wehub-resource-sync
215 lines
7.3 KiB
Python
215 lines
7.3 KiB
Python
# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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from op_test import get_device_place
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import paddle
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np.random.seed(10)
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paddle.seed(10)
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def log_normal_mean(mean, std):
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return np.exp(mean + np.power(std, 2) / 2.0)
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def log_normal_var(mean, std):
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var = np.power(std, 2)
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return (np.exp(var) - 1.0) * np.exp(2 * mean + var)
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class TestLogNormalAPI(unittest.TestCase):
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def setUp(self):
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self.mean = 0.0
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self.std = 0.5
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self.shape = None
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self.mean_duplicate = None
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self.std_duplicate = None
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self.duplicates = 1000
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self.set_attrs()
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self.dtype = self.get_dtype()
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self.place = get_device_place()
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def set_attrs(self):
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self.shape = [self.duplicates]
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def get_shape(self):
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if isinstance(self.mean, np.ndarray):
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shape = self.mean_duplicate.shape
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elif isinstance(self.std, np.ndarray):
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shape = self.std_duplicate.shape
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else:
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shape = self.shape
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return list(shape)
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def get_dtype(self):
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if isinstance(self.mean, np.ndarray):
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return self.mean.dtype
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elif isinstance(self.std, np.ndarray):
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return self.std.dtype
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else:
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return 'float32'
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def static_api(self):
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shape = self.get_shape()
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main_program = paddle.static.Program()
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if isinstance(self.mean, np.ndarray) and isinstance(
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self.std, np.ndarray
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):
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with paddle.static.program_guard(main_program):
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mean = paddle.static.data(
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'Mean', self.mean_duplicate.shape, self.mean_duplicate.dtype
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)
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std = paddle.static.data(
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'Std', self.std_duplicate.shape, self.std_duplicate.dtype
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)
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out = paddle.log_normal(mean, std, self.shape)
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exe = paddle.static.Executor(self.place)
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ret = exe.run(
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feed={
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'Mean': self.mean_duplicate,
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'Std': self.std_duplicate.reshape(shape),
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},
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fetch_list=[out],
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)
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return ret[0]
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elif isinstance(self.mean, np.ndarray):
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with paddle.static.program_guard(main_program):
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mean = paddle.static.data(
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'Mean', self.mean_duplicate.shape, self.mean_duplicate.dtype
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)
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out = paddle.log_normal(mean, self.std, self.shape)
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exe = paddle.static.Executor(self.place)
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ret = exe.run(
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feed={'Mean': self.mean_duplicate}, fetch_list=[out]
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)
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return ret[0]
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elif isinstance(self.std, np.ndarray):
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with paddle.static.program_guard(main_program):
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std = paddle.static.data(
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'Std', self.std_duplicate.shape, self.std_duplicate.dtype
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)
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out = paddle.log_normal(self.mean, std, self.shape)
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exe = paddle.static.Executor(self.place)
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ret = exe.run(
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feed={'Std': self.std_duplicate}, fetch_list=[out]
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)
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return ret[0]
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else:
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with paddle.static.program_guard(main_program):
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out = paddle.log_normal(self.mean, self.std, self.shape)
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exe = paddle.static.Executor(self.place)
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ret = exe.run(fetch_list=[out])
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return ret[0]
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def dygraph_api(self):
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paddle.disable_static(self.place)
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shape = self.get_shape()
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mean = (
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paddle.to_tensor(self.mean_duplicate)
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if isinstance(self.mean, np.ndarray)
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else self.mean
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)
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std = (
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paddle.to_tensor(self.std_duplicate)
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if isinstance(self.std, np.ndarray)
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else self.std
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)
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out = paddle.log_normal(mean, std, self.shape)
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ret = out.numpy()
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paddle.enable_static()
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return ret
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def test_api(self):
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paddle.enable_static()
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ret_static = self.static_api()
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ret_dygraph = self.dygraph_api()
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for ret in [ret_static, ret_dygraph]:
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shape_ref = self.get_shape()
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self.assertEqual(shape_ref, list(ret.shape))
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mean = np.mean(ret, axis=0, keepdims=True)
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var = np.var(ret, axis=0, keepdims=True)
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mean_ref = log_normal_mean(self.mean, self.std)
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var_ref = log_normal_var(self.mean, self.std)
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np.testing.assert_allclose(mean_ref, mean, rtol=0.2, atol=0.2)
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np.testing.assert_allclose(var_ref, var, rtol=0.2, atol=0.2)
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class TestLogNormalAPI_mean_is_tensor(TestLogNormalAPI):
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def set_attrs(self):
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self.mean = np.random.uniform(-0.5, -0.1, [1, 2]).astype('float64')
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self.mean_duplicate = np.broadcast_to(self.mean, [self.duplicates, 2])
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self.std = 0.5
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class TestLogNormalAPI_std_is_tensor(TestLogNormalAPI):
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def set_attrs(self):
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self.std = np.random.uniform(0.1, 0.5, [1, 2]).astype('float64')
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self.std_duplicate = np.broadcast_to(self.std, [self.duplicates, 2])
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class TestLogNormalAPI_mean_std_are_tensor(TestLogNormalAPI):
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def set_attrs(self):
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self.mean = np.random.uniform(0.1, 0.5, [1, 2]).astype('float64')
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self.mean_duplicate = np.broadcast_to(self.mean, [self.duplicates, 2])
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self.std = np.random.uniform(0.1, 0.5, [1, 2]).astype('float64')
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self.std_duplicate = np.broadcast_to(self.std, [self.duplicates, 2])
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class TestLogNormalAlias(unittest.TestCase):
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def test_alias(self):
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paddle.disable_static()
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shape = [1, 2, 3]
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out1 = paddle.log_normal(shape=shape)
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out2 = paddle.tensor.log_normal(shape=shape)
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out3 = paddle.tensor.random.log_normal(shape=shape)
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paddle.enable_static()
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class TestLogNormalErrors(unittest.TestCase):
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def test_errors(self):
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main_program = paddle.static.Program()
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with paddle.static.program_guard(main_program):
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mean = [1, 2, 3]
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self.assertRaises(TypeError, paddle.log_normal, mean)
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std = [1, 2, 3]
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self.assertRaises(TypeError, paddle.log_normal, std=std)
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mean = paddle.static.data('Mean', [100], 'int32')
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self.assertRaises(TypeError, paddle.log_normal, mean)
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std = paddle.static.data('Std', [100], 'int32')
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self.assertRaises(TypeError, paddle.log_normal, mean=1.0, std=std)
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self.assertRaises(TypeError, paddle.log_normal, shape=1)
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self.assertRaises(TypeError, paddle.log_normal, shape=[1.0])
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shape = paddle.static.data('Shape', [100], 'float32')
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self.assertRaises(TypeError, paddle.log_normal, shape=shape)
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if __name__ == "__main__":
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unittest.main()
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