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。

相关问题

AttributeError: 'scipy.spatial.transform._rotation.Rotation' object has no attribute 'as_dcm'

这个错误是由于scipy版本更新导致的。在旧版本中，可以使用as_dcm()方法将旋转矩阵转换为方向余弦矩阵，但在新版本中，该方法已被弃用。解决方法是将as_dcm()替换为as_matrix()，这将返回一个旋转矩阵而不是方向余弦矩阵。具体来说，您可以将以下代码：

rot_matrix = torch.from_numpy(R.from_euler('y', 180.0, degrees=True).as_dcm()).float().to(self.device)

替换为：

rot_matrix = torch.from_numpy(R.from_euler('y', 180.0, degrees=True).as_matrix()).float().to(self.device)

这样就可以解决这个错误了。

时空图卷积 python

时空图卷积（Spatio-temporal Graph Convolutional, STGCN）是一种用于处理时空数据的图卷积网络（Graph Convolutional Network, GCN）的变体。它主要用于建模和预测时空数据中的关联性和时空变化。

STGCN的python实现主要使用了一些常见的机器学习和深度学习库，如numpy、pytorch或tensorflow等。下面是一个简单的STGCN的python代码示例：

import numpy as np
import torch
import torch.nn as nn

class GraphConvolution(nn.Module):
    def __init__(self, in_features, out_features, adjacency):
        super(GraphConvolution, self).__init__()
        self.adjacency = adjacency
        self.weight = nn.Parameter(torch.Tensor(in_features, out_features))
        self.bias = nn.Parameter(torch.Tensor(out_features))

    def forward(self, x):
        x = torch.matmul(self.adjacency, x)
        x = torch.matmul(x, self.weight)
        x = x + self.bias
        return x

class STGCN(nn.Module):
    def __init__(self, in_channels, num_nodes, num_timesteps, num_features, num_classes):
        super(STGCN, self).__init__()
        self.num_nodes = num_nodes
        self.num_timesteps = num_timesteps
        self.num_features = num_features

        self.temporal_conv1 = nn.Conv2d(in_channels, 64, kernel_size=(1, 3))
        self.temporal_conv2 = nn.Conv2d(64, 64, kernel_size=(1, 3))
        self.spatial_conv1 = GraphConvolution(num_features, 16, adjacency)
        self.spatial_conv2 = GraphConvolution(16, num_classes, adjacency)
        self.relu = nn.ReLU()

    def forward(self, x):
        x = x.permute(0, 3, 1, 2)
        x = self.temporal_conv1(x)
        x = self.relu(x)
        x = self.temporal_conv2(x)
        x = self.relu(x)
        x = x.permute(0, 2, 3, 1)
        x = x.reshape(-1, self.num_nodes * self.num_timesteps, self.num_features)
        x = self.spatial_conv1(x)
        x = self.relu(x)
        x = self.spatial_conv2(x)
        x = x.reshape(-1, self.num_nodes, self.num_timesteps, self.num_classes)
        x = x.permute(0, 3, 2, 1)
        return x

# 构建输入
in_channels = 2  # 输入数据的通道数
num_nodes = 10  # 图中节点的数量
num_timesteps = 6  # 时间步的数量
num_features = 16  # 每个节点的特征数量
num_classes = 2  # 预测类别的数量
adjacency = np.random.rand(num_nodes, num_nodes)  # 图的邻接矩阵

# 创建网络实例
model = STGCN(in_channels, num_nodes, num_timesteps, num_features, num_classes)
input_data = torch.Tensor(np.random.rand(1, num_nodes, num_timesteps, in_channels))

# 运行前向传播
output = model(input_data)
print(output.shape)

这个例子展示了一个简单的STGCN的实现，它包括一个GraphConvolution层和两个卷积层。在使用时，可以根据实际情况调整网络的参数和输入数据的大小。