from __future__ import absolute_import
import sugartensor as tf
import logging
import os
import time
import sys
import re
from tensorflow.python.ops import gen_logging_ops
__author__ = 'namju.kim@kakaobrain.com'
#
# summary function
#
def sg_summary_loss(tensor, prefix='losses', name=None):
r"""Register `tensor` to summary report as `loss`
Args:
tensor: A `Tensor` to log as loss
prefix: A `string`. A prefix to display in the tensor board web UI.
name: A `string`. A name to display in the tensor board web UI.
Returns:
None
"""
# defaults
prefix = '' if prefix is None else prefix + '/'
# summary name
name = prefix + _pretty_name(tensor) if name is None else prefix + name
# summary statistics
_scalar(name, tf.reduce_mean(tensor))
_histogram(name + '-h', tensor)
def sg_summary_metric(tensor, prefix='metrics', name=None):
r"""Register `tensor` to summary report as `metric`
Args:
tensor: A `Tensor` to log as metric
prefix: A `string`. A prefix to display in the tensor board web UI.
name: A `string`. A name to display in the tensor board web UI.
Returns:
None
"""
# defaults
prefix = '' if prefix is None else prefix + '/'
# summary name
name = prefix + _pretty_name(tensor) if name is None else prefix + name
# summary statistics
_scalar(name, tf.reduce_mean(tensor))
_histogram(name + '-h', tensor)
def sg_summary_gradient(tensor, gradient, prefix=None, name=None):
r"""Register `tensor` to summary report as `gradient`
Args:
tensor: A `Tensor` to log as gradient
gradient: A 0-D `Tensor`. A gradient to log
prefix: A `string`. A prefix to display in the tensor board web UI.
name: A `string`. A name to display in the tensor board web UI.
Returns:
None
"""
# defaults
prefix = '' if prefix is None else prefix + '/'
# summary name
name = prefix + _pretty_name(tensor) if name is None else prefix + name
# summary statistics
# noinspection PyBroadException
_scalar(name + '/grad', tf.reduce_mean(tf.abs(gradient)))
_histogram(name + '/grad-h', tf.abs(gradient))
def sg_summary_activation(tensor, prefix=None, name=None):
r"""Register `tensor` to summary report as `activation`
Args:
tensor: A `Tensor` to log as activation
prefix: A `string`. A prefix to display in the tensor board web UI.
name: A `string`. A name to display in the tensor board web UI.
Returns:
None
"""
# defaults
prefix = '' if prefix is None else prefix + '/'
# summary name
name = prefix + _pretty_name(tensor) if name is None else prefix + name
# summary statistics
_scalar(name + '/ratio',
tf.reduce_mean(tf.cast(tf.greater(tensor, 0), tf.sg_floatx)))
_histogram(name + '/ratio-h', tensor)
def sg_summary_param(tensor, prefix=None, name=None):
r"""Register `tensor` to summary report as `parameters`
Args:
tensor: A `Tensor` to log as parameters
prefix: A `string`. A prefix to display in the tensor board web UI.
name: A `string`. A name to display in the tensor board web UI.
Returns:
None
"""
# defaults
prefix = '' if prefix is None else prefix + '/'
# summary name
name = prefix + _pretty_name(tensor) if name is None else prefix + name
# summary statistics
_scalar(name + '/abs', tf.reduce_mean(tf.abs(tensor)))
_histogram(name + '/abs-h', tf.abs(tensor))
def sg_summary_image(tensor, prefix=None, name=None):
r"""Register `tensor` to summary report as `image`
Args:
tensor: A tensor to log as image
prefix: A `string`. A prefix to display in the tensor board web UI.
name: A `string`. A name to display in the tensor board web UI.
Returns:
None
"""
# defaults
prefix = '' if prefix is None else prefix + '/'
# summary name
name = prefix + _pretty_name(tensor) if name is None else prefix + name
# summary statistics
if not tf.get_variable_scope().reuse:
tf.summary.image(name + '-im', tensor)
def sg_summary_audio(tensor, sample_rate=16000, prefix=None, name=None):
r"""Register `tensor` to summary report as audio
Args:
tensor: A `Tensor` to log as audio
sample_rate : An int. Sample rate to report. Default is 16000.
prefix: A `string`. A prefix to display in the tensor board web UI.
name: A `string`. A name to display in the tensor board web UI.
Returns:
None
"""
# defaults
prefix = '' if prefix is None else prefix + '/'
# summary name
name = prefix + _pretty_name(tensor) if name is None else prefix + name
# summary statistics
if not tf.get_variable_scope().reuse:
tf.summary.audio(name + '-au', tensor, sample_rate)
def _pretty_name(tensor):
name = ''.join(tensor.name.split(':')[:-1])
return re.sub(r'gpu_[0-9]+/', '', name)
def _scalar(name, tensor):
if not tf.get_variable_scope().reuse and not tf.sg_get_context().reuse:
val = gen_logging_ops._scalar_summary(name, tensor)
tf.add_to_collection(tf.GraphKeys.SUMMARIES, val)
def _histogram(name, tensor):
if not tf.get_variable_scope().reuse and not tf.sg_get_context().reuse:
val = gen_logging_ops._histogram_summary(name, tensor)
tf.add_to_collection(tf.GraphKeys.SUMMARIES, val)
#
# logger wrappers
#
_logger = logging.getLogger('SugarTensor')
_logger.addHandler(logging.StreamHandler())
def _log_prefix():
# Returns (filename, line number) for the stack frame.
def _get_file_line():
# pylint: disable=protected-access
# noinspection PyProtectedMember
f = sys._getframe()
# pylint: enable=protected-access
our_file = f.f_code.co_filename
f = f.f_back
while f:
code = f.f_code
if code.co_filename != our_file:
return code.co_filename, f.f_lineno
f = f.f_back
return '<unknown>', 0
# current time
now = time.time()
now_tuple = time.localtime(now)
now_millisecond = int(1e3 * (now % 1.0))
# current filename and line
filename, line = _get_file_line()
basename = os.path.basename(filename)
s = '%02d%02d:%02d:%02d:%02d.%03d:%s:%d] ' % (
now_tuple[1], # month
now_tuple[2], # day
now_tuple[3], # hour
now_tuple[4], # min
now_tuple[5], # sec
now_millisecond,
basename,
line)
return s
def sg_verbosity(verbosity=0):
_logger.setLevel(verbosity)
def sg_debug(msg, *args, **kwargs):
_logger.debug('D ' + _log_prefix() + msg, *args, **kwargs)
def sg_info(msg, *args, **kwargs):
_logger.info('I ' + _log_prefix() + msg, *args, **kwargs)
def sg_warn(msg, *args, **kwargs):
_logger.warn('W ' + _log_prefix() + msg, *args, **kwargs)
def sg_error(msg, *args, **kwargs):
_logger.error('E ' + _log_prefix() + msg, *args, **kwargs)
def sg_fatal(msg, *args, **kwargs):
_logger.fatal('F ' + _log_prefix() + msg, *args, **kwargs)
