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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 evaluation plan for the Rotator model."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
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', 24, '')
flags.DEFINE_integer('batch_size', 2, '')
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', 'ptn_proj',
'Name of the model used in naming the TF job. Must be different for each run.')
# Optimization
flags.DEFINE_float('image_weight', 10, '')
flags.DEFINE_float('mask_weight', 1, '')
flags.DEFINE_float('learning_rate', 0.0001, 'Learning rate.')
flags.DEFINE_float('weight_decay', 0.001, '')
flags.DEFINE_float('clip_gradient_norm', 0, '')
# Summary
flags.DEFINE_integer('save_summaries_secs', 15, '')
flags.DEFINE_integer('eval_interval_secs', 60 * 5, '')
# Scheduling
flags.DEFINE_string('master', '', '')
FLAGS = flags.FLAGS
def main(argv=()):
del argv # Unused.
eval_dir = os.path.join(FLAGS.checkpoint_dir,
FLAGS.model_name, 'train')
log_dir = os.path.join(FLAGS.checkpoint_dir,
FLAGS.model_name, 'eval')
if not os.path.exists(eval_dir):
os.makedirs(eval_dir)
if not os.path.exists(log_dir):
os.makedirs(log_dir)
g = tf.Graph()
if FLAGS.step_size < FLAGS.num_views:
raise ValueError('Impossible step_size, must not be less than num_views.')
g = tf.Graph()
with g.as_default():
##########
## data ##
##########
val_data = model.get_inputs(
FLAGS.inp_dir,
FLAGS.dataset_name,
'val',
FLAGS.batch_size,
FLAGS.image_size,
is_training=False)
inputs = model.preprocess(val_data, FLAGS.step_size)
###########
## model ##
###########
model_fn = model.get_model_fn(FLAGS, is_training=False)
outputs = model_fn(inputs)
#############
## metrics ##
#############
names_to_values, names_to_updates = model.get_metrics(
inputs, outputs, FLAGS)
del names_to_values
################
## evaluation ##
################
num_batches = int(val_data['num_samples'] / FLAGS.batch_size)
slim.evaluation.evaluation_loop(
master=FLAGS.master,
checkpoint_dir=eval_dir,
logdir=log_dir,
num_evals=num_batches,
eval_op=names_to_updates.values(),
eval_interval_secs=FLAGS.eval_interval_secs)
if __name__ == '__main__':
app.run()
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