Xuanhong Chen
67 lines
2.0 KiB
Python
67 lines
2.0 KiB
Python
import torch
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import torch.nn as nn
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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),
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norm_layer(64),
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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),
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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),
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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),
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nn.LeakyReLU(0.2, True)
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)
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self.down5 = nn.Sequential(
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nn.Conv2d(512, 512, kernel_size=kw, stride=2, 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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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),
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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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x = self.down5(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 |