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generate_pseudo_label.py
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#!/usr/bin/env python
import argparse
import os
import os.path as osp
import torch.nn.functional as F
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
import torch
from torch.autograd import Variable
import tqdm
from dataloaders import fundus_dataloader as DL
from torch.utils.data import DataLoader
from dataloaders import custom_transforms as tr
from torchvision import transforms
from matplotlib.pyplot import imsave
from utils.Utils import *
from metrics import *
from datetime import datetime
import pytz
from networks.deeplabv3 import *
import cv2
import torch.backends.cudnn as cudnn
import random
bceloss = torch.nn.BCELoss()
seed = 3377
savefig = False
get_hd = False
if True:
cudnn.benchmark = False
cudnn.deterministic = True
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--model-file', type=str, default='./logs/Domain2/1/checkpoint_200.pth.tar')
parser.add_argument('--dataset', type=str, default='Domain2')
parser.add_argument('--batchsize', type=int, default=8)
parser.add_argument('--source', type=str, default='Domain3')
parser.add_argument('-g', '--gpu', type=int, default=0)
parser.add_argument('--data-dir', default='/mnt/data1/llr_data/Fundus/')
parser.add_argument('--out-stride',type=int,default=16)
parser.add_argument('--save-root-ent',type=str,default='./results/ent/')
parser.add_argument('--save-root-mask',type=str,default='./results/mask/')
parser.add_argument('--sync-bn',type=bool,default=True)
parser.add_argument('--freeze-bn',type=bool,default=False)
parser.add_argument('--test-prediction-save-path', type=str,default='./results/baseline/')
args = parser.parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu)
model_file = args.model_file
# 1. dataset
composed_transforms_test = transforms.Compose([
tr.Resize(512),
tr.Normalize_tf1(),
tr.ToTensor()
])
db_train = DL.FundusSegmentationRIGA(base_dir=args.data_dir, dataset=args.dataset, split='train/', transform=composed_transforms_test)
db_test = DL.FundusSegmentationRIGA(base_dir=args.data_dir, dataset=args.dataset, split='test/', transform=composed_transforms_test)
db_source = DL.FundusSegmentationRIGA(base_dir=args.data_dir, dataset=args.source, split='train/ROIs', transform=composed_transforms_test)
train_loader = DataLoader(db_train, batch_size=args.batchsize, shuffle=False, num_workers=1,drop_last=True)
test_loader = DataLoader(db_test, batch_size=args.batchsize, shuffle=False, num_workers=1)
source_loader = DataLoader(db_source, batch_size=args.batchsize, shuffle=False, num_workers=1)
# 2. model
model = DeepLab(num_classes=2, backbone='mobilenet', output_stride=args.out_stride, sync_bn=args.sync_bn, freeze_bn=args.freeze_bn)
if torch.cuda.is_available():
model = model.cuda()
print('==> Loading %s model file: %s' %
(model.__class__.__name__, model_file))
checkpoint = torch.load(model_file)
model.load_state_dict(checkpoint['model_state_dict'])
#model.load_state_dict({k.replace('module.',''):v for k,v in torch.load(model_file).items()})
model.train()
pseudo_label_dic = {}
uncertain_dic = {}
proto_pseudo_dic = {}
distance_0_obj_dic = {}
distance_0_bck_dic = {}
distance_1_bck_dic = {}
distance_1_obj_dic = {}
centroid_0_obj_dic = {}
centroid_0_bck_dic = {}
centroid_1_obj_dic = {}
centroid_1_bck_dic = {}
# fundus
with torch.no_grad():
for batch_idx, (sample) in tqdm.tqdm(enumerate(test_loader),
total=len(train_loader),
ncols=80, leave=False):
data, target, img_name = sample['image'], sample['map'], sample['img_name']
if torch.cuda.is_available():
data, target = data.cuda(), target.cuda()
data, target = Variable(data), Variable(target)
