forked from mindspore-Ecosystem/mindspore
60 lines
2.4 KiB
Python
60 lines
2.4 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 pytest
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import numpy as np
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import mindspore.nn as nn
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from mindspore.ops import operations as P
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from mindspore import Tensor, context
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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class AssertTEST(nn.Cell):
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def __init__(self, summarize):
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super(AssertTEST, self).__init__()
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self.assert1 = P.Assert(summarize)
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def construct(self, cond, x):
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return self.assert1(cond, x)
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@pytest.mark.level0
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@pytest.mark.env_onecard
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@pytest.mark.platform_x86_gpu_training
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def test_assert_op():
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"""
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Feature: Assert gpu kernel
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Description: test the assert summarize = 10.
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Expectation: match to np benchmark.
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"""
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assert1 = AssertTEST(10)
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a = Tensor(np.array([1.0, -1.0, 1.0, 2.0, -1.0, 1.0, 2.0, -1.0, 1.0, 2.0, -1.0, 1.0, 2.0]).astype(np.float32))
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b = Tensor(np.array([2.0, -1.0, 1.0, 2.0, -1.0, 1.0, 2.0, -1.0, 1.0, 2.0, -1.0, 1.0, 2.0]).astype(np.float16))
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c = Tensor(np.array([3.0, -1.0, 1.0, 2.0, -1.0, 1.0, 2.0, -1.0, 1.0, 2.0, -1.0, 1.0, 2.0]).astype(np.float64))
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d = Tensor(np.array([4, -4]).astype(np.int16))
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e = Tensor(np.array([5, 6, 7, -4]).astype(np.int32))
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f = Tensor(np.array([5, 6, 7, 5, 6, 7, 5, 6, 7, -4]).astype(np.int64))
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g = Tensor(np.array([6, -4]).astype(np.int8))
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h = Tensor(np.array([7]).astype(np.uint16))
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i = Tensor(np.array([8, 6, 7]).astype(np.uint32))
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j = Tensor(np.array([9, 6, 7, 5, 6, 7, 5, 6, 7]).astype(np.uint64))
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k = Tensor(np.array([10]).astype(np.uint8))
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l = Tensor(np.array([True, False]).astype(np.bool))
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context.set_context(mode=context.GRAPH_MODE)
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assert1(True, [a, b, c, d, e, f, g, h, i, j, k, l])
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context.set_context(mode=context.PYNATIVE_MODE)
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assert1(False, [a, b, c, d, e, f, g, h, i, j, k, l])
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