forked from mindspore-Ecosystem/mindspore
158 lines
5.1 KiB
Python
158 lines
5.1 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 torch
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import mindspore.ops.operations as P
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from mindspore import Tensor
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from mindspore.nn import Cell
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from mindspore import ops
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from mindspore.ops import Complex as ComplexOp
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shape_2d = (7, 6)
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shape_1d = (6,)
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rand_shape_1d_1 = np.random.rand(*shape_1d).astype(np.float32)
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rand_shape_1d_2 = np.random.rand(*shape_1d).astype(np.float32)
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rand_shape_2d_1 = np.random.rand(*shape_2d).astype(np.float32)
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rand_shape_2d_2 = np.random.rand(*shape_2d).astype(np.float32)
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real_op = ops.Real()
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imag_op = ops.Imag()
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class Complex(Cell):
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def __init__(self):
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super().__init__()
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self.complex = ComplexOp()
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def construct(self, x, y):
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return self.complex(x, y)
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def complex_compare(complex1, complex2):
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real1 = real_op(Tensor(complex1)).asnumpy()
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real2 = np.real(complex2)
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imag1 = imag_op(Tensor(complex1)).asnumpy()
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imag2 = np.imag(complex2)
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return np.allclose(real1, real2, rtol=5e-03, atol=5e-03) and np.allclose(imag1, imag2, rtol=5e-03, atol=5e-03)
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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_complex_elemwise():
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"""
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Feature: complex basic Operation.
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Description: Test complex basic Operation.
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Expectation: the result match given one.
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"""
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real_ms = Tensor(rand_shape_2d_1)
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imag_ms = Tensor(rand_shape_2d_2)
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real_to = rand_shape_2d_1
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imag_to = rand_shape_2d_2
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complex1 = Complex()(real_ms, imag_ms)
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complex2 = Complex()(imag_ms, real_ms)
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complex_1 = np.vectorize(complex)(real_to, imag_to)
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complex_2 = np.vectorize(complex)(imag_to, real_to)
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assert complex_compare(complex1, complex_1)
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res_ms = ops.Add()(complex1, complex2)
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res_to = np.add(complex_1, complex_2)
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assert complex_compare(res_ms, res_to)
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res_ms = ops.Mul()(complex1, complex2)
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res_to = np.multiply(complex_1, complex_2)
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assert complex_compare(res_ms, res_to)
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res_ms = ops.Sub()(complex1, complex2)
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res_to = np.subtract(complex_1, complex_2)
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assert complex_compare(res_ms, res_to)
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res_ms = ops.Div()(complex1, complex2)
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res_to = np.divide(complex_1, complex_2)
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assert complex_compare(res_ms, res_to)
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res_ms = complex1 / complex2
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res_to = np.divide(complex_1, complex_2)
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assert complex_compare(res_ms, res_to)
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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_complex_broadcast():
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"""
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Feature: complex broadcast Operation.
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Description: Test complex broadcast Operation.
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Expectation: the result match given one.
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"""
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real_ms_1 = Tensor(rand_shape_2d_1)
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imag_ms_1 = Tensor(rand_shape_2d_2)
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real_ms_2 = Tensor(rand_shape_1d_1)
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imag_ms_2 = Tensor(rand_shape_1d_2)
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real_to_1 = rand_shape_2d_1
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imag_to_1 = rand_shape_2d_2
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real_to_2 = rand_shape_1d_1
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imag_to_2 = rand_shape_1d_2
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complex1 = Complex()(real_ms_1, imag_ms_1)
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complex2 = Complex()(real_ms_2, imag_ms_2)
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complex_1 = np.vectorize(complex)(real_to_1, imag_to_1)
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complex_2 = np.vectorize(complex)(real_to_2, imag_to_2)
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assert complex_compare(complex1, complex_1)
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res_ms = ops.Add()(complex1, complex2)
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res_to = np.add(complex_1, complex_2)
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assert complex_compare(res_ms, res_to)
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res_ms = ops.Mul()(complex1, complex2)
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res_to = np.multiply(complex_1, complex_2)
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assert complex_compare(res_ms, res_to)
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res_ms = ops.Sub()(complex1, complex2)
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res_to = np.subtract(complex_1, complex_2)
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assert complex_compare(res_ms, res_to)
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res_ms = ops.Div()(complex1, complex2)
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res_to = np.divide(complex_1, complex_2)
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assert complex_compare(res_ms, res_to)
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res_ms = complex1 / complex2
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res_to = np.divide(complex_1, complex_2)
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assert complex_compare(res_ms, res_to)
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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_complex_reducesum():
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"""
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Feature: complex reduce Operation.
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Description: Test complex reduce Operation.
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Expectation: the result match given one.
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"""
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x0_real = np.random.rand(2, 3, 4).astype(np.float32)
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x0_imag = np.random.rand(2, 3, 4).astype(np.float32)
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x0 = Complex()(Tensor(x0_real), Tensor(x0_imag))
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axis0 = 2
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keep_dims0 = True
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res_ms = P.ReduceSum(keep_dims0)(x0, axis0)
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x0_torch = torch.complex(torch.tensor(x0_real), torch.tensor(x0_imag))
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res_torch = torch.sum(x0_torch, axis0, keep_dims0)
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np.allclose(res_ms.asnumpy(), res_torch.numpy(), rtol=5e-03, atol=1.e-8)
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