preds = torch.zeros([10, data.shape[0], 2, data.shape[2], data.shape[3]]).cuda()
features = torch.zeros([10, data.shape[0], 305, 128, 128]).cuda()
for i in range(10):
with torch.no_grad():
preds[i,...], _, features[i,...] = model(data)
preds1 = torch.sigmoid(preds)
preds = torch.sigmoid(preds/2.0)
std_map = torch.std(preds,dim=0)
prediction=torch.mean(preds1,dim=0)
pseudo_label = prediction.clone()
pseudo_label[pseudo_label > 0.75] = 1.0; pseudo_label[pseudo_label <= 0.75] = 0.0
feature = torch.mean(features,dim=0)
target_0_obj = F.interpolate(pseudo_label[:,0:1,...], size=feature.size()[2:], mode='nearest')
target_1_obj = F.interpolate(pseudo_label[:, 1:, ...], size=feature.size()[2:], mode='nearest')
prediction_small = F.interpolate(prediction, size=feature.size()[2:], mode='bilinear', align_corners=True)
std_map_small = F.interpolate(std_map, size=feature.size()[2:], mode='bilinear', align_corners=True)
target_0_bck = 1.0 - target_0_obj;target_1_bck = 1.0 - target_1_obj
mask_0_obj = torch.zeros([std_map_small.shape[0], 1, std_map_small.shape[2], std_map_small.shape[3]]).cuda()
mask_0_bck = torch.zeros([std_map_small.shape[0], 1, std_map_small.shape[2], std_map_small.shape[3]]).cuda()
mask_1_obj = torch.zeros([std_map_small.shape[0], 1, std_map_small.shape[2], std_map_small.shape[3]]).cuda()
mask_1_bck = torch.zeros([std_map_small.shape[0], 1, std_map_small.shape[2], std_map_small.shape[3]]).cuda()
mask_0_obj[std_map_small[:, 0:1, ...] < 0.05] = 1.0
mask_0_bck[std_map_small[:, 0:1, ...] < 0.05] = 1.0
mask_1_obj[std_map_small[:, 1:, ...] < 0.05] = 1.0
mask_1_bck[std_map_small[:, 1:, ...] < 0.05] = 1.0
mask_0 = mask_0_obj + mask_0_bck
mask_1 = mask_1_obj + mask_1_bck
mask = torch.cat((mask_0, mask_1), dim=1)
feature_0_obj = feature * target_0_obj*mask_0_obj;feature_1_obj = feature * target_1_obj*mask_1_obj
feature_0_bck = feature * target_0_bck*mask_0_bck;feature_1_bck = feature * target_1_bck*mask_1_bck
centroid_0_obj = torch.sum(feature_0_obj*prediction_small[:,0:1,...], dim=[0,2,3], keepdim=True)
centroid_1_obj = torch.sum(feature_1_obj*prediction_small[:,1:,...], dim=[0,2,3], keepdim=True)
centroid_0_bck = torch.sum(feature_0_bck*(1.0-prediction_small[:,0:1,...]), dim=[0,2,3], keepdim=True)
centroid_1_bck = torch.sum(feature_1_bck*(1.0-prediction_small[:,1:,...]), dim=[0,2,3], keepdim=True)
target_0_obj_cnt = torch.sum(mask_0_obj*target_0_obj*prediction_small[:,0:1,...], dim=[0,2,3], keepdim=True)
target_1_obj_cnt = torch.sum(mask_1_obj*target_1_obj*prediction_small[:,1:,...], dim=[0,2,3], keepdim=True)
target_0_bck_cnt = torch.sum(mask_0_bck*target_0_bck*(1.0-prediction_small[:,0:1,...]), dim=[0,2,3], keepdim=True)
target_1_bck_cnt = torch.sum(mask_1_bck*target_1_bck*(1.0-prediction_small[:,1:,...]), dim=[0,2,3], keepdim=True)
centroid_0_obj /= target_0_obj_cnt; centroid_1_obj /= target_1_obj_cnt
centroid_0_bck /= target_0_bck_cnt; centroid_1_bck /= target_1_bck_cnt
distance_0_obj = torch.sum(torch.pow(feature - centroid_0_obj, 2), dim=1, keepdim=True)
distance_0_bck = torch.sum(torch.pow(feature - centroid_0_bck, 2), dim=1, keepdim=True)
distance_1_obj = torch.sum(torch.pow(feature - centroid_1_obj, 2), dim=1, keepdim=True)
distance_1_bck = torch.sum(torch.pow(feature - centroid_1_bck, 2), dim=1, keepdim=True)
proto_pseudo_0 = torch.zeros([data.shape[0], 1, feature.shape[2], feature.shape[3]]).cuda()
proto_pseudo_1 = torch.zeros([data.shape[0], 1, feature.shape[2], feature.shape[3]]).cuda()
proto_pseudo_0[distance_0_obj < distance_0_bck] = 1.0
proto_pseudo_1[distance_1_obj < distance_1_bck] = 1.0
proto_pseudo = torch.cat((proto_pseudo_0, proto_pseudo_1), dim=1)
proto_pseudo = F.interpolate(proto_pseudo, size=data.size()[2:], mode='nearest')
debugc = 1
