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

tutorials/09 - Image Captioning/resize.py

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+from PIL import Image
+import os
+
+
+def resize_image(image, size):
+    """Resizes an image to the given size."""
+    return image.resize(size, Image.ANTIALIAS)
+
+def resize_images(image_dir, output_dir, size):
+    """Resizes the images in the image_dir and save into the output_dir."""
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+
+    images = os.listdir(image_dir)
+    num_images = len(images)
+    for i, image in enumerate(images):
+        with open(os.path.join(image_dir, image), 'r+b') as f:
+            with Image.open(f) as img:
+                img = resize_image(img, size)
+                img.save(
+                    os.path.join(output_dir, image), img.format)
+        if i % 100 == 0:
+            print ('[%d/%d] Resized the images and saved into %s.' 
+                   %(i, num_images, output_dir)) 
+            
+def main():
+    splits = ['train', 'val']
+    for split in splits:
+        image_dir = './data/%s2014/' %split
+        output_dir = './data/%s2014resized' %split
+        resize_images(image_dir, output_dir, (256, 256))
+        
+
+if __name__ == '__main__':
+    main()

tutorials/09 - Image Captioning/train.py

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+from data import get_loader 
+from vocab import Vocabulary
+from models import EncoderCNN, DecoderRNN 
+from torch.autograd import Variable 
+from torch.nn.utils.rnn import pack_padded_sequence
+import torch
+import torch.nn as nn 
+import numpy as np 
+import torchvision.transforms as T 
+import pickle 
+
+# Hyper Parameters
+num_epochs = 5
+batch_size = 100
+embed_size = 128
+hidden_size = 512
+num_layers = 1
+learning_rate = 0.001
+train_image_path = './data/train2014resized/'
+train_json_path = './data/annotations/captions_train2014.json'
+
+# Image Preprocessing
+transform = T.Compose([
+    T.RandomHorizontalFlip(), 
+    T.ToTensor(), 
+    T.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))])
+
+# Load Vocabulary Wrapper
+with open('./data/vocab.pkl', 'rb') as f:
+        vocab = pickle.load(f)
+        
+# Build Dataset Loader
+train_loader = get_loader(train_image_path, train_json_path, vocab, transform, 
+                          batch_size=batch_size, shuffle=True, num_workers=2) 
+total_step = len(train_loader)
+
+# Build Models
+encoder = EncoderCNN(embed_size)
+decoder = DecoderRNN(embed_size, hidden_size, len(vocab), num_layers)
+encoder.cuda()
+decoder.cuda()
+        
+# Loss and Optimizer
+criterion = nn.CrossEntropyLoss()
+optimizer = torch.optim.Adam(decoder.parameters(), lr=learning_rate)
+
+# Train the Decoder
+for epoch in range(num_epochs):
+    for i, (images, captions, lengths) in enumerate(train_loader):
+        # Set mini-batch dataset
+        images = Variable(images).cuda()
+        captions = Variable(captions).cuda()
+        targets = pack_padded_sequence(captions, lengths, batch_first=True)[0]
+        
+        # Forward, Backward and Optimize
+        decoder.zero_grad()
+        features = encoder(images)
+        outputs = decoder(features, captions, lengths)
+        loss = criterion(outputs, targets)
+        loss.backward()
+        optimizer.step()
+        
+        if i % 100 == 0:
+            print('Epoch [%d/%d], Step [%d/%d], Loss: %.4f, Perplexity: %5.4f'
+                  %(epoch, num_epochs, i, total_step, loss.data[0], np.exp(loss.data[0])))    
+            
+# Save the Model
+torch.save(decoder, 'decoder.pkl')
+torch.save(encoder, 'encoder.pkl')

tutorials/09 - Image Captioning/vocab.py

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+# Create a vocabulary wrapper
+import nltk
+import pickle
+from collections import Counter
+from pycocotools.coco import COCO
+
+
+class Vocabulary(object):
+    """Simple vocabulary wrapper."""
+    def __init__(self):
+        self.word2idx = {}
+        self.idx2word = {}
+        self.idx = 0
+    
+    def add_word(self, word):
+        if not word in self.word2idx:
+            self.word2idx[word] = self.idx
+            self.idx2word[self.idx] = word
+            self.idx += 1
+    
+    def __call__(self, word):
+        if not word in self.word2idx:
+            return self.word2idx['<unk>']
+        return self.word2idx[word]
+    
+    def __len__(self):
+        return len(self.word2idx)
+
+def build_vocab(json, threshold):
+    """Build a simple vocabulary wrapper."""
+    coco = COCO(json)
+    counter = Counter()
+    ids = coco.anns.keys()
+    for i, id in enumerate(ids):
+        caption = str(coco.anns[id]['caption'])
+        tokens = nltk.tokenize.word_tokenize(caption.lower())
+        counter.update(tokens)
+        
+        if i % 1000 == 0:
+            print("[%d/%d] tokenized the captions." %(i, len(ids)))
+        
+    # Discard if the occurrence of the word is less than min_word_cnt.
+    words = [word for word, cnt in counter.items() if cnt >= threshold]
+
+    # Create a vocab wrapper and add some special tokens.
+    vocab = Vocabulary()
+    vocab.add_word('<pad>') 
+    vocab.add_word('<start>') 
+    vocab.add_word('<end>') 
+    vocab.add_word('<unk>') 
+    
+    # Add words to the vocabulary.
+    for i, word in enumerate(words):
+        vocab.add_word(word)
+    return vocab
+
+def main():
+    vocab = create_vocab(json='./data/annotations/captions_train2014.json',
+                         threshold=4)
+    with open('./data/vocab.pkl', 'wb') as f:
+        pickle.dump(vocab, f, pickle.HIGHEST_PROTOCOL)
+    print("Saved vocabulary file to ", './data/vocab.pkl')
+    
+if __name__ == '__main__':
+    main()