'resume', False,
'Tries to resume if True. Throws an error if False and any of the log files exist'
' unless F.overwrite is True')
flags.DEFINE_boolean('overwrite', False, '')
flags.DEFINE_string('gpu', '0', 'Id of the gpu to allocate')
flags.DEFINE_boolean('debug', False, 'Adds a lot of summaries if True')
F = flags.FLAGS
os.environ['CUDA_VISIBLE_DEVICES'] = F.gpu
if __name__ == '__main__':
_load_flags(F.model_config, F.data_config)
flags = parse_flags()
assert_all_flags_parsed()
flag_path = os.path.join(F.checkpoint_dir, FLAG_FILE)
restored_flags = json_load(flag_path)
flags.update(restored_flags)
_restore_flags(flags)
print('Processing:', F.checkpoint_dir)
checkpoint_state = tf.train.get_checkpoint_state(F.checkpoint_dir)
if checkpoint_state is None:
print('No checkpoints found in {}'.format(F.checkpoint_dir))
checkpoint_paths = checkpoint_state.all_model_checkpoint_paths
if F.from_itr > 0:
flags.DEFINE_string(
'schedule', '4,6,10',
'Uses a learning rate schedule if True. Schedule = \'4,6,10\' '
'means that F.train_itr will be split in proportions 4/s, 6/s, 10/s,'
'where s = sum(schedule)')
flags.DEFINE_boolean('test_run', False, 'Only a small run if True')
flags.DEFINE_string('gpu', '0', 'Id of the gpu to use for this job.')
flags.DEFINE_boolean('debug', False,
'Adds a lot of tensorboard summaries if True.')
F = flags.FLAGS
os.environ['CUDA_VISIBLE_DEVICES'] = F.gpu
# Parse flags
parse_flags()
F = flags.FLAGS
if F.test_run:
# F.run_name = 'duke_test'
# F.data_config = 'configs/duke_data.py'
# F.data_path = 'pruned_merged225.pickle'
# F.model_config = 'configs/duke_model.py'
# F.glimpse_size = '32x12'
F.run_name = 'mnist_test'
F.data_config = 'configs/small_new_seq_mnist_data.py'
F.model_config = 'configs/mlp_mnist_model.py'
F.seq_len = 2
F.eval_on_train = False