forked from mindspore-Ecosystem/mindspore
183 lines
7.5 KiB
Python
183 lines
7.5 KiB
Python
# Copyright 2022 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.common.dtype as mstype
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from mindspore.ops import deformable_conv2d
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from mindspore import Tensor
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context.set_context(device_target="Ascend")
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@pytest.mark.level0
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.env_onecard
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def test_deformable_conv2d():
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""""
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Feature: deformable_conv2d function
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Description: Test case for simplest deformable_conv2d
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Expectation: The results are as expected
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"""
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kh, kw = 1, 1
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deformable_group = 1
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stride_h, stride_w = 1, 1
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pad_h, pad_w = 0, 0
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dilation_h, dilation_w = 1, 1
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# x shape [1, 64, 1, 2]
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x = np.array([[[[-0.41675785, -0.05626683]],
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[[1.41675785, -0.25626683]],
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[[-0.41675785, 1.79979878]],
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[[1.41634355, -0.05626683]],
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[[-0.5475785, -2.9879797]],
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[[0.4543585, -0.9792279]],
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[[-1.5435465, -0.79898799]],
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[[1.41634355, -0.05626683]],
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[[-0.41675785, -0.05626683]],
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[[1.41675785, -0.25626683]],
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[[-0.41675785, 1.79979878]],
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[[1.41634355, -0.05626683]],
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[[-0.5475785, -2.9879797]],
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[[0.4543585, -0.9792279]],
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[[-1.5435465, -0.79898799]],
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[[1.41634355, -0.05626683]],
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[[-0.41675785, -0.05626683]],
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[[1.41675785, -0.25626683]],
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[[-0.41675785, 1.79979878]],
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[[1.41634355, -0.05626683]],
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[[-0.5475785, -2.9879797]],
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[[0.4543585, -0.9792279]],
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[[-1.5435465, -0.79898799]],
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[[1.41634355, -0.05626683]],
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[[-0.41675785, -0.05626683]],
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[[1.41675785, -0.25626683]],
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[[-0.41675785, 1.79979878]],
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[[1.41634355, -0.05626683]],
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[[-0.5475785, -2.9879797]],
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[[0.4543585, -0.9792279]],
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[[-1.5435465, -0.79898799]],
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[[1.41634355, -0.05626683]],
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[[-0.41675785, -0.05626683]],
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[[1.41675785, -0.25626683]],
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[[-0.41675785, 1.79979878]],
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[[1.41634355, -0.05626683]],
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[[-0.5475785, -2.9879797]],
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[[0.4543585, -0.9792279]],
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[[-1.5435465, -0.79898799]],
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[[1.41634355, -0.05626683]],
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[[-0.41675785, -0.05626683]],
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[[1.41675785, -0.25626683]],
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[[-0.41675785, 1.79979878]],
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[[1.41634355, -0.05626683]],
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[[-0.5475785, -2.9879797]],
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[[0.4543585, -0.9792279]],
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[[-1.5435465, -0.79898799]],
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[[1.41634355, -0.05626683]],
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[[-0.41675785, -0.05626683]],
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[[1.41675785, -0.25626683]],
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[[-0.41675785, 1.79979878]],
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[[1.41634355, -0.05626683]],
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[[-0.5475785, -2.9879797]],
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[[0.4543585, -0.9792279]],
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[[-1.5435465, -0.79898799]],
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[[1.41634355, -0.05626683]],
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[[-0.41675785, -0.05626683]],
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[[1.41675785, -0.25626683]],
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[[-0.41675785, 1.79979878]],
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[[0.4543585, -0.9792279]],
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[[-1.5435465, -0.79898799]],
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[[1.41634355, -0.05626683]],
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[[-0.41675785, -0.05626683]],
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[[0.5645654, 0.4656564]]]]).astype(np.float32)
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x = Tensor(x, mstype.float32)
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# weight shape [1, 64, 1, 1]
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weight = np.array([[[[-2.1361961]],
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[[-1.767576]],
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[[0.454354]],
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[[-2.1361961]],
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[[0.56756]],
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[[-0.80899]],
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[[0.767676]],
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[[-1.767576]],
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[[0.454354]],
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[[-2.1361961]],
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[[0.56756]],
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[[-0.80899]],
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[[0.767676]],
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[[-1.767576]],
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[[0.454354]],
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[[-2.1361961]],
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[[0.56756]],
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[[-0.80899]],
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[[0.767676]],
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[[-1.767576]],
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[[0.454354]],
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[[-2.1361961]],
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[[0.56756]],
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[[-0.80899]],
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[[0.767676]],
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[[-1.767576]],
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[[0.454354]],
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[[-2.1361961]],
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[[0.56756]],
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[[-0.80899]],
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[[0.767676]],
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[[-1.767576]],
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[[0.454354]],
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[[-2.1361961]],
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[[0.56756]],
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[[-0.80899]],
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[[0.767676]],
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[[-1.767576]],
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[[0.454354]],
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[[-2.1361961]],
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[[0.56756]],
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[[-0.80899]],
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[[0.767676]],
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[[-1.767576]],
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[[0.454354]],
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[[-2.1361961]],
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[[0.56756]],
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[[-0.80899]],
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[[0.767676]],
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[[-1.767576]],
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[[0.454354]],
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[[-2.1361961]],
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[[0.56756]],
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[[-0.80899]],
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[[0.767676]],
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[[-1.767576]],
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[[0.454354]],
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[[-2.1361961]],
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[[0.56756]],
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[[-0.80899]],
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[[0.767676]],
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[[-0.80899]],
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[[0.767676]],
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[[-1.675756]]]]).astype(np.float32)
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weight = Tensor(weight, mstype.float32)
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# offsets shape [1, 3, 1, 2]
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offsets = np.array([[[[1.6402708, -1.7934356]],
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[[-0.84174734, 0.5028814]],
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[[-1.2452881, -1.0579522]]]]).astype(np.float32)
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offsets = Tensor(offsets, mstype.float32)
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out = deformable_conv2d(x, weight, offsets, (kh, kw), (1, 1, stride_h, stride_w), (pad_h, pad_h, pad_w, pad_w),
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dilations=(1, 1, dilation_h, dilation_w), deformable_groups=deformable_group)
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# expected output: [1, 1, 1, 2]
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expected = np.array([[[[-0.45592457, 7.7579584]]]]).astype(np.float32)
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assert np.allclose(out.asnumpy(), expected, 0.0001, 0.0001)
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