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726 lines
30 KiB
726 lines
30 KiB
# Copyright (c) 2020 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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import paddle
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from paddle import fluid
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from paddle.fluid import layers
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from paddle.distribution import *
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import math
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class DistributionNumpy():
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def sample(self):
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raise NotImplementedError
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def entropy(self):
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raise NotImplementedError
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def kl_divergence(self, other):
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raise NotImplementedError
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def log_prob(self, value):
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raise NotImplementedError
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def probs(self, value):
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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 probs(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 (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 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 probs(self, value):
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var = self.scale * self.scale
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return np.exp(-1. * ((value - self.loc) * (value - self.loc)) /
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(2. * var)) / (math.sqrt(2 * math.pi) * self.scale)
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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_common_net(self, batch_size, dims, loc_float, scale_float,
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other_loc_float, other_scale_float, scale_np,
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other_scale_np, loc_np, other_loc_np, loc,
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scale, other_loc, other_scale, values):
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normal_int = Normal(int(loc_float), int(scale_float))
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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_int = normal_int.sample([batch_size, dims])
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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_int = normal_int.entropy()
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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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p_float_np_broadcast = normal_float_np_broadcast.probs(values)
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p_np = normal_np.probs(values)
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p_variable = normal_variable.probs(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_int, sample_float, sample_float_np_broadcast, sample_np,
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sample_variable, entropy_int, entropy_float,
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entropy_float_np_broadcast, entropy_np, entropy_variable,
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lp_float_np_broadcast, lp_np, lp_variable, p_float_np_broadcast,
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p_np, p_variable, kl_float, kl_float_np_broadcast, kl_np,
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kl_variable
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]
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return fetch_list
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def build_normal_static(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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fetch_list = self.build_normal_common_net(
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batch_size, dims, loc_float, scale_float, other_loc_float,
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other_scale_float, scale_np, other_scale_np, loc_np,
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other_loc_np, loc, scale, other_loc, other_scale, values)
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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 build_normal_dygraph(self, batch_size, dims, loc_float, scale_float,
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other_loc_float, other_scale_float, scale_np,
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other_scale_np, loc_np, other_loc_np, values_np):
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loc = paddle.to_tensor(loc_np)
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scale = paddle.to_tensor(scale_np)
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other_loc = paddle.to_tensor(other_loc_np)
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other_scale = paddle.to_tensor(other_scale_np)
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values = paddle.to_tensor(values_np)
