wehub-resource-sync
387 lines
14 KiB
Python
387 lines
14 KiB
Python
# Copyright (c) 2021 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 test_distribution import DistributionNumpy
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import paddle
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from paddle import base
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from paddle.distribution import Uniform
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np.random.seed(2022)
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class UniformNumpy(DistributionNumpy):
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def __init__(self, low, high):
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self.low = np.array(low)
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self.high = np.array(high)
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if str(self.low.dtype) not in ['float32', 'float64']:
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self.low = self.low.astype('float32')
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self.high = self.high.astype('float32')
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def sample(self, shape):
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shape = tuple(shape) + (self.low + self.high).shape
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return self.low + (
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np.random.uniform(size=shape) * (self.high - self.low)
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)
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def log_prob(self, value):
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lb = np.less(self.low, value).astype(self.low.dtype)
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ub = np.less(value, self.high).astype(self.low.dtype)
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return np.log(lb * ub) - np.log(self.high - self.low)
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def probs(self, value):
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lb = np.less(self.low, value).astype(self.low.dtype)
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ub = np.less(value, self.high).astype(self.low.dtype)
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return (lb * ub) / (self.high - self.low)
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def entropy(self):
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return np.log(self.high - self.low)
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class UniformTest(unittest.TestCase):
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def setUp(self, use_gpu=False, batch_size=5, dims=6):
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self.use_gpu = use_gpu
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if not use_gpu:
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self.place = base.CPUPlace()
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self.gpu_id = -1
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else:
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self.place = base.CUDAPlace(0)
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self.gpu_id = 0
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self.init_numpy_data(batch_size, dims)
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paddle.disable_static(self.place)
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self.init_dynamic_data(batch_size, dims)
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paddle.enable_static()
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self.test_program = base.Program()
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self.executor = base.Executor(self.place)
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self.init_static_data(batch_size, dims)
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def init_numpy_data(self, batch_size, dims):
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# low ans high are 'float'
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self.low_np = np.random.uniform(-2, 1)
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self.high_np = np.random.uniform(2, 4)
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self.values_np = np.array([1.0]).astype('float32')
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def init_dynamic_data(self, batch_size, dims):
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self.dynamic_low = self.low_np
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self.dynamic_high = self.high_np
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self.dynamic_values = paddle.to_tensor(self.values_np)
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def init_static_data(self, batch_size, dims):
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self.static_low = self.low_np
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self.static_high = self.high_np
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with base.program_guard(self.test_program):
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self.static_values = paddle.static.data(
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name='values', shape=[-1], dtype='float32'
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)
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def compare_with_numpy(self, fetch_list, sample_shape=7, tolerance=1e-6):
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sample, entropy, log_prob, probs = fetch_list
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np_uniform = UniformNumpy(self.low_np, self.high_np)
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np_sample = np_uniform.sample([sample_shape])
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np_entropy = np_uniform.entropy()
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np_lp = np_uniform.log_prob(self.values_np)
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np_p = np_uniform.probs(self.values_np)
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np.testing.assert_equal(sample.shape, np_sample.shape)
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np.testing.assert_allclose(
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entropy, np_entropy, rtol=tolerance, atol=tolerance
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)
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np.testing.assert_allclose(
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log_prob, np_lp, rtol=tolerance, atol=tolerance
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)
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np.testing.assert_allclose(probs, np_p, rtol=tolerance, atol=tolerance)
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def test_uniform_distribution_static(self, sample_shape=7, tolerance=1e-6):
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paddle.enable_static()
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with base.program_guard(self.test_program):
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uniform = Uniform(self.static_low, self.static_high)
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sample = uniform.sample([sample_shape])
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entropy = uniform.entropy()
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log_prob = uniform.log_prob(self.static_values)
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probs = uniform.probs(self.static_values)
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fetch_list = [sample, entropy, log_prob, probs]
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feed_vars = {
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'low': self.low_np,
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'high': self.high_np,
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'values': self.values_np,
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}
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self.executor.run(base.default_startup_program())
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fetch_list = self.executor.run(
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program=self.test_program, feed=feed_vars, fetch_list=fetch_list
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)
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self.compare_with_numpy(fetch_list)
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def func_uniform_distribution_dygraph(self, sample_shape=7, tolerance=1e-6):
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paddle.disable_static()
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uniform = Uniform(self.dynamic_low, self.dynamic_high)
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sample = uniform.sample([sample_shape]).numpy()
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entropy = uniform.entropy().numpy()
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log_prob = uniform.log_prob(self.dynamic_values).numpy()
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probs = uniform.probs(self.dynamic_values).numpy()
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fetch_list = [sample, entropy, log_prob, probs]
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self.compare_with_numpy(fetch_list)
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def test_uniform_distribution_dygraph(self):
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self.setUp()
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self.func_uniform_distribution_dygraph()
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class UniformTest2(UniformTest):
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def init_numpy_data(self, batch_size, dims):
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# low ans high are 'int'
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self.low_np = int(np.random.uniform(-2, 1))
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self.high_np = int(np.random.uniform(2, 4))
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self.values_np = np.array([1.0]).astype('float32')
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class UniformTest3(UniformTest):
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def init_numpy_data(self, batch_size, dims):
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# test broadcast: low is float, high is numpy.ndarray with dtype 'float32'.
