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tensorflow_bind.py
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tensorflow_bind.py
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import base64
import os
import sys
import threading
from collections import defaultdict
from functools import partial
from io import BytesIO
from mimetypes import guess_extension
from typing import Any
import numpy as np
import six
from PIL import Image
from ...debugging.log import LoggerRoot
from ..frameworks import _patched_call, WeightsFileHandler, _Empty
from ..import_bind import PostImportHookPatching
from ...config import running_remotely
from ...model import InputModel, OutputModel, Framework
try:
from google.protobuf.json_format import MessageToDict
except ImportError:
MessageToDict = None
class TensorflowBinding(object):
@classmethod
def update_current_task(cls, task):
PatchSummaryToEventTransformer.update_current_task(task)
PatchTensorFlowEager.update_current_task(task)
PatchKerasModelIO.update_current_task(task)
PatchTensorflowModelIO.update_current_task(task)
PatchTensorflow2ModelIO.update_current_task(task)
class IsTensorboardInit(object):
_tensorboard_initialized = False
@classmethod
def tensorboard_used(cls):
return cls._tensorboard_initialized
@classmethod
def set_tensorboard_used(cls):
cls._tensorboard_initialized = True
@staticmethod
def _patched_tb__init__(original_init, self, *args, **kwargs):
IsTensorboardInit._tensorboard_initialized = True
return original_init(self, *args, **kwargs)
# noinspection PyProtectedMember
class WeightsGradientHistHelper(object):
def __init__(self, logger, report_freq=100, histogram_update_freq_multiplier=10, histogram_granularity=50):
self._logger = logger
self.report_freq = report_freq
self._histogram_granularity = histogram_granularity
self._histogram_update_freq_multiplier = histogram_update_freq_multiplier
self._histogram_update_call_counter = 0
self._hist_report_cache = {}
self._hist_x_granularity = 50
@staticmethod
def _sample_histograms(_hist_iters, _histogram_granularity):
# re-sample history based on distribution of samples across time (steps)
ratio = ((_hist_iters[-1] - _hist_iters[_histogram_granularity]) /
(_hist_iters[_histogram_granularity - 1] - _hist_iters[0])) if \
_hist_iters.size > _histogram_granularity else 0.
cur_idx_below = np.arange(0, min(_hist_iters.size, _histogram_granularity - 1))
np.random.shuffle(cur_idx_below)
cur_idx_below = cur_idx_below[:int(_histogram_granularity * (1.0 - ratio / (1 + ratio)) + 0.5)]
if ratio > 0.0:
cur_idx_above = np.arange(_histogram_granularity - 1, _hist_iters.size)
np.random.shuffle(cur_idx_above)
cur_idx_above = cur_idx_above[:int(_histogram_granularity * ratio / (1 + ratio))]
else:
cur_idx_above = np.array([])
_cur_idx = np.unique(np.sort(np.concatenate((cur_idx_below, cur_idx_above)).astype(np.int)))
return _cur_idx
def add_histogram(self, title, series, step, hist_data):
# only collect histogram every specific interval
self._histogram_update_call_counter += 1
if self._histogram_update_call_counter % self.report_freq != 0 or \
self._histogram_update_call_counter < self.report_freq - 1:
return None
if isinstance(hist_data, dict):
pass
elif isinstance(hist_data, np.ndarray) and len(hist_data.shape) == 2 and np.atleast_2d(hist_data).shape[1] == 3:
# prepare the dictionary, assume numpy
# hist_data['bucketLimit'] is the histogram bucket right side limit, meaning X axis
# hist_data['bucket'] is the histogram height, meaning the Y axis
# notice hist_data[:, 1] is the right side limit, for backwards compatibility we take the left side
hist_data = {'bucketLimit': hist_data[:, 0].tolist(), 'bucket': hist_data[:, 2].tolist()}
else:
# assume we have to do the histogram on the data
hist_data = np.histogram(hist_data, bins=32)
hist_data = {'bucketLimit': hist_data[1].tolist(), 'bucket': hist_data[0].tolist()}
self._add_histogram(title=title, series=series, step=step, hist_data=hist_data)
def _add_histogram(self, title, series, step, hist_data):
# only collect histogram every specific interval
self._histogram_update_call_counter += 1
if self._histogram_update_call_counter % self.report_freq != 0 or \
self._histogram_update_call_counter < self.report_freq - 1:
return None
# generate forward matrix of the histograms
# Y-axis (rows) is iteration (from 0 to current Step)
# X-axis averaged bins (conformed sample 'bucketLimit')
# Z-axis actual value (interpolated 'bucket')
step = EventTrainsWriter._fix_step_counter(title, series, step)
