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

这里提供一个基于DPN模型的图像分类与检测的完整程序，包括训练和测试代码。这个程序使用的是PyTorch框架。

首先，我们需要安装PyTorch和其他必要的Python库。可以使用以下命令来安装：

pip install torch torchvision opencv-python

接下来，我们需要准备数据集。这里我们使用COCO数据集进行训练和测试。可以从COCO官网下载数据集。

接下来，我们需要定义模型。这里我们使用DPN模型，并在其基础上添加一个检测头，以输出坐标、大小和种类等信息。以下是模型的代码：

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

class DPN(nn.Module):
    def __init__(self, num_classes=1000, seg=False, dpn_type='dpn92', small_inputs=False, num_init_features=64, k_r=96, groups=32, b=True, c=None, k_sec=(3, 4, 20, 3), inc_sec=(16, 32, 24, 128), test_time_pool=False):
        super(DPN, self).__init__()

        self.test_time_pool = test_time_pool

        blocks = OrderedDict()
        if dpn_type == 'dpn68':
            blocks['conv1_1'] = nn.Conv2d(3, num_init_features, kernel_size=3, stride=1, padding=1, bias=False)
            blocks['bn1_1'] = nn.BatchNorm2d(num_init_features)
            blocks['conv1_2'] = nn.Conv2d(num_init_features, num_init_features, kernel_size=3, stride=1, padding=1, bias=False)
            blocks['bn1_2'] = nn.BatchNorm2d(num_init_features)
            blocks['conv1_3'] = nn.Conv2d(num_init_features, num_init_features*2, kernel_size=3, stride=2, padding=1, bias=False)
            blocks['bn1_3'] = nn.BatchNorm2d(num_init_features*2)
            blocks['conv2_1'] = DualPathBlock(num_init_features*2, k_r, groups, b, c, 1, inc_sec[0], 'proj', True, 0.2)
            for i in range(k_sec[0]):
                blocks['conv2_{}'.format(i+2)] = DualPathBlock(inc_sec[0], k_r, groups, b, c, 1, inc_sec[0], 'normal', False, 0.2)
            blocks['conv3_1'] = DualPathBlock(inc_sec[0], k_r, groups, b, c, 2, inc_sec[1], 'down', True, 0.2)
            for i in range(k_sec[1]):
                blocks['conv3_{}'.format(i+2)] = DualPathBlock(inc_sec[1], k_r, groups, b, c, 1, inc_sec[1], 'normal', False, 0.2)
            blocks['conv4_1'] = DualPathBlock(inc_sec[1], k_r, groups, b, c, 2, inc_sec[2], 'down', True, 0.2)
            for i in range(k_sec[2]):
                blocks['conv4_{}'.format(i+2)] = DualPathBlock(inc_sec[2], k_r, groups, b, c, 1, inc_sec[2], 'normal', False, 0.2)
            blocks['conv5_1'] = DualPathBlock(inc_sec[2], k_r, groups, b, c, 2, inc_sec[3], 'down', True, 0.2)
            for i in range(k_sec[3]):
                blocks['conv5_{}'.format(i+2)] = DualPathBlock(inc_sec[3], k_r, groups, b, c, 1, inc_sec[3], 'normal', False, 0.2)
            blocks['bn5'] = nn.BatchNorm2d(inc_sec[3]+inc_sec[2])
            blocks['relu5'] = nn.ReLU(inplace=True)

            if small_inputs:
                blocks['pool5'] = nn.AvgPool2d(kernel_size=7, stride=1, padding=0)
            else:
                blocks['pool5'] = nn.AvgPool2d(kernel_size=7, stride=2, padding=0)

            self.features = nn.Sequential(blocks)
            self.last_linear = nn.Linear(inc_sec[3]+inc_sec[2], num_classes)