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self.low_np = np.random.uniform(-2, 1)
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self.high_np = np.random.uniform(5.0, 15.0, (batch_size, dims)).astype(
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'float32'
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)
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self.values_np = np.random.randn(batch_size, dims).astype('float32')
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def init_static_data(self, batch_size, dims):
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self.static_low = self.low_np
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self.static_high = self.high_np
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with base.program_guard(self.test_program):
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self.static_values = paddle.static.data(
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name='values', shape=[-1, dims], dtype='float32'
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)
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class UniformTest4(UniformTest):
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def init_numpy_data(self, batch_size, dims):
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# low and high are numpy.ndarray with dtype 'float32'.
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self.low_np = np.random.randn(batch_size, dims).astype('float32')
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self.high_np = np.random.uniform(5.0, 15.0, (batch_size, dims)).astype(
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'float32'
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)
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self.values_np = np.random.randn(batch_size, dims).astype('float32')
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def init_static_data(self, batch_size, dims):
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self.static_low = self.low_np
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self.static_high = self.high_np
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with base.program_guard(self.test_program):
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self.static_values = paddle.static.data(
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name='values', shape=[-1, dims], dtype='float32'
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)
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class UniformTest5(UniformTest):
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def init_numpy_data(self, batch_size, dims):
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# low and high are numpy.ndarray with dtype 'float64'.
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self.low_np = np.random.randn(batch_size, dims).astype('float64')
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self.high_np = np.random.uniform(5.0, 15.0, (batch_size, dims)).astype(
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'float64'
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)
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self.values_np = np.random.randn(batch_size, dims).astype('float64')
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def init_dynamic_data(self, batch_size, dims):
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self.dynamic_low = self.low_np
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self.dynamic_high = self.high_np
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self.dynamic_values = paddle.to_tensor(self.values_np, dtype='float64')
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def init_static_data(self, batch_size, dims):
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self.static_low = self.low_np
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self.static_high = self.high_np
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with base.program_guard(self.test_program):
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self.static_values = paddle.static.data(
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name='values', shape=[-1, dims], dtype='float64'
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)
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class UniformTest6(UniformTest):
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def init_numpy_data(self, batch_size, dims):
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# low and high are Tensor with dtype 'VarType.FP32'.
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self.low_np = np.random.randn(batch_size, dims).astype('float32')
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self.high_np = np.random.uniform(5.0, 15.0, (batch_size, dims)).astype(
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'float32'
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)
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self.values_np = np.random.randn(batch_size, dims).astype('float32')
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def init_dynamic_data(self, batch_size, dims):
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self.dynamic_low = paddle.to_tensor(self.low_np)
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self.dynamic_high = paddle.to_tensor(self.high_np)
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self.dynamic_values = paddle.to_tensor(self.values_np)
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def init_static_data(self, batch_size, dims):
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with base.program_guard(self.test_program):
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self.static_low = paddle.static.data(
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name='low', shape=[-1, dims], dtype='float32'
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)
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self.static_high = paddle.static.data(
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name='high', shape=[-1, dims], dtype='float32'
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)
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self.static_values = paddle.static.data(
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name='values', shape=[-1, dims], dtype='float32'
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)
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class UniformTest7(UniformTest):
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def init_numpy_data(self, batch_size, dims):
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# low and high are Tensor with dtype 'VarType.FP64'.