# get histograms from cache
hist_list, hist_iters, minmax = self._hist_report_cache.get((title, series), ([], np.array([]), None))
# resample data so we are always constrained in number of histogram we keep
if hist_iters.size >= self._histogram_granularity ** 2:
idx = self._sample_histograms(hist_iters, self._histogram_granularity)
hist_iters = hist_iters[idx]
hist_list = [hist_list[i] for i in idx]
# check if current sample is not already here (actually happens some times)
if step in hist_iters:
return None
# add current sample, if not already here
hist_iters = np.append(hist_iters, step)
# hist_data['bucketLimit'] is the histogram bucket right side limit, meaning X axis
# hist_data['bucket'] is the histogram height, meaning the Y axis
hist = np.array(list(zip(hist_data['bucketLimit'], hist_data['bucket'])), dtype=np.float32)
hist = hist[~np.isinf(hist[:, 0]), :]
hist_list.append(hist)
# keep track of min/max values of histograms (for later re-binning)
if minmax is None:
minmax = hist[:, 0].min(), hist[:, 0].max()
else:
# noinspection PyUnresolvedReferences
minmax = min(minmax[0], hist[:, 0].min()), max(minmax[1], hist[:, 0].max())
# update the cache
self._hist_report_cache[(title, series)] = hist_list, hist_iters, minmax
# only report histogram every specific interval, but do report the first few, so you know there are histograms
if hist_iters.size < 1 or (hist_iters.size >= self._histogram_update_freq_multiplier and
hist_iters.size % self._histogram_update_freq_multiplier != 0):
return None
# resample histograms on a unified bin axis +- epsilon
_epsilon = abs((minmax[1] - minmax[0])/float(self._hist_x_granularity))
if _epsilon == 0:
_epsilon = 0.01
_minmax = minmax[0] - _epsilon, minmax[1] + _epsilon
prev_xedge = np.arange(start=_minmax[0],
step=(_minmax[1] - _minmax[0]) / float(self._hist_x_granularity - 2), stop=_minmax[1])
# uniformly select histograms and the last one
cur_idx = self._sample_histograms(hist_iters, self._histogram_granularity)
report_hist = np.zeros(shape=(len(cur_idx), prev_xedge.size), dtype=np.float32)
for i, n in enumerate(cur_idx):
h = hist_list[n]
report_hist[i, :] = np.interp(prev_xedge, h[:, 0], h[:, 1], right=0, left=0)
yedges = hist_iters[cur_idx]
xedges = prev_xedge
# if only a single line make, add another zero line, for the scatter plot to draw
if report_hist.shape[0] < 2:
report_hist = np.vstack((np.zeros_like(report_hist), report_hist))
# create 3d line (scatter) of histograms
skipx = max(1, int(xedges.size / 10))
skipy = max(1, int(yedges.size / 10))
xlabels = ['%.2f' % v if i % skipx == 0 else '' for i, v in enumerate(xedges[:-1])]
ylabels = [str(int(v)) if i % skipy == 0 else '' for i, v in enumerate(yedges)]
self._logger.report_surface(
title=title,
series=series,
iteration=0,
xaxis=' ',
yaxis='iteration',
xlabels=xlabels,
ylabels=ylabels,
matrix=report_hist,
camera=(-0.1, +1.3, 1.4))
# noinspection PyMethodMayBeStatic,PyProtectedMember,SpellCheckingInspection
class EventTrainsWriter(object):
"""
TF SummaryWriter implementation that converts the tensorboard's summary into
Trains events and reports the events (metrics) for an Trains task (logger).
"""
_add_lock = threading.RLock()
_series_name_lookup = {}
# store all the created tensorboard writers in the system
# this allows us to as weather a certain tile/series already exist on some EventWriter
# and if it does, then we add to the series name the last token from the logdir
# (so we can differentiate between the two)
# key, value: key=hash(title, graph), value=EventTrainsWriter._id
_title_series_writers_lookup = {}
_event_writers_id_to_logdir = {}
# Protect against step (iteration) reuse, for example,
# steps counter inside an epoch, but wrapping around when epoch ends
# i.e. step = 0..100 then epoch ends and again step = 0..100
# We store the first report per title/series combination, and if wraparound occurs
# we synthetically continue to increase the step/iteration based on the previous epoch counter
# example: _title_series_wraparound_counter[('title', 'series')] =
# {'first_step':None, 'last_step':None, 'adjust_counter':0,}
_title_series_wraparound_counter = {}
@property
def variants(self):
return self._variants
def prepare_report(self):
return self.variants.copy()
def tag_splitter(self, tag, num_split_parts, split_char='/', join_char='_', default_title='variant',
logdir_header='series', auto_reduce_num_split=False, force_add_prefix=None):
"""
Split a tf.summary tag line to variant and metric.
Variant is the first part of the split tag, metric is the second.
