Spaces:
Runtime error
Runtime error
File size: 6,672 Bytes
113789d |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 |
import torch
import torch.nn as nn
import torch.nn.functional as F
class Transformer(nn.Module):
def __init__(self):
super(Transformer, self).__init__()
#
self.refpad01_1 = nn.ReflectionPad2d(3)
self.conv01_1 = nn.Conv2d(3, 64, 7)
self.in01_1 = InstanceNormalization(64)
# relu
self.conv02_1 = nn.Conv2d(64, 128, 3, 2, 1)
self.conv02_2 = nn.Conv2d(128, 128, 3, 1, 1)
self.in02_1 = InstanceNormalization(128)
# relu
self.conv03_1 = nn.Conv2d(128, 256, 3, 2, 1)
self.conv03_2 = nn.Conv2d(256, 256, 3, 1, 1)
self.in03_1 = InstanceNormalization(256)
# relu
## res block 1
self.refpad04_1 = nn.ReflectionPad2d(1)
self.conv04_1 = nn.Conv2d(256, 256, 3)
self.in04_1 = InstanceNormalization(256)
# relu
self.refpad04_2 = nn.ReflectionPad2d(1)
self.conv04_2 = nn.Conv2d(256, 256, 3)
self.in04_2 = InstanceNormalization(256)
# + input
## res block 2
self.refpad05_1 = nn.ReflectionPad2d(1)
self.conv05_1 = nn.Conv2d(256, 256, 3)
self.in05_1 = InstanceNormalization(256)
# relu
self.refpad05_2 = nn.ReflectionPad2d(1)
self.conv05_2 = nn.Conv2d(256, 256, 3)
self.in05_2 = InstanceNormalization(256)
# + input
## res block 3
self.refpad06_1 = nn.ReflectionPad2d(1)
self.conv06_1 = nn.Conv2d(256, 256, 3)
self.in06_1 = InstanceNormalization(256)
# relu
self.refpad06_2 = nn.ReflectionPad2d(1)
self.conv06_2 = nn.Conv2d(256, 256, 3)
self.in06_2 = InstanceNormalization(256)
# + input
## res block 4
self.refpad07_1 = nn.ReflectionPad2d(1)
self.conv07_1 = nn.Conv2d(256, 256, 3)
self.in07_1 = InstanceNormalization(256)
# relu
self.refpad07_2 = nn.ReflectionPad2d(1)
self.conv07_2 = nn.Conv2d(256, 256, 3)
self.in07_2 = InstanceNormalization(256)
# + input
## res block 5
self.refpad08_1 = nn.ReflectionPad2d(1)
self.conv08_1 = nn.Conv2d(256, 256, 3)
self.in08_1 = InstanceNormalization(256)
# relu
self.refpad08_2 = nn.ReflectionPad2d(1)
self.conv08_2 = nn.Conv2d(256, 256, 3)
self.in08_2 = InstanceNormalization(256)
# + input
## res block 6
self.refpad09_1 = nn.ReflectionPad2d(1)
self.conv09_1 = nn.Conv2d(256, 256, 3)
self.in09_1 = InstanceNormalization(256)
# relu
self.refpad09_2 = nn.ReflectionPad2d(1)
self.conv09_2 = nn.Conv2d(256, 256, 3)
self.in09_2 = InstanceNormalization(256)
# + input
## res block 7
self.refpad10_1 = nn.ReflectionPad2d(1)
self.conv10_1 = nn.Conv2d(256, 256, 3)
self.in10_1 = InstanceNormalization(256)
# relu
self.refpad10_2 = nn.ReflectionPad2d(1)
self.conv10_2 = nn.Conv2d(256, 256, 3)
self.in10_2 = InstanceNormalization(256)
# + input
## res block 8
self.refpad11_1 = nn.ReflectionPad2d(1)
self.conv11_1 = nn.Conv2d(256, 256, 3)
self.in11_1 = InstanceNormalization(256)
# relu
self.refpad11_2 = nn.ReflectionPad2d(1)
self.conv11_2 = nn.Conv2d(256, 256, 3)
