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train_inception_v3_baseline_2.py
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train_inception_v3_baseline_2.py
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# Copyright 2019 Adam Byerly. 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.
# ==============================================================================
import argparse
import tensorflow as tf
from datetime import datetime
from input_sieve import DataSet, train_inputs, eval_inputs
from input_sieve import non_blacklisted_eval_inputs
from inception_v3.output import Output
from inception_v3.model import run_towers
def train(out, sess, epoch, training_steps, train_op, loss_op,
global_step, learning_rate, is_training_ph, is_validating_nbl_ph):
g_step = 0
for i in range(training_steps):
out.train_step_begin(i)
_, l, g_step, lr = sess.run(
[train_op, loss_op, global_step, learning_rate],
feed_dict={is_training_ph: True,
is_validating_nbl_ph: False},
options=out.get_run_options(),
run_metadata=out.get_run_metadata())
out.train_step_end(
sess, epoch, g_step, i, l, lr, training_steps,
feed_dict={is_training_ph: True,
is_validating_nbl_ph: False})
out.train_end(sess, epoch, g_step)
def validate(out, sess, epoch, validation_steps, loss_op,
acc_top_1_op, acc_top_5_op, global_step, learning_rate,
is_training_ph, is_validating_nbl_ph):
g_step, acc_top1, acc_top5, test_loss, lr = (0, 0, 0, 0, 0)
for i in range(validation_steps):
out.validation_step_begin(i, validation_steps)
g_step, l, acc1, acc5, lr = sess.run(
[global_step, loss_op, acc_top_1_op, acc_top_5_op, learning_rate],
feed_dict={is_training_ph: False,
is_validating_nbl_ph: False})
acc_top1 = (acc1 + (i * acc_top1)) / (i + 1)
acc_top5 = (acc5 + (i * acc_top5)) / (i + 1)
test_loss = (l + (i * test_loss)) / (i + 1)
out.validation_end(sess, epoch, g_step, False,
test_loss, lr, acc_top1, acc_top5)
def validate_nbl(out, sess, epoch, nbl_validation_steps, loss_op,
acc_top_1_op, acc_top_5_op, global_step, learning_rate,
is_training_ph, is_validating_nbl_ph):
g_step, acc_top1, acc_top5, test_loss, lr = (0, 0, 0, 0, 0)
for i in range(nbl_validation_steps):
out.validation_step_begin(i, nbl_validation_steps)
g_step, l, acc1, acc5, lr = sess.run(
[global_step, loss_op, acc_top_1_op, acc_top_5_op, learning_rate],
feed_dict={is_training_ph: False,
is_validating_nbl_ph: True})
acc_top1 = (acc1 + (i * acc_top1)) / (i + 1)
acc_top5 = (acc5 + (i * acc_top5)) / (i + 1)
test_loss = (l + (i * test_loss)) / (i + 1)
out.validation_end(sess, epoch, g_step, True,
test_loss, lr, acc_top1, acc_top5)
def go(start_epoch, end_epoch, run_name, weights_file,
profile_compute_time_every_n_steps, save_summary_info_every_n_steps,
log_annotated_images, image_size, batch_size, num_gpus,
data_dir, black_list_file, log_dir, do_validate_all, do_validate_nbl):
tf.reset_default_graph()
out = Output(log_dir, run_name, profile_compute_time_every_n_steps,
save_summary_info_every_n_steps)
############################################################################
# Data feeds
############################################################################
out.log_msg("Setting up data feeds...")
