File size: 18,288 Bytes
97b6013 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 |
# Lint as: python2, python3
# Copyright 2018 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.
# ==============================================================================
"""Training script for the DeepLab model.
See model.py for more details and usage.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import six
import tensorflow as tf
from tensorflow.contrib import quantize as contrib_quantize
from tensorflow.contrib import tfprof as contrib_tfprof
from deeplab import common
from deeplab import model
from deeplab.datasets import data_generator
from deeplab.utils import train_utils
from deployment import model_deploy
slim = tf.contrib.slim
flags = tf.app.flags
FLAGS = flags.FLAGS
# Settings for multi-GPUs/multi-replicas training.
flags.DEFINE_integer('num_clones', 1, 'Number of clones to deploy.')
flags.DEFINE_boolean('clone_on_cpu', False, 'Use CPUs to deploy clones.')
flags.DEFINE_integer('num_replicas', 1, 'Number of worker replicas.')
flags.DEFINE_integer('startup_delay_steps', 15,
'Number of training steps between replicas startup.')
flags.DEFINE_integer(
'num_ps_tasks', 0,
'The number of parameter servers. If the value is 0, then '
'the parameters are handled locally by the worker.')
flags.DEFINE_string('master', '', 'BNS name of the tensorflow server')
flags.DEFINE_integer('task', 0, 'The task ID.')
# Settings for logging.
flags.DEFINE_string('train_logdir', None,
'Where the checkpoint and logs are stored.')
flags.DEFINE_integer('log_steps', 10,
'Display logging information at every log_steps.')
flags.DEFINE_integer('save_interval_secs', 1200,
'How often, in seconds, we save the model to disk.')
flags.DEFINE_integer('save_summaries_secs', 600,
'How often, in seconds, we compute the summaries.')
flags.DEFINE_boolean(
'save_summaries_images', False,
'Save sample inputs, labels, and semantic predictions as '
'images to summary.')
# Settings for profiling.
flags.DEFINE_string('profile_logdir', None,
'Where the profile files are stored.')
# Settings for training strategy.
flags.DEFINE_enum('optimizer', 'momentum', ['momentum', 'adam'],
'Which optimizer to use.')
# Momentum optimizer flags
flags.DEFINE_enum('learning_policy', 'poly', ['poly', 'step'],
'Learning rate policy for training.')
# Use 0.007 when training on PASCAL augmented training set, train_aug. When
# fine-tuning on PASCAL trainval set, use learning rate=0.0001.
flags.DEFINE_float('base_learning_rate', .0001,
'The base learning rate for model training.')
flags.DEFINE_float('decay_steps', 0.0,
'Decay steps for polynomial learning rate schedule.')
flags.DEFINE_float('end_learning_rate', 0.0,
'End learning rate for polynomial learning rate schedule.')
flags.DEFINE_float('learning_rate_decay_factor', 0.1,
'The rate to decay the base learning rate.')
flags.DEFINE_integer('learning_rate_decay_step', 2000,
'Decay the base learning rate at a fixed step.')
flags.DEFINE_float('learning_power', 0.9,
'The power value used in the poly learning policy.')
flags.DEFINE_integer('training_number_of_steps', 30000,
'The number of steps used for training')
flags.DEFINE_float('momentum', 0.9, 'The momentum value to use')
# Adam optimizer flags
flags.DEFINE_float('adam_learning_rate', 0.001,
'Learning rate for the adam optimizer.')
flags.DEFINE_float('adam_epsilon', 1e-08, 'Adam optimizer epsilon.')
# When fine_tune_batch_norm=True, use at least batch size larger than 12
# (batch size more than 16 is better). Otherwise, one could use smaller batch
# size and set fine_tune_batch_norm=False.
flags.DEFINE_integer('train_batch_size', 8,
'The number of images in each batch during training.')
# For weight_decay, use 0.00004 for MobileNet-V2 or Xcpetion model variants.
# Use 0.0001 for ResNet model variants.
flags.DEFINE_float('weight_decay', 0.00004,
'The value of the weight decay for training.')
flags.DEFINE_list('train_crop_size', '513,513',
'Image crop size [height, width] during training.')
flags.DEFINE_float(
'last_layer_gradient_multiplier', 1.0,
'The gradient multiplier for last layers, which is used to '
'boost the gradient of last layers if the value > 1.')
flags.DEFINE_boolean('upsample_logits', True,
'Upsample logits during training.')
