defining encoder and decoder

model.py

@@ -20,7 +20,7 @@ def forward(self, images):
         features = self.embed(features)
         return features
 
-
+'''
 class DecoderRNN(nn.Module):
     def __init__(self, embed_size, hidden_size, vocab_size, num_layers=1):
         pass
@@ -30,4 +30,41 @@ def forward(self, features, captions):
 
     def sample(self, inputs, states=None, max_len=20):
         " accepts pre-processed image tensor (inputs) and returns predicted sentence (list of tensor ids of length max_len) "
-        pass
+        pass
+'''
+
+#https://pytorch.org/tutorials/intermediate/seq2seq_translation_tutorial.html
+
+class DecoderRNN(nn.Module):
+    def __init__(self, embed_size, hidden_size, vocab_size, num_layers=2, dropout=0.2):
+        """Set the hyper-parameters and build the layers."""
+        super(DecoderRNN, self).__init__()
+        self.embed = nn.Embedding(vocab_size, embed_size) ## outputsize, hidden_size
+        # The LSTM takes word embeddings as inputs, and outputs hidden states
+        # with dimensionality hidden_size
+        self.lstm = nn.LSTM(embed_size, hidden_size, num_layers, dropout=dropout, batch_first=True) ## embedding_dim, hidden_dim 
+        self.dropout = nn.Dropout(dropout)
+        self.linear = nn.Linear(hidden_size, vocab_size)
+
+    def forward(self, features, captions):
+        """Decode image feature vectors and generates captions."""
+        features = features.view(len(features), 1, -1)
+        embeddings = self.embed(captions[:, :-1]) ## remove the last `word`
+
+        inputs = torch.cat((features, embeddings), 1)
+        ltsm_out, hidden = self.lstm(inputs)
+        ltsm_out = self.dropout(ltsm_out)
+        out = self.linear(ltsm_out)
+        return out
+
+    def sample(self, inputs, states=None, max_len=20):
+        " accepts pre-processed image tensor (inputs) and returns predicted sentence (list of tensor ids of length max_len) "
+        result = []
+        for _ in range(max_len):
+            lstm_out, states = self.lstm(inputs, states)
+            out = self.linear(lstm_out.view(len(lstm_out), -1))
+            idx = out.max(1)[1]
+            result.append(int(idx.item()))
+            inputs = self.embed(idx)
+            inputs = inputs.unsqueeze(1)
+        return result