(ECCV2024)SMFA.py

import torch
import torch.nn as nn
import torch.nn.functional as F
#GitHub地址： https://github.com/Zheng-MJ/SMFANet
#论文地址：https://openaccess.thecvf.com/content/ICCV2023/papers/Sun_Spatially-Adaptive_Feature_Modulation_for_Efficient_Image_Super-Resolution_ICCV_2023_paper.pdf
class DMlp(nn.Module):
    def __init__(self, dim, growth_rate=2.0):
        super().__init__()
        hidden_dim = int(dim * growth_rate)
        self.conv_0 = nn.Sequential(
            nn.Conv2d(dim, hidden_dim, 3, 1, 1, groups=dim),
            nn.Conv2d(hidden_dim, hidden_dim, 1, 1, 0)
        )
        self.act = nn.GELU()
        self.conv_1 = nn.Conv2d(hidden_dim, dim, 1, 1, 0)

    def forward(self, x):
        x = self.conv_0(x)
        x = self.act(x)
        x = self.conv_1(x)
        return x


class SMFA(nn.Module):
    def __init__(self, dim=36):
        super(SMFA, self).__init__()
        self.linear_0 = nn.Conv2d(dim, dim * 2, 1, 1, 0)
        self.linear_1 = nn.Conv2d(dim, dim, 1, 1, 0)
        self.linear_2 = nn.Conv2d(dim, dim, 1, 1, 0)

        self.lde = DMlp(dim, 2)

        self.dw_conv = nn.Conv2d(dim, dim, 3, 1, 1, groups=dim)

        self.gelu = nn.GELU()
        self.down_scale = 8

        self.alpha = nn.Parameter(torch.ones((1, dim, 1, 1)))
        self.belt = nn.Parameter(torch.zeros((1, dim, 1, 1)))

    def forward(self, f):
        _, _, h, w = f.shape
        y, x = self.linear_0(f).chunk(2, dim=1)
        x_s = self.dw_conv(F.adaptive_max_pool2d(x, (h // self.down_scale, w // self.down_scale)))
        x_v = torch.var(x, dim=(-2, -1), keepdim=True)
        x_l = x * F.interpolate(self.gelu(self.linear_1(x_s * self.alpha + x_v * self.belt)), size=(h, w),
                                mode='nearest')
        y_d = self.lde(y)
        return self.linear_2(x_l + y_d)


if __name__ == '__main__':
    input = torch.randn(3, 36, 64, 64)  # 输入b c h w

    block = SMFA(dim=36)
    output = block(input)
    print(output.size())