【mmdetection】中dataloader加载COCO数据集

【mmdetection】中dataloader加载COCO数据集
时间:2022年9月10日

平常调试mmdetection代码,需要载入部分数据,所以写个脚本,方便数据加载。

参考:
datasets的构建参考./tools/train.py
data_loaders的构建参考./mmdet/apis/train.py

注意:
datasets载入的数据就已经是数据增强后的了,已经是经过缩放、翻转、正则化、填充后的了

代码如下:

from mmdet.datasets import build_dataset, build_dataloader
from mmcv import Config
from mmdet.apis import init_random_seed, set_random_seed
import torch.distributed as dist
import argparse

def parse_args():
    parser = argparse.ArgumentParser(description='Train a detector')
    parser.add_argument('--config', default='./configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py', help='train config file path')
    parser.add_argument('--seed', type=int, default=None, help='random seed')
    parser.add_argument(
        '--diff-seed',
        action='store_true',
        help='Whether or not set different seeds for different ranks')
    parser.add_argument(
        '--deterministic',
        action='store_true',
        help='whether to set deterministic options for CUDNN backend.')
    group_gpus = parser.add_mutually_exclusive_group()
    group_gpus.add_argument(
        '--gpus',
        type=int,
        help='(Deprecated, please use --gpu-id) number of gpus to use '
             '(only applicable to non-distributed training)')
    group_gpus.add_argument(
        '--gpu-ids',
        type=int,
        nargs='+',
        help='(Deprecated, please use --gpu-id) ids of gpus to use '
             '(only applicable to non-distributed training)')
    group_gpus.add_argument(
        '--gpu-id',
        type=int,
        default=0,
        help='id of gpu to use '
             '(only applicable to non-distributed training)')
    args = parser.parse_args()
    return args

def get_data():
    args = parse_args()
    cfg = Config.fromfile(args.config)
    datasets = build_dataset(cfg.data.train)
    print(f'datasets build finish! total : {len(datasets)}')

    # set random seeds
    seed = init_random_seed(args.seed)
    seed = seed + dist.get_rank() if args.diff_seed else seed
    set_random_seed(seed, deterministic=args.deterministic)
    cfg.seed = seed

    # set gpu_ids
    if args.gpu_ids is not None:
        cfg.gpu_ids = args.gpu_ids
    else:
        cfg.gpu_ids = range(1) if args.gpus is None else range(args.gpus)
    # set runner_type
    runner_type = 'EpochBasedRunner' if 'runner' not in cfg else cfg.runner[
        'type']

    data_loaders = build_dataloader(
        datasets,
        samples_per_gpu=2,
        workers_per_gpu=2,
        # `num_gpus` will be ignored if distributed
        num_gpus=len(cfg.gpu_ids),
        dist=False,
        seed=cfg.seed,
        runner_type=runner_type,
        persistent_workers=cfg.data.get('persistent_workers', False))

    print(f'data_loaders build finish! total : {len(data_loaders)}')

    for i, data_batch in enumerate(data_loaders):  # data_batch ['img_metas', 'img', 'gt_bboxes', 'gt_labels']
        img_metas_batch = data_batch['img_metas'].data[0]  # len = 2
        img_batch = data_batch['img'].data[0]  # [2, 3, 736, 758]
        gt_bboxes_batch = data_batch['gt_bboxes'].data[0]  # ([n1, 4], [n2, 4]) = ([n1, [x, y, w, h]], [n2, [x, y, w, h]])
        gt_labels_batch = data_batch['gt_labels'].data[0]  # ([n1], [n2])
        break
        
    return img_metas_batch, img_batch, gt_bboxes_batch, gt_labels_batch

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