把后面的卷积程序改成深度可分离卷积的程序 self.conv2 = nn.Conv2d(in_channels=features * 4, out_channels=features * 8, kernel_size=3,padding=1,bias=False

好的，您希望我将前面的卷积程序改成深度可分离卷积的程序，这里提供一种可能的修改方法：

import torch.nn as nn

class MyModel(nn.Module):
    def __init__(self, features):
        super(MyModel, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=3, out_channels=features, kernel_size=3, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(num_features=features)
        self.relu1 = nn.ReLU(inplace=True)

        self.conv2 = nn.Sequential(
            nn.Conv2d(in_channels=features, out_channels=features, kernel_size=3, padding=1, groups=features, bias=False),
            nn.Conv2d(in_channels=features, out_channels=features * 2, kernel_size=1, padding=0, bias=False),
            nn.BatchNorm2d(num_features=features * 2),
            nn.ReLU(inplace=True),

            nn.Conv2d(in_channels=features * 2, out_channels=features * 2, kernel_size=3, padding=1, groups=features * 2, bias=False),
            nn.Conv2d(in_channels=features * 2, out_channels=features * 4, kernel_size=1, padding=0, bias=False),
            nn.BatchNorm2d(num_features=features * 4),
            nn.ReLU(inplace=True),

            nn.Conv2d(in_channels=features * 4, out_channels=features * 4, kernel_size=3, padding=1, groups=features * 4, bias=False),
            nn.Conv2d(in_channels=features * 4, out_channels=features * 8, kernel_size=1, padding=0, bias=False),
            nn.BatchNorm2d(num_features=features * 8),
            nn.ReLU(inplace=True)
        )

        self.avgpool = nn.AdaptiveAvgPool2d(output_size=(1, 1))
        self.fc = nn.Linear(in_features=features * 8, out_features=10)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu1(x)

        x = self.conv2(x)

        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)

        return x

这里使用了 `nn.Sequential` 以及多个深度可分离卷积层来实现 `self.conv2`。其中，每个深度可分离卷积层都由一个深度卷积层和一个逐点卷积层组成，然后再接上 BN 和 ReLU 激活函数。

需要注意的是，由于深度可分离卷积的计算方式和普通卷积有所不同，因此在这里我们要将 `groups` 参数设为输入通道数，以实现深度可分离卷积的效果。

希望这个修改方案可以帮助到您，如有任何疑问请随时提出。