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import torch
import torch.nn as nn
class SegmentationHead(nn.Module):
def __init__(self, in_channels: int, num_classes: int):
super().__init__()
self.head = nn.Sequential(
nn.Conv2d(in_channels, 256, kernel_size=3, padding=1),
nn.BatchNorm2d(256),
nn.ReLU(),
nn.Conv2d(256, 256, kernel_size=3, padding=1),
nn.BatchNorm2d(256),
nn.ReLU(),
nn.Upsample(size=(64, 64), mode="bilinear"),
nn.Conv2d(256, 128, kernel_size=3, padding=1),
nn.BatchNorm2d(128),
nn.ReLU(),
nn.Conv2d(128, 128, kernel_size=3, padding=1),
nn.BatchNorm2d(128),
nn.ReLU(),
nn.Upsample(size=(128, 128), mode="bilinear"),
nn.Conv2d(128, 64, kernel_size=3, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(),
nn.Conv2d(64, 64, kernel_size=3, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(),
nn.Upsample(size=(224, 224), mode="bilinear"),
nn.Conv2d(64, 32, kernel_size=3, padding=1),
nn.BatchNorm2d(32),
nn.ReLU(),
nn.Conv2d(32, 32, kernel_size=3, padding=1),
nn.BatchNorm2d(32),
nn.ReLU(),
nn.Conv2d(32, num_classes, kernel_size=3, padding=1),
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.head(x) |