写一个输入图像数据集的卷积GRU的pytorch代码

好的，下面是代码示例：

import torch
import torch.nn as nn
import torch.nn.functional as F

class ConvGRU(nn.Module):
    def __init__(self, input_size, hidden_size, kernel_size, stride=1, padding=0):
        super(ConvGRU, self).__init__()

        self.input_size = input_size
        self.hidden_size = hidden_size
        self.kernel_size = kernel_size
        self.stride = stride
        self.padding = padding

        # define convolutional layers for input gate
        self.conv_i = nn.Conv2d(input_size + hidden_size, hidden_size, kernel_size,
                                stride=stride, padding=padding)
        # define convolutional layers for forget gate
        self.conv_f = nn.Conv2d(input_size + hidden_size, hidden_size, kernel_size,
                                stride=stride, padding=padding)
        # define convolutional layers for output gate
        self.conv_o = nn.Conv2d(input_size + hidden_size, hidden_size, kernel_size,
                                stride=stride, padding=padding)
        # define convolutional layers for cell state
        self.conv_c = nn.Conv2d(input_size + hidden_size, hidden_size, kernel_size,
                                stride=stride, padding=padding)

    def forward(self, input, hidden=None):
        if hidden is None:
            # initialize hidden state with zeros
            hx = torch.zeros(input.size(0), self.hidden_size, input.size(2), input.size(3)).to(input.device)
        else:
            hx = hidden

        # concatenate input with previous hidden state
        combined = torch.cat((input, hx), dim=1)

        # compute input, forget, and output gates as well as cell state
        i = torch.sigmoid(self.conv_i(combined))
        f = torch.sigmoid(self.conv_f(combined))
        o = torch.sigmoid(self.conv_o(combined))
        c = torch.tanh(self.conv_c(combined))

        # compute new cell state and new hidden state
        cell_state = f * hx + i * c
        output = o * torch.tanh(cell_state)

        return output, cell_state

这个 ConvGRU 模型的输入是一个 4 维的张量，形状为 (batch_size, input_size, height, width)，其中 `input_size` 表示输入特征的数量，`height` 和 `width` 表示输入特征的尺寸。

这个模型包含四个卷积层，用于计算输入门、遗忘门、输出门和记忆单元。在每个时间步，将输入和上一个时间步的隐藏状态进行拼接，然后通过这四个卷积层计算四个门和记忆单元。最后，根据门的权重和记忆单元计算新时间步的隐藏状态和输出。