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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.
# ==============================================================================
"""Eval Cross Convolutional Model."""
import io
import os
import sys
import time
import numpy as np
from six.moves import xrange
import tensorflow as tf
import model as cross_conv_model
import reader
FLAGS = tf.flags.FLAGS
tf.flags.DEFINE_string('log_root', '/tmp/moving_obj', 'The root dir of output.')
tf.flags.DEFINE_string('data_filepattern',
'est',
'training data file pattern.')
tf.flags.DEFINE_integer('batch_size', 1, 'Batch size.')
tf.flags.DEFINE_integer('image_size', 64, 'Image height and width.')
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_integer('eval_batch_count', 100,
'Average the result this number of examples.')
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 _Eval():
params = dict()
params['batch_size'] = FLAGS.batch_size
params['seq_len'] = FLAGS.sequence_length
params['image_size'] = FLAGS.image_size
params['is_training'] = False
params['norm_scale'] = FLAGS.norm_scale
params['scale'] = FLAGS.scale
params['l2_loss'] = FLAGS.l2_loss
params['reconstr_loss'] = FLAGS.reconstr_loss
params['kl_loss'] = FLAGS.kl_loss
eval_dir = os.path.join(FLAGS.log_root, 'eval')
images = reader.ReadInput(
FLAGS.data_filepattern, shuffle=False, 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()
summary_writer = tf.summary.FileWriter(eval_dir)
saver = tf.train.Saver()
sess = tf.Session('', config=tf.ConfigProto(allow_soft_placement=True))
tf.train.start_queue_runners(sess)
while True:
time.sleep(60)
try:
ckpt_state = tf.train.get_checkpoint_state(FLAGS.log_root)
except tf.errors.OutOfRangeError as e:
sys.stderr.write('Cannot restore checkpoint: %s\n' % e)
continue
if not (ckpt_state and ckpt_state.model_checkpoint_path):
sys.stderr.write('No model to eval yet at %s\n' % FLAGS.log_root)
continue
sys.stderr.write('Loading checkpoint %s\n' %
ckpt_state.model_checkpoint_path)
saver.restore(sess, ckpt_state.model_checkpoint_path)
# Use the empirical distribution of z from training set.
if not tf.gfile.Exists(os.path.join(FLAGS.log_root, 'z_mean.npy')):
sys.stderr.write('No z at %s\n' % FLAGS.log_root)
continue
with tf.gfile.Open(os.path.join(FLAGS.log_root, 'z_mean.npy')) as f:
sample_z_mean = np.load(io.BytesIO(f.read()))
with tf.gfile.Open(
os.path.join(FLAGS.log_root, 'z_stddev_log.npy')) as f:
sample_z_stddev_log = np.load(io.BytesIO(f.read()))
total_loss = 0.0
for _ in xrange(FLAGS.eval_batch_count):
loss_val, total_steps, summaries = sess.run(
[model.loss, model.global_step, model.summary_op],
feed_dict={model.z_mean: sample_z_mean,
model.z_stddev_log: sample_z_stddev_log})
total_loss += loss_val
summary_writer.add_summary(summaries, total_steps)
sys.stderr.write('steps: %d, loss: %f\n' %
(total_steps, total_loss / FLAGS.eval_batch_count))
def main(_):
_Eval()
if __name__ == '__main__':
tf.app.run()
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