写一个卷积GRU的pytorch代码

当然，我可以为您提供一个简单的卷积GRU的PyTorch代码示例：

import torch
import torch.nn as nn

# 定义卷积GRU模型
class ConvGRU(nn.Module):
    def __init__(self, input_size, hidden_size, kernel_size, num_layers):
        super(ConvGRU, self).__init__()

        self.input_size = input_size
        self.hidden_size = hidden_size
        self.kernel_size = kernel_size
        self.num_layers = num_layers

        self.conv_gates = nn.ModuleList()
        self.conv_towers = nn.ModuleList()
        self.height, self.width = None, None

        for i in range(num_layers):
            input_c = input_size if i == 0 else hidden_size
            conv_gate = nn.Conv2d(in_channels=input_c + hidden_size,
                                  out_channels=2 * hidden_size,
                                  kernel_size=kernel_size,
                                  padding=kernel_size // 2)
            conv_tower = nn.Conv2d(in_channels=input_c,
                                   out_channels=hidden_size,
                                   kernel_size=kernel_size,
                                   padding=kernel_size // 2)
            self.conv_gates.append(conv_gate)
            self.conv_towers.append(conv_tower)

    def forward(self, input_, hidden):
        if hidden is None:
            size_h = [input_.size()[0], self.hidden_size, self.height, self.width]
            hidden = torch.zeros(size_h, device=input_.device)
        hidden_seq = []
        h = hidden
        for layer_idx in range(self.num_layers):
            c_gate = self.conv_gates[layer_idx]
            c_tower = self.conv_towers[layer_idx]
            # 堆叠输入和隐藏状态的通道维度，对应于门控卷积层的in_channels
            stacked_inputs = torch.cat([input_, h], dim=1)
            # 计算门控和隐藏状态更新值
            gates = c_gate(stacked_inputs)
            reset_gate, update_gate = torch.split(gates, self.hidden_size, dim=1)
            reset_gate = torch.sigmoid(reset_gate)
            update_gate = torch.sigmoid(update_gate)
            # 计算新的隐藏状态
            content_tower = c_tower(input_)
            transformed_content = reset_gate * h
            transformed_tower = content_tower * update_gate
            h = transformed_content + transformed_tower
            hidden_seq.append(h)

        return h, hidden_seq

    def init_hidden(self, batch_size, height, width, device):
        self.height, self.width = height, width
        hidden = torch.zeros(batch_size, self.hidden_size, height, width, device=device)
        return hidden

这是一个4层的卷积GRU模型，使用的是卷积门控单元。您可以使用此代码作为起点，根据自己的需求进行修改和扩展。希望对您有所帮助！