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Use view instead of unsqueeze since ONNX v1.0 doesn't support it (#94)

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Michael Tu
2017-12-08 15:04:29 -05:00
committed by GitHub
parent 85f35bb994
commit eee160ea41
1 changed files with 11 additions and 7 deletions
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sm_cnn/model.py
+11 -7
View File
@@ -39,21 +39,25 @@ class SmPlusPlus(nn.Module):
self.combined_feature_vector = nn.Linear(n_hidden, n_hidden)
self.hidden = nn.Linear(n_hidden, n_classes)
def _unsqueeze(self, tensor):
dim = tensor.size()
return tensor.view(dim[0], 1, dim[1], dim[2])
def forward(self, x_question, x_answer, x_ext):
if self.mode == 'rand':
question = self.question_embed(x_question).unsqueeze(1)
answer = self.answer_embed(x_answer).unsqueeze(1) # (batch, sent_len, embed_dim)
question = self._unsqueeze(self.question_embed(x_question))
answer = self._unsqueeze(self.answer_embed(x_answer)) # (batch, 1, sent_len, embed_dim)
x = [F.tanh(self.conv_q(question)).squeeze(3), F.tanh(self.conv_a(answer)).squeeze(3)]
x = [F.max_pool1d(i, i.size(2)).squeeze(2) for i in x] # max-over-time pooling
# actual SM model mode (Severyn & Moschitti, 2015)
elif self.mode == 'static':
question = self.static_question_embed(x_question).unsqueeze(1)
answer = self.static_answer_embed(x_answer).unsqueeze(1) # (batch, sent_len, embed_dim)
question = self._unsqueeze(self.static_question_embed(x_question))
answer = self._unsqueeze(self.static_answer_embed(x_answer)) # (batch, 1, sent_len, embed_dim)
x = [F.tanh(self.conv_q(question)).squeeze(3), F.tanh(self.conv_a(answer)).squeeze(3)]
x = [F.max_pool1d(i, i.size(2)).squeeze(2) for i in x] # max-over-time pooling
elif self.mode == 'non-static':
question = self.nonstatic_question_embed(x_question).unsqueeze(1)
answer = self.nonstatic_answer_embed(x_answer).unsqueeze(1) # (batch, sent_len, embed_dim)
question = self._unsqueeze(self.nonstatic_question_embed(x_question))
answer = self._unsqueeze(self.nonstatic_answer_embed(x_answer)) # (batch, 1, sent_len, embed_dim)
x = [F.tanh(self.conv_q(question)).squeeze(3), F.tanh(self.conv_a(answer)).squeeze(3)]
x = [F.max_pool1d(i, i.size(2)).squeeze(2) for i in x] # max-over-time pooling
elif self.mode == 'multichannel':
@@ -76,4 +80,4 @@ class SmPlusPlus(nn.Module):
x = F.tanh(self.combined_feature_vector(x))
x = self.dropout(x)
x = self.hidden(x)
return x
return x