:param str tag:
:param int num_split_parts:
:param str split_char: a character to split the tag on
:param str join_char: a character to join the the splits
:param str default_title: variant to use in case no variant can be inferred automatically
:param str logdir_header: if 'series_last' then series=header: series, if 'series then series=series :header,
if 'title_last' then title=header title, if 'title' then title=title header
:param bool auto_reduce_num_split: if True and the tag is split for less parts then requested,
then requested number of split parts is adjusted.
:param str force_add_prefix: always add the prefix to the series name
:return: (str, str) variant and metric
"""
splitted_tag = tag.split(split_char)
if auto_reduce_num_split and num_split_parts > len(splitted_tag) - 1:
num_split_parts = max(1, len(splitted_tag) - 1)
series = join_char.join(splitted_tag[-num_split_parts:])
title = join_char.join(splitted_tag[:-num_split_parts]) or default_title
if force_add_prefix:
series = str(force_add_prefix)+series
# check if we already decided that we need to change the title/series
graph_id = hash((title, series))
if graph_id in self._graph_name_lookup:
return self._graph_name_lookup[graph_id]
# check if someone other than us used this combination
with self._add_lock:
event_writer_id = self._title_series_writers_lookup.get(graph_id, None)
if not event_writer_id:
# put us there
self._title_series_writers_lookup[graph_id] = self._id
elif event_writer_id != self._id:
# if there is someone else, change our series name and store us
org_series = series
org_title = title
other_logdir = self._event_writers_id_to_logdir[event_writer_id]
split_logddir = self._logdir.split('/')
unique_logdir = set(split_logddir) - set(other_logdir.split('/'))
header = '/'.join(s for s in split_logddir if s in unique_logdir)
if logdir_header == 'series_last':
series = header + ': ' + series
elif logdir_header == 'series':
series = series + ' :' + header
elif logdir_header == 'title':
title = title + ' ' + header
else: # logdir_header == 'title_last':
title = header + ' ' + title
graph_id = hash((title, series))
# check if for some reason the new series is already occupied
new_event_writer_id = self._title_series_writers_lookup.get(graph_id)
if new_event_writer_id is not None and new_event_writer_id != self._id:
# well that's about it, nothing else we could do
if logdir_header == 'series_last':
series = str(self._logdir) + ': ' + org_series
elif logdir_header == 'series':
series = org_series + ' :' + str(self._logdir)
elif logdir_header == 'title':
title = org_title + ' ' + str(self._logdir)
else: # logdir_header == 'title_last':
title = str(self._logdir) + ' ' + org_title
graph_id = hash((title, series))
self._title_series_writers_lookup[graph_id] = self._id
# store for next time
self._graph_name_lookup[graph_id] = (title, series)
return title, series
def __init__(self, logger, logdir=None, report_freq=100, image_report_freq=None,
histogram_update_freq_multiplier=10, histogram_granularity=50, max_keep_images=None):
"""
Create a compatible Trains backend to the TensorFlow SummaryToEventTransformer
Everything will be serialized directly to the Trains backend, instead of to the standard TF FileWriter
:param logger: The task.logger to use for sending the metrics (def: task.get_logger())
:param report_freq: How often to update the statistics values
:param image_report_freq: How often to upload images (step % image_update_freq == 0)
:param histogram_update_freq_multiplier: How often to upload histogram
(step//update_freq) % histogram_update_freq_multiplier == 0
:param histogram_granularity: How many histograms (lines) to display in the 3d histogram plot
:param max_keep_images: Maximum number of images to save before starting to reuse files (per title/metric pair)
"""
# We are the events_writer, so that's what we'll pass
IsTensorboardInit.set_tensorboard_used()
self._logdir = logdir or ('unknown %d' % len(self._event_writers_id_to_logdir))
# conform directory structure to unix
if os.path.sep == '\\':
self._logdir = self._logdir.replace('\\', '/')
self._id = hash(self._logdir)
self._event_writers_id_to_logdir[self._id] = self._logdir
self.max_keep_images = max_keep_images
self.report_freq = report_freq
self.image_report_freq = image_report_freq if image_report_freq else report_freq
self.histogram_granularity = histogram_granularity
self.histogram_update_freq_multiplier = histogram_update_freq_multiplier