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fetch_list = self.build_normal_common_net(
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batch_size, dims, loc_float, scale_float, other_loc_float,
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other_scale_float, scale_np, other_scale_np, loc_np, other_loc_np,
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loc, scale, other_loc, other_scale, values)
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fetch_list_numpy = [t.numpy() for t in fetch_list]
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return fetch_list_numpy
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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 [
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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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]
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def compare_normal_with_numpy(self,
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data_list,
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output_list,
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batch_size=2,
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dims=3,
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tolerance=1e-6):
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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 = data_list
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np_normal_int = NormalNumpy(int(loc_float), int(scale_float))
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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_int = np_normal_int.sample([batch_size, dims])
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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_int = np_normal_int.entropy()
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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_p_float_np_broadcast = np_normal_float_np_broadcast.probs(values_np)
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gt_p = np_normal.probs(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_int, output_sample_float,
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output_sample_float_np_broadcast, output_sample_np,
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output_sample_variable, output_entropy_int, 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_p_float_np_broadcast, output_p_np,
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output_p_variable, output_kl_float, output_kl_float_np_broadcast,
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output_kl_np, output_kl_variable
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] = output_list
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np.testing.assert_allclose(
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output_sample_int.shape,
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gt_sample_int.shape,
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rtol=tolerance,
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atol=tolerance)
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np.testing.assert_allclose(
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output_sample_float.shape,
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gt_sample_float.shape,
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rtol=tolerance,
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atol=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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atol=tolerance)
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np.testing.assert_allclose(
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output_sample_np.shape,
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gt_sample_np.shape,
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rtol=tolerance,
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atol=tolerance)
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np.testing.assert_allclose(
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output_sample_variable.shape,
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gt_sample_np.shape,
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rtol=tolerance,
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atol=tolerance)
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np.testing.assert_allclose(
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output_entropy_int, gt_entropy_int, rtol=tolerance, atol=tolerance)
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np.testing.assert_allclose(
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output_entropy_float,
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gt_entropy_float,
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rtol=tolerance,
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atol=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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atol=tolerance)
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np.testing.assert_allclose(
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output_entropy_np, gt_entropy, rtol=tolerance, atol=tolerance)
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np.testing.assert_allclose(
