能写出ContextBlock的pytorch代码吗

当然可以，以下是一个简单的ContextBlock的PyTorch实现代码：

import torch
import torch.nn as nn

class ContextBlock(nn.Module):
    def __init__(self, in_channels, ratio=8):
        super(ContextBlock, self).__init__()
        self.in_channels = in_channels
        self.inter_channels = in_channels // ratio

        self.g = nn.Conv2d(in_channels=self.in_channels, out_channels=self.inter_channels, kernel_size=1)
        self.theta = nn.Conv2d(in_channels=self.in_channels, out_channels=self.inter_channels, kernel_size=1)
        self.phi = nn.Conv2d(in_channels=self.in_channels, out_channels=self.inter_channels, kernel_size=1)
        self.out = nn.Conv2d(in_channels=self.inter_channels, out_channels=self.in_channels, kernel_size=1)

    def forward(self, x):
        batch_size = x.size(0)
        g_x = self.g(x).view(batch_size, self.inter_channels, -1)
        g_x = g_x.permute(0, 2, 1)
        theta_x = self.theta(x).view(batch_size, self.inter_channels, -1)
        theta_x = theta_x.permute(0, 2, 1)
        phi_x = self.phi(x).view(batch_size, self.inter_channels, -1)
        f = torch.matmul(theta_x, phi_x)
        f_div_C = F.softmax(f, dim=-1)
        y = torch.matmul(f_div_C, g_x)
        y = y.permute(0, 2, 1).contiguous()
        y = y.view(batch_size, self.inter_channels, *x.size()[2:])
        y = self.out(y)
        z = y + x

        return z

这里的ContextBlock包含了三个卷积层和一个softmax操作，其中g、theta、phi分别代表映射到低维空间的三个卷积操作，f代表关联矩阵，f_div_C代表关联矩阵归一化后的权重，y为上下文信息，z为输出结果。