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https://github.com/pytorch/pytorch.git
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775788f93b
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144556 Approved by: https://github.com/ezyang
34 lines
1.2 KiB
Python
34 lines
1.2 KiB
Python
# Owner(s): ["oncall: package/deploy"]
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import torch
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class TestNnModule(torch.nn.Module):
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def __init__(self, nz=6, ngf=9, nc=3):
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super().__init__()
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self.main = torch.nn.Sequential(
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# input is Z, going into a convolution
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torch.nn.ConvTranspose2d(nz, ngf * 8, 4, 1, 0, bias=False),
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torch.nn.BatchNorm2d(ngf * 8),
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torch.nn.ReLU(True),
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# state size. (ngf*8) x 4 x 4
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torch.nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False),
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torch.nn.BatchNorm2d(ngf * 4),
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torch.nn.ReLU(True),
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# state size. (ngf*4) x 8 x 8
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torch.nn.ConvTranspose2d(ngf * 4, ngf * 2, 4, 2, 1, bias=False),
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torch.nn.BatchNorm2d(ngf * 2),
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torch.nn.ReLU(True),
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# state size. (ngf*2) x 16 x 16
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torch.nn.ConvTranspose2d(ngf * 2, ngf, 4, 2, 1, bias=False),
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torch.nn.BatchNorm2d(ngf),
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torch.nn.ReLU(True),
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# state size. (ngf) x 32 x 32
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torch.nn.ConvTranspose2d(ngf, nc, 4, 2, 1, bias=False),
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torch.nn.Tanh(),
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# state size. (nc) x 64 x 64
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)
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def forward(self, input):
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return self.main(input)
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