image captioning completed'

tutorials/09 - Image Captioning/configuration.py

@@ -1,3 +1,6 @@
+import torchvision.transforms as T 
+
+
 class Config(object):
     """Wrapper class for hyper-parameters."""
     def __init__(self):
@@ -8,6 +11,21 @@ class Config(object):
         self.word_count_threshold = 4
         self.num_threads = 2
         
+        # Image preprocessing in training phase
+        self.train_transform = T.Compose([
+            T.Scale(self.image_size),    
+            T.RandomCrop(self.crop_size),
+            T.RandomHorizontalFlip(), 
+            T.ToTensor(), 
+            T.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
+        
+        # Image preprocessing in test phase
+        self.test_transform = T.Compose([
+            T.Scale(self.crop_size),
+            T.CenterCrop(self.crop_size),
+            T.ToTensor(),
+            T.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
+        
         # Training 
         self.num_epochs = 5
         self.batch_size = 64
@@ -23,4 +41,7 @@ class Config(object):
         # Path 
         self.image_path = './data/'
         self.caption_path = './data/annotations/'
-        self.vocab_path = './data/'
+        self.vocab_path = './data/'
+        self.model_path = './model/'
+        self.trained_encoder = 'encoder-4-6000.pkl'
+        self.trained_decoder = 'decoder-4-6000.pkl'

tutorials/09 - Image Captioning/data.py

@@ -2,11 +2,13 @@ import torch
 import torchvision.transforms as transforms
 import torch.utils.data as data
 import os
+import sys
 import pickle
 import numpy as np
 import nltk
 from PIL import Image
 from vocab import Vocabulary
+sys.path.append('../../../coco/PythonAPI')
 from pycocotools.coco import COCO
 
 

tutorials/09 - Image Captioning/model.py

@@ -53,14 +53,14 @@ class DecoderRNN(nn.Module):
         return outputs
     
     def sample(self, features, states):
-        """Samples captions for given image features."""
+        """Samples captions for given image features (Greedy search)."""
         sampled_ids = []
         inputs = features.unsqueeze(1)
         for i in range(20):
             hiddens, states = self.lstm(inputs, states)                # (batch_size, 1, hidden_size)
-            outputs = self.linear(hiddens.unsqueeze())  # (batch_size, vocab_size)
+            outputs = self.linear(hiddens.squeeze(1))                  # (batch_size, vocab_size)
             predicted = outputs.max(1)[1]
             sampled_ids.append(predicted)
             inputs = self.embed(predicted)
-        sampled_ids = torch.cat(sampled_ids, 1)   # (batch_size, 20)
+        sampled_ids = torch.cat(sampled_ids, 1)                        # (batch_size, 20)
-        return sampled_ids
+        return sampled_ids.squeeze()

tutorials/09 - Image Captioning/requirements.txt

@@ -0,0 +1,5 @@
+matplotlib==2.0.0
+nltk==3.2.2
+numpy==1.12.0
+Pillow==4.0.0
+argparse

tutorials/09 - Image Captioning/sample.py

@@ -1,58 +1,77 @@
-import os
-import numpy as np
-import torch
-import torchvision.transforms as T
-import pickle
-import matplotlib.pyplot as plt
-from PIL import Image
-from model import EncoderCNN, DecoderRNN
 from vocab import Vocabulary
-from torch.autograd import Variable
+from model import EncoderCNN, DecoderRNN 
+from configuration import Config
+from PIL import Image
+from torch.autograd import Variable 
+import torch
+import torchvision.transforms as T 
+import matplotlib.pyplot as plt
+import numpy as np 
+import argparse
+import pickle 
+import os
 
-# Image processing 
-transform = T.Compose([
-    T.CenterCrop(224),
-    T.ToTensor(),
-    T.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))])
 
-# Hyper Parameters
+def main(params):
-embed_size = 128
+    # Configuration for hyper-parameters
-hidden_size = 512
+    config = Config()
-num_layers = 1
-
-# Load vocabulary
-with open('./data/vocab.pkl', 'rb') as f:
-    vocab = pickle.load(f)
     
-# Load an image array
+    # Image Preprocessing
-images = os.listdir('./data/train2014resized/')
+    transform = config.test_transform
-image_path = './data/train2014resized/' + images[12] 
-img = Image.open(image_path)
-image = transform(img).unsqueeze(0) 
 
-# Load the trained models
+    # Load vocabulary
-encoder = torch.load('./encoder.pkl')
+    with open(os.path.join(config.vocab_path, 'vocab.pkl'), 'rb') as f:
-decoder = torch.load('./decoder.pkl')
+        vocab = pickle.load(f)
 
