Spaces:
Running
on
L40S
Running
on
L40S
File size: 2,707 Bytes
2252f3d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 |
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("--------------------")
|