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378 lines
16 KiB
378 lines
16 KiB
# Copyright (c) 2019 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 numpy as np
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import unittest
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from paddle import fluid
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from paddle.fluid import layers
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from paddle.fluid.layers.distributions import *
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import math
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class DistributionNumpy():
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"""
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Distribution is the abstract base class for probability distributions.
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"""
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def sample(self):
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"""Sampling from the distribution."""
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raise NotImplementedError
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def entropy(self):
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"""The entropy of the distribution."""
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raise NotImplementedError
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def kl_divergence(self, other):
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"""The KL-divergence between self distributions and other."""
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raise NotImplementedError
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def log_prob(self, value):
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"""Log probability density/mass function."""
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raise NotImplementedError
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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).astype('float32')
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self.high = np.array(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 + (np.random.uniform(size=shape) *
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(self.high - self.low))
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def log_prob(self, value):
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lb = np.less(self.low, value).astype('float32')
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ub = np.less(value, self.high).astype('float32')
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return np.log(lb * ub) - np.log(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 NormalNumpy(DistributionNumpy):
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def __init__(self, loc, scale):
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self.loc = np.array(loc).astype('float32')
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self.scale = np.array(scale).astype('float32')
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def sample(self, shape):
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shape = tuple(shape) + (self.loc + self.scale).shape
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return self.loc + (np.random.randn(*shape) * self.scale)
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def log_prob(self, value):
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var = self.scale * self.scale
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log_scale = np.log(self.scale)
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return -((value - self.loc) * (value - self.loc)) / (
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2. * var) - log_scale - math.log(math.sqrt(2. * math.pi))
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def entropy(self):
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return 0.5 + 0.5 * np.log(np.array(2. * math.pi).astype(
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'float32')) + np.log(self.scale)
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def kl_divergence(self, other):
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var_ratio = (self.scale / other.scale)
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var_ratio = var_ratio * var_ratio
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t1 = ((self.loc - other.loc) / other.scale)
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t1 = (t1 * t1)
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return 0.5 * (var_ratio + t1 - 1 - np.log(var_ratio))
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class DistributionTest(unittest.TestCase):
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def setUp(self, use_gpu=False):
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self.use_gpu = use_gpu
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if not use_gpu:
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place = fluid.CPUPlace()
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self.gpu_id = -1
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else:
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place = fluid.CUDAPlace(0)
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self.gpu_id = 0
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self.executor = fluid.Executor(place)
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def build_normal_program(self, test_program, batch_size, dims, loc_float,
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scale_float, other_loc_float, other_scale_float,
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scale_np, other_scale_np, loc_np, other_loc_np,
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values_np):
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with fluid.program_guard(test_program):
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loc = layers.data(name='loc', shape=[dims], dtype='float32')
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scale = layers.data(name='scale', shape=[dims], dtype='float32')
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other_loc = layers.data(
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name='other_loc', shape=[dims], dtype='float32')
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other_scale = layers.data(
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name='other_scale', shape=[dims], dtype='float32')
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values = layers.data(name='values', shape=[dims], dtype='float32')
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normal_float = Normal(loc_float, scale_float)
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other_normal_float = Normal(other_loc_float, other_scale_float)
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normal_float_np_broadcast = Normal(loc_float, scale_np)
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other_normal_float_np_broadcast = Normal(other_loc_float,
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other_scale_np)
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normal_np = Normal(loc_np, scale_np)
