test_useless.py

# point cloud 텍스트 파일의 첫 1024개의 point가 샘플링 되는지 확인하기

from data_utils.ModelNetDataLoader import ModelNetDataLoader
import torch
import argparse

def parse_args():
    '''PARAMETERS'''
    parser = argparse.ArgumentParser('training')
    parser.add_argument('--use_cpu', action='store_true', default=False, help='use cpu mode')
    parser.add_argument('--gpu', type=str, default='0', help='specify gpu device')
    parser.add_argument('--batch_size', type=int, default=24, help='batch size in training')
    parser.add_argument('--model', default='pointnet_cls', help='model name [default: pointnet_cls]')
    parser.add_argument('--num_category', default=40, type=int, choices=[10, 40],  help='training on ModelNet10/40')
    parser.add_argument('--epoch', default=200, type=int, help='number of epoch in training')
    parser.add_argument('--learning_rate', default=0.001, type=float, help='learning rate in training')
    parser.add_argument('--num_point', type=int, default=1024, help='Point Number')
    parser.add_argument('--optimizer', type=str, default='Adam', help='optimizer for training')
    parser.add_argument('--log_dir', type=str, default=None, help='experiment root')
    parser.add_argument('--decay_rate', type=float, default=1e-4, help='decay rate')
    parser.add_argument('--use_normals', action='store_true', default=False, help='use normals')
    parser.add_argument('--process_data', action='store_true', default=False, help='save data offline')
    parser.add_argument('--use_uniform_sample', action='store_true', default=False, help='use uniform sampiling')
    return parser.parse_args()

def main(args):
    data_path = 'data/modelnet40_normal_resampled/'

    train_dataset = ModelNetDataLoader(root=data_path, args=args, split='train', process_data=args.process_data) # ModelNetDataLoader: Custom dataset을 반환하는 클래스
    test_dataset = ModelNetDataLoader(root=data_path, args=args, split='test', process_data=args.process_data)
    trainDataLoader = torch.utils.data.DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True, num_workers=10, drop_last=True) # DataLoader는 utils에 있는 거 그냥 사용하면 됨!
    testDataLoader = torch.utils.data.DataLoader(test_dataset, batch_size=args.batch_size, shuffle=False, num_workers=10)
    print(train_dataset[625])

if __name__ == '__main__': # Step 1
    args = parse_args()
    main(args)