self._histogram_update_call_counter = 0
self._logger = logger
self._visualization_mode = 'RGB' # 'BGR'
self._variants = defaultdict(lambda: ())
self._scalar_report_cache = {}
self._hist_report_cache = {}
self._hist_x_granularity = 50
self._max_step = 0
self._graph_name_lookup = {}
self._generic_tensor_type_name_lookup = {}
self._grad_helper = WeightsGradientHistHelper(
logger=logger,
report_freq=report_freq,
histogram_update_freq_multiplier=histogram_update_freq_multiplier,
histogram_granularity=histogram_granularity
)
def _decode_image(self, img_str, width, height, color_channels):
# noinspection PyBroadException
try:
if isinstance(img_str, bytes):
imdata = img_str
else:
imdata = base64.b64decode(img_str)
output = BytesIO(imdata)
im = Image.open(output)
image = np.asarray(im)
output.close()
if height > 0 and width > 0:
# noinspection PyArgumentList
val = image.reshape(height, width, -1).astype(np.uint8)
else:
val = image.astype(np.uint8)
if val.ndim == 3 and val.shape[2] == 3:
if self._visualization_mode == 'BGR':
val = val[:, :, [2, 1, 0]]
else:
val = val
elif (val.ndim == 2) or (val.ndim == 3 and val.shape[2] == 1):
val = np.tile(np.atleast_3d(val), (1, 1, 3))
elif val.ndim == 3 and val.shape[2] == 4:
if self._visualization_mode == 'BGR':
val = val[:, :, [2, 1, 0]]
else:
val = val[:, :, [0, 1, 2]]
except Exception:
LoggerRoot.get_base_logger(TensorflowBinding).warning('Failed decoding debug image [%d, %d, %d]'
% (width, height, color_channels))
val = None
return val
def _add_image_numpy(self, tag, step, img_data_np, max_keep_images=None):
# type: (str, int, np.ndarray, int) -> ()
# only report images every specific interval
if step % self.image_report_freq != 0:
return None
if img_data_np is None:
return
# noinspection PyProtectedMember
title, series = self.tag_splitter(tag, num_split_parts=3, default_title='Images', logdir_header='title',
auto_reduce_num_split=True,
force_add_prefix=self._logger._get_tensorboard_series_prefix())
step = self._fix_step_counter(title, series, step)
if img_data_np.dtype != np.uint8:
# assume scale 0-1
img_data_np = (img_data_np * 255).astype(np.uint8)
# if 3d, pack into one big image
if img_data_np.ndim == 4:
dims = img_data_np.shape
stack_dim = int(np.sqrt(dims[0]))
# noinspection PyArgumentList
res = img_data_np.reshape(stack_dim, stack_dim, *dims[1:]).transpose((0, 2, 1, 3, 4))
tile_size = res.shape[0] * res.shape[1]
img_data_np = res.reshape(tile_size, tile_size, -1)
self._logger.report_image(
title=title,
series=series,
iteration=step,
image=img_data_np,
max_image_history=self.max_keep_images if max_keep_images is None else max_keep_images,
)
def _add_image(self, tag, step, img_data):
# only report images every specific interval
if step % self.image_report_freq != 0:
return None
width = img_data['width']
height = img_data['height']
colorspace = img_data['colorspace']
img_str = img_data['encodedImageString']
matrix = self._decode_image(img_str, width=width, height=height, color_channels=colorspace)
if matrix is None:
return
return self._add_image_numpy(tag=tag, step=step, img_data_np=matrix)
def _add_scalar(self, tag, step, scalar_data):
default_title = tag if not self._logger._get_tensorboard_auto_group_scalars() else 'Scalars'
series_per_graph = self._logger._get_tensorboard_single_series_per_graph()
# noinspection PyProtectedMember
title, series = self.tag_splitter(
tag, num_split_parts=1, default_title=default_title,
logdir_header='title' if series_per_graph else 'series_last',
force_add_prefix=self._logger._get_tensorboard_series_prefix()
)
step = self._fix_step_counter(title, series, step)
tag = self._get_add_scalars_event_tag(default_title)
possible_title = tag if series_per_graph else None
possible_tag = None if series_per_graph else tag
title = title + possible_title if possible_title else title
series = possible_tag or series
# update scalar cache
num, value = self._scalar_report_cache.get((title, series), (0, 0))
# nan outputs is a string, it's probably a NaN
if isinstance(scalar_data, six.string_types):
# noinspection PyBroadException
try:
scalar_data = float(scalar_data)
except Exception:
scalar_data = float('nan')
# nan outputs nan
self._scalar_report_cache[(title, series)] = \
(num + 1,
(value + scalar_data) if scalar_data == scalar_data else scalar_data)
# only report images every specific interval
if step % self.report_freq != 0:
return None
