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

以下是基于DPN模型的图像检测和分类的完整程序，包含训练代码和测试代码。本程序使用PyTorch框架实现。

首先，需要安装PyTorch和其他必要的库，如torchvision、numpy等。

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import torchvision.transforms as transforms
from torch.utils.data import Dataset, DataLoader
from PIL import Image

# 定义超参数
learning_rate = 0.001
num_epochs = 10
batch_size = 32

# 定义数据预处理
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])

# 定义数据集类
class CustomDataset(Dataset):
    def __init__(self, data, transform=None):
        self.data = data
        self.transform = transform
    
    def __len__(self):
        return len(self.data)
    
    def __getitem__(self, idx):
        img_path = self.data[idx][0]
        label = self.data[idx][1]
        img = Image.open(img_path)
        if self.transform:
            img = self.transform(img)
        return img, label

# 定义模型
class DPN(nn.Module):
    def __init__(self):
        super(DPN, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
        )
        self.classifier = nn.Sequential(
            nn.Linear(512 * 28 * 28, 4096),
            nn.ReLU(inplace=True),
            nn.Dropout(),
            nn.Linear(4096, 4096),
            nn.ReLU(inplace=True),
            nn.Dropout(),
            nn.Linear(4096, 10)
        )
    
    def forward(self, x):
        x = self.features(x)
        x = x.view(x.size(0), -1)
        x = self.classifier(x)
        return x

# 加载数据集
train_data = [('path/to/image1', 0), ('path/to/image2', 1), ...]
test_data = [('path/to/image3', 0), ('path/to/image4', 1), ...]
train_dataset = CustomDataset(train_data, transform=transform)
test_dataset = CustomDataset(test_data, transform=transform)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

# 初始化模型和损失函数
model = DPN()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

# 训练模型
for epoch in range(num_epochs):
    running_loss = 0.0
    for i, (inputs, labels) in enumerate(train_loader):
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
        if (i+1) % 100 == 0:
            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
                  .format(epoch+1, num_epochs, i+1, len(train_loader), running_loss/100))
            running_loss = 0.0

# 测试模型
with torch.no_grad():
    correct = 0
    total = 0
    for inputs, labels in test_loader:
        outputs = model(inputs)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()
    print('Accuracy of the network on the test images: {} %'.format(100 * correct / total))

上述代码中的模型是一个基于DPN的简单卷积神经网络，包含3个卷积层、3个池化层和3个全连接层。训练时使用Adam优化器和交叉熵损失函数，测试时计算模型在测试集上的精度。

接下来，我们可以将上述代码保存为一个py文件，并在命令行中输入以下命令来运行程序：

python train.py

其中train.py为保存的py文件名。程序将加载数据集、训练模型并在测试集上测试模型。