lib/dataloader_demo.py

import os
import sys
root_path = os.path.abspath(__file__)
root_path = '/'.join(root_path.split('/')[:-2])
sys.path.append(root_path)

import argparse
from lib.common.config import get_cfg_defaults
from lib.dataset.PIFuDataset import PIFuDataset

if __name__ == '__main__':

    parser = argparse.ArgumentParser()
    parser.add_argument('-v',
                        '--show',
                        action='store_true',
                        help='vis sampler 3D')
    parser.add_argument('-s',
                        '--speed',
                        action='store_true',
                        help='vis sampler 3D')
    parser.add_argument('-l',
                        '--list',
                        action='store_true',
                        help='vis sampler 3D')
    parser.add_argument('-c',
                        '--config',
                        default='./configs/train/icon-filter.yaml',
                        help='vis sampler 3D')
    parser.add_argument('-d', '--dataset', default='thuman')
    args_c = parser.parse_args()

    args = get_cfg_defaults()
    args.merge_from_file(args_c.config)
    print(args_c.dataset)
    if args_c.dataset == 'cape':

        # for cape test set
        cfg_test_mode = [
            "test_mode", True, "dataset.types", ["cape"], "dataset.scales",
            [100.0], "dataset.rotation_num", 3,"root","./data/"
        ]
        args.merge_from_list(cfg_test_mode)

    # dataset sampler
    dataset = PIFuDataset(args, split='test', vis=args_c.show)
    print(f"Number of subjects :{len(dataset.subject_list)}")
    data_dict = dataset[1]

    if args_c.list:
        for k in data_dict.keys():
            if not hasattr(data_dict[k], "shape"):
                print(f"{k}: {data_dict[k]}")
            else:
                print(f"{k}: {data_dict[k].shape}")

    if args_c.show:
        # for item in dataset:
        item = dataset[0]
        dataset.visualize_sampling3D(item, mode='cmap')
        # dataset.visualize_sampling3D(item, mode='occ')
        # dataset.visualize_sampling3D(item, mode='normal')
        # dataset.visualize_sampling3D(item, mode='sdf')
        # dataset.visualize_sampling3D(item, mode='vis')

    if args_c.speed:
        # original: 2 it/s
        # smpl online compute: 2 it/s
        # normal online compute: 1.5 it/s
        from tqdm import tqdm
        for item in tqdm(dataset):
            # pass
            for k in item.keys():
                if 'voxel' in k:
                    if not hasattr(item[k], "shape"):
                        print(f"{k}: {item[k]}")
                    else:
                        print(f"{k}: {item[k].shape}")
            print("--------------------")