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other_normal_np = Normal(other_loc_np, other_scale_np)
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normal_variable = Normal(loc, scale)
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other_normal_variable = Normal(other_loc, other_scale)
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sample_float = normal_float.sample([batch_size, dims])
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sample_float_np_broadcast = normal_float_np_broadcast.sample(
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[batch_size, dims])
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sample_np = normal_np.sample([batch_size, dims])
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sample_variable = normal_variable.sample([batch_size, dims])
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entropy_float = normal_float.entropy()
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entropy_float_np_broadcast = normal_float_np_broadcast.entropy()
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entropy_np = normal_np.entropy()
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entropy_variable = normal_variable.entropy()
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lp_float_np_broadcast = normal_float_np_broadcast.log_prob(values)
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lp_np = normal_np.log_prob(values)
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lp_variable = normal_variable.log_prob(values)
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kl_float = normal_float.kl_divergence(other_normal_float)
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kl_float_np_broadcast = normal_float_np_broadcast.kl_divergence(
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other_normal_float_np_broadcast)
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kl_np = normal_np.kl_divergence(other_normal_np)
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kl_variable = normal_variable.kl_divergence(other_normal_variable)
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fetch_list = [
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sample_float, sample_float_np_broadcast, sample_np, sample_variable,
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entropy_float, entropy_float_np_broadcast, entropy_np,
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entropy_variable, lp_float_np_broadcast, lp_np, lp_variable,
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kl_float, kl_float_np_broadcast, kl_np, kl_variable
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]
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feed_vars = {
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'loc': loc_np,
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'scale': scale_np,
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'other_loc': other_loc_np,
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'other_scale': other_scale_np,
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'values': values_np
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}
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return feed_vars, fetch_list
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def get_normal_random_input(self, batch_size, dims):
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loc_np = np.random.randn(batch_size, dims).astype('float32')
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other_loc_np = np.random.randn(batch_size, dims).astype('float32')
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loc_float = (np.random.ranf() - 0.5) * 4
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scale_float = (np.random.ranf() - 0.5) * 4
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while scale_float < 0:
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scale_float = (np.random.ranf() - 0.5) * 4
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other_loc_float = (np.random.ranf() - 0.5) * 4
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other_scale_float = (np.random.ranf() - 0.5) * 4
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while other_scale_float < 0:
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other_scale_float = (np.random.ranf() - 0.5) * 4
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scale_np = np.random.randn(batch_size, dims).astype('float32')
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other_scale_np = np.random.randn(batch_size, dims).astype('float32')
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values_np = np.random.randn(batch_size, dims).astype('float32')
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while not np.all(scale_np > 0):
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scale_np = np.random.randn(batch_size, dims).astype('float32')
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while not np.all(other_scale_np > 0):
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other_scale_np = np.random.randn(batch_size, dims).astype('float32')
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return loc_np, other_loc_np, loc_float, scale_float, other_loc_float, \
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other_scale_float, scale_np, other_scale_np, values_np
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def test_normal_distribution(self, batch_size=2, dims=3, tolerance=1e-6):
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test_program = fluid.Program()
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loc_np, other_loc_np, loc_float, scale_float, other_loc_float, other_scale_float, scale_np, other_scale_np, values_np = self.get_normal_random_input(
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batch_size, dims)
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feed_vars, fetch_list = self.build_normal_program(
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test_program, batch_size, dims, loc_float, scale_float,
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other_loc_float, other_scale_float, scale_np, other_scale_np,
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loc_np, other_loc_np, values_np)
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self.executor.run(fluid.default_startup_program())
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np_normal_float = NormalNumpy(loc_float, scale_float)
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np_other_normal_float = NormalNumpy(other_loc_float, other_scale_float)
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np_normal_float_np_broadcast = NormalNumpy(loc_float, scale_np)
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np_other_normal_float_np_broadcast = NormalNumpy(other_loc_float,
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other_scale_np)
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np_normal = NormalNumpy(loc_np, scale_np)
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np_other_normal = NormalNumpy(other_loc_np, other_scale_np)
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gt_sample_float = np_normal_float.sample([batch_size, dims])
