65 lines
3.0 KiB
Python
65 lines
3.0 KiB
Python
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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"""ECCV2024(https://github.com/Zheng-MJ/SMFANet)
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基于Transformer的恢复方法取得了显著的效果，因为Transformer的自注意力机制（SA）可以探索非局部信息，从而实现更好的高分辨率图像重建。然而，关键的点积自注意力需要大量的计算资源，这限制了其在低功耗设备上的应用。
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此外，自注意力机制的低通滤波特性限制了其捕捉局部细节的能力，从而导致重建结果过于平滑。为了解决这些问题，我们提出了一种自调制特征聚合（SMFA）模块，协同利用局部和非局部特征交互，以实现更精确的重建。
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具体而言，SMFA模块采用了高效的自注意力近似（EASA）分支来建模非局部信息，并使用局部细节估计（LDE）分支来捕捉局部细节。此外，我们还引入了基于部分卷积的前馈网络（PCFN），以进一步优化从SMFA提取的代表性特征。
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大量实验表明，所提出的SMFANet系列在公共基准数据集上实现了更好的重建性能与计算效率的平衡。
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特别是，与SwinIR-light的×4放大相比，SMFANet+在五个公共测试集上的平均性能提高了0.14dB，运行速度提升了约10倍，且模型复杂度（如FLOPs）仅为其约43%。
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"""
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class DMlp(nn.Module):
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def __init__(self, dim, growth_rate=2.0):
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super().__init__()
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hidden_dim = int(dim * growth_rate)
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self.conv_0 = nn.Sequential(
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nn.Conv2d(dim, hidden_dim, 3, 1, 1, groups=dim),
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nn.Conv2d(hidden_dim, hidden_dim, 1, 1, 0)
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)
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self.act = nn.GELU()
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self.conv_1 = nn.Conv2d(hidden_dim, dim, 1, 1, 0)
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def forward(self, x):
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x = self.conv_0(x)
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x = self.act(x)
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x = self.conv_1(x)
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return x
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class SMFA(nn.Module):
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def __init__(self, dim=36):
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super(SMFA, self).__init__()
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self.linear_0 = nn.Conv2d(dim, dim * 2, 1, 1, 0)
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self.linear_1 = nn.Conv2d(dim, dim, 1, 1, 0)
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self.linear_2 = nn.Conv2d(dim, dim, 1, 1, 0)
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self.lde = DMlp(dim, 2)
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self.dw_conv = nn.Conv2d(dim, dim, 3, 1, 1, groups=dim)
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self.gelu = nn.GELU()
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self.down_scale = 8
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self.alpha = nn.Parameter(torch.ones((1, dim, 1, 1)))
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self.belt = nn.Parameter(torch.zeros((1, dim, 1, 1)))
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def forward(self, f):
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_, _, h, w = f.shape
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y, x = self.linear_0(f).chunk(2, dim=1)
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x_s = self.dw_conv(F.adaptive_max_pool2d(x, (h // self.down_scale, w // self.down_scale)))
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x_v = torch.var(x, dim=(-2, -1), keepdim=True)
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x_l = x * F.interpolate(self.gelu(self.linear_1(x_s * self.alpha + x_v * self.belt)), size=(h, w),
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mode='nearest')
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y_d = self.lde(y)
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return self.linear_2(x_l + y_d)
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if __name__ == '__main__':
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block = SMFA(dim=36)
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input = torch.randn(3, 36, 64, 64)
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output = block(input)
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print(input.size())
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print(output.size())
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