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batch_normalization2.py
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batch_normalization2.py
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import numpy
from chainer import configuration
from chainer import cuda
from chainer import functions
from chainer import initializers
from chainer import link
from chainer.utils import argument
from chainer import variable
"""
Copyright (c) 2015 Preferred Infrastructure, Inc.
Copyright (c) 2015 Preferred Networks, Inc.
Pls see LICENSE-chainer.txt in the 'docs' directory
This is a change of
<\python3\Lib\site-packages\chainer\links\normalization\batch_normalization.py>
Change: add initial_avg_mean, initial_avg_var
add enforce_compute flag, to compute mean and var even if test mode, config.train is false
Date: 2018.6
"""
class BatchNormalization2(link.Link):
"""Batch normalization layer on outputs of linear or convolution functions.
This link wraps the :func:`~chainer.functions.batch_normalization` and
:func:`~chainer.functions.fixed_batch_normalization` functions.
It runs in three modes: training mode, fine-tuning mode, and testing mode.
In training mode, it normalizes the input by *batch statistics*. It also
maintains approximated population statistics by moving averages, which can
be used for instant evaluation in testing mode.
In fine-tuning mode, it accumulates the input to compute *population
statistics*. In order to correctly compute the population statistics, a
user must use this mode to feed mini-batches running through whole training
dataset.
In testing mode, it uses pre-computed population statistics to normalize
the input variable. The population statistics is approximated if it is
computed by training mode, or accurate if it is correctly computed by
fine-tuning mode.
Args:
size (int or tuple of ints): Size (or shape) of channel
dimensions.
decay (float): Decay rate of moving average. It is used on training.
eps (float): Epsilon value for numerical stability.
dtype (numpy.dtype): Type to use in computing.
use_gamma (bool): If ``True``, use scaling parameter. Otherwise, use
unit(1) which makes no effect.
use_beta (bool): If ``True``, use shifting parameter. Otherwise, use
unit(0) which makes no effect.
See: `Batch Normalization: Accelerating Deep Network Training by Reducing\
Internal Covariate Shift <https://arxiv.org/abs/1502.03167>`_
.. seealso::
:func:`~chainer.functions.batch_normalization`,
:func:`~chainer.functions.fixed_batch_normalization`
Attributes:
gamma (~chainer.Variable): Scaling parameter.
beta (~chainer.Variable): Shifting parameter.
avg_mean (numpy.ndarray or cupy.ndarray): Population mean.
avg_var (numpy.ndarray or cupy.ndarray): Population variance.
N (int): Count of batches given for fine-tuning.
decay (float): Decay rate of moving average. It is used on training.
~BatchNormalization.eps (float): Epsilon value for numerical stability.
This value is added to the batch variances.
"""
def __init__(self, size, decay=0.9, eps=2e-5, dtype=numpy.float32,
use_gamma=True, use_beta=True,
initial_gamma=None, initial_beta=None,
initial_avg_mean=None, initial_avg_var=None,
enforce_compute=False):
super(BatchNormalization2, self).__init__()
if initial_avg_mean is not None:
self.avg_mean = numpy.array(initial_avg_mean, dtype=dtype)
else:
self.avg_mean = numpy.zeros(size, dtype=dtype)
self.register_persistent('avg_mean')
if initial_avg_var is not None:
self.avg_var = numpy.array(initial_avg_var, dtype=dtype)
else:
self.avg_var = numpy.zeros(size, dtype=dtype)
self.register_persistent('avg_var')
self.N = 0
self.register_persistent('N')
self.decay = decay
self.eps = eps
self.enforce_compute=enforce_compute
with self.init_scope():
if use_gamma:
if initial_gamma is None:
initial_gamma = 1
initial_gamma = initializers._get_initializer(initial_gamma)
initial_gamma.dtype = dtype
self.gamma = variable.Parameter(initial_gamma, size)
if use_beta:
if initial_beta is None:
initial_beta = 0
initial_beta = initializers._get_initializer(initial_beta)
initial_beta.dtype = dtype
self.beta = variable.Parameter(initial_beta, size)
def __call__(self, x, **kwargs):
"""__call__(self, x, finetune=False)
Invokes the forward propagation of BatchNormalization.
In training mode, the BatchNormalization computes moving averages of
mean and variance for evaluation during training, and normalizes the
input using batch statistics.
.. warning::
``test`` argument is not supported anymore since v2.
Instead, use ``chainer.using_config('train', False)``.
See :func:`chainer.using_config`.
Args:
x (Variable): Input variable.
finetune (bool): If it is in the training mode and ``finetune`` is
``True``, BatchNormalization runs in fine-tuning mode; it
accumulates the input array to compute population statistics
for normalization, and normalizes the input using batch
statistics.
"""
argument.check_unexpected_kwargs(
kwargs, test='test argument is not supported anymore. '
'Use chainer.using_config')
finetune, = argument.parse_kwargs(kwargs, ('finetune', False))
if hasattr(self, 'gamma'):
gamma = self.gamma
else:
with cuda.get_device_from_id(self._device_id):
gamma = variable.Variable(self.xp.ones(
self.avg_mean.shape, dtype=x.dtype))
if hasattr(self, 'beta'):
beta = self.beta
else:
with cuda.get_device_from_id(self._device_id):
beta = variable.Variable(self.xp.zeros(
self.avg_mean.shape, dtype=x.dtype))
if configuration.config.train or self.enforce_compute:
# if self.force_compute is true, then compute following...
if finetune:
self.N += 1
decay = 1. - 1. / self.N
else:
decay = self.decay
ret = functions.batch_normalization(
x, gamma, beta, eps=self.eps, running_mean=self.avg_mean,
running_var=self.avg_var, decay=decay)
else: # if is not train like test
# Use running average statistics or fine-tuned statistics.
mean = variable.Variable(self.avg_mean)
var = variable.Variable(self.avg_var)
ret = functions.fixed_batch_normalization(
x, gamma, beta, mean, var, self.eps)
return ret
def start_finetuning(self):
"""Resets the population count for collecting population statistics.
This method can be skipped if it is the first time to use the
fine-tuning mode. Otherwise, this method should be called before
starting the fine-tuning mode again.
"""
self.N = 0