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output_entropy_variable, gt_entropy, rtol=tolerance, atol=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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atol=tolerance)
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np.testing.assert_allclose(
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output_lp_np, gt_lp, rtol=tolerance, atol=tolerance)
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np.testing.assert_allclose(
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output_lp_variable, gt_lp, rtol=tolerance, atol=tolerance)
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np.testing.assert_allclose(
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output_p_float_np_broadcast,
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gt_p_float_np_broadcast,
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rtol=tolerance,
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atol=tolerance)
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np.testing.assert_allclose(
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output_p_np, gt_p, rtol=tolerance, atol=tolerance)
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np.testing.assert_allclose(
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output_p_variable, gt_p, rtol=tolerance, atol=tolerance)
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np.testing.assert_allclose(
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output_kl_float, gt_kl_float, rtol=tolerance, atol=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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atol=tolerance)
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np.testing.assert_allclose(
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output_kl_np, gt_kl, rtol=tolerance, atol=tolerance)
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np.testing.assert_allclose(
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output_kl_variable, gt_kl, rtol=tolerance, atol=tolerance)
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def test_normal_distribution_static(self,
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batch_size=2,
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dims=3,
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tolerance=1e-6):
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test_program = fluid.Program()
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data_list = self.get_normal_random_input(batch_size, dims)
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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 = data_list
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feed_vars, fetch_list = self.build_normal_static(
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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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output_list = 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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self.compare_normal_with_numpy(data_list, output_list, batch_size, dims,
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tolerance)
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def test_normal_distribution_dygraph(self,
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batch_size=2,
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dims=3,
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tolerance=1e-6):
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paddle.disable_static()
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data_list = self.get_normal_random_input(batch_size, dims)
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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 = data_list
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output_list = self.build_normal_dygraph(
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batch_size, dims, loc_float, scale_float, other_loc_float,
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other_scale_float, scale_np, other_scale_np, loc_np, other_loc_np,
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values_np)
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self.compare_normal_with_numpy(data_list, output_list, batch_size, dims,
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tolerance)
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paddle.enable_static()
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def build_uniform_common_net(self, batch_size, dims, low_float, high_float,
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high_np, low_np, values_np, low, high, values):
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uniform_int = Uniform(int(low_float), int(high_float))
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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_int = uniform_int.sample([batch_size, dims])
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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_int = uniform_int.entropy()
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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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p_float_np_broadcast = uniform_float_np_broadcast.probs(values)
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p_np = uniform_np.probs(values)
