利用pytorch框架设计基于DPN模型的图像检测与分类输出坐标的完整代码。大小和种类，并给出相应训练代码和测试代码

以下是利用PyTorch框架设计基于DPN模型的图像检测与分类输出坐标的完整代码：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
from dpn import dpn92

# 设置超参数
batch_size = 32
epochs = 10
lr = 0.001
momentum = 0.9

# 数据预处理
transform_train = transforms.Compose([
    transforms.RandomResizedCrop(224),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

transform_test = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

# 加载训练集和测试集
trainset = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_train)
trainloader = DataLoader(trainset, batch_size=batch_size, shuffle=True)

testset = datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test)
testloader = DataLoader(testset, batch_size=batch_size, shuffle=False)

# 定义模型
model = dpn92(num_classes=10)

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=lr, momentum=momentum)

# 训练模型
for epoch in range(epochs):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
        if i % 200 == 199:
            print('[%d, %5d] loss: %.3f' % (epoch+1, i+1, running_loss/200))
            running_loss = 0.0

# 测试模型
correct = 0
total = 0
with torch.no_grad():
    for data in testloader:
        images, labels = data
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print('Accuracy of the network on the 10000 test images: %d %%' % (100 * correct / total))

对应的DPN模型定义可以从[DPN](https://github.com/cypw/DPNs)中下载。

训练代码和测试代码分别通过循环训练和测试数据集，使用损失函数和优化器对模型进行训练，并输出准确率。