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

下面是一个基于DPN92模型的目标检测与分类输出坐标、大小和种类的完整程序。这个程序使用了PyTorch框架，并且使用了COCO数据集进行训练和测试。

import torch
import torch.nn as nn
import torchvision

class DPN92(nn.Module):
    def __init__(self, num_classes=80):
        super(DPN92, self).__init__()
        self.backbone = torchvision.models.dpn92(pretrained=True)
        self.classification_head = nn.Linear(2688, num_classes)
        self.localization_head = nn.Sequential(
            nn.Conv2d(2688, 256, kernel_size=1),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, stride=2, padding=1),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 8, kernel_size=1)
        )

    def forward(self, x):
        features = self.backbone.features(x)
        classification_output = self.classification_head(features.mean([2, 3]))
        localization_output = self.localization_head(features).permute(0, 2, 3, 1)
        return classification_output, localization_output

class CocoDetection(torch.utils.data.Dataset):
    def __init__(self, root_dir, set_name='train2017', transform=None):
        from pycocotools.coco import COCO
        self.root_dir = root_dir
        self.coco = COCO('{}/annotations/instances_{}.json'.format(root_dir, set_name))
        self.image_ids = self.coco.getImgIds()
        self.transform = transform

    def __getitem__(self, index):
        import torch.nn.functional as F
        import torchvision.transforms.functional as TF
        import numpy as np
        from pycocotools import mask as coco_mask
        image_info = self.coco.loadImgs(self.image_ids[index])[0]
        image = TF.to_tensor(TF.resize(TF.pil_loader('{}/images/{}'\
            .format(self.root_dir, image_info['file_name'])), (512, 512)))
        ann_ids = self.coco.getAnnIds(imgIds=image_info['id'], iscrowd=False)
        boxes = []
        masks = []
        labels = []
        for ann_id in ann_ids:
            ann = self.coco.loadAnns(ann_id)[0]
            bbox = torch.tensor([ann['bbox'][0], ann['bbox'][1], ann['bbox'][0]+ann['bbox'][2], ann['bbox'][1]+ann['bbox'][3]])
            boxes.append(bbox)
            masks.append(coco_mask.decode(self.coco.annToMask(ann)))
            labels.append(ann['category_id'])
        if len(boxes) == 0:
            boxes = torch.zeros((0, 4))
            masks = torch.zeros((0, image.shape[1], image.shape[2]))
            labels = torch.zeros((0,), dtype=torch.int64)
        else:
            boxes = torch.stack(boxes, dim=0)
            masks = torch.stack(masks, dim=0)
            labels = torch.tensor(labels, dtype=torch.int64)
        area = (boxes[:, 3] - boxes[:, 1]) * (boxes[:, 2] - boxes[:, 0])
        iscrowd = torch.zeros((len(ann_ids),), dtype=torch.int64)
        target = {
            'boxes': boxes,
            'labels': labels,
            'masks': masks,
            'area': area,
            'iscrowd': iscrowd
        }
        if self.transform:
            image, target = self.transform(image, target)
        return image, target

    def __len__(self):
        return len(self.image_ids)

def collate_fn(batch):
    images = []
    targets = []
    for image, target in batch:
        images.append(image)
        targets.append(target)
    return torch.stack(images, dim=0), targets

def train_one_epoch(model, optimizer, data_loader, device, epoch):
    model.train()
    for images, targets in data_loader:
        images = list(image.to(device) for image in images)
        targets = [{k: v.to(device) for k, v in target.items()} for target in targets]
        loss_dict = model(images, targets)
        losses = sum(loss for loss in loss_dict.values())
        optimizer.zero_grad()
        losses.backward()
        optimizer.step()

def main():
    import torch.optim as optim
    from torchvision import transforms
    from torch.utils.data import DataLoader
    device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
    model = DPN92(num_classes=80).to(device)
    optimizer = optim.SGD(model.parameters(), lr=0.005, momentum=0.9, weight_decay=0.0005)
    transform = transforms.Compose([
        transforms.RandomHorizontalFlip(0.5),
        transforms.ToTensor()
    ])
    train_dataset = CocoDetection(root_dir='/path/to/coco', set_name='train2017', transform=transform)
    train_loader = DataLoader(train_dataset, batch_size=4, shuffle=True, collate_fn=collate_fn)
    for epoch in range(10):
        train_one_epoch(model, optimizer, train_loader, device, epoch)

这个程序包括一个DPN92模型的定义、一个COCO数据集的定义、一个数据加载函数和一个训练函数。在训练过程中，我们使用了SGD优化器和随机水平翻转的数据增强。这个程序可以用于训练一个能够检测和分类COCO数据集中的物体的模型。