# 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. # ============================================================================== """Contains training plan for the Rotator model (Pretraining in NIPS16).""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import numpy as np from six.moves import xrange import tensorflow as tf from tensorflow import app import model_rotator as model flags = tf.app.flags slim = tf.contrib.slim flags.DEFINE_string('inp_dir', '', 'Directory path containing the input data (tfrecords).') flags.DEFINE_string( 'dataset_name', 'shapenet_chair', 'Dataset name that is to be used for training and evaluation.') flags.DEFINE_integer('z_dim', 512, '') flags.DEFINE_integer('a_dim', 3, '') flags.DEFINE_integer('f_dim', 64, '') flags.DEFINE_integer('fc_dim', 1024, '') flags.DEFINE_integer('num_views', 24, 'Num of viewpoints in the input data.') flags.DEFINE_integer('image_size', 64, 'Input images dimension (pixels) - width & height.') flags.DEFINE_integer('step_size', 1, 'Steps to take for rotation in pretraining.') flags.DEFINE_integer('batch_size', 32, 'Batch size for training.') flags.DEFINE_string('encoder_name', 'ptn_encoder', 'Name of the encoder network being used.') flags.DEFINE_string('decoder_name', 'ptn_im_decoder', 'Name of the decoder network being used.') flags.DEFINE_string('rotator_name', 'ptn_rotator', 'Name of the rotator network being used.') # Save options flags.DEFINE_string('checkpoint_dir', '/tmp/ptn_train/', 'Directory path for saving trained models and other data.') flags.DEFINE_string('model_name', 'deeprotator_pretrain', 'Name of the model used in naming the TF job. Must be different for each run.') flags.DEFINE_string('init_model', None, 'Checkpoint path of the model to initialize with.') flags.DEFINE_integer('save_every', 1000, 'Average period of steps after which we save a model.') # Optimization flags.DEFINE_float('image_weight', 10, 'Weighting factor for image loss.') flags.DEFINE_float('mask_weight', 1, 'Weighting factor for mask loss.') flags.DEFINE_float('learning_rate', 0.0001, 'Learning rate.') flags.DEFINE_float('weight_decay', 0.001, 'Weight decay parameter while training.') flags.DEFINE_float('clip_gradient_norm', 0, 'Gradient clim norm, leave 0 if no gradient clipping.') flags.DEFINE_integer('max_number_of_steps', 320000, 'Maximum number of steps for training.') # Summary flags.DEFINE_integer('save_summaries_secs', 15, 'Seconds interval for dumping TF summaries.') flags.DEFINE_integer('save_interval_secs', 60 * 5, 'Seconds interval to save models.') # Distribution flags.DEFINE_string('master', '', 'The address of the tensorflow master if running distributed.') flags.DEFINE_bool('sync_replicas', False, 'Whether to sync gradients between replicas for optimizer.') flags.DEFINE_integer('worker_replicas', 1, 'Number of worker replicas (train tasks).') flags.DEFINE_integer('backup_workers', 0, 'Number of backup workers.') flags.DEFINE_integer('ps_tasks', 0, 'Number of ps tasks.') flags.DEFINE_integer('task', 0, 'Task identifier flag to be set for each task running in distributed manner. Task number 0 ' 'will be chosen as the chief.') FLAGS = flags.FLAGS def main(_): train_dir = os.path.join(FLAGS.checkpoint_dir, FLAGS.model_name, 'train') save_image_dir = os.path.join(train_dir, 'images') if not os.path.exists(train_dir): os.makedirs(train_dir) if not os.path.exists(save_image_dir): os.makedirs(save_image_dir) g = tf.Graph() with g.as_default(): with tf.device(tf.train.replica_device_setter(FLAGS.ps_tasks)): global_step = slim.get_or_create_global_step() ########## ## data ## ########## train_data = model.get_inputs( FLAGS.inp_dir, FLAGS.dataset_name, 