forked from mindspore-Ecosystem/mindspore
78 lines
2.8 KiB
Python
78 lines
2.8 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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# This example should be run with multiple processes.
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# Please refer to the Programming Guide > Distributed Training -> Distributed Parallel Usage Example
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# on mindspore.cn and focus on the contents of these three parts: Configuring Distributed Environment
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# Variables, Calling the Collective Communication Library, Running the Script.
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import pytest
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import mindspore
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from mindspore import context
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from mindspore import Tensor
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from mindspore.ops import operations as P
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from mindspore import nn
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class NetApproximateEqual(nn.Cell):
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def __init__(self, tolerance):
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super(NetApproximateEqual, self).__init__()
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self.tolerance = tolerance
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self.approximate_equal = P.ApproximateEqual(self.tolerance)
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def construct(self, input_x1, input_x2):
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output = self.approximate_equal(input_x1, input_x2)
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return output
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_approximate_equal_fp16():
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"""
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Feature: ALL To ALL
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Description: test cases for ApproximateEqual
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Expectation: the result match to expect result
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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approximate_equal = NetApproximateEqual(tolerance=0.01)
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x1 = Tensor([1., 2., 3., 4.], mindspore.float16)
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x2 = Tensor([1., 2., 3., 4.], mindspore.float16)
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output = approximate_equal(x1, x2)
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expect = Tensor(([True, True, True, True]), mindspore.bool_)
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assert (output.asnumpy() == expect).all()
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_approximate_equal_fp32():
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"""
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Feature: ALL To ALL
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Description: test cases for ApproximateEqual
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Expectation: the result match to expect result
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"""
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context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
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approximate_equal = NetApproximateEqual(tolerance=0.07)
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x1 = Tensor([1., 2., 3., 4.], mindspore.float32)
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x2 = Tensor([3., 1., 3., 1.], mindspore.float32)
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output = approximate_equal(x1, x2)
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expect = Tensor(([False, False, True, False]), mindspore.bool_)
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assert (output.asnumpy() == expect).all()
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