File size: 907 Bytes
5d21dd2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 |
import torch.nn as nn
class ConvINRelu(nn.Module):
"""
A sequence of Convolution, Instance Normalization, and ReLU activation
"""
def __init__(self, channels_in, channels_out, stride):
super(ConvINRelu, self).__init__()
self.layers = nn.Sequential(
nn.Conv2d(channels_in, channels_out, 3, stride, padding=1),
nn.InstanceNorm2d(channels_out),
nn.ReLU(inplace=True)
)
def forward(self, x):
return self.layers(x)
class ConvBlock(nn.Module):
'''
Network that composed by layers of ConvINRelu
'''
def __init__(self, in_channels, out_channels, blocks=1, stride=1):
super(ConvBlock, self).__init__()
layers = [ConvINRelu(in_channels, out_channels, stride)] if blocks != 0 else []
for _ in range(blocks - 1):
layer = ConvINRelu(out_channels, out_channels, 1)
layers.append(layer)
self.layers = nn.Sequential(*layers)
def forward(self, x):
return self.layers(x)
|