/
config.py
executable file
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/
config.py
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#-*- coding: utf-8 -*-
import argparse
def str2bool(v):
return v.lower() in ('true', '1')
arg_lists = []
parser = argparse.ArgumentParser()
def add_argument_group(name):
arg = parser.add_argument_group(name)
arg_lists.append(arg)
return arg
# Network
net_arg = add_argument_group('Network')
# net_arg.add_argument('--input_scale_size', type=int, default=64,
# help='input image will be resized with the given value as width and height')
net_arg.add_argument('--img_H', type=int, default=128,
help='input image height')
net_arg.add_argument('--img_W', type=int, default=64,
help='input image width')
net_arg.add_argument('--conv_hidden_num', type=int, default=128,
choices=[64, 128],help='n in the paper')
# net_arg.add_argument('--z_num', type=int, default=64, choices=[64, 128])
net_arg.add_argument('--z_num', type=int, default=64)
# net_arg.add_argument('--noise_dim', type=int, default=10, choices=[10, 128])
# Data
data_arg = add_argument_group('Data')
data_arg.add_argument('--dataset', type=str, default='CelebA')
data_arg.add_argument('--split', type=str, default='train')
data_arg.add_argument('--batch_size', type=int, default=16)
data_arg.add_argument('--grayscale', type=str2bool, default=False)
data_arg.add_argument('--num_worker', type=int, default=4)
# Training / test parameters
train_arg = add_argument_group('Training')
train_arg.add_argument('--is_train', type=str2bool, default=True)
train_arg.add_argument('--test_one_by_one', type=str2bool, default=False)
train_arg.add_argument('--optimizer', type=str, default='adam')
train_arg.add_argument('--start_step', type=int, default=0)
data_arg.add_argument('--ckpt_path', type=str, default=None)
data_arg.add_argument('--pretrained_path', type=str, default=None)
train_arg.add_argument('--max_step', type=int, default=500000)
# train_arg.add_argument('--lr_update_step', type=int, default=100000, choices=[100000, 75000])
train_arg.add_argument('--lr_update_step', type=int, default=100000, choices=[50000, 100000])
train_arg.add_argument('--d_lr', type=float, default=0.00008)
train_arg.add_argument('--g_lr', type=float, default=0.00008)
train_arg.add_argument('--beta1', type=float, default=0.5)
train_arg.add_argument('--beta2', type=float, default=0.999)
train_arg.add_argument('--gamma', type=float, default=0.5)
train_arg.add_argument('--lambda_k', type=float, default=0.001)
train_arg.add_argument('--use_gpu', type=str2bool, default=True)
train_arg.add_argument('--gpu', type=int, default=-1)
train_arg.add_argument('--model', type=int, default=0)
train_arg.add_argument('--D_arch', type=str, default='DCGAN') # 'DCGAN' 'noNormDCGAN' 'MultiplicativeDCGAN' 'tanhNonlinearDCGAN' 'resnet101'
# Misc
misc_arg = add_argument_group('Misc')
misc_arg.add_argument('--load_path', type=str, default='')
misc_arg.add_argument('--log_step', type=int, default=200)
misc_arg.add_argument('--save_model_secs', type=int, default=1000)
misc_arg.add_argument('--num_log_samples', type=int, default=3)
misc_arg.add_argument('--log_level', type=str, default='INFO', choices=['INFO', 'DEBUG', 'WARN'])
misc_arg.add_argument('--log_dir', type=str, default='logs')
misc_arg.add_argument('--model_dir', type=str, default=None)
misc_arg.add_argument('--data_dir', type=str, default='data')
misc_arg.add_argument('--test_data_path', type=str, default=None,
help='directory with images which will be used in test sample generation')
misc_arg.add_argument('--sample_per_image', type=int, default=64,
help='# of sample per image during test sample generation')
misc_arg.add_argument('--random_seed', type=int, default=123)
def get_config():
config, unparsed = parser.parse_known_args()
if config.use_gpu:
data_format = 'NCHW'
else:
data_format = 'NHWC'
setattr(config, 'data_format', data_format)
return config, unparsed