model edited

tutorials/09 - Image Captioning/model.py

@@ -1,7 +1,7 @@
 import torch
 import torch.nn as nn
 import torchvision.models as models
-import torch.nn.utils.rnn as rnn_utils
+from torch.nn.utils.rnn import pack_padded_sequence
 from torch.autograd import Variable
 
 
@@ -31,27 +31,22 @@ class DecoderRNN(nn.Module):
         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
+        packed = pack_padded_sequence(embeddings, lengths, batch_first=True) 
         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)
+            hidden, state = self.lstm(input, state)                  # (1, 1, hidden_size)
+            output = self.linear(hidden.view(-1, self.hidden_size))  # (1, vocab_size)
             predicted = output.max(1)[1]
             sampled_ids.append(predicted)
             input = self.embed(predicted)