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import torch
import torch.nn as nn
import torch.optim as optim
class UNet(nn.Module):
def __init__(self):
super(UNet, self).__init__()
# Encoder
self.encoder = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=4, stride=2, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(64, 128, kernel_size=4, stride=2, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(128, 256, kernel_size=4, stride=2, padding=1),
nn.ReLU(inplace=True),
)
# Decoder
self.decoder = nn.Sequential(
nn.ConvTranspose2d(256, 128, kernel_size=4, stride=2, padding=1),
nn.ReLU(inplace=True),
nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1),
nn.ReLU(inplace=True),
nn.ConvTranspose2d(64, 3, kernel_size=4, stride=2, padding=1),
nn.Tanh()
)
def forward(self, x):
enc = self.encoder(x)
dec = self.decoder(enc)
return dec |