class MobileNetV2(nn.Module): def __init__(self, num_classes=1000, width_mult=1.0, inverted_residual_setting=None, round_nearest=8, block=None, norm_layer=None):如何修改参数

你可以通过实例化 `MobileNetV2` 类时传入不同的参数来修改模型的参数，例如：

model = MobileNetV2(num_classes=10, width_mult=0.5)

其中，`num_classes` 参数表示模型的输出类别数，`width_mult` 参数表示模型的通道数缩放比例。你还可以根据需要调整其他参数，例如 `inverted_residual_setting` 表示模型的结构设置，`round_nearest` 表示通道数取整时的最小单位，`block` 表示模型基本块的类型，`norm_layer` 表示模型使用的归一化层类型等等。如果不传入这些参数，会使用默认值。

相关问题

请你把MobileNetV3_Small 代码模型写出来

好的，这里是 MobileNetV3_Small 的代码实现：

import torch
import torch.nn as nn

class ConvBNReLU(nn.Sequential):
    """Convolutional layer with batch normalization and ReLU activation."""
    def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, groups=1):
        padding = (kernel_size - 1) // 2
        super(ConvBNReLU, self).__init__(
            nn.Conv2d(in_planes, out_planes, kernel_size, stride, padding, groups=groups, bias=False),
            nn.BatchNorm2d(out_planes),
            nn.ReLU6(inplace=True)
        )

class InvertedResidual(nn.Module):
    """Inverted residual block."""
    def __init__(self, inp, oup, stride, expand_ratio):
        super(InvertedResidual, self).__init__()
        hidden_dim = round(inp * expand_ratio)
        self.use_res_connect = stride == 1 and inp == oup

        layers = []
        if expand_ratio != 1:
            # pointwise
            layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1))
        layers.extend([
            # depthwise
            ConvBNReLU(hidden_dim, hidden_dim, stride=stride, groups=hidden_dim),
            # pointwise-linear
            nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
            nn.BatchNorm2d(oup),
        ])
        self.conv = nn.Sequential(*layers)

    def forward(self, x):
        if self.use_res_connect:
            return x + self.conv(x)
        else:
            return self.conv(x)

class MobileNetv3_small(nn.Module):
    """MobileNetV3_Small model."""
    def __init__(self, num_classes=1000, width_mult=1.0):
        super(MobileNetv3_small, self).__init__()
        # setting of inverted residual blocks
        self.cfgs = [
            # k, t, c, SE, s
            [3,  16,  16, 0, 1],
            [3,  72,  24, 0, 2],
            [3,  88,  24, 0, 1],
            [5,  96,  40, 1, 2],
            [5, 240,  40, 1, 1],
            [5, 240,  40, 1, 1],
            [5, 120,  48, 1, 1],
            [5, 144,  48, 1, 1],
            [5, 288,  96, 1, 2],
            [5, 576,  96, 1, 1],
            [5, 576,  96, 1, 1],
        ]

        # building first layer
        input_channel = int(16 * width_mult)
        last_channel = int(576 * width_mult) if width_mult > 1.0 else 576
        self.features = [ConvBNReLU(3, input_channel, stride=2)]
        # building inverted residual blocks
        for k, exp_size, c, se, s in self.cfgs:
            output_channel = int(c * width_mult)
            self.features.append(InvertedResidual(input_channel, output_channel, stride=s, expand_ratio=exp_size/ input_channel))
            input_channel = output_channel
        # building last several layers
        self.features.append(ConvBNReLU(input_channel, last_channel, kernel_size=1))
        self.features.append(nn.AdaptiveAvgPool2d((1, 1)))
        self.features.append(nn.Conv2d(last_channel, 1024, kernel_size=1, stride=1, padding=0, bias=False))
        self.features.append(nn.Hardswish())
        self.features.append(nn.Conv2d(1024, num_classes, kernel_size=1, stride=1, padding=0, bias=True))
        self.features = nn.Sequential(*self.features)

    def forward(self, x):
        x = self.features(x)
        x = x.view(x.size(0), -1)
        return x

这个实现中包含了 MobileNetV3_Small 模型中使用的 Inverted Residual Block 和 ConvBNReLU 等基本组件。同时，在实现模型的过程中，作者还考虑到了模型的可配置性，可以通过调整宽度因子 `width_mult` 来控制模型的宽度。

帮我用pytorch框架复现mobilenetV2，要求：1.源代码2.写好注释文档

好的，以下是使用 PyTorch 框架复现 MobileNet V2 的代码和注释文档。

import torch
import torch.nn as nn

class Block(nn.Module):
    '''Depthwise convolutional block.'''
    def __init__(self, in_channels, out_channels, stride=1, expansion=6):
        super(Block, self).__init__()
        self.stride = stride
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.use_residual = self.stride == 1 and self.in_channels == self.out_channels
        
        self.conv1 = nn.Sequential(
            nn.Conv2d(in_channels, in_channels * expansion, kernel_size=1, bias=False),
            nn.BatchNorm2d(in_channels * expansion),
            nn.ReLU6(inplace=True)
        )
        self.conv2 = nn.Sequential(
            nn.Conv2d(in_channels * expansion, in_channels * expansion, kernel_size=3, stride=stride, padding=1, groups=in_channels * expansion, bias=False),
            nn.BatchNorm2d(in_channels * expansion),
            nn.ReLU6(inplace=True)
        )
        self.conv3 = nn.Sequential(
            nn.Conv2d(in_channels * expansion, out_channels, kernel_size=1, bias=False),
            nn.BatchNorm2d(out_channels),
        )
        
    def forward(self, x):
        residual = x
        out = self.conv1(x)
        out = self.conv2(out)
        out = self.conv3(out)
        if self.use_residual:
            out += residual
        return out

class MobileNetV2(nn.Module):
    '''MobileNetV2 implementation.'''
    def __init__(self, num_classes=1000, width_mult=1.0):
        super(MobileNetV2, self).__init__()
        self.num_classes = num_classes
        
        # Define the network architecture
        self.features = nn.Sequential(
            nn.Conv2d(3, 32, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(32),
            nn.ReLU6(inplace=True),
            
            Block(32, 16, stride=1, expansion=1),
            Block(16, 24, stride=2, expansion=6),
            Block(24, 24, stride=1, expansion=6),
            Block(24, 32, stride=2, expansion=6),
            Block(32, 32, stride=1, expansion=6),
            Block(32, 32, stride=1, expansion=6),
            Block(32, 64, stride