有没有基于pytorch框架的dmtcnn代码

是的，有基于PyTorch框架的DMTCNN（Deep Multi-Task Convolutional Neural Network）代码。DMTCNN是一种用于人脸检测的深度学习模型，它可以同时检测人脸、关键点和姿态。以下是一个示例代码：

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

class PNet(nn.Module):
    def __init__(self):
        super(PNet, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 10, kernel_size=3, stride=1), 
            nn.PReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True),
            nn.Conv2d(10, 16, kernel_size=3, stride=1), 
            nn.PReLU(),
            nn.Conv2d(16, 32, kernel_size=3, stride=1), 
            nn.PReLU()
        )
        self.conv4_1 = nn.Conv2d(32, 1, kernel_size=1, stride=1)
        self.conv4_2 = nn.Conv2d(32, 4, kernel_size=1, stride=1)

    def forward(self, x):
        x = self.features(x)
        label = F.sigmoid(self.conv4_1(x))
        offset = self.conv4_2(x)
        return label, offset

class RNet(nn.Module):
    def __init__(self):
        super(RNet, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 28, kernel_size=3, stride=1), 
            nn.PReLU(),
            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
            nn.Conv2d(28, 48, kernel_size=3, stride=1), 
            nn.PReLU(),
            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
            nn.Conv2d(48, 64, kernel_size=2, stride=1), 
            nn.PReLU()
        )
        self.fc6 = nn.Linear(64 * 2 * 2, 128)
        self.prelu6 = nn.PReLU()
        self.fc7_1 = nn.Linear(128, 1)
        self.fc7_2 = nn.Linear(128, 4)

    def forward(self, x):
        x = self.features(x)
        x = x.view(x.size(0), -1)
        x = self.fc6(x)
        x = self.prelu6(x)
        label = F.sigmoid(self.fc7_1(x))
        offset = self.fc7_2(x)
        return label, offset

class ONet(nn.Module):
    def __init__(self):
        super(ONet, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 32, kernel_size=3, stride=1), 
            nn.PReLU(),
            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
            nn.Conv2d(32, 64, kernel_size=3, stride=1), 
            nn.PReLU(),
            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
            nn.Conv2d(64, 64, kernel_size=3, stride=1), 
            nn.PReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True),
            nn.Conv2d(64, 128, kernel_size=2, stride=1), 
            nn.PReLU()
        )
        self.fc6 = nn.Linear(128 * 2 * 2, 256)
        self.prelu6 = nn.PReLU()
        self.fc7_1 = nn.Linear(256, 1)
        self.fc7_2 = nn.Linear(256, 4)
        self.fc7_3 = nn.Linear(256, 10)

    def forward(self, x):
        x = self.features(x)
        x = x.view(x.size(0), -1)
        x = self.fc6(x)
        x = self.prelu6(x)
        label = F.sigmoid(self.fc7_1(x))
        offset = self.fc7_2(x)
        landmark = self.fc7_3(x)
        return label, offset, landmark

这里展示了三个模型的代码：PNet、RNet和ONet。它们分别用于不同尺度下的人脸检测。你可以将这些模型的代码整合到一个Python文件中，然后使用PyTorch进行训练和测试。