forked from mindspore-Ecosystem/mindspore
105 lines
3.5 KiB
Python
105 lines
3.5 KiB
Python
# Copyright 2019-2021 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.common.api import ms_function
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from mindspore.ops.operations import _grad_ops as G
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context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
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class SliceGrad(nn.Cell):
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def __init__(self):
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super(SliceGrad, self).__init__()
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self.slice_grad = G.SliceGrad()
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@ms_function
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def construct(self, dy, x):
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return self.slice_grad(dy, x, (0, 1, 0), (2, 1, 3))
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_slice():
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x = Tensor(np.array([[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]], [[5, 5, 5], [6, 6, 6]]]).astype(np.float32))
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dy = Tensor(np.array([[[3., 1., 2.]], [[4., 1., 4.]]]).astype(np.float32))
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slice_grad = SliceGrad()
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output = slice_grad(dy, x)
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expect = [[[0., 0., 0.],
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[3., 1., 2.]],
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[[0., 0., 0.],
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[4., 1., 4.]],
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[[0., 0., 0.],
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[0., 0., 0.]]]
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assert (output.asnumpy() == expect).all()
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_slice_float64():
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x = Tensor(np.array([[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]], [[5, 5, 5], [6, 6, 6]]]).astype(np.float64))
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dy = Tensor(np.array([[[3., 1., 2.]], [[4., 1., 4.]]]).astype(np.float64))
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slice_grad = SliceGrad()
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output = slice_grad(dy, x)
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expect = np.array([[[0., 0., 0.],
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[3., 1., 2.]],
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[[0., 0., 0.],
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[4., 1., 4.]],
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[[0., 0., 0.],
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[0., 0., 0.]]]).astype(np.float64)
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assert (output.asnumpy() == expect).all()
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class SliceGrad7D(nn.Cell):
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def __init__(self):
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super(SliceGrad7D, self).__init__()
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self.slice_grad = G.SliceGrad()
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@ms_function
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def construct(self, dy, x):
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return self.slice_grad(dy, x, (1, 0, 2, 0, 0, 0, 0), (1, 2, 1, 1, 1, 1, 2))
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def test_slice_grad_7d():
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"""
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Feature: SliceGrad
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Description: test SliceGrad with 7D input
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Expectation: the output is as expected
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"""
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x = Tensor(np.array([[[[[[[3, 4]]]], [[[[8, 9]]]], [[[[3, 2]]]]],
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[[[[[4, 4]]]], [[[[8, 6]]]], [[[[1, 7]]]]]],
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[[[[[[7, 2]]]], [[[[3, 7]]]], [[[[5, 8]]]]],
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[[[[[3, 2]]]], [[[[6, 0]]]], [[[[7, 6]]]]]]]).astype(np.int32))
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dy = Tensor(np.arange(1 * 2 * 1 * 1 * 1 * 1 * 2).reshape(1, 2, 1, 1, 1, 1, 2).astype(np.int32))
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slice_grad = SliceGrad7D()
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output = slice_grad(dy, x)
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expect = np.zeros((2, 2, 3, 1, 1, 1, 2))
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expect[1:2, 0:2, 2:3, 0:1, 0:1, 0:1, 0:2] = dy
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print("output:\n", output)
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assert (output.asnumpy() == expect).all()
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if __name__ == '__main__':
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test_slice()
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test_slice_float64()
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test_slice_grad_7d()
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