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c6d5483 | 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 | import torch.nn as nn
import torch
import albumentations as A
# CNN block will be used repeatly later
class CNNBlock(nn.Module):
def __init__(self, in_channels, out_channels, stride=2):
super().__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_channels, out_channels, 4, stride, bias=False, padding_mode='reflect'),
nn.BatchNorm2d(out_channels),
nn.LeakyReLU(0.2)
)
def forward(self, x):
return self.conv(x)
class PatchGan(torch.nn.Module):
""" Patch GAN Architecture """
@staticmethod
def create_contracting_block(in_channels: int, out_channels: int):
"""
Create encoding layer
:param in_channels:
:param out_channels:
:return:
"""
conv_layer = torch.nn.Sequential(
torch.nn.Conv2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=3,
padding=1,
),
torch.nn.ReLU(),
torch.nn.Conv2d(
in_channels=out_channels,
out_channels=out_channels,
kernel_size=3,
padding=1,
),
torch.nn.ReLU(),
)
max_pool = torch.nn.Sequential(
torch.nn.MaxPool2d(
stride=2,
kernel_size=2,
),
)
layer = torch.nn.Sequential(
conv_layer,
max_pool,
)
return layer
def __init__(self, input_channels: int, hidden_channels: int) -> None:
super().__init__()
self.resize_channels = torch.nn.Conv2d(
in_channels=input_channels,
out_channels=hidden_channels,
kernel_size=1,
)
self.enc1 = self.create_contracting_block(
in_channels=hidden_channels,
out_channels=hidden_channels * 2
)
self.enc2 = self.create_contracting_block(
in_channels=hidden_channels * 2,
out_channels=hidden_channels * 4
)
self.enc3 = self.create_contracting_block(
in_channels=hidden_channels * 4,
out_channels=hidden_channels * 8
)
self.enc4 = self.create_contracting_block(
in_channels=hidden_channels * 8,
out_channels=hidden_channels * 16
)
self.final_layer = torch.nn.Conv2d(
in_channels=hidden_channels * 16,
out_channels=1,
kernel_size=1,
)
def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
""" Forward patch gan layer """
inpt = torch.cat([x, y], axis=1)
resize_img = self.resize_channels(inpt)
enc1 = self.enc1(resize_img)
enc2 = self.enc2(enc1)
enc3 = self.enc3(enc2)
enc4 = self.enc4(enc3)
final_layer = self.final_layer(enc4)
return final_layer
# x, y <- concatenate the gen image and the input image to determin the gen image is real or not
class Discriminator(nn.Module):
def __init__(self, in_channels=3, features=[64, 128, 256, 512]):
super().__init__()
self.initial = nn.Sequential(
nn.Conv2d(in_channels * 2, features[0], kernel_size=4, stride=2, padding=1, padding_mode='reflect'),
nn.LeakyReLU(.2)
)
# save layers into a list
layers = []
in_channels = features[0]
for feature in features[1:]:
layers.append(
CNNBlock(
in_channels,
feature,
stride=1 if feature == features[-1] else 2
),
)
in_channels = feature
# append last conv layer
layers.append(
nn.Conv2d(in_channels, 1, kernel_size=4, stride=1, padding=1, padding_mode='reflect')
)
# create a model using the list of layers
self.model = nn.Sequential(*layers)
def forward(self, x, y):
x = torch.cat([x, y], dim=1)
x = self.initial(x)
return self.model(x)
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