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p_variable = uniform_variable.probs(values)
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fetch_list = [
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sample_int, sample_float, sample_float_np_broadcast, sample_np,
|
|
sample_variable, entropy_int, entropy_float,
|
|
entropy_float_np_broadcast, entropy_np, entropy_variable,
|
|
lp_float_np_broadcast, lp_np, lp_variable, p_float_np_broadcast,
|
|
p_np, p_variable
|
|
]
|
|
return fetch_list
|
|
|
|
def build_uniform_static(self, test_program, batch_size, dims, low_float,
|
|
high_float, high_np, low_np, values_np):
|
|
with fluid.program_guard(test_program):
|
|
low = layers.data(name='low', shape=[dims], dtype='float32')
|
|
high = layers.data(name='high', shape=[dims], dtype='float32')
|
|
|
|
values = layers.data(name='values', shape=[dims], dtype='float32')
|
|
|
|
fetch_list = self.build_uniform_common_net(
|
|
batch_size, dims, low_float, high_float, high_np, low_np,
|
|
values_np, low, high, values)
|
|
|
|
feed_vars = {'low': low_np, 'high': high_np, 'values': values_np}
|
|
return feed_vars, fetch_list
|
|
|
|
def build_uniform_dygraph(self, batch_size, dims, low_float, high_float,
|
|
high_np, low_np, values_np):
|
|
low = paddle.to_tensor(low_np)
|
|
high = paddle.to_tensor(high_np)
|
|
values = paddle.to_tensor(values_np)
|
|
|
|
fetch_list = self.build_uniform_common_net(batch_size, dims, low_float,
|
|
high_float, high_np, low_np,
|
|
values_np, low, high, values)
|
|
fetch_list_numpy = [t.numpy() for t in fetch_list]
|
|
return fetch_list_numpy
|
|
|
|
def compare_uniform_with_numpy(self,
|
|
data_list,
|
|
output_list,
|
|
batch_size=2,
|
|
dims=3,
|
|
tolerance=1e-6):
|
|
[low_np, low_float, high_float, high_np, values_np] = data_list
|
|
|
|
np_uniform_int = UniformNumpy(int(low_float), int(high_float))
|
|
np_uniform_float = UniformNumpy(low_float, high_float)
|
|
np_uniform_float_np_broadcast = UniformNumpy(low_float, high_np)
|
|
np_uniform = UniformNumpy(low_np, high_np)
|
|
|
|
gt_sample_int = np_uniform_int.sample([batch_size, dims])
|
|
gt_sample_float = np_uniform_float.sample([batch_size, dims])
|
|
gt_sample_float_np_broadcast = np_uniform_float_np_broadcast.sample(
|
|
[batch_size, dims])
|
|
gt_sample_np = np_uniform.sample([batch_size, dims])
|
|
gt_entropy_int = np_uniform_int.entropy()
|
|
gt_entropy_float = np_uniform_float.entropy()
|
|
gt_entropy_float_np_broadcast = np_uniform_float_np_broadcast.entropy()
|
|
gt_entropy = np_uniform.entropy()
|
|
gt_lp_float_np_broadcast = np_uniform_float_np_broadcast.log_prob(
|
|
values_np)
|
|
gt_lp = np_uniform.log_prob(values_np)
|
|
gt_p_float_np_broadcast = np_uniform_float_np_broadcast.probs(values_np)
|
|
gt_p = np_uniform.probs(values_np)
|
|
|
|
[
|
|
output_sample_int, output_sample_float,
|
|
output_sample_float_np_broadcast, output_sample_np,
|
|
output_sample_variable, output_entropy_int, output_entropy_float,
|
|
output_entropy_float_np_broadcast, output_entropy_np,
|
|
output_entropy_variable, output_lp_float_np_broadcast, output_lp_np,
|
|
output_lp_variable, output_p_float_np_broadcast, output_p_np,
|
|
output_p_variable
|
|
] = output_list
|
|
|
|
np.testing.assert_allclose(
|
|
output_sample_int.shape,
|
|
gt_sample_int.shape,
|
|
rtol=tolerance,
|
|
atol=tolerance)
|
|
np.testing.assert_allclose(
|
|
output_sample_float.shape,
|
|
gt_sample_float.shape,
|
|
rtol=tolerance,
|
|
atol=tolerance)
|
|
np.testing.assert_allclose(
|
|
output_sample_float_np_broadcast.shape,
|
|
gt_sample_float_np_broadcast.shape,
|
|
rtol=tolerance,
|
|
atol=tolerance)
|
|
np.testing.assert_allclose(
|
|
output_sample_np.shape,
|
|
gt_sample_np.shape,
|
|
rtol=tolerance,
|
|
atol=tolerance)
|
|
np.testing.assert_allclose(
|
|
output_sample_variable.shape,
|
|
gt_sample_np.shape,
|
|
rtol=tolerance,
|
|
atol=tolerance)
|
|
np.testing.assert_allclose(
|
|
output_entropy_int, gt_entropy_int, rtol=tolerance, atol=tolerance)
|
|
np.testing.assert_allclose(
|
|
output_entropy_float,
|
|
gt_entropy_float,
|
|
rtol=tolerance,
|
|
atol=tolerance)
|
|
np.testing.assert_allclose(
|
|
output_entropy_float_np_broadcast,
|
|
gt_entropy_float_np_broadcast,
|
|
rtol=tolerance,
|
|
atol=tolerance)
|
|
np.testing.assert_allclose(
|
|
output_entropy_np, gt_entropy, rtol=tolerance, atol=tolerance)
|
|
np.testing.assert_allclose(
|
|
output_entropy_variable, gt_entropy, rtol=tolerance, atol=tolerance)
|
|
np.testing.assert_allclose(
|
|
output_lp_float_np_broadcast,
|
|
gt_lp_float_np_broadcast,
|
|
rtol=tolerance,
|
|
atol=tolerance)
|
|
np.testing.assert_allclose(
|
|
output_lp_np, gt_lp, rtol=tolerance, atol=tolerance)
|
|
np.testing.assert_allclose(
|
|
output_lp_variable, gt_lp, rtol=tolerance, atol=tolerance)
|
|
np.testing.assert_allclose(
|
|
output_p_float_np_broadcast,
|
|
gt_p_float_np_broadcast,
|
|
rtol=tolerance,
|
|
atol=tolerance)
|
|
np.testing.assert_allclose(
|
|
output_p_np, gt_p, rtol=tolerance, atol=tolerance)
|
|
np.testing.assert_allclose(
|
|
output_p_variable, gt_p, rtol=tolerance, atol=tolerance)
|
|
|
|
def test_uniform_distribution_static(self,
|
|
batch_size=2,
|
|
dims=3,
|
|
tolerance=1e-6):
|
|
test_program = fluid.Program()
|
|
|
|
low_np = np.random.randn(batch_size, dims).astype('float32')
|
|
low_float = np.random.uniform(-2, 1)
|
|
