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  1. if use_cuda:
  2.     encoder = encoder.cuda()
  3.     decoder = decoder.cuda()
  4.  
  5.     encoder = nn.DataParallel(encoder, dim=0)
  6.     decoder = nn.DataParallel(decoder, dim=0)
  7.  
  8. class EncoderRNN(nn.Module):
  9.     def __init__(self, vocal_size, hidden_size):
  10.         super(EncoderRNN, self).__init__()
  11.         self.hidden_size = hidden_size
  12.         self.embedding = nn.Embedding(vocal_size, hidden_size)
  13.         self.gru = nn.GRU(hidden_size, hidden_size, batch_first=True)
  14.  
  15.     def forward(self, input_batch, input_batch_length, hidden):
  16.         print(input_batch)
  17.         print(input_batch_length)
  18.         print(hidden)
  19.         embedded = self.embedding(input_batch)
  20.         packed_input = nn.utils.rnn.pack_padded_sequence(embedded, input_batch_length.cpu().numpy(), batch_first=True)
  21.         output, hidden = self.gru(packed_input, hidden)
  22.         return output, hidden
  23.  
  24.     def init_hidden(self, batch_size):
  25.         result = torch.autograd.Variable(torch.zeros(1, batch_size, self.hidden_size))
  26.  
  27.         if use_cuda:
  28.             return result.cuda()
  29.         else:
  30.             return result
  31.    
  32. Traceback (most recent call last):
  33.   File "train.py", line 156, in <module>
  34.     train_iteration(encoder, decoder, fileDataSet)
  35.   File "train.py", line 122, in train_iteration
  36.     target_indices, encoder, decoder, encoder_optimizer, decoder_optimizer, criterion)
  37.   File "train.py", line 49, in train
  38.     encoder_output, encoder_hidden = encoder(input_batch, input_batch_length, encoder_hidden)
  39.   File "/home/cjunjie/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 357, in __call__
  40.     result = self.forward(*input, **kwargs)
  41.   File "/home/cjunjie/anaconda3/lib/python3.6/site-packages/torch/nn/parallel/data_parallel.py", line 74, in forward
  42.     return self.gather(outputs, self.output_device)
  43.   File "/home/cjunjie/anaconda3/lib/python3.6/site-packages/torch/nn/parallel/data_parallel.py", line 86, in gather
  44.     return gather(outputs, output_device, dim=self.dim)
  45.   File "/home/cjunjie/anaconda3/lib/python3.6/site-packages/torch/nn/parallel/scatter_gather.py", line 65, in gather
  46.     return gather_map(outputs)
  47.   File "/home/cjunjie/anaconda3/lib/python3.6/site-packages/torch/nn/parallel/scatter_gather.py", line 60, in gather_map
  48.     return type(out)(map(gather_map, zip(*outputs)))
  49.   File "/home/cjunjie/anaconda3/lib/python3.6/site-packages/torch/nn/parallel/scatter_gather.py", line 60, in gather_map
  50.     return type(out)(map(gather_map, zip(*outputs)))
  51.   File "/home/cjunjie/anaconda3/lib/python3.6/site-packages/torch/nn/utils/rnn.py", line 39, in __new__
  52.     return super(PackedSequence, cls).__new__(cls, *args[0])
  53.   File "/home/cjunjie/anaconda3/lib/python3.6/site-packages/torch/nn/parallel/scatter_gather.py", line 57, in gather_map
  54.     return Gather.apply(target_device, dim, *outputs)
  55.   File "/home/cjunjie/anaconda3/lib/python3.6/site-packages/torch/nn/parallel/_functions.py", line 58, in forward
  56.     assert all(map(lambda i: i.is_cuda, inputs))
  57. AssertionError
  58.    
  59. 2.0000e+00  6.2900e+02  5.4000e+01  ...   0.0000e+00  0.0000e+00  0.0000e+00
  60.  2.0000e+00  1.6759e+04  6.0000e+00  ...   0.0000e+00  0.0000e+00  0.0000e+00
  61.  2.0000e+00  7.2000e+01  3.3500e+02  ...   0.0000e+00  0.0000e+00  0.0000e+00
  62.  2.0000e+00  5.4000e+01  1.2900e+02  ...   0.0000e+00  0.0000e+00  0.0000e+00
  63. [torch.cuda.LongTensor of size (4,2687) (GPU 0)]
  64.    
  65. 1844
  66.  1507
  67.  1219
  68.  1021
  69. [torch.cuda.LongTensor of size (4,) (GPU 0)]
  70.    
  71. ( 0 ,.,.) =
  72.    0   0   0  ...    0   0   0
  73.    0   0   0  ...    0   0   0
  74.    0   0   0  ...    0   0   0
  75.    0   0   0  ...    0   0   0
  76. [torch.cuda.FloatTensor of size (1,4,256) (GPU 0)]
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