utils.py

from bunch import Bunch
import json
import argparse
import os
import tensorflow as tf


def parse_args():
    """
    Parse the arguments of the program
    :return: (config_args)
    :rtype: tuple
    """
    # Create a parser
    parser = argparse.ArgumentParser(description="ShuffleNet Tensorflow implementation")
    parser.add_argument('--version', action='version', version='%(prog)s 0.0.1')
    parser.add_argument('--config', default=None, type=str, help='Configuration file')

    # Parse the arguments
    args = parser.parse_args()

    # parse the configurations from the config json file provided
    with open(args.config, 'r') as config_file:
        config_args_dict = json.load(config_file)
    # convert the dictionary to a namespace using bunch lib
    config_args = Bunch(config_args_dict)

    print(config_args)
    return config_args


def create_experiment_dirs(exp_dir):
    """
    Create Directories of a regular tensorflow experiment directory
    :param exp_dir:
    :return summary_dir, checkpoint_dir:
    """
    experiment_dir = os.path.realpath(os.path.join(os.path.dirname(__file__))) + "/experiments/" + exp_dir + "/"
    summary_dir = experiment_dir + 'summaries/'
    checkpoint_dir = experiment_dir + 'checkpoints/'
    # output_dir = experiment_dir + 'output/'
    # test_dir = experiment_dir + 'test/'
    # dirs = [summary_dir, checkpoint_dir, output_dir, test_dir]
    dirs = [summary_dir, checkpoint_dir]
    try:
        for dir_ in dirs:
            if not os.path.exists(dir_):
                os.makedirs(dir_)
        print("Experiment directories created!")
        # return experiment_dir, summary_dir, checkpoint_dir, output_dir, test_dir
        return experiment_dir, summary_dir, checkpoint_dir
    except Exception as err:
        print("Creating directories error: {0}".format(err))
        exit(-1)


def calculate_flops():
    # Print to stdout an analysis of the number of floating point operations in the
    # model broken down by individual operations.
    tf.profiler.profile(
        tf.get_default_graph(),
        options=tf.profiler.ProfileOptionBuilder.float_operation(), cmd='scope')


def show_parameters():
    tf.profiler.profile(
        tf.get_default_graph(),
        options=tf.profiler.ProfileOptionBuilder.trainable_variables_parameter(), cmd='scope')


def load_obj(filename):
    import pickle
    with open(filename, 'rb') as file:
        return pickle.load(file)