File size: 4,471 Bytes
998bb30 |
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 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 |
# Code is copied from
# github.com/Alibaba-AAIG/Beyond-ImageNet-Attack:generator.py@863b7
import torch
from torch import nn
class ResidualBlock(nn.Module):
def __init__(self, num_filters):
super(ResidualBlock, self).__init__()
self.block = nn.Sequential(
nn.ReflectionPad2d(1),
nn.Conv2d(
in_channels=num_filters,
out_channels=num_filters,
kernel_size=3,
stride=1,
padding=0,
bias=False,
),
nn.BatchNorm2d(num_filters),
nn.ReLU(True),
nn.Dropout(0.5),
nn.ReflectionPad2d(1),
nn.Conv2d(
in_channels=num_filters,
out_channels=num_filters,
kernel_size=3,
stride=1,
padding=0,
bias=False,
),
nn.BatchNorm2d(num_filters),
)
def forward(self, x):
residual = self.block(x)
return x + residual
ngf = 64
class ResNetGenerator(nn.Module):
"""
https://github.com/Alibaba-AAIG/Beyond-ImageNet-Attack/blob/863b758ee4f4a6d3d4e7777c5f94f457fa449f73/generator.py#L14
Test Case:
>>> netG = ResNetGenerator()
>>> test_sample = torch.rand(1, 3, 224, 224)
>>> print("Generator output:", netG(test_sample).size())
>>> print(
>>> "Generator parameters:",
>>> sum(p.numel() for p in netG.parameters() if p.requires_grad),
>>> )
"""
def __init__(self, inception=False):
super(ResNetGenerator, self).__init__()
self.inception = inception
self.block1 = nn.Sequential(
nn.ReflectionPad2d(3),
nn.Conv2d(3, ngf, kernel_size=7, padding=0, bias=False),
nn.BatchNorm2d(ngf),
nn.ReLU(True),
)
# output: (ngf) x (n) x (n)
self.block2 = nn.Sequential(
nn.Conv2d(
ngf, ngf * 2, kernel_size=3, stride=2, padding=1, bias=False
),
nn.BatchNorm2d(ngf * 2),
nn.ReLU(True),
)
# output: (ngf*2) x (n/2) x (n/2)
self.block3 = nn.Sequential(
nn.Conv2d(
ngf * 2,
ngf * 4,
kernel_size=3,
stride=2,
padding=1,
bias=False,
),
nn.BatchNorm2d(ngf * 4),
nn.ReLU(True),
)
# output: (ngf*4) x (n/4) x (n/4)
self.resblock1 = ResidualBlock(ngf * 4)
self.resblock2 = ResidualBlock(ngf * 4)
self.resblock3 = ResidualBlock(ngf * 4)
self.resblock4 = ResidualBlock(ngf * 4)
self.resblock5 = ResidualBlock(ngf * 4)
self.resblock6 = ResidualBlock(ngf * 4)
# output: (ngf*4) x (n/4) x (n/4)
self.upsampl1 = nn.Sequential(
nn.ConvTranspose2d(
ngf * 4,
ngf * 2,
kernel_size=3,
stride=2,
padding=1,
output_padding=1,
bias=False,
),
nn.BatchNorm2d(ngf * 2),
nn.ReLU(True),
)
# output: (ngf*2) x (n/2) x (n/2)
self.upsampl2 = nn.Sequential(
nn.ConvTranspose2d(
ngf * 2,
ngf,
kernel_size=3,
stride=2,
padding=1,
output_padding=1,
bias=False,
),
nn.BatchNorm2d(ngf),
nn.ReLU(True),
)
# output: (ngf) x (n) x (n)
self.blockf = nn.Sequential(
nn.ReflectionPad2d(3), nn.Conv2d(ngf, 3, kernel_size=7, padding=0)
)
self.crop = nn.ConstantPad2d((0, -1, -1, 0), 0)
def forward(self, input):
x = self.block1(input)
x = self.block2(x)
x = self.block3(x)
x = self.resblock1(x)
x = self.resblock2(x)
x = self.resblock3(x)
x = self.resblock4(x)
x = self.resblock5(x)
x = self.resblock6(x)
x = self.upsampl1(x)
x = self.upsampl2(x)
x = self.blockf(x)
if self.inception:
x = self.crop(x)
return (torch.tanh(x) + 1) / 2
CDAGenerator = ResNetGenerator
|