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121 lines
3.8 KiB
Python
121 lines
3.8 KiB
Python
"""
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---
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title: Deep Convolutional Generative Adversarial Networks (DCGAN)
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summary: A simple PyTorch implementation/tutorial of Deep Convolutional Generative Adversarial Networks (DCGAN).
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---
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# Deep Convolutional Generative Adversarial Networks (DCGAN)
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This is a [PyTorch](https://pytorch.org) implementation of paper
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[Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://arxiv.org/abs/1511.06434).
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This implementation is based on the [PyTorch DCGAN Tutorial](https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html).
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"""
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import torch.nn as nn
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from labml import experiment
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from labml.configs import calculate
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from labml_helpers.module import Module
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from labml_nn.gan.simple_mnist_experiment import Configs
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class Generator(Module):
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"""
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### Convolutional Generator Network
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This is similar to the de-convolutional network used for CelebA faces,
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but modified for MNIST images.
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<img src="https://pytorch.org/tutorials/_images/dcgan_generator.png" style="max-width:90%" />
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"""
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def __init__(self):
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super().__init__()
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# The input is $1 \times 1$ with 100 channels
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self.layers = nn.Sequential(
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# This gives $3 \times 3$ output
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nn.ConvTranspose2d(100, 1024, 3, 1, 0, bias=False),
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nn.BatchNorm2d(1024),
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nn.ReLU(True),
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# This gives $7 \times 7$
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nn.ConvTranspose2d(1024, 512, 3, 2, 0, bias=False),
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nn.BatchNorm2d(512),
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nn.ReLU(True),
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# This gives $14 \times 14$
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nn.ConvTranspose2d(512, 256, 4, 2, 1, bias=False),
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nn.BatchNorm2d(256),
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nn.ReLU(True),
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# This gives $28 \times 28$
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nn.ConvTranspose2d(256, 1, 4, 2, 1, bias=False),
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nn.Tanh()
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)
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self.apply(_weights_init)
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def __call__(self, x):
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# Change from shape `[batch_size, 100]` to `[batch_size, 100, 1, 1]`
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x = x.unsqueeze(-1).unsqueeze(-1)
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x = self.layers(x)
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return x
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class Discriminator(Module):
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"""
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### Convolutional Discriminator Network
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"""
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def __init__(self):
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super().__init__()
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# The input is $28 \times 28$ with one channel
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self.layers = nn.Sequential(
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# This gives $14 \times 14$
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nn.Conv2d(1, 256, 4, 2, 1, bias=False),
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nn.LeakyReLU(0.2, inplace=True),
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# This gives $7 \times 7$
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nn.Conv2d(256, 512, 4, 2, 1, bias=False),
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nn.BatchNorm2d(512),
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nn.LeakyReLU(0.2, inplace=True),
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# This gives $3 \times 3$
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nn.Conv2d(512, 1024, 3, 2, 0, bias=False),
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nn.BatchNorm2d(1024),
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nn.LeakyReLU(0.2, inplace=True),
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# This gives $1 \times 1$
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nn.Conv2d(1024, 1, 3, 1, 0, bias=False),
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)
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self.apply(_weights_init)
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def forward(self, x):
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x = self.layers(x)
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return x.view(x.shape[0], -1)
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def _weights_init(m):
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classname = m.__class__.__name__
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if classname.find('Conv') != -1:
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nn.init.normal_(m.weight.data, 0.0, 0.02)
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elif classname.find('BatchNorm') != -1:
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nn.init.normal_(m.weight.data, 1.0, 0.02)
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nn.init.constant_(m.bias.data, 0)
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# We import the [simple gan experiment]((simple_mnist_experiment.html) and change the
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# generator and discriminator networks
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calculate(Configs.generator, 'cnn', lambda c: Generator().to(c.device))
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calculate(Configs.discriminator, 'cnn', lambda c: Discriminator().to(c.device))
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def main():
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conf = Configs()
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experiment.create(name='mnist_dcgan', comment='test')
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experiment.configs(conf,
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{'discriminator': 'cnn',
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'generator': 'cnn',
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'label_smoothing': 0.01})
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with experiment.start():
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conf.run()
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if __name__ == '__main__':
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main()
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