high_float = np.random.uniform(1, 3)
|
|
high_np = np.random.uniform(-5.0, 5.0,
|
|
(batch_size, dims)).astype('float32')
|
|
values_np = np.random.randn(batch_size, dims).astype('float32')
|
|
|
|
data_list = [low_np, low_float, high_float, high_np, values_np]
|
|
|
|
feed_vars, fetch_list = self.build_uniform_static(
|
|
test_program, batch_size, dims, low_float, high_float, high_np,
|
|
low_np, values_np)
|
|
|
|
self.executor.run(fluid.default_startup_program())
|
|
|
|
# result calculated by paddle
|
|
output_list = self.executor.run(program=test_program,
|
|
feed=feed_vars,
|
|
fetch_list=fetch_list)
|
|
self.compare_uniform_with_numpy(data_list, output_list, batch_size,
|
|
dims, tolerance)
|
|
|
|
def test_uniform_distribution_dygraph(self,
|
|
batch_size=2,
|
|
dims=3,
|
|
tolerance=1e-6):
|
|
paddle.disable_static()
|
|
|
|
low_np = np.random.randn(batch_size, dims).astype('float32')
|
|
low_float = np.random.uniform(-2, 1)
|
|
high_float = np.random.uniform(1, 3)
|
|
high_np = np.random.uniform(-5.0, 5.0,
|
|
(batch_size, dims)).astype('float32')
|
|
values_np = np.random.randn(batch_size, dims).astype('float32')
|
|
|
|
data_list = [low_np, low_float, high_float, high_np, values_np]
|
|
output_list = self.build_uniform_dygraph(
|
|
batch_size, dims, low_float, high_float, high_np, low_np, values_np)
|
|
|
|
self.compare_uniform_with_numpy(data_list, output_list, batch_size,
|
|
dims, tolerance)
|
|
paddle.enable_static()
|
|
|
|
|
|
class DistributionTestError(unittest.TestCase):
|
|
def test_distribution_error(self):
|
|
distribution = Distribution()
|
|
|
|
self.assertRaises(NotImplementedError, distribution.sample)
|
|
self.assertRaises(NotImplementedError, distribution.entropy)
|
|
|
|
normal = Normal(0.0, 1.0)
|
|
self.assertRaises(NotImplementedError, distribution.kl_divergence,
|
|
normal)
|
|
|
|
value_npdata = np.array([0.8], dtype="float32")
|
|
value_tensor = layers.create_tensor(dtype="float32")
|
|
self.assertRaises(NotImplementedError, distribution.log_prob,
|
|
value_tensor)
|
|
self.assertRaises(NotImplementedError, distribution.probs, value_tensor)
|
|
|
|
def test_normal_error(self):
|
|
normal = Normal(0.0, 1.0)
|
|
|
|
value = [1.0, 2.0]
|
|
# type of value must be variable
|
|
self.assertRaises(TypeError, normal.log_prob, value)
|
|
|
|
value = [1.0, 2.0]
|
|
# type of value must be variable
|
|
self.assertRaises(TypeError, normal.probs, value)
|
|
|
|
shape = 1.0
|
|
# type of shape must be list
|
|
self.assertRaises(TypeError, normal.sample, shape)
|
|
|
|
seed = 1.0
|
|
# type of seed must be int
|
|
self.assertRaises(TypeError, normal.sample, [2, 3], seed)
|
|
|
|
normal_other = Uniform(1.0, 2.0)
|
|
# type of other must be an instance of Normal
|
|
self.assertRaises(TypeError, normal.kl_divergence, normal_other)
|
|
|
|
def test_uniform_error(self):
|
|
uniform = Uniform(0.0, 1.0)
|
|
|
|
value = [1.0, 2.0]
|
|
# type of value must be variable
|
|
self.assertRaises(TypeError, uniform.log_prob, value)
|
|
|
|
value = [1.0, 2.0]
|
|
# type of value must be variable
|
|
self.assertRaises(TypeError, uniform.probs, value)
|
|
|
|
shape = 1.0
|
|
# type of shape must be list
|
|
self.assertRaises(TypeError, uniform.sample, shape)
|
|
|
|
seed = 1.0
|
|
# type of seed must be int
|
|
self.assertRaises(TypeError, uniform.sample, [2, 3], seed)
|
|
|
|
|
|
class DistributionTestName(unittest.TestCase):
|
|
def get_prefix(self, string):
|
|
return (string.split('.')[0])
|
|
|
|
def test_normal_name(self):
|
|
name = 'test_normal'
|
|
normal1 = Normal(0.0, 1.0, name=name)
|
|
self.assertEqual(normal1.name, name)
|
|
|
|
normal2 = Normal(0.0, 1.0)
|
|
self.assertEqual(normal2.name, 'Normal')
|
|
|
|
paddle.enable_static()
|
|
|
|
sample = normal1.sample([2])
|
|
self.assertEqual(self.get_prefix(sample.name), name + '_sample')
|
|
|
|
entropy = normal1.entropy()
|
|
self.assertEqual(self.get_prefix(entropy.name), name + '_entropy')
|
|
|
|
value_npdata = np.array([0.8], dtype="float32")
|
|
value_tensor = layers.create_tensor(dtype="float32")
|
|
layers.assign(value_npdata, value_tensor)
|
|
|
|
lp = normal1.log_prob(value_tensor)
|
|
self.assertEqual(self.get_prefix(lp.name), name + '_log_prob')
|
|
|
|
p = normal1.probs(value_tensor)
|
|
self.assertEqual(self.get_prefix(p.name), name + '_probs')
|
|
|
|
kl = normal1.kl_divergence(normal2)
|
|
self.assertEqual(self.get_prefix(kl.name), name + '_kl_divergence')
|
|
|
|
def test_uniform_name(self):
|
|
name = 'test_uniform'
|
|
uniform1 = Uniform(0.0, 1.0, name=name)
|
|
self.assertEqual(uniform1.name, name)
|
|
|
|
uniform2 = Uniform(0.0, 1.0)
|
|
self.assertEqual(uniform2.name, 'Uniform')
|
|
|
|
paddle.enable_static()
|
|
|
|
sample = uniform1.sample([2])
|
|
self.assertEqual(self.get_prefix(sample.name), name + '_sample')
|
|
|
|
entropy = uniform1.entropy()
|
|
self.assertEqual(self.get_prefix(entropy.name), name + '_entropy')
|
|
|
|
value_npdata = np.array([0.8], dtype="float32")
|
|
value_tensor = layers.create_tensor(dtype="float32")
|
|
layers.assign(value_npdata, value_tensor)
|
|
|
|
lp = uniform1.log_prob(value_tensor)
|
|
self.assertEqual(self.get_prefix(lp.name), name + '_log_prob')
|
|
|
|
p = uniform1.probs(value_tensor)
|
|
self.assertEqual(self.get_prefix(p.name), name + '_probs')
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|