def __init__(self):
super(TransformerNet, self).__init__()
# Initial convolution layers
self.conv1 = ConvLayer(3, 32, kernel_size=9, stride=1)
self.in1 = nn.InstanceNorm2d(32, affine=True)
self.conv2 = ConvLayer(32, 64, kernel_size=3, stride=2)
self.in2 = nn.InstanceNorm2d(64, affine=True)
self.conv3 = ConvLayer(64, 128, kernel_size=3, stride=2)
self.in3 = nn.InstanceNorm2d(128, affine=True)
# Residual layers
self.res1 = ResidualBlock(128)
self.res2 = ResidualBlock(128)
self.res3 = ResidualBlock(128)
self.res4 = ResidualBlock(128)
self.res5 = ResidualBlock(128)
# Upsampling Layers
self.deconv1 = UpsampleConvLayer(128,
64,
kernel_size=3,
stride=1,
upsample=2)
self.in4 = nn.InstanceNorm2d(64, affine=True)
self.deconv2 = UpsampleConvLayer(64,
32,
kernel_size=3,
stride=1,
upsample=2)
self.in5 = nn.InstanceNorm2d(32, affine=True)
self.deconv3 = ConvLayer(32, 3, kernel_size=9, stride=1)
# Non-linearities
self.relu = nn.ReLU()
def __init__(self, channels):
super(ResidualBlock, self).__init__()
self.conv1 = ConvLayer(channels,
channels,
kernel_size=3,
stride=1)
self.in1 = nn.InstanceNorm2d(channels, affine=True)
self.conv2 = ConvLayer(channels,
channels,
kernel_size=3,
stride=1)
self.in2 = nn.InstanceNorm2d(channels, affine=True)
self.relu = nn.ReLU()