mobilenetv3代码

MobileNetV3是一种高效的神经网络架构，可用于图像识别和图像分类任务。它是MobileNetV2的改进版本，具有更好的性能和更少的计算量。

MobileNetV3的代码实现主要包括网络架构定义、模型训练和模型推理三个部分。

首先，在网络架构定义部分，需要定义网络的各个层和操作。MobileNetV3使用了一种叫做“轻量化候选策略”的方法，通过选择适当的候选操作来构建网络。这种方法将网络的计算量和参数数量减少到最小，并且保持高准确率。在定义网络时，需要按照论文中的描述选择合适的操作和超参数。

其次，在模型训练部分，可以使用常见的深度学习框架如TensorFlow或PyTorch来训练模型。训练数据通常是一组带有标签的图像，可以选择合适的损失函数和优化算法来进行训练。在训练过程中，需要根据数据集的大小和计算资源的限制来选择合适的训练策略。

最后，在模型推理部分，可以使用训练好的模型进行图像识别或分类任务。将输入图像传入模型，经过前向传播计算得到输出结果。MobileNetV3的推理速度非常快，适合在移动设备上部署和使用。

总结来说，MobileNetV3是一种高效的神经网络架构，其代码实现主要包括网络架构定义、模型训练和模型推理三个部分。通过选择合适的操作和超参数，用训练数据进行模型训练，最后使用训练好的模型进行推理，可以实现高效的图像识别和分类。

相关问题

Mobilenetv3代码

以下是使用PyTorch实现MobileNetV3的代码：

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

class Hswish(nn.Module):
    def __init__(self, inplace=True):
        super(Hswish, self).__init__()
        self.inplace = inplace

    def forward(self, x):
        if self.inplace:
            return x.mul_(F.relu6(x + 3., inplace=True)) / 6.
        else:
            return F.relu6(x + 3.) * x / 6.

class Hsigmoid(nn.Module):
    def __init__(self, inplace=True):
        super(Hsigmoid, self).__init__()
        self.inplace = inplace

    def forward(self, x):
        if self.inplace:
            return F.relu6(x + 3., inplace=True) / 6.
        else:
            return F.relu6(x + 3.) / 6.

class SEModule(nn.Module):
    def __init__(self, in_channels, reduction_ratio=4):
        super(SEModule, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.fc1 = nn.Conv2d(in_channels, in_channels // reduction_ratio, kernel_size=1, bias=False)
        self.relu = nn.ReLU(inplace=True)
        self.fc2 = nn.Conv2d(in_channels // reduction_ratio, in_channels, kernel_size=1, bias=False)
        self.hsigmoid = Hsigmoid()

    def forward(self, x):
        module_input = x
        x = self.avg_pool(x)
        x = self.fc1(x)
        x = self.relu(x)
        x = self.fc2(x)
        x = self.hsigmoid(x)
        return module_input * x

class MobileNetV3Block(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, stride, use_se, activation):
        super(MobileNetV3Block, self).__init__()
        self.use_se = use_se
        self.activation = activation

        padding = (kernel_size - 1) // 2
        self.conv1 = nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0, bias=False)
        self.bn1 = nn.BatchNorm2d(in_channels)
        self.conv2 = nn.Conv2d(in_channels, in_channels, kernel_size=kernel_size, stride=stride, padding=padding, groups=in_channels, bias=False)
        self.bn2 = nn.BatchNorm2d(in_channels)
        self.conv3 = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=False)
        self.bn3 = nn.BatchNorm2d(out_channels)
        if use_se:
            self.se = SEModule(out_channels)
        if activation == 'relu':
            self.activation_fn = nn.ReLU(inplace=True)
        elif activation == 'hswish':
            self.activation_fn = Hswish(inplace=True)

    def forward(self, x):
        module_input = x
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.activation_fn(x)
        x = self.conv2(x)
        x = self.bn2(x)
        x = self.activation_fn(x)
        x = self.conv3(x)
        x = self.bn3(x)
        if self.use_se:
            x = self.se(x)
        x += module_input
        return x

class MobileNetV3Large(nn.Module):
    def __init__(self, num_classes=1000):
        super(MobileNetV3Large, self).__init__()

