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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() | |