pseudo_label = pseudo_label.detach().cpu().numpy()
# save pseudo label image
# mask_oc = construct_color_img(pseudo_label[0][0,:,:])
# mask_oc = cv2.cvtColor(mask_oc, cv2.COLOR_BGR2GRAY)
# print(pseudo_label.shape)
# mask_od = construct_color_img(pseudo_label[0][1,:,:])
# mask_od = cv2.cvtColor(mask_od, cv2.COLOR_BGR2GRAY)
# cv2.imwrite(os.path.join('/mnt/data1/llr_data/results/pseudolabel/boundary', args.dataset, 'oc',sample['img_name'][0]), mask_oc)
# cv2.imwrite(os.path.join('/mnt/data1/llr_data/results/pseudolabel/boundary', args.dataset, 'od',sample['img_name'][0]), mask_od)
# save pseudo boundary image
# mask_oc = construct_color_img(pseudo_label[0][0,:,:])
# mask_oc = cv2.cvtColor(mask_oc, cv2.COLOR_BGR2GRAY)
# img = np.zeros((512,512,3),np.uint8)
# #img = Image.fromarray(img)
# ret, thresh = cv2.threshold(mask_oc, 255, 255, 0)
# contours, im = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) #第一个参数是轮廓
# result_oc = cv2.drawContours(image=img, contours=contours, contourIdx=-1, color=(255, 255, 255), thickness=5)
# mask_od = cv2.imread(os.path.join('/mnt/data1/llr_data/results/pseudolabel/Domain2/oc',sample['img_name'][0]))
# mask_od = cv2.cvtColor(mask_od, cv2.COLOR_BGR2GRAY)
# ret, thresh = cv2.threshold(mask_od, 127, 255, 0)
# contours, im = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) #第一个参数是轮廓
# result_od = cv2.drawContours(image=img, contours=contours, contourIdx=-1, color=(255, 255, 255), thickness=5)
# cv2.imwrite(os.path.join('/mnt/data1/llr_data/results/pseudolabel/Domain2/boundary/oc',sample['img_name'][0]), result_oc)
# cv2.imwrite(os.path.join('/mnt/data1/llr_data/results/pseudolabel/Domain2/boundary/od',sample['img_name'][0]), result_od)
std_map = std_map.detach().cpu().numpy()
proto_pseudo = proto_pseudo.detach().cpu().numpy()
distance_0_obj = distance_0_obj.detach().cpu().numpy()
distance_0_bck = distance_0_bck.detach().cpu().numpy()
distance_1_obj = distance_1_obj.detach().cpu().numpy()
distance_1_bck = distance_1_bck.detach().cpu().numpy()
centroid_0_obj = centroid_0_obj.detach().cpu().numpy()
centroid_0_bck = centroid_0_bck.detach().cpu().numpy()
centroid_1_obj = centroid_1_obj.detach().cpu().numpy()
centroid_1_bck = centroid_1_bck.detach().cpu().numpy()
for i in range(prediction.shape[0]):
pseudo_label_dic[img_name[i]] = pseudo_label[i]
uncertain_dic[img_name[i]] = std_map[i]
proto_pseudo_dic[img_name[i]] = proto_pseudo[i]
distance_0_obj_dic[img_name[i]] = distance_0_obj[i]
distance_0_bck_dic[img_name[i]] = distance_0_bck[i]
distance_1_obj_dic[img_name[i]] = distance_1_obj[i]
distance_1_bck_dic[img_name[i]] = distance_1_bck[i]
centroid_0_obj_dic[img_name[i]] = centroid_0_obj
centroid_0_bck_dic[img_name[i]] = centroid_0_bck
centroid_1_obj_dic[img_name[i]] = centroid_1_obj
centroid_1_bck_dic[img_name[i]] = centroid_1_bck
if args.dataset=="Domain1":#pseudolabel_D1
np.savez('/mnt/data1/llr_data/results/cell/pseudolabel_D1', pseudo_label_dic, uncertain_dic, proto_pseudo_dic,
distance_0_obj_dic, distance_0_bck_dic, distance_1_obj_dic, distance_1_bck_dic,
centroid_0_obj_dic, centroid_0_bck_dic, centroid_1_obj_dic, centroid_1_bck_dic
)
elif args.dataset=="Domain2":
np.savez('/mnt/data1/llr_data/results/pseudolabel_D2', pseudo_label_dic, uncertain_dic, proto_pseudo_dic,
distance_0_obj_dic, distance_0_bck_dic, distance_1_obj_dic, distance_1_bck_dic,
centroid_0_obj_dic, centroid_0_bck_dic, centroid_1_obj_dic, centroid_1_bck_dic
)
elif args.dataset=="RIGA":
np.savez('/mnt/data1/llr_data/results/prototype/test_D6', pseudo_label_dic, uncertain_dic, proto_pseudo_dic,
distance_0_obj_dic, distance_0_bck_dic, distance_1_obj_dic, distance_1_bck_dic,
centroid_0_obj_dic, centroid_0_bck_dic, centroid_1_obj_dic, centroid_1_bck_dic
)