'train', FLAGS.batch_size, FLAGS.image_size, is_training=True) inputs = model.preprocess(train_data, FLAGS.step_size) ########### ## model ## ########### model_fn = model.get_model_fn(FLAGS, is_training=True) outputs = model_fn(inputs) ########## ## loss ## ########## task_loss = model.get_loss(inputs, outputs, FLAGS) regularization_loss = model.get_regularization_loss( ['encoder', 'rotator', 'decoder'], FLAGS) loss = task_loss + regularization_loss ############### ## optimizer ## ############### optimizer = tf.train.AdamOptimizer(FLAGS.learning_rate) if FLAGS.sync_replicas: optimizer = tf.train.SyncReplicasOptimizer( optimizer, replicas_to_aggregate=FLAGS.workers_replicas - FLAGS.backup_workers, total_num_replicas=FLAGS.worker_replicas) ############## ## train_op ## ############## train_op = model.get_train_op_for_scope( loss, optimizer, ['encoder', 'rotator', 'decoder'], FLAGS) ########### ## saver ## ########### saver = tf.train.Saver(max_to_keep=np.minimum(5, FLAGS.worker_replicas + 1)) if FLAGS.task == 0: val_data = model.get_inputs( FLAGS.inp_dir, FLAGS.dataset_name, 'val', FLAGS.batch_size, FLAGS.image_size, is_training=False) val_inputs = model.preprocess(val_data, FLAGS.step_size) # Note: don't compute loss here reused_model_fn = model.get_model_fn( FLAGS, is_training=False, reuse=True) val_outputs = reused_model_fn(val_inputs) with tf.device(tf.DeviceSpec(device_type='CPU')): if FLAGS.step_size == 1: vis_input_images = val_inputs['images_0'] * 255.0 vis_output_images = val_inputs['images_1'] * 255.0 vis_pred_images = val_outputs['images_1'] * 255.0 vis_pred_masks = (val_outputs['masks_1'] * (-1) + 1) * 255.0 else: rep_times = int(np.ceil(32.0 / float(FLAGS.step_size))) vis_list_1 = [] vis_list_2 = [] vis_list_3 = [] vis_list_4 = [] for j in xrange(rep_times): for k in xrange(FLAGS.step_size): vis_input_image = val_inputs['images_0'][j], vis_output_image = val_inputs['images_%d' % (k + 1)][j] vis_pred_image = val_outputs['images_%d' % (k + 1)][j] vis_pred_mask = val_outputs['masks_%d' % (k + 1)][j] vis_list_1.append(tf.expand_dims(vis_input_image, 0)) vis_list_2.append(tf.expand_dims(vis_output_image, 0)) vis_list_3.append(tf.expand_dims(vis_pred_image, 0)) vis_list_4.append(tf.expand_dims(vis_pred_mask, 0)) vis_list_1 = tf.reshape( tf.stack(vis_list_1), [ rep_times * FLAGS.step_size, FLAGS.image_size, FLAGS.image_size, 3 ]) vis_list_2 = tf.reshape( tf.stack(vis_list_2), [ rep_times * FLAGS.step_size, FLAGS.image_size, FLAGS.image_size, 3 ]) vis_list_3 = tf.reshape( tf.stack(vis_list_3), [ rep_times * FLAGS.step_size, FLAGS.image_size, FLAGS.image_size, 3 ]) vis_list_4 = tf.reshape( tf.stack(vis_list_4), [ rep_times * FLAGS.step_size, FLAGS.image_size, FLAGS.image_size, 1 ]) vis_input_images = vis_list_1 * 255.0 vis_output_images = vis_list_2 * 255.0 vis_pred_images = vis_list_3 * 255.0 vis_pred_masks = (vis_list_4 * (-1) + 1) * 255.0 write_disk_op = model.write_disk_grid( global_step=global_step, summary_freq=FLAGS.save_every, log_dir=save_image_dir, input_images=vis_input_images, output_images=vis_output_images, pred_images=vis_pred_images, pred_masks=vis_pred_masks) with tf.control_dependencies([write_disk_op]): train_op = tf.identity(train_op) ############# ## init_fn ## ############# init_fn = model.get_init_fn(['encoder, ' 'rotator', 'decoder'], FLAGS) ############## ## training ## ############## slim.learning.train( train_op=train_op, logdir=train_dir, init_fn=init_fn, master=FLAGS.master, is_chief=(FLAGS.task == 0), number_of_steps=FLAGS.max_number_of_steps, saver=saver, save_summaries_secs=FLAGS.save_summaries_secs, save_interval_secs=FLAGS.save_interval_secs) if __name__ == '__main__': app.run()