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train_ptn.py
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train_ptn.py
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# 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 Im2vox model."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import numpy as np
import tensorflow as tf
from tensorflow import app
import model_ptn
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('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('vox_size', 32, 'Voxel prediction dimension.')
flags.DEFINE_integer('step_size', 24, 'Steps to take in rotation to fetch viewpoints.')
flags.DEFINE_integer('batch_size', 6, 'Batch size while training.')
flags.DEFINE_float('focal_length', 0.866, 'Focal length parameter used in perspective projection.')
flags.DEFINE_float('focal_range', 1.732, 'Focal length parameter used in perspective projection.')
flags.DEFINE_string('encoder_name', 'ptn_encoder',
'Name of the encoder network being used.')
flags.DEFINE_string('decoder_name', 'ptn_vox_decoder',
'Name of the decoder network being used.')
flags.DEFINE_string('projector_name', 'perspective_projector',
'Name of the projector 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', 'ptn_finetune',
'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('proj_weight', 10, 'Weighting factor for projection loss.')
flags.DEFINE_float('volume_weight', 0, 'Weighting factor for volume loss.')
flags.DEFINE_float('viewpoint_weight', 1, 'Weighting factor for viewpoint 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', 10000, '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.')
# Scheduling
flags.DEFINE_string('master', '', 'The address of the tensorflow master')
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()
###########
## model ##
###########
model = model_ptn.model_PTN(FLAGS)
##########
## data ##
##########
train_data = model.get_inputs(
FLAGS.inp_dir,
FLAGS.dataset_name,
'train',
FLAGS.batch_size,
FLAGS.image_size,
FLAGS.vox_size,
is_training=True)
inputs = model.preprocess(train_data, FLAGS.step_size)
##############
## model_fn ##
##############
model_fn = model.get_model_fn(
is_training=True, reuse=False, run_projection=True)
outputs = model_fn(inputs)
##################
## train_scopes ##
##################
if FLAGS.init_model:
train_scopes = ['decoder']
init_scopes = ['encoder']
else:
train_scopes = ['encoder', 'decoder']
##########
## loss ##
##########
task_loss = model.get_loss(inputs, outputs)
regularization_loss = model.get_regularization_loss(train_scopes)
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, train_scopes)
###########
## saver ##
###########
saver = tf.train.Saver(max_to_keep=np.minimum(5,
FLAGS.worker_replicas + 1))
if FLAGS.task == 0:
params = FLAGS
params.batch_size = params.num_views
params.step_size = 1
model.set_params(params)
val_data = model.get_inputs(
params.inp_dir,
params.dataset_name,
'val',
params.batch_size,
params.image_size,
params.vox_size,
is_training=False)
val_inputs = model.preprocess(val_data, params.step_size)
# Note: don't compute loss here
reused_model_fn = model.get_model_fn(is_training=False, reuse=True)
val_outputs = reused_model_fn(val_inputs)
with tf.device(tf.DeviceSpec(device_type='CPU')):
vis_input_images = val_inputs['images_1'] * 255.0
vis_gt_projs = (val_outputs['masks_1'] * (-1) + 1) * 255.0
vis_pred_projs = (val_outputs['projs_1'] * (-1) + 1) * 255.0
vis_gt_projs = tf.concat([vis_gt_projs] * 3, axis=3)
vis_pred_projs = tf.concat([vis_pred_projs] * 3, axis=3)
# rescale
new_size = [FLAGS.image_size] * 2
vis_gt_projs = tf.image.resize_nearest_neighbor(
vis_gt_projs, new_size)
vis_pred_projs = tf.image.resize_nearest_neighbor(
vis_pred_projs, new_size)
# flip
# vis_gt_projs = utils.image_flipud(vis_gt_projs)
# vis_pred_projs = utils.image_flipud(vis_pred_projs)
# vis_gt_projs is of shape [batch, height, width, channels]
write_disk_op = model.write_disk_grid(
global_step=global_step,
log_dir=save_image_dir,
input_images=vis_input_images,
gt_projs=vis_gt_projs,
pred_projs=vis_pred_projs,
input_voxels=val_inputs['voxels'],
output_voxels=val_outputs['voxels_1'])
with tf.control_dependencies([write_disk_op]):
train_op = tf.identity(train_op)
#############
## init_fn ##
#############
if FLAGS.init_model:
init_fn = model.get_init_fn(init_scopes)
else:
init_fn = None
##############
## 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()