test_complex_abs.py

# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
# 
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# 
#     http://www.apache.org/licenses/LICENSE-2.0
# 
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from __future__ import print_function, division

import unittest
import numpy as np

import paddle
from op_test import OpTest


class TestComplexAbsOp(OpTest):
    def setUp(self):
        paddle.enable_static()
        self.op_type = "abs"
        self.dtype = np.float64
        self.shape = (2, 3, 4, 5)
        self.init_input_output()
        self.init_grad_input_output()

        self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(self.x)}
        self.outputs = {'Out': self.out}

    def init_input_output(self):
        self.x = np.random.random(self.shape).astype(
            self.dtype) + 1J * np.random.random(self.shape).astype(self.dtype)
        self.out = np.abs(self.x)

    def init_grad_input_output(self):
        self.grad_out = np.ones(self.shape, self.dtype)
        self.grad_x = self.grad_out * (self.x / np.abs(self.x))

    def test_check_output(self):
        self.check_output()

    def test_check_grad(self):
        self.check_grad(
            ['X'],
            'Out',
            user_defined_grads=[self.grad_x],
            user_defined_grad_outputs=[self.grad_out])


class TestComplexAbsOpZeroValues(OpTest):
    def setUp(self):
        paddle.enable_static()
        self.op_type = "abs"
        self.dtype = np.float64
        self.shape = (2, 3, 4, 5)
        self.init_input_output()
        self.init_grad_input_output()

        self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(self.x)}
        self.outputs = {'Out': self.out}

    def init_input_output(self):
        self.x = np.zeros(self.shape).astype(self.dtype) + 1J * np.zeros(
            self.shape).astype(self.dtype)
        self.out = np.abs(self.x)

    def init_grad_input_output(self):
        self.grad_out = np.ones(self.shape, self.dtype)
        self.grad_x = np.zeros(self.shape, self.dtype)

    def test_check_output(self):
        self.check_output()

    def test_check_grad(self):
        self.check_grad(
            ['X'],
            'Out',
            user_defined_grads=[self.grad_x],
            user_defined_grad_outputs=[self.grad_out])


if __name__ == '__main__':
    unittest.main()