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175 lines
6.8 KiB
175 lines
6.8 KiB
# 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://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, 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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import sys
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import math
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from op_test import OpTest
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def box_coder(target_box, prior_box, prior_box_var, output_box, code_type,
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box_normalized):
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prior_box_x = (
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(prior_box[:, 2] + prior_box[:, 0]) / 2).reshape(1, prior_box.shape[0])
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prior_box_y = (
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(prior_box[:, 3] + prior_box[:, 1]) / 2).reshape(1, prior_box.shape[0])
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prior_box_width = (
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(prior_box[:, 2] - prior_box[:, 0])).reshape(1, prior_box.shape[0])
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prior_box_height = (
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(prior_box[:, 3] - prior_box[:, 1])).reshape(1, prior_box.shape[0])
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prior_box_var = prior_box_var.reshape(1, prior_box_var.shape[0],
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prior_box_var.shape[1])
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if not box_normalized:
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prior_box_height = prior_box_height + 1
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prior_box_width = prior_box_width + 1
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if (code_type == "EncodeCenterSize"):
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target_box_x = ((target_box[:, 2] + target_box[:, 0]) / 2).reshape(
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target_box.shape[0], 1)
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target_box_y = ((target_box[:, 3] + target_box[:, 1]) / 2).reshape(
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target_box.shape[0], 1)
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target_box_width = ((target_box[:, 2] - target_box[:, 0])).reshape(
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target_box.shape[0], 1)
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target_box_height = ((target_box[:, 3] - target_box[:, 1])).reshape(
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target_box.shape[0], 1)
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if not box_normalized:
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target_box_height = target_box_height + 1
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target_box_width = target_box_width + 1
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output_box[:,:,0] = (target_box_x - prior_box_x) / prior_box_width / \
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prior_box_var[:,:,0]
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output_box[:,:,1] = (target_box_y - prior_box_y) / prior_box_height / \
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prior_box_var[:,:,1]
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output_box[:,:,2] = np.log(np.fabs(target_box_width / prior_box_width)) / \
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prior_box_var[:,:,2]
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output_box[:,:,3] = np.log(np.fabs(target_box_height / prior_box_height)) / \
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prior_box_var[:,:,3]
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elif (code_type == "DecodeCenterSize"):
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target_box_x = prior_box_var[:,:,0] * target_box[:,:,0] * \
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prior_box_width + prior_box_x
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target_box_y = prior_box_var[:,:,1] * target_box[:,:,1] * \
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prior_box_height + prior_box_y
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target_box_width = np.exp(prior_box_var[:,:,2] * target_box[:,:,2]) * \
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prior_box_width
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target_box_height = np.exp(prior_box_var[:,:,3] * target_box[:,:,3]) * \
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prior_box_height
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output_box[:, :, 0] = target_box_x - target_box_width / 2
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output_box[:, :, 1] = target_box_y - target_box_height / 2
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output_box[:, :, 2] = target_box_x + target_box_width / 2
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output_box[:, :, 3] = target_box_y + target_box_height / 2
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if not box_normalized:
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output_box[:, :, 2] = output_box[:, :, 2] - 1
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output_box[:, :, 3] = output_box[:, :, 3] - 1
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def batch_box_coder(prior_box, prior_box_var, target_box, lod, code_type,
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box_normalized):
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n = target_box.shape[0]
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m = prior_box.shape[0]
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output_box = np.zeros((n, m, 4), dtype=np.float32)
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for i in range(len(lod) - 1):
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if (code_type == "EncodeCenterSize"):
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box_coder(target_box[lod[i]:lod[i + 1], :], prior_box,
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prior_box_var, output_box[lod[i]:lod[i + 1], :, :],
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code_type, box_normalized)
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elif (code_type == "DecodeCenterSize"):
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box_coder(target_box[lod[i]:lod[i + 1], :, :], prior_box,
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prior_box_var, output_box[lod[i]:lod[i + 1], :, :],
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code_type, box_normalized)
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return output_box
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class TestBoxCoderOp(OpTest):
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def test_check_output(self):
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self.check_output()
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def setUp(self):
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self.op_type = "box_coder"
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lod = [[0, 1, 2, 3, 4, 5]]
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prior_box = np.random.random((10, 4)).astype('float32')
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prior_box_var = np.random.random((10, 4)).astype('float32')
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target_box = np.random.random((5, 10, 4)).astype('float32')
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code_type = "DecodeCenterSize"
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box_normalized = False
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output_box = batch_box_coder(prior_box, prior_box_var, target_box,
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lod[0], code_type, box_normalized)
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self.inputs = {
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'PriorBox': prior_box,
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'PriorBoxVar': prior_box_var,
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'TargetBox': target_box,
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}
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self.attrs = {
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'code_type': 'decode_center_size',
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'box_normalized': False
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}
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self.outputs = {'OutputBox': output_box}
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class TestBoxCoderOpWithoutBoxVar(OpTest):
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def test_check_output(self):
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self.check_output()
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def setUp(self):
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self.op_type = "box_coder"
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lod = [[0, 1, 2, 3, 4, 5]]
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prior_box = np.random.random((10, 4)).astype('float32')
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prior_box_var = np.ones((10, 4)).astype('float32')
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target_box = np.random.random((5, 10, 4)).astype('float32')
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code_type = "DecodeCenterSize"
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box_normalized = False
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output_box = batch_box_coder(prior_box, prior_box_var, target_box,
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lod[0], code_type, box_normalized)
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self.inputs = {
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'PriorBox': prior_box,
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'TargetBox': target_box,
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}
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self.attrs = {
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'code_type': 'decode_center_size',
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'box_normalized': False
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}
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self.outputs = {'OutputBox': output_box}
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class TestBoxCoderOpWithLoD(OpTest):
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def test_check_output(self):
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self.check_output()
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def setUp(self):
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self.op_type = "box_coder"
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lod = [[0, 4, 12, 20]]
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prior_box = np.random.random((10, 4)).astype('float32')
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prior_box_var = np.random.random((10, 4)).astype('float32')
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target_box = np.random.random((20, 4)).astype('float32')
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code_type = "EncodeCenterSize"
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box_normalized = True
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output_box = batch_box_coder(prior_box, prior_box_var, target_box,
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lod[0], code_type, box_normalized)
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self.inputs = {
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'PriorBox': prior_box,
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'PriorBoxVar': prior_box_var,
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'TargetBox': (target_box, lod),
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}
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self.attrs = {'code_type': 'encode_center_size', 'box_normalized': True}
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self.outputs = {'OutputBox': output_box}
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if __name__ == '__main__':
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unittest.main()
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