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Running
on
Zero
File size: 2,309 Bytes
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import torch
import torch.nn as nn
import torch.nn.functional as F
class DoubleConv(nn.Module):
"""(Conv2d => ReLU) * 2 with padding"""
def __init__(self, in_channels, out_channels):
super().__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1), # preserve spatial
nn.ReLU(inplace=True),
nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1), # preserve spatial
nn.ReLU(inplace=True),
)
def forward(self, x):
return self.conv(x)
class UNet(nn.Module):
def __init__(self, in_channels, out_channels, features=[64, 128, 256, 512]):
super().__init__()
self.downs = nn.ModuleList()
self.ups = nn.ModuleList()
# Downsampling part
for feature in features:
self.downs.append(DoubleConv(in_channels, feature))
in_channels = feature
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
# Bottleneck
self.bottleneck = DoubleConv(features[-1], features[-1] * 2)
# Upsampling part
for feature in reversed(features):
self.ups.append(nn.ConvTranspose2d(feature * 2, feature, kernel_size=2, stride=2))
self.ups.append(DoubleConv(feature * 2, feature))
# Final output
self.final_conv = nn.Conv2d(features[0], out_channels, kernel_size=1)
def forward(self, x):
skip_connections = []
# Encoder
for down in self.downs:
x = down(x)
skip_connections.append(x)
x = self.pool(x)
x = self.bottleneck(x)
# Decoder
skip_connections = skip_connections[::-1]
for idx in range(0, len(self.ups), 2):
x = self.ups[idx](x) # ConvTranspose2d
skip_connection = skip_connections[idx // 2]
if x.shape != skip_connection.shape:
x = F.interpolate(x, size=skip_connection.shape[2:]) # Fix mismatched shapes
x = torch.cat((skip_connection, x), dim=1)
x = self.ups[idx + 1](x)
return self.final_conv(x)
if __name__ == "__main__":
model = UNet(in_channels=3, out_channels=16)
x = torch.randn(1, 3, 256, 256)
out = model(x)
print(out.shape)
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