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imagenet_train.py
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imagenet_train.py
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# Copyright 2016 Google Inc. 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.
# ==============================================================================
"""Trains a ResNet-ACT model."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from tensorflow.contrib import slim
import imagenet_data_provider
import imagenet_model
import summary_utils
import training_utils
import utils
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_string('master', '',
'Name of the TensorFlow master to use.')
tf.app.flags.DEFINE_string('train_log_dir', '/tmp/resnet/',
'Directory where to write event logs.')
tf.app.flags.DEFINE_string(
'split_name', 'train',
"""The name of the train/test split, either 'train' or 'validation'.""")
tf.app.flags.DEFINE_integer('worker_replicas', 1, 'Number of worker replicas.')
tf.app.flags.DEFINE_integer(
'ps_tasks', 0,
'The number of parameter servers. If the value is 0, then the parameters '
'are handled locally by the worker.')
tf.app.flags.DEFINE_integer(
'save_summaries_secs', 600,
'The frequency with which summaries are saved, in seconds.')
tf.app.flags.DEFINE_integer('save_interval_secs', 600,
'The frequency with which the model is saved, in seconds.')
tf.app.flags.DEFINE_integer('startup_delay_steps', 15,
'Number of training steps between replicas startup.')
tf.app.flags.DEFINE_integer('task', 0, 'Task id of the replica running the training.')
tf.app.flags.DEFINE_string('dataset_dir', None, 'Directory with ImageNet data.')
# Training parameters.
tf.app.flags.DEFINE_integer('batch_size', 32,
'The number of images in each batch.')
tf.app.flags.DEFINE_float('learning_rate', 0.05, """Initial learning rate.""")
tf.app.flags.DEFINE_float('momentum', 0.9, """Momentum.""")
tf.app.flags.DEFINE_float('learning_rate_decay_factor', 0.1,
'Learning rate decay factor.')
tf.app.flags.DEFINE_float('num_epochs_per_decay', 30.0,
'Number of epochs after which learning rate decays.')
tf.app.flags.DEFINE_integer(
'replicas_to_aggregate', 1,
'The Number of gradients to collect before updating params.')
tf.app.flags.DEFINE_float('moving_average_decay', 0.9999,
'The decay to use for the moving average.')
tf.app.flags.DEFINE_integer('image_size', 224,
'Image resolution for resize.')
tf.app.flags.DEFINE_string(
'model', '101',
'Depth of the network to train (50, 101, 152, 200), or number of layers'
' in each block (e.g. 3_4_23_3).')
tf.app.flags.DEFINE_string(
'model_type', 'vanilla',
'Options: vanilla (basic ResNet model), act (Adaptive Computation Time), '
'act_early_stopping (act implementation which actually saves time), '
'sact (Spatially Adaptive Computation Time)')
tf.app.flags.DEFINE_float('tau', 1.0, 'Target value of tau (ponder relative cost).')
tf.app.flags.DEFINE_string('finetune_path', '',
'Path for the initial checkpoint for finetuning.')
def main(_):
g = tf.Graph()
with g.as_default():
# If ps_tasks is zero, the local device is used. When using multiple
# (non-local) replicas, the ReplicaDeviceSetter distributes the variables
# across the different devices.
with tf.device(tf.train.replica_device_setter(
FLAGS.ps_tasks, merge_devices=True)):
data_tuple = imagenet_data_provider.provide_data(
FLAGS.split_name,
FLAGS.batch_size,
dataset_dir=FLAGS.dataset_dir,
is_training=True,
image_size=FLAGS.image_size)
images, labels, examples_per_epoch, num_classes = data_tuple
# Define the model:
with slim.arg_scope(imagenet_model.resnet_arg_scope(is_training=True)):
model = utils.split_and_int(FLAGS.model)
logits, end_points = imagenet_model.get_network(
images,
model,
num_classes,
model_type=FLAGS.model_type)
# Specify the loss function:
tf.losses.softmax_cross_entropy(
onehot_labels=labels, logits=logits, label_smoothing=0.1, weights=1.0)
if FLAGS.model_type in ('act', 'act_early_stopping', 'sact'):
training_utils.add_all_ponder_costs(end_points, weights=FLAGS.tau)
total_loss = tf.losses.get_total_loss()
# Configure the learning rate using an exponetial decay.
decay_steps = int(examples_per_epoch / FLAGS.batch_size *
FLAGS.num_epochs_per_decay)
learning_rate = tf.train.exponential_decay(
FLAGS.learning_rate,
slim.get_or_create_global_step(),
decay_steps,
FLAGS.learning_rate_decay_factor,
staircase=True)
opt = tf.train.MomentumOptimizer(learning_rate, FLAGS.momentum)
init_fn = training_utils.finetuning_init_fn(FLAGS.finetune_path)
train_tensor = slim.learning.create_train_op(
total_loss,
optimizer=opt,
update_ops=tf.get_collection(tf.GraphKeys.UPDATE_OPS))
# Summaries:
tf.summary.scalar('losses/Total Loss', total_loss)
tf.summary.scalar('training/Learning Rate', learning_rate)
metric_map = {} # summary_utils.flops_metric_map(end_points, False)
if FLAGS.model_type in ('act', 'act_early_stopping', 'sact'):
metric_map.update(summary_utils.act_metric_map(end_points, False))
for name, value in metric_map.iteritems():
tf.summary.scalar(name, value)
if FLAGS.model_type == 'sact':
summary_utils.add_heatmaps_image_summary(end_points, border=10)
startup_delay_steps = FLAGS.task * FLAGS.startup_delay_steps
slim.learning.train(
train_tensor,
init_fn=init_fn,
logdir=FLAGS.train_log_dir,
master=FLAGS.master,
is_chief=(FLAGS.task == 0),
startup_delay_steps=startup_delay_steps,
save_summaries_secs=FLAGS.save_summaries_secs,
save_interval_secs=FLAGS.save_interval_secs)
if __name__ == '__main__':
tf.app.run()