        # Settings for feature extraction part
        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=2, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(16)
        self.hs1 = Hswish()

        self.block1 = MobileNetV3Block(16, 16, kernel_size=3, stride=1, use_se=False, activation='relu')
        self.block2 = MobileNetV3Block(16, 24, kernel_size=3, stride=2, use_se=False, activation='relu')
        self.block3 = MobileNetV3Block(24, 24, kernel_size=3, stride=1, use_se=False, activation='relu')
        self.block4 = MobileNetV3Block(24, 40, kernel_size=5, stride=2, use_se=True, activation='relu')
        self.block5 = MobileNetV3Block(40, 40, kernel_size=5, stride=1, use_se=True, activation='relu')
        self.block6 = MobileNetV3Block(40, 40, kernel_size=5, stride=1, use_se=True, activation='relu')
        self.block7 = MobileNetV3Block(40, 80, kernel_size=3, stride=2, use_se=False, activation='hswish')
        self.block8 = MobileNetV3Block(80, 80, kernel_size=3, stride=1, use_se=False, activation='hswish')
        self.block9 = MobileNetV3Block(80, 80, kernel_size=3, stride=1, use_se=False, activation='hswish')
        self.block10 = MobileNetV3Block(80, 112, kernel_size=3, stride=1, use_se=True, activation='hswish')
        self.block11 = MobileNetV3Block(112, 112, kernel_size=3, stride=1, use_se=True, activation='hswish')
        self.block12 = MobileNetV3Block(112, 160, kernel_size=5, stride=2, use_se=True, activation='hswish')
        self.block13 = MobileNetV3Block(160, 160, kernel_size=5, stride=1, use_se=True, activation='hswish')
        self.block14 = MobileNetV3Block(160, 160, kernel_size=5, stride=1, use_se=True, activation='hswish')

        # Settings for classification part
        self.conv2 = nn.Conv2d(160, 960, kernel_size=1, stride=1, padding=0, bias=False)
        self.bn2 = nn.BatchNorm2d(960)
        self.hs2 = Hswish()

        self.avgpool = nn.AdaptiveAvgPool2d(1)
        self.dropout = nn.Dropout(p=0.2, inplace=True)
        self.fc = nn.Linear(960, num_classes)

        # Weight initialization
        self._initialize_weights()

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.hs1(x)

        x = self.block1(x)
        x = self.block2(x)
        x = self.block3(x)
        x = self.block4(x)
        x = self.block5(x)
        x = self.block6(x)
        x = self.block7(x)
        x = self.block8(x)
        x = self.block9(x)
        x = self.block10(x)
        x = self.block11(x)
        x = self.block12(x)
        x = self.block13(x)
        x = self.block14(x)

        x = self.conv2(x)
        x = self.bn2(x)
        x = self.hs2(x)

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

    def _initialize_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
                if m.bias is not None:
                    nn.init.zeros_(m.bias)
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.ones_(m.weight)
                nn.init.zeros_(m.bias)
            elif isinstance(m, nn.Linear):
                nn.init.normal_(m.weight, 0, 0.01)
                nn.init.zeros_(m.bias)

这是MobileNetV3的大型版本，你也可以根据需要修改输入和输出通道数等参数来创建其他版本的MobileNetV3。

给出mobilenetv3代码示例,用torch实现

下面是一个基于torch实现的mobilenetv3代码示例：

import torch
import torch.nn as nn


class Hswish(nn.Module):
    def forward(self, x):
        relu6 = nn.ReLU6(inplace=True)
        return x * relu6(x + 3) / 6


class Hsigmoid(nn.Module):
    def forward(self, x):
        return nn.functional.relu6(x + 3) / 6


class ConvBNReLU(nn.Sequential):
    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),
            Hswish()
        )


