import numpy as np import torch from torch import nn from torch.nn import init def spatial_shift1(x): b, w, h, c = x.size() x[:, 1:, :, :c // 4] = x[:, :w - 1, :, :c // 4] x[:, :w - 1, :, c // 4:c // 2] = x[:, 1:, :, c // 4:c // 2] x[:, :, 1:, c // 2:c * 3 // 4] = x[:, :, :h - 1, c // 2:c * 3 // 4] x[:, :, :h - 1, 3 * c // 4:] = x[:, :, 1:, 3 * c // 4:] return x def spatial_shift2(x): b, w, h, c = x.size() x[:, :, 1:, :c // 4] = x[:, :, :h - 1, :c // 4] x[:, :, :h - 1, c // 4:c // 2] = x[:, :, 1:, c // 4:c // 2] x[:, 1:, :, c // 2:c * 3 // 4] = x[:, :w - 1, :, c // 2:c * 3 // 4] x[:, :w - 1, :, 3 * c // 4:] = x[:, 1:, :, 3 * c // 4:] return x class SplitAttention(nn.Module): def __init__(self, channel=512, k=3): super().__init__() self.channel = channel self.k = k self.mlp1 = nn.Linear(channel, channel, bias=False) self.gelu = nn.GELU() self.mlp2 = nn.Linear(channel, channel * k, bias=False) self.softmax = nn.Softmax(1) def forward(self, x_all): b, k, h, w, c = x_all.shape x_all = x_all.reshape(b, k, -1, c) # bs,k,n,c a = torch.sum(torch.sum(x_all, 1), 1) # bs,c hat_a = self.mlp2(self.gelu(self.mlp1(a))) # bs,kc hat_a = hat_a.reshape(b, self.k, c) # bs,k,c bar_a = self.softmax(hat_a) # bs,k,c attention = bar_a.unsqueeze(-2) # #bs,k,1,c out = attention * x_all # #bs,k,n,c out = torch.sum(out, 1).reshape(b, h, w, c) return out class S2Attention(nn.Module): def __init__(self, channels=512): super().__init__() self.mlp1 = nn.Linear(channels, channels * 3) self.mlp2 = nn.Linear(channels, channels) self.split_attention = SplitAttention() def forward(self, x): b, c, w, h = x.size() x = x.permute(0, 2, 3, 1) x = self.mlp1(x) x1 = spatial_shift1(x[:, :, :, :c]) x2 = spatial_shift2(x[:, :, :, c:c * 2]) x3 = x[:, :, :, c * 2:] x_all = torch.stack([x1, x2, x3], 1) a = self.split_attention(x_all) x = self.mlp2(a) x = x.permute(0, 3, 1, 2) return x

这是一个使用Python中的NumPy和PyTorch库实现的类和函数的代码。其中，spatial_shift1和spatial_shift2函数都是用来对输入的张量进行空间平移操作的，参数x为一个四维张量，分别代表batch size、宽、高和通道数。SplitAttention类实现了分组注意力机制，其中包含一个MLP网络和Softmax层，用于计算注意力权重，输入x_all为一个五维张量，分别代表batch size、组数、宽、高和通道数。在forward函数中，首先将张量reshape成三维张量，然后通过MLP和Softmax计算注意力权重，最后再将注意力加权后的结果reshape回原来的形状。