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Change functional padding with padding layer

This commit is contained in:
Eren 2018-09-18 16:00:47 +02:00
parent 00c0c9cde6
commit 2bcd7dbb6f
1 changed files with 7 additions and 4 deletions
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11
layers/tacotron.py
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@ -57,6 +57,7 @@ class BatchNormConv1d(nn.Module):
activation=None): activation=None):
super(BatchNormConv1d, self).__init__() super(BatchNormConv1d, self).__init__()
self.padding = padding self.padding = padding
self.padder = nn.ConstantPad1d(padding, 0)
self.conv1d = nn.Conv1d( self.conv1d = nn.Conv1d(
in_channels, in_channels,
out_channels, out_channels,
@ -69,7 +70,7 @@ class BatchNormConv1d(nn.Module):
self.activation = activation self.activation = activation
def forward(self, x): def forward(self, x):
x = nn.functional.pad(x, self.padding) x = self.padder(x)
x = self.conv1d(x) x = self.conv1d(x)
if self.activation is not None: if self.activation is not None:
x = self.activation(x) x = self.activation(x)
@ -135,9 +136,11 @@ class CBHG(nn.Module):
padding=[(k - 1) // 2, k // 2], padding=[(k - 1) // 2, k // 2],
activation=self.relu) for k in range(1, K + 1) activation=self.relu) for k in range(1, K + 1)
]) ])
# max pooling of conv bank, padding with nn.functional # max pooling of conv bank, padding with nn.functional
# TODO: try average pooling OR larger kernel size # TODO: try average pooling OR larger kernel size
self.max_pool1d = nn.MaxPool1d(kernel_size=2, stride=1, padding=0) self.max_pool1d = nn.Sequential(
nn.ConstantPad1d([0, 1], value=0),
nn.MaxPool1d(kernel_size=2, stride=1, padding=0))
out_features = [K * conv_bank_features] + conv_projections[:-1] out_features = [K * conv_bank_features] + conv_projections[:-1]
activations = [self.relu] * (len(conv_projections) - 1) activations = [self.relu] * (len(conv_projections) - 1)
activations += [None] activations += [None]
@ -186,7 +189,6 @@ class CBHG(nn.Module):
outs.append(out) outs.append(out)
x = torch.cat(outs, dim=1) x = torch.cat(outs, dim=1)
assert x.size(1) == self.conv_bank_features * len(self.conv1d_banks) assert x.size(1) == self.conv_bank_features * len(self.conv1d_banks)
x = nn.functional.pad(x, [0, 1])
x = self.max_pool1d(x) x = self.max_pool1d(x)
for conv1d in self.conv1d_projections: for conv1d in self.conv1d_projections:
x = conv1d(x) x = conv1d(x)
@ -256,6 +258,7 @@ class PostCBHG(nn.Module):
highway_features=128, highway_features=128,
gru_features=128, gru_features=128,
num_highways=4) num_highways=4)
def forward(self, x): def forward(self, x):
return self.cbhg(x) return self.cbhg(x)