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postprocessing_loop.py
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import os
from os import path, mkdir, listdir, makedirs
import sys
import shutil
import random
import math
import gdal
import glob
import timeit
import copy
import argparse
import time
import numpy as np
import pandas as pd
import geopandas as gpd
from tqdm import tqdm
from collections import defaultdict
from multiprocessing import Pool, Queue, Process
from functools import partial
import rasterio
from rasterio import features
from affine import Affine
import torch
from torch import nn
from torch.backends import cudnn
from torch.utils.data import Dataset, DataLoader
import torch.optim.lr_scheduler as lr_scheduler
import torch.nn.functional as F
import torchvision
from torch.autograd import Variable
import torchvision.transforms as transforms
import skimage
import skimage.segmentation
from skimage import measure, io
from skimage.morphology import square, erosion, dilation, remove_small_objects, remove_small_holes
from skimage.color import label2rgb
from scipy import ndimage
from shapely.wkt import dumps, loads
from shapely.geometry import shape, Polygon
import cv2
from PIL import Image
from SwinUnet import SwinTransformerSys
try:
from torch.hub import load_state_dict_from_url
except ImportError:
from torch.utils.model_zoo import load_url as load_state_dict_from_url
import base
import geopandas as gpd
import rasterio as rs
from rasterio.plot import show # imshow for raster
import matplotlib.pyplot as plt
import segmentation_models_pytorch as smp
from segmentation_models_pytorch.base import SegmentationModel, SegmentationHead
from segmentation_models_pytorch.base import modules as md
from segmentation_models_pytorch.encoders import get_encoder
from segmentation_models_pytorch.base import (
SegmentationModel,
SegmentationHead,
ClassificationHead,
)
from segmentation_models_pytorch.decoders.unet import UnetDecoder
from torch.utils.tensorboard import SummaryWriter
from math import ceil
from typing import Optional, Union, List
#from utils import PixelwiseContrastiveLoss
from utils_cutmix import *
from sklearn.manifold import TSNE
#import seaborn as sns
import warnings
warnings.filterwarnings("ignore")
print(os.path.basename(__file__))
os.environ["MKL_NUM_THREADS"] = "1"
os.environ["NUMEXPR_NUM_THREADS"] = "1"
os.environ["OMP_NUM_THREADS"] = "1"
os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
seed = 0
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
cudnn.benchmark = False
cudnn.deterministic = True
torch.backends.cudnn.enabled = False
#cudnn.enabled = config.CUDNN.ENABLED
cv2.setNumThreads(0)
cv2.ocl.setUseOpenCL(False)
class SemanticSegmentationTarget:
def __init__(self, category, mask):
self.category = category
self.mask = torch.from_numpy(mask)
if torch.cuda.is_available():
self.mask = self.mask.cuda()
def __call__(self, model_output):
return (model_output[self.category, :, : ]*self.mask).sum()
#print((model_output[self.category, :, : ]*self.mask).sum())
#print((self.mask).sum())
#return (self.mask).sum()
################################# MODEL
class Unet_OC_cutmix_CL(SegmentationModel):
def __init__(
self,
encoder_name: str = "efficientnet-b3",
encoder_depth: int = 5,
encoder_weights: Optional[str] = "imagenet",
decoder_use_batchnorm: bool = False,
decoder_channels: List[int] = (256, 128, 64, 32, 16),
in_channels: int = 3,
classes: int = 3
):
super().__init__()
self.encoder = get_encoder(
encoder_name,
in_channels=in_channels,
depth=encoder_depth,
weights=encoder_weights,
)
classes = 3
self.decoder = UnetDecoder(
encoder_channels=(3,40,32,48,136,384),
decoder_channels=decoder_channels,
n_blocks=encoder_depth,
use_batchnorm=decoder_use_batchnorm,
)
self.segmentation_head = SegmentationHead(
in_channels=decoder_channels[-1],
out_channels=classes,
)
self.OClayer1 = nn.Conv2d(40,56,kernel_size=3, stride=1, padding=1)
self.OC1_bn = nn.BatchNorm2d(56)
self.OClayer2 = nn.Conv2d(56,64,kernel_size=3, stride=1, padding=1)
self.OC2_bn = nn.BatchNorm2d(64)
self.OClayer3 = nn.Conv2d(64,128,kernel_size=3, stride=1, padding=1)
self.OC3_bn = nn.BatchNorm2d(128)
