}, {
'params': reco_model.parameters(),
'lr': args.reco_lr
}]
optimizer = optim.SGD(param, lr=args.lr, weight_decay=args.weight_decay)
criterion = Maploss()
#criterion = torch.nn.MSELoss(reduce=True, size_average=True)
net.train()
step_index = 0
loss_time = 0
loss_value = 0
compare_loss = 1
batch_time = AverageMeter(100)
iter_time = AverageMeter(100)
loss_value = AverageMeter(10)
reco_loss_value = AverageMeter(10)
args.max_iters = args.num_epoch * len(train_loader)
for epoch in range(args.num_epoch):
# if epoch % 50 == 0 and epoch != 0:
# step_index += 1
# adjust_learning_rate(optimizer, args.gamma, step_index)
for index, batches in enumerate(train_loader):
st = time.time()
images, gh_label, gah_label, mask, _, word_region_torch = batches[