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Make local variables out of nn.sequential members for onnx exportability #14

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Make local variables out of nn.sequential members for onnx exportability #14

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34 changes: 16 additions & 18 deletions TCN/tcn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,27 +15,25 @@ def forward(self, x):
class TemporalBlock(nn.Module):
def __init__(self, n_inputs, n_outputs, kernel_size, stride, dilation, padding, dropout=0.2):
super(TemporalBlock, self).__init__()
self.conv1 = weight_norm(nn.Conv1d(n_inputs, n_outputs, kernel_size,
stride=stride, padding=padding, dilation=dilation))
self.chomp1 = Chomp1d(padding)
self.relu1 = nn.ReLU()
self.dropout1 = nn.Dropout(dropout)

self.conv2 = weight_norm(nn.Conv1d(n_outputs, n_outputs, kernel_size,
stride=stride, padding=padding, dilation=dilation))
self.chomp2 = Chomp1d(padding)
self.relu2 = nn.ReLU()
self.dropout2 = nn.Dropout(dropout)

self.net = nn.Sequential(self.conv1, self.chomp1, self.relu1, self.dropout1,
self.conv2, self.chomp2, self.relu2, self.dropout2)
conv1 = weight_norm(nn.Conv1d(n_inputs, n_outputs, kernel_size,
stride=stride, padding=padding, dilation=dilation))
chomp1 = Chomp1d(padding)
relu1 = nn.ReLU()
dropout1 = nn.Dropout(dropout)

conv2 = weight_norm(nn.Conv1d(n_outputs, n_outputs, kernel_size,
stride=stride, padding=padding, dilation=dilation))
chomp2 = Chomp1d(padding)
relu2 = nn.ReLU()
dropout2 = nn.Dropout(dropout)

self.net = nn.Sequential(conv1, chomp1, relu1, dropout1,
conv2, chomp2, relu2, dropout2)
self.downsample = nn.Conv1d(n_inputs, n_outputs, 1) if n_inputs != n_outputs else None
self.relu = nn.ReLU()
self.init_weights()

def init_weights(self):
self.conv1.weight.data.normal_(0, 0.01)
self.conv2.weight.data.normal_(0, 0.01)
conv1.weight.data.normal_(0, 0.01)
conv2.weight.data.normal_(0, 0.01)
if self.downsample is not None:
self.downsample.weight.data.normal_(0, 0.01)

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