resolved concat size err

@@ -28,8 +28,6 @@ DATASET_PATH = '/content/drive/My Drive/CD2 Project/data/BraTS_Training/train_fr
 TRAIN_DATASET_PATH = '/content/drive/My Drive/CD2 Project/data/BraTS_Training/train_frame/'
 TRAIN_DATASET_PATH = '/content/drive/My Drive/CD2 Project/data/BraTS_Training/train_frame/'
 VAL_DATASET_PATH = '/content/drive/My Drive/CD2 Project/data/BraTS_Training/val_frame/'
 VAL_DATASET_PATH = '/content/drive/My Drive/CD2 Project/data/BraTS_Training/val_frame/'
 
 
-TRAIN_TARGET_PATH = '/content/drive/My Drive/CD2 Project/data/BraTS_Training/train_frame.csv'
-VAL_TARGET_PATH = '/content/drive/My Drive/CD2 Project/data/BraTS_Training/val_frame.csv'
 
 
 current_epoch = 0
 current_epoch = 0
 
 
@@ -65,14 +63,36 @@ def concat_image_features(image, features, max_features=3):
     image_feature = image.clone()
     image_feature = image.clone()
 
 
     for i in range(max_features):
     for i in range(max_features):
+        # features torch.Size([64, 16, 16])
+
         feature = features[i:i+1]
         feature = features[i:i+1]
+        #torch.Size([1, 16, 16])
+
         _min, _max = torch.min(feature), torch.max(feature)
         _min, _max = torch.min(feature), torch.max(feature)
         feature = (feature - _min) / (_max - _min + 1e-6)
         feature = (feature - _min) / (_max - _min + 1e-6)
-        feature = torch.cat([feature]*3, 0)
-        feature = feature.view(1, 3, feature.size(1), feature.size(2))
+        # torch.Size([1, 16, 16])
+
+        feature = torch.cat([feature]*1, 0)
+        #feature = torch.cat([feature]*3, 0)
+        # torch.Size([3, 16, 16]) -> [1, 16, 16]
+
+        feature = feature.view(1, 1, feature.size(1), feature.size(2))
+        #feature = feature.view(1, 3, feature.size(1), feature.size(2))
+        # torch.Size([1, 3, 16, 16])-> [1, 1, 16, 16]
+
         feature = F.upsample(feature, size=(h,w), mode="bilinear")
         feature = F.upsample(feature, size=(h,w), mode="bilinear")
-        feature = feature.view(3, h, w)
-        image_feature = torch.cat((image_feature, feature), 2)
+        # torch.Size([1, 3, 32, 32])-> [1, 1, 32, 32]
+
+        feature = feature.view(1, h, w) #(3, h, w) input of size 3072
+        # torch.Size([3, 32, 32])->[1, 32, 32]
+
+        print("img_feature & feature size:\n", image_feature.size(),"\n", feature.size()) 
+        # img_feature & feature size:
+        # torch.Size([1, 32, 32]) -> [1, 32, 64]
+        # torch.Size([3, 32, 32] ->[1, 32, 32]
+        
+
+        image_feature = torch.cat((image_feature, feature), 2) ### dim = 2
 
 
     return image_feature
     return image_feature
 
 
@@ -148,7 +168,7 @@ def select_model(args):
         Net = getattr(importlib.import_module('networks.{}'.format(args.network)), 'Net')
         Net = getattr(importlib.import_module('networks.{}'.format(args.network)), 'Net')
         model = Net(args)
         model = Net(args)
 
 
-    print(model)
+    #print(model) # print model architecture
     return model
     return model
 
 
 
 
@@ -197,10 +217,38 @@ class CustomDataset(Dataset):
        targets = self.targets[idx]
        targets = self.targets[idx]
        #img = self.img[idx]
        #img = self.img[idx]
        image = Image.open(img_loc)
        image = Image.open(img_loc)
+       #print("Image:\n", image)
+       #print("type of img:\n", type(image)) #<class 'PIL.PngImagePlugin.PngImageFile'>
+       #w, h = image.size
+       #print(image.size) #(240, 240)
+       #image = image.reshape(w, h)
        