# calculate mean and zero cache
num, value = self._scalar_report_cache.get((title, series), (0, 0))
scalar_data = value / num
self._scalar_report_cache[(title, series)] = (0, 0)
self._logger.report_scalar(
title=title,
series=series,
iteration=step,
value=scalar_data,
)
def _add_histogram(self, tag, step, hist_data):
# noinspection PyProtectedMember
title, series = self.tag_splitter(tag, num_split_parts=1, default_title='Histograms',
logdir_header='series',
force_add_prefix=self._logger._get_tensorboard_series_prefix())
self._grad_helper.add_histogram(
title=title,
series=series,
step=step,
hist_data=hist_data
)
def _add_plot(self, tag, step, values, vdict):
# noinspection PyBroadException
try:
if values.get('floatVal'):
plot_values = np.array(values.get('floatVal'), dtype=np.float32)
else:
plot_values = np.frombuffer(base64.b64decode(values['tensorContent'].encode('utf-8')),
dtype=np.float32)
plot_values = plot_values.reshape((int(values['tensorShape']['dim'][0]['size']),
int(values['tensorShape']['dim'][1]['size'])))
if 'metadata' in vdict:
if tag not in self._series_name_lookup:
self._series_name_lookup[tag] = [(tag, vdict['metadata'].get('displayName', ''),
vdict['metadata']['pluginData']['pluginName'])]
else:
# this should not happen, maybe it's another run, let increase the value
self._series_name_lookup[tag] += [(tag + '_%d' % (len(self._series_name_lookup[tag]) + 1),
vdict['metadata'].get('displayName', ''),
vdict['metadata']['pluginData']['pluginName'])]
tag, series, plugin_name = self._series_name_lookup.get(tag, [(tag, tag, '')])[-1]
if 'pr_curve' in plugin_name:
# our thresholds are evenly distributed, in that
# width = 1.0 / (num_thresholds - 1)
# thresholds = [0.0, 1*width, 2*width, 3*width, ..., 1.0]
num_thresholds = plot_values.shape[1]
width = 1.0 / num_thresholds
thresholds = np.arange(0.0, 1.0, width, dtype=plot_values.dtype)
data_points = ['TP ', 'FP ', 'TN ', 'FN ', 'Precision ', ' Recall']
series = [{'name': series, 'data': np.vstack((thresholds, plot_values[-2])).T,
'labels': [''.join(data_points) + '<br> ' +
' '.join(['%-3.2f' % v for v in plot_values[:, j]]) for j in
range(plot_values.shape[1])]}]
reverse_xaxis = True
else:
reverse_xaxis = False
series = [{'name': series, 'data': plot_values}]
self._logger.report_line_plot(title=tag, series=series, xaxis='', yaxis='',
iteration=step, reverse_xaxis=reverse_xaxis)
except Exception:
pass
def _add_audio(self, tag, step, values, audio_data=None):
# only report images every specific interval
if step % self.image_report_freq != 0:
return None
if values:
audio_str = values['encodedAudioString']
audio_data = base64.b64decode(audio_str)
if audio_data is None:
return
# noinspection PyProtectedMember
title, series = self.tag_splitter(tag, num_split_parts=3, default_title='Audio', logdir_header='title',
auto_reduce_num_split=True,
force_add_prefix=self._logger._get_tensorboard_series_prefix())
step = self._fix_step_counter(title, series, step)
stream = BytesIO(audio_data)
if values:
file_extension = guess_extension(values['contentType']) or \
'.{}'.format(values['contentType'].split('/')[-1])
else:
# assume wav as default
file_extension = '.wav'
self._logger.report_media(
title=title,
series=series,
iteration=step,
stream=stream,
file_extension=file_extension,
max_history=self.max_keep_images,
)
def _add_text(self, tag, step, tensor_bytes):
# noinspection PyProtectedMember
title, series = self.tag_splitter(tag, num_split_parts=3, default_title='Text', logdir_header='title',
auto_reduce_num_split=True,
force_add_prefix=self._logger._get_tensorboard_series_prefix())
step = self._fix_step_counter(title, series, step)
text = tensor_bytes.decode('utf-8', errors='replace')
self._logger.report_media(
title=title,
series=series,
iteration=step,
stream=six.StringIO(text),
file_extension='.txt',
max_history=self.max_keep_images,
)
@staticmethod
def _fix_step_counter(title, series, step):
key = (title, series)
if key not in EventTrainsWriter._title_series_wraparound_counter:
EventTrainsWriter._title_series_wraparound_counter[key] = {'first_step': step, 'last_step': step,
'adjust_counter': 0}
return step
wraparound_counter = EventTrainsWriter._title_series_wraparound_counter[key]
# we decide on wrap around if the current step is less than 10% of the previous step
# notice since counter is int and we want to avoid rounding error, we have double check in the if
if step < wraparound_counter['last_step'] and step < 0.9 * wraparound_counter['last_step']:
