# 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 import paddle import paddle.fluid as fluid import paddle.fluid.core as core from paddle.fluid.op import Operator from paddle.fluid.executor import Executor from op_test import OpTest class TestGaussianRandomOp(OpTest): def setUp(self): self.op_type = "gaussian_random" self.set_attrs() self.inputs = {} self.use_mkldnn = False self.attrs = { "shape": [123, 92], "mean": self.mean, "std": self.std, "seed": 10, "use_mkldnn": self.use_mkldnn } paddle.manual_seed(10) self.outputs = {'Out': np.zeros((123, 92), dtype='float32')} def set_attrs(self): self.mean = 1.0 self.std = 2. def test_check_output(self): self.check_output_customized(self.verify_output) def verify_output(self, outs): self.assertEqual(outs[0].shape, (123, 92)) hist, _ = np.histogram(outs[0], range=(-3, 5)) hist = hist.astype("float32") hist /= float(outs[0].size) data = np.random.normal(size=(123, 92), loc=1, scale=2) hist2, _ = np.histogram(data, range=(-3, 5)) hist2 = hist2.astype("float32") hist2 /= float(outs[0].size) self.assertTrue( np.allclose( hist, hist2, rtol=0, atol=0.01), "hist: " + str(hist) + " hist2: " + str(hist2)) class TestMeanStdAreInt(TestGaussianRandomOp): def set_attrs(self): self.mean = 1 self.std = 2 # Situation 2: Attr(shape) is a list(with tensor) class TestGaussianRandomOp_ShapeTensorList(TestGaussianRandomOp): def setUp(self): '''Test gaussian_random op with specified value ''' self.op_type = "gaussian_random" self.init_data() shape_tensor_list = [] for index, ele in enumerate(self.shape): shape_tensor_list.append(("x" + str(index), np.ones( (1)).astype('int32') * ele)) self.attrs = { 'shape': self.infer_shape, 'mean': self.mean, 'std': self.std, 'seed': self.seed, 'use_mkldnn': self.use_mkldnn } self.inputs = {"ShapeTensorList": shape_tensor_list} self.outputs = {'Out': np.zeros((123, 92), dtype='float32')} def init_data(self): self.shape = [123, 92] self.infer_shape = [-1, 92] self.use_mkldnn = False self.mean = 1.0 self.std = 2.0 self.seed = 10 def test_check_output(self): self.check_output_customized(self.verify_output) class TestGaussianRandomOp2_ShapeTensorList( TestGaussianRandomOp_ShapeTensorList): def init_data(self): self.shape = [123, 92] self.infer_shape = [-1, -1] self.use_mkldnn = False self.mean = 1.0 self.std = 2.0 self.seed = 10 class TestGaussianRandomOp3_ShapeTensorList( TestGaussianRandomOp_ShapeTensorList): def init_data(self): self.shape = [123, 92] self.infer_shape = [123, -1] self.use_mkldnn = True self.mean = 1.0 self.std = 2.0 self.seed = 10 class TestGaussianRandomOp4_ShapeTensorList( TestGaussianRandomOp_ShapeTensorList): def init_data(self): self.shape = [123, 92] self.infer_shape = [123, -1] self.use_mkldnn = False self.mean = 1.0 self.std = 2.0 self.seed = 10 # Situation 3: shape is a tensor class TestGaussianRandomOp1_ShapeTensor(TestGaussianRandomOp): def setUp(self): '''Test gaussian_random op with specified value ''' self.op_type = "gaussian_random" self.init_data() self.use_mkldnn = False self.inputs = {"ShapeTensor": np.array(self.shape).astype("int32")} self.attrs = { 'mean': self.mean, 'std': self.std, 'seed': self.seed, 'use_mkldnn': self.use_mkldnn } self.outputs = {'Out': np.zeros((123, 92), dtype='float32')} def init_data(self): self.shape = [123, 92] self.use_mkldnn = False self.mean = 1.0 self.std = 2.0 self.seed = 10 # Test python API class TestGaussianRandomAPI(unittest.TestCase): def test_api(self): positive_2_int32 = fluid.layers.fill_constant([1], "int32", 2000) positive_2_int64 = fluid.layers.fill_constant([1], "int64", 500) shape_tensor_int32 = fluid.data( name="shape_tensor_int32", shape=[2], dtype="int32") shape_tensor_int64 = fluid.data( name="shape_tensor_int64", shape=[2], dtype="int64") out_1 = fluid.layers.gaussian_random( shape=[2000, 500], dtype="float32", mean=0.0, std=1.0, seed=10) out_2 = fluid.layers.gaussian_random( shape=[2000, positive_2_int32], dtype="float32", mean=0., std=1.0, seed=10) out_3 = fluid.layers.gaussian_random( shape=[2000, positive_2_int64], dtype="float32", mean=0., std=1.0, seed=10) out_4 = fluid.layers.gaussian_random( shape=shape_tensor_int32, dtype="float32", mean=0., std=1.0, seed=10) out_5 = fluid.layers.gaussian_random( shape=shape_tensor_int64, dtype="float32", mean=0., std=1.0, seed=10) out_6 = fluid.layers.gaussian_random( shape=shape_tensor_int64, dtype=np.float32, mean=0., std=1.0, seed=10) exe = fluid.Executor(place=fluid.CPUPlace()) res_1, res_2, res_3, res_4, res_5, res_6 = exe.run( fluid.default_main_program(), feed={ "shape_tensor_int32": np.array([2000, 500]).astype("int32"), "shape_tensor_int64": np.array([2000, 500]).astype("int64"), }, fetch_list=[out_1, out_2, out_3, out_4, out_5, out_6]) self.assertAlmostEqual(np.mean(res_1), 0.0, delta=0.1) self.assertAlmostEqual(np.std(res_1), 1., delta=0.1) self.assertAlmostEqual(np.mean(res_2), 0.0, delta=0.1) self.assertAlmostEqual(np.std(res_2), 1., delta=0.1) self.assertAlmostEqual(np.mean(res_3), 0.0, delta=0.1) self.assertAlmostEqual(np.std(res_3), 1., delta=0.1) self.assertAlmostEqual(np.mean(res_4), 0.0, delta=0.1) self.assertAlmostEqual(np.std(res_5), 1., delta=0.1) self.assertAlmostEqual(np.mean(res_5), 0.0, delta=0.1) self.assertAlmostEqual(np.std(res_5), 1., delta=0.1) self.assertAlmostEqual(np.mean(res_6), 0.0, delta=0.1) self.assertAlmostEqual(np.std(res_6), 1., delta=0.1) if __name__ == "__main__": unittest.main()