Spaces:
Build error
Build error
File size: 2,634 Bytes
28b1a6e |
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 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 |
# Taken from here: https://huggingface.co/spaces/awacke1/Image2LineDrawing
from torch import nn
norm_layer = nn.InstanceNorm2d
class ResidualBlock(nn.Module):
def __init__(self, in_features):
super(ResidualBlock, self).__init__()
conv_block = [
nn.ReflectionPad2d(1),
nn.Conv2d(in_features, in_features, 3),
norm_layer(in_features),
nn.ReLU(inplace=True),
nn.ReflectionPad2d(1),
nn.Conv2d(in_features, in_features, 3),
norm_layer(in_features),
]
self.conv_block = nn.Sequential(*conv_block)
def forward(self, x):
return x + self.conv_block(x)
class Generator(nn.Module):
def __init__(self, input_nc, output_nc, n_residual_blocks=9, sigmoid=True):
super(Generator, self).__init__()
# Initial convolution block
model0 = [
nn.ReflectionPad2d(3),
nn.Conv2d(input_nc, 64, 7),
norm_layer(64),
nn.ReLU(inplace=True),
]
self.model0 = nn.Sequential(*model0)
# Downsampling
model1 = []
in_features = 64
out_features = in_features * 2
for _ in range(2):
model1 += [
nn.Conv2d(in_features, out_features, 3, stride=2, padding=1),
norm_layer(out_features),
nn.ReLU(inplace=True),
]
in_features = out_features
out_features = in_features * 2
self.model1 = nn.Sequential(*model1)
model2 = []
# Residual blocks
for _ in range(n_residual_blocks):
model2 += [ResidualBlock(in_features)]
self.model2 = nn.Sequential(*model2)
# Upsampling
model3 = []
out_features = in_features // 2
for _ in range(2):
model3 += [
nn.ConvTranspose2d(
in_features, out_features, 3, stride=2, padding=1, output_padding=1
),
norm_layer(out_features),
nn.ReLU(inplace=True),
]
in_features = out_features
out_features = in_features // 2
self.model3 = nn.Sequential(*model3)
# Output layer
model4 = [nn.ReflectionPad2d(3), nn.Conv2d(64, output_nc, 7)]
if sigmoid:
model4 += [nn.Sigmoid()]
self.model4 = nn.Sequential(*model4)
def forward(self, x, cond=None):
out = self.model0(x)
out = self.model1(out)
out = self.model2(out)
out = self.model3(out)
out = self.model4(out)
return out
|