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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() | |