# Copyright (c) 2018 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 import unittest import numpy as np from scipy.special import erf from op_test import OpTest import paddle import paddle.fluid as fluid import paddle.fluid.dygraph as dg class TestErfOp(OpTest): def setUp(self): self.op_type = "erf" self.python_api = paddle.erf self.dtype = self._init_dtype() self.x_shape = [11, 17] x = np.random.uniform(-1, 1, size=self.x_shape).astype(self.dtype) y_ref = erf(x).astype(self.dtype) self.inputs = {'X': x} self.outputs = {'Out': y_ref} def _init_dtype(self): return "float64" def test_check_output(self): self.check_output(check_eager=True) def test_check_grad(self): self.check_grad(['X'], 'Out', check_eager=True) # class TestErfLayer(unittest.TestCase): # def _test_case(self, place): # x = np.random.uniform(-1, 1, size=(11, 17)).astype(np.float64) # y_ref = erf(x) # with dg.guard(place) as g: # x_var = dg.to_variable(x) # y_var = fluid.layers.erf(x_var) # y_test = y_var.numpy() # self.assertTrue(np.allclose(y_ref, y_test)) # def test_case(self): # self._test_case(fluid.CPUPlace()) # if fluid.is_compiled_with_cuda(): # self._test_case(fluid.CUDAPlace(0)) # def test_name(self): # with fluid.program_guard(fluid.Program()): # x = paddle.static.data('x', [3, 4]) # y = paddle.erf(x, name='erf') # self.assertTrue('erf' in y.name) if __name__ == '__main__': paddle.enable_static() unittest.main()