self.OClayer4 = nn.Conv2d(128,16,kernel_size=3, stride=1, padding=1)
self.OC4_bn = nn.BatchNorm2d(16)
def forward(self, x):
features = self.encoder(x)
OCout = F.relu(self.OC1_bn(F.interpolate(self.OClayer1(features[1]),scale_factor =(1.2,1.2)))) #layersize256 #output320
_,_,h1,w1 = features[0].shape #512
#print(h1,w1)
OCout = F.relu(self.OC2_bn(F.interpolate(self.OClayer2(OCout), scale_factor =(1.2,1.2))))#layersize320 #output400
#print(OCout.shape)
OCout_CL = F.relu(self.OC3_bn(F.interpolate(self.OClayer3(OCout), scale_factor =(1.2,1.2))))#layersize400 output500
#print(OCout.shape)
OCout = F.relu(self.OC4_bn(F.interpolate(self.OClayer4(OCout_CL), scale_factor =(1.15,1.15))))#layersize500 output625
#print(OCout.shape)
OCout = F.interpolate(OCout, size = (h1,w1))#625 to 512
logit = self.decoder(*features) #16
_,_,h,w = logit.shape
if(logit.shape==OCout.shape):
logit = torch.add(OCout, logit)
else:
OCout = F.interpolate(OCout,size=(h,w),mode='bilinear')
logit = torch.add(OCout, logit)
logit = self.segmentation_head(logit)
return logit, OCout_CL
#################### Simple Unet model ############################
class Simple_Unet(SegmentationModel):
def __init__(
self,
encoder_name: str = "efficientnet-b3",
encoder_depth: int = 5,
encoder_weights: Optional[str] = "imagenet",
decoder_use_batchnorm: bool = False,
decoder_channels: List[int] = (256, 128, 64, 32, 16),
in_channels: int = 3,
classes: int = 3
):
super().__init__()
self.encoder = get_encoder(
encoder_name,
in_channels=in_channels,
depth=encoder_depth,
weights=encoder_weights,
)
classes = 3
self.decoder = UnetDecoder(
encoder_channels=(3,40,32,48,136,384),
decoder_channels=decoder_channels,
n_blocks=encoder_depth,
use_batchnorm=decoder_use_batchnorm,
)
self.segmentation_head = SegmentationHead(
in_channels=decoder_channels[-1],
out_channels=classes,
)
def forward(self, x):
features = self.encoder(x)
logit = self.decoder(*features)
logit = self.segmentation_head(logit)
return logit
#########################SQUEEZE AND EXCITATION##############
class ConvBNReLU(nn.Module):
def __init__(self, in_chan, out_chan, ks=3, stride=1, padding=1, *args, **kwargs):
super(ConvBNReLU, self).__init__()
self.conv = nn.Conv2d(in_chan,
out_chan,
kernel_size = ks,
stride = stride,
padding = padding,
bias = False)
# self.bn = BatchNorm2d(out_chan)
self.bn = nn.BatchNorm2d(out_chan)
self.relu = nn.ReLU()
self.init_weight()
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
x = self.relu(x)
return x
def init_weight(self):
for ly in self.children():
if isinstance(ly, nn.Conv2d):
nn.init.kaiming_normal_(ly.weight, a=1)
if not ly.bias is None: nn.init.constant_(ly.bias, 0)
class FeatureFusionModuleSCSE_V2(nn.Module):
def __init__(self, in_chan, out_chan, *args, **kwargs):
super().__init__()
self.scse_1 = md.SCSEModule(in_chan)
self.scse_2 = md.SCSEModule(in_chan)
self.convblk = ConvBNReLU(in_chan*2, out_chan, ks=1, stride=1, padding=0)
self.scse = md.SCSEModule(out_chan)
self.init_weight()
def forward(self, fsp, fcp):
fsp = self.scse_1(fsp)
fcp = self.scse_2(fcp)
fcat = torch.cat([fsp, fcp], dim=1)
feat = self.convblk(fcat)
feat_out = self.scse(feat)
return feat_out
def init_weight(self):
for ly in self.children():
if isinstance(ly, nn.Conv2d):
nn.init.kaiming_normal_(ly.weight, a=1)
if not ly.bias is None: nn.init.constant_(ly.bias, 0)
class Unet(SegmentationModel):
def __init__(
self,
encoder_name: str = "efficientnet-b3",
encoder_depth: int = 5,
encoder_weights: Optional[str] = "imagenet",
decoder_use_batchnorm: bool = False,
decoder_channels: List[int] = (256, 128, 64, 32, 16),
in_channels: int = 3,
classes: int = 3
):
super().__init__()
self.encoder = get_encoder(
encoder_name,
in_channels=in_channels,
depth=encoder_depth,
weights=encoder_weights,
)
classes = 3
self.decoder = UnetDecoder(
encoder_channels=(3,40,32,48,136,384),
decoder_channels=decoder_channels,
n_blocks=encoder_depth,
use_batchnorm=decoder_use_batchnorm,
)
self.segmentation_head = SegmentationHead(
in_channels=decoder_channels[-1],
out_channels=classes,
)
self.OClayer1 = nn.Conv2d(40,56,kernel_size=3, stride=1, padding=1)
self.OC1_bn = nn.BatchNorm2d(56)
