写一个输入图像数据集的卷积GRU的pytorch-cpu代码

好的，以下是一个输入图像数据集的卷积GRU的PyTorch-CPU代码示例：

import torch.nn as nn

class ConvGRU(nn.Module):
    def __init__(self, input_dim, hidden_dim, kernel_size, num_layers):
        super(ConvGRU, self).__init__()

        self.input_dim = input_dim
        self.hidden_dim = hidden_dim
        self.kernel_size = kernel_size
        self.num_layers = num_layers
        padding = kernel_size // 2

        # Create list of convolutional GRU layers
        self.conv_gru_layers = nn.ModuleList()
        for i in range(self.num_layers):
            input_dim = self.input_dim if i == 0 else self.hidden_dim
            conv_gru_layer = ConvGRULayer(input_dim, self.hidden_dim, self.kernel_size, padding)
            self.conv_gru_layers.append(conv_gru_layer)

    def forward(self, x, hidden_state=None):
        current_hidden_state = hidden_state

        # Iterate through layers
        for layer_idx in range(self.num_layers):
            conv_gru_layer = self.conv_gru_layers[layer_idx]
            conv_input = x if layer_idx == 0 else current_hidden_state

            # Run convolutional GRU layer
            layer_hidden_state = conv_gru_layer(conv_input, current_hidden_state)
            if current_hidden_state is None:
                current_hidden_state = layer_hidden_state
            else:
                current_hidden_state = current_hidden_state + layer_hidden_state

        return current_hidden_state

class ConvGRULayer(nn.Module):
    def __init__(self, input_dim, hidden_dim, kernel_size, padding):
        super(ConvGRULayer, self).__init__()

        self.input_dim = input_dim
        self.hidden_dim = hidden_dim
        self.kernel_size = kernel_size
        self.padding = padding

        # Convolutional layers to compute gates
        self.conv_gates = nn.Conv2d(input_dim + hidden_dim, 2 * hidden_dim, kernel_size, padding)

        # Convolutional layer to compute candidate hidden state
        self.conv_candidate = nn.Conv2d(input_dim + hidden_dim, hidden_dim, kernel_size, padding)

    def forward(self, x, hidden_state=None):
        if hidden_state is None:
            hidden_state = torch.zeros(1, self.hidden_dim, x.size(2), x.size(3))

        # Concatenate inputs along channel dimension
        combined_input = torch.cat([x, hidden_state], dim=1)

        # Compute gates
        gate_activations = self.conv_gates(combined_input)
        reset_gate, update_gate = gate_activations.chunk(2, dim=1)
        reset_gate = torch.sigmoid(reset_gate)
        update_gate = torch.sigmoid(update_gate)

        # Compute candidate hidden state
        combined_input_reset = torch.cat([x, hidden_state * reset_gate], dim=1)
        candidate = self.conv_candidate(combined_input_reset)
        candidate = torch.tanh(candidate)

        # Compute new hidden state
        new_hidden_state = hidden_state * (1 - update_gate) + candidate * update_gate

        return new_hidden_state

此代码实现了一个由多个卷积GRU层组成的ConvGRU网络，可以处理输入图像序列。

注意：这里的代码仅供参考，实现细节和输入数据格式等取决于具体的应用场景。