# 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 import paddle.fluid.dygraph as dg 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]) class TestAbs(unittest.TestCase): def setUp(self): self._dtypes = ["float32", "float64"] self._places = [paddle.CPUPlace()] if paddle.is_compiled_with_cuda(): self._places.append(paddle.CUDAPlace(0)) def test_all_positive(self): for dtype in self._dtypes: x = 1 + 10 * np.random.random([13, 3, 3]).astype(dtype) for place in self._places: with dg.guard(place): y = paddle.abs(paddle.to_tensor(x)) self.assertTrue(np.allclose(np.abs(x), y.numpy())) class TestRealAbsOp(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 = 1 + 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]) if __name__ == '__main__': unittest.main()