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| 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 = F.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 | |