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import random |
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import torch |
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from .densepose_base import DensePoseBaseSampler |
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class DensePoseUniformSampler(DensePoseBaseSampler): |
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""" |
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Samples DensePose data from DensePose predictions. |
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Samples for each class are drawn uniformly over all pixels estimated |
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to belong to that class. |
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""" |
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def __init__(self, count_per_class: int = 8): |
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""" |
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Constructor |
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Args: |
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count_per_class (int): the sampler produces at most `count_per_class` |
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samples for each category |
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""" |
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super().__init__(count_per_class) |
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def _produce_index_sample(self, values: torch.Tensor, count: int): |
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""" |
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Produce a uniform sample of indices to select data |
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Args: |
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values (torch.Tensor): an array of size [n, k] that contains |
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estimated values (U, V, confidences); |
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n: number of channels (U, V, confidences) |
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k: number of points labeled with part_id |
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count (int): number of samples to produce, should be positive and <= k |
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Return: |
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list(int): indices of values (along axis 1) selected as a sample |
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""" |
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k = values.shape[1] |
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return random.sample(range(k), count) |
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