self.OClayer2 = nn.Conv2d(56,64,kernel_size=3, stride=1, padding=1)
self.OC2_bn = nn.BatchNorm2d(64)
self.OClayer3 = nn.Conv2d(64,128,kernel_size=3, stride=1, padding=2, dilation=2)
self.OC3_bn = nn.BatchNorm2d(128)
self.OClayer4 = nn.Conv2d(128,16,kernel_size=3, stride=1, padding=2, dilation =2)
self.OC4_bn = nn.BatchNorm2d(16)
#self.ffm = FeatureFusionModuleSCSE_V2(decoder_channels[-1], decoder_channels[-1])
#self.SEblock = torchvision.ops.SqueezeExcitation(16,16)
def forward(self, x):
features = self.encoder(x)
OCout = F.relu(self.OC1_bn(F.interpolate(self.OClayer1(features[1]),scale_factor =(1.2,1.2)))) #layersize256 #output320
_,_,h1,w1 = features[0].shape #512
#print(h1,w1)
OCout = F.relu(self.OC2_bn(F.interpolate(self.OClayer2(OCout), scale_factor =(1.2,1.2))))#layersize320 #output400
#print(OCout.shape)
OCout = F.relu(self.OC3_bn(F.interpolate(self.OClayer3(OCout), scale_factor =(1.2,1.2))))#layersize400 output500
#print(OCout.shape)
OCout = F.relu(self.OC4_bn(F.interpolate(self.OClayer4(OCout), scale_factor =(1.15,1.15))))#layersize500 output625
#print(OCout.shape)
OCout = F.interpolate(OCout, size = (h1,w1))#625 to 512
#OCout = self.SEblock(OCout)
logit = self.decoder(*features) #16
_,_,h,w = logit.shape
if(logit.shape==OCout.shape):
logit = torch.add(OCout, logit)
else:
OCout = F.interpolate(OCout,size=(h,w),mode='bilinear')
logit = torch.add(OCout, logit)
#fused_features = self.ffm(logit,OCout)
logit = self.segmentation_head(logit)
return logit
class SwinUnetmodel(nn.Module):
def __init__(self, img_size=224, num_classes=21843, zero_head=False, vis=False):
super(SwinUnetmodel, self).__init__()
self.num_classes = num_classes
self.zero_head = zero_head
self.swin_unet = SwinTransformerSys(img_size=512,
patch_size=4,
in_chans=3,
num_classes=3,
embed_dim=96,
depths=[2,2,6,2],
num_heads=[3,6,12,24],
window_size=8,
mlp_ratio=4,
qkv_bias=True,
qk_scale=None,
drop_rate=0.0,
drop_path_rate=0.1,
ape=False,
patch_norm=True,
use_checkpoint=False)
def forward(self, x):
if x.size()[1] == 1:
x = x.repeat(1,3,1,1)
logits = self.swin_unet(x)
return logits
class UnetOCSE(SegmentationModel):
def __init__(
self,
encoder_name: str = "efficientnet-b3",
encoder_depth: int = 5,
encoder_weights: Optional[str] = "imagenet",
decoder_use_batchnorm: bool = False,
decoder_channels: List[int] = (256, 128, 64, 32, 16),
in_channels: int = 3,
classes: int = 3
):
super().__init__()
self.encoder = get_encoder(
encoder_name,
in_channels=in_channels,
depth=encoder_depth,
weights=encoder_weights,
)
classes = 3
self.decoder = UnetDecoder(
encoder_channels=(3,40,32,48,136,384),
decoder_channels=decoder_channels,
n_blocks=encoder_depth,
use_batchnorm=decoder_use_batchnorm,
)
self.segmentation_head = SegmentationHead(
in_channels=decoder_channels[-1],
out_channels=classes,
)
self.OClayer1 = nn.Conv2d(40,56,kernel_size=3, stride=1, padding=1)
self.OC1_bn = nn.BatchNorm2d(56)
self.OClayer2 = nn.Conv2d(56,64,kernel_size=3, stride=1, padding=1)
self.OC2_bn = nn.BatchNorm2d(64)
self.OClayer3 = nn.Conv2d(64,128,kernel_size=3, stride=1, padding=1)
self.OC3_bn = nn.BatchNorm2d(128)
self.OClayer4 = nn.Conv2d(128,16,kernel_size=3, stride=1, padding=1)
self.OC4_bn = nn.BatchNorm2d(16)
self.ffm = FeatureFusionModuleSCSE_V2(decoder_channels[-1], decoder_channels[-1])
#self.SEblock = torchvision.ops.SqueezeExcitation(16,16)
def forward(self, x):
features = self.encoder(x)
OCout = F.relu(self.OC1_bn(F.interpolate(self.OClayer1(features[1]),scale_factor =(1.2,1.2)))) #layersize256 #output320
_,_,h1,w1 = features[0].shape #512
#print(h1,w1)
OCout = F.relu(self.OC2_bn(F.interpolate(self.OClayer2(OCout), scale_factor =(1.2,1.2))))#layersize320 #output400
#print(OCout.shape)
OCout = F.relu(self.OC3_bn(F.interpolate(self.OClayer3(OCout), scale_factor =(1.2,1.2))))#layersize400 output500
#print(OCout.shape)
OCout = F.relu(self.OC4_bn(F.interpolate(self.OClayer4(OCout), scale_factor =(1.15,1.15))))#layersize500 output625
#print(OCout.shape)
OCout = F.interpolate(OCout, size = (h1,w1))#625 to 512
#OCout = self.SEblock(OCout)
logit = self.decoder(*features) #16
'''_,_,h,w = logit.shape
if(logit.shape==OCout.shape):
logit = torch.add(OCout, logit)
else:
