# net = st_resnet.resnet_st_seg01.resnet50(pretrained=False, num_classes=args.num_classes) # net.load_state_dict(torch.load(model_path), strict = True) args.batch_size = 1 st_crf_layer = sec.sec_org_net.STCRFLayer(True) print(args) # if flag_use_cuda: # net.cuda() dataloader = VOCData(args) max_iou = 0 iou_obj = common_function.iou_calculator() num_train_batch = len(dataloader.dataloaders["train"]) # net.train(False) with torch.no_grad(): train_iou = 0 eval_iou = 0 counter = 0 for data in dataloader.dataloaders["train"]: inputs, labels, mask_gt, img, cues = data # if flag_use_cuda: # inputs = inputs.cuda(); labels = labels.cuda(); cues = cues.cuda() # # sm_mask = net(inputs)
print(args) print(model_path) if flag_use_cuda: net.cuda() dataloader = multi_scale.voc_data_mul_scale_w_cues.VOCData(args) optimizer = optim.Adam(net.parameters(), lr=args.lr) # L2 penalty: norm weight_decay=0.0001 # main_scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=args.step_size) main_scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=3, gamma=0.5) max_iou = 0 iou_obj_train = common_function.iou_calculator() iou_obj_eval = common_function.iou_calculator() num_train_batch = len(dataloader.dataloaders["train"]) weight_STBCE = 0.1 weight_dec = 0.9 iter_counter = 0 for epoch in range(args.epochs): train_seed_loss = 0.0 train_expand_loss = 0.0 train_constraint_loss = 0.0 train_st_BEC_loss = 0.0 train_st_half_BCE_loss = 0.0