-# Encode the image
+    # Build Models
-feature = encoder(Variable(image).cuda())
+    encoder = EncoderCNN(config.embed_size)
+    encoder.eval()  # evaluation mode (BN uses moving mean/variance)
+    decoder = DecoderRNN(config.embed_size, config.hidden_size, 
+                         len(vocab), config.num_layers)
+    
 
-# Set initial states
+    # Load the trained model parameters
-state = (Variable(torch.zeros(num_layers, 1, hidden_size).cuda()),
+    encoder.load_state_dict(torch.load(os.path.join(config.model_path, 
-         Variable(torch.zeros(num_layers, 1, hidden_size)).cuda())
+                                                    config.trained_encoder)))
+    decoder.load_state_dict(torch.load(os.path.join(config.model_path, 
+                                                    config.trained_decoder)))
 
-# Decode the feature to caption
+    # Prepare Image       
-ids = decoder.sample(feature, state)
+    image = Image.open(params['image'])
-
+    image_tensor = Variable(transform(image).unsqueeze(0))
-words = []
+    
-for id in ids:
+    # Set initial states
-    word = vocab.idx2word[id.data[0, 0]]
+    state = (Variable(torch.zeros(config.num_layers, 1, config.hidden_size)),
-    words.append(word)
+             Variable(torch.zeros(config.num_layers, 1, config.hidden_size)))
-    if  word == '<end>':
+    
-        break    
+    # If use gpu
-caption = ' '.join(words)
+    if torch.cuda.is_available():
-
+        encoder.cuda()
-# Display the image and generated caption
+        decoder.cuda()
-plt.imshow(img)
+        state = [s.cuda() for s in state]
-plt.show()
+        image_tensor = image_tensor.cuda()
-print (caption)
+    
+    # Generate caption from image
+    feature = encoder(image_tensor)
+    sampled_ids = decoder.sample(feature, state)
+    sampled_ids = sampled_ids.cpu().data.numpy()
+    
+    # Decode word_ids to words
+    sampled_caption = []
+    for word_id in sampled_ids:
+        word = vocab.idx2word[word_id]
+        sampled_caption.append(word)
+        if word == '<end>':
+            break
+    sentence = ' '.join(sampled_caption)
+    
+    # Print out image and generated caption.
+    print (sentence)
+    plt.imshow(np.asarray(image))
+    
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--image', type=str, required=True, help='image for generating caption')
+    args = parser.parse_args()
+    params = vars(args)
+    main(params)

tutorials/09 - Image Captioning/train.py

@@ -6,7 +6,6 @@ from torch.autograd import Variable
 from torch.nn.utils.rnn import pack_padded_sequence
 import torch
 import torch.nn as nn
-import torchvision.transforms as T 
 import numpy as np 
 import pickle 
 import os
@@ -16,14 +15,13 @@ def main():
     # Configuration for hyper-parameters
     config = Config()
     
+    # Create model directory
+    if not os.path.exists(config.model_path):
+        os.makedirs(config.model_path)
+    
     # Image preprocessing
-    transform = T.Compose([
+    transform = config.train_transform
-        T.Scale(config.image_size),     # no resize
+    
-        T.RandomCrop(config.crop_size),
-        T.RandomHorizontalFlip(), 
-        T.ToTensor(), 
-        T.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
-
     # Load vocabulary wrapper
     with open(os.path.join(config.vocab_path, 'vocab.pkl'), 'rb') as f:
         vocab = pickle.load(f)
@@ -40,22 +38,28 @@ def main():
     encoder = EncoderCNN(config.embed_size)
     decoder = DecoderRNN(config.embed_size, config.hidden_size, 
                          len(vocab), config.num_layers)
-    encoder.cuda()
+    
-    decoder.cuda()
+    if torch.cuda.is_available()
+        encoder.cuda()
+        decoder.cuda()
 
     # Loss and Optimizer
     criterion = nn.CrossEntropyLoss()
     params = list(decoder.parameters()) + list(encoder.resnet.fc.parameters())
     optimizer = torch.optim.Adam(params, lr=config.learning_rate)
-
+    
     # Train the Models
     for epoch in range(config.num_epochs):
         for i, (images, captions, lengths) in enumerate(train_loader):
             
             # Set mini-batch dataset
-            images = Variable(images).cuda()
+            images = Variable(images)
-            captions = Variable(captions).cuda()
+            captions = Variable(captions)
             targets = pack_padded_sequence(captions, lengths, batch_first=True)[0]
+            
+            if torch.cuda.is_available():
+                images = images.cuda()
+                captions = captions.cuda()
 
             # Forward, Backward and Optimize
             decoder.zero_grad()
@@ -80,5 +84,6 @@ def main():
                 torch.save(encoder.state_dict(), 
                            os.path.join(config.model_path, 
                                         'encoder-%d-%d.pkl' %(epoch+1, i+1)))
+                
 if __name__ == '__main__':
     main()