OCout = F.interpolate(OCout,size=(h,w),mode='bilinear')
logit = torch.add(OCout, logit)'''
fused_features = self.ffm(logit,OCout)
logit = self.segmentation_head(fused_features)
return logit
############################## DATASET
def _blend(img1, img2, alpha):
return img1 * alpha + (1 - alpha) * img2
_alpha = np.asarray([0.25, 0.25, 0.25, 0.25]).reshape((1, 1, 4))
def _grayscale(img):
return np.sum(_alpha * img, axis=2, keepdims=True)
def saturation(img, alpha):
gs = _grayscale(img)
return _blend(img, gs, alpha)
def brightness(img, alpha):
gs = np.zeros_like(img)
return _blend(img, gs, alpha)
def contrast(img, alpha):
gs = _grayscale(img)
gs = np.repeat(gs.mean(), 4)
return _blend(img, gs, alpha)
def parse_img_id(file_path, orients):
file_name = file_path.split('/')[-1]
stripname = '_'.join(file_name.split('_')[-4:-2])
direction = int(orients.loc[stripname]['direction'])
direction = torch.from_numpy(np.reshape(np.asarray([direction]), (1,1,1))).float()
val = int(orients.loc[stripname]['val'])
strip = torch.Tensor(np.zeros((len(orients.index), 1, 1))).float()
strip[val] = 1
coord = np.asarray([orients.loc[stripname]['coord_y']])
coord = torch.from_numpy(np.reshape(coord, (1,1,1))).float() - 0.5
return direction, strip, coord
class MyData(Dataset):
def __init__(self, image_sar_paths, image_rgb_paths, label_paths, train, test, crop_size = None,
rot_prob = 0.3, scale_prob = 0.5, color_aug_prob = 0.0, fliplr_prob = 0.0, train_min_building_size=0,normalize=False):
super().__init__()
self.image_sar_paths = image_sar_paths
self.image_rgb_paths = image_rgb_paths
self.label_paths = label_paths
self.train = train
self.test = test
self.crop_size = crop_size
self.rot_prob = rot_prob
self.scale_prob = scale_prob
self.color_aug_prob = color_aug_prob
self.fliplr_prob = fliplr_prob
self.train_min_building_size = train_min_building_size
self.normalize = normalize
self.orients = pd.read_csv(rot_out_path, index_col = 0)
self.orients['val'] = list(range(len(self.orients.index)))
def __len__(self):
return len(self.image_rgb_paths)
def __getitem__(self,idx):
if not self.test:
sar = skimage.io.imread(self.image_sar_paths[idx])
rgb = skimage.io.imread(self.image_rgb_paths[idx])
rgb_full = rgb
m = np.where((rgb.sum(axis=2) > 0).any(1))
ymin, ymax = np.amin(m), np.amax(m) + 1
m = np.where((rgb.sum(axis=2) > 0).any(0))
xmin, xmax = np.amin(m), np.amax(m) + 1
if not self.test:
rgb = rgb[ymin:ymax, xmin:xmax]
sar = sar[ymin:ymax, xmin:xmax]
msk = skimage.io.imread(self.label_paths[idx])
msk_full = msk
msk = msk[ymin:ymax, xmin:xmax]
if self.train:
msk = skimage.io.imread(self.label_paths[idx])
msk = msk[ymin:ymax, xmin:xmax]
pad = max(0, self.crop_size - rgb.shape[0])
msk = cv2.copyMakeBorder(msk, 0, pad, 0, 0, cv2.BORDER_CONSTANT, 0.0)
rgb = cv2.copyMakeBorder(rgb, 0, pad, 0, 0, cv2.BORDER_CONSTANT, 0.0)
rgb_org = copy.deepcopy(rgb)
msk_org = copy.deepcopy(msk)
sar = cv2.copyMakeBorder(sar, 0, pad, 0, 0, cv2.BORDER_CONSTANT, 0.0)
x0 = random.randint(0, rgb.shape[1] - self.crop_size)
y0 = random.randint(0, rgb.shape[0] - self.crop_size)
msk = msk[y0 : y0 + self.crop_size, x0 : x0 + self.crop_size]
rgb = rgb[y0 : y0 + self.crop_size, x0 : x0 + self.crop_size]
sar = sar[y0 : y0 + self.crop_size, x0 : x0 + self.crop_size]
rgb_512 = copy.deepcopy(rgb)
msk_512 = copy.deepcopy(msk)
if random.random() < args.rot_prob:
rot_mat = cv2.getRotationMatrix2D((rgb.shape[0] // 2, rgb.shape[1] // 2), random.randint(0, 10) - 5, 1.0)
msk_rot = cv2.warpAffine(msk, rot_mat, msk.shape[:2], flags=cv2.INTER_NEAREST, borderMode=cv2.BORDER_REFLECT_101)
rgb_rot = cv2.warpAffine(rgb, rot_mat, rgb.shape[:2], flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT_101)
if(np.sum(msk_rot)>=0):
msk = msk_rot
rgb = rgb_rot
sar = cv2.warpAffine(sar, rot_mat, sar.shape[:2], flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT_101)
if random.random() < args.scale_prob:
rot_mat = cv2.getRotationMatrix2D((rgb.shape[0] // 2, rgb.shape[1] // 2), 0, random.uniform(0.5,2.0))
msk_scale = cv2.warpAffine(msk, rot_mat, msk.shape[:2], flags=cv2.INTER_NEAREST, borderMode=cv2.BORDER_REFLECT_101)
rgb_scale = cv2.warpAffine(rgb, rot_mat, rgb.shape[:2], flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT_101)
