + per_pt_cov = central_diff.unsqueeze(4) * central_diff.unsqueeze(3) + # per-point covariances + covariances = per_pt_cov.mean(2) + return covariances, k_nearest_neighbors, idx + + +def estimate_pointcloud_local_coord_frames_from_two_pcds( + pcd1, pcd2, + neighborhood_size: int = 50, + disambiguate_directions: bool = True, + radius=None +) -> Tuple[torch.Tensor, torch.Tensor]: + """s + Estimates the principal directions of curvature (which includes normals) + of a batch of `pointclouds`. + + The algorithm first finds `neighborhood_size` nearest neighbors for each + point of the point clouds, followed by obtaining principal vectors of + covariance matrices of each of the point neighborhoods. + The main principal vector corresponds to the normals, while the + other 2 are the direction of the highest curvature and the 2nd highest + curvature. + + Note that each principal direction is given up to a sign. Hence, + the function implements `disambiguate_directions` switch that allows + to ensure consistency of the sign of neighboring normals. The implementation + follows the sign disabiguation from SHOT descriptors [1]. + + The algorithm also returns the curvature values themselves. + These are the eigenvalues of the estimated covariance matrices + of each point neighborhood. + + Args: + **pointclouds**: Batch of 3-dimensional points of shape + `(minibatch, num_point, 3)` or a `Pointclouds` object. + **neighborhood_size**: The size of the neighborhood used to estimate the + geometry around each point. + **disambiguate_directions**: If `True`, uses the algorithm from [1] to + ensure sign consistency of the normals of neigboring points. + + Returns: + **curvatures**: The three principal curvatures of each point + of shape `(minibatch, num_point, 3)`. + If `pointclouds` are of `Pointclouds` class, returns a padded tensor. + **local_coord_frames**: The three principal directions of the curvature + around each point of shape `(minibatch, num_point, 3, 3)`. + The principal directions are stored in columns of the output. + E.g. `local_coord_frames[i, j, :, 0]` is the normal of + `j`-th point in the `i`-th pointcloud. + If `pointclouds` are of `Pointclouds` class, returns a padded tensor. + + References: + [1] Tombari, Salti, Di Stefano: Unique Signatures of Histograms for + Local Surface Description, ECCV 2010. + """ + pcd1, num_points = convert_pointclouds_to_tensor(pcd1) + pcd2, _ = convert_pointclouds_to_tensor(pcd2) + + ba, N, dim = pcd1.shape + if dim != 3: + raise ValueError( + "The pointclouds argument has to be of shape (minibatch, N, 3)" + ) + + if (num_points <= neighborhood_size).any(): + raise ValueError( + "The neighborhood_size argument has to be" + + " >= size of each of the point clouds." + ) + + # undo global mean for stability + # TODO: replace with tutil.wmean once landed + pcl_mean = pcd2.sum(1) / num_points[:, None] #[B, 3] + pcd2_centered = pcd2 - pcl_mean[:, None, :] + pcd1_centered = pcd1 - pcl_mean[:, None, :] + + # get the per-point covariance and nearest neighbors used to compute it + cov, knns, knns_idx = get_point_covariances_from_two_pcds(pcd1_centered, pcd2_centered, num_points, neighborhood_size, radius) + + # get the local coord frames as principal directions of + # the per-point covariance + # this is done with torch.symeig, which returns the + # eigenvectors (=principal directions) in an ascending order of their + # corresponding eigenvalues, while the smallest eigenvalue's eigenvector + # corresponds to the normal direction + # curvatures, local_coord_frames = torch.symeig(cov, eigenvectors=True) + + curvatures, local_coord_frames = torch.symeig(cov.detach().cpu(), eigenvectors=True) + curvatures, local_coord_frames = curvatures.cuda(), local_coord_frames.cuda() + + # disambiguate the directions of individual principal vectors + if disambiguate_directions: + # disambiguate normal + n = _disambiguate_vector_directions( + pcd2_centered, knns, local_coord_frames[:, :, :, 0] + ) + # disambiguate the main curvature + z = _disambiguate_vector_directions( + pcd2_centered, knns, local_coord_frames[:, :, :, 2] + ) + # the secondary curvature is just a cross between n and z + y = torch.cross(n, z, dim=2) + # cat to form the set of principal directions + local_coord_frames = torch.stack((n, y, z), dim=3) + + # knn:[B, N, K, 3] + return curvatures, local_coord_frames, knns + pcl_mean[:, None, None, :], knns_idx + + + +def square_dists(points1, points2): + ''' + Calculate square dists between two group points + :param points1: shape=(B, N, C) + :param points2: shape=(B, M, C) + :return: + ''' + B, N, C = points1.shape + _, M, _ = points2.shape + dists = torch.sum(torch.pow(points1, 2), dim=-1).view(B, N, 1) + \ + torch.sum(torch.pow(points2, 2), dim=-1).view(B, 1, M) + dists -= 2 * torch.matmul(points1, points2.permute(0, 2, 1)) + return dists.float() + + +def batch_transform(batch_pc, batch_R, batch_t=None): + ''' + + :param batch_pc: shape=(B, N, 3) + :param batch_R: shape=(B, 3, 3) + :param batch_t: shape=(B, 3) + :return: shape(B, N, 3) + ''' + transformed_pc = torch.matmul(batch_pc, batch_R.permute(0, 2, 1).contiguous()) + if batch_t is not None: + transformed_pc = transformed_pc + torch.unsqueeze(batch_t, 1) + return transformed_pc + + +def batch_quat2mat(batch_quat): + ''' + + :param batch_quat: shape=(B, 4) + :return: + ''' + w, x, y, z = batch_quat[:, 0], batch_quat[:, 1], batch_quat[:, 2], \ + batch_quat[:, 3] + device = batch_quat.device + B = batch_quat.size()[0] + R = torch.zeros(dtype=torch.float, size=(B, 3, 3)).to(device) + R[:, 0, 0] = 1 - 2 * y * y - 2 * z * z + R[:, 0, 1] = 2 * x * y - 2 * z * w + R[:, 0, 2] = 2 * x * z + 2 * y * w + R[:, 1, 0] = 2 * x * y + 2 * z * w + R[:, 1, 1] = 1 - 2 * x * x - 2 * z * z + R[:, 1, 2] = 2 * y * z - 2 * x * w + R[:, 2, 0] = 2 * x * z - 2 * y * w + R[:, 2, 1] = 2 * y * z + 2 * x * w + R[:, 2, 2] = 1 - 2 * x * x - 2 * y * y + return R + +def batch_mat2quat(mat): + """ + [B, 4, 4] + [B, 4] + """ + w = torch.sqrt(mat[:, 0, 0] + mat[:, 1, 1] + mat[:, 2, 2] + 1 + 1e-8) / 2 + x = (mat[:, 2, 1] - mat[:, 1, 2]) / (4 * w + 1e-8) + y = (mat[:, 0, 2] - mat[:, 2, 0]) / (4 * w + 1e-8) + z = (mat[:, 1, 0] - mat[:, 0, 1]) / (4 * w + 1e-8) + return torch.stack([w, x, y, z], dim=1) + + +def angle(v1: torch.Tensor, v2: torch.Tensor): + """Compute angle between 2 vectors + + For robustness, we use the same formulation as in PPFNet, i.e. + angle(v1, v2) = atan2(cross(v1, v2), dot(v1, v2)). + This handles the case where one of the vectors is 0.0, since torch.atan2(0.0, 0.0)=0.0 + + Args: + v1: (B, *, 3) + v2: (B, *, 3) + + Returns: + + """ + + cross_prod = torch.stack([v1[..., 1] * v2[..., 2] - v1[..., 2] * v2[..., 1], + v1[..., 2] * v2[..., 0] - v1[..., 0] * v2[..., 2], + v1[..., 0] * v2[..., 1] - v1[..., 1] * v2[..., 0]], dim=-1) + cross_prod_norm = torch.norm(cross_prod, dim=-1) + dot_prod = torch.sum(v1 * v2, dim=-1) + + return torch.atan2(cross_prod_norm, dot_prod) + + + +def quaternion_to_matrix_torch(quaternions): + """ + Convert rotations given as quaternions to rotation matrices. + + Args: + quaternions: quaternions with real part first, + as tensor of shape (..., 4). + + Returns: + Rotation matrices as tensor of shape (..., 3, 3). + """ + r, i, j, k = torch.unbind(quaternions, -1) + two_s = 2.0 / (quaternions * quaternions).sum(-1) + + o = torch.stack( + ( + 1 - two_s * (j * j + k * k), + two_s * (i * j - k * r), + two_s * (i * k + j * r), + two_s * (i * j + k * r), + 1 - two_s * (i * i + k * k), + two_s * (j * k - i * r), + two_s * (i * k - j * r), + two_s * (j * k + i * r), + 1 - two_s * (i * i + j * j), + ), + -1, + ) + return o.reshape(quaternions.shape[:-1] + (3, 3)) + + +def _sqrt_positive_part(x: torch.Tensor) -> torch.Tensor: + """ + Returns torch.sqrt(torch.max(0, x)) + but with a zero subgradient where x is 0. + """ + ret = torch.zeros_like(x) + positive_mask = x > 0 + ret[positive_mask] = torch.sqrt(x[positive_mask]) + return ret + +def matrix_to_quaternion_torch(matrix: torch.Tensor) -> torch.Tensor: + """ + Convert rotations given as rotation matrices to quaternions. + + Args: + matrix: Rotation matrices as tensor of shape (..., 3, 3). + + Returns: + quaternions with real part first, as tensor of shape (..., 4). + """ + if matrix.size(-1) != 3 or matrix.size(-2) != 3: + raise ValueError(f"Invalid rotation matrix shape {matrix.shape}.") + + batch_dim = matrix.shape[:-2] + m00, m01, m02, m10, m11, m12, m20, m21, m22 = torch.unbind( + matrix.reshape(*batch_dim, 9), dim=-1 + ) + + q_abs = _sqrt_positive_part( + torch.stack( + [ + 1.0 + m00 + m11 + m22, + 1.0 + m00 - m11 - m22, + 1.0 - m00 + m11 - m22, + 1.0 - m00 - m11 + m22, + ], + dim=-1, + ) + ) + + # we produce the desired quaternion multiplied by each of r, i, j, k + quat_by_rijk = torch.stack( + [ + torch.stack([q_abs[..., 0] ** 2, m21 - m12, m02 - m20, m10 - m01], dim=-1), + torch.stack([m21 - m12, q_abs[..., 1] ** 2, m10 + m01, m02 + m20], dim=-1), + torch.stack([m02 - m20, m10 + m01, q_abs[..., 2] ** 2, m12 + m21], dim=-1), + torch.stack([m10 - m01, m20 + m02, m21 + m12, q_abs[..., 3] ** 2], dim=-1), + ], + dim=-2, + ) + + # We floor here at 0.1 but the exact level is not important; if q_abs is small, + # the candidate won't be picked. + quat_candidates = quat_by_rijk / (2.0 * q_abs[..., None].max(q_abs.new_tensor(0.1))) + + # if not for numerical problems, quat_candidates[i] should be same (up to a sign), + # forall i; we pick the best-conditioned one (with the largest denominator) + + return quat_candidates[F.one_hot(q_abs.argmax(dim=-1), num_classes=4) > 0.5, : # pyre-ignore[16] +].reshape(*batch_dim, 4) + + +def inv_R_t(R, t): + inv_R = R.permute(0, 2, 1).contiguous() + inv_t = - inv_R @ t[..., None] + return inv_R, torch.squeeze(inv_t, -1) + + +def fps(xyz, M): + ''' + Sample M points from points according to farthest point sampling (FPS) algorithm. + :param xyz: shape=(B, N, 3) + :return: inds: shape=(B, M) + ''' + device = xyz.device + B, N, C = xyz.shape + centroids = torch.zeros(size=(B, M), dtype=torch.long).to(device) + dists = torch.ones(B, N).to(device) * 1e10 + inds = torch.randint(0, N, size=(B, ), dtype=torch.long).to(device) + batchlists = torch.arange(0, B, dtype=torch.long).to(device) + for i in range(M): + centroids[:, i] = inds + cur_point = xyz[batchlists, inds, :] # (B, 3) + cur_dist = torch.squeeze(square_dists(torch.unsqueeze(cur_point, 1), xyz), dim=1) + dists[cur_dist < dists] = cur_dist[cur_dist < dists] + inds = torch.max(dists, dim=1)[1] + return centroids + + +def gather_points(points, inds): + ''' + :param points: shape=(B, N, C) + :param inds: shape=(B, M) or shape=(B, M, K) + :return: sampling points: shape=(B, M, C) or shape=(B, M, K, C) + ''' + device = points.device + B, N, C = points.shape + inds_shape = list(inds.shape) + inds_shape[1:] = [1] * len(inds_shape[1:]) + repeat_shape = list(inds.shape) + repeat_shape[0] = 1 + batchlists = torch.arange(0, B, dtype=torch.long).to(device).reshape(inds_shape).repeat(repeat_shape) + return points[batchlists, inds, :] + + +def ball_query(xyz, new_xyz, radius, K, rt_density=False): + ''' + + :param xyz: shape=(B, N, 3) + :param new_xyz: shape=(B, M, 3) + :param radius: int + :param K: int, an upper limit samples + :return: shape=(B, M, K) + ''' + device = xyz.device + B, N, C = xyz.shape + M = new_xyz.shape[1] + grouped_inds = torch.arange(0, N, dtype=torch.long).to(device).view(1, 1, N).repeat(B, M, 1) + dists = square_dists(new_xyz, xyz) + grouped_inds[dists > radius ** 2] = N + if rt_density: + density = torch.sum(grouped_inds < N, dim=-1) + density = density / N + grouped_inds = torch.sort(grouped_inds, dim=-1)[0][:, :, :K] + grouped_min_inds = grouped_inds[:, :, 0:1].repeat(1, 1, K) + grouped_inds[grouped_inds == N] = grouped_min_inds[grouped_inds == N] + if rt_density: + return grouped_inds, density + return grouped_inds + + +def sample_and_group(xyz, points, M, radius, K, use_xyz=True, rt_density=False): + ''' + :param xyz: shape=(B, N, 3) + :param points: shape=(B, N, C) + :param M: int + :param radius:float + :param K: int + :param use_xyz: bool, if True concat XYZ with local point features, otherwise just use point features + :return: new_xyz, shape=(B, M, 3); new_points, shape=(B, M, K, C+3); + group_inds, shape=(B, M, K); grouped_xyz, shape=(B, M, K, 3) + ''' + if M < 0: + new_xyz = xyz + else: + new_xyz = gather_points(xyz, fps(xyz, M)) + if rt_density: + grouped_inds, density = ball_query(xyz, new_xyz, radius, K, + rt_density=True) + else: + grouped_inds = ball_query(xyz, new_xyz, radius, K, rt_density=False) + grouped_xyz = gather_points(xyz, grouped_inds) + grouped_xyz -= torch.unsqueeze(new_xyz, 2).repeat(1, 1, K, 1) + if points is not None: + grouped_points = gather_points(points, grouped_inds) + if use_xyz: + new_points = torch.cat((grouped_xyz.float(), grouped_points.float()), dim=-1) + else: + new_points = grouped_points + else: + new_points = grouped_xyz + if rt_density: + return new_xyz, new_points, grouped_inds, grouped_xyz, density + return new_xyz, new_points, grouped_inds, grouped_xyz + + +def weighted_icp(src, tgt, weights, _EPS = 1e-8): + """Compute rigid transforms between two point sets + + Args: + src (torch.Tensor): (B, M, 3) points + tgt (torch.Tensor): (B, M, 3) points + weights (torch.Tensor): (B, M) + + Returns: + R, t, transformed_src: (B, 3, 3), (B, 3), (B, M, 3) + + Modified from open source code: + https://github.com/yewzijian/RPMNet/blob/master/src/models/rpmnet.py + """ + weights_normalized = weights[..., None] / (torch.sum(weights[..., None], dim=1, keepdim=True) + _EPS) + centroid_src = torch.sum(src * weights_normalized, dim=1) + centroid_tgt = torch.sum(tgt * weights_normalized, dim=1) + src_centered = src - centroid_src[:, None, :] + tgt_centered = tgt - centroid_tgt[:, None, :] + cov = src_centered.transpose(-2, -1) @ (tgt_centered * weights_normalized) + + # Compute rotation using Kabsch algorithm. Will compute two copies with +/-V[:,:3] + # and choose based on determinant to avoid flips + u, s, v = torch.svd(cov, some=False, compute_uv=True) + rot_mat_pos = v @ u.transpose(-1, -2) + v_neg = v.clone() + v_neg[:, :, 2] *= -1 + rot_mat_neg = v_neg @ u.transpose(-1, -2) + rot_mat = torch.where(torch.det(rot_mat_pos)[:, None, None] > 0, rot_mat_pos, rot_mat_neg) + assert torch.all(torch.det(rot_mat) > 0) + + # Compute translation (uncenter centroid) + translation = -rot_mat @ centroid_src[:, :, None] + centroid_tgt[:, :, None] + translation = torch.squeeze(translation, -1) + transformed_src = batch_transform(src, rot_mat, translation) + return rot_mat, translation, transformed_src + + +def weighted_icp_flow(src_raw, flow, overlap, topk): + """ + src_raw: [B, N, 3] + flow: [B, N, 3] + overlap: [B, N] + return: + T: [B, 3, 4] + src_t: [B, N, 3] + + """ + assert topk <= overlap.shape[1] + ol_score_raw, ol_inds_raw = torch.sort(overlap, dim=-1, descending=True) + ol_inds = ol_inds_raw[:, :topk] #[B, K] + ol_score= ol_score_raw[:, :topk] #[B, K] + + src_ol = gather_points(src_raw, ol_inds) + flow_ol = gather_points(flow, ol_inds) + + R, t, _ = weighted_icp(src_ol, src_ol + flow_ol, weights=ol_score) + + src_t = batch_transform(src_raw, R, t) + + return torch.cat([R, t.unsqueeze(2)], dim=2), src_t diff --git a/utils/metrics.py b/utils/metrics.py new file mode 100644 index 0000000..4107742 --- /dev/null +++ b/utils/metrics.py @@ -0,0 +1,131 @@ +import torch +# from pytorch_lightning.metrics import TensorMetric +# from pytorch_lightning import metrics +from typing import Any, Optional +from losses.supervised_losses import * +from losses.unsupervised_losses import * +from losses.common_losses import * + + +class EPE3D(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, pc_source: torch.Tensor, pc_target: torch.Tensor, pred_flow: torch.Tensor, gt_flow: torch.Tensor) -> torch.Tensor: + epe3d = torch.norm(pred_flow - gt_flow, dim=2).mean() + return epe3d + +class Acc3DR(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, pc_source: torch.Tensor, pc_target: torch.Tensor, pred_flow: torch.Tensor, gt_flow: torch.Tensor) -> torch.Tensor: + l2_norm = torch.norm(pred_flow - gt_flow, dim=2) + sf_norm = torch.norm(gt_flow, dim=2) + relative_err = l2_norm / (sf_norm + 1e-4) + acc3d_relax = (torch.logical_or(l2_norm < 0.1, relative_err < 0.1)).float().mean() + return acc3d_relax + +class Acc3DS(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, pc_source: torch.Tensor, pc_target: torch.Tensor, pred_flow: torch.Tensor, gt_flow: torch.Tensor) -> torch.Tensor: + l2_norm = torch.norm(pred_flow - gt_flow, dim=2) + sf_norm = torch.norm(gt_flow, dim=2) + relative_err = l2_norm / (sf_norm + 1e-4) + acc3d_strict = (torch.logical_or(l2_norm < 0.05, relative_err < 0.05)).float().mean() + return acc3d_strict + +class EPE3DOutliers(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, pc_source: torch.Tensor, pc_target: torch.Tensor, pred_flow: torch.Tensor, gt_flow: torch.Tensor) -> torch.Tensor: + l2_norm = torch.norm(pred_flow - gt_flow, dim=2) + sf_norm = torch.norm(gt_flow, dim=2) + relative_err = l2_norm / (sf_norm + 1e-4) + epe3d_outliers = (torch.logical_or(l2_norm > 0.3, relative_err > 0.1)).float().mean() + return epe3d_outliers + +class SupervisedL1LossMetric(torch.nn.Module): + def __init__(self): + super(SupervisedL1LossMetric, self).__init__() + self.loss = SupervisedL1Loss() + def forward(self, pc_source: torch.Tensor, pc_target: torch.Tensor, pred_flow: torch.Tensor, gt_flow: torch.Tensor) -> torch.Tensor: + loss_metric = self.loss(pc_source, pc_target, pred_flow, gt_flow) + return loss_metric + + +class SmoothnessLossMetric(torch.nn.Module): + def __init__(self, smoothness_loss_params): + super(SmoothnessLossMetric, self).__init__() + self.loss = SmoothnessLoss(**smoothness_loss_params) + def forward(self, pc_source: torch.Tensor, pc_target: torch.Tensor, pred_flow: torch.Tensor, gt_flow: torch.Tensor) -> torch.Tensor: + loss_metric = self.loss(pc_source, pred_flow) + return loss_metric + +class ChamferLossMetric(torch.nn.Module): + def __init__(self, chamfer_loss_params): + super(ChamferLossMetric, self).