| """ | |
| Evaluate P-IS of a batch of point clouds. | |
| The point cloud batch should be saved to an npz file, where there is an | |
| arr_0 key of shape [N x K x 3], where K is the dimensionality of each | |
| point cloud and N is the number of clouds. | |
| """ | |
| import argparse | |
| from point_e.evals.feature_extractor import PointNetClassifier, get_torch_devices | |
| from point_e.evals.fid_is import compute_inception_score | |
| from point_e.evals.npz_stream import NpzStreamer | |
| def main(): | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--cache_dir", type=str, default=None) | |
| parser.add_argument("batch", type=str) | |
| args = parser.parse_args() | |
| print("creating classifier...") | |
| clf = PointNetClassifier(devices=get_torch_devices(), cache_dir=args.cache_dir) | |
| print("computing batch predictions") | |
| _, preds = clf.features_and_preds(NpzStreamer(args.batch)) | |
| print(f"P-IS: {compute_inception_score(preds)}") | |
| if __name__ == "__main__": | |
| main() | |