Hardswish代码

Hardswish 代码是这样的：

import torch.nn.functional as F

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

这是一个 PyTorch 实现的 Hardswish 激活函数，它是一种类似于 ReLU 的激活函数，但比 ReLU 更平滑，可以在一定程度上缓解梯度消失问题。

相关问题

怎么调用HardSwish

### 回答1：
要使用HardSwish激活函数，需要引入以下两个库：

import torch
import torch.nn.functional as F

然后，在模型的层中使用以下代码：

class ConvBlock(nn.Module):
    def __init__(self, in_channel, out_channel, act=F.relu):
        super(ConvBlock, self).__init__()

        self.conv = nn.Conv2d(
            in_channel,
            out_channel,
            kernel_size=3,
            stride=1,
            padding=1,
            bias=False)
        self.bn = nn.BatchNorm2d(out_channel)
        self.act = act

    def forward(self, x):
        return self.act(self.bn(self.conv(x)))

在上述代码中，我们传入了act变量并将其设置为F.relu。如果要使用HardSwish，只需将F.relu更改为以下内容即可：

self.act = lambda x: x * F.relu6(x + 3) / 6

这应该可以让模型在 ConvBlock 中使用 HardSwish 激活函数。希望这回答了你的问题。

### 回答2：
HardSwish是一种激活函数，常用于深度学习模型中的卷积神经网络（CNN）。调用HardSwish需要在代码中定义并使用相应的函数。

以下为一个示例的Python代码，演示了如何调用HardSwish函数：

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

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

    def forward(self, x):
        if self.inplace:
            return x.mul_(F.relu6(x + 3.).div_(6.))
        else:
            return F.relu6(x + 3.).div_(6.).mul_(x)

# 对于一个输入张量x
x = torch.randn(1, 3, 32, 32)  # 假设输入的维度是1x3x32x32

# 创建HardSwish激活函数的实例
hardswish = HardSwish()

# 使用HardSwish函数对输入张量进行激活
output = hardswish(x)

# 输出激活后的张量
print(output)

在上述代码中，我们首先定义了一个名为`HardSwish`的类，继承自`nn.Module`。在类的`forward`方法中，我们实现了HardSwish的计算逻辑。

在使用HardSwish时，首先需要创建HardSwish的实例（例如`hardswish = HardSwish()`），然后将输入张量传递给实例的`forward`方法（例如`output = hardswish(x)`）。最后，我们可以打印输出张量以查看激活后的结果。

总之，要调用HardSwish，你需要创建HardSwish的实例，并使用其`forward`方法对输入张量进行激活。

### 回答3：
HardSwish是一种激活函数，用于在神经网络中增加非线性。它是由Google的研究人员提出的，并在深度学习模型中取得了很好的效果。在调用HardSwish时，我们需要遵循以下步骤：

1. 导入库：首先，我们需要导入使用HardSwish激活函数所需的库。通常，在深度学习框架中已经提供了HardSwish的实现。如果使用PyTorch，我们可以使用torch.nn中的Hardswish模块。

2. 定义模型：在定义网络模型时，我们需要使用HardSwish激活函数作为某个层的激活函数。可以将其用作卷积层、全连接层或其他需要激活函数的地方。

3. 调用HardSwish：在定义网络层时，我们可以使用HardSwish函数对输入进行激活。如果使用PyTorch，可以通过torch.nn.Hardswish()来调用HardSwish函数。

例如，在PyTorch中调用HardSwish的示例代码如下：

import torch
import torch.nn as nn

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 16, kernel_size=3)
        self.hswish = nn.Hardswish()

    def forward(self, x):
        x = self.conv1(x)
        x = self.hswish(x)
        return x

model = Net()

以上代码定义了一个包含一个卷积层和一个HardSwish激活函数的网络模型。在forward函数中，我们通过self.hswish(x)调用HardSwish对输入x进行激活。这样，我们就成功地调用了HardSwish函数。

总之，要调用HardSwish，我们需要导入相关库，并将HardSwish函数作为激活函数应用于我们的网络模型中的相应层。这样就可以利用HardSwish的非线性特性来提升模型的表达能力和性能。

mobilenet V3模型代码

以下是使用PyTorch实现的MobileNet V3模型代码示例：

import torch
import torch.nn as nn

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

    def forward(self, x):
        if self.inplace:
            x *= torch.clamp(x + 3, 0, 6) / 6
        else:
            x = x * (torch.nn.functional.relu6(x + 3, inplace=True) / 6)
        return x