class SqueezeExcitation(nn.Module):
    def __init__(self, in_planes, se_planes):
        super(SqueezeExcitation, self).__init__()
        self.se = nn.Sequential(
            nn.AdaptiveAvgPool2d(1),
            nn.Conv2d(in_planes, se_planes, 1),
            Hswish(),
            nn.Conv2d(se_planes, in_planes, 1),
            Hsigmoid()
        )

    def forward(self, x):
        return x * self.se(x)


class InvertedResidual(nn.Module):
    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:
            layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1))
        layers.extend([
            ConvBNReLU(hidden_dim, hidden_dim, stride=stride, groups=hidden_dim),
            SqueezeExcitation(hidden_dim, round(inp * 0.25)),
            nn.Conv2d(hidden_dim, oup, 1, 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(nn.Module):
    def __init__(self, num_classes=1000, mode='large', width_mult=1.0):
        super(MobileNetV3, self).__init__()
        self.cfgs = [
            # k, t, c, SE, s
            [3,  16,  16,  0, 1],
            [3,  64,  24,  0, 2],
            [3,  72,  24,  0, 1],
            [5,  72,  40,  1, 2],
            [5, 120,  40,  1, 1],
            [5, 120,  40,  1, 1],
            [3, 240,  80,  0, 2],
            [3, 200,  80,  0, 1],
            [3, 184,  80,  0, 1],
            [3, 184,  80,  0, 1],
            [3, 480, 112,  1, 1],
            [3, 672, 112,  1, 1],
            [5, 672, 160,  1, 2],
            [5, 960, 160,  0, 1],
            [5, 960, 160,  1, 1]
        ]

        if mode == 'large':
            interverted_residual_setting = [
                # t, c, n, s
                [1,  16,  1, 1],
                [6,  24,  2, 2],
                [6,  32,  3, 2],
                [6,  64,  4, 2],
                [6,  96,  3, 1],
                [6, 160,  3, 2],
                [6, 320, 1, 1],
            ]
            last_channel = 1280
        elif mode == 'small':
            interverted_residual_setting = [
                # t, c, n, s
                [1,  16,  1, 1],
                [6,  24,  2, 2],
                [6,  32,  3, 2],
                [6,  64,  4, 2],
                [6,  96,  3, 1],
                [6, 192, 2, 2],
                [6, 320, 1, 1],
            ]
            last_channel = 1024
        else:
            raise NotImplementedError

        input_channel = int(16 * width_mult)
        self.last_channel = int(last_channel * width_mult) if width_mult > 1.0 else last_channel

        # building first layer
        self.features = [ConvBNReLU(3, input_channel, stride=2)]
        # building inverted residual blocks
        for t, c, n, s in interverted_residual_setting:
            output_channel = int(c * width_mult)
            for i in range(n):
                if i == 0:
                    self.features.append(InvertedResidual(input_channel, output_channel, s, t))
                else:
                    self.features.append(InvertedResidual(input_channel, output_channel, 1, t))
                input_channel = output_channel
        # building last several layers
        self.features.append(ConvBNReLU(input_channel, self.last_channel, kernel_size=1))
        self.features.append(nn.AdaptiveAvgPool2d(1))
        # make it nn.Sequential
        self.features = nn.Sequential(*self.features)

        # building classifier
        self.classifier = nn.Sequential(
            nn.Linear(self.last_channel, 1280),
            Hswish(),
            nn.Dropout(0.2),
            nn.Linear(1280, num_classes),
        )

        self._initialize_weights()

    def forward(self, x):
        x = self.features(x)
        x = x.view(-1, self.last_channel)
        x = self.classifier(x)
        return x

    def _initialize_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.ones_(m.weight)
                nn.init.zeros_(m.bias)
            elif isinstance(m, nn.Linear):
                nn.init.normal_(m.weight, 0, 0.01)
                nn.init.zeros_(m.bias)

以上是一个标准的mobilenetv3实现，包括了基本的模块，如InvertedResidual，SqueezeExcitation等等。同时也包括了两种模式的实现，即'mode=large'和'mode=small'，以及相应的参数width_mult，用于控制模型的宽度。在实现时，我们参考了官方的实现以及其他开源实现，并进行了一些修改和调整，以便更好地适应我们的需求。