sar = cv2.warpAffine(sar, rot_mat, sar.shape[:2], flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT_101)
if(np.sum(msk_scale)>=0):
msk = msk_scale
rgb = rgb_scale
if random.random() < self.fliplr_prob:
msk_flip = np.fliplr(msk)
rgb_flip = np.fliplr(rgb)
if(np.sum(msk_flip)>=0):
msk = msk_flip
rgb = rgb_flip
sar = np.fliplr(sar)
#direction, strip, coord = parse_img_id(self.image_sar_paths[idx], self.orients)
'''if direction.item():
sar = np.fliplr(np.flipud(sar))
small_sar = np.fliplr(np.flipud(small_sar))
rgb = np.fliplr(np.flipud(rgb))
small_rgb = np.fliplr(np.flipud(small_rgb))
msk = np.fliplr(np.flipud(msk))
small_msk = np.fliplr(np.flipud(small_msk))'''
sar = torch.from_numpy(sar.transpose((2, 0, 1)).copy()).float()
weights = np.ones_like(msk[:,:,:1], dtype=float)
regionlabels, regioncount = measure.label(msk[:,:,0], background=0, connectivity=1, return_num=True)
regionproperties = measure.regionprops(regionlabels)
weights_512 = np.ones_like(msk_512[:,:,:1], dtype=float)
regionlabels_512, regioncount_512 = measure.label(msk_512[:,:,0], background=0, connectivity=1, return_num=True)
regionproperties_512 = measure.regionprops(regionlabels_512)
for bl in range(regioncount):
if regionproperties[bl].area < self.train_min_building_size:
msk[:,:,0][regionlabels == bl+1] = 0
msk[:,:,1][regionlabels == bl+1] = 0
weights[regionlabels == bl+1] = 1024.0 / regionproperties[bl].area
for bl in range(regioncount_512):
if regionproperties_512[bl].area < self.train_min_building_size:
msk_512[:,:,0][regionlabels_512 == bl+1] = 0
msk_512[:,:,1][regionlabels_512 == bl+1] = 0
weights_512[regionlabels_512 == bl+1] = 1024.0 / regionproperties_512[bl].area
msk_512[:, :, :3] = (msk_512[:, :, :3] > 1) * 1
msk[:, :, :3] = (msk[:, :, :3] > 1) * 1
weights = torch.from_numpy(weights.transpose((2, 0, 1)).copy()).float()
msk = torch.from_numpy(msk.transpose((2, 0, 1)).copy()).float()
rgb_512 = torch.from_numpy(rgb_512.transpose((2, 0, 1)).copy()).float()
msk_512 = torch.from_numpy(msk_512.transpose((2, 0, 1)).copy()).float()
rgb = torch.from_numpy(rgb.transpose((2, 0, 1)).copy()).float()
else:
rgb_full = torch.from_numpy(rgb_full.transpose((2, 0, 1)).copy()).float()
rgb = torch.from_numpy(rgb.transpose((2, 0, 1)).copy()).float()
sar = torch.from_numpy(sar.transpose((2, 0, 1)).copy()).float()
rgb_512 = rgb
weights = regioncount = torch.Tensor([0])
msk=msk_512= torch.from_numpy(msk.transpose((2, 0, 1)).copy()).float()
x0=y0=weights=regioncount=torch.Tensor([0])
return {"mask": msk, "sar": sar, "rgb": rgb, "rgb_512": rgb_512, "msk_512": msk_512, "rgb_full": rgb_full, "mask_full": msk_full, "x0" : x0, "y0" : y0, 'img_name': self.image_rgb_paths[idx],
'ymin': ymin, 'xmin': xmin, 'b_count': regioncount, 'weights': weights}
#################################### EVAL
seedth_list = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9]
#seedth_list = np.arange(0.04,0.27,0.01)
predth_list = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9]
#predth_list = np.arange(0.54,0.77,0.01)
#seedth_list = [0.06,0.08,0.1,0.12,0.14]
#predth_list = [0.66,0.68,0.7,0.72,0.74]
#seedth_list = [0.75]
#predth_list = [0.5]
#areath_list = [40,100,150,200,225,500,1000,1500,2000]
areath_list = [40]
def test_postprocess(pred_folder, pred_csv, seed_msk_th, pred_th, areath, **kwargs):
np.seterr(over = 'ignore')
print('pred_folder',pred_folder)
sourcefiles = sorted(glob.glob(os.path.join(pred_folder, '*'))) #"/*.csv")
#print('sourcefiles',sourcefiles)
'''with Pool() as pool:
proposals = [p for p in tqdm(pool.imap_unordered(partial(test_postprocess_single, **kwargs), sourcefiles), total = len(sourcefiles))]
#print('proposals',proposals)'''
proposals = []
for i in tqdm(range(len(sourcefiles))):
#if i%48==0:
pro = test_postprocess_single(sourcefiles[i],seed_msk_th=seed_msk_th,pred_th=pred_th, area_th_for_seed=areath)
proposals.append(pro)
'''else:
#print("here")
pro = test_postprocess_single(sourcefiles[i],seed_msk_th=thresh1,pred_th=thresh2)
proposals.append(pro)'''
#proposals = [p for p in tqdm(test_postprocess_single(sourcefiles[i]))]
pd.concat(proposals).to_csv(pred_csv, index=False)