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gt_sample_float_np_broadcast = np_normal_float_np_broadcast.sample(
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[batch_size, dims])
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gt_sample_np = np_normal.sample([batch_size, dims])
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gt_entropy_float = np_normal_float.entropy()
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gt_entropy_float_np_broadcast = np_normal_float_np_broadcast.entropy()
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gt_entropy = np_normal.entropy()
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gt_lp_float_np_broadcast = np_normal_float_np_broadcast.log_prob(
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values_np)
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gt_lp = np_normal.log_prob(values_np)
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gt_kl_float = np_normal_float.kl_divergence(np_other_normal_float)
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gt_kl_float_np_broadcast = np_normal_float_np_broadcast.kl_divergence(
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np_other_normal_float_np_broadcast)
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gt_kl = np_normal.kl_divergence(np_other_normal)
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[
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output_sample_float, output_sample_float_np_broadcast,
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output_sample_np, output_sample_variable, output_entropy_float,
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output_entropy_float_np_broadcast, output_entropy_np,
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output_entropy_variable, output_lp_float_np_broadcast, output_lp_np,
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output_lp_variable, output_kl_float, output_kl_float_np_broadcast,
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output_kl_np, output_kl_variable
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] = self.executor.run(program=test_program,
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feed=feed_vars,
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fetch_list=fetch_list)
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np.testing.assert_allclose(
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output_sample_float.shape, gt_sample_float.shape, rtol=tolerance)
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np.testing.assert_allclose(
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output_sample_float_np_broadcast.shape,
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gt_sample_float_np_broadcast.shape,
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rtol=tolerance)
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np.testing.assert_allclose(
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output_sample_np.shape, gt_sample_np.shape, rtol=tolerance)
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np.testing.assert_allclose(
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output_sample_variable.shape, gt_sample_np.shape, rtol=tolerance)
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np.testing.assert_allclose(
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output_entropy_float, gt_entropy_float, rtol=tolerance)
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np.testing.assert_allclose(
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output_entropy_float_np_broadcast,
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gt_entropy_float_np_broadcast,
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rtol=tolerance)
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np.testing.assert_allclose(
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output_entropy_np, gt_entropy, rtol=tolerance)
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np.testing.assert_allclose(
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output_entropy_variable, gt_entropy, rtol=tolerance)
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np.testing.assert_allclose(
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output_lp_float_np_broadcast,
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gt_lp_float_np_broadcast,
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rtol=tolerance)
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np.testing.assert_allclose(output_lp_np, gt_lp, rtol=tolerance)
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np.testing.assert_allclose(output_lp_variable, gt_lp, rtol=tolerance)
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np.testing.assert_allclose(output_kl_float, gt_kl_float, rtol=tolerance)
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np.testing.assert_allclose(
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output_kl_float_np_broadcast,
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gt_kl_float_np_broadcast,
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rtol=tolerance)
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np.testing.assert_allclose(output_kl_np, gt_kl, rtol=tolerance)
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np.testing.assert_allclose(output_kl_variable, gt_kl, rtol=tolerance)
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def build_uniform_program(self, test_program, batch_size, dims, low_float,
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high_float, high_np, low_np, values_np):
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with fluid.program_guard(test_program):
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low = layers.data(name='low', shape=[dims], dtype='float32')
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high = layers.data(name='high', shape=[dims], dtype='float32')
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values = layers.data(name='values', shape=[dims], dtype='float32')
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uniform_float = Uniform(low_float, high_float)
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uniform_float_np_broadcast = Uniform(low_float, high_np)
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uniform_np = Uniform(low_np, high_np)
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uniform_variable = Uniform(low, high)
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sample_float = uniform_float.sample([batch_size, dims])
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sample_float_np_broadcast = uniform_float_np_broadcast.sample(
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[batch_size, dims])
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sample_np = uniform_np.sample([batch_size, dims])
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sample_variable = uniform_variable.sample([batch_size, dims])
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entropy_float = uniform_float.entropy()
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entropy_float_np_broadcast = uniform_float_np_broadcast.entropy()
