utility.py

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import distutils.util
import six
import sys
import numpy as np
import paddle.fluid as fluid


def print_arguments(args):
    """Print argparse's arguments.

    Usage:

    .. code-block:: python

        parser = argparse.ArgumentParser()
        parser.add_argument("name", default="Jonh", type=str, help="User name.")
        args = parser.parse_args()
        print_arguments(args)

    :param args: Input argparse.Namespace for printing.
    :type args: argparse.Namespace
    """
    print("-----------  Configuration Arguments -----------")
    for arg, value in sorted(six.iteritems(vars(args))):
        print("%s: %s" % (arg, value))
    print("------------------------------------------------")


def add_arguments(argname, type, default, help, argparser, **kwargs):
    """Add argparse's argument.

    Usage:

    .. code-block:: python

        parser = argparse.ArgumentParser()
        add_argument("name", str, "Jonh", "User name.", parser)
        args = parser.parse_args()
    """
    type = distutils.util.strtobool if type == bool else type
    argparser.add_argument(
        "--" + argname,
        default=default,
        type=type,
        help=help + ' Default: %(default)s.',
        **kwargs)

def check_cuda(use_cuda, err = \
    "\nYou can not set use_cuda = True in the model because you are using paddlepaddle-cpu.\n \
    Please: 1. Install paddlepaddle-gpu to run your models on GPU or 2. Set use_cuda = False to run models on CPU.\n"
                                                                                                                     ):
    try:
        if use_cuda == True and fluid.is_compiled_with_cuda() == False:
            print(err)
            sys.exit(1)
    except Exception as e:
        pass


class AvgrageMeter(object):
    def __init__(self):
        self.reset()

    def reset(self):
        self.avg = 0
        self.sum = 0
        self.cnt = 0

    def update(self, val, n=1):
        self.sum += val * n
        self.cnt += n
        self.avg = self.sum / self.cnt


def get_parameters(all_params, prefix):
    parameters_name = []
    parameters_val = []
    for param in all_params:
        if param.name.startswith(prefix) and param.trainable:
            parameters_name.append(param.name)
            parameters_val.append(param)
    return parameters_name, parameters_val


def count_parameters_in_MB(all_params, prefix='model'):
    parameters_number = 0
    for param in all_params:
        if param.name.startswith(
                prefix) and param.trainable and 'aux' not in param.name:
            parameters_number += np.prod(param.shape)
    return parameters_number / 1e6