        
+    #    image = np.array(image) * 255
+    #    image = image.astype('uint8')
+    #    image = Image.fromarray(image, mode = 'L')
+
+
        if self.transform is not None:
        if self.transform is not None:
             #img = self.transform(img)
             #img = self.transform(img)
-            tensor_image = self.transform(image)
+           # print("\ngetitem image max:\n", np.amax(np.array(image)), np.array(image).shape)
+           #image [0, 255]
+
+            tensor_image = self.transform(image) ##
+
+            """
+            range [0, 1] -> [0, 255]
+            RuntimeError: Input type (torch.cuda.DoubleTensor) and weight type (torch.cuda.FloatTensor) should be the same
+            # tensor_image = np.array(tensor_image) * 255
+            # tensor_image = tensor_image.astype('uint8')
+            # tensor_image = np.reshape(tensor_image, (32, 32))
+            # tensor_image = Image.fromarray(tensor_image, mode = 'L')
+            # tensor_image = np.reshape(tensor_image, (1, 32, 32))
+            # tensor_image = tensor_image.astype('float')
+            """
+        
+            #print("\ngetitem tensor_image max:\n", np.amax(np.array(tensor_image)), np.array(tensor_image).shape)
+            # tensor_image range: [0, 1], shape: (1, 32, 32)
+
        #return img, targets
        #return img, targets
        return tensor_image, targets
        return tensor_image, targets
 
 
@@ -273,7 +321,7 @@ def get_inf_dataloader(args, dataset):
 
 
 def get_train_transform(args, model, log_dir=None):
 def get_train_transform(args, model, log_dir=None):
     if args.fast_auto_augment:
     if args.fast_auto_augment:
-        assert args.dataset == 'BraTS' # TODO: FastAutoAugment for Imagenet
+        #assert args.dataset == 'BraTS' # TODO: FastAutoAugment for Imagenet
 
 
         from fast_auto_augment import fast_auto_augment
         from fast_auto_augment import fast_auto_augment
         if args.augment_path:
         if args.augment_path:
@@ -281,7 +329,7 @@ def get_train_transform(args, model, log_dir=None):
             os.system('cp {} {}'.format(
             os.system('cp {} {}'.format(
                 args.augment_path, os.path.join(log_dir, 'augmentation.cp')))
                 args.augment_path, os.path.join(log_dir, 'augmentation.cp')))
         else:
         else:
-            transform = fast_auto_augment(args, model, K=4, B=1, num_process=4)
+            transform = fast_auto_augment(args, model, K=4, B=1, num_process=4) ##
             if log_dir:
             if log_dir:
                 cp.dump(transform, open(os.path.join(log_dir, 'augmentation.cp'), 'wb'))
                 cp.dump(transform, open(os.path.join(log_dir, 'augmentation.cp'), 'wb'))
 
 
@@ -302,14 +350,14 @@ def get_train_transform(args, model, log_dir=None):
             transforms.ToTensor()
             transforms.ToTensor()
         ])
         ])
 
 
-    elif args.dataset == 'BraTS':
-        resize_h, resize_w = 256, 256
-        transform = transforms.Compose([
-            transforms.Resize([resize_h, resize_w]),
-            transforms.RandomCrop(model.img_size),
-            transforms.RandomHorizontalFlip(),
-            transforms.ToTensor()
-        ])
+    # elif args.dataset == 'BraTS':
+    #     resize_h, resize_w = 256, 256
+    #     transform = transforms.Compose([
+    #         transforms.Resize([resize_h, resize_w]),
+    #         transforms.RandomCrop(model.img_size),
+    #         transforms.RandomHorizontalFlip(),
+    #         transforms.ToTensor()
+    #     ])
     else:
     else:
         raise Exception('Unknown Dataset')
         raise Exception('Unknown Dataset')
 
 
@@ -393,7 +441,7 @@ def validate(args, model, criterion, valid_loader, step, writer, device=None):
     infer_t = 0
     infer_t = 0
 
 
     with torch.no_grad():
     with torch.no_grad():
-        for i, (images, target) in enumerate(valid_loader):
+        for i, (images, target) in enumerate(valid_loader): ##
 
 
             start_t = time.time()
             start_t = time.time()
             if device:
             if device: