image captionig added

tutorials/09 - Image Captioning/data.py

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+import torch
+import torchvision.transforms as transforms
+import torch.utils.data as data
+import os
+import pickle
+import numpy as np
+import nltk
+from PIL import Image
+from vocab import Vocabulary
+from pycocotools.coco import COCO
+
+
+class CocoDataset(data.Dataset):
+    """COCO Custom Dataset compatible with torch.utils.data.DataLoader."""
+    def __init__(self, root, json, vocab, transform=None):
+        """
+        Args:
+            root: image directory.
+            json: coco annotation file path.
+            vocab: vocabulary wrapper.
+            transform: transformer for image.
+        """
+        self.root = root
+        self.coco = COCO(json)
+        self.ids = list(self.coco.anns.keys())
+        self.vocab = vocab
+        self.transform = transform
+
+    def __getitem__(self, index):
+        """This function should return one data pair(image and caption)."""
+        coco = self.coco
+        vocab = self.vocab
+        ann_id = self.ids[index]
+        caption = coco.anns[ann_id]['caption']
+        img_id = coco.anns[ann_id]['image_id']
+        path = coco.loadImgs(img_id)[0]['file_name']
+
+        image = Image.open(os.path.join(self.root, path)).convert('RGB')
+        if self.transform is not None:
+            image = self.transform(image)
+            
+        # Convert caption (string) to word ids.
+        tokens = nltk.tokenize.word_tokenize(str(caption).lower())
+        caption = []
+        caption.append(vocab('<start>'))
+        caption.extend([vocab(token) for token in tokens])
+        caption.append(vocab('<end>'))
+        target = torch.Tensor(caption)
+        return image, target
+
+    def __len__(self):
+        return len(self.ids)
+
+    
+def collate_fn(data):
+    """Build mini-batch tensors from a list of (image, caption) tuples.
+    Args:
+        data: list of (image, caption) tuple. 
+            - image: torch tensor of shape (3, 256, 256).
+            - caption: torch tensor of shape (?); variable length.
+        
+    Returns:
+        images: torch tensor of shape (batch_size, 3, 256, 256).
+        targets: torch tensor of shape (batch_size, padded_length).
+        lengths: list; valid length for each padded caption.
+    """
+    # Sort a data list by caption length
+    data.sort(key=lambda x: len(x[1]), reverse=True)
+    images, captions = zip(*data)
+    
+    # Merge images (convert tuple of 3D tensor to 4D tensor)
+    images = torch.stack(images, 0)
+    
+    # Merget captions (convert tuple of 1D tensor to 2D tensor)
+    lengths = [len(cap) for cap in captions]
+    targets = torch.zeros(len(captions), max(lengths)).long()
+    for i, cap in enumerate(captions):
+        end = lengths[i]
+        targets[i, :end] = cap[:end]        
+    return images, targets, lengths
+
+
+def get_loader(root, json, vocab, transform, batch_size=100, shuffle=True, num_workers=2):
+    """Returns torch.utils.data.DataLoader for custom coco dataset."""
+    # COCO custom dataset
+    coco = CocoDataset(root=root,
+                       json=json,
+                       vocab = vocab,
+                       transform=transform)
+    
+    # Data loader
+    data_loader = torch.utils.data.DataLoader(dataset=coco, 
+                                              batch_size=batch_size,
+                                              shuffle=True,
+                                              num_workers=num_workers,
+                                              collate_fn=collate_fn)
+    return data_loader