forked from mindspore-Ecosystem/mindspore
64 lines
2.5 KiB
Python
64 lines
2.5 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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"""smoke tests for SparseSlice"""
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import numpy as np
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import pytest
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from mindspore import Tensor, context
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from mindspore.ops.operations.sparse_ops import SparseSlice
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def generate_data(input_type="float32"):
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"""
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generate data for sparse slice op test cases.
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"""
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indices = Tensor(np.array([[0, 1], [1, 2], [1, 3], [2, 2]])).astype(np.int64)
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values = Tensor(np.array([1, 2, 3, 4])).astype(input_type)
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shape = Tensor(np.array([3, 4])).astype(np.int64)
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start = Tensor(np.array([0, 1])).astype(np.int64)
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size = Tensor(np.array([2, 3])).astype(np.int64)
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data = indices, values, shape, start, size
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return data
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@pytest.mark.level1
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@pytest.mark.parametrize('input_type', ["int8", "uint8", "int16", "uint16",
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"int32", "int64", "float16", "float32", "float64"])
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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_slice(input_type):
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"""
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Feature: Test sparse slice ops.
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Description: Test 2D sparse slice ops.
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Expectation: Success.
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"""
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context.set_context(mode=context.GRAPH_MODE)
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data = generate_data(input_type=input_type)
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net = SparseSlice()
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out = net(data[0], data[1], data[2], data[3], data[4])
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out_indices = out[0].asnumpy()
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out_values = out[1].asnumpy()
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out_size = out[2].asnumpy()
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expected_indices = np.array([[0, 0], [1, 1], [1, 2]]).astype(np.int64)
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expected_values = np.array([1, 2, 3]).astype(input_type)
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expected_size = np.array([2, 3]).astype(np.int64)
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eps_indices = 1e-6 * np.array(np.ones_like(out_indices))
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eps_values = 1e-6 * np.array(np.ones_like(out_values))
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eps_size = 1e-6 * np.array(np.ones_like(out_size))
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assert np.all(expected_indices - out_indices < eps_indices)
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assert np.all(expected_values - out_values < eps_values)
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assert np.all(expected_size - out_size < eps_size)
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