def test_postprocess_single(sourcefile, watershed_line=True, conn = 2, polygon_buffer = 0.5, tolerance = 0.5, seed_msk_th = 0.75, area_th_for_seed = 40, pred_th = 0.5, area_th = 40, ## area_th=80, area_th_for_seed=110, seed_msk_th=0.75##
contact_weight = 1.0, edge_weight = 0.0, seed_contact_weight = 1.0, seed_edge_weight = 1.0):
mask = gdal.Open(sourcefile).ReadAsArray() # logits
mask = 1.0 / (1 + np.exp(-mask))
mask[0] = mask[0] * (1 - contact_weight * mask[2]) * (1 - edge_weight * mask[1])
seed_msk = mask[0] * (1 - seed_contact_weight * mask[2]) * (1 - seed_edge_weight * mask[1])
seed_msk = measure.label((seed_msk > seed_msk_th), connectivity=conn, background=0)
props = measure.regionprops(seed_msk)
for i in range(len(props)):
if props[i].area < area_th_for_seed:
seed_msk[seed_msk == i + 1] = 0
seed_msk = measure.label(seed_msk, connectivity=conn, background=0)
mask = skimage.segmentation.watershed(-mask[0], seed_msk, mask=(mask[0] > pred_th), watershed_line=watershed_line)
mask = measure.label(mask, connectivity=conn, background=0).astype('uint8')
polygon_generator = rasterio.features.shapes(mask, mask)
polygons = []
for polygon, value in polygon_generator:
p = shape(polygon).buffer(polygon_buffer)
if p.area >= area_th:
p = dumps(p.simplify(tolerance=tolerance), rounding_precision=0)
polygons.append(p)
tilename = '_'.join(os.path.splitext(os.path.basename(sourcefile))[0].split('_')[-4:])
csvaddition = pd.DataFrame({'ImageId': tilename, 'BuildingId': range(len(polygons)), 'PolygonWKT_Pix': polygons, 'Confidence': 1 })
return csvaddition
def evaluation(pred_csv, gt_csv,areath):
evaluator = base.Evaluator(gt_csv)
evaluator.load_proposal(pred_csv, proposalCSV=True, conf_field_list=[])
report = evaluator.eval_iou_spacenet_csv(miniou=0.5, min_area=areath)
tp = 0
fp = 0
fn = 0
for entry in report:
tp += entry['TruePos']
fp += entry['FalsePos']
fn += entry['FalseNeg']
f1score = (2*tp) / ((2*tp) + fp + fn)
if(tp!=0):
Precision = (tp) / (tp + fp)
Recall = (tp) / (tp + fn)
print('Validation F1 {} tp {} fp {} fn {}'.format(f1score, tp, fp, fn))
return f1score, tp,fp,fn
############################### TRAIN
class FocalLoss2d(torch.nn.Module):
def __init__(self, gamma=2, ignore_index=255, eps=1e-6):
super().__init__()
self.gamma = gamma
self.ignore_index = ignore_index
self.eps = eps
def forward(self, outputs, targets, weights = 1.0):
outputs = torch.sigmoid(outputs)
outputs = outputs.contiguous()
targets = targets.contiguous()
weights = weights.contiguous()
non_ignored = targets.view(-1) != self.ignore_index
targets = targets.view(-1)[non_ignored].float()
outputs = outputs.contiguous().view(-1)[non_ignored]
weights = weights.contiguous().view(-1)[non_ignored]
outputs = torch.clamp(outputs, self.eps, 1. - self.eps)
targets = torch.clamp(targets, self.eps, 1. - self.eps)
pt = (1 - targets) * (1 - outputs) + targets * outputs
return ((-(1. - pt) ** self.gamma * torch.log(pt)) * weights).mean()
class DiceLoss(torch.nn.Module):
def __init__(self, weight=None, size_average=True, per_image=False, eps = 1e-6):
super().__init__()
self.size_average = size_average
self.register_buffer('weight', weight)
self.per_image = per_image
self.eps = eps
def forward(self, outputs, targets):
outputs = torch.sigmoid(outputs)
batch_size = outputs.size()[0]
if not self.per_image:
batch_size = 1
dice_target = targets.contiguous().view(batch_size, -1).float()
dice_output = outputs.contiguous().view(batch_size, -1)
intersection = torch.sum(dice_output * dice_target, dim=1)
union = torch.sum(dice_output, dim=1) + torch.sum(dice_target, dim=1) + self.eps
loss = (1 - (2 * intersection + self.eps) / union).mean()
return loss
def load_state_dict(model, state_dict):
missing_keys = []
unexpected_keys = []
metadata = getattr(state_dict, '_metadata', None)
state_dict = state_dict.copy()
if metadata is not None:
state_dict._metadata = metadata
def load(module, prefix=''):
local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
module._load_from_state_dict(state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, [])
for name, child in module._modules.items():
if child is not None:
load(child, prefix + name + '.')
load(model)
print('Unexpected key(s) in state_dict: {} '.format(', '.join('"{}"'.format(k) for k in unexpected_keys)))
print('Missing key(s) in state_dict: {} '.format(', '.join('"{}"'.format(k) for k in missing_keys)))
def load_state_dict_for_resume(model, state_dict, optimizer_state_dict, loss):
missing_keys = []
unexpected_keys = []
metadata = getattr(state_dict, '_metadata', optimizer_state_dict, loss, None)
state_dict = state_dict.copy()
optimizer_state_dict = optimizer_state_dict.copy()
if metadata is not None:
state_dict._metadata = metadata
optimizer_state_dict._metadata = metadata
loss._metadata = metadata
def load(module, prefix=''):
local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
module._load_from_state_dict(state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, [])
for name, child in module._modules.items():
if child is not None:
load(child, prefix + name + '.')
load(model)
print('Unexpected key(s) in state_dict: {} '.format(', '.join('"{}"'.format(k) for k in unexpected_keys)))
print('Missing key(s) in state_dict: {} '.format(', '.join('"{}"'.format(k) for k in missing_keys)))
rot_in_path = '/home/user/Perception/A600_Backup/Sumanth/Spacenet_codes/SAR_orientations.txt'
rot_out_path = '/home/user/Perception/A600_Backup/Sumanth/Spacenet_codes/SAR_orientations.csv'
models_folder = '/home/user/Perception/A600_Backup/Sumanth/Spacenet_codes/wdata_Swin_Unet/weights'
cutmix_rgb_folder = '/home/user/Perception/SN6_dataset/train_set/AOI_11_Rotterdam/cu_RGB'
cutmix_mas_folder = '/home/user/Perception/SN6_dataset/train_set/AOI_11_Rotterdam/cu_mas'
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='SpaceNet 6 Baseline Algorithm')
parser.add_argument('--split_folds', action='store_true')
parser.add_argument('--train', action='store_true')
parser.add_argument('--val', action='store_true')
parser.add_argument('--test', action='store_true')
parser.add_argument('--merge', action='store_true')
parser.add_argument('--masks_csv', default='/home/user/Perception/SN6_dataset/val_set/AOI_11_Rotterdam/val_masks_csv', type=str)
parser.add_argument('--pred_csv', default='./wdata_Swin_Unet/pred_fold_{0}_csv', type=str)
parser.add_argument('--pred_folder', default='./wdata_Swin_Unet/pred_fold_{0}_0', type=str)
parser.add_argument('--snapshot_last', default='snapshot_fold_{0}_last', type=str)
parser.add_argument('--snapshot_best', default='snapshot_fold_{0}_best', type=str)
parser.add_argument('--edge_width', default=3, type=int)
parser.add_argument('--contact_width', default=9, type=int)
parser.add_argument('--train_min_building_size', default=0, type=int)
parser.add_argument('--start_val_epoch', default=1, type=int)
parser.add_argument('--num_workers', default=4, type=int)
parser.add_argument('--batch_size', default=8, type=int)
parser.add_argument('--crop_size', default=512, type=int) ##512##
parser.add_argument('--lr', default=2e-4, type=float)
parser.add_argument('--warm_up_lr_scale', default=1.0, type=float)
parser.add_argument('--warm_up_lr_epochs', default=0, type=int)
parser.add_argument('--warm_up_dec_epochs', default=0, type=int)
parser.add_argument('--wd', default=1e-2, type=float)
parser.add_argument('--gamma', default=1.0, type=float) ###0.5###
parser.add_argument('--pos_weight', default=0.5, type=float)
parser.add_argument('--b_count_weight', default=0.5, type=float)
parser.add_argument('--b_count_div', default=8, type=float)
parser.add_argument('--b_rev_size_weight', default=0.0, type=float)
parser.add_argument('--focal_weight', default=1.0, type=float) ## 1.0 ##
parser.add_argument('--edge_weight', default=0.25, type=float)
parser.add_argument('--contact_weight', default=0.1, type=float)
parser.add_argument('--height_scale', default=0.0, type=float)
parser.add_argument('--rgb_weight', default=0.0, type=float)
parser.add_argument('--loss_eps', default=1e-6, type=float)
parser.add_argument('--clip_grad_norm_value', default=1.2, type=float)
parser.add_argument('--focal_gamma', default=2.0, type=float)
parser.add_argument('--rot_prob', default=0.7, type=float) ##0.7##
parser.add_argument('--scale_prob', default=1.0, type=float)
parser.add_argument('--color_aug_prob', default=0.0, type=float)
parser.add_argument('--fliplr_prob', default=0.0, type=float) ##0.5##
parser.add_argument('--input_scale', default=1.0, type=float)
parser.add_argument('--strip_scale', default=1.0, type=float)
parser.add_argument('--direction_scale', default=1.0, type=float)
parser.add_argument('--coord_scale', default=1.0, type=float)
args = parser.parse_args(sys.argv[1:])
if not (args.train or args.val or args.test):
