def eval_model(model_name, test_img_path, submit_path, save_flag=True):
if os.path.exists(submit_path):
shutil.rmtree(submit_path)
os.mkdir(submit_path)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = EAST(False).to(device)
model.load_state_dict(torch.load(model_name))
model.eval()
start_time = time.time()
detect_dataset(model, device, test_img_path, submit_path)
os.chdir(submit_path)
res = subprocess.getoutput('zip -q submit.zip *.txt')
res = subprocess.getoutput('mv submit.zip ../')
os.chdir('../')
res = subprocess.getoutput(
'python ./evaluate/script.py –g=./evaluate/gt.zip –s=./submit.zip')
print(res)
os.remove('./submit.zip')
print('eval time is {}'.format(time.time() - start_time))
if not save_flag:
shutil.rmtree(submit_path)
f.write(strResult)
if args.vis:
for bbox in boxes:
# bbox = bbox / scale.repeat(int(len(bbox) / 2))
bbox = np.array(bbox,np.int)
cv2.drawContours(orign_img, [bbox[:8].reshape(int(bbox.shape[0] / 2), 2)], -1, (0, 0, 255), 2)
cv2.imwrite(vis_file, orign_img)
if not args.is_test:
f_score_new = getresult(output_path,args.gt_name)
if __name__ == '__main__':
device = torch.device("cuda")
model = EAST()
data_parallel = False
# if torch.cuda.device_count() > 1:
# model = nn.DataParallel(model)
# data_parallel = True
model.to(device)
args.workspace = os.path.join(args.workspace, args.exp_name)
args.resume = os.path.join(args.workspace,args.resume)
print("loading pretrained model from ",args.resume)
model.load_state_dict(torch.load(args.resume))
for ther in range(1,10):
ther_ = ther*0.1
print("threshold:",ther_)
test(model,args,ther_)
for box in boxes
])
with open(
os.path.join(
submit_path, 'res_' +
os.path.basename(img_file).replace('.jpg', '.txt')),
'w') as f:
f.writelines(seq)
if __name__ == '__main__':
img_path = '/data/data_weijiawu/Sence_Text_detection/Paper-ACCV/DomainAdaptive/ICDAR2015/EAST_v1/ICDAR15/Test/image/'
model_path = '/data/data_weijiawu/Sence_Text_detection/Paper-ACCV/DomainAdaptive/ICDAR2015/EAST_v4/Model_save/model_epoch_1.pth'
res_img = '/data/data_weijiawu/Sence_Text_detection/Paper-ACCV/DomainAdaptive/ICDAR2015/EAST_v1/ICDAR15/Test/submit/'
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = EAST().to(device)
model.load_state_dict(torch.load(model_path))
model.eval()
image_list = os.listdir(img_path)
for one_image in tqdm(image_list):
image_path = os.path.join(img_path, one_image)
img = Image.open(image_path)
output_path = os.path.join(
"/home/wwj/workspace/Sence_Text_detection/Paper-ACCV/DomainAdaptive/ICDAR2013/EAST_v1/show/",
one_image)
img_show = cv2.imread(image_path)
filename, file_ext = os.path.splitext(os.path.basename(one_image))
res_file = res_img + "res_" + filename + '.txt'
boxes, score = detect(img, model, device)
epoch + 1, args.epoch_iter, i + 1, int(len(train_loader_source)), time.time() - start_time, loss.item()))
# if i>4000 and i%1000==0:
# f_score = test(epoch, model, args.t_eval_path, args.t_output_path, f_score, args.save_model)
logger.info('epoch_loss is {:.8f}, epoch_time is {:.8f}'.format(epoch_loss / int(7200 / args.batch_size),time.time() - epoch_time))
logger.info(time.asctime(time.localtime(time.time())))
if __name__ == '__main__':
args.workspace = os.path.join(args.workspace, args.exp_name)
os.makedirs(args.workspace, exist_ok=True)
logger = setup_logger(os.path.join(args.workspace, 'train_MSRA_log'))
criterion = Loss()
device = torch.device("cuda")
model = EAST()
# model = nn.DataParallel(model)
data_parallel = False
if torch.cuda.device_count() > 1:
model = nn.DataParallel(model)
data_parallel = True
model.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=[150,220], gamma=0.1)
# 先产生第一次的pseudo-label
logger.info("loading pretrained model from "+args.resume)
# model.load_state_dict(torch.load(args.resume))
trainset = MSRA_TD500(args.msra_data,args.hust_path,args.is_box_pseudo)
train_loader_target = data.DataLoader(trainset, batch_size=args.batch_size,
# print(" best_f_score:", f_score)
# print(" ---------------------------------------------------------")
return f_score_new
if __name__ == '__main__':
train_img_path = os.path.abspath('/data/data_weijiawu/SynthText')
train_gt_path = os.path.abspath('/data/data_weijiawu/SynthText/gt.mat')
args.workspace = os.path.join(args.workspace, args.exp_name)
os.makedirs(args.workspace, exist_ok=True)
logger = setup_logger(os.path.join(args.workspace, 'train_synthtext_log'))
criterion = Loss()
device = torch.device("cuda")
model = EAST()
data_parallel = False
if torch.cuda.device_count() > 1:
model = nn.DataParallel(model)
data_parallel = True
model.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
scheduler = lr_scheduler.MultiStepLR(optimizer,
milestones=[args.epoch_iter // 2],
gamma=0.1)
# 先产生第一次的pseudo-label
# logger.info("loading pretrained model from ",args.resume)
# model.load_state_dict(torch.load(args.resume))
trainset_ = SynthText(train_img_path, train_gt_path)
transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))])
score_map, geo_map, ignored_map = get_score_geo(
img, vertices, labels, self.scale, self.length)
return transform(img), score_map, geo_map, ignored_map
if __name__ == "__main__":
from network.model import EAST
from torch import nn
train_img_path = os.path.abspath(
'/data/data_weijiawu/ICDAR17/train_image/')
train_gt_path = os.path.abspath("/data/data_weijiawu/ICDAR17/train_gt/")
device = torch.device("cuda")
model = EAST()
data_parallel = False
if torch.cuda.device_count() > 1:
model = nn.DataParallel(model)
data_parallel = True
model.to(device)
trainset = ICDAR17(train_img_path, train_gt_path)
train_loader = data.DataLoader(trainset,
batch_size=2,
shuffle=True,
num_workers=10,
drop_last=True)
for i, (img, gt_score, gt_geo, ignored_map) in enumerate(train_loader):
image_path = "/home/wwj/workspace/Sence_Text_detection/AAAI_EAST/Baseline/EAST_v1/show/image.jpg"
logger.info(
" ---------------------------------------------------------")
logger.info(" current f_score: {:.3f} best_f_score: {:.3f}".format(
f_score_new, f_score))
logger.info(
" ---------------------------------------------------------")
return f_score
if __name__ == '__main__':
args.workspace = os.path.join(args.workspace, args.exp_name)
os.makedirs(args.workspace, exist_ok=True)
logger = setup_logger(os.path.join(args.workspace, 'train_icdar17_log'))
criterion = Loss()
device = torch.device("cuda")
model = EAST()
data_parallel = False
if torch.cuda.device_count() > 1:
model = nn.DataParallel(model)
data_parallel = True
model.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
scheduler = lr_scheduler.MultiStepLR(optimizer,
milestones=[100, 200],
gamma=0.1)
# 先产生第一次的pseudo-label
logger.info("loading pretrained model from " + args.resume)
model.load_state_dict(torch.load(args.resume))
# target domain