import argparse


def get_arguments():
    parser = argparse.ArgumentParser()

    parser.add_argument('--not_cuda', action='store_true', help='disables cuda', default=0)
    parser.add_argument('--manualSeed', type=int, help='manual seed')

    parser.add_argument('--nfc', type=int, help='number of filters per conv layer', default=32)
    parser.add_argument('--ker_size', type=int, help='kernel size', default=3)
    parser.add_argument('--num_layer', type=int, help='number of layers per stage', default=3)
    parser.add_argument('--padd_size', type=int, help='net pad size', default=0)

    parser.add_argument('--nc_im', type=int, help='image # channels', default=1)
    parser.add_argument('--noise_amp', type=float, help='additive noise cont weight', default=0.1)
    parser.add_argument('--min_size', type=int, help='image minimal size at the coarser scale', default=25)
    parser.add_argument('--max_size', type=int, help='image minimal size at the coarser scale', default=500)
    parser.add_argument('--train_depth', type=int, help='how many layers are trained if growing', default=3)
    parser.add_argument('--start_scale', type=int, help='at which stage to start training', default=0)
    parser.add_argument('--niter', type=int, default=2000, help='number of epochs to train per scale')
    parser.add_argument('--gamma', type=float, help='scheduler gamma', default=0.1)
    parser.add_argument('--lr_g', type=float, default=0.0005, help='learning rate, default=0.0005')
    parser.add_argument('--lr_d', type=float, default=0.0005, help='learning rate, default=0.0005')
    parser.add_argument('--beta1', type=float, default=0.5, help='beta1 for adam. default=0.5')
    parser.add_argument('--Gsteps', type=int, help='Generator inner steps', default=1)
    parser.add_argument('--Dsteps', type=int, help='Discriminator inner steps', default=1)
    parser.add_argument('--lambda_grad', type=float, help='gradient penalty weight', default=0.1)
    parser.add_argument('--alpha', type=float, help='reconstruction loss weight', default=10)
    parser.add_argument('--activation', default='lrelu', help="activation function {lrelu, prelu, elu, selu}")
    parser.add_argument('--lrelu_alpha', type=float, help='alpha for leaky relu', default=0.05)
    parser.add_argument('--batch_norm', action='store_true', help='use batch norm in generator', default=1)

    return parser
ac4017b908887418217799a4f147dd574a3be79b
<mask token>

<mask token>


def get_arguments():
    parser = argparse.ArgumentParser()
    parser.add_argument('--not_cuda', action='store_true', help=
        'disables cuda', default=0)
    parser.add_argument('--manualSeed', type=int, help='manual seed')
    parser.add_argument('--nfc', type=int, help=
        'number of filters per conv layer', default=32)
    parser.add_argument('--ker_size', type=int, help='kernel size', default=3)
    parser.add_argument('--num_layer', type=int, help=
        'number of layers per stage', default=3)
    parser.add_argument('--padd_size', type=int, help='net pad size', default=0
        )
    parser.add_argument('--nc_im', type=int, help='image # channels', default=1
        )
    parser.add_argument('--noise_amp', type=float, help=
        'additive noise cont weight', default=0.1)
    parser.add_argument('--min_size', type=int, help=
        'image minimal size at the coarser scale', default=25)
    parser.add_argument('--max_size', type=int, help=
        'image minimal size at the coarser scale', default=500)
    parser.add_argument('--train_depth', type=int, help=
        'how many layers are trained if growing', default=3)
    parser.add_argument('--start_scale', type=int, help=
        'at which stage to start training', default=0)
    parser.add_argument('--niter', type=int, default=2000, help=
        'number of epochs to train per scale')
    parser.add_argument('--gamma', type=float, help='scheduler gamma',
        default=0.1)
    parser.add_argument('--lr_g', type=float, default=0.0005, help=
        'learning rate, default=0.0005')
    parser.add_argument('--lr_d', type=float, default=0.0005, help=
        'learning rate, default=0.0005')
    parser.add_argument('--beta1', type=float, default=0.5, help=
        'beta1 for adam. default=0.5')
    parser.add_argument('--Gsteps', type=int, help='Generator inner steps',
        default=1)
    parser.add_argument('--Dsteps', type=int, help=
        'Discriminator inner steps', default=1)
    parser.add_argument('--lambda_grad', type=float, help=
        'gradient penalty weight', default=0.1)
    parser.add_argument('--alpha', type=float, help=
        'reconstruction loss weight', default=10)
    parser.add_argument('--activation', default='lrelu', help=
        'activation function {lrelu, prelu, elu, selu}')
    parser.add_argument('--lrelu_alpha', type=float, help=
        'alpha for leaky relu', default=0.05)
    parser.add_argument('--batch_norm', action='store_true', help=
        'use batch norm in generator', default=1)
    return parser

