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pack_padded_sequence cuda fix #87

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pack_padded_sequence cuda fix #87

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6 changes: 3 additions & 3 deletions flowtron.py
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Original file line number Diff line number Diff line change
Expand Up @@ -213,7 +213,7 @@ def run_padded_sequence(self, sorted_idx, unsort_idx, lens, padded_data, recurre
# sort the data by decreasing length using provided index
# we assume batch index is in dim=1
padded_data = padded_data[:, sorted_idx]
padded_data = nn.utils.rnn.pack_padded_sequence(padded_data, lens)
padded_data = nn.utils.rnn.pack_padded_sequence(padded_data, lens.cpu())
hidden_vectors = recurrent_model(padded_data)[0]
hidden_vectors, _ = nn.utils.rnn.pad_packed_sequence(hidden_vectors)
# unsort the results at dim=1 and return
Expand Down Expand Up @@ -320,7 +320,7 @@ def forward(self, x, in_lens):
for conv in self.convolutions:
x = F.dropout(F.relu(conv(x)), 0.5, self.training)
x = x.transpose(1, 2)
x = nn.utils.rnn.pack_padded_sequence(x, in_lens, batch_first=True)
x = nn.utils.rnn.pack_padded_sequence(x, in_lens.cpu(), batch_first=True)

self.lstm.flatten_parameters()
outputs, _ = self.lstm(x)
Expand Down Expand Up @@ -448,7 +448,7 @@ def run_padded_sequence(self, sorted_idx, unsort_idx, lens, padded_data,
# sort the data by decreasing length using provided index
# we assume batch index is in dim=1
padded_data = padded_data[:, sorted_idx]
padded_data = nn.utils.rnn.pack_padded_sequence(padded_data, lens)
padded_data = nn.utils.rnn.pack_padded_sequence(padded_data, lens.cpu())
hidden_vectors = recurrent_model(padded_data)[0]
hidden_vectors, _ = nn.utils.rnn.pad_packed_sequence(hidden_vectors)
# unsort the results at dim=1 and return
Expand Down