请举例说明ResetNet用法，Python

下面是一个基于Python和PyTorch实现的ResetNet的示例代码：

import torch
import torch.nn as nn

# 定义Reset Unit
class ResetUnit(nn.Module):
    def __init__(self, channels):
        super(ResetUnit, self).__init__()
        self.conv1 = nn.Conv2d(channels, channels, kernel_size=3, padding=1)
        self.bn1 = nn.BatchNorm2d(channels)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(channels, channels, kernel_size=3, padding=1)
        self.bn2 = nn.BatchNorm2d(channels)

    def forward(self, x):
        residual = x
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        out = self.conv2(out)
        out = self.bn2(out)
        out = out + residual
        out = self.relu(out)
        return out

# 定义ResetNet
class ResetNet(nn.Module):
    def __init__(self, num_classes=10):
        super(ResetNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1)
        self.bn1 = nn.BatchNorm2d(32)
        self.relu = nn.ReLU(inplace=True)
        self.residual_block1 = ResetUnit(32)
        self.residual_block2 = ResetUnit(32)
        self.residual_block3 = ResetUnit(32)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
        self.bn2 = nn.BatchNorm2d(64)
        self.residual_block4 = ResetUnit(64)
        self.residual_block5 = ResetUnit(64)
        self.residual_block6 = ResetUnit(64)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(64, num_classes)

    def forward(self, x):
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        out = self.residual_block1(out)
        out = self.residual_block2(out)
        out = self.residual_block3(out)
        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)
        out = self.residual_block4(out)
        out = self.residual_block5(out)
        out = self.residual_block6(out)
        out = self.avgpool(out)
        out = out.view(out.size(0), -1)
        out = self.fc(out)
        return out

在上述代码中，我们首先定义了Reset Unit，它包含两个卷积层和归一化层，并且使用了ReLU激活函数和残差连接。然后，我们定义了ResetNet，它由多个Residual Block和全连接层组成。在每个Residual Block中，我们使用了Reset Unit来防止梯度消失和梯度爆炸的问题。最终，我们将全连接层的输出用于分类预测。

你可以根据自己的需求和数据集特点，修改和调整ResetNet的结构和超参数，并使用训练数据集对模型进行训练和测试。