import argparse


def get_arguments():
    parser = argparse.ArgumentParser()
    parser.add_argument('--not_cuda', action='store_true', help=
        'disables cuda', default=0)
    parser.add_argument('--manualSeed', type=int, help='manual seed')
    parser.add_argument('--nfc', type=int, help=
        'number of filters per conv layer', default=32)
    parser.add_argument('--ker_size', type=int, help='kernel size', default=3)
    parser.add_argument('--num_layer', type=int, help=
        'number of layers per stage', default=3)
    parser.add_argument('--padd_size', type=int, help='net pad size', default=0
        )
    parser.add_argument('--nc_im', type=int, help='image # channels', default=1
        )
    parser.add_argument('--noise_amp', type=float, help=
        'additive noise cont weight', default=0.1)
    parser.add_argument('--min_size', type=int, help=
        'image minimal size at the coarser scale', default=25)
    parser.add_argument('--max_size', type=int, help=
        'image minimal size at the coarser scale', default=500)
    parser.add_argument('--train_depth', type=int, help=
        'how many layers are trained if growing', default=3)
    parser.add_argument('--start_scale', type=int, help=
        'at which stage to start training', default=0)
    parser.add_argument('--niter', type=int, default=2000, help=
        'number of epochs to train per scale')
    parser.add_argument('--gamma', type=float, help='scheduler gamma',
        default=0.1)
    parser.add_argument('--lr_g', type=float, default=0.0005, help=
        'learning rate, default=0.0005')
    parser.add_argument('--lr_d', type=float, default=0.0005, help=
        'learning rate, default=0.0005')
    parser.add_argument('--beta1', type=float, default=0.5, help=
        'beta1 for adam. default=0.5')
    parser.add_argument('--Gsteps', type=int, help='Generator inner steps',
        default=1)
    parser.add_argument('--Dsteps', type=int, help=
        'Discriminator inner steps', default=1)
    parser.add_argument('--lambda_grad', type=float, help=
        'gradient penalty weight', default=0.1)
    parser.add_argument('--alpha', type=float, help=
        'reconstruction loss weight', default=10)
    parser.add_argument('--activation', default='lrelu', help=
        'activation function {lrelu, prelu, elu, selu}')
    parser.add_argument('--lrelu_alpha', type=float, help=
        'alpha for leaky relu', default=0.05)
    parser.add_argument('--batch_norm', action='store_true', help=
        'use batch norm in generator', default=1)
    return parser

import argparse


def get_arguments():
    parser = argparse.ArgumentParser()

    parser.add_argument('--not_cuda', action='store_true', help='disables cuda', default=0)
    parser.add_argument('--manualSeed', type=int, help='manual seed')

    parser.add_argument('--nfc', type=int, help='number of filters per conv layer', default=32)
    parser.add_argument('--ker_size', type=int, help='kernel size', default=3)
    parser.add_argument('--num_layer', type=int, help='number of layers per stage', default=3)
    parser.add_argument('--padd_size', type=int, help='net pad size', default=0)

    parser.add_argument('--nc_im', type=int, help='image # channels', default=1)
    parser.add_argument('--noise_amp', type=float, help='additive noise cont weight', default=0.1)
    parser.add_argument('--min_size', type=int, help='image minimal size at the coarser scale', default=25)
    parser.add_argument('--max_size', type=int, help='image minimal size at the coarser scale', default=500)
    parser.add_argument('--train_depth', type=int, help='how many layers are trained if growing', default=3)
    parser.add_argument('--start_scale', type=int, help='at which stage to start training', default=0)
    parser.add_argument('--niter', type=int, default=2000, help='number of epochs to train per scale')
    parser.add_argument('--gamma', type=float, help='scheduler gamma', default=0.1)
    parser.add_argument('--lr_g', type=float, default=0.0005, help='learning rate, default=0.0005')
    parser.add_argument('--lr_d', type=float, default=0.0005, help='learning rate, default=0.0005')
    parser.add_argument('--beta1', type=float, default=0.5, help='beta1 for adam. default=0.5')
    parser.add_argument('--Gsteps', type=int, help='Generator inner steps', default=1)
    parser.add_argument('--Dsteps', type=int, help='Discriminator inner steps', default=1)
    parser.add_argument('--lambda_grad', type=float, help='gradient penalty weight', default=0.1)
    parser.add_argument('--alpha', type=float, help='reconstruction loss weight', default=10)
    parser.add_argument('--activation', default='lrelu', help="activation function {lrelu, prelu, elu, selu}")
    parser.add_argument('--lrelu_alpha', type=float, help='alpha for leaky relu', default=0.05)
    parser.add_argument('--batch_norm', action='store_true', help='use batch norm in generator', default=1)

    return parser