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  • I am building a DecoderRNN using PyTorch (This is an image-caption decoder):

    class DecoderRNN(nn.Module):
        def __init__(self, embed_size, hidden_size, vocab_size):
            super(DecoderRNN, self).__init__()
            self.hidden_size = hidden_size
            self.gru = nn.GRU(embed_size, hidden_size, hidden_size)
            self.softmax = nn.LogSoftmax(dim=1)
        def forward(self, features, captions):
            print (features.shape)
            print (captions.shape)
            output, hidden = self.gru(features, captions)
            output = self.softmax(self.out(output[0]))
            return output, hidden 
    

    The data have the following shapes:

    torch.Size([10, 200])  <- features.shape (10 for batch size)
    torch.Size([10, 12])   <- captions.shape (10 for batch size)
    

    Then I got the following errors. Any ideas what I missed here? Thanks!

    ---------------------------------------------------------------------------
    RuntimeError                              Traceback (most recent call last)
    <ipython-input-2-76e05ba08b1d> in <module>()
         44         # Pass the inputs through the CNN-RNN model.
         45         features = encoder(images)
    ---> 46         outputs = decoder(features, captions)
         48         # Calculate the batch loss.
    /opt/conda/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
        323         for hook in self._forward_pre_hooks.values():
        324             hook(self, input)
    --> 325         result = self.forward(*input, **kwargs)
        326         for hook in self._forward_hooks.values():
        327             hook_result = hook(self, input, result)
    /home/workspace/model.py in forward(self, features, captions)
         37         print (captions.shape)
         38         # features = features.unsqueeze(1)
    ---> 39         output, hidden = self.gru(features, captions)
         40         output = self.softmax(self.out(output[0]))
         41         return output, hidden
    /opt/conda/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
        323         for hook in self._forward_pre_hooks.values():
        324             hook(self, input)
    --> 325         result = self.forward(*input, **kwargs)
        326         for hook in self._forward_hooks.values():
        327             hook_result = hook(self, input, result)
    /opt/conda/lib/python3.6/site-packages/torch/nn/modules/rnn.py in forward(self, input, hx)
        167             flat_weight=flat_weight
        168         )
    --> 169         output, hidden = func(input, self.all_weights, hx)
        170         if is_packed:
        171             output = PackedSequence(output, batch_sizes)
    /opt/conda/lib/python3.6/site-packages/torch/nn/_functions/rnn.py in forward(input, *fargs, **fkwargs)
        383             return hack_onnx_rnn((input,) + fargs, output, args, kwargs)
        384         else:
    --> 385             return func(input, *fargs, **fkwargs)
        387     return forward
    /opt/conda/lib/python3.6/site-packages/torch/autograd/function.py in _do_forward(self, *input)
        326         self._nested_input = input
        327         flat_input = tuple(_iter_variables(input))
    --> 328         flat_output = super(NestedIOFunction, self)._do_forward(*flat_input)
        329         nested_output = self._nested_output
        330         nested_variables = _unflatten(flat_output, self._nested_output)
    /opt/conda/lib/python3.6/site-packages/torch/autograd/function.py in forward(self, *args)
        348     def forward(self, *args):
        349         nested_tensors = _map_variable_tensor(self._nested_input)
    --> 350         result = self.forward_extended(*nested_tensors)
        351         del self._nested_input
        352         self._nested_output = result
    /opt/conda/lib/python3.6/site-packages/torch/nn/_functions/rnn.py in forward_extended(self, input, weight, hx)
        292             hy = tuple(h.new() for h in hx)
    --> 294         cudnn.rnn.forward(self, input, hx, weight, output, hy)
        296         self.save_for_backward(input, hx, weight, output)
    /opt/conda/lib/python3.6/site-packages/torch/backends/cudnn/rnn.py in forward(fn, input, hx, weight, output, hy)
        206         if (not is_input_packed and input.dim() != 3) or (is_input_packed and input.dim() != 2):
        207             raise RuntimeError(
    --> 208                 'input must have 3 dimensions, got {}'.format(input.dim()))
        209         if fn.input_size != input.size(-1):
        210             raise RuntimeError('input.size(-1) must be equal to input_size. Expected {}, got {}'.format(
    RuntimeError: input must have 3 dimensions, got 2
      

    input of shape (seq_len, batch, input_size): tensor containing the features of the input sequence.

    Further you need to provide the hidden state (last encoder hidden state in this case) as second parameter:

    self.gru(input, h_0)
    

    Where input is your actual input and h_0 the hidden state which needs to be 3-dimensional as well:

    h_0 of shape (num_layers * num_directions, batch, hidden_size): tensor containing the initial hidden state for each element in the batch. Defaults to zero if not provided.

    https://pytorch.org/docs/master/nn.html#torch.nn.GRU

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