forked from mindspore-Ecosystem/mindspore
142 lines
4.4 KiB
Python
142 lines
4.4 KiB
Python
# Copyright 2020 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
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import mindspore.nn as nn
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from mindspore import Tensor, context
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from mindspore.ops import operations as P
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context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
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class TruncateDiv(nn.Cell):
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def __init__(self):
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super(TruncateDiv, self).__init__()
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self.truncdiv = P.TruncateDiv()
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def construct(self, x, y):
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res = self.truncdiv(x, y)
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return res
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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_truncatediv_output_diff_types():
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"""
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Feature: TruncateDiv cpu op
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Description: Test output values for different dtypes
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Expectation: Output matching expected values
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"""
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input_x = Tensor(np.array([1, 4, -7]), mindspore.int32)
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input_y = Tensor(np.array([3, 3, 5]), mindspore.float32)
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input_x_1 = Tensor(np.array([1, 4, -3]), mindspore.float32)
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input_y_1 = Tensor(np.array([3, 3, 5]), mindspore.float32)
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input_x_2 = Tensor(np.array([1, 4, -3]), mindspore.int32)
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input_y_2 = Tensor(np.array([3, 3, 5]), mindspore.int32)
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input_x_3 = Tensor(np.array([1, 4, -3]), mindspore.int32)
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input_y_3 = Tensor(np.array([True]), mindspore.bool_)
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truncatediv_op = TruncateDiv()
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out = truncatediv_op(input_x, input_y).asnumpy()
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exp = np.array([0.33333334, 1.33333334, -1.4])
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diff = np.abs(out - exp)
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err = np.ones(shape=exp.shape) * 1.0e-5
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assert np.all(diff < err)
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assert out.shape == exp.shape
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out_1 = truncatediv_op(input_x_1, input_y_1).asnumpy()
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exp = np.array([0.33333334, 1.33333334, -0.6])
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diff = np.abs(out_1 - exp)
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err = np.ones(shape=exp.shape) * 1.0e-5
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assert np.all(diff < err)
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assert out.shape == exp.shape
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out_2 = truncatediv_op(input_x_2, input_y_2).asnumpy()
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exp = np.array([0, 1, 0])
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diff = np.abs(out_2 - exp)
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err = np.ones(shape=exp.shape) * 1.0e-5
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assert np.all(diff < err)
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assert out.shape == exp.shape
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out_3 = truncatediv_op(input_x_3, input_y_3).asnumpy()
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exp = np.array([1, 4, -3])
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diff = np.abs(out_3 - exp)
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err = np.ones(shape=exp.shape) * 1.0e-5
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assert np.all(diff < err)
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assert out.shape == exp.shape
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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_truncatediv_output_broadcasting():
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"""
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Feature: TruncateDiv cpu op
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Description: Test output values with broadcasting
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Expectation: Output matching expected values
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"""
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input_x = Tensor(np.array([1, 4, -7]), mindspore.int32)
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input_y = Tensor(np.array([3]), mindspore.int32)
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out = TruncateDiv()(input_x, input_y).asnumpy()
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exp = np.array([0, 1, -2])
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diff = np.abs(out - exp)
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err = np.ones(shape=exp.shape) * 1.0e-5
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assert np.all(diff < err)
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assert out.shape == exp.shape
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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_truncatediv_output_broadcasting_scalar():
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"""
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Feature: TruncateDiv cpu op
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Description: Test output values for scalar input
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Expectation: Output matching expected values
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"""
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input_x = Tensor(np.array([1, 4, -7]), mindspore.int32)
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input_y = 3
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out = TruncateDiv()(input_x, input_y).asnumpy()
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exp = np.array([0, 1, -2])
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diff = np.abs(out - exp)
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err = np.ones(shape=exp.shape) * 1.0e-5
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assert np.all(diff < err)
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assert out.shape == exp.shape
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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_truncatediv_dtype_not_supported():
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"""
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Feature: TruncateDiv cpu op
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Description: Test output for unsupported dtype
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Expectation: Raise TypeError exception
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"""
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with pytest.raises(TypeError):
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input_x = Tensor(np.array([True, False]), mindspore.bool_)
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input_y = Tensor(np.array([True]), mindspore.bool_)
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_ = TruncateDiv()(input_x, input_y).asnumpy()
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