model code added

tutorials/09 - Image Captioning/model.py

@@ -0,0 +1,58 @@
+import torch
+import torch.nn as nn
+import torchvision.models as models
+import torch.nn.utils.rnn as rnn_utils
+from torch.autograd import Variable
+
+
+class EncoderCNN(nn.Module):
+    def __init__(self, embed_size):
+        """Load pretrained ResNet-152 and replace top fc layer."""
+        super(EncoderCNN, self).__init__()
+        self.resnet = models.resnet152(pretrained=True)
+        self.resnet.fc = nn.Linear(self.resnet.fc.in_features, embed_size)
+        for param in self.resnet.parameters():
+            param.requires_grad = False
+        
+    def forward(self, images):
+        """Extract image feature vectors."""
+        features = self.resnet(images)
+        return features
+    
+    
+class DecoderRNN(nn.Module):
+    def __init__(self, embed_size, hidden_size, vocab_size, num_layers):
+        """Set hyper-parameters and build layers."""
+        super(DecoderRNN, self).__init__()
+        self.embed_size = embed_size
+        self.hidden_size = hidden_size
+        self.vocab_size = vocab_size
+        self.embed = nn.Embedding(vocab_size, embed_size)
+        self.lstm = nn.LSTM(embed_size, hidden_size, num_layers)
+        self.linear = nn.Linear(hidden_size, vocab_size)
+        
+    def init_weights(self):
+        pass
+        
+    def forward(self, features, captions, lengths):
+        """Decode image feature vectors and generate caption."""
+        embeddings = self.embed(captions)
+        embeddings = torch.cat((features.unsqueeze(1), embeddings), 1)
+        packed = rnn_utils.pack_padded_sequence(embeddings, lengths, batch_first=True) # lengths is ok
+        hiddens, _ = self.lstm(packed)
+        outputs = self.linear(hiddens[0])
+        return outputs
+    
+    def sample(self, feature, state):
+        """Sample a caption for given a image feature."""
+        # (batch_size, seq_length, embed_size)
+        # features: (1, 128)
+        sampled_ids = []
+        input = feature.unsqueeze(1)
+        for i in range(20):
+            hidden, state = self.lstm(input, state)  # (1, 1, 512)
+            output = self.linear(hidden.view(-1, self.hidden_size))  # (1, 10000)
+            predicted = output.max(1)[1]
+            sampled_ids.append(predicted)
+            input = self.embed(predicted)
+        return sampled_ids

tutorials/09 - Image Captioning/vocab.py

@@ -55,7 +55,7 @@ def build_vocab(json, threshold):
     return vocab
 
 def main():
-    vocab = create_vocab(json='./data/annotations/captions_train2014.json',
+    vocab = build_vocab(json='./data/annotations/captions_train2014.json',
                          threshold=4)
     with open('./data/vocab.pkl', 'wb') as f:
         pickle.dump(vocab, f, pickle.HIGHEST_PROTOCOL)