class MobileNetV2(nn.Module): def __init__(self, downsample_factor=8, pretrained=True): super(MobileNetV2, self).__init__() from functools import partial model = mobilenetv2(pretrained) self.features = model.features[:-1] self.total_idx = len(self.features) self.down_idx = [2, 4, 7, 14] if downsample_factor == 8: for i in range(self.down_idx[-2], self.down_idx[-1]): self.features[i].apply( partial(self._nostride_dilate, dilate=2) ) for i in range(self.down_idx[-1], self.total_idx): self.features[i].apply( partial(self._nostride_dilate, dilate=4) ) elif downsample_factor == 16: for i in range(self.down_idx[-1], self.total_idx): self.features[i].apply( partial(self._nostride_dilate, dilate=2) ) def _nostride_dilate(self, m, dilate): classname = m.__class__.__name__ if classname.find('Conv') != -1: if m.stride == (2, 2): m.stride = (1, 1) if m.kernel_size == (3, 3): m.dilation = (dilate // 2, dilate // 2) m.padding = (dilate // 2, dilate // 2) else: if m.kernel_size == (3, 3): m.dilation = (dilate, dilate) m.padding = (dilate, dilate)

这是一个使用 PyTorch 实现的 MobileNetV2 模型。MobileNetV2 是一种轻量级的卷积神经网络，适用于移动设备和嵌入式设备等资源受限的环境。

该模型的构造函数中有两个参数：downsample_factor 和 pretrained。downsample_factor 控制最后一层卷积的下采样倍数，可以选择 8 或 16。pretrained 控制是否使用预训练的权重。

该模型使用了 MobileNetV2 的预训练模型作为基础模型，然后通过修改其中的一些层来适应不同的下采样倍数。具体来说，它通过将一些卷积层的步幅从 2 改为 1，并在这些层上应用不同的膨胀率来实现下采样倍数的调整。

_nostride_dilate 是一个辅助函数，用于在不改变卷积层大小的情况下增大感受野。它通过在卷积核周围填充一些像素来实现膨胀卷积。

总体来说，这个 MobileNetV2 模型是一个轻量级的卷积神经网络，适用于在资源受限的环境中进行图像分类等任务。

相关问题

# New module: utils.pyimport torchfrom torch import nnclass ConvBlock(nn.Module): """A convolutional block consisting of a convolution layer, batch normalization layer, and ReLU activation.""" def __init__(self, in_chans, out_chans, drop_prob): super().__init__() self.conv = nn.Conv2d(in_chans, out_chans, kernel_size=3, padding=1) self.bn = nn.BatchNorm2d(out_chans) self.relu = nn.ReLU(inplace=True) self.dropout = nn.Dropout2d(p=drop_prob) def forward(self, x): x = self.conv(x) x = self.bn(x) x = self.relu(x) x = self.dropout(x) return x# Refactored U-Net modelfrom torch import nnfrom utils import ConvBlockclass UnetModel(nn.Module): """PyTorch implementation of a U-Net model.""" def __init__(self, in_chans, out_chans, chans, num_pool_layers, drop_prob, pu_args=None): super().__init__() PUPS.__init__(self, *pu_args) self.in_chans = in_chans self.out_chans = out_chans self.chans = chans self.num_pool_layers = num_pool_layers self.drop_prob = drop_prob # Calculate input and output channels for each ConvBlock ch_list = [chans] + [chans * 2 ** i for i in range(num_pool_layers - 1)] in_chans_list = [in_chans] + [ch_list[i] for i in range(num_pool_layers - 1)] out_chans_list = ch_list[::-1] # Create down-sampling layers self.down_sample_layers = nn.ModuleList() for i in range(num_pool_layers): self.down_sample_layers.append(ConvBlock(in_chans_list[i], out_chans_list[i], drop_prob)) # Create up-sampling layers self.up_sample_layers = nn.ModuleList() for i in range(num_pool_layers - 1): self.up_sample_layers.append(ConvBlock(out_chans_list[i], out_chans_list[i + 1] // 2, drop_prob)) self.up_sample_layers.append(ConvBlock(out_chans_list[-1], out_chans_list[-1], drop_prob)) # Create final convolution layer self.conv2 = nn.Sequential( nn.Conv2d(out_chans_list[-1], out_chans_list[-1] // 2, kernel_size=1), nn.Conv2d(out_chans_list[-1] // 2, out_chans, kernel_size=1), nn.Conv2d(out_chans, out_chans, kernel_size=1), ) def forward(self, x): # Down-sampling path encoder_outs = [] for layer in self.down_sample_layers: x = layer(x) encoder_outs.append(x) x = nn.MaxPool2d(kernel_size=2)(x) # Bottom layer x = self.conv(x) # Up-sampling path for i, layer in enumerate(self.up_sample_layers): x = nn.functional.interpolate(x, scale_factor=2, mode='bilinear', align_corners=True) x = torch.cat([x, encoder_outs[-(i + 1)]], dim=1) x = layer(x) # Final convolution layer x = self.conv2(x) return x

