train.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-

"""Trains, evaluates and saves the TensorDetect model."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import json
import logging
import os
import sys

import collections

# configure logging
if 'TV_IS_DEV' in os.environ and os.environ['TV_IS_DEV']:
    logging.basicConfig(format='%(asctime)s %(levelname)s %(message)s',
                        level=logging.INFO,
                        stream=sys.stdout)
else:
    logging.basicConfig(format='%(asctime)s %(levelname)s %(message)s',
                        level=logging.INFO,
                        stream=sys.stdout)

# https://github.com/tensorflow/tensorflow/issues/2034#issuecomment-220820070
import numpy as np
import tensorflow as tf

flags = tf.app.flags
FLAGS = flags.FLAGS

sys.path.insert(1, 'incl')

import tensorvision.train as train
import tensorvision.utils as utils

flags.DEFINE_string('name', None,
                    'Append a name Tag to run.')

flags.DEFINE_string('project', None,
                    'Append a name Tag to run.')

flags.DEFINE_string('hypes', None,
                    'File storing model parameters.')

flags.DEFINE_string('mod', None,
                    'Modifier for model parameters.')

tf.app.flags.DEFINE_boolean(
    'save', True, ('Whether to save the run. In case --nosave (default) '
                   'output will be saved to the folder TV_DIR_RUNS/debug, '
                   'hence it will get overwritten by further runs.'))


def dict_merge(dct, merge_dct):
    """ Recursive dict merge. Inspired by :meth:``dict.update()``, instead of
    updating only top-level keys, dict_merge recurses down into dicts nested
    to an arbitrary depth, updating keys. The ``merge_dct`` is merged into
    ``dct``.
    :param dct: dict onto which the merge is executed
    :param merge_dct: dct merged into dct
    :return: None
    """
    for k, v in merge_dct.iteritems():
        if (k in dct and isinstance(dct[k], dict) and
                isinstance(merge_dct[k], collections.Mapping)):
            dict_merge(dct[k], merge_dct[k])
        else:
            dct[k] = merge_dct[k]


def main(_):
    utils.set_gpus_to_use()

    try:
        import tensorvision.train
        import tensorflow_fcn.utils
    except ImportError:
        logging.error("Could not import the submodules.")
        logging.error("Please execute:"
                      "'git submodule update --init --recursive'")
        exit(1)

    if tf.app.flags.FLAGS.hypes is None:
        logging.error("No hype file is given.")
        logging.info("Usage: python train.py --hypes hypes/KittiClass.json")
        exit(1)

    with open(tf.app.flags.FLAGS.hypes, 'r') as f:
        logging.info("f: %s", f)
        hypes = json.load(f)
    utils.load_plugins()

    if tf.app.flags.FLAGS.mod is not None:
        import ast
        mod_dict = ast.literal_eval(tf.app.flags.FLAGS.mod)
        dict_merge(hypes, mod_dict)

    if 'TV_DIR_RUNS' in os.environ:
        os.environ['TV_DIR_RUNS'] = os.path.join(os.environ['TV_DIR_RUNS'],
                                                 'KittiClass')
    utils.set_dirs(hypes, tf.app.flags.FLAGS.hypes)

    utils._add_paths_to_sys(hypes)

    logging.info("Initialize training folder")
    train.initialize_training_folder(hypes)
    train.maybe_download_and_extract(hypes)
    logging.info("Start training")
    train.do_training(hypes)


if __name__ == '__main__':
    tf.app.run()