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

    def forward(self, x):
        if self.inplace:
            x = torch.clamp(x + 3, 0, 6) / 6
        else:
            x = torch.nn.functional.relu6(x + 3, inplace=True) / 6
        return x

class SeModule(nn.Module):
    def __init__(self, in_size, reduction=4):
        super(SeModule, self).__init__()
        self.se = nn.Sequential(
            nn.AdaptiveAvgPool2d(1),
            nn.Conv2d(in_size, in_size // reduction, kernel_size=1, stride=1, padding=0, bias=True),
            nn.Hardswish(inplace=True),
            nn.Conv2d(in_size // reduction, in_size, kernel_size=1, stride=1, padding=0, bias=True),
            Hsigmoid(inplace=True)
        )

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

class ConvBNActivation(nn.Sequential):
    def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, groups=1, norm_layer=None, activation_layer=None):
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        if activation_layer is None:
            activation_layer = nn.ReLU(inplace=True)
        padding = (kernel_size - 1) // 2
        super(ConvBNActivation, self).__init__(
            nn.Conv2d(in_planes, out_planes, kernel_size, stride, padding, groups=groups, bias=False),
            norm_layer(out_planes),
            activation_layer(inplace=True)
        )

class SqueezeExcite(nn.Module):
    def __init__(self, in_channels, se_ratio=0.25):
        super().__init__()
        self.se_ratio = se_ratio
        self.avgpool = nn.AdaptiveAvgPool2d(1)
        num_squeezed_channels = max(1, int(in_channels * se_ratio))
        self.fc1 = nn.Conv2d(in_channels, num_squeezed_channels, kernel_size=1)
        self.relu = nn.ReLU(inplace=True)
        self.fc2 = nn.Conv2d(num_squeezed_channels, in_channels, kernel_size=1)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        out = self.avgpool(x)
        out = self.fc1(out)
        out = self.relu(out)
        out = self.fc2(out)
        out = self.sigmoid(out)
        return out * x

class InvertedResidual(nn.Module):
    def __init__(self, inp, oup, stride, expand_ratio, se_ratio):
        super(InvertedResidual, self).__init__()
        self.stride = stride
        assert stride in [1, 2]
        self.use_res_connect = self.stride == 1 and inp == oup

        layers = []
        # expand
        expand_inp = int(round(inp * expand_ratio))
        if expand_ratio != 1:
            layers.append(ConvBNActivation(inp, expand_inp, kernel_size=1))

        # depthwise
        layers.append(ConvBNActivation(expand_inp, expand_inp, stride=stride, groups=expand_inp, norm_layer=nn.BatchNorm2d, activation_layer=Hswish()))

        # squeeze-and-excite
        layers.append(SqueezeExcite(expand_inp, se_ratio))

        # project
        layers.append(nn.Conv2d(expand_inp, oup, kernel_size=1, bias=False))
        layers.append(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.num_classes = num_classes
        if mode == 'large':
            layers = [
                # stage 1
                ConvBNActivation(3, int(16 * width_mult), kernel_size=3, stride=2, norm_layer=nn.BatchNorm2d, activation_layer=Hswish()),

                # stage 2
                InvertedResidual(int(16 * width_mult), int(16 * width_mult), stride=1, expand_ratio=1, se_ratio=0.25),
                InvertedResidual(int(16 * width_mult), int(24 * width_mult), stride=2, expand_ratio=4, se_ratio=0.25),
                InvertedResidual(int(24 * width_mult), int(24 * width_mult), stride=1, expand_ratio=3, se_ratio=0.25),

                # stage 3
                InvertedResidual(int(24 * width_mult), int(40 * width_mult), stride=2, expand_ratio=3, se_ratio=0.25),
                InvertedResidual(int(40 * width_mult), int(40 * width_mult), stride=1, expand_ratio=3, se_ratio=0.25),
                InvertedResidual(int(40 * width_mult), int(40 * width_mult), stride=1, expand_ratio=3, se_ratio=0.25),

