forked from mindspore-Ecosystem/mindspore
89 lines
3.4 KiB
Python
89 lines
3.4 KiB
Python
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# 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 pytest
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import mindspore
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from mindspore import Tensor
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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 P
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import numpy as np
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class SparseSparseMinimumNet(nn.Cell):
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def __init__(self):
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super(SparseSparseMinimumNet, self).__init__()
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self.sparsesparseminimum = P.SparseSparseMinimum()
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def construct(self, x1_indices, x1_values, x1_shape, x2_indices, x2_values, x2_shape):
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return self.sparsesparseminimum(x1_indices, x1_values, x1_shape, x2_indices, x2_values, x2_shape)
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def sparse_sparse_minimum(loss):
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loss1 = loss
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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indices1 = Tensor([[0, 1], [0, 1], [2, 2], [0, 2]])
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values1 = Tensor([4, 2, 3, 4], dtype=mindspore.float32)
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shape1 = Tensor([3, 4])
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indices2 = Tensor([[0, 1], [2, 3]])
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values2 = Tensor([2, 3], dtype=mindspore.float32)
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shape2 = Tensor([3, 4])
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net = SparseSparseMinimumNet()
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m, n = net(indices1, values1, shape1, indices2, values2, shape2)
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expected_m = np.array([[0, 1], [0, 1], [2, 2], [0, 2], [2, 3]], dtype=np.int64)
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expected_n = np.array([2, 0, 0, 0, 0], dtype=np.float32)
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assert np.allclose(m.asnumpy(), expected_m, loss, loss)
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assert np.allclose(n.asnumpy(), expected_n, loss1, loss1)
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def sparse_sparse_minimum_pynative(loss):
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loss1 = loss
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context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
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indices1 = Tensor([[0, 1], [0, 1], [2, 2], [0, 2]])
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values1 = Tensor([4, 2, 3, 4], dtype=mindspore.float32)
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shape1 = Tensor([3, 4])
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indices2 = Tensor([[0, 1], [2, 3]])
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values2 = Tensor([2, 3], dtype=mindspore.float32)
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shape2 = Tensor([3, 4])
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net = SparseSparseMinimumNet()
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m, n = net(indices1, values1, shape1, indices2, values2, shape2)
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expected_m = np.array([[0, 1], [0, 1], [2, 2], [0, 2], [2, 3]], dtype=np.int64)
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expected_n = np.array([2, 0, 0, 0, 0], dtype=np.float32)
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assert np.allclose(m.asnumpy(), expected_m, loss, loss)
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assert np.allclose(n.asnumpy(), expected_n, loss1, loss1)
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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_sparse_sparse_maximum_graph_float32():
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"""
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Feature: ALL To ALL
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Description: test cases for SparseSparseMinimum
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Expectation: the result match to tensorflow
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"""
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sparse_sparse_minimum(loss=1.0e-4)
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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_sparse_sparse_minimum_pynative_float32():
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"""
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Feature: ALL To ALL
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Description: test cases for SparseSparseMinimum
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Expectation: the result match to tensorflow
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"""
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sparse_sparse_minimum_pynative(loss=1.0e-5)
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