idx,
args.max_iters,
batch_time=batch_time,
reco_loss=reco_loss_value,
iter_time=iter_time,
loss=loss_value))
logger.info("remain_time: {}".format(remain_time))
# if loss < compare_loss:
# print('save the lower loss iter, loss:',loss)
# compare_loss = loss
# torch.save(net.module.state_dict(),
# '/data/CRAFT-pytorch/real_weights/lower_loss.pth'
if index % args.eval_iter == 0 and index != 0:
print('Saving state, index:', index)
torch.save(
net.module.state_dict(),
'./checkpoint/{}/synweights_'.format(args.exp_name) +
repr(index) + '.pth')
test('./checkpoint/{}/synweights_'.format(args.exp_name) +
repr(index) + '.pth',
args=args,
result_folder='./checkpoint/{}/result/'.format(
args.exp_name))
#test('/data/CRAFT-pytorch/craft_mlt_25k.pth')
res_dict = getresult('./checkpoint/{}/result/'.format(
args.exp_name))
logger.info(res_dict['method'])
loss = criterion(gh_label, gah_label, out1, out2, mask)
loss.backward()
optimizer.step()
loss_value += loss.item()
if index % 2 == 0 and index > 0:
et = time.time()
print(
'epoch {}:({}/{}) batch || training time for 2 batch {} || training loss {} ||'
.format(epoch, index, len(train_loader), et - st,
loss_value / 2))
loss_time = 0
loss_value = 0
st = time.time()
# if loss < compare_loss:
# print('save the lower loss iter, loss:',loss)
# compare_loss = loss
# torch.save(net.module.state_dict(),
# '/data/CRAFT-pytorch/real_weights/lower_loss.pth'
if index % 5000 == 0 and index != 0:
print('Saving state, index:', index)
torch.save(
net.module.state_dict(),
'/data/CRAFT-pytorch/synweights/synweights_' +
repr(index) + '.pth')
test('/data/CRAFT-pytorch/synweights/synweights_' +
repr(index) + '.pth')
#test('/data/CRAFT-pytorch/craft_mlt_25k.pth')
getresult()
parser.add_argument('--cuda',
default=True,
type=str2bool,
help='Use cuda to train model')
parser.add_argument('--canvas_size',
default=1920,
type=int,
help='image size for inference')
parser.add_argument('--mag_ratio',
default=2,
type=float,
help='image magnification ratio')
parser.add_argument('--poly',
default=False,
action='store_true',
help='enable polygon type')
parser.add_argument('--show_time',
default=False,
action='store_true',
help='show processing time')
parser.add_argument('--test_folder',
default='/data/',
type=str,
help='folder path to input images')
args = parser.parse_args()
test(args.trained_model, result_folder="./result/", args=args)
res_dict = getresult('./result/')
print(res_dict['method'])