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Optimize the peak memory usage when loading dataset. #514

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Optimize the peak memory usage when loading dataset. #514

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2 changes: 1 addition & 1 deletion ann_benchmarks/distance.py
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Original file line number Diff line number Diff line change
Expand Up @@ -115,7 +115,7 @@ def dataset_transform(dataset: h5py.Dataset) -> Tuple[Union[np.ndarray, List[np.
Tuple[Union[np.ndarray, List[np.ndarray]], Union[np.ndarray, List[np.ndarray]]]: Tuple of training and testing data in conventional format.
"""
if dataset.attrs.get("type", "dense") != "sparse":
return np.array(dataset["train"]), np.array(dataset["test"])
return np.asarray(dataset["train"]), np.asarray(dataset["test"])

# we store the dataset as a list of integers, accompanied by a list of lengths in hdf5
# so we transform it back to the format expected by the algorithms here (array of array of ints)
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4 changes: 2 additions & 2 deletions ann_benchmarks/runner.py
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Expand Up @@ -152,8 +152,8 @@ def load_and_transform_dataset(dataset_name: str) -> Tuple[
Tuple: Transformed datasets.
"""
D, dimension = get_dataset(dataset_name)
X_train = numpy.array(D["train"])
X_test = numpy.array(D["test"])
X_train = numpy.asarray(D["train"])
X_test = numpy.asarray(D["test"])
distance = D.attrs["distance"]

print(f"Got a train set of size ({X_train.shape[0]} * {dimension})")
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