config.py

""" config.py
"""
import argparse
import time

parser = argparse.ArgumentParser("PGGAN")

## general settings.
parser.add_argument(
    "--train_data_root", type=str, default="/home/veesion/nabirds/images/"
)
parser.add_argument("--random_seed", type=int, default=int(time.time()))
parser.add_argument("--n_gpu", type=int, default=1)  # for Multi-GPU training.

## training parameters.
parser.add_argument("--lr", type=float, default=0.001)  # learning rate.
parser.add_argument(
    "--lr_decay", type=float, default=0.9
)  # learning rate decay at every resolution transition.
parser.add_argument(
    "--eps_drift", type=float, default=0.001
)  # coeff for the drift loss.
parser.add_argument(
    "--smoothing", type=float, default=0.997
)  # smoothing factor for smoothed generator.
parser.add_argument("--nc", type=int, default=3)  # number of input channel.
parser.add_argument("--nz", type=int, default=512)  # input dimension of noise.
parser.add_argument(
    "--ngf", type=int, default=512
)  # feature dimension of final layer of generator.
parser.add_argument(
    "--ndf", type=int, default=512
)  # feature dimension of first layer of discriminator.
parser.add_argument(
    "--TICK", type=int, default=1000
)  # 1 tick = 1000 images = (1000/batch_size) iter.
parser.add_argument("--max_resl", type=int, default=8)  # 10-->1024, 9-->512, 8-->256
parser.add_argument("--trns_tick", type=int, default=200)  # transition tick
parser.add_argument("--stab_tick", type=int, default=500)  # stabilization tick
parser.add_argument("--resume", type=int, default=0)  # stabilization tick


## network structure.
parser.add_argument(
    "--flag_wn", type=bool, default=True
)  # use of equalized-learning rate.
parser.add_argument(
    "--flag_bn", type=bool, default=False
)  # use of batch-normalization. (not recommended)
parser.add_argument(
    "--flag_pixelwise", type=bool, default=True
)  # use of pixelwise normalization for generator.
parser.add_argument(
    "--flag_gdrop", type=bool, default=True
)  # use of generalized dropout layer for discriminator.
parser.add_argument(
    "--flag_leaky", type=bool, default=True
)  # use of leaky relu instead of relu.
parser.add_argument(
    "--flag_tanh", type=bool, default=False
)  # use of tanh at the end of the generator.
parser.add_argument(
    "--flag_sigmoid", type=bool, default=False
)  # use of sigmoid at the end of the discriminator.
parser.add_argument(
    "--flag_add_noise", type=bool, default=True
)  # add noise to the real image(x)
parser.add_argument(
    "--flag_norm_latent", type=bool, default=False
)  # pixelwise normalization of latent vector (z)
parser.add_argument("--flag_add_drift", type=bool, default=True)  # add drift loss


## optimizer setting.
parser.add_argument("--optimizer", type=str, default="adam")  # optimizer type.
parser.add_argument("--beta1", type=float, default=0.0)  # beta1 for adam.
parser.add_argument("--beta2", type=float, default=0.99)  # beta2 for adam.


## display and save setting.
parser.add_argument(
    "--use_tb", type=bool, default=True
)  # enable tensorboard visualization
parser.add_argument(
    "--save_img_every", type=int, default=20
)  # save images every specified iteration.
parser.add_argument(
    "--display_tb_every", type=int, default=5
)  # display progress every specified iteration.


## parse and save config.
config, _ = parser.parse_known_args()