forked from mindspore-Ecosystem/mindspore
97 lines
3.3 KiB
Python
97 lines
3.3 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.nn as nn
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from mindspore import Tensor, context
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from mindspore.ops import operations as P
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from mindspore import dtype as mstype
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context.set_context(mode=context.GRAPH_MODE)
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class StridedSlice(nn.Cell):
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def __init__(self, begin=(2, 0, 0), end=(3, 2, 3), strides=(1, 1, 1)):
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super().__init__()
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self.stridedslice = P.StridedSlice()
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self.begin = begin
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self.end = end
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self.strides = strides
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def construct(self, x):
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return self.stridedslice(x, self.begin, self.end, self.strides)
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class StridedSliceDynamicRank(nn.Cell):
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def __init__(self, begin_mask=0, end_mask=0, ellipsis_mask=0, new_axis_mask=0, shrink_axis_mask=0):
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super().__init__()
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self.stridedslice = P.StridedSlice(
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begin_mask, end_mask, ellipsis_mask, new_axis_mask, shrink_axis_mask)
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self.reduce_sum = P.ReduceSum()
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def construct(self, x, begin, end, strides, axis):
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x = self.reduce_sum(x, axis)
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return self.stridedslice(x, begin, end, strides)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_stridedslice_dynamic_shape():
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"""
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Feature: Dynamic shape.
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Description: Test StridedSlice dynamic shape.
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Expectation: Success.
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"""
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x = Tensor(np.array([[[1., 1., 1.], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]], [[5, 5, 5], [6, 7, 8]]]).astype(np.float32))
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x_dyn = Tensor(shape=[None for _ in x.shape], dtype=x.dtype)
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net = StridedSlice()
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net.set_inputs(x_dyn)
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output = net(x)
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expect = [[[5., 5., 5.],
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[6., 7., 8.]]]
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assert (output.asnumpy() == expect).all()
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_stridedslice_dynamic_rank():
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"""
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Feature: Dynamic rank.
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Description: Test StridedSlice dynamic rank.
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Expectation: Success.
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"""
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begin_mask = (0, 1, 2)
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end_mask = (4, 5, 6)
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strides_mask = (1, 1, 2)
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x = Tensor(np.random.randn(5, 6, 7, 8, 9).astype(np.float32))
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begin_ms = Tensor(begin_mask, mstype.int64)
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end_ms = Tensor(end_mask, mstype.int64)
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strides_ms = Tensor(strides_mask, mstype.int64)
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axis_ms = Tensor(np.array([0, 0, 1, 1]))
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net = StridedSliceDynamicRank()
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out = net(x, begin_ms, end_ms, strides_ms, axis_ms)
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axis_dyn = Tensor(shape=(None,), dtype=axis_ms.dtype)
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net_dyn = StridedSliceDynamicRank()
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net_dyn.set_inputs(x, begin_ms, end_ms, strides_ms, axis_dyn)
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out_dyn = net_dyn(x, begin_ms, end_ms, strides_ms, axis_ms)
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assert(out.asnumpy() == out_dyn.asnumpy()).all()
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