wehub-resource-sync
63 lines
2.3 KiB
Python
63 lines
2.3 KiB
Python
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://w_idxw.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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def anchor_generator_in_python(
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input_feat, anchor_sizes, aspect_ratios, variances, stride, offset
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):
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num_anchors = len(aspect_ratios) * len(anchor_sizes)
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layer_h = input_feat.shape[2]
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layer_w = input_feat.shape[3]
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out_dim = (layer_h, layer_w, num_anchors, 4)
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out_anchors = np.zeros(out_dim).astype('float32')
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for h_idx in range(layer_h):
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for w_idx in range(layer_w):
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x_ctr = (w_idx * stride[0]) + offset * (stride[0] - 1)
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y_ctr = (h_idx * stride[1]) + offset * (stride[1] - 1)
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idx = 0
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for r in range(len(aspect_ratios)):
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ar = aspect_ratios[r]
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for s in range(len(anchor_sizes)):
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anchor_size = anchor_sizes[s]
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area = stride[0] * stride[1]
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area_ratios = area / ar
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base_w = np.round(np.sqrt(area_ratios))
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base_h = np.round(base_w * ar)
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scale_w = anchor_size / stride[0]
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scale_h = anchor_size / stride[1]
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w = scale_w * base_w
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h = scale_h * base_h
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out_anchors[h_idx, w_idx, idx, :] = [
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(x_ctr - 0.5 * (w - 1)),
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(y_ctr - 0.5 * (h - 1)),
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(x_ctr + 0.5 * (w - 1)),
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(y_ctr + 0.5 * (h - 1)),
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]
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idx += 1
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# set the variance.
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out_var = np.tile(variances, (layer_h, layer_w, num_anchors, 1))
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out_anchors = out_anchors.astype('float32')
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out_var = out_var.astype('float32')
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return out_anchors, out_var
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if __name__ == '__main__':
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unittest.main()
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