Viewing file:  beholder.py (7.31 KB) -rw-r--r--
Select action/file-type:
 (+) |  (+) |  (+) | Code (+) | Session (+) |  (+) | SDB (+) |  (+) |  (+) |  (+) |  (+) |  (+) |
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import time
import numpy as np
import tensorflow as tf
from tensorboard.plugins.beholder import im_util
from tensorboard.plugins.beholder.file_system_tools import read_pickle,\
write_pickle, write_file
from tensorboard.plugins.beholder.shared_config import PLUGIN_NAME, TAG_NAME,\
SUMMARY_FILENAME, DEFAULT_CONFIG, CONFIG_FILENAME, SUMMARY_COLLECTION_KEY_NAME
from tensorboard.plugins.beholder import video_writing
from tensorboard.plugins.beholder.visualizer import Visualizer
class Beholder(object):
def __init__(self, logdir):
self.PLUGIN_LOGDIR = logdir + '/plugins/' + PLUGIN_NAME
self.is_recording = False
self.video_writer = video_writing.VideoWriter(
self.PLUGIN_LOGDIR,
outputs=[
video_writing.FFmpegVideoOutput,
video_writing.PNGVideoOutput])
self.frame_placeholder = tf.placeholder(tf.uint8, [None, None, None])
self.summary_op = tf.summary.tensor_summary(TAG_NAME,
self.frame_placeholder,
collections=[
SUMMARY_COLLECTION_KEY_NAME
])
self.last_image_shape = []
self.last_update_time = time.time()
self.config_last_modified_time = -1
self.previous_config = dict(DEFAULT_CONFIG)
if not tf.gfile.Exists(self.PLUGIN_LOGDIR + '/config.pkl'):
tf.gfile.MakeDirs(self.PLUGIN_LOGDIR)
write_pickle(DEFAULT_CONFIG, '{}/{}'.format(self.PLUGIN_LOGDIR,
CONFIG_FILENAME))
self.visualizer = Visualizer(self.PLUGIN_LOGDIR)
def _get_config(self):
'''Reads the config file from disk or creates a new one.'''
filename = '{}/{}'.format(self.PLUGIN_LOGDIR, CONFIG_FILENAME)
modified_time = os.path.getmtime(filename)
if modified_time != self.config_last_modified_time:
config = read_pickle(filename, default=self.previous_config)
self.previous_config = config
else:
config = self.previous_config
self.config_last_modified_time = modified_time
return config
def _write_summary(self, session, frame):
'''Writes the frame to disk as a tensor summary.'''
summary = session.run(self.summary_op, feed_dict={
self.frame_placeholder: frame
})
path = '{}/{}'.format(self.PLUGIN_LOGDIR, SUMMARY_FILENAME)
write_file(summary, path)
def _get_final_image(self, session, config, arrays=None, frame=None):
if config['values'] == 'frames':
if frame is None:
final_image = im_util.get_image_relative_to_script('frame-missing.png')
else:
frame = frame() if callable(frame) else frame
final_image = im_util.scale_image_for_display(frame)
elif config['values'] == 'arrays':
if arrays is None:
final_image = im_util.get_image_relative_to_script('arrays-missing.png')
# TODO: hack to clear the info. Should be cleaner.
self.visualizer._save_section_info([], [])
else:
final_image = self.visualizer.build_frame(arrays)
elif config['values'] == 'trainable_variables':
arrays = [session.run(x) for x in tf.trainable_variables()]
final_image = self.visualizer.build_frame(arrays)
if len(final_image.shape) == 2:
# Map grayscale images to 3D tensors.
final_image = np.expand_dims(final_image, -1)
return final_image
def _enough_time_has_passed(self, FPS):
'''For limiting how often frames are computed.'''
if FPS == 0:
return False
else:
earliest_time = self.last_update_time + (1.0 / FPS)
return time.time() >= earliest_time
def _update_frame(self, session, arrays, frame, config):
final_image = self._get_final_image(session, config, arrays, frame)
self._write_summary(session, final_image)
self.last_image_shape = final_image.shape
return final_image
def _update_recording(self, frame, config):
'''Adds a frame to the current video output.'''
# pylint: disable=redefined-variable-type
should_record = config['is_recording']
if should_record:
if not self.is_recording:
self.is_recording = True
tf.logging.info(
'Starting recording using %s',
self.video_writer.current_output().name())
self.video_writer.write_frame(frame)
elif self.is_recording:
self.is_recording = False
self.video_writer.finish()
tf.logging.info('Finished recording')
# TODO: blanket try and except for production? I don't someone's script to die
# after weeks of running because of a visualization.
def update(self, session, arrays=None, frame=None):
'''Creates a frame and writes it to disk.
Args:
arrays: a list of np arrays. Use the "custom" option in the client.
frame: a 2D np array. This way the plugin can be used for video of any
kind, not just the visualization that comes with the plugin.
frame can also be a function, which only is evaluated when the
"frame" option is selected by the client.
'''
new_config = self._get_config()
if self._enough_time_has_passed(self.previous_config['FPS']):
self.visualizer.update(new_config)
self.last_update_time = time.time()
final_image = self._update_frame(session, arrays, frame, new_config)
self._update_recording(final_image, new_config)
##############################################################################
@staticmethod
def gradient_helper(optimizer, loss, var_list=None):
'''A helper to get the gradients out at each step.
Args:
optimizer: the optimizer op.
loss: the op that computes your loss value.
Returns: the gradient tensors and the train_step op.
'''
if var_list is None:
var_list = tf.trainable_variables()
grads_and_vars = optimizer.compute_gradients(loss, var_list=var_list)
grads = [pair[0] for pair in grads_and_vars]
return grads, optimizer.apply_gradients(grads_and_vars)
class BeholderHook(tf.train.SessionRunHook):
"""SessionRunHook implementation that runs Beholder every step.
Convenient when using tf.train.MonitoredSession:
```python
beholder_hook = BeholderHook(LOG_DIRECTORY)
with MonitoredSession(..., hooks=[beholder_hook]) as sess:
sess.run(train_op)
```
"""
def __init__(self, logdir):
"""Creates new Hook instance
Args:
logdir: Directory where Beholder should write data.
"""
self._logdir = logdir
self.beholder = None
def begin(self):
self.beholder = Beholder(self._logdir)
def after_run(self, run_context, unused_run_values):
self.beholder.update(run_context.session)
|