                # stage 4
                InvertedResidual(int(40 * width_mult), int(80 * width_mult), stride=2, expand_ratio=6, se_ratio=0.25),
                InvertedResidual(int(80 * width_mult), int(80 * width_mult), stride=1, expand_ratio=2.5, se_ratio=0.25),
                InvertedResidual(int(80 * width_mult), int(80 * width_mult), stride=1, expand_ratio=2.3, se_ratio=0.25),
                InvertedResidual(int(80 * width_mult), int(80 * width_mult), stride=1, expand_ratio=2.3, se_ratio=0.25),

                # stage 5
                InvertedResidual(int(80 * width_mult), int(112 * width_mult), stride=1, expand_ratio=6, se_ratio=0.25),
                InvertedResidual(int(112 * width_mult), int(112 * width_mult), stride=1, expand_ratio=6, se_ratio=0.25),

                # stage 6
                InvertedResidual(int(112 * width_mult), int(160 * width_mult), stride=2, expand_ratio=6, se_ratio=0.25),
                InvertedResidual(int(160 * width_mult), int(160 * width_mult), stride=1, expand_ratio=6, se_ratio=0.25),
                InvertedResidual(int(160 * width_mult), int(160 * width_mult), stride=1, expand_ratio=6, se_ratio=0.25),

                # stage 7
                ConvBNActivation(int(160 * width_mult), int(960 * width_mult), kernel_size=1, norm_layer=nn.BatchNorm2d, activation_layer=Hswish())
            ]
        elif mode == 'small':
            layers = [
                # stage 1
                ConvBNActivation(3, int(8 * width_mult), kernel_size=3, stride=2, norm_layer=nn.BatchNorm2d, activation_layer=Hswish()),

                # stage 2
                InvertedResidual(int(8 * width_mult), int(16 * width_mult), stride=2, expand_ratio=3, se_ratio=0.25),
                InvertedResidual(int(16 * width_mult), int(16 * width_mult), stride=1, expand_ratio=3, se_ratio=0.25),

                # stage 3
                InvertedResidual(int(16 * width_mult), int(24 * width_mult), stride=2, expand_ratio=3, se_ratio=0.25),
                InvertedResidual(int(24 * width_mult), int(24 * width_mult), stride=1, expand_ratio=3, se_ratio=0.25),

                # stage 4
                InvertedResidual(int(24 * width_mult), int(40 * width_mult), stride=2, expand_ratio=6, se_ratio=0.25),
                InvertedResidual(int(40 * width_mult), int(40 * width_mult), stride=1, expand_ratio=6, se_ratio=0.25),
                InvertedResidual(int(40 * width_mult), int(40 * width_mult), stride=1, expand_ratio=6, se_ratio=0.25),

                # stage 5
                InvertedResidual(int(40 * width_mult), int(48 * width_mult), stride=1, expand_ratio=6, se_ratio=0.25),
                InvertedResidual(int(48 * width_mult), int(48 * width_mult), stride=1, expand_ratio=6, se_ratio=0.25),

                # stage 6
                InvertedResidual(int(48 * width_mult), int(96 * width_mult), stride=2, expand_ratio=6, se_ratio=0.25),
                InvertedResidual(int(96 * width_mult), int(96 * width_mult), stride=1, expand_ratio=6, se_ratio=0.25),

                # stage 7
                InvertedResidual(int(96 * width_mult), int(576 * width_mult), kernel_size=1, stride=1, expand_ratio=6, se_ratio=0.25),
                ConvBNActivation(int(576 * width_mult), int(1024 * width_mult), kernel_size=1, norm_layer=nn.BatchNorm2d, activation_layer=Hswish())
            ]
        else:
            raise ValueError('Unsupported MobileNetV3 model mode: ', mode)

        # build the network
        self.features = nn.Sequential(*layers)

        # build the head
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.classifier = nn.Sequential(
            nn.Linear(int(1024 * width_mult), int(1280 * width_mult)),
            Hswish(inplace=True),
            nn.Dropout(p=0.2, inplace=True) if self.num_classes > 0 else nn.Identity(),
            nn.Linear(int(1280 * width_mult), self.num_classes),
            nn.Softmax(dim=1)
        )

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