forked from mindspore-Ecosystem/mindspore
131 lines
4.3 KiB
Python
131 lines
4.3 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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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.common.initializer import initializer
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from mindspore.common.parameter import Parameter
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from mindspore.ops import operations as P
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from mindspore.common import dtype as mstype
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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class NetEqBool(nn.Cell):
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def __init__(self):
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super(NetEqBool, self).__init__()
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self.equal = P.Equal()
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x = Tensor(np.array([True, True, False]).astype(np.bool))
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y = Tensor(np.array([True, False, True]).astype(np.bool))
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self.x = Parameter(initializer(x, x.shape), name="x")
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self.y = Parameter(initializer(y, y.shape), name="y")
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def construct(self):
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return self.equal(self.x, self.y)
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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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def test_equal_bool():
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equal_net = NetEqBool()
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output = equal_net()
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print("================================")
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expect = np.array([True, False, False]).astype(np.bool)
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print(output)
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assert (output.asnumpy() == expect).all()
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class NetEqInt(nn.Cell):
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def __init__(self):
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super(NetEqInt, self).__init__()
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self.equal = P.Equal()
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x = Tensor(np.array([1, 20, 5]).astype(np.int32))
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y = Tensor(np.array([2, 20, 5]).astype(np.int32))
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self.x = Parameter(initializer(x, x.shape), name="x")
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self.y = Parameter(initializer(y, y.shape), name="y")
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def construct(self):
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return self.equal(self.x, self.y)
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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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def test_equal_int():
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equal_net = NetEqInt()
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output = equal_net()
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print("================================")
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expect = np.array([False, True, True]).astype(np.bool)
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print(output)
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assert (output.asnumpy() == expect).all()
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class NetEqFloat(nn.Cell):
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def __init__(self):
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super(NetEqFloat, self).__init__()
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self.equal = P.Equal()
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x = Tensor(np.array([1.2, 10.4, 5.5]).astype(np.float32))
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y = Tensor(np.array([1.2, 10.3, 5.4]).astype(np.float32))
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self.x = Parameter(initializer(x, x.shape), name="x")
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self.y = Parameter(initializer(y, y.shape), name="y")
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def construct(self):
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return self.equal(self.x, self.y)
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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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def test_equal_float():
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equal_net = NetEqFloat()
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output = equal_net()
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print("================================")
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expect = np.array([True, False, False]).astype(np.bool)
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print(output)
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assert (output.asnumpy() == expect).all()
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def test_equal_tensor_api():
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"""
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Feature: test equal tensor API.
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Description: testcase for 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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y = Tensor(np.array([1, 2, 4]), mstype.int32)
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output = x.equal(y)
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expected = np.array([True, True, False])
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np.testing.assert_array_equal(output.asnumpy(), expected)
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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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def test_equal_tensor_modes():
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"""
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Feature: test equal tensor API in PyNative and Graph modes.
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Description: test case for equal tensor 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="CPU")
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test_equal_tensor_api()
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context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
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test_equal_tensor_api()
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