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dohe0342 2023-01-28 02:37:56 +09:00
parent 2b3776ecba
commit 6433d0e801
2 changed files with 1 additions and 44 deletions
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egs/librispeech/ASR/pruned_transducer_stateless_gtrans/.conformer_randomcombine.py.swp
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egs/librispeech/ASR/pruned_transducer_stateless_gtrans/conformer_randomcombine.py
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@ -213,56 +213,13 @@ class Conformer(EncoderInterface):
x = x.permute(1, 0, 2) # (T, N, C) ->(N, T, C)
layer_outputs = [x.permute(1, 0, 2) for x in layer_outputs]
'''
if self.group_num == 4:
x = self.layer_norm(1/4*(self.sigmoid(self.alpha[0])*layer_outputs[2] + \
self.sigmoid(self.alpha[1])*layer_outputs[5] + \
self.sigmoid(self.alpha[2])*layer_outputs[8] + \
self.sigmoid(self.alpha[3])*layer_outputs[11]
)
)
elif self.group_num == 6:
x = self.layer_norm(1/6*(self.sigmoid(self.alpha[0])*layer_outputs[1] + \
self.sigmoid(self.alpha[1])*layer_outputs[3] + \
self.sigmoid(self.alpha[2])*layer_outputs[5] + \
self.sigmoid(self.alpha[3])*layer_outputs[7] + \
self.sigmoid(self.alpha[4])*layer_outputs[9] + \
self.sigmoid(self.alpha[5])*layer_outputs[11]
)
)
elif self.group_num == 12:
x = self.layer_norm(1/12*(self.sigmoid(self.alpha[0])*layer_outputs[0] + \
self.sigmoid(self.alpha[1])*layer_outputs[1] + \
self.sigmoid(self.alpha[2])*layer_outputs[2] + \
self.sigmoid(self.alpha[3])*layer_outputs[3] + \
self.sigmoid(self.alpha[4])*layer_outputs[4] + \
self.sigmoid(self.alpha[5])*layer_outputs[5] + \
self.sigmoid(self.alpha[6])*layer_outputs[6] + \
self.sigmoid(self.alpha[7])*layer_outputs[7] + \
self.sigmoid(self.alpha[8])*layer_outputs[8] + \
self.sigmoid(self.alpha[9])*layer_outputs[9] + \
self.sigmoid(self.alpha[10])*layer_outputs[10] + \
self.sigmoid(self.alpha[11])*layer_outputs[11]
)
)
'''
if self.group_num != 0:
x = 0
for enum, alpha in enumerate(self.alpha):
x += self.sigmoid(alpha) * layer_outputs[(enum+1)*self.group_layer_num-1]
x = self.layer_norm(x/self.group_num)
'''
layer_outputs = [x.permute(1, 0, 2) for x in layer_outputs]
x = 0
for enum, alpha in enumerate(self.alpha):
x += self.sigmoid(alpha*layer_outputs[(enum+1)*self.group_layer_num-1])
x = self.layer_norm(x/self.group_num)
'''
return x, lengths
@torch.jit.export