#copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve. # #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. from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import sys import six import argparse import functools import distutils.util import trainer def print_arguments(args): ''' Print argparse's argument 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 base_parse_args(parser): add_arg = functools.partial(add_arguments, argparser=parser) # yapf: disable add_arg('model_net', str, "CGAN", "The model used.") add_arg('dataset', str, "mnist", "The dataset used.") add_arg('data_dir', str, "./data", "The dataset root directory") add_arg('train_list', str, None, "The train list file name") add_arg('test_list', str, None, "The test list file name") add_arg('batch_size', int, 1, "Minibatch size.") add_arg('epoch', int, 200, "The number of epoch to be trained.") add_arg('g_base_dims', int, 64, "Base channels in generator") add_arg('d_base_dims', int, 64, "Base channels in discriminator") add_arg('load_size', int, 286, "the image size when load the image") add_arg('crop_type', str, 'Centor', "the crop type, choose = ['Centor', 'Random']") add_arg('crop_size', int, 256, "crop size when preprocess image") add_arg('save_checkpoints', bool, True, "Whether to save checkpoints.") add_arg('run_test', bool, True, "Whether to run test.") add_arg('use_gpu', bool, True, "Whether to use GPU to train.") add_arg('profile', bool, False, "Whether to profile.") add_arg('dropout', bool, False, "Whether to use drouput.") add_arg('drop_last', bool, False, "Whether to drop the last images that cannot form a batch") add_arg('shuffle', bool, True, "Whether to shuffle data") add_arg('output', str, "./output", "The directory the model and the test result to be saved to.") add_arg('init_model', str, None, "The init model file of directory.") add_arg('gan_mode', str, "vanilla", "The init model file of directory.") add_arg('norm_type', str, "batch_norm", "Which normalization to used") add_arg('learning_rate', float, 0.0002, "the initialize learning rate") add_arg('lambda_L1', float, 100.0, "the initialize lambda parameter for L1 loss") add_arg('num_generator_time', int, 1, "the generator run times in training each epoch") add_arg('num_discriminator_time', int, 1, "the discriminator run times in training each epoch") add_arg('print_freq', int, 10, "the frequency of print loss") # yapf: enable return parser def parse_args(): parser = argparse.ArgumentParser(description=__doc__) parser = base_parse_args(parser) cfg, _ = parser.parse_known_args() model_name = cfg.model_net model_cfg = trainer.get_special_cfg(model_name) parser = model_cfg(parser) args = parser.parse_args() return args