        elif dpn_type == 'dpn92':
            blocks['conv1_1'] = nn.Conv2d(3, num_init_features, kernel_size=7, stride=2, padding=3, bias=False)
            blocks['bn1_1'] = nn.BatchNorm2d(num_init_features)
            blocks['relu1_1'] = nn.ReLU(inplace=True)
            blocks['pool1'] = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
            blocks['conv2_1'] = DualPathBlock(num_init_features, k_r, groups, b, c, 1, inc_sec[0], 'proj', True, 0.0)
            for i in range(k_sec[0]):
                blocks['conv2_{}'.format(i+2)] = DualPathBlock(inc_sec[0], k_r, groups, b, c, 1, inc_sec[0], 'normal', False, 0.0)
            blocks['conv3_1'] = DualPathBlock(inc_sec[0], k_r, groups, b, c, 2, inc_sec[1], 'down', True, 0.0)
            for i in range(k_sec[1]):
                blocks['conv3_{}'.format(i+2)] = DualPathBlock(inc_sec[1], k_r, groups, b, c, 1, inc_sec[1], 'normal', False, 0.0)
            blocks['conv4_1'] = DualPathBlock(inc_sec[1], k_r, groups, b, c, 2, inc_sec[2], 'down', True, 0.0)
            for i in range(k_sec[2]):
                blocks['conv4_{}'.format(i+2)] = DualPathBlock(inc_sec[2], k_r, groups, b, c, 1, inc_sec[2], 'normal', False, 0.0)
            blocks['conv5_1'] = DualPathBlock(inc_sec[2], k_r, groups, b, c, 2, inc_sec[3], 'down', True, 0.0)
            for i in range(k_sec[3]):
                blocks['conv5_{}'.format(i+2)] = DualPathBlock(inc_sec[3], k_r, groups, b, c, 1, inc_sec[3], 'normal', False, 0.0)
            blocks['bn5'] = nn.BatchNorm2d(inc_sec[3]+inc_sec[2])
            blocks['relu5'] = nn.ReLU(inplace=True)
            blocks['pool5'] = nn.AvgPool2d(kernel_size=7, stride=1, padding=0)

            self.features = nn.Sequential(blocks)
            self.last_linear = nn.Linear(inc_sec[3]+inc_sec[2], num_classes)

        else:
            raise ValueError('Unsupported DPNet version with number of layers {}'.format(dpn_type))

        self.seg = seg

        if seg:
            self.final_conv = nn.Conv2d(inc_sec[3]+inc_sec[2], num_classes, kernel_size=1, stride=1, padding=0, bias=True)
            self.linear_up = nn.ConvTranspose2d(num_classes, num_classes, kernel_size=32, stride=16, padding=8, output_padding=0, groups=num_classes, bias=False)

        else:
            self.final_conv = nn.Conv2d(inc_sec[3]+inc_sec[2], 4, kernel_size=1, stride=1, padding=0, bias=True)

    def forward(self, x):
        x = self.features(x)

        if self.test_time_pool:
            x = F.avg_pool2d(x, kernel_size=x.size()[2:])
        else:
            x = self.pool(x)

        if self.seg:
            x = self.final_conv(x)
            x = self.linear_up(x)
            return x

        else:
            x = self.final_conv(x)
            x = x.permute(0,2,3,1)
            return x

其中，`DPN`类定义了DPN模型，包括DPN-68和DPN-92两个版本。`DualPathBlock`类是DPN的基本模块。`num_classes`指定分类的类别数；`seg`指定是否进行语义分割；`dpn_type`指定DPN模型的版本；`small_inputs`指定输入图像的大小；`num_init_features`指定初始卷积层输出的通道数；`k_r`和`groups`指定DualPathBlock中的参数；`b`和`c`指定是否使用bottleneck和是否使用SE模块；`k_sec`和`inc_sec`指定每个阶段的参数；`test_time_pool`指定是否在测试时使用平均池化。`forward`方法定义了前向传播过程。

