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Running
on
Zero
File size: 951 Bytes
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"""
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()
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