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：

• torch.utils.data.Dataset
• torchvision.datasets.vision.VisionDataset

自定义实现 YOLOv5 数据集的例子：

import os
from pathlib import Path
from typing import Any, Callable, Optional, Tuple

import numpy as np
import torch
import torchvision
from 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: 128
dataset[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

• datasets.py
• test_datasets.py

参考

• Loading data in PyTorch
• Datasets & Dataloaders
• Writing Custom Datasets, DataLoaders and Transforms

APIs:

• torch.utils.data
• torchvision.datasets

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