# Copyright 2017 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 model.""" # Example usage: # # python train.py \ # --logtostderr \ # --data_dir ~/vid2depth/data/kitti_raw_eigen \ # --seq_length 3 \ # --reconstr_weight 0.85 \ # --smooth_weight 0.05 \ # --ssim_weight 0.15 \ # --icp_weight 0.1 \ # --checkpoint_dir ~/vid2depth/checkpoints from __future__ import absolute_import from __future__ import division from __future__ import print_function import math import os import random import time from absl import app from absl import flags from absl import logging import model import numpy as np import tensorflow as tf import util gfile = tf.gfile HOME_DIR = os.path.expanduser('~') DEFAULT_DATA_DIR = os.path.join(HOME_DIR, 'vid2depth/data/kitti_raw_eigen') DEFAULT_CHECKPOINT_DIR = os.path.join(HOME_DIR, 'vid2depth/checkpoints') flags.DEFINE_string('data_dir', DEFAULT_DATA_DIR, 'Preprocessed data.') flags.DEFINE_float('learning_rate', 0.0002, 'Adam learning rate.') flags.DEFINE_float('beta1', 0.9, 'Adam momentum.') flags.DEFINE_float('reconstr_weight', 0.85, 'Frame reconstruction loss weight.') flags.DEFINE_float('smooth_weight', 0.05, 'Smoothness loss weight.') flags.DEFINE_float('ssim_weight', 0.15, 'SSIM loss weight.') flags.DEFINE_float('icp_weight', 0.0, 'ICP loss weight.') flags.DEFINE_integer('batch_size', 4, 'The size of a sample batch') flags.DEFINE_integer('img_height', 128, 'Input frame height.') flags.DEFINE_integer('img_width', 416, 'Input frame width.') # Note: Training time grows linearly with sequence length. Use 2 or 3. flags.DEFINE_integer('seq_length', 3, 'Number of frames in sequence.') flags.DEFINE_string('pretrained_ckpt', None, 'Path to checkpoint with ' 'pretrained weights. Do not include .data* extension.') flags.DEFINE_string('checkpoint_dir', DEFAULT_CHECKPOINT_DIR, 'Directory to save model checkpoints.') flags.DEFINE_integer('train_steps', 200000, 'Number of training steps.') flags.DEFINE_integer('summary_freq', 100, 'Save summaries every N steps.') flags.DEFINE_bool('legacy_mode', False, 'Whether to limit losses to using only ' 'the middle frame in sequence as the target frame.') FLAGS = flags.FLAGS # Maximum number of checkpoints to keep. MAX_TO_KEEP = 100 def main(_): # Fixed seed for repeatability seed = 8964 tf.set_random_seed(seed) np.random.seed(seed) random.seed(seed) if FLAGS.legacy_mode and FLAGS.seq_length < 3: raise ValueError('Legacy mode supports sequence length > 2 only.') if not gfile.Exists(FLAGS.checkpoint_dir): gfile.MakeDirs(FLAGS.checkpoint_dir) train_model = model.Model(data_dir=FLAGS.data_dir, is_training=True, learning_rate=FLAGS.learning_rate, beta1=FLAGS.beta1, reconstr_weight=FLAGS.reconstr_weight, smooth_weight=FLAGS.smooth_weight, ssim_weight=FLAGS.ssim_weight, icp_weight=FLAGS.icp_weight, batch_size=FLAGS.batch_size, img_height=FLAGS.img_height, img_width=FLAGS.img_width, seq_length=FLAGS.seq_length, legacy_mode=FLAGS.legacy_mode) train(train_model, FLAGS.pretrained_ckpt, FLAGS.checkpoint_dir, FLAGS.train_steps, FLAGS.summary_freq) def train(train_model, pretrained_ckpt, checkpoint_dir, train_steps, summary_freq): """Train model.""" if pretrained_ckpt is not None: vars_to_restore = util.get_vars_to_restore(pretrained_ckpt) pretrain_restorer = tf.train.Saver(vars_to_restore) vars_to_save = util.get_vars_to_restore() saver = tf.train.Saver(vars_to_save + [train_model.global_step], max_to_keep=MAX_TO_KEEP) sv = tf.train.Supervisor(logdir=checkpoint_dir, save_summaries_secs=0, saver=None) config = tf.ConfigProto() config.gpu_options.allow_growth = True with sv.managed_session(config=config) as sess: if pretrained_ckpt is not None: logging.info('Restoring pretrained weights from %s', pretrained_ckpt) pretrain_restorer.restore(sess, pretrained_ckpt) logging.info('Attempting to resume training from %s...', checkpoint_dir) checkpoint = tf.train.latest_checkpoint(checkpoint_dir) logging.info('Last checkpoint found: %s', checkpoint) if checkpoint: saver.restore(sess, checkpoint) logging.info('Training...') start_time = time.time() last_summary_time = time.time() steps_per_epoch = train_model.reader.steps_per_epoch step = 1 while step <= train_steps: fetches = { 'train': train_model.train_op, 'global_step': train_model.global_step, 'incr_global_step': train_model.incr_global_step } if step % summary_freq == 0: fetches['loss'] = train_model.total_loss fetches['summary'] = sv.summary_op results = sess.run(fetches) global_step = results['global_step'] if step % summary_freq == 0: sv.summary_writer.add_summary(results['summary'], global_step) train_epoch = math.ceil(global_step / steps_per_epoch) train_step = global_step - (train_epoch - 1) * steps_per_epoch this_cycle = time.time() - last_summary_time last_summary_time += this_cycle logging.info( 'Epoch: [%2d] [%5d/%5d] time: %4.2fs (%ds total) loss: %.3f', train_epoch, train_step, steps_per_epoch, this_cycle, time.time() - start_time, results['loss']) if step % steps_per_epoch == 0: logging.info('[*] Saving checkpoint to %s...', checkpoint_dir) saver.save(sess, os.path.join(checkpoint_dir, 'model'), global_step=global_step) # Setting step to global_step allows for training for a total of # train_steps even if the program is restarted during training. step = global_step + 1 if __name__ == '__main__': app.run(main)