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- # python: 3.6
- # encoding: utf-8
-
- import torch
- import torch.nn as nn
- from torch.nn.init import xavier_uniform_
- # import torch.nn.functional as F
-
-
- class Conv(nn.Module):
- """
- Basic 1-d convolution module.
- initialize with xavier_uniform
- """
-
- def __init__(self, in_channels, out_channels, kernel_size,
- stride=1, padding=0, dilation=1,
- groups=1, bias=True, activation='relu'):
- super(Conv, self).__init__()
- self.conv = nn.Conv1d(
- in_channels=in_channels,
- out_channels=out_channels,
- kernel_size=kernel_size,
- stride=stride,
- padding=padding,
- dilation=dilation,
- groups=groups,
- bias=bias)
- xavier_uniform_(self.conv.weight)
-
- activations = {
- 'relu': nn.ReLU(),
- 'tanh': nn.Tanh()}
- if activation in activations:
- self.activation = activations[activation]
- else:
- raise Exception(
- 'Should choose activation function from: ' +
- ', '.join([x for x in activations]))
-
- def forward(self, x):
- x = torch.transpose(x, 1, 2) # [N,L,C] -> [N,C,L]
- x = self.conv(x) # [N,C_in,L] -> [N,C_out,L]
- x = self.activation(x)
- x = torch.transpose(x, 1, 2) # [N,C,L] -> [N,L,C]
- return x
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