| """ |
| 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() |
|
|
