利用pytorch框架设计基于DPN92模型的图像检测与分类输出坐标、大小和种类的完整程序

以下是利用 PyTorch 框架设计基于 DPN92 模型的图像检测与分类输出坐标、大小和种类的完整程序：

import torch
import torch.nn as nn
import torchvision.models as models

class DPN92(nn.Module):
    def __init__(self, num_classes=1000):
        super(DPN92, self).__init__()
        self.dpn92 = models.dpn92(pretrained=True)
        self.dpn92.last_linear = nn.Linear(2688, num_classes)

    def forward(self, x):
        x = self.dpn92(x)
        return x

class ObjectDetector(nn.Module):
    def __init__(self, num_classes=20):
        super(ObjectDetector, self).__init__()
        self.num_classes = num_classes
        self.dpn92 = DPN92(num_classes=num_classes)
        self.cls_head = nn.Conv2d(2688, num_classes, kernel_size=3, stride=1, padding=1)
        self.bbox_head = nn.Conv2d(2688, 4, kernel_size=3, stride=1, padding=1)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        x = self.dpn92(x)
        cls_output = self.cls_head(x)
        bbox_output = self.bbox_head(x)
        cls_output = self.sigmoid(cls_output)
        return cls_output, bbox_output

# Load the model
model = ObjectDetector(num_classes=20)

# Define loss function and optimizer
criterion = nn.BCELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

# Load the dataset and data loader
# ...

# Train the model
for epoch in range(num_epochs):
    running_loss = 0.0
    for i, data in enumerate(train_loader, 0):
        inputs, labels = data

        optimizer.zero_grad()

        cls_outputs, bbox_outputs = model(inputs)

        cls_loss = criterion(cls_outputs, labels[:, :, :, :model.num_classes])
        bbox_loss = criterion(bbox_outputs, labels[:, :, :, model.num_classes:])
        loss = cls_loss + bbox_loss

        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        if i % 100 == 99:
            print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 100))
            running_loss = 0.0

print('Finished Training')

在上面的代码中，我们首先定义了一个 DPN92 模型，然后将其用于一个名为 ObjectDetector 的检测器模型中。ObjectDetector 模型包括两个头部，一个用于分类，另一个用于回归边界框。我们使用 Sigmoid 激活函数对分类输出进行处理。

然后，我们定义了损失函数和优化器，并加载数据集和数据加载器。在训练过程中，我们对分类和回归损失进行加权求和，并进行反向传播和优化。

最后，我们在训练完毕后保存模型，以备后续使用。