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- if use_cuda:
- encoder = encoder.cuda()
- decoder = decoder.cuda()
- encoder = nn.DataParallel(encoder, dim=0)
- decoder = nn.DataParallel(decoder, dim=0)
- class EncoderRNN(nn.Module):
- def __init__(self, vocal_size, hidden_size):
- super(EncoderRNN, self).__init__()
- self.hidden_size = hidden_size
- self.embedding = nn.Embedding(vocal_size, hidden_size)
- self.gru = nn.GRU(hidden_size, hidden_size, batch_first=True)
- def forward(self, input_batch, input_batch_length, hidden):
- print(input_batch)
- print(input_batch_length)
- print(hidden)
- embedded = self.embedding(input_batch)
- packed_input = nn.utils.rnn.pack_padded_sequence(embedded, input_batch_length.cpu().numpy(), batch_first=True)
- output, hidden = self.gru(packed_input, hidden)
- return output, hidden
- def init_hidden(self, batch_size):
- result = torch.autograd.Variable(torch.zeros(1, batch_size, self.hidden_size))
- if use_cuda:
- return result.cuda()
- else:
- return result
- Traceback (most recent call last):
- File "train.py", line 156, in <module>
- train_iteration(encoder, decoder, fileDataSet)
- File "train.py", line 122, in train_iteration
- target_indices, encoder, decoder, encoder_optimizer, decoder_optimizer, criterion)
- File "train.py", line 49, in train
- encoder_output, encoder_hidden = encoder(input_batch, input_batch_length, encoder_hidden)
- File "/home/cjunjie/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 357, in __call__
- result = self.forward(*input, **kwargs)
- File "/home/cjunjie/anaconda3/lib/python3.6/site-packages/torch/nn/parallel/data_parallel.py", line 74, in forward
- return self.gather(outputs, self.output_device)
- File "/home/cjunjie/anaconda3/lib/python3.6/site-packages/torch/nn/parallel/data_parallel.py", line 86, in gather
- return gather(outputs, output_device, dim=self.dim)
- File "/home/cjunjie/anaconda3/lib/python3.6/site-packages/torch/nn/parallel/scatter_gather.py", line 65, in gather
- return gather_map(outputs)
- File "/home/cjunjie/anaconda3/lib/python3.6/site-packages/torch/nn/parallel/scatter_gather.py", line 60, in gather_map
- return type(out)(map(gather_map, zip(*outputs)))
- File "/home/cjunjie/anaconda3/lib/python3.6/site-packages/torch/nn/parallel/scatter_gather.py", line 60, in gather_map
- return type(out)(map(gather_map, zip(*outputs)))
- File "/home/cjunjie/anaconda3/lib/python3.6/site-packages/torch/nn/utils/rnn.py", line 39, in __new__
- return super(PackedSequence, cls).__new__(cls, *args[0])
- File "/home/cjunjie/anaconda3/lib/python3.6/site-packages/torch/nn/parallel/scatter_gather.py", line 57, in gather_map
- return Gather.apply(target_device, dim, *outputs)
- File "/home/cjunjie/anaconda3/lib/python3.6/site-packages/torch/nn/parallel/_functions.py", line 58, in forward
- assert all(map(lambda i: i.is_cuda, inputs))
- AssertionError
- 2.0000e+00 6.2900e+02 5.4000e+01 ... 0.0000e+00 0.0000e+00 0.0000e+00
- 2.0000e+00 1.6759e+04 6.0000e+00 ... 0.0000e+00 0.0000e+00 0.0000e+00
- 2.0000e+00 7.2000e+01 3.3500e+02 ... 0.0000e+00 0.0000e+00 0.0000e+00
- 2.0000e+00 5.4000e+01 1.2900e+02 ... 0.0000e+00 0.0000e+00 0.0000e+00
- [torch.cuda.LongTensor of size (4,2687) (GPU 0)]
- 1844
- 1507
- 1219
- 1021
- [torch.cuda.LongTensor of size (4,) (GPU 0)]
- ( 0 ,.,.) =
- 0 0 0 ... 0 0 0
- 0 0 0 ... 0 0 0
- 0 0 0 ... 0 0 0
- 0 0 0 ... 0 0 0
- [torch.cuda.FloatTensor of size (1,4,256) (GPU 0)]
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