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from torch import nn |
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import torch |
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import torch.nn.functional as F |
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ngf = 64 |
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num_channels = 3 |
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class Generator(nn.Module): |
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def __init__(self, latent_size): |
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super(Generator, self).__init__() |
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self.latent_size = latent_size |
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self.conv1 = nn.ConvTranspose2d( |
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self.latent_size, ngf*8, 4, 1, 0, bias=False) |
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self.bn1 = nn.BatchNorm2d(ngf*8) |
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self.conv2 = nn.ConvTranspose2d(ngf*8, ngf*4, 4, 2, 1, bias=False) |
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self.bn2 = nn.BatchNorm2d(ngf*4) |
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self.conv3 = nn.ConvTranspose2d(ngf*4, ngf*2, 4, 2, 1, bias=False) |
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self.bn3 = nn.BatchNorm2d(ngf*2) |
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self.conv4 = nn.ConvTranspose2d(ngf*2, ngf, 4, 2, 1, bias=False) |
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self.bn4 = nn.BatchNorm2d(ngf) |
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self.conv5 = nn.ConvTranspose2d(ngf, num_channels, 4, 2, 1, bias=False) |
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def forward(self, x): |
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x = F.relu(self.bn1(self.conv1(x)), inplace=True) |
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x = F.relu(self.bn2(self.conv2(x)), inplace=True) |
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x = F.relu(self.bn3(self.conv3(x)), inplace=True) |
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x = F.relu(self.bn4(self.conv4(x)), inplace=True) |
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return torch.tanh(self.conv5(x)) |
