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