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# Copyright 2016 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.
# ==============================================================================
"""Train the cross convolutional model."""
import os
import sys
import numpy as np
import tensorflow as tf
import model as cross_conv_model
import reader
FLAGS = tf.flags.FLAGS
tf.flags.DEFINE_string('master', '', 'Session address.')
tf.flags.DEFINE_string('log_root', '/tmp/moving_obj', 'The root dir of output.')
tf.flags.DEFINE_string('data_filepattern', '',
'training data file pattern.')
tf.flags.DEFINE_integer('image_size', 64, 'Image height and width.')
tf.flags.DEFINE_integer('batch_size', 1, 'Batch size.')
tf.flags.DEFINE_float('norm_scale', 1.0, 'Normalize the original image')
tf.flags.DEFINE_float('scale', 10.0,
'Scale the image after norm_scale and move the diff '
'to the positive realm.')
tf.flags.DEFINE_integer('sequence_length', 2, 'tf.SequenceExample length.')
tf.flags.DEFINE_float('learning_rate', 0.8, 'Learning rate.')
tf.flags.DEFINE_bool('l2_loss', True, 'If true, include l2_loss.')
tf.flags.DEFINE_bool('reconstr_loss', False, 'If true, include reconstr_loss.')
tf.flags.DEFINE_bool('kl_loss', True, 'If true, include KL loss.')
slim = tf.contrib.slim
def _Train():
params = dict()
params['batch_size'] = FLAGS.batch_size
params['seq_len'] = FLAGS.sequence_length
params['image_size'] = FLAGS.image_size
params['is_training'] = True
params['norm_scale'] = FLAGS.norm_scale
params['scale'] = FLAGS.scale
params['learning_rate'] = FLAGS.learning_rate
params['l2_loss'] = FLAGS.l2_loss
params['reconstr_loss'] = FLAGS.reconstr_loss
params['kl_loss'] = FLAGS.kl_loss
train_dir = os.path.join(FLAGS.log_root, 'train')
images = reader.ReadInput(FLAGS.data_filepattern, shuffle=True, params=params)
images *= params['scale']
# Increase the value makes training much faster.
image_diff_list = reader.SequenceToImageAndDiff(images)
model = cross_conv_model.CrossConvModel(image_diff_list, params)
model.Build()
tf.contrib.tfprof.model_analyzer.print_model_analysis(tf.get_default_graph())
summary_writer = tf.summary.FileWriter(train_dir)
sv = tf.train.Supervisor(logdir=FLAGS.log_root,
summary_op=None,
is_chief=True,
save_model_secs=60,
global_step=model.global_step)
sess = sv.prepare_or_wait_for_session(
FLAGS.master, config=tf.ConfigProto(allow_soft_placement=True))
total_loss = 0.0
step = 0
sample_z_mean = np.zeros(model.z_mean.get_shape().as_list())
sample_z_stddev_log = np.zeros(model.z_stddev_log.get_shape().as_list())
sample_step = 0
while True:
_, loss_val, total_steps, summaries, z_mean, z_stddev_log = sess.run(
[model.train_op, model.loss, model.global_step,
model.summary_op,
model.z_mean, model.z_stddev_log])
sample_z_mean += z_mean
sample_z_stddev_log += z_stddev_log
total_loss += loss_val
step += 1
sample_step += 1
if step % 100 == 0:
summary_writer.add_summary(summaries, total_steps)
sys.stderr.write('step: %d, loss: %f\n' %
(total_steps, total_loss / step))
total_loss = 0.0
step = 0
# Sampled z is used for eval.
# It seems 10k is better than 1k. Maybe try 100k next?
if sample_step % 10000 == 0:
with tf.gfile.Open(os.path.join(FLAGS.log_root, 'z_mean.npy'), 'w') as f:
np.save(f, sample_z_mean / sample_step)
with tf.gfile.Open(
os.path.join(FLAGS.log_root, 'z_stddev_log.npy'), 'w') as f:
np.save(f, sample_z_stddev_log / sample_step)
sample_z_mean = np.zeros(model.z_mean.get_shape().as_list())
sample_z_stddev_log = np.zeros(
model.z_stddev_log.get_shape().as_list())
sample_step = 0
def main(_):
_Train()
if __name__ == '__main__':
tf.app.run()
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