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generator.py
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generator.py
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import torch
import torch.nn as nn
class Generator(nn.Module):
def __init__(self, latent_dimension: int) -> None:
super().__init__()
self.verbose = True
self.model = nn.Sequential(
nn.Conv2d(latent_dimension, 64, 1, 1, 0, bias=False), # 1
# nn.BatchNorm2d(32),
nn.ReLU(inplace=True),
nn.ConvTranspose2d(64, 64, 2, 2, 0, bias=False), # 2
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.Conv2d(64, 128, 3, 1, 1, bias=False),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
nn.ConvTranspose2d(128, 128, 4, 2, 1, bias=False), # 4
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
nn.Conv2d(128, 128, 3, 1, 1, bias=False),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
nn.ConvTranspose2d(128, 128, 4, 2, 1, bias=False), # 8
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
nn.Conv2d(128, 128, 3, 1, 1, bias=False),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
nn.ConvTranspose2d(128, 128, 4, 2, 1, bias=False), # 16
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
nn.Conv2d(128, 128, 3, 1, 1, bias=False),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
nn.ConvTranspose2d(128, 128, 4, 2, 1, bias=False), # 32
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
nn.Conv2d(128, 128, 3, 1, 1, bias=False),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
nn.ConvTranspose2d(128, 128, 4, 2, 1, bias=False), # 64
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
nn.Conv2d(128, 64, 3, 1, 1, bias=False),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.Conv2d(64, 3, 3, 1, 1, bias=False),
nn.Tanh()
)
def forward(self, z: torch.Tensor) -> torch.Tensor:
x = z
for depth, module in enumerate(self.model.children()):
shape_before = x.size()
x = module(x)
shape_after = x.size()
if self.verbose is True:
print(f"{depth:02d}: {shape_before} --> {shape_after}")
self.verbose = False
output = x
# output: torch.Tensor = self.model(z)
return output