• Home
  • Line#
  • Scopes#
  • Navigate#
  • Raw
  • Download
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 NetIOU(nn.Cell):
27    def __init__(self, mode):
28        super(NetIOU, self).__init__()
29        self.encode = P.IOU(mode=mode)
30
31    def construct(self, anchor, groundtruth):
32        return self.encode(anchor, groundtruth)
33
34@pytest.mark.level0
35@pytest.mark.platform_x86_cpu
36@pytest.mark.env_onecard
37def test_iou():
38    pos1 = [[101, 169, 246, 429], [107, 150, 277, 400], [103, 130, 220, 400]]
39    pos2 = [[121, 138, 304, 374], [97, 130, 250, 400]]
40    mode = "iou"
41    pos1_box = Tensor(np.array(pos1), mindspore.float32)
42    pos2_box = Tensor(np.array(pos2), mindspore.float32)
43    expect_result = np.array([[0.46551168, 0.6898875, 0.4567706], [0.73686045, 0.74506813, 0.76623374]], np.float32)
44
45    error = np.ones(shape=[1]) * 1.0e-6
46
47    context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
48    overlaps = NetIOU(mode)
49    output = overlaps(pos1_box, pos2_box)
50    diff = output.asnumpy() - expect_result
51    assert np.all(abs(diff) < error)
52
53    context.set_context(mode=context.PYNATIVE_MODE, device_target='CPU')
54    overlaps = NetIOU(mode)
55    output = overlaps(pos1_box, pos2_box)
56    diff = output.asnumpy() - expect_result
57    assert np.all(abs(diff) < error)
58