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PyTorch 自定义数据集

准备数据#

准备 COCO128 数据集,其是 COCO train2017 前 128 个数据。按 YOLOv5 组织的目录:

$ tree ~/datasets/coco128 -L 2/home/john/datasets/coco128├── images│   └── train2017│       ├── ...│       └── 000000000650.jpg├── labels│   └── train2017│       ├── ...│       └── 000000000650.txt├── LICENSE└── README.txt

详见 Train Custom Data

定义 Dataset#

torch.utils.data.Dataset 是一个数据集的抽象类。自定义数据集时,需继承 Dataset 并覆盖如下方法:

  • __len__: len(dataset) 获取数据集大小。
  • __getitem__: dataset[i] 访问第 i 个数据。

详见:

自定义实现 YOLOv5 数据集的例子:

import osfrom pathlib import Pathfrom typing import Any, Callable, Optional, Tuple
import numpy as npimport torchimport torchvisionfrom PIL import Image

class YOLOv5(torchvision.datasets.vision.VisionDataset):
  def __init__(    self,    root: str,    name: str,    transform: Optional[Callable] = None,    target_transform: Optional[Callable] = None,    transforms: Optional[Callable] = None,  ) -> None:    super(YOLOv5, self).__init__(root, transforms, transform, target_transform)    images_dir = Path(root) / 'images' / name    labels_dir = Path(root) / 'labels' / name    self.images = [n for n in images_dir.iterdir()]    self.labels = []    for image in self.images:      base, _ = os.path.splitext(os.path.basename(image))      label = labels_dir / f'{base}.txt'      self.labels.append(label if label.exists() else None)
  def __getitem__(self, idx: int) -> Tuple[Any, Any]:    img = Image.open(self.images[idx]).convert('RGB')
    label_file = self.labels[idx]    if label_file is not None:  # found      with open(label_file, 'r') as f:        labels = [x.split() for x in f.read().strip().splitlines()]        labels = np.array(labels, dtype=np.float32)    else:  # missing      labels = np.zeros((0, 5), dtype=np.float32)
    boxes = []    classes = []    for label in labels:      x, y, w, h = label[1:]      boxes.append([        (x - w/2) * img.width,        (y - h/2) * img.height,        (x + w/2) * img.width,        (y + h/2) * img.height])      classes.append(label[0])
    target = {}    target["boxes"] = torch.as_tensor(boxes, dtype=torch.float32)    target["labels"] = torch.as_tensor(classes, dtype=torch.int64)
    if self.transforms is not None:      img, target = self.transforms(img, target)
    return img, target
  def __len__(self) -> int:    return len(self.images)

以上实现,继承了 VisionDataset 子类。其 __getitem__ 返回了:

  • image: PIL Image, 大小为 (H, W)
  • target: dict, 含以下字段:
    • boxes (FloatTensor[N, 4]): 真实标注框 [x1, y1, x2, y2], x 范围 [0,W], y 范围 [0,H]
    • labels (Int64Tensor[N]): 上述标注框的类别标识

读取 Dataset#

dataset = YOLOv5(Path.home() / 'datasets/coco128', 'train2017')print(f'dataset: {len(dataset)}')print(f'dataset[0]: {dataset[0]}')

输出:

dataset: 128dataset[0]: (<PIL.Image.Image image mode=RGB size=640x480 at 0x7F6F9464ADF0>, {'boxes': tensor([[249.7296, 200.5402, 460.5399, 249.1901],        [448.1702, 363.7198, 471.1501, 406.2300],        ...        [  0.0000, 188.8901, 172.6400, 280.9003]]), 'labels': tensor([44, 51, 51, 51, 51, 44, 44, 44, 44, 44, 45, 45, 45, 45, 45, 45, 45, 45,        45, 50, 50, 50, 51, 51, 60, 42, 44, 45, 45, 45, 50, 51, 51, 51, 51, 51,        51, 44, 50, 50, 50, 45])})

预览:

使用 DataLoader#

训练需要批量提取数据,可以使用 DataLoader :

dataset = YOLOv5(Path.home() / 'datasets/coco128', 'train2017',  transform=torchvision.transforms.Compose([    torchvision.transforms.ToTensor()  ]))
dataloader = DataLoader(dataset, batch_size=64, shuffle=True,                        collate_fn=lambda batch: tuple(zip(*batch)))
for batch_i, (images, targets) in enumerate(dataloader):  print(f'batch {batch_i}, images {len(images)}, targets {len(targets)}')  print(f'  images[0]: shape={images[0].shape}')  print(f'  targets[0]: {targets[0]}')

输出:

batch 0, images 64, targets 64  images[0]: shape=torch.Size([3, 480, 640])  targets[0]: {'boxes': tensor([[249.7296, 200.5402, 460.5399, 249.1901],        [448.1702, 363.7198, 471.1501, 406.2300],        ...        [  0.0000, 188.8901, 172.6400, 280.9003]]), 'labels': tensor([44, 51, 51, 51, 51, 44, 44, 44, 44, 44, 45, 45, 45, 45, 45, 45, 45, 45,        45, 50, 50, 50, 51, 51, 60, 42, 44, 45, 45, 45, 50, 51, 51, 51, 51, 51,        51, 44, 50, 50, 50, 45])}batch 1, images 64, targets 64  images[0]: shape=torch.Size([3, 248, 640])  targets[0]: {'boxes': tensor([[337.9299, 167.8500, 378.6999, 191.3100],        [383.5398, 148.4501, 452.6598, 191.4701],        [467.9299, 149.9001, 540.8099, 193.2401],        [196.3898, 142.7200, 271.6896, 190.0999],        [134.3901, 154.5799, 193.9299, 189.1699],        [ 89.5299, 162.1901, 124.3798, 188.3301],        [  1.6701, 154.9299,  56.8400, 188.3700]]), 'labels': tensor([20, 20, 20, 20, 20, 20, 20])}

源码#

参考#

APIs: