# Copyright (c) 2020 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. import unittest import numpy as np import paddle import copy np.random.seed(10) class TestNormalAPI(unittest.TestCase): def setUp(self): self.mean = 1.0 self.std = 0.0 self.shape = None self.repeat_num = 1000 self.set_attrs() self.dtype = self.get_dtype() self.place=paddle.CUDAPlace(0) \ if paddle.fluid.core.is_compiled_with_cuda() \ else paddle.CPUPlace() def set_attrs(self): self.shape = [8, 12] def get_shape(self): if isinstance(self.mean, np.ndarray): shape = self.mean.shape elif isinstance(self.std, np.ndarray): shape = self.std.shape else: shape = self.shape return list(shape) def get_dtype(self): if isinstance(self.mean, np.ndarray): return self.mean.dtype elif isinstance(self.std, np.ndarray): return self.std.dtype else: return 'float32' def static_api(self): shape = self.get_shape() ret_all_shape = copy.deepcopy(shape) ret_all_shape.insert(0, self.repeat_num) ret_all = np.zeros(ret_all_shape, self.dtype) if isinstance(self.mean, np.ndarray) \ and isinstance(self.std, np.ndarray): with paddle.static.program_guard(paddle.static.Program()): mean = paddle.data('Mean', self.mean.shape, self.mean.dtype) std = paddle.data('Std', self.std.shape, self.std.dtype) out = paddle.normal(mean, std, self.shape) exe = paddle.static.Executor(self.place) for i in range(self.repeat_num): ret = exe.run(feed={ 'Mean': self.mean, 'Std': self.std.reshape(shape) }, fetch_list=[out]) ret_all[i] = ret[0] return ret_all elif isinstance(self.mean, np.ndarray): with paddle.static.program_guard(paddle.static.Program()): mean = paddle.data('Mean', self.mean.shape, self.mean.dtype) out = paddle.normal(mean, self.std, self.shape) exe = paddle.static.Executor(self.place) for i in range(self.repeat_num): ret = exe.run(feed={'Mean': self.mean}, fetch_list=[out]) ret_all[i] = ret[0] return ret_all elif isinstance(self.std, np.ndarray): with paddle.static.program_guard(paddle.static.Program()): std = paddle.data('Std', self.std.shape, self.std.dtype) out = paddle.normal(self.mean, std, self.shape) exe = paddle.static.Executor(self.place) for i in range(self.repeat_num): ret = exe.run(feed={'Std': self.std}, fetch_list=[out]) ret_all[i] = ret[0] return ret_all else: with paddle.static.program_guard(paddle.static.Program()): out = paddle.normal(self.mean, self.std, self.shape) exe = paddle.static.Executor(self.place) for i in range(self.repeat_num): ret = exe.run(fetch_list=[out]) ret_all[i] = ret[0] return ret_all def dygraph_api(self): paddle.disable_static(self.place) shape = self.get_shape() ret_all_shape = copy.deepcopy(shape) ret_all_shape.insert(0, self.repeat_num) ret_all = np.zeros(ret_all_shape, self.dtype) mean = paddle.to_tensor(self.mean) \ if isinstance(self.mean, np.ndarray) else self.mean std = paddle.to_tensor(self.std) \ if isinstance(self.std, np.ndarray) else self.std for i in range(self.repeat_num): out = paddle.normal(mean, std, self.shape) ret_all[i] = out.numpy() paddle.enable_static() return ret_all def test_api(self): ret_static = self.static_api() ret_dygraph = self.dygraph_api() for ret in [ret_static, ret_dygraph]: shape_ref = self.get_shape() self.assertEqual(shape_ref, list(ret[0].shape)) ret = ret.flatten().reshape([self.repeat_num, -1]) mean = np.mean(ret, axis=0) std = np.std(ret, axis=0) mean_ref=self.mean.reshape([1, -1]) \ if isinstance(self.mean, np.ndarray) else self.mean std_ref=self.std.reshape([1, -1]) \ if isinstance(self.std, np.ndarray) else self.std self.assertTrue(np.allclose(mean_ref, mean, 0.1, 0.1)) self.assertTrue(np.allclose(std_ref, std, 0.1, 0.1)) class TestNormalAPI_mean_is_tensor(TestNormalAPI): def set_attrs(self): self.mean = np.random.uniform(-2, -1, [2, 3, 4, 5]).astype('float64') class TestNormalAPI_std_is_tensor(TestNormalAPI): def set_attrs(self): self.std = np.random.uniform(0.7, 1, [2, 3, 17]).astype('float64') class TestNormalAPI_mean_std_are_tensor(TestNormalAPI): def set_attrs(self): self.mean = np.random.uniform(1, 2, [1, 100]).astype('float64') self.std = np.random.uniform(0.5, 1, [1, 100]).astype('float64') class TestNormalAPI_mean_std_are_tensor_with_different_dtype(TestNormalAPI): def set_attrs(self): self.mean = np.random.uniform(1, 2, [100]).astype('float64') self.std = np.random.uniform(1, 2, [100]).astype('float32') class TestNormalAlias(unittest.TestCase): def test_alias(self): paddle.disable_static() shape = [1, 2, 3] out1 = paddle.normal(shape=shape) out2 = paddle.tensor.normal(shape=shape) out3 = paddle.tensor.random.normal(shape=shape) paddle.enable_static() class TestNormalErrors(unittest.TestCase): def test_errors(self): with paddle.static.program_guard(paddle.static.Program()): mean = [1, 2, 3] self.assertRaises(TypeError, paddle.normal, mean) std = [1, 2, 3] self.assertRaises(TypeError, paddle.normal, std=std) mean = paddle.data('Mean', [100], 'int32') self.assertRaises(TypeError, paddle.normal, mean) std = paddle.data('Std', [100], 'int32') self.assertRaises(TypeError, paddle.normal, mean=1.0, std=std) self.assertRaises(TypeError, paddle.normal, shape=1) self.assertRaises(TypeError, paddle.normal, shape=[1.0]) shape = paddle.data('Shape', [100], 'float32') self.assertRaises(TypeError, paddle.normal, shape=shape) if __name__ == "__main__": unittest.main()