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import argparse
import logging
import os.path as osp
import random
import torch
from data.pose_attr_dataset import DeepFashionAttrPoseDataset
from models import create_model
from utils.logger import get_root_logger
from utils.options import dict2str, dict_to_nonedict, parse
from utils.util import make_exp_dirs, set_random_seed
def main():
# options
parser = argparse.ArgumentParser()
parser.add_argument('-opt', type=str, help='Path to option YAML file.')
args = parser.parse_args()
opt = parse(args.opt, is_train=False)
# mkdir and loggers
make_exp_dirs(opt)
log_file = osp.join(opt['path']['log'], f"test_{opt['name']}.log")
logger = get_root_logger(
logger_name='base', log_level=logging.INFO, log_file=log_file)
logger.info(dict2str(opt))
# convert to NoneDict, which returns None for missing keys
opt = dict_to_nonedict(opt)
# random seed
seed = opt['manual_seed']
if seed is None:
seed = random.randint(1, 10000)
logger.info(f'Random seed: {seed}')
set_random_seed(seed)
test_dataset = DeepFashionAttrPoseDataset(
pose_dir=opt['pose_dir'],
texture_ann_dir=opt['texture_ann_file'],
shape_ann_path=opt['shape_ann_path'])
test_loader = torch.utils.data.DataLoader(
dataset=test_dataset, batch_size=4, shuffle=False)
logger.info(f'Number of test set: {len(test_dataset)}.')
model = create_model(opt)
_ = model.inference(test_loader, opt['path']['results_root'])
if __name__ == '__main__':
main()
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