forked from mindspore-Ecosystem/mindspore
130 lines
4.9 KiB
Python
130 lines
4.9 KiB
Python
# Copyright 2020-2022 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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from mindspore.common.tensor import Tensor
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from mindspore.nn import Cell
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from mindspore.ops import operations as P
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from mindspore.ops import functional as F
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from mindspore.common import dtype as mstype
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class Net(Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.lessequal = P.LessEqual()
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def construct(self, x, y):
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return self.lessequal(x, y)
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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_lessequal():
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x = Tensor(np.array([[1, 2, 3]]).astype(np.float32))
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y = Tensor(np.array([[2, 2, 2]]).astype(np.float32))
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expect = np.array([[True, True, False]])
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x1 = Tensor(np.array([[1, 2, 3]]).astype(np.int16))
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y1 = Tensor(np.array([[2]]).astype(np.int16))
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expect1 = np.array([[True, True, False]])
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x2 = Tensor(np.array([[1, 2, 3]]).astype(np.uint8))
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y2 = Tensor(np.array([[2]]).astype(np.uint8))
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expect2 = np.array([[True, True, False]])
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x3 = Tensor(np.array([[1, 2, 3]]).astype(np.float64))
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y3 = Tensor(np.array([[2]]).astype(np.float64))
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expect3 = np.array([[True, True, False]])
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x4 = Tensor(np.array([[1, 2, 3]]).astype(np.float16))
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y4 = Tensor(np.array([[2]]).astype(np.float16))
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expect4 = np.array([[True, True, False]])
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x5 = Tensor(np.array([[1, 2, 3]]).astype(np.int64))
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y5 = Tensor(np.array([[2]]).astype(np.int64))
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expect5 = np.array([[True, True, False]])
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x6 = Tensor(np.array([[1, 2, 3]]).astype(np.int32))
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y6 = Tensor(np.array([[2, 2, 2]]).astype(np.int32))
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expect6 = np.array([[True, True, False]])
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x7 = Tensor(np.array([[1, 2, 3]]).astype(np.int8))
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y7 = Tensor(np.array([[2]]).astype(np.int8))
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expect7 = np.array([[True, True, False]])
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x8 = Tensor(np.array([[6.67054e-10, 6.67054e-10]]).astype(np.float32))
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y8 = Tensor(np.array([[0, 6.67054e-10]]).astype(np.float32))
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expect8 = np.array([[False, True]])
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x = [x, x1, x2, x3, x4, x5, x6, x7, x8]
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y = [y, y1, y2, y3, y4, y5, y6, y7, y8]
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expect = [expect, expect1, expect2, expect3, expect4, expect5, expect6, expect7, expect8]
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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lessequal = Net()
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for i, xi in enumerate(x):
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output = lessequal(xi, y[i])
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assert np.all(output.asnumpy() == expect[i])
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assert output.shape == expect[i].shape
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print('test [%d/%d] passed!' % (i, len(x)))
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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lessequal = Net()
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for i, xi in enumerate(x):
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output = lessequal(xi, y[i])
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assert np.all(output.asnumpy() == expect[i])
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assert output.shape == expect[i].shape
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print('test [%d/%d] passed!' % (i, len(x)))
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def test_less_equal_functional_api():
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"""
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Feature: test less_equal functional API.
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Description: test less_equal functional API.
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Expectation: the result match with expected result.
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"""
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x = Tensor(np.array([1, 2, 3]), mstype.int32)
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other = Tensor(np.array([1, 1, 4]), mstype.int32)
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output = F.less_equal(x, other)
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expected = np.array([True, False, True])
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np.testing.assert_array_equal(output.asnumpy(), expected)
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def test_less_equal_tensor_api():
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"""
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Feature: test less_equal tensor API.
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Description: test case for less_equal tensor API.
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Expectation: the result match with expected result.
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"""
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x = Tensor(np.array([1, 2, 3]), mstype.int32)
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other = Tensor(np.array([1, 1, 4]), mstype.int32)
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output = x.less_equal(other)
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expected = np.array([True, False, True])
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np.testing.assert_array_equal(output.asnumpy(), expected)
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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_less_equal_functional_tensor_modes():
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"""
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Feature: test less_equal functional and tensor APIs in PyNative and Graph modes.
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Description: test case for erfiless_equalnv functional API.
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Expectation: the result match with expected result.
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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test_less_equal_functional_api()
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test_less_equal_tensor_api()
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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test_less_equal_functional_api()
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test_less_equal_tensor_api()
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