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import numpy as np |
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import sklearn.metrics |
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__all__ = ["compute_prdc"] |
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def compute_pairwise_distance(data_x, data_y=None): |
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""" |
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Parameters |
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---------- |
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data_x: numpy.ndarray([N, feature_dim], dtype=np.float32) |
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data_y: numpy.ndarray([N, feature_dim], dtype=np.float32) |
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Returns |
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------- |
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numpy.ndarray([N, N], dtype=np.float32) of pairwise distances. |
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""" |
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if data_y is None: |
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data_y = data_x |
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dists = sklearn.metrics.pairwise_distances( |
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data_x, data_y, metric="euclidean", n_jobs=8 |
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) |
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return dists |
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def get_kth_value(unsorted, k, axis=-1): |
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""" |
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Parameters |
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---------- |
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unsorted: numpy.ndarray of any dimensionality. |
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k: int |
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axis: int |
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Returns |
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------- |
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kth values along the designated axis. |
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""" |
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indices = np.argpartition(unsorted, k, axis=axis)[..., :k] |
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k_smallests = np.take_along_axis(unsorted, indices, axis=axis) |
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kth_values = k_smallests.max(axis=axis) |
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return kth_values |
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def compute_nearest_neighbour_distances(input_features, nearest_k): |
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""" |
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Parameters |
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---------- |
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input_features: numpy.ndarray([N, feature_dim], dtype=np.float32) |
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nearest_k: int |
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Returns |
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------- |
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Distances to kth nearest neighbours. |
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""" |
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distances = compute_pairwise_distance(input_features) |
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radii = get_kth_value(distances, k=nearest_k + 1, axis=-1) |
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return radii |
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def compute_prdc(real_features, fake_features, nearest_k): |
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""" |
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Computes precision, recall, density, and coverage given two manifolds. |
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Parameters |
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---------- |
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real_features: numpy.ndarray([N, feature_dim], dtype=np.float32) |
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fake_features: numpy.ndarray([N, feature_dim], dtype=np.float32) |
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nearest_k: int. |
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Returns |
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------- |
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dict of precision, recall, density, and coverage. |
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""" |
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print( |
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"Num real: {} Num fake: {}".format( |
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real_features.shape[0], fake_features.shape[0] |
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) |
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) |
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real_nearest_neighbour_distances = compute_nearest_neighbour_distances( |
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real_features, nearest_k |
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) |
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fake_nearest_neighbour_distances = compute_nearest_neighbour_distances( |
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fake_features, nearest_k |
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) |
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distance_real_fake = compute_pairwise_distance(real_features, fake_features) |
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precision = ( |
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(distance_real_fake < np.expand_dims(real_nearest_neighbour_distances, axis=1)) |
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.any(axis=0) |
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.mean() |
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) |
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recall = ( |
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(distance_real_fake < np.expand_dims(fake_nearest_neighbour_distances, axis=0)) |
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.any(axis=1) |
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.mean() |
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) |
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density = (1.0 / float(nearest_k)) * ( |
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distance_real_fake < np.expand_dims(real_nearest_neighbour_distances, axis=1) |
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).sum(axis=0).mean() |
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coverage = ( |
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distance_real_fake.min(axis=1) < real_nearest_neighbour_distances |
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).mean() |
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return dict(precision=precision, recall=recall, density=density, coverage=coverage) |
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