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87 lines
3.0 KiB
87 lines
3.0 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
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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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# ============================================================================
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import numpy as np
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import pytest
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import mindspore.context as context
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.ops import operations as P
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class NetCheckValid(nn.Cell):
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def __init__(self):
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super(NetCheckValid, self).__init__()
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self.valid = P.CheckValid()
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def construct(self, anchor, image_metas):
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return self.valid(anchor, image_metas)
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def check_valid(nptype):
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anchor = np.array([[50, 0, 100, 700], [-2, 2, 8, 100], [10, 20, 300, 2000]], nptype)
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image_metas = np.array([768, 1280, 1], nptype)
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anchor_box = Tensor(anchor)
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image_metas_box = Tensor(image_metas)
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expect = np.array([True, False, False], np.bool)
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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boundingbox_decode = NetCheckValid()
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output = boundingbox_decode(anchor_box, image_metas_box)
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assert np.array_equal(output.asnumpy(), expect)
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context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
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boundingbox_decode = NetCheckValid()
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output = boundingbox_decode(anchor_box, image_metas_box)
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assert np.array_equal(output.asnumpy(), expect)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_check_valid_float32():
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check_valid(np.float32)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_check_valid_float16():
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check_valid(np.float16)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_check_valid_int16():
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check_valid(np.int16)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_check_valid_uint8():
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anchor = np.array([[5, 0, 10, 70], [2, 2, 8, 10], [1, 2, 30, 200]], np.uint8)
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image_metas = np.array([76, 128, 1], np.uint8)
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anchor_box = Tensor(anchor)
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image_metas_box = Tensor(image_metas)
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expect = np.array([True, True, False], np.bool)
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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boundingbox_decode = NetCheckValid()
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output = boundingbox_decode(anchor_box, image_metas_box)
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assert np.array_equal(output.asnumpy(), expect)
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context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
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boundingbox_decode = NetCheckValid()
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output = boundingbox_decode(anchor_box, image_metas_box)
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assert np.array_equal(output.asnumpy(), expect)
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