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entropy_np = uniform_np.entropy()
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entropy_variable = uniform_variable.entropy()
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lp_float_np_broadcast = uniform_float_np_broadcast.log_prob(values)
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lp_np = uniform_np.log_prob(values)
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lp_variable = uniform_variable.log_prob(values)
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fetch_list = [
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sample_float, sample_float_np_broadcast, sample_np, sample_variable,
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entropy_float, entropy_float_np_broadcast, entropy_np,
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entropy_variable, lp_float_np_broadcast, lp_np, lp_variable
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]
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feed_vars = {'low': low_np, 'high': high_np, 'values': values_np}
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return feed_vars, fetch_list
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def test_uniform_distribution(self, batch_size=2, dims=3, tolerance=1e-6):
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test_program = fluid.Program()
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low_np = np.random.randn(batch_size, dims).astype('float32')
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low_float = np.random.uniform(-2, 1)
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high_float = np.random.uniform(1, 3)
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high_np = np.random.uniform(-5.0, 5.0,
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(batch_size, dims)).astype('float32')
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values_np = np.random.randn(batch_size, dims).astype('float32')
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feed_vars, fetch_list = self.build_uniform_program(
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test_program, batch_size, dims, low_float, high_float, high_np,
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low_np, values_np)
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self.executor.run(fluid.default_startup_program())
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np_uniform_float = UniformNumpy(low_float, high_float)
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np_uniform_float_np_broadcast = UniformNumpy(low_float, high_np)
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np_uniform = UniformNumpy(low_np, high_np)
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gt_sample_float = np_uniform_float.sample([batch_size, dims])
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gt_sample_float_np_broadcast = np_uniform_float_np_broadcast.sample(
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[batch_size, dims])
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gt_sample_np = np_uniform.sample([batch_size, dims])
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gt_entropy_float = np_uniform_float.entropy()
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gt_entropy_float_np_broadcast = np_uniform_float_np_broadcast.entropy()
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gt_entropy = np_uniform.entropy()
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gt_lp_float_np_broadcast = np_uniform_float_np_broadcast.log_prob(
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values_np)
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gt_lp = np_uniform.log_prob(values_np)
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# result calculated by paddle
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[
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output_sample_float, output_sample_float_np_broadcast,
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output_sample_np, output_sample_variable, output_entropy_float,
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output_entropy_float_np_broadcast, output_entropy_np,
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output_entropy_variable, output_lp_float_np_broadcast, output_lp_np,
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output_lp_variable
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] = self.executor.run(program=test_program,
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feed=feed_vars,
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fetch_list=fetch_list)
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np.testing.assert_allclose(
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output_sample_float.shape, gt_sample_float.shape, rtol=tolerance)
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np.testing.assert_allclose(
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output_sample_float_np_broadcast.shape,
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gt_sample_float_np_broadcast.shape,
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rtol=tolerance)
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np.testing.assert_allclose(
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output_sample_np.shape, gt_sample_np.shape, rtol=tolerance)
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np.testing.assert_allclose(
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output_sample_variable.shape, gt_sample_np.shape, rtol=tolerance)
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np.testing.assert_allclose(
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output_entropy_float, gt_entropy_float, rtol=tolerance)
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np.testing.assert_allclose(
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output_entropy_float_np_broadcast,
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gt_entropy_float_np_broadcast,
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rtol=tolerance)
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np.testing.assert_allclose(
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output_entropy_np, gt_entropy, rtol=tolerance)
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np.testing.assert_allclose(
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output_entropy_variable, gt_entropy, rtol=tolerance)
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np.testing.assert_allclose(
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output_lp_float_np_broadcast,
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gt_lp_float_np_broadcast,
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rtol=tolerance)
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np.testing.assert_allclose(output_lp_np, gt_lp, rtol=tolerance)
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np.testing.assert_allclose(output_lp_variable, gt_lp, rtol=tolerance)
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if __name__ == '__main__':
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unittest.main()
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