8000 resolving padding issue by rainyrainyguo · Pull Request #18 · jakezhaojb/ARAE · GitHub
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resolving padding issue #18

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resolving padding issue #18

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2237347
resolve pack_padded_sequence issue of models.py
rainyrainyguo Aug 24, 2018
f12499d
resolving pack_padded_sequence issue of models.py
rainyrainyguo Aug 24, 2018
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6 changes: 3 additions & 3 deletions lang/models.py
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Original file line number Diff line number Diff line change
Expand Up @@ -186,7 +186,7 @@ def forward(self, indices, lengths, noise, encode_only=False):

def encode(self, indices, lengths, noise):
embeddings = self.embedding(indices)
packed_embeddings = pack_padded_sequence(input=embeddings,
packed_embeddings = pack_padded_sequence(embeddings,
lengths=lengths,
batch_first=True)

Expand All @@ -195,7 +195,7 @@ def encode(self, indices, lengths, noise):
hidden = hidden / torch.norm(hidden, p=2, dim=1, keepdim=True)

if noise and self.noise_r > 0:
gauss_noise = torch.normal(means=torch.zeros(hidden.size()),
gauss_noise = torch.normal(mean=torch.zeros(hidden.size()),
std=self.noise_r)
hidden = hidden + Variable(gauss_noise.cuda())

Expand All @@ -213,7 +213,7 @@ def decode(self, hidden, batch_size, maxlen, indices=None, lengths=None):

embeddings = self.embedding_decoder(indices)
augmented_embeddings = torch.cat([embeddings, all_hidden], 2)
packed_embeddings = pack_padded_sequence(input=augmented_embeddings,
packed_embeddings = pack_padded_sequence(augmented_embeddings,
lengths=lengths,
batch_first=True)

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12 changes: 6 additions & 6 deletions yelp/models.py
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Original file line number Diff line number Diff line change
Expand Up @@ -155,7 +155,7 @@ def forward(self, whichdecoder, indices, lengths, noise=False, encode_only=False

def encode(self, indices, lengths, noise):
embeddings = self.embedding(indices)
packed_embeddings = pack_padded_sequence(input=embeddings,
packed_embeddings = pack_padded_sequence(embeddings,
lengths=lengths,
batch_first=True)

Expand All @@ -175,7 +175,7 @@ def encode(self, indices, lengths, noise):
# hidden = torch.div(hidden, norms.unsqueeze(1).expand_as(hidden))

if noise and self.noise_r > 0:
gauss_noise = torch.normal(means=torch.zeros(hidden.size()),
gauss_noise = torch.normal(mean=torch.zeros(hidden.size()),
std=self.noise_r)
hidden = hidden + to_gpu(self.gpu, Variable(gauss_noise))

Expand All @@ -197,7 +197,7 @@ def decode(self, whichdecoder, hidden, batch_size, maxlen, indices=None, lengths
embeddings = self.embedding_decoder2(indices)

augmented_embeddings = torch.cat([embeddings, all_hidden], 2)
packed_embeddings = pack_padded_sequence(input=augmented_embeddings,
packed_embeddings = pack_padded_sequence(augmented_embeddings,
lengths=lengths,
batch_first=True)

Expand Down Expand Up @@ -443,7 +443,7 @@ def forward(self, indices, lengths, noise, encode_only=False):

def encode(self, indices, lengths, noise):
embeddings = self.embedding(indices)
packed_embeddings = pack_padded_sequence(input=embeddings,
packed_embeddings = pack_padded_sequence(embeddings,
lengths=lengths,
batch_first=True)

Expand All @@ -463,7 +463,7 @@ def encode(self, indices, lengths, noise):
# hidden = torch.div(hidden, norms.unsqueeze(1).expand_as(hidden))

if noise and self.noise_r > 0:
gauss_noise = torch.normal(means=torch.zeros(hidden.size()),
gauss_noise = torch.normal(mean=torch.zeros(hidden.size()),
std=self.noise_r)
hidden = hidden + to_gpu(self.gpu, Variable(gauss_noise))

Expand All @@ -481,7 +481,7 @@ def decode(self, hidden, batch_size, maxlen, indices=None, lengths=None):

embeddings = self.embedding_decoder(indices)
augmented_embeddings = torch.cat([embeddings, all_hidden], 2)
packed_embeddings = pack_padded_sequence(input=augmented_embeddings,
packed_embeddings = pack_padded_sequence(augmented_embeddings,
46C0 lengths=lengths,
batch_first=True)

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