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# Owner(s): ["oncall: package/deploy"]
import torch
class TestNnModule(torch.nn.Module):
def __init__(self, nz=6, ngf=9, nc=3):
super(TestNnModule, self).__init__()
self.main = torch.nn.Sequential(
# input is Z, going into a convolution
torch.nn.ConvTranspose2d(nz, ngf * 8, 4, 1, 0, bias=False),
torch.nn.BatchNorm2d(ngf * 8),
torch.nn.ReLU(True),
# state size. (ngf*8) x 4 x 4
torch.nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False),
torch.nn.BatchNorm2d(ngf * 4),
torch.nn.ReLU(True),
# state size. (ngf*4) x 8 x 8
torch.nn.ConvTranspose2d(ngf * 4, ngf * 2, 4, 2, 1, bias=False),
torch.nn.BatchNorm2d(ngf * 2),
torch.nn.ReLU(True),
# state size. (ngf*2) x 16 x 16
torch.nn.ConvTranspose2d(ngf * 2, ngf, 4, 2, 1, bias=False),
torch.nn.BatchNorm2d(ngf),
torch.nn.ReLU(True),
# state size. (ngf) x 32 x 32
torch.nn.ConvTranspose2d(ngf, nc, 4, 2, 1, bias=False),
torch.nn.Tanh()
# state size. (nc) x 64 x 64
)
def forward(self, input):
return self.main(input)
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