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1# Copyright 2021 Huawei Technologies Co., Ltd
2#
3# Licensed under the Apache License, Version 2.0 (the "License");
4# you may not use this file except in compliance with the License.
5# You may obtain a copy of the License at
6#
7# http://www.apache.org/licenses/LICENSE-2.0
8#
9# Unless required by applicable law or agreed to in writing, software
10# distributed under the License is distributed on an "AS IS" BASIS,
11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12# See the License for the specific language governing permissions and
13# limitations under the License.
14# ============================================================================
15
16import numpy as np
17import pytest
18
19import mindspore
20import mindspore.context as context
21import mindspore.nn as nn
22from mindspore import Tensor
23from mindspore.ops import operations as P
24
25
26class NetBoundingBoxEncode(nn.Cell):
27    def __init__(self, means=(0.0, 0.0, 0.0, 0.0), stds=(1.0, 1.0, 1.0, 1.0)):
28        super(NetBoundingBoxEncode, self).__init__()
29        self.encode = P.BoundingBoxEncode(means=means, stds=stds)
30
31    def construct(self, anchor, groundtruth):
32        return self.encode(anchor, groundtruth)
33
34def bbox2delta(proposals, gt, means, stds):
35    p_width = proposals[..., 2] - proposals[..., 0] + 1.0
36    p_height = proposals[..., 3] - proposals[..., 1] + 1.0
37
38    dx = (((gt[..., 0] + gt[..., 2]) * 0.5) - ((proposals[..., 0] + proposals[..., 2]) * 0.5)) / p_width
39    dy = (((gt[..., 1] + gt[..., 3]) * 0.5) - ((proposals[..., 1] + proposals[..., 3]) * 0.5)) / p_height
40    dw = np.log((gt[..., 2] - gt[..., 0] + 1.0) / p_width)
41    dh = np.log((gt[..., 3] - gt[..., 1] + 1.0) / p_height)
42    means = np.array(means, np.float32)
43    stds = np.array(stds, np.float32)
44    deltas = np.stack([(dx - means[0]) / stds[0], (dy - means[1]) / stds[1],
45                       (dw - means[2]) / stds[2], (dh - means[3]) / stds[3]], axis=-1)
46
47    return deltas
48
49@pytest.mark.level0
50@pytest.mark.platform_x86_cpu
51@pytest.mark.env_onecard
52def test_boundingbox_encode():
53    anchor = np.array([[4, 1, 6, 9], [2, 5, 5, 9]]).astype(np.float32)
54    gt = np.array([[3, 2, 7, 7], [1, 5, 5, 8]]).astype(np.float32)
55    means = (0.1, 0.1, 0.2, 0.2)
56    stds = (2.0, 2.0, 3.0, 3.0)
57    anchor_box = Tensor(anchor, mindspore.float32)
58    groundtruth_box = Tensor(gt, mindspore.float32)
59    expect_deltas = bbox2delta(anchor, gt, means, stds)
60
61    error = np.ones(shape=[2, 4]) * 1.0e-6
62
63    context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
64    boundingbox_encode = NetBoundingBoxEncode(means, stds)
65    output = boundingbox_encode(anchor_box, groundtruth_box)
66    diff = output.asnumpy() - expect_deltas
67    assert np.all(abs(diff) < error)
68
69    context.set_context(mode=context.PYNATIVE_MODE, device_target='CPU')
70    boundingbox_encode = NetBoundingBoxEncode(means, stds)
71    output = boundingbox_encode(anchor_box, groundtruth_box)
72    diff = output.asnumpy() - expect_deltas
73    assert np.all(abs(diff) < error)
74