接下来，我们需要定义训练和测试代码。以下是训练代码：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import numpy as np
import argparse
import os

from dpn import DPN

def train(train_loader, model, criterion, optimizer, epoch):
    model.train()
    running_loss = 0.0
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.cuda(), target.cuda()
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, target)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()

        if batch_idx % 100 == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                100. * batch_idx / len(train_loader), running_loss / 100))
            running_loss = 0.0

def test(test_loader, model, criterion):
    model.eval()
    test_loss = 0.0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.cuda(), target.cuda()
            output = model(data)
            test_loss += criterion(output, target).item()
            pred = output.argmax(dim=1, keepdim=True)  # get the index of the max log-probability
            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)))

def main():
    parser = argparse.ArgumentParser(description='DPN Example')
    parser.add_argument('--batch-size', type=int, default=64, metavar='N',
                        help='input batch size for training (default: 64)')
    parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
                        help='input batch size for testing (default: 1000)')
    parser.add_argument('--epochs', type=int, default=10, metavar='N',
                        help='number of epochs to train (default: 10)')
    parser.add_argument('--lr', type=float, default=0.001, metavar='LR',
                        help='learning rate (default: 0.001)')
    parser.add_argument('--momentum', type=float, default=0.9, metavar='M',
                        help='SGD momentum (default: 0.9)')
    parser.add_argument('--no-cuda', action='store_true', default=False,
                        help='disables CUDA training')
    parser.add_argument('--seed', type=int, default=1, metavar='S',
                        help='random seed (default: 1)')
    parser.add_argument('--log-interval', type=int, default=10, metavar='N',
                        help='how many batches to wait before logging training status')
    parser.add_argument('--save-model', action='store_true', default=False,
                        help='For Saving the current Model')
    args = parser.parse_args()
    use_cuda = not args.no_cuda and torch.cuda.is_available()

    torch.manual_seed(args.seed)

    device = torch.device("cuda" if use_cuda else "cpu")

    train_dataset = datasets.CocoDetection(root='path/to/coco', annFile='path/to/annotations', transform=transforms.ToTensor())
    train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True)

    test_dataset = datasets.CocoDetection(root='path/to/coco', annFile='path/to/annotations', transform=transforms.ToTensor())
    test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=args.test_batch_size, shuffle=True)

    model = DPN(num_classes=81).to(device)
    optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
    criterion = nn.CrossEntropyLoss()

    for epoch in range(1, args.epochs + 1):
        train(train_loader, model, criterion, optimizer, epoch)
        test(test_loader, model, criterion)

    if args.save_model:
        torch.save(model.state_dict(), "dpn_coco.pt")

if __name__ == '__main__':
    main()

其中，`train`和`test`函数分别用于训练和测试。`main`函数定义了训练过程，包括数据准备、模型定义、优化器和损失函数定义，以及训练和测试循环。

最后，以下是测试代码：

import torch
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import numpy as np
import argparse
import os

from dpn import DPN

def test(test_loader, model):
    model.eval()
    with torch.no_grad():
        for data, target in test_loader:
            data = data.cuda()
            output = model(data)
            print(output)

def main():
    parser = argparse.ArgumentParser(description='DPN Example')
    parser.add_argument('--test-batch-size', type=int, default=1, metavar='N',
                        help='input batch size for testing (default: 1)')
    parser.add_argument('--no-cuda', action='store_true', default=False,
                        help='disables CUDA training')
    parser.add_argument('--seed', type=int, default=1, metavar='S',
                        help='random seed (default: 1)')
    args = parser.parse_args()
    use_cuda = not args.no_cuda and torch.cuda.is_available()

    torch.manual_seed(args.seed)

    device = torch.device("cuda" if use_cuda else "cpu")

    test_dataset = datasets.CocoDetection(root='path/to/coco', annFile='path/to/annotations', transform=transforms.ToTensor())
    test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=args.test_batch_size, shuffle=False)

    model = DPN(num_classes=81).to(device)
    model.load_state_dict(torch.load("dpn_coco.pt"))

    test(test_loader, model)

if __name__ == '__main__':
    main()

其中，`test`函数用于测试模型。`main`函数定义了测试过程，包括数据准备、模型定义和测试循环。