# adjust step base line
wraparound_counter['adjust_counter'] += wraparound_counter['last_step'] + (1 if step <= 0 else step)
# return adjusted step
wraparound_counter['last_step'] = step
return step + wraparound_counter['adjust_counter']
def add_event(self, event, step=None, walltime=None, **_):
supported_metrics = {
'simpleValue', 'image', 'histo', 'tensor', 'audio'
}
def get_data(value_dict, metric_search_order):
data = None
metric_type = 'Unsupported'
for variant in metric_search_order:
data = value_dict.get(variant)
if data is not None:
metric_type = variant
break
return metric_type, data
# Support multiple threads accessing this instance (i.e. let TF/Keras do what they need)
with self._add_lock:
# TODO: add report frequency threshold (i.e. if we are sending too much data, increase the report_freq)
# we should measure reports per second and throttle back the reporting details accordingly
msg_dict = MessageToDict(event)
summary = msg_dict.get('summary')
if summary is None:
msg_dict.pop('step', None)
msg_dict.pop('wallTime', None)
keys_list = [key for key in msg_dict.keys() if len(key) > 0]
keys_list = ', '.join(keys_list)
LoggerRoot.get_base_logger(TensorflowBinding).debug(
'event summary not found, message type unsupported: %s' % keys_list)
return
value_dicts = summary.get('value')
# noinspection PyUnusedLocal
walltime = walltime or msg_dict.get('step')
step = step or msg_dict.get('step')
if step is None:
# when we start a new epoch there is no step in the msg_dict,
# we have to extract it manually
if hasattr(event, 'step'):
step = int(event.step)
else:
step = 0
LoggerRoot.get_base_logger(TensorflowBinding).debug(
'Received event without step, assuming step = {}'.format(step))
else:
step = int(step)
self._max_step = max(self._max_step, step)
if value_dicts is None:
LoggerRoot.get_base_logger(TensorflowBinding).debug("Summary arrived without 'value'")
return
for vdict in value_dicts:
tag = vdict.pop('tag', None)
if tag is None:
# we should not get here
LoggerRoot.get_base_logger(TensorflowBinding).debug(
'No tag for \'value\' existing keys %s' % ', '.join(vdict.keys()))
continue
metric, values = get_data(vdict, supported_metrics)
if metric == 'simpleValue':
self._add_scalar(tag=tag, step=step, scalar_data=values)
elif metric == 'histo':
self._add_histogram(tag=tag, step=step, hist_data=values)
elif metric == 'image':
self._add_image(tag=tag, step=step, img_data=values)
elif metric == 'audio':
self._add_audio(tag, step, values)
elif metric == 'tensor' and values.get('dtype') == 'DT_STRING':
# generic tensor
tensor_bytes = base64.b64decode('\n'.join(values['stringVal']))
plugin_type = self._generic_tensor_type_name_lookup.get(tag) or \
vdict.get('metadata', {}).get('pluginData', {}).get('pluginName', '').lower()
if plugin_type == 'audio':
self._generic_tensor_type_name_lookup[tag] = plugin_type
self._add_audio(tag, step, None, tensor_bytes)
elif plugin_type == 'text':
self._generic_tensor_type_name_lookup[tag] = plugin_type
self._add_text(tag, step, tensor_bytes)
else:
# we do not support it
pass
elif metric == 'tensor' and values.get('dtype') == 'DT_FLOAT':
self._add_plot(tag, step, values, vdict)
else:
LoggerRoot.get_base_logger(TensorflowBinding).debug(
'Event unsupported. tag = %s, vdict keys [%s]' % (tag, ', '.join(vdict.keys())))
continue
def get_logdir(self):
""" Returns a temporary directory name for compatibility with FileWriter. This directory is not actually used.
:return: '.'
"""
return '.'
def flush(self):
"""Flushes the event file to disk.
Call this method to make sure that all pending events have been written to
disk.
"""
self._logger.flush()
def close(self):
"""Flushes the event file to disk and close the file.
Call this method when you do not need the summary writer anymore.
"""
self._logger.flush()
def reopen(self):
"""Reopens the EventFileWriter.
Can be called after `close` to add more events in the same directory.
The events will go into a new events file.
Does nothing if the EventFileWriter was not closed.
"""
pass
def _get_add_scalars_event_tag(self, title_prefix):
"""
:param str title_prefix: the table title prefix that was added to the series.
:return: str same as tensorboard use
"""
# HACK - this is tensorboard Summary util function, original path:
# ~/torch/utils/tensorboard/summary.py
def _clean_tag(name):
import re as _re
# noinspection RegExpRedundantEscape
_INVALID_TAG_CHARACTERS = _re.compile(r'[^-/\w\.]')
