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：

首先，需要安装PyTorch和其他必要的库。可以使用以下命令来安装它们：

pip install torch torchvision opencv-python

然后，导入必要的库：

import torch
import torch.nn as nn
import torch.optim as optim
import torch.utils.data as data
import torchvision.datasets as datasets
import torchvision.transforms as transforms
import numpy as np
import cv2

接下来，定义DPN92模型：

class DPN92(nn.Module):
    def __init__(self, num_classes=1000):
        super(DPN92, self).__init__()

        self.features = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 1024, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(1024),
            nn.ReLU(inplace=True),
            nn.Conv2d(1024, 1024, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(1024),
            nn.ReLU(inplace=True),
            nn.Conv2d(1024, 1024, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(1024),
            nn.ReLU(inplace=True),
            nn.Conv2d(1024, num_classes, kernel_size=1, stride=1, padding=0, bias=True),
        )

        self.avgpool = nn.AvgPool2d(kernel_size=7, stride=1)
        self.fc = nn.Linear(num_classes, num_classes)

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

接下来，定义训练和测试函数：

def train(model, device, train_loader, optimizer, epoch):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = nn.CrossEntropyLoss()(output, target)
        loss.backward()
        optimizer.step()
        if batch_idx % 10 == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                100. * batch_idx / len(train_loader), loss.item()))


def test(model, device, test_loader):
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            output = model(data)
            test_loss += nn.CrossEntropyLoss()(output, target).item()
            pred = output.max(1, keepdim=True)[1]
            correct += pred.eq(target.view_as(pred)).sum().item()

    test_loss /= len(test_loader.dataset)

    print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
        test_loss, correct, len(test_loader.dataset),
        100. * correct / len(test_loader.dataset)))

然后，定义数据集和数据加载器：

transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
])

train_dataset = datasets.ImageFolder(
    root='train',
    transform=transform
)

test_dataset = datasets.ImageFolder(
    root='test',
    transform=transform
)

train_loader = data.DataLoader(
    train_dataset,
    batch_size=32,
    shuffle=True,
    num_workers=4
)

test_loader = data.DataLoader(
    test_dataset,
    batch_size=32,
    shuffle=False,
    num_workers=4
)

最后，定义训练和测试模型：

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

model = DPN92(num_classes=10).to(device)

optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)

for epoch in range(1, 21):
    train(model, device, train_loader, optimizer, epoch)
    test(model, device, test_loader)

通过以上代码，可以完成基于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。需要注意的是，以上代码仅供参考，可能需要根据实际情况进行修改和调整。