change data.py

data.py

@@ -11,7 +11,7 @@ class CLIPDataset(torch.utils.data.Dataset):
     """
     Dataset class for getting image and text using a dataset loader.
     """
-    def __init__(self, image_filenames, captions, tokenizer, transforms):
+    def __init__(self, image_filenames, encoded_captions, data_len):
         """
         image_filenames and cpations must have the same length; so, if there are
         multiple captions for each image, the image_filenames must have repetitive
@@ -19,11 +19,20 @@ class CLIPDataset(torch.utils.data.Dataset):
         """
 
         self.image_filenames = image_filenames
-        self.captions = list(captions)
-        self.encoded_captions = tokenizer(
-            list(captions), padding=True, truncation=True, max_length=CFG.max_length
+        self.encoded_captions = encoded_captions
+        self.datalen = data_len
+        self.imgtransforms = A.Compose(
+            [
+                A.Resize(CFG.size, CFG.size, always_apply=True),
+                A.Normalize(max_pixel_value=255.0, always_apply=True),
+            ]
         )
-        self.transforms = transforms
+
+    def __getimage(self, idx):
+        image = cv2.imread(f"{CFG.image_path}/{self.image_filenames[idx]}")
+        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        image = self.imgtransforms(image=image)['image']
+        return torch.tensor(image).permute(2, 0, 1).float()
 
     def __getitem__(self, idx):
         """
@@ -33,13 +42,7 @@ class CLIPDataset(torch.utils.data.Dataset):
             key: torch.tensor(values[idx])
             for key, values in self.encoded_captions.items()
         }
-
-        image = cv2.imread(f"{CFG.image_path}/{self.image_filenames[idx]}")
-        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-        image = self.transforms(image=image)['image']
-        item['image'] = torch.tensor(image).permute(2, 0, 1).float()
-        item['caption'] = self.captions[idx]
-
+        item['image'] = self.__getimage(idx)
         return item
 
 
@@ -47,34 +50,28 @@ class CLIPDataset(torch.utils.data.Dataset):
         """
         Returns size of our training data.
         """
-        return len(self.captions)
+        return self.datalen
 
-    
-def image_transforms():
-    """
-    Implements image transformations.
-    """
-    return A.Compose(
-        [
-            A.Resize(CFG.size, CFG.size, always_apply=True),
-            A.Normalize(max_pixel_value=255.0, always_apply=True),
-        ]
-    )
 
 def gen_train_valid_dfs():
     """
     Split dataset into train and validation dataset.
     """
     dataframe = pd.read_csv(f"{CFG.captions_path}/captions.csv")
+
+    # Get max number of images
     max_id = dataframe["id"].max() + 1 if not CFG.debug else 100
     image_ids = np.arange(0, max_id)
     np.random.seed(CFG.seed)
-    valid_ids = np.random.choice(
+
+    valid_data_ids = np.random.choice(
         image_ids, size=int(0.2 * len(image_ids)), replace=False
     )
-    train_ids = [id_ for id_ in image_ids if id_ not in valid_ids]
-    train_dataframe = dataframe[dataframe["id"].isin(train_ids)].reset_index(drop=True)
-    valid_dataframe = dataframe[dataframe["id"].isin(valid_ids)].reset_index(drop=True)
+
+    # Generate train and validation data frames.
+    train_data_ids = [id_ for id_ in image_ids if id_ not in valid_data_ids]
+    train_dataframe = dataframe[dataframe["id"].isin(train_data_ids)].reset_index(drop=True)
+    valid_dataframe = dataframe[dataframe["id"].isin(valid_data_ids)].reset_index(drop=True)
     return train_dataframe, valid_dataframe
 
 
@@ -82,13 +79,19 @@ def get_dataset_loader(dataframe, tokenizer, mode):
     """
     Build a dataset loader using CLIPDataset.
     """
-    transforms = image_transforms()
+    # Generate encoded caption for tokeniser provided
+    encoded_captions = tokenizer(
+            list(dataframe["caption"].values), padding=True, truncation=True, max_length=CFG.max_length
+    )
+    
+    # Pass image and encoded caption to the dataset
     dataset = CLIPDataset(
         dataframe["image"].values,
-        dataframe["caption"].values,
-        tokenizer=tokenizer,
-        transforms=transforms,
+        encoded_captions,
+        len(list(dataframe["caption"].values))
     )
+
+    # Create dataset loader
     dataloader = torch.utils.data.DataLoader(
         dataset,
         batch_size=CFG.batch_size,