这里给出了一个 PyTorch 实现的 U-Net 模型，并且对代码进行了重构：

1. 将 ConvBlock 提取到了 utils.py 中，以便在 U-Net 中复用。

2. 在 U-Net 模型的 forward 方法中，使用了 ModuleList 对 down-sampling 和 up-sampling 层进行了封装，使得代码更加简洁和易于扩展。

3. 使用了 nn.functional.interpolate 对 feature map 进行了上采样，避免了使用 nn.ConvTranspose2d 带来的一些问题。

4. 在最后的卷积层中，使用了 nn.Sequential 对多个卷积层进行了封装，使得代码更加简洁。

这些重构都是为了提高代码的可读性、可维护性和可扩展性，使得代码更加清晰，易于理解和修改。

mmsegmentation框架下给mobilenetv2主干加入SKNet

在`mmsegmentation`框架中，可以通过修改`mmseg/models/backbones/mobilenet_v2.py`文件来给`MobileNetV2`主干加入`SKNet`。下面是实现的步骤：

1. 首先需要导入`SKNet`的相关模块，可以在文件最上面添加以下代码：

from mmcv.cnn import build_conv_layer, build_norm_layer, build_plugin_layer
from ..builder import BACKBONES

2. 接着在`MobileNetV2`类中定义`SKNet`的结构，可以在`_make_stem_layer`函数中加入以下代码：

def _make_stem_layer(self, in_channels, stem_channels):
    layers = []
    layers.append(ConvModule(
        in_channels,
        stem_channels,
        3,
        stride=2,
        padding=1,
        bias=False,
        norm_cfg=dict(type='BN', momentum=0.1, eps=1e-5),
        activation='relu',
        inplace=True))
    in_channels = stem_channels

    layers.append(ConvModule(
        in_channels,
        in_channels,
        3,
        stride=1,
        padding=1,
        bias=False,
        norm_cfg=dict(type='BN', momentum=0.1, eps=1e-5),
        activation='relu',
        inplace=True))

    # add SKNet module
    channels = in_channels
    mid_channels = channels // 2
    squeeze_channels = max(1, mid_channels // 8)
    layers.append(
        build_plugin_layer(dict(
            type='SKConv',
            channels=channels,
            squeeze_channels=squeeze_channels,
            kernel_size=3,
            stride=1,
            padding=1,
            dilation=1,
            groups=32,
            sk_mode='two',
            norm_cfg=dict(type='BN', momentum=0.1, eps=1e-5),
            act_cfg=dict(type='ReLU', inplace=True),
        ),
        [build_conv_layer(
            dict(type='Conv2d'),
            channels,
            channels,
            kernel_size=3,
            stride=1,
            padding=1,
            bias=False),
        build_norm_layer(dict(type='BN', momentum=0.1, eps=1e-5), channels)[1]]))
    return nn.Sequential(*layers)

3. 最后在`BACKBONES`中注册`MobileNetV2`主干即可。完整代码如下：

import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.cnn import build_conv_layer, build_norm_layer, build_plugin_layer
from ..builder import BACKBONES


