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import torch
import torch.nn as nn
import torch.nn.functional as F
class UNet(nn.Module):
def __init__(self, in_channels, out_channels):
super(UNet, self).__init__()
def conv_block(in_channels, out_channels):
return nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
nn.ReLU(inplace=True)
)
self.encoder1 = conv_block(in_channels, 64)
self.encoder2 = conv_block(64, 128)
self.encoder3 = conv_block(128, 256)
self.encoder4 = conv_block(256, 512)
self.bottleneck = conv_block(512, 1024)
self.upconv4 = nn.ConvTranspose2d(1024, 512, kernel_size=2, stride=2)
self.decoder4 = conv_block(1024, 512)
self.upconv3 = nn.ConvTranspose2d(512, 256, kernel_size=2, stride=2)
self.decoder3 = conv_block(512, 256)
self.upconv2 = nn.ConvTranspose2d(256, 128, kernel_size=2, stride=2)
self.decoder2 = conv_block(256, 128)
self.upconv1 = nn.ConvTranspose2d(128, 64, kernel_size=2, stride=2)
self.decoder1 = conv_block(128, 64)
self.final = nn.Conv2d(64, out_channels, kernel_size=1)
def forward(self, x):
enc1 = self.encoder1(x)
enc2 = self.encoder2(F.max_pool2d(enc1, kernel_size=2, stride=2))
enc3 = self.encoder3(F.max_pool2d(enc2, kernel_size=2, stride=2))
enc4 = self.encoder4(F.max_pool2d(enc3, kernel_size=2, stride=2))
bottleneck = self.bottleneck(F.max_pool2d(enc4, kernel_size=2, stride=2))
dec4 = self.upconv4(bottleneck)
dec4 = torch.cat((dec4, enc4), dim=1)
dec4 = self.decoder4(dec4)
dec3 = self.upconv3(dec4)
dec3 = torch.cat((dec3, enc3), dim=1)
dec3 = self.decoder3(dec3)
dec2 = self.upconv2(dec3)
dec2 = torch.cat((dec2, enc2), dim=1)
dec2 = self.decoder2(dec2)
dec1 = self.upconv1(dec2)
dec1 = torch.cat((dec1, enc1), dim=1)
dec1 = self.decoder1(dec1)
return self.final(dec1)
if __name__ == "__main__":
model = UNet(in_channels=3,out_channels=7)
fake_img = torch.rand(size=(2,3,224,224))
print(fake_img.shape)
# torch.Size([2, 3, 224, 224])
out = model(fake_img)
print(out.shape)
# torch.Size([2, 7, 224, 224]) |