# = param =
# ==============================================================================
# command line
py.arg('--dataset', default='fashion_mnist', choices=['cifar10', 'fashion_mnist', 'mnist', 'celeba', 'anime', 'custom'])
py.arg('--batch_size', type=int, default=64)
py.arg('--epochs', type=int, default=25)
py.arg('--lr', type=float, default=0.0002)
py.arg('--beta_1', type=float, default=0.5)
py.arg('--n_d', type=int, default=1) # # d updates per g update
py.arg('--z_dim', type=int, default=128)
py.arg('--adversarial_loss_mode', default='gan', choices=['gan', 'hinge_v1', 'hinge_v2', 'lsgan', 'wgan'])
py.arg('--gradient_penalty_mode', default='none', choices=['none', 'dragan', 'wgan-gp'])
py.arg('--gradient_penalty_weight', type=float, default=10.0)
py.arg('--experiment_name', default='none')
args = py.args()
# output_dir
if args.experiment_name == 'none':
args.experiment_name = '%s_%s' % (args.dataset, args.adversarial_loss_mode)
if args.gradient_penalty_mode != 'none':
args.experiment_name += '_%s' % args.gradient_penalty_mode
output_dir = py.join('output', '%s_BN%d_DPG%d' % (args.experiment_name, args.batch_size, args.n_d ) )
py.mkdir(output_dir)
# save settings
py.args_to_yaml(py.join(output_dir, 'settings.yml'), args)
# ==============================================================================
# = data and model =
# ==============================================================================
py.arg(
'--img_dir',
default=
'./data/img_celeba/aligned/align_size(572,572)_move(0.250,0.000)_face_factor(0.450)_jpg/data'
)
py.arg('--test_label_path', default='./data/img_celeba/test_label.txt')
py.arg('--test_att_names',
choices=data.ATT_ID.keys(),
nargs='+',
default=['Bangs', 'Mustache'])
py.arg('--test_ints', type=float, nargs='+', default=2)
py.arg('--experiment_name', default='default')
args_ = py.args()
# output_dir
output_dir = py.join('output', args_.experiment_name)
# save settings
args = py.args_from_yaml(py.join(output_dir, 'settings.yml'))
args.__dict__.update(args_.__dict__)
# others
n_atts = len(args.att_names)
if not isinstance(args.test_ints, list):
args.test_ints = [args.test_ints] * len(args.test_att_names)
elif len(args.test_ints) == 1:
args.test_ints = args.test_ints * len(args.test_att_names)
import imlib as im
import numpy as np
import pylib as py
import tensorflow as tf
import tf2lib as tl
import data
import module
# ==============================================================================
# = param =
# ==============================================================================
py.arg('--experiment_dir')
py.arg('--batch_size', type=int, default=32)
test_args = py.args()
args = py.args_from_yaml(py.join(test_args.experiment_dir, 'settings.yml'))
args.__dict__.update(test_args.__dict__)
# ==============================================================================
# = test =
# ==============================================================================
# data
A_img_paths_test = py.glob(py.join(args.datasets_dir, args.dataset, 'testA'),
'*.jpg')
B_img_paths_test = py.glob(py.join(args.datasets_dir, args.dataset, 'testB'),
'*.jpg')
A_dataset_test = data.make_dataset(A_img_paths_test,
args.batch_size,
args.load_size,
