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# Copyright 2019 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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from mindspore import Tensor
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from mindspore.ops import operations as P
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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_roi_align():
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def roi_align_case(data_type):
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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x = Tensor(np.array([[
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[[1, 2, 3, 4, 5, 6],
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[7, 8, 9, 10, 11, 12],
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[13, 14, 15, 16, 17, 18],
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[19, 20, 21, 22, 23, 24],
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[25, 26, 27, 28, 29, 30],
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[31, 32, 33, 34, 35, 36]]
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]], data_type))
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# test case 1
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rois = Tensor(np.array([[0, -2.0, -2.0, 21.0, 21.0]], data_type))
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pooled_height, pooled_width, spatial_scale, sample_num = 3, 3, 0.25, 2
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roi_align = P.ROIAlign(pooled_height, pooled_width,
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spatial_scale, sample_num, 1)
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output = roi_align(x, rois)
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print(output)
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expect = [[[[4.5, 6.5, 8.5],
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[16.5, 18.5, 20.5],
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[28.5, 30.5, 32.5]]]]
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assert (output.asnumpy() == expect).all()
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# test case 2
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rois = Tensor(np.array([[0, -2.0, -2.0, 22.0, 22.0]], data_type))
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pooled_height, pooled_width, spatial_scale, sample_num = 3, 3, 0.25, 2
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roi_align = P.ROIAlign(pooled_height, pooled_width,
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spatial_scale, sample_num, 0)
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output = roi_align(x, rois)
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print(output)
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expect = [[[[4.5, 6.5, 8.5],
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[16.5, 18.5, 20.5],
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[28.5, 30.5, 32.5]]]]
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assert (output.asnumpy() == expect).all()
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# test case 3
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pooled_height, pooled_width, spatial_scale, sample_num = 2, 2, 1.0, -1
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rois = Tensor(np.array([[0, -2.0, -2.0, 22.0, 22.0]], data_type))
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roi_align = P.ROIAlign(pooled_height, pooled_width,
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spatial_scale, sample_num, 0)
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output = roi_align(x, rois)
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print(output)
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expect = [[[[6.295, 0.],
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[0., 0.]]]]
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np.testing.assert_almost_equal(output.asnumpy(), expect, decimal=2)
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roi_align_case(np.float32)
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roi_align_case(np.float16)
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