class Mish(nn.Module): # 定义Mish激活函数的类 def __init__(self): super().__init__() def forward(self,x): x = x*(torch.tanh(F.softplus(x))) return x

这是一个定义Mish激活函数的类，继承自nn.Module。在初始化中，调用了nn.Module的初始化方法。在forward()方法中，实现了Mish激活函数的计算，即x = x*(torch.tanh(F.softplus(x)))，然后返回x。Mish激活函数是一种比较新的激活函数，相比于ReLU等常用的激活函数，它有更好的性能。它的表达式为：f(x) = x * tanh(softplus(x))，其中softplus(x) = ln(1 + exp(x))。

相关问题

class ThreelinearModel(nn.Module): def __init__(self): super.__init__() self.linear1 = nn.Linear(12, 12) self.mish1 = Mish() self.linear2 = nn.Linear(12, 8) self.mish2 = Mish() self.linear3 = nn.Linear(8, 2) self.softmax = nn.Softmax(dim=1) self.criterion = nn.CrossEntropyLoss # 确定交叉熵系数 def forward(self,x): # 定义一个全连接网络 lin1_out = self.linear1(x) out1 = self.mish1(lin1_out) out2 = self.mish2(self.linear2(out1)) out3 = self.softmax(self.linear3(out2)) def getloss(self,x,y): y_pred = self.forward(x) loss = self.criterion(y_pred,y) return loss

这是一个使用 PyTorch 框架搭建的三层全连接神经网络模型，包括两个隐藏层和一个输出层。输入层有12个神经元，输出层有2个神经元，使用了 Mish 激活函数和 Softmax 函数，损失函数为交叉熵损失函数。该模型可以用于分类任务。在 forward 函数中，通过调用自定义的 Mish 激活函数和 Softmax 函数，将输入的 x 逐层传递到输出层，得到预测结果 y_pred。在 getloss 函数中，通过调用 forward 函数和交叉熵损失函数，计算预测值 y_pred 和真实值 y 之间的损失。

class CSPDarkNet(nn.Module):

CSPDarkNet 是一个深度神经网络模型，它是 YOLOv4 目标检测算法的基础模型之一，其核心是 CSP 模块（Cross Stage Partial Network）。它具有以下特点：

1. 使用 CSP 模块分离卷积计算，减少了计算量和参数数量。
2. 采用 DarkNet53 作为主干网络，具有较高的精度和速度。
3. 通过 SPP、PAN 等技术增强了模型的感受野和多尺度特征表达能力。
4. 基于 YOLOv4 的思想，使用 Mish 激活函数和多尺度训练等技术进一步提升了精度。

下面是 CSPDarkNet 的代码实现：

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

class CSPBlock(nn.Module):
    def __init__(self, in_channels, out_channels, n=1, shortcut=True):
        super(CSPBlock, self).__init__()
        self.shortcut = shortcut
        hidden_channels = out_channels // 2
        self.conv1 = nn.Conv2d(in_channels, hidden_channels, 1, bias=False)
        self.bn1 = nn.BatchNorm2d(hidden_channels)
        self.conv2 = nn.Conv2d(in_channels, hidden_channels, 1, bias=False)
        self.bn2 = nn.BatchNorm2d(hidden_channels)
        self.conv3 = nn.Conv2d(hidden_channels, hidden_channels, 3, padding=1, groups=n, bias=False)
        self.bn3 = nn.BatchNorm2d(hidden_channels)
        self.conv4 = nn.Conv2d(hidden_channels, hidden_channels, 1, bias=False)
        self.bn4 = nn.BatchNorm2d(hidden_channels)
        self.conv5 = nn.Conv2d(hidden_channels, hidden_channels, 3, padding=1, groups=n, bias=False)
        self.bn5 = nn.BatchNorm2d(hidden_channels)
        self.conv6 = nn.Conv2d(hidden_channels, out_channels, 1, bias=False)
        self.bn6 = nn.BatchNorm2d(out_channels)
        self.act = nn.LeakyReLU(0.1, inplace=True)

    def forward(self, x):
        if self.shortcut:
            shortcut = x
        else:
            shortcut = 0
        x1 = self.conv1(x)
        x1 = self.bn1(x1)
        x1 = self.act(x1)
        x2 = self.conv2(x)
        x2 = self.bn2(x2)
        x2 = self.act(x2)
        x3 = self.conv3(x2)
        x3 = self.bn3(x3)
        x3 = self.act(x3)
        x4 = self.conv4(x3)
        x4 = self.bn4(x4)
        x4 = self.act(x4)
        x5 = self.conv5(x4)
        x5 = self.bn5(x5)
        x5 = self.act(x5)
        x6 = self.conv6(x5)
        x6 = self.bn6(x6)
        x6 = self.act(x6)
        out = torch.cat([x1, x6], dim=1)
        return out + shortcut

class CSPDarkNet(nn.Module):
    def __init__(self, num_classes=80):
        super(CSPDarkNet, self).__init__()
        self.stem = nn.Sequential(
            nn.Conv2d(3, 32, 3, padding=1, bias=False),
            nn.BatchNorm2d(32),
            nn.LeakyReLU(0.1, inplace=True),
            nn.Conv2d(32, 64, 3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(64),
            nn.LeakyReLU(0.1, inplace=True)
        )
        self.layer1 = nn.Sequential(
            CSPBlock(64, 64, n=1, shortcut=False),
            *[CSPBlock(64, 64, n=1) for _ in range(1, 3)]
        )
        self.layer2 = nn.Sequential(
            CSPBlock(64, 128, n=2, shortcut=False),
            *[CSPBlock(128, 128, n=2) for _ in range(1, 9)]
        )
        self.layer3 = nn.Sequential(
            CSPBlock(128, 256, n=4, shortcut=False),
            *[CSPBlock(256, 256, n=4) for _ in range(1, 9)]
        )
        self.layer4 = nn.Sequential(
            CSPBlock(256, 512, n=8, shortcut=False),
            *[CSPBlock(512, 512, n=8) for _ in range(1, 5)],
            nn.Conv2d(512, 1024, 1, bias=False),
            nn.BatchNorm2d(1024),
            nn.LeakyReLU(0.1, inplace=True)
        )
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(1024, num_classes)

    def forward(self, x):
        x = self.stem(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x