# = 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,