training_dataset = DataSet('train', image_size, batch_size, num_gpus,
data_dir, None)
validation_dataset = DataSet('validation', image_size, batch_size, num_gpus,
data_dir, black_list_file)
training_data = train_inputs(training_dataset, log_annotated_images)
validation_data = eval_inputs(validation_dataset, log_annotated_images)
nbl_val_data = non_blacklisted_eval_inputs(
validation_dataset, log_annotated_images)
training_steps = training_dataset.training_batches_per_epoch()
validation_steps = validation_dataset.validation_batches_per_epoch()
nbl_val_steps = validation_dataset.nbl_validation_batches_per_epoch()
############################################################################
# Tensorflow placeholders and operations
############################################################################
with tf.device("/device:CPU:0"): # set the default device to the CPU
with tf.name_scope("input/placeholders"):
is_training_ph = tf.placeholder(tf.bool)
is_validating_nbl_ph = tf.placeholder(tf.bool)
global_step = tf.train.get_or_create_global_step()
decay_steps = int(training_steps*30.0)
learning_rate_op = tf.train.exponential_decay(0.1,
global_step, decay_steps,
0.16, staircase=True)
opt = tf.train.RMSPropOptimizer(learning_rate_op,
decay=0.9, momentum=0.9, epsilon=1.0)
train_op, loss_op,\
acc_top_1_op, \
acc_top_5_op = run_towers(opt, global_step, is_training_ph,
is_validating_nbl_ph, training_data,
validation_data, nbl_val_data,
DataSet.num_classes(), num_gpus)
out.log_msg("Starting Session...")
############################################################################
# Tensorflow session
############################################################################
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
out.set_session_graph(sess.graph)
if weights_file is not None:
out.log_msg("Restoring weights file: {}".format(weights_file))
tf.train.Saver().restore(sess, weights_file)
else:
tf.global_variables_initializer().run()
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
try:
for e in range(start_epoch, end_epoch + 1):
train(out, sess, e, training_steps, train_op, loss_op,
global_step, learning_rate_op,
is_training_ph, is_validating_nbl_ph)
if do_validate_all:
validate(out, sess, e, validation_steps, loss_op,
acc_top_1_op, acc_top_5_op, global_step,
learning_rate_op, is_training_ph,
is_validating_nbl_ph)
if do_validate_nbl:
validate_nbl(out, sess, e, nbl_val_steps, loss_op,
acc_top_1_op, acc_top_5_op, global_step,
learning_rate_op, is_training_ph,
is_validating_nbl_ph)
except tf.errors.OutOfRangeError:
out.log_msg("Finished.")
finally:
coord.request_stop()
coord.join(threads)
sess.close()
out.close_files()
################################################################################
# Entry point
################################################################################
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="inception_v3")
parser.add_argument("-se", "--start_epoch", default=1, type=int)
parser.add_argument("-ee", "--end_epoch", default=175, type=int)
parser.add_argument("-rn", "--run_name",
default=datetime.now().strftime("%Y%m%d%H%M%S"))
parser.add_argument("-wf", "--weights_file", default=None)
parser.add_argument("-pct", "--profile_compute_time_every_n_steps",
default=None, type=int)
parser.add_argument("-ssi", "--save_summary_info_every_n_steps",
default=None, type=int)
parser.add_argument("-lai", "--log_annotated_images",
default=False, type=bool)
parser.add_argument("-is", "--image_size", default=299, type=int)
parser.add_argument("-bs", "--batch_size", default=96, type=int)
parser.add_argument("-g", "--gpus", default=2, type=int)
parser.add_argument("-ld", "--log_dir", default="logs")
parser.add_argument("-dd", "--data_dir",
default="D:\\workingbasedir\\processed")
parser.add_argument("-blf", "--black_list_file",
default="ILSVRC2015_clsloc_validation_blacklist.txt")
parser.add_argument("-vall", "--validate_all", default=True, type=bool)
parser.add_argument("-vnbl", "--validate_nbl", default=True, type=bool)
args = parser.parse_args()
print(args)
go(args.start_epoch, args.end_epoch, args.run_name, args.weights_file,
args.profile_compute_time_every_n_steps,
args.save_summary_info_every_n_steps, args.log_annotated_images,
args.image_size, args.batch_size, args.gpus,
args.data_dir, args.black_list_file, args.log_dir,
args.validate_all, args.validate_nbl)