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
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 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：
image
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:
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