forked from mindspore-Ecosystem/mindspore
73 lines
2.9 KiB
Python
73 lines
2.9 KiB
Python
# Copyright 2020-2021 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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import mindspore.ops.operations.sparse_ops as sparse_ops
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from mindspore import Tensor
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from mindspore.common import dtype as mstype
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.op = sparse_ops.SparseReshape()
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def construct(self, indices, shape, new_shape):
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return self.op(indices, shape, new_shape)
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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_pynative():
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'''
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Feature: SparseReshape gpu TEST (PYNATIVE_MODE).
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Description: (2, 3) int64 indices, (3, ) float64 shape, (3, ) int64 new_shape
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Expectation: The result matches expected output.
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'''
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context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
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indices = Tensor([[1, 1, 0], [2, 2, 1]], dtype=mstype.int64)
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shape = Tensor([3, 4, 2], dtype=mstype.int64)
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new_shape = Tensor([2, 3, -1], dtype=mstype.int64)
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y_indices_expect = np.array([[0, 2, 2], [1, 2, 1]]).astype(np.int64)
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y_shape_expect = np.array([2, 3, 4]).astype(np.int64)
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net = Net()
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y_indices, y_shape = net(indices, shape, new_shape)
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np.testing.assert_almost_equal(y_indices.asnumpy(), y_indices_expect)
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np.testing.assert_almost_equal(y_shape.asnumpy(), y_shape_expect)
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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_grath():
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'''
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Feature: SparseReshape gpu TEST (GRAPH_MODE).
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Description: (2, 3) int64 indices, (3, ) float64 shape, (3, ) int64 new_shape
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Expectation: The result matches expected output.
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'''
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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indices = Tensor([[1, 1, 0], [2, 2, 1]], dtype=mstype.int64)
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shape = Tensor([3, 4, 2], dtype=mstype.int64)
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new_shape = Tensor([2, -1, 4], dtype=mstype.int64)
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y_indices_expect = np.array([[0, 2, 2], [1, 2, 1]]).astype(np.int64)
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y_shape_expect = np.array([2, 3, 4]).astype(np.int64)
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net = Net()
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y_indices, y_shape = net(indices, shape, new_shape)
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np.testing.assert_almost_equal(y_indices.asnumpy(), y_indices_expect)
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np.testing.assert_almost_equal(y_shape.asnumpy(), y_shape_expect)
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