215 lines
7.7 KiB
Python
215 lines
7.7 KiB
Python
"""
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Copyright (C) 2019 NVIDIA Corporation. All rights reserved.
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Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
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"""
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import torch
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import torch.nn as nn
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class InstanceNorm(nn.Module):
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def __init__(self, epsilon=1e-8):
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"""
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@notice: avoid in-place ops.
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https://discuss.pytorch.org/t/encounter-the-runtimeerror-one-of-the-variables-needed-for-gradient-computation-has-been-modified-by-an-inplace-operation/836/3
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"""
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super(InstanceNorm, self).__init__()
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self.epsilon = epsilon
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def forward(self, x):
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x = x - torch.mean(x, (2, 3), True)
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tmp = torch.mul(x, x) # or x ** 2
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tmp = torch.rsqrt(torch.mean(tmp, (2, 3), True) + self.epsilon)
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return x * tmp
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class ApplyStyle(nn.Module):
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"""
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@ref: https://github.com/lernapparat/lernapparat/blob/master/style_gan/pytorch_style_gan.ipynb
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"""
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def __init__(self, latent_size, channels):
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super(ApplyStyle, self).__init__()
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self.linear = nn.Linear(latent_size, channels * 2)
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def forward(self, x, latent):
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style = self.linear(latent) # style => [batch_size, n_channels*2]
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shape = [-1, 2, x.size(1), 1, 1]
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style = style.view(shape) # [batch_size, 2, n_channels, ...]
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#x = x * (style[:, 0] + 1.) + style[:, 1]
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x = x * (style[:, 0] * 1 + 1.) + style[:, 1] * 1
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return x
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class ResnetBlock_Adain(nn.Module):
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def __init__(self, dim, latent_size, padding_type, activation=nn.ReLU(True)):
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super(ResnetBlock_Adain, self).__init__()
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p = 0
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conv1 = []
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if padding_type == 'reflect':
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conv1 += [nn.ReflectionPad2d(1)]
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elif padding_type == 'replicate':
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conv1 += [nn.ReplicationPad2d(1)]
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elif padding_type == 'zero':
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p = 1
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else:
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raise NotImplementedError('padding [%s] is not implemented' % padding_type)
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conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding = p), InstanceNorm()]
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self.conv1 = nn.Sequential(*conv1)
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self.style1 = ApplyStyle(latent_size, dim)
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self.act1 = activation
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p = 0
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conv2 = []
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if padding_type == 'reflect':
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conv2 += [nn.ReflectionPad2d(1)]
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elif padding_type == 'replicate':
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conv2 += [nn.ReplicationPad2d(1)]
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elif padding_type == 'zero':
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p = 1
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else:
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raise NotImplementedError('padding [%s] is not implemented' % padding_type)
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conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p), InstanceNorm()]
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self.conv2 = nn.Sequential(*conv2)
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self.style2 = ApplyStyle(latent_size, dim)
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def forward(self, x, dlatents_in_slice):
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y = self.conv1(x)
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y = self.style1(y, dlatents_in_slice)
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y = self.act1(y)
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y = self.conv2(y)
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y = self.style2(y, dlatents_in_slice)
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out = x + y
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return out
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class Generator_Adain_Upsample(nn.Module):
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def __init__(self, input_nc, output_nc, latent_size, n_blocks=6, deep=False,
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norm_layer=nn.BatchNorm2d,
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padding_type='reflect'):
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assert (n_blocks >= 0)
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super(Generator_Adain_Upsample, self).__init__()
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activation = nn.ReLU(True)
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self.deep = deep
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self.first_layer = nn.Sequential(nn.ReflectionPad2d(3), nn.Conv2d(input_nc, 64, kernel_size=7, padding=0),
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norm_layer(64), activation)
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### downsample
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self.down1 = nn.Sequential(nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1),
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norm_layer(128), activation)
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self.down2 = nn.Sequential(nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1),
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norm_layer(256), activation)
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self.down3 = nn.Sequential(nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1),
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norm_layer(512), activation)
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if self.deep:
