# 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
from ..proto.summary_pb2 import Summary
from ..proto.summary_pb2 import SummaryMetadata
from ..proto.tensor_pb2 import TensorProto
from ..proto.tensor_shape_pb2 import TensorShapeProto
import os
import time
import numpy as np
# import tensorflow as tf
# from tensorboard.plugins.beholder import im_util
# from . import im_util
from .file_system_tools import read_pickle,\
write_pickle, write_file
from .shared_config import PLUGIN_NAME, TAG_NAME,\
SUMMARY_FILENAME, DEFAULT_CONFIG, CONFIG_FILENAME, SUMMARY_COLLECTION_KEY_NAME, SECTION_INFO_FILENAME
from . import video_writing
# from .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.last_image_shape = []
self.last_update_time = time.time()
self.config_last_modified_time = -1
self.previous_config = dict(DEFAULT_CONFIG)
if not os.path.exists(self.PLUGIN_LOGDIR + '/config.pkl'):
os.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, frame):
'''Writes the frame to disk as a tensor summary.'''
path = '{}/{}'.format(self.PLUGIN_LOGDIR, SUMMARY_FILENAME)
smd = SummaryMetadata()
tensor = TensorProto(
dtype='DT_FLOAT',
float_val=frame.reshape(-1).tolist(),
tensor_shape=TensorShapeProto(
dim=[TensorShapeProto.Dim(size=frame.shape[0]),
TensorShapeProto.Dim(size=frame.shape[1]),
TensorShapeProto.Dim(size=frame.shape[2])]
)
)
summary = Summary(value=[Summary.Value(
tag=TAG_NAME, metadata=smd, tensor=tensor)]).SerializeToString()
write_file(summary, path)
@staticmethod
def stats(tensor_and_name):
imgstats = []
for (img, name) in tensor_and_name:
immax = img.max()
immin = img.min()
imgstats.append(
{
'height': img.shape[0],
'max': str(immax),
'mean': str(img.mean()),
'min': str(immin),
'name': name,
'range': str(immax - immin),
'shape': str((img.shape[1], img.shape[2]))
})
return imgstats
def _get_final_image(self, config, trainable=None, arrays=None, frame=None):
if config['values'] == 'frames':
# print('===frames===')
final_image = frame
elif config['values'] == 'arrays':
# print('===arrays===')
final_image = np.concatenate([arr for arr, _ in arrays])
stat = self.stats(arrays)
write_pickle(
stat, '{}/{}'.format(self.PLUGIN_LOGDIR, SECTION_INFO_FILENAME))
elif config['values'] == 'trainable_variables':
# print('===trainable===')
final_image = np.concatenate([arr for arr, _ in trainable])
stat = self.stats(trainable)
write_pickle(
stat, '{}/{}'.format(self.PLUGIN_LOGDIR, SECTION_INFO_FILENAME))
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, trainable, arrays, frame, config):
final_image = self._get_final_image(config, trainable, arrays, frame)
self._write_summary(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
print('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()
print('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, trainable=None, arrays=None, frame=None):
'''Creates a frame and writes it to disk.
Args:
trainable: a list of namedtuple (tensors, name).
arrays: a list of namedtuple (tensors, name).
frame: lalala
'''
new_config = self._get_config()
if True or 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(
trainable, 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)
# implements pytorch backward later
class BeholderHook():
pass
# """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)