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https://github.com/aladdinpersson/Machine-Learning-Collection.git
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103 lines
3.5 KiB
Python
103 lines
3.5 KiB
Python
"""
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Discriminator and Generator implementation from DCGAN paper,
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with removed Sigmoid() as output from Discriminator (and therefor
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it should be called critic)
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Programmed by Aladdin Persson <aladdin.persson at hotmail dot com>
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* 2020-11-01: Initial coding
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* 2022-12-20: Small revision of code, checked that it works with latest PyTorch version
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"""
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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, channels_img, features_d):
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super(Discriminator, self).__init__()
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self.disc = nn.Sequential(
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# input: N x channels_img x 64 x 64
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nn.Conv2d(
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channels_img, features_d, kernel_size=4, stride=2, padding=1
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),
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nn.LeakyReLU(0.2),
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# _block(in_channels, out_channels, kernel_size, stride, padding)
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self._block(features_d, features_d * 2, 4, 2, 1),
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self._block(features_d * 2, features_d * 4, 4, 2, 1),
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self._block(features_d * 4, features_d * 8, 4, 2, 1),
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# After all _block img output is 4x4 (Conv2d below makes into 1x1)
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nn.Conv2d(features_d * 8, 1, kernel_size=4, stride=2, padding=0),
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)
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def _block(self, in_channels, out_channels, kernel_size, stride, padding):
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return nn.Sequential(
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nn.Conv2d(
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in_channels,
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out_channels,
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kernel_size,
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stride,
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padding,
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bias=False,
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),
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nn.InstanceNorm2d(out_channels, affine=True),
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nn.LeakyReLU(0.2),
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)
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def forward(self, x):
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return self.disc(x)
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class Generator(nn.Module):
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def __init__(self, channels_noise, channels_img, features_g):
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super(Generator, self).__init__()
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self.net = nn.Sequential(
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# Input: N x channels_noise x 1 x 1
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self._block(channels_noise, features_g * 16, 4, 1, 0), # img: 4x4
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self._block(features_g * 16, features_g * 8, 4, 2, 1), # img: 8x8
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self._block(features_g * 8, features_g * 4, 4, 2, 1), # img: 16x16
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self._block(features_g * 4, features_g * 2, 4, 2, 1), # img: 32x32
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nn.ConvTranspose2d(
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features_g * 2, channels_img, kernel_size=4, stride=2, padding=1
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),
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# Output: N x channels_img x 64 x 64
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nn.Tanh(),
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)
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def _block(self, in_channels, out_channels, kernel_size, stride, padding):
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return nn.Sequential(
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nn.ConvTranspose2d(
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in_channels,
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out_channels,
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kernel_size,
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stride,
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padding,
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bias=False,
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),
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nn.BatchNorm2d(out_channels),
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nn.ReLU(),
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)
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def forward(self, x):
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return self.net(x)
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def initialize_weights(model):
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# Initializes weights according to the DCGAN paper
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for m in model.modules():
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if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d, nn.BatchNorm2d)):
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nn.init.normal_(m.weight.data, 0.0, 0.02)
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def test():
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N, in_channels, H, W = 8, 3, 64, 64
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noise_dim = 100
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x = torch.randn((N, in_channels, H, W))
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disc = Discriminator(in_channels, 8)
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assert disc(x).shape == (N, 1, 1, 1), "Discriminator test failed"
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gen = Generator(noise_dim, in_channels, 8)
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z = torch.randn((N, noise_dim, 1, 1))
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assert gen(z).shape == (N, in_channels, H, W), "Generator test failed"
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print("Success, tests passed!")
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if __name__ == "__main__":
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test() |