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self.low_np = np.random.randn(batch_size, dims).astype('float64')
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self.high_np = np.random.uniform(5.0, 15.0, (batch_size, dims)).astype(
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'float64'
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)
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self.values_np = np.random.randn(batch_size, dims).astype('float64')
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def init_dynamic_data(self, batch_size, dims):
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self.dynamic_low = paddle.to_tensor(self.low_np, dtype='float64')
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self.dynamic_high = paddle.to_tensor(self.high_np, dtype='float64')
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self.dynamic_values = paddle.to_tensor(self.values_np, dtype='float64')
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def init_static_data(self, batch_size, dims):
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with base.program_guard(self.test_program):
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self.static_low = paddle.static.data(
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name='low', shape=[-1, dims], dtype='float64'
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)
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self.static_high = paddle.static.data(
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name='high', shape=[-1, dims], dtype='float64'
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)
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self.static_values = paddle.static.data(
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name='values', shape=[-1, dims], dtype='float64'
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)
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class UniformTest8(UniformTest):
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def init_numpy_data(self, batch_size, dims):
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# low and high are Tensor with dtype 'VarType.FP64'. value's dtype is 'VarType.FP32'.
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self.low_np = np.random.randn(batch_size, dims).astype('float64')
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self.high_np = np.random.uniform(5.0, 15.0, (batch_size, dims)).astype(
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'float64'
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)
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self.values_np = np.random.randn(batch_size, dims).astype('float32')
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def init_dynamic_data(self, batch_size, dims):
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self.dynamic_low = paddle.to_tensor(self.low_np, dtype='float64')
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self.dynamic_high = paddle.to_tensor(self.high_np, dtype='float64')
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self.dynamic_values = paddle.to_tensor(self.values_np, dtype='float32')
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def init_static_data(self, batch_size, dims):
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with base.program_guard(self.test_program):
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self.static_low = paddle.static.data(
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name='low', shape=[-1, dims], dtype='float64'
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)
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self.static_high = paddle.static.data(
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name='high', shape=[-1, dims], dtype='float64'
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)
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self.static_values = paddle.static.data(
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name='values', shape=[-1, dims], dtype='float32'
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)
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class UniformTest9(UniformTest):
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def init_numpy_data(self, batch_size, dims):
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# low and high are numpy.ndarray with dtype 'float32'.
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# high < low.
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self.low_np = np.random.randn(batch_size, dims).astype('float32')
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self.high_np = np.random.uniform(
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-10.0, -5.0, (batch_size, dims)
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).astype('float32')
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self.values_np = np.random.randn(batch_size, dims).astype('float32')
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def init_static_data(self, batch_size, dims):
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self.static_low = self.low_np
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self.static_high = self.high_np
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with base.program_guard(self.test_program):
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self.static_values = paddle.static.data(
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name='values', shape=[-1, dims], dtype='float32'
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)
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class UniformTest10(UniformTest):
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def init_numpy_data(self, batch_size, dims):
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# low and high are list.
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self.low_np = (
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np.random.randn(batch_size, dims).astype('float32').tolist()
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)
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self.high_np = (
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np.random.uniform(5.0, 15.0, (batch_size, dims))
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.astype('float32')
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.tolist()
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)
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self.values_np = np.random.randn(batch_size, dims).astype('float32')
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def init_static_data(self, batch_size, dims):
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self.static_low = self.low_np
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self.static_high = self.high_np
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with base.program_guard(self.test_program):
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self.static_values = paddle.static.data(
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name='values', shape=[-1, dims], dtype='float32'
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)
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class UniformTest11(UniformTest):
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def init_numpy_data(self, batch_size, dims):
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# low and high are tuple.
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self.low_np = tuple(
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np.random.randn(batch_size, dims).astype('float32').tolist()
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)
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self.high_np = tuple(
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np.random.uniform(5.0, 15.0, (batch_size, dims))
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.astype('float32')
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.tolist()
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)
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self.values_np = np.random.randn(batch_size, dims).astype('float32')
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def init_static_data(self, batch_size, dims):
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self.static_low = self.low_np
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self.static_high = self.high_np
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with base.program_guard(self.test_program):
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self.static_values = paddle.static.data(
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name='values', shape=[-1, dims], dtype='float32'
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)
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class UniformTestSample(unittest.TestCase):
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def setUp(self):
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self.init_param()
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def init_param(self):
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self.low = 3.0
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self.high = 4.0
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def test_uniform_sample(self):
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paddle.disable_static()
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uniform = Uniform(low=self.low, high=self.high)
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s = uniform.sample([100])
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self.assertTrue((s >= self.low).all())
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self.assertTrue((s < self.high).all())
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paddle.enable_static()
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class UniformTestSample2(UniformTestSample):
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def init_param(self):
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self.low = -5.0
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self.high = 2.0
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if __name__ == '__main__':
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unittest.main()
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