update classifier

@@ -33,12 +33,9 @@ def eval(model_path):
     model.load_state_dict(torch.load(weight_path))
     model.load_state_dict(torch.load(weight_path))
 
 
     print('\n[+] Load dataset')
     print('\n[+] Load dataset')
-    test_transform = get_valid_transform(args, model)
-    #print('\nTEST Transform\n', test_transform)
     test_dataset = get_dataset(args, 'test') 
     test_dataset = get_dataset(args, 'test') 
 
 
    
    
-   
     test_loader = iter(get_dataloader(args, test_dataset)) ###
     test_loader = iter(get_dataloader(args, test_dataset)) ###
 
 
     print('\n[+] Start testing')
     print('\n[+] Start testing')

@@ -16,6 +16,8 @@ class BaseNet(nn.Module):
         x = self.after(f)
         x = self.after(f)
         x = x.reshape(x.size(0), -1)
         x = x.reshape(x.size(0), -1)
         x = self.fc(x)
         x = self.fc(x)
+
+        # output, first
         return x, f
         return x, f
 
 
 """
 """

@@ -24,7 +24,7 @@ def train(**kwargs):
 
 
     print('\n[+] Create log dir')
     print('\n[+] Create log dir')
     model_name = get_model_name(args)
     model_name = get_model_name(args)
-    log_dir = os.path.join('/content/drive/My Drive/CD2 Project/classify/', model_name)
+    log_dir = os.path.join('/content/drive/My Drive/CD2 Project/runs/classify/', model_name)
     os.makedirs(os.path.join(log_dir, 'model'))
     os.makedirs(os.path.join(log_dir, 'model'))
     json.dump(kwargs, open(os.path.join(log_dir, 'kwargs.json'), 'w'))
     json.dump(kwargs, open(os.path.join(log_dir, 'kwargs.json'), 'w'))
     writer = SummaryWriter(log_dir=log_dir)
     writer = SummaryWriter(log_dir=log_dir)
@@ -42,13 +42,11 @@ def train(**kwargs):
     if args.use_cuda:
     if args.use_cuda:
         model = model.cuda()
         model = model.cuda()
         criterion = criterion.cuda()
         criterion = criterion.cuda()
-    writer.add_graph(model)
+    #writer.add_graph(model)
 
 
     print('\n[+] Load dataset')
     print('\n[+] Load dataset')
-    transform = get_train_transform(args, model, log_dir)
-    val_transform = get_valid_transform(args, model)
-    train_dataset = get_dataset(args, transform, 'train')
-    valid_dataset = get_dataset(args, val_transform, 'val')
+    train_dataset = get_dataset(args, 'train')
+    valid_dataset = get_dataset(args, 'val')
     train_loader = iter(get_inf_dataloader(args, train_dataset))
     train_loader = iter(get_inf_dataloader(args, train_dataset))
     max_epoch = len(train_dataset) // args.batch_size
     max_epoch = len(train_dataset) // args.batch_size
     best_acc = -1
     best_acc = -1
@@ -82,6 +80,7 @@ def train(**kwargs):
             print('  BW Time : {:.3f}ms'.format(_train_res[4]*1000))
             print('  BW Time : {:.3f}ms'.format(_train_res[4]*1000))
 
 
         if step % args.val_step == args.val_step-1:
         if step % args.val_step == args.val_step-1:
+            # print("\nstep, args.val_step: ", step, args.val_step)
             valid_loader = iter(get_dataloader(args, valid_dataset))
             valid_loader = iter(get_dataloader(args, valid_dataset))
             _valid_res = validate(args, model, criterion, valid_loader, step, writer)
             _valid_res = validate(args, model, criterion, valid_loader, step, writer)
             print('\n[+] Valid results')
             print('\n[+] Valid results')