@BACKBONES.register_module()
class MobileNetV2(nn.Module):
    def __init__(self,
                 widen_factor=1.0,
                 output_stride=32,
                 norm_cfg=dict(type='BN', momentum=0.1, eps=1e-5),
                 with_cp=False,
                 ):
        super(MobileNetV2, self).__init__()

        assert output_stride in [8, 16, 32]
        self.output_stride = output_stride
        self.with_cp = with_cp
        self.norm_cfg = norm_cfg

        input_channel = int(32 * widen_factor)
        self.stem = self._make_stem_layer(3, input_channel)

        self.layer1 = self._make_layer(
            input_channel, int(16 * widen_factor), 1, 1, 16, 2)
        self.layer2 = self._make_layer(
            int(16 * widen_factor), int(24 * widen_factor), 2, 6, 16, 2)
        self.layer3 = self._make_layer(
            int(24 * widen_factor), int(32 * widen_factor), 3, 6, 24, 2)
        self.layer4 = self._make_layer(
            int(32 * widen_factor), int(64 * widen_factor), 4, 6, 32, 2)
        self.layer5 = self._make_layer(
            int(64 * widen_factor), int(96 * widen_factor), 3, 6, 64, 1)
        self.layer6 = self._make_layer(
            int(96 * widen_factor), int(160 * widen_factor), 3, 6, 96, 1)
        self.layer7 = self._make_layer(
            int(160 * widen_factor), int(320 * widen_factor), 1, 6, 160, 1)

        if self.output_stride == 8:
            self.layer2[0].conv2.stride = (1, 1)
            self.layer2[0].downsample[0].stride = (1, 1)
            self.layer3[0].conv2.stride = (1, 1)
            self.layer3[0].downsample[0].stride = (1, 1)
        elif self.output_stride == 16:
            self.layer3[0].conv2.stride = (1, 1)
            self.layer3[0].downsample[0].stride = (1, 1)

        self._freeze_stages()

    def _make_stem_layer(self, in_channels, stem_channels):
        layers = []
        layers.append(ConvModule(
            in_channels,
            stem_channels,
            3,
            stride=2,
            padding=1,
            bias=False,
            norm_cfg=dict(type='BN', momentum=0.1, eps=1e-5),
            activation='relu',
            inplace=True))
        in_channels = stem_channels

        layers.append(ConvModule(
            in_channels,
            in_channels,
            3,
            stride=1,
            padding=1,
            bias=False,
            norm_cfg=dict(type='BN', momentum=0.1, eps=1e-5),
            activation='relu',
            inplace=True))

        # add SKNet module
        channels = in_channels
        mid_channels = channels // 2
        squeeze_channels = max(1, mid_channels // 8)
        layers.append(
            build_plugin_layer(dict(
                type='SKConv',
                channels=channels,
                squeeze_channels=squeeze_channels,
                kernel_size=3,
                stride=1,
                padding=1,
                dilation=1,
                groups=32,
                sk_mode='two',
                norm_cfg=dict(type='BN', momentum=0.1, eps=1e-5),
                act_cfg=dict(type='ReLU', inplace=True),
            ),
            [build_conv_layer(
                dict(type='Conv2d'),
                channels,
                channels,
                kernel_size=3,
                stride=1,
                padding=1,
                bias=False),
            build_norm_layer(dict(type='BN', momentum=0.1, eps=1e-5), channels)[1]]))
        return nn.Sequential(*layers)

    def _make_layer(self,
                    in_channels,
                    out_channels,
                    num_blocks,
                    stride,
                    t,
                    dilation=1):
        layers = []
        layers.append(InvertedResidual(
            in_channels,
            out_channels,
            stride,
            expand_ratio=t,
            dilation=dilation,
            norm_cfg=self.norm_cfg,
            with_cp=self.with_cp))
        for i in range(1, num_blocks):
            layers.append(InvertedResidual(
                out_channels,
                out_channels,
                1,
                expand_ratio=t,
                dilation=dilation,
                norm_cfg=self.norm_cfg,
                with_cp=self.with_cp))

        return nn.Sequential(*layers)

    def _freeze_stages(self):
        if self.output_stride == 8:
            self.stem.eval()
            for param in self.stem.parameters():
                param.requires_grad = False
        elif self.output_stride == 16:
            self.stem[0].eval()
            self.stem[1].eval()
            for param in self.stem[0].parameters():
                param.requires_grad = False
            for param in self.stem[1].parameters():
                param.requires_grad = False

这样就给`MobileNetV2`主干加入了`SKNet`结构。