sys.exit(0)
############# TRAINING
print("In training")
if args.train:
for f in glob.glob("/home/user/Perception/SN6_dataset/train_set/AOI_11_Rotterdam/cu_RGB/*_cutmix_aug_*"):
os.remove(f)
for f in glob.glob("/home/user/Perception/SN6_dataset/train_set/AOI_11_Rotterdam/cu_mas/*_cutmix_aug_*"):
os.remove(f)
train_sar_img_files = sorted([f for f in glob.glob(os.path.join('/home/user/Perception/SN6_dataset/train_set/AOI_11_Rotterdam/SAR-3ch/*.tif'))])
train_rgb_img_files = sorted([f for f in glob.glob(os.path.join('/home/user/Perception/SN6_dataset/train_set/AOI_11_Rotterdam/PS-RGB/*.tif'))])
train_label_files = sorted([f for f in glob.glob(os.path.join('/home/user/Perception/SN6_dataset/train_set/AOI_11_Rotterdam/masks/*.tif'))])
train_label_index_files = sorted([f for f in glob.glob(os.path.join('/home/user/Perception/SN6_dataset/train_set/AOI_11_Rotterdam/label_index_masks/*.tif'))])
if args.val:
val_sar_img_files = sorted([f for f in glob.glob(os.path.join('/home/user/Perception/SN6_dataset/val_set/AOI_11_Rotterdam/SAR-3ch/*.tif'))])
val_rgb_img_files = sorted([f for f in glob.glob(os.path.join('/home/user/Perception/SN6_dataset/val_set/AOI_11_Rotterdam/PS-RGB/*.tif'))])
val_label_files = sorted([f for f in glob.glob(os.path.join('/home/user/Perception/SN6_dataset/val_set/AOI_11_Rotterdam/masks/*.tif'))])
makedirs(models_folder, exist_ok=True)
#val_data_loader = DataLoader(MyData(val_sar_img_files, val_rgb_img_files, val_label_files, train_label_index_files,train=False, test=False), batch_size=1, num_workers=args.num_workers, pin_memory=True, shuffle=False)
val_data_loader = DataLoader(MyData(val_sar_img_files, val_rgb_img_files, val_label_files,train=False, test=False), batch_size=1, num_workers=args.num_workers, pin_memory=True, shuffle=False)
#model = Simple_Unet().cuda()
model = SwinUnetmodel().cuda()
#model = smp.FPN("efficientnet-b3",encoder_depth=5,encoder_weights='imagenet',classes=3).cuda()
#model = smp.DeepLabV3Plus(encoder_name="efficientnet-b3",encoder_depth=5,encoder_weights='imagenet',encoder_output_stride=16,decoder_atrous_rates=(12, 24, 36),decoder_channels=256,in_channels=3,classes=3).cuda()
###############
retrain = False
if not args.train or retrain:
loaded = torch.load(path.join(models_folder, args.snapshot_best))
print("loaded checkpoint '{}' (epoch {}, f1 score {})".format(args.snapshot_best, loaded['epoch'], loaded['best_score']))
load_state_dict(model, loaded['state_dict'])
#load_state_dict_for_resume(model, loaded['state_dict','optimizer_state_dict','loss'])
optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=args.wd)
pytorch_total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
print("Total_params: {}".format(pytorch_total_params))
#print("High dimensionality OC")
def lr_comp(epoch):
if epoch < args.warm_up_lr_epochs:
return args.warm_up_lr_scale
elif epoch < 60:
return 1.0
elif epoch < 80:
return 0.33
elif epoch < 90:
return 0.1
scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=[80,100,120], gamma=args.gamma)
dice_loss = DiceLoss(eps=args.loss_eps).cuda()
focal_loss = FocalLoss2d(gamma = args.focal_gamma, eps=args.loss_eps).cuda()
criterion = nn.L1Loss()
#contra_criterion = PixelwiseContrastiveLoss().cuda()
#q = Queue()
best_f1 = -1.0
for epoch in range(150 if args.train else 1):
if args.train:
time2 = time.time()
if(epoch<=46):
data_train = MyData(train_sar_img_files, train_rgb_img_files, train_label_files, train_label_index_files, train=True, test=False, crop_size=args.crop_size,
rot_prob = args.rot_prob, scale_prob = args.scale_prob, color_aug_prob = args.color_aug_prob, fliplr_prob = args.fliplr_prob, train_min_building_size = args.train_min_building_size, normalize=False)
train_data_loader = DataLoader(data_train, batch_size=args.batch_size, num_workers=args.num_workers, pin_memory=True, shuffle=True)
iterator = tqdm(train_data_loader)
model.train()
torch.cuda.empty_cache()
for sample in iterator:
time1 = time.time()
load = time1 - time2
rgb = sample["rgb"].cuda(non_blocking=True).to('cuda:0')
target = sample["mask"].cuda(non_blocking=True).to('cuda:0')
b_count = sample["b_count"].cuda(non_blocking=True) / args.b_count_div
b_weights = b_count * args.b_count_weight + 1.0 * (1.0 - args.b_count_weight)
b_rev_size_weights = sample["weights"].cuda(non_blocking=True)
b_rev_size_weights = b_rev_size_weights * args.b_rev_size_weight + 1.0 * (1.0 - args.b_rev_size_weight)
weights = torch.ones(size=target.shape).cuda()
weights[target > 0.0] *= args.pos_weight
weights[:, :1] *= b_rev_size_weights
weights[:, 1:2] *= b_rev_size_weights
for i in range(weights.shape[0]):