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145 lines
5.0 KiB
145 lines
5.0 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.common import dtype as mstype
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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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from mindspore.ops import operations as P
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context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
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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.slicegrad = G.SliceGrad()
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@ms_function
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def construct(self, dy, x):
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return self.slicegrad(dy, x, (0, 1, 0), (2, 1, 3))
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_slice_grad():
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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]]]), mstype.float32)
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dy = Tensor(np.array([[[3., 1., 2.]], [[4., 1., 4.]]]), mstype.float32)
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slicegrad = SliceGrad()
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output = slicegrad(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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print("output:\n", output)
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assert (output.asnumpy() == expect).all()
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class SliceGrad2(nn.Cell):
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def __init__(self):
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super(SliceGrad2, self).__init__()
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self.slicegrad = G.SliceGrad()
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def construct(self, dy, x):
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return self.slicegrad(dy, x, (0, 1, 0), (2, 2, 2))
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_slice_grad2():
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dy = Tensor(np.array([[[2., 3.], [4., 5.]], [[8., 9.], [10., 11.]]]), mstype.float32)
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x = Tensor(np.arange(2 * 3 * 2).reshape(2, 3, 2), mstype.float32)
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grad = SliceGrad2()
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output = grad(dy, x)
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print("output:\n", output)
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expect = [[[0., 0.], [2., 3.], [4., 5.]],
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[[0., 0.], [8., 9.], [10., 11.]]]
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assert (output.asnumpy() == expect).all()
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def test_slice_grad3():
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x = Tensor(np.array([[[1.0, 3.5, 5.8], [2.5, 4, 1]], [[3.5, 15.3, 3.1], [2.2, 4.0, 1.1]],
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[[43.4, 1.1, 12.1], [2.4, 6.5, 6.3]]]), mstype.float64)
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dy = Tensor(np.array([[[3.1, 1.1, 2.2]], [[4.4, 1.2, 4.2]]]), mstype.float64)
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slicegrad = SliceGrad()
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output = slicegrad(dy, x)
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expect = [[[0., 0., 0.],
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[3.1, 1.1, 2.2]],
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[[0., 0., 0.],
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[4.4, 1.2, 4.2]],
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[[0., 0., 0.],
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[0., 0., 0.]]]
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print("output:\n", output)
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assert (output.asnumpy() == expect).all()
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class StridedSliceGrad(nn.Cell):
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def __init__(self, x, begin, end, stride):
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super(StridedSliceGrad, self).__init__()
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self.shape_op = P.Shape()
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self.shapex = self.shape_op(x)
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self.begin = begin
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self.end = end
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self.stride = stride
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self.stride_slice = G.StridedSliceGrad()
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def construct(self, dy):
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return self.stride_slice(dy, self.shapex, self.begin, self.end, self.stride)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_strided_slice_grad_bool_type():
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x = Tensor([[[False, False, True], [False, True, False]], [[False, True, False], [True, False, False]],
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[[False, True, True], [True, False, True]]], mstype.bool_)
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dy = Tensor([False, True, False], mstype.bool_)
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begin = (1, 0, 0)
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end = (2, 1, 3)
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stride = (1, 1, 1)
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slice_op = StridedSliceGrad(x, begin, end, stride)
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output = slice_op(dy)
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expected_output = np.array([[[False, False, False], [False, False, False]],
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[[False, True, False], [False, False, False]],
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[[False, False, False], [False, False, False]]])
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assert (output.asnumpy() == expected_output).all()
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_strided_slice_grad_float32_type():
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x = Tensor([[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]], [[5, 5, 5], [6, 6, 6]]], mstype.float32)
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dy = Tensor([3, 3, 3], mstype.float32)
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begin = (1, 0, 0)
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end = (2, 1, 3)
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stride = (1, 1, 1)
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slice_op = StridedSliceGrad(x, begin, end, stride)
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output = slice_op(dy)
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expected_output = np.array([[[0, 0, 0], [0, 0, 0]], [[3, 3, 3], [0, 0, 0]], [[0, 0, 0], [0, 0, 0]]])
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assert (output.asnumpy() == expected_output).all()
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if __name__ == '__main__':
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test_slice_grad()
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test_slice_grad2()
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test_strided_slice_grad_bool_type()
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test_strided_slice_grad_float32_type()
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