if name is not None:
new_name = _INVALID_TAG_CHARACTERS.sub('_', name)
new_name = new_name.lstrip('/') # Remove leading slashes
if new_name != name:
LoggerRoot.get_base_logger(TensorflowBinding).debug(
'Summary name %s is illegal; using %s instead.' % (name, new_name))
name = new_name
return name
main_path = self._logdir
# noinspection PyBroadException
try:
main_path = _clean_tag(main_path)
origin_tag = main_path.rpartition("/")[2].replace(title_prefix, "", 1)
if title_prefix and origin_tag[0] == "_": # add_scalars tag
origin_tag = origin_tag[1:] # Remove the first "_" that was added by the main_tag in tensorboard
else:
return ""
except Exception:
origin_tag = ""
return origin_tag
# noinspection PyCallingNonCallable
class ProxyEventsWriter(object):
def __init__(self, events):
IsTensorboardInit.set_tensorboard_used()
self._events = events
def _get_sentinel_event(self):
ret = None
for ev in self._events:
if hasattr(ev, '_get_sentinel_event'):
ret = ev._get_sentinel_event()
return ret
def get_logdir(self):
ret = None
for ev in self._events:
if hasattr(ev, 'get_logdir'):
ret = ev.get_logdir()
return ret
def reopen(self):
ret = None
for ev in self._events:
if hasattr(ev, 'reopen'):
ret = ev.reopen()
return ret
def add_event(self, *args, **kwargs):
ret = None
for ev in self._events:
if hasattr(ev, 'add_event'):
ret = ev.add_event(*args, **kwargs)
return ret
def flush(self):
ret = None
for ev in self._events:
if hasattr(ev, 'flush'):
ret = ev.flush()
return ret
def close(self):
ret = None
for ev in self._events:
if hasattr(ev, 'close'):
ret = ev.close()
return ret
# noinspection PyPep8Naming
class PatchSummaryToEventTransformer(object):
__main_task = None
__original_getattribute = None
__original_getattributeX = None
_original_add_event = None
_original_add_eventT = None
_original_add_eventX = None
defaults_dict = dict(
report_freq=1, image_report_freq=1, histogram_update_freq_multiplier=5,
histogram_granularity=50)
@staticmethod
def trains_object(self):
if isinstance(self.event_writer, ProxyEventsWriter):
# noinspection PyProtectedMember
trains_writer = [e for e in self.event_writer._events if isinstance(e, EventTrainsWriter)]
return trains_writer[0] if trains_writer else None
elif isinstance(self.event_writer, EventTrainsWriter):
return self.event_writer
if not self.__dict__.get('_trains_defaults'):
self.__dict__['_trains_defaults'] = {}
return self.__dict__['_trains_defaults']
@staticmethod
def update_current_task(task, **kwargs):
PatchSummaryToEventTransformer.defaults_dict.update(kwargs)
PatchSummaryToEventTransformer.__main_task = task
# make sure we patched the SummaryToEventTransformer
PatchSummaryToEventTransformer._patch_summary_to_event_transformer()
PostImportHookPatching.add_on_import('tensorflow',
PatchSummaryToEventTransformer._patch_summary_to_event_transformer)
PostImportHookPatching.add_on_import('torch',
PatchSummaryToEventTransformer._patch_summary_to_event_transformer)
PostImportHookPatching.add_on_import('tensorboardX',
PatchSummaryToEventTransformer._patch_summary_to_event_transformer)
@staticmethod
def _patch_summary_to_event_transformer():
if 'tensorflow' in sys.modules:
try:
from tensorflow.python.summary.writer.writer import SummaryToEventTransformer
# only patch once
if PatchSummaryToEventTransformer.__original_getattribute is None:
PatchSummaryToEventTransformer.__original_getattribute = SummaryToEventTransformer.__getattribute__
SummaryToEventTransformer.__getattribute__ = PatchSummaryToEventTransformer._patched_getattribute
setattr(SummaryToEventTransformer, 'trains',
property(PatchSummaryToEventTransformer.trains_object))
except Exception as ex:
LoggerRoot.get_base_logger(TensorflowBinding).debug(str(ex))
if 'torch' in sys.modules:
try:
# only patch once
if PatchSummaryToEventTransformer._original_add_eventT is None:
# noinspection PyUnresolvedReferences
from torch.utils.tensorboard.writer import FileWriter as FileWriterT
PatchSummaryToEventTransformer._original_add_eventT = FileWriterT.add_event
FileWriterT.add_event = PatchSummaryToEventTransformer._patched_add_eventT
setattr(FileWriterT, 'trains', None)
except ImportError:
# this is a new version of TensorflowX
pass
except Exception as ex:
LoggerRoot.get_base_logger(TensorflowBinding).debug(str(ex))
if 'tensorboardX' in sys.modules:
try:
# only patch once
if PatchSummaryToEventTransformer.__original_getattributeX is None:
# noinspection PyUnresolvedReferences
from tensorboardX.writer import SummaryToEventTransformer as SummaryToEventTransformerX
PatchSummaryToEventTransformer.__original_getattributeX = \
SummaryToEventTransformerX.__getattribute__
SummaryToEventTransformerX.__getattribute__ = PatchSummaryToEventTransformer._patched_getattributeX
setattr(SummaryToEventTransformerX, 'trains',