# Hyper-parameters for NAS training strategy.
flags.DEFINE_float(
'drop_path_keep_prob', 1.0,
'Probability to keep each path in the NAS cell when training.')
# Settings for fine-tuning the network.
flags.DEFINE_string('tf_initial_checkpoint', None,
'The initial checkpoint in tensorflow format.')
# Set to False if one does not want to re-use the trained classifier weights.
flags.DEFINE_boolean('initialize_last_layer', True,
'Initialize the last layer.')
flags.DEFINE_boolean('last_layers_contain_logits_only', False,
'Only consider logits as last layers or not.')
flags.DEFINE_integer('slow_start_step', 0,
'Training model with small learning rate for few steps.')
flags.DEFINE_float('slow_start_learning_rate', 1e-4,
'Learning rate employed during slow start.')
# Set to True if one wants to fine-tune the batch norm parameters in DeepLabv3.
# Set to False and use small batch size to save GPU memory.
flags.DEFINE_boolean('fine_tune_batch_norm', True,
'Fine tune the batch norm parameters or not.')
flags.DEFINE_float('min_scale_factor', 0.5,
'Mininum scale factor for data augmentation.')
flags.DEFINE_float('max_scale_factor', 2.,
'Maximum scale factor for data augmentation.')
flags.DEFINE_float('scale_factor_step_size', 0.25,
'Scale factor step size for data augmentation.')
# For `xception_65`, use atrous_rates = [12, 24, 36] if output_stride = 8, or
# rates = [6, 12, 18] if output_stride = 16. For `mobilenet_v2`, use None. Note
# one could use different atrous_rates/output_stride during training/evaluation.
flags.DEFINE_multi_integer('atrous_rates', None,
'Atrous rates for atrous spatial pyramid pooling.')
flags.DEFINE_integer('output_stride', 16,
'The ratio of input to output spatial resolution.')
# Hard example mining related flags.
flags.DEFINE_integer(
'hard_example_mining_step', 0,
'The training step in which exact hard example mining kicks off. Note we '
'gradually reduce the mining percent to the specified '
'top_k_percent_pixels. For example, if hard_example_mining_step=100K and '
'top_k_percent_pixels=0.25, then mining percent will gradually reduce from '
'100% to 25% until 100K steps after which we only mine top 25% pixels.')
flags.DEFINE_float(
'top_k_percent_pixels', 1.0,
'The top k percent pixels (in terms of the loss values) used to compute '
'loss during training. This is useful for hard pixel mining.')
# Quantization setting.
flags.DEFINE_integer(
'quantize_delay_step', -1,
'Steps to start quantized training. If < 0, will not quantize model.')
# Dataset settings.
flags.DEFINE_string('dataset', 'pascal_voc_seg',
'Name of the segmentation dataset.')
flags.DEFINE_string('train_split', 'train',
'Which split of the dataset to be used for training')
flags.DEFINE_string('dataset_dir', None, 'Where the dataset reside.')
def _build_deeplab(iterator, outputs_to_num_classes, ignore_label):
"""Builds a clone of DeepLab.
Args:
iterator: An iterator of type tf.data.Iterator for images and labels.
outputs_to_num_classes: A map from output type to the number of classes. For
example, for the task of semantic segmentation with 21 semantic classes,
we would have outputs_to_num_classes['semantic'] = 21.
ignore_label: Ignore label.
"""
samples = iterator.get_next()
# Add name to input and label nodes so we can add to summary.
samples[common.IMAGE] = tf.identity(samples[common.IMAGE], name=common.IMAGE)
samples[common.LABEL] = tf.identity(samples[common.LABEL], name=common.LABEL)
model_options = common.ModelOptions(
outputs_to_num_classes=outputs_to_num_classes,
crop_size=[int(sz) for sz in FLAGS.train_crop_size],
atrous_rates=FLAGS.atrous_rates,
output_stride=FLAGS.output_stride)
outputs_to_scales_to_logits = model.multi_scale_logits(
samples[common.IMAGE],
model_options=model_options,
image_pyramid=FLAGS.image_pyramid,
weight_decay=FLAGS.weight_decay,
is_training=True,
fine_tune_batch_norm=FLAGS.fine_tune_batch_norm,
nas_training_hyper_parameters={
'drop_path_keep_prob': FLAGS.drop_path_keep_prob,
'total_training_steps': FLAGS.training_number_of_steps,
})
