wehub-resource-sync
70 lines
2.3 KiB
Python
70 lines
2.3 KiB
Python
# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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"""Numpy reference implementation for get_valid_counts."""
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import numpy as np
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def get_valid_counts_python(data, score_threshold=0, id_index=0, score_index=1):
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"""Numpy reference for get_valid_counts.
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Parameters
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----------
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data : numpy.ndarray
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3-D array with shape [batch_size, num_anchors, elem_length].
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score_threshold : float
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Lower limit of score for valid bounding boxes.
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id_index : int
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Index of the class categories, -1 to disable.
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score_index : int
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Index of the scores/confidence of boxes.
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Returns
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-------
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valid_count : numpy.ndarray
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1-D array, shape [batch_size].
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out_tensor : numpy.ndarray
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Rearranged data, shape [batch_size, num_anchors, elem_length].
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out_indices : numpy.ndarray
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Indices mapping, shape [batch_size, num_anchors].
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"""
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batch_size, num_anchors, box_data_length = data.shape
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valid_count = np.zeros(batch_size, dtype="int32")
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out_tensor = np.full_like(data, -1.0)
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out_indices = np.full((batch_size, num_anchors), -1, dtype="int32")
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for i in range(batch_size):
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cnt = 0
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for j in range(num_anchors):
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score = data[i, j, score_index]
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if id_index < 0:
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is_valid = score > score_threshold
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else:
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is_valid = score > score_threshold and data[i, j, id_index] >= 0
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if is_valid:
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out_tensor[i, cnt, :] = data[i, j, :]
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out_indices[i, cnt] = j
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cnt += 1
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valid_count[i] = cnt
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return valid_count, out_tensor, out_indices
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