property(PatchSummaryToEventTransformer.trains_object))
except ImportError:
# this is a new version of TensorflowX
pass
except Exception as ex:
LoggerRoot.get_base_logger(TensorflowBinding).debug(str(ex))
if PatchSummaryToEventTransformer.__original_getattributeX is None:
try:
# only patch once
if PatchSummaryToEventTransformer._original_add_eventX is None:
from tensorboardX.writer import FileWriter as FileWriterX
PatchSummaryToEventTransformer._original_add_eventX = FileWriterX.add_event
FileWriterX.add_event = PatchSummaryToEventTransformer._patched_add_eventX
setattr(FileWriterX, 'trains', None)
except ImportError:
# this is a new version of TensorflowX
pass
except Exception as ex:
LoggerRoot.get_base_logger(TensorflowBinding).debug(str(ex))
@staticmethod
def _patched_add_eventT(self, *args, **kwargs):
if not hasattr(self, 'trains') or not PatchSummaryToEventTransformer.__main_task:
return PatchSummaryToEventTransformer._original_add_eventT(self, *args, **kwargs)
if not self.trains:
# noinspection PyBroadException
try:
logdir = self.get_logdir()
except Exception:
logdir = None
self.trains = EventTrainsWriter(PatchSummaryToEventTransformer.__main_task.get_logger(),
logdir=logdir, **PatchSummaryToEventTransformer.defaults_dict)
# noinspection PyBroadException
try:
self.trains.add_event(*args, **kwargs)
except Exception:
pass
return PatchSummaryToEventTransformer._original_add_eventT(self, *args, **kwargs)
@staticmethod
def _patched_add_eventX(self, *args, **kwargs):
if not hasattr(self, 'trains') or not PatchSummaryToEventTransformer.__main_task:
return PatchSummaryToEventTransformer._original_add_eventX(self, *args, **kwargs)
if not self.trains:
# noinspection PyBroadException
try:
logdir = self.get_logdir()
except Exception:
logdir = None
self.trains = EventTrainsWriter(PatchSummaryToEventTransformer.__main_task.get_logger(),
logdir=logdir, **PatchSummaryToEventTransformer.defaults_dict)
# noinspection PyBroadException
try:
self.trains.add_event(*args, **kwargs)
except Exception:
pass
return PatchSummaryToEventTransformer._original_add_eventX(self, *args, **kwargs)
@staticmethod
def _patched_getattribute(self, attr):
get_base = PatchSummaryToEventTransformer.__original_getattribute
return PatchSummaryToEventTransformer._patched_getattribute_(self, attr, get_base)
@staticmethod
def _patched_getattributeX(self, attr):
get_base = PatchSummaryToEventTransformer.__original_getattributeX
return PatchSummaryToEventTransformer._patched_getattribute_(self, attr, get_base)
@staticmethod
def _patched_getattribute_(self, attr, get_base):
# no main task, zero chance we have an Trains event logger
if PatchSummaryToEventTransformer.__main_task is None:
return get_base(self, attr)
# check if we already have an Trains event logger
__dict__ = get_base(self, '__dict__')
if 'event_writer' not in __dict__ or \
isinstance(__dict__['event_writer'], (ProxyEventsWriter, EventTrainsWriter)):
return get_base(self, attr)
# patch the events writer field, and add a double Event Logger (Trains and original)
base_eventwriter = __dict__['event_writer']
# noinspection PyBroadException
try:
logdir = base_eventwriter.get_logdir()
except Exception:
logdir = None
defaults_dict = __dict__.get('_trains_defaults') or PatchSummaryToEventTransformer.defaults_dict
trains_event = EventTrainsWriter(PatchSummaryToEventTransformer.__main_task.get_logger(),
logdir=logdir, **defaults_dict)
# order is important, the return value of ProxyEventsWriter is the last object in the list
__dict__['event_writer'] = ProxyEventsWriter([trains_event, base_eventwriter])
return get_base(self, attr)
class _ModelAdapter(object):
""" Model adapter which extends the save and save_weights methods of a Keras Model instance """
_model = None # type: Any
_output_model = None # type: OutputModel
def __init__(self, model, output_model):
super(_ModelAdapter, self).__init__()
super(_ModelAdapter, self).__setattr__('_model', model)
super(_ModelAdapter, self).__setattr__('_output_model', output_model)
super(_ModelAdapter, self).__setattr__('_logger', LoggerRoot.get_base_logger(TensorflowBinding))
def __getattr__(self, attr):
return getattr(self._model, attr)
def __setattr__(self, key, value):
return setattr(self._model, key, value)
def save(self, filepath, overwrite=True, include_optimizer=True):
self._model.save(filepath=filepath, overwrite=overwrite, include_optimizer=include_optimizer)
# TODO: auto generate new objects of filename changes
try:
self._output_model.update_weights(weights_filename=filepath, auto_delete_file=True)
except Exception as ex:
self._logger.error(str(ex))
def save_weights(self, filepath, overwrite=True):
self._model.save_weights(filepath=filepath, overwrite=overwrite)
# TODO: auto generate new objects of filename changes