# Add name to graph node so we can add to summary.
output_type_dict = outputs_to_scales_to_logits[common.OUTPUT_TYPE]
output_type_dict[model.MERGED_LOGITS_SCOPE] = tf.identity(
output_type_dict[model.MERGED_LOGITS_SCOPE], name=common.OUTPUT_TYPE)
for output, num_classes in six.iteritems(outputs_to_num_classes):
train_utils.add_softmax_cross_entropy_loss_for_each_scale(
outputs_to_scales_to_logits[output],
samples[common.LABEL],
num_classes,
ignore_label,
loss_weight=model_options.label_weights,
upsample_logits=FLAGS.upsample_logits,
hard_example_mining_step=FLAGS.hard_example_mining_step,
top_k_percent_pixels=FLAGS.top_k_percent_pixels,
scope=output)
def main(unused_argv):
tf.logging.set_verbosity(tf.logging.INFO)
# Set up deployment (i.e., multi-GPUs and/or multi-replicas).
config = model_deploy.DeploymentConfig(
num_clones=FLAGS.num_clones,
clone_on_cpu=FLAGS.clone_on_cpu,
replica_id=FLAGS.task,
num_replicas=FLAGS.num_replicas,
num_ps_tasks=FLAGS.num_ps_tasks)
# Split the batch across GPUs.
assert FLAGS.train_batch_size % config.num_clones == 0, (
'Training batch size not divisble by number of clones (GPUs).')
clone_batch_size = FLAGS.train_batch_size // config.num_clones
tf.gfile.MakeDirs(FLAGS.train_logdir)
tf.logging.info('Training on %s set', FLAGS.train_split)
with tf.Graph().as_default() as graph:
with tf.device(config.inputs_device()):
dataset = data_generator.Dataset(
dataset_name=FLAGS.dataset,
split_name=FLAGS.train_split,
dataset_dir=FLAGS.dataset_dir,
batch_size=clone_batch_size,
crop_size=[int(sz) for sz in FLAGS.train_crop_size],
min_resize_value=FLAGS.min_resize_value,
max_resize_value=FLAGS.max_resize_value,
resize_factor=FLAGS.resize_factor,
min_scale_factor=FLAGS.min_scale_factor,
max_scale_factor=FLAGS.max_scale_factor,
scale_factor_step_size=FLAGS.scale_factor_step_size,
model_variant=FLAGS.model_variant,
num_readers=4,
is_training=True,
should_shuffle=True,
should_repeat=True)
# Create the global step on the device storing the variables.
with tf.device(config.variables_device()):
global_step = tf.train.get_or_create_global_step()
# Define the model and create clones.
model_fn = _build_deeplab
model_args = (dataset.get_one_shot_iterator(), {
common.OUTPUT_TYPE: dataset.num_of_classes
}, dataset.ignore_label)
clones = model_deploy.create_clones(config, model_fn, args=model_args)
# Gather update_ops from the first clone. These contain, for example,
# the updates for the batch_norm variables created by model_fn.
first_clone_scope = config.clone_scope(0)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS, first_clone_scope)
# Gather initial summaries.
summaries = set(tf.get_collection(tf.GraphKeys.SUMMARIES))
# Add summaries for model variables.
for model_var in tf.model_variables():
summaries.add(tf.summary.histogram(model_var.op.name, model_var))
# Add summaries for images, labels, semantic predictions
if FLAGS.save_summaries_images:
summary_image = graph.get_tensor_by_name(
('%s/%s:0' % (first_clone_scope, common.IMAGE)).strip('/'))
summaries.add(
tf.summary.image('samples/%s' % common.IMAGE, summary_image))
first_clone_label = graph.get_tensor_by_name(
('%s/%s:0' % (first_clone_scope, common.LABEL)).strip('/'))
# Scale up summary image pixel values for better visualization.
pixel_scaling = max(1, 255 // dataset.num_of_classes)
summary_label = tf.cast(first_clone_label * pixel_scaling, tf.uint8)
summaries.add(
tf.summary.image('samples/%s' % common.LABEL, summary_label))
first_clone_output = graph.get_tensor_by_name(
('%s/%s:0' % (first_clone_scope, common.OUTPUT_TYPE)).strip('/'))
predictions = tf.expand_dims(tf.argmax(first_clone_output, 3), -1)
summary_predictions = tf.cast(predictions * pixel_scaling, tf.uint8)
summaries.add(
tf.summary.image(
'samples/%s' % common.OUTPUT_TYPE, summary_predictions))
