class ResNet13Light(nn.Module):
def __init__(self, channels, down_pad=False):
super(ResNet13Light, self).__init__()
down_padding = 0
if down_pad:
down_padding = 1
# inchannels, outchannels, kernel size
self.conv1 = nn.Conv2d(3, channels, 3, stride=2, padding=down_padding)
self.block1 = ResBlockStrided(channels,
stride=2,
down_padding=down_padding)
self.block15 = ResBlock(channels)
self.block2 = ResBlockStrided(channels,
stride=2,
down_padding=down_padding)
self.block25 = ResBlock(channels)
self.block3 = ResBlockStrided(channels,
stride=1,
down_padding=down_padding)
self.block35 = ResBlock(channels)
self.res_norm = nn.InstanceNorm2d(channels)
def get_downscale_factor(self):
return 8
def init_weights(self):
self.block1.init_weights()
self.block2.init_weights()
self.block3.init_weights()
self.block15.init_weights()
self.block25.init_weights()
self.block35.init_weights()
torch.nn.init.kaiming_uniform_(self.conv1.weight)
self.conv1.bias.data.fill_(0)
def forward(self, input):
x = self.conv1(input)
x = self.block1(x)
x = self.block15(x)
x = self.block2(x)
x = self.block25(x)
x = self.block3(x)
x = self.block35(x)
x = self.res_norm(x)
return x
def __init__(self, channels, down_pad=False):
super(ResNet13S, self).__init__()
down_padding = 0
if down_pad:
down_padding = 1
# inchannels, outchannels, kernel size
self.conv1 = nn.Conv2d(3, channels, 3, stride=1, padding=down_padding)
self.block1 = ResBlockStrided(channels, stride=2, down_padding=down_padding)
self.block15 = ResBlock(channels)
self.block2 = ResBlockStrided(channels, stride=2, down_padding=down_padding)
self.block25 = ResBlock(channels)
self.block3 = ResBlockStrided(channels, stride=1, down_padding=down_padding)
self.block35 = ResBlock(channels)
self.res_norm = nn.InstanceNorm2d(channels)
class ResNet30(torch.nn.Module):
def __init__(self, channels, down_pad=False):
super(ResNet30, self).__init__()
down_padding = 0
if down_pad:
down_padding = 1
# inchannels, outchannels, kernel size
self.conv1 = nn.Conv2d(3, channels, 3, stride=1, padding=down_padding)
self.block1 = ResBlock(channels)
self.block2 = ResBlock(channels)
self.block3 = ResBlock(channels)
self.block4 = ResBlockStrided(channels,
stride=2,
down_padding=down_padding)
self.block5 = ResBlock(channels)
self.block6 = ResBlock(channels)
self.block7 = ResBlock(channels)
self.block8 = ResBlockStrided(channels,
stride=2,
down_padding=down_padding)
self.block9 = ResBlock(channels)
self.block10 = ResBlock(channels)
self.block11 = ResBlock(channels)
self.block12 = ResBlockStrided(channels,
stride=2,
down_padding=down_padding)
self.res_norm = nn.InstanceNorm2d(channels)
def init_weights(self):
self.block1.init_weights()
self.block2.init_weights()
self.block3.init_weights()
self.block4.init_weights()
self.block5.init_weights()
self.block6.init_weights()
self.block7.init_weights()
self.block8.init_weights()
self.block9.init_weights()
self.block10.init_weights()
self.block11.init_weights()
self.block12.init_weights()
torch.nn.init.kaiming_uniform(self.conv1.weight)
self.conv1.bias.data.fill_(0)
def forward(self, input):
x = self.conv1(input)
x = self.block1(x)
x = self.block2(x)
x = self.block3(x)
x = self.block4(x)
x = self.block5(x)
x = self.block6(x)
x = self.block7(x)
x = self.block8(x)
x = self.block9(x)
x = self.block10(x)
x = self.block11(x)
x = self.block12(x)
x = self.res_norm(x)
return x
def __init__(self, embed_size, channels, c_out):
super(ResNetConditional, self).__init__()
self.block1 = ResBlock(channels) # RF: 5x5
self.block1a = ResBlock(channels) # RF: 9x9
self.cblock1 = ResBlockConditional(embed_size, channels) # RF: 9x9
self.block2 = ResBlock(channels) # RF: 13x13
self.block2a = ResBlock(channels) # RF: 17x17
self.cblock2 = ResBlockConditional(embed_size, channels) # RF: 17x17
self.block3 = ResBlock(channels) # RF: 21x21
self.block3a = ResBlock(channels) # RF: 25x25
self.cblock3 = ResBlockConditional(embed_size, channels) # RF: 25x25
self.block4 = ResBlock(channels) # RF: 29x29
self.block4a = ResBlock(channels) # RF: 33x33
self.cblock4 = ResBlockConditional(embed_size, channels) # RF: 33x33
self.block5 = ResBlock(channels) # RF: 37x37
self.block5a = ResBlock(channels) # RF: 41x41
self.cblock5 = ResBlockConditional(embed_size, channels) # RF: 41x41
self.block6 = ResBlock(channels) # RF: 45x45
self.block6a = ResBlock(channels) # RF: 49x49
self.cblock6 = ResBlockConditional(embed_size, channels) # RF: 49x49
self.block7 = ResBlock(channels) # RF: 53x53
self.block7a = ResBlock(channels) # RF: 57x57
self.cblock7 = ResBlockConditional(embed_size, channels) # RF: 57x57
self.block8 = ResBlock(channels) # RF: 61x61
self.block8a = ResBlock(channels) # RF: 65x65
self.cblock8 = ResBlockConditional(embed_size, channels,
c_out) # RF: 65x65