__init__() + self.loss = ChamferLoss(**chamfer_loss_params) + def forward(self, pc_source: torch.Tensor, pc_target: torch.Tensor, pred_flow: torch.Tensor, gt_flow: torch.Tensor) -> torch.Tensor: + loss_metric = self.loss(pc_source, pc_target, pred_flow) + return loss_metric + + +class SceneFlowMetrics(): + """ + An object of relevant metrics for scene flow. + """ + + def __init__(self, split: str, loss_params: dict, reduce_op: Optional[Any] = None): + """ + Initializes a dictionary of metrics for scene flow + keep reduction as 'none' to allow metrics computation per sample. + + Arguments: + split : a string with split type, should be used to allow logging of same metrics for different aplits + loss_params: loss configuration dictionary + reduce_op: the operation to perform during reduction within DDP (only needed for DDP training). + Defaults to sum. + """ + + self.metrics = { + split + '_epe3d': EPE3D(), + + } + if loss_params['loss_type'] == 'sv_l1_reg': + self.metrics[f'{split}_data_loss'] = SupervisedL1LossMetric() + self.metrics[f'{split}_smoothness_loss'] = SmoothnessLossMetric(loss_params['smoothness_loss_params']) + + if loss_params['loss_type'] == 'unsup_l1': + self.metrics[f'{split}_chamfer_loss'] = ChamferLossMetric(loss_params['chamfer_loss_params']) + self.metrics[f'{split}_smoothness_loss'] = SmoothnessLossMetric(loss_params['smoothness_loss_params']) + + if split in ['test', 'val']: + self.metrics[f'{split}_acc3dr'] = Acc3DR() + self.metrics[f'{split}_acc3ds'] = Acc3DS() + self.metrics[f'{split}_epe3d_outliers'] = EPE3DOutliers() + + def __call__(self, pc_source: torch.Tensor, pc_target: torch.Tensor, pred_flows: list, gt_flow: torch.Tensor) -> dict: + """ + Compute and scale the resulting metrics + + Arguments: + pc_source : a tensor containing source point cloud + pc_target : a tensor containing target point cloud + pred_flows : list of tensors containing model's predictions + gt_flow : a tensor containing ground truth labels + + Return: + A dictionary of copmuted metrics + """ + with torch.no_grad(): + result = {} + for key, metric in self.metrics.items(): + for i, pred_flow in enumerate(pred_flows): + val = metric(pc_source, pc_target, pred_flow, gt_flow) + result.update({f'{key}_i#{i}': val}) + + return result diff --git a/utils/utils.py b/utils/utils.py new file mode 100644 index 0000000..36438c7 --- /dev/null +++ b/utils/utils.py @@ -0,0 +1,57 @@ +import os +import numpy as np +import torch + +class Unbuffered: + def __init__(self, stream, filename): + self.stream = stream + assert os.path.exists(os.path.dirname(filename)) + self.file = open(filename, "w") + + def write(self, data): + self.stream.write(data) + self.stream.flush() + self.file.write(data) # Write the data of stdout here to a text file as wel + + def flush(self): + pass + + def __exit__(self, *args): + self.file.close() + + def close(self): + self.file.close() + +def debugger_mode(): + import sys + gettrace = getattr(sys, 'gettrace', None) + if gettrace is None: + return False + elif gettrace(): + return True + + +def get_num_workers(num_workers): + if debugger_mode(): + return 0 + else: + return num_workers + +def convert_dict_to_device(input, device): + input_new = {} + for key, value in input.items(): + input_new[key] = torch.tensor(value, dtype=torch.float32, device=device) + return input_new + +def detach_tensor(x): + if isinstance(x, torch.Tensor): + x = x.detach().cpu().numpy() + return x + + +def setup_seed(seed): + torch.backends.cudnn.deterministic = True + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + np.random.seed(seed) +