forked from mindspore-Ecosystem/mindspore
78 lines
2.7 KiB
Python
78 lines
2.7 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 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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from mindspore import Tensor
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from mindspore.ops import operations as P
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class Net(nn.Cell):
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def __init__(self, rtol, atol, equal_nan):
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super(Net, self).__init__()
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self.ops = P.IsClose(rtol=rtol, atol=atol, equal_nan=equal_nan)
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def construct(self, a, b):
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return self.ops(a, b)
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def rand_int(*shape):
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"""return an random integer array with parameter shape"""
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res = np.random.randint(low=1, high=5, size=shape)
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if isinstance(res, np.ndarray):
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return res.astype(np.float32)
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return float(res)
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def compare_with_numpy(a, b, rtol=1e-05, atol=1e-08, equal_nan=False):
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# Graph Mode
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context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
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ms_result_graph = Net(rtol, atol, equal_nan)(Tensor(a), Tensor(b))
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# PyNative Mode
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context.set_context(mode=context.PYNATIVE_MODE, device_target='CPU')
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ms_result_pynative = Net(rtol, atol, equal_nan)(Tensor(a), Tensor(b))
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np_result = np.isclose(a, b, rtol, atol, equal_nan)
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return np.array_equal(ms_result_graph, np_result) and np.array_equal(ms_result_pynative, np_result)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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@pytest.mark.parametrize('equal_nan', [True, False])
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def test_net(equal_nan):
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"""
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Feature: ALL TO ALL
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Description: test cases for IsClose operator.
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Expectation: the result match numpy isclose.
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"""
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a = [0, 1, 2, float('inf'), float('inf'), float('nan')]
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b = [0, 1, -2, float('-inf'), float('inf'), float('nan')]
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assert compare_with_numpy(a, b, equal_nan=equal_nan)
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a = rand_int(2, 3, 4, 5)
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diff = (np.random.random((2, 3, 4, 5)).astype("float32") - 0.5) / 1000
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b = a + diff
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assert compare_with_numpy(a, b, atol=1e-3)
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assert compare_with_numpy(a, b, atol=1e-3, rtol=1e-4)
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assert compare_with_numpy(a, b, atol=1e-2, rtol=1e-6)
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a = rand_int(2, 3, 4, 5)
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b = rand_int(4, 5)
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assert compare_with_numpy(a, b, equal_nan=equal_nan)
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