self.in11_2 = InstanceNormalization(256)
# + input
##------------------------------------##
self.deconv01_1 = nn.ConvTranspose2d(256, 128, 3, 2, 1, 1)
self.deconv01_2 = nn.Conv2d(128, 128, 3, 1, 1)
self.in12_1 = InstanceNormalization(128)
# relu
self.deconv02_1 = nn.ConvTranspose2d(128, 64, 3, 2, 1, 1)
self.deconv02_2 = nn.Conv2d(64, 64, 3, 1, 1)
self.in13_1 = InstanceNormalization(64)
# relu
self.refpad12_1 = nn.ReflectionPad2d(3)
self.deconv03_1 = nn.Conv2d(64, 3, 7)
# tanh
def forward(self, x):
y = F.relu(self.in01_1(self.conv01_1(self.refpad01_1(x))))
y = F.relu(self.in02_1(self.conv02_2(self.conv02_1(y))))
t04 = F.relu(self.in03_1(self.conv03_2(self.conv03_1(y))))
##
y = F.relu(self.in04_1(self.conv04_1(self.refpad04_1(t04))))
t05 = self.in04_2(self.conv04_2(self.refpad04_2(y))) + t04
y = F.relu(self.in05_1(self.conv05_1(self.refpad05_1(t05))))
t06 = self.in05_2(self.conv05_2(self.refpad05_2(y))) + t05
y = F.relu(self.in06_1(self.conv06_1(self.refpad06_1(t06))))
t07 = self.in06_2(self.conv06_2(self.refpad06_2(y))) + t06
y = F.relu(self.in07_1(self.conv07_1(self.refpad07_1(t07))))
t08 = self.in07_2(self.conv07_2(self.refpad07_2(y))) + t07
y = F.relu(self.in08_1(self.conv08_1(self.refpad08_1(t08))))
t09 = self.in08_2(self.conv08_2(self.refpad08_2(y))) + t08
y = F.relu(self.in09_1(self.conv09_1(self.refpad09_1(t09))))
t10 = self.in09_2(self.conv09_2(self.refpad09_2(y))) + t09
y = F.relu(self.in10_1(self.conv10_1(self.refpad10_1(t10))))
t11 = self.in10_2(self.conv10_2(self.refpad10_2(y))) + t10
y = F.relu(self.in11_1(self.conv11_1(self.refpad11_1(t11))))
y = self.in11_2(self.conv11_2(self.refpad11_2(y))) + t11
##
y = F.relu(self.in12_1(self.deconv01_2(self.deconv01_1(y))))
y = F.relu(self.in13_1(self.deconv02_2(self.deconv02_1(y))))
y = torch.tanh(self.deconv03_1(self.refpad12_1(y)))
return y
class InstanceNormalization(nn.Module):
def __init__(self, dim, eps=1e-9):
super(InstanceNormalization, self).__init__()
self.scale = nn.Parameter(torch.FloatTensor(dim))
self.shift = nn.Parameter(torch.FloatTensor(dim))
self.eps = eps
self._reset_parameters()
def _reset_parameters(self):
self.scale.data.uniform_()
self.shift.data.zero_()
def __call__(self, x):
n = x.size(2) * x.size(3)
t = x.view(x.size(0), x.size(1), n)
mean = torch.mean(t, 2).unsqueeze(2).unsqueeze(3).expand_as(x)
# Calculate the biased var. torch.var returns unbiased var
var = torch.var(t, 2).unsqueeze(2).unsqueeze(3).expand_as(x) * (
(n - 1) / float(n)
)
scale_broadcast = self.scale.unsqueeze(1).unsqueeze(1).unsqueeze(0)
scale_broadcast = scale_broadcast.expand_as(x)
shift_broadcast = self.shift.unsqueeze(1).unsqueeze(1).unsqueeze(0)
shift_broadcast = shift_broadcast.expand_as(x)
out = (x - mean) / torch.sqrt(var + self.eps)
out = out * scale_broadcast + shift_broadcast
return out
|