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self.down4 = nn.Sequential(nn.Conv2d(512, 512, kernel_size=3, stride=2, padding=1),
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norm_layer(512), activation)
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### resnet blocks
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BN = []
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for i in range(n_blocks):
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BN += [
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ResnetBlock_Adain(512, latent_size=latent_size, padding_type=padding_type, activation=activation)]
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self.BottleNeck = nn.Sequential(*BN)
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if self.deep:
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self.up4 = nn.Sequential(
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nn.Upsample(scale_factor=2, mode='bilinear'),
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nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
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nn.BatchNorm2d(512), activation
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)
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self.up3 = nn.Sequential(
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nn.Upsample(scale_factor=2, mode='bilinear'),
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nn.Conv2d(512, 256, kernel_size=3, stride=1, padding=1),
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nn.BatchNorm2d(256), activation
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)
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self.up2 = nn.Sequential(
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nn.Upsample(scale_factor=2, mode='bilinear'),
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nn.Conv2d(256, 128, kernel_size=3, stride=1, padding=1),
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nn.BatchNorm2d(128), activation
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)
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self.up1 = nn.Sequential(
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nn.Upsample(scale_factor=2, mode='bilinear'),
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nn.Conv2d(128, 64, kernel_size=3, stride=1, padding=1),
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nn.BatchNorm2d(64), activation
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)
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self.last_layer = nn.Sequential(nn.ReflectionPad2d(3), nn.Conv2d(64, output_nc, kernel_size=7, padding=0),
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nn.Tanh())
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def forward(self, input, dlatents):
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x = input # 3*224*224
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skip1 = self.first_layer(x)
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skip2 = self.down1(skip1)
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skip3 = self.down2(skip2)
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if self.deep:
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skip4 = self.down3(skip3)
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x = self.down4(skip4)
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else:
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x = self.down3(skip3)
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for i in range(len(self.BottleNeck)):
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x = self.BottleNeck[i](x, dlatents)
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if self.deep:
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x = self.up4(x)
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x = self.up3(x)
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x = self.up2(x)
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x = self.up1(x)
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x = self.last_layer(x)
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x = (x + 1) / 2
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return x
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class Discriminator(nn.Module):
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def __init__(self, input_nc, norm_layer=nn.BatchNorm2d, use_sigmoid=False):
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super(Discriminator, self).__init__()
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kw = 4
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padw = 1
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self.down1 = nn.Sequential(
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nn.Conv2d(input_nc, 64, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)
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)
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self.down2 = nn.Sequential(
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nn.Conv2d(64, 128, kernel_size=kw, stride=2, padding=padw),
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norm_layer(128), nn.LeakyReLU(0.2, True)
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)
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self.down3 = nn.Sequential(
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nn.Conv2d(128, 256, kernel_size=kw, stride=2, padding=padw),
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norm_layer(256), nn.LeakyReLU(0.2, True)
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)
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self.down4 = nn.Sequential(
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nn.Conv2d(256, 512, kernel_size=kw, stride=2, padding=padw),
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norm_layer(512), nn.LeakyReLU(0.2, True)
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)
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self.conv1 = nn.Sequential(
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nn.Conv2d(512, 512, kernel_size=kw, stride=1, padding=padw),
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norm_layer(512),
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nn.LeakyReLU(0.2, True)
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)
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if use_sigmoid:
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self.conv2 = nn.Sequential(
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nn.Conv2d(512, 1, kernel_size=kw, stride=1, padding=padw), nn.Sigmoid()
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)
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else:
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self.conv2 = nn.Sequential(
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nn.Conv2d(512, 1, kernel_size=kw, stride=1, padding=padw)
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)
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def forward(self, input):
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out = []
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x = self.down1(input)
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out.append(x)
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x = self.down2(x)
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out.append(x)
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x = self.down3(x)
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out.append(x)
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x = self.down4(x)
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out.append(x)
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x = self.conv1(x)
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out.append(x)
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x = self.conv2(x)
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out.append(x)
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return out |