layers.py

import torch
import torch.nn as nn
import torch.nn.functional as F


class MultiScaleFusion(nn.Module):
    def __init__(self, in_ch: int, out_ch: int):
        super().__init__()
        self.scale1 = nn.Conv2d(in_ch, in_ch, 3, padding="same")
        self.scale2 = nn.Conv2d(in_ch, in_ch, 3, padding="same", dilation=2)
        self.scale3 = nn.Conv2d(in_ch, in_ch, 3, padding="same", dilation=4)
        self.proj = nn.Conv2d(in_ch * 3, out_ch, 1, bias=False) if in_ch * 3 != out_ch else nn.Identity()

    def forward(self, x):
        f1 = self.scale1(x)
        f2 = self.scale2(x)
        f3 = self.scale3(x)
        fused = torch.cat([f1, f2, f3], dim=1)
        return self.proj(fused)