# Add summaries for losses.
for loss in tf.get_collection(tf.GraphKeys.LOSSES, first_clone_scope):
summaries.add(tf.summary.scalar('losses/%s' % loss.op.name, loss))
# Build the optimizer based on the device specification.
with tf.device(config.optimizer_device()):
learning_rate = train_utils.get_model_learning_rate(
FLAGS.learning_policy,
FLAGS.base_learning_rate,
FLAGS.learning_rate_decay_step,
FLAGS.learning_rate_decay_factor,
FLAGS.training_number_of_steps,
FLAGS.learning_power,
FLAGS.slow_start_step,
FLAGS.slow_start_learning_rate,
decay_steps=FLAGS.decay_steps,
end_learning_rate=FLAGS.end_learning_rate)
summaries.add(tf.summary.scalar('learning_rate', learning_rate))
if FLAGS.optimizer == 'momentum':
optimizer = tf.train.MomentumOptimizer(learning_rate, FLAGS.momentum)
elif FLAGS.optimizer == 'adam':
optimizer = tf.train.AdamOptimizer(
learning_rate=FLAGS.adam_learning_rate, epsilon=FLAGS.adam_epsilon)
else:
raise ValueError('Unknown optimizer')
if FLAGS.quantize_delay_step >= 0:
if FLAGS.num_clones > 1:
raise ValueError('Quantization doesn\'t support multi-clone yet.')
contrib_quantize.create_training_graph(
quant_delay=FLAGS.quantize_delay_step)
startup_delay_steps = FLAGS.task * FLAGS.startup_delay_steps
with tf.device(config.variables_device()):
total_loss, grads_and_vars = model_deploy.optimize_clones(
clones, optimizer)
total_loss = tf.check_numerics(total_loss, 'Loss is inf or nan.')
summaries.add(tf.summary.scalar('total_loss', total_loss))
# Modify the gradients for biases and last layer variables.
last_layers = model.get_extra_layer_scopes(
FLAGS.last_layers_contain_logits_only)
grad_mult = train_utils.get_model_gradient_multipliers(
last_layers, FLAGS.last_layer_gradient_multiplier)
if grad_mult:
grads_and_vars = slim.learning.multiply_gradients(
grads_and_vars, grad_mult)
# Create gradient update op.
grad_updates = optimizer.apply_gradients(
grads_and_vars, global_step=global_step)
update_ops.append(grad_updates)
update_op = tf.group(*update_ops)
with tf.control_dependencies([update_op]):
train_tensor = tf.identity(total_loss, name='train_op')
# Add the summaries from the first clone. These contain the summaries
# created by model_fn and either optimize_clones() or _gather_clone_loss().
summaries |= set(
tf.get_collection(tf.GraphKeys.SUMMARIES, first_clone_scope))
# Merge all summaries together.
summary_op = tf.summary.merge(list(summaries))
# Soft placement allows placing on CPU ops without GPU implementation.
session_config = tf.ConfigProto(
allow_soft_placement=True, log_device_placement=False)
# Start the training.
profile_dir = FLAGS.profile_logdir
if profile_dir is not None:
tf.gfile.MakeDirs(profile_dir)
with contrib_tfprof.ProfileContext(
enabled=profile_dir is not None, profile_dir=profile_dir):
init_fn = None
if FLAGS.tf_initial_checkpoint:
init_fn = train_utils.get_model_init_fn(
FLAGS.train_logdir,
FLAGS.tf_initial_checkpoint,
FLAGS.initialize_last_layer,
last_layers,
ignore_missing_vars=True)
slim.learning.train(
train_tensor,
logdir=FLAGS.train_logdir,
log_every_n_steps=FLAGS.log_steps,
master=FLAGS.master,
number_of_steps=FLAGS.training_number_of_steps,
is_chief=(FLAGS.task == 0),
session_config=session_config,
startup_delay_steps=startup_delay_steps,
init_fn=init_fn,
summary_op=summary_op,
save_summaries_secs=FLAGS.save_summaries_secs,
save_interval_secs=FLAGS.save_interval_secs)
if __name__ == '__main__':
flags.mark_flag_as_required('train_logdir')
flags.mark_flag_as_required('dataset_dir')
tf.app.run()
|