iGAN_main.py

import sys
import argparse
import qdarkstyle
from PyQt4.QtGui import QApplication, QIcon
from PyQt4.QtCore import Qt
from ui import gui_design
from pydoc import locate

import model_def
def parse_args():
    parser = argparse.ArgumentParser(description='iGAN: Interactive Visual Synthesis Powered by GAN')
    parser.add_argument('--model_name', dest='model_name', help='the model name', default='outdoor_64', type=str)
    parser.add_argument('--model_type', dest='model_type', help='the generative models and its deep learning framework', default='dcgan_theano', type=str)
    parser.add_argument('--framework', dest='framework', help='deep learning framework', default='theano')
    parser.add_argument('--win_size', dest='win_size', help='the size of the main window', type=int, default=384)
    parser.add_argument('--batch_size', dest='batch_size', help='the number of random initializations', type=int, default=64)
    parser.add_argument('--n_iters', dest='n_iters', help='the number of total optimization iterations', type=int, default=40)
    parser.add_argument('--top_k', dest='top_k', help='the number of the thumbnail results being displayed', type=int, default=16)
    parser.add_argument('--morph_steps', dest='morph_steps', help='the number of intermediate frames of morphing sequence', type=int, default=16)
    parser.add_argument('--model_file', dest='model_file', help='the file that stores the generative model', type=str, default=None)
    parser.add_argument('--d_weight', dest='d_weight', help='captures the visual realism based on GAN discriminator', type=float, default=0.0)
    parser.add_argument('--interp', dest='interp', help='the interpolation method (linear or slerp)', type=str, default='linear')

    args = parser.parse_args()
    return args

if __name__ == '__main__':
    args = parse_args()
    if not args.model_file:  #if the model_file is not specified
        args.model_file = './models/%s.%s' % (args.model_name, args.model_type)

    for arg in vars(args):
        print '[%s] =' % arg, getattr(args, arg)

    args.win_size = int(args.win_size / 4) * 4  # make sure the width of the image can be divided by 4
    # initialize model and constrained optimization problem
    model_class = locate('model_def.%s' % args.model_type)
    model_G = model_class.Model(model_name=args.model_name, model_file=args.model_file)
    opt_class = locate('constrained_opt_%s' % args.framework)
    opt_engine = opt_class.Constrained_OPT(model_G, batch_size=args.batch_size, n_iters=args.n_iters, topK=args.top_k, morph_steps=args.morph_steps, interp=args.interp)
    # initialize application
    app = QApplication(sys.argv)
    window = gui_design.GUIDesign(opt_engine, batch_size=args.batch_size,
                                  n_iters=args.n_iters, win_size=args.win_size, topK=args.top_k)
    app.setStyleSheet(qdarkstyle.load_stylesheet(pyside=False))  # comment this if you do not like dark stylesheet
    app.setWindowIcon(QIcon('pics/logo.png'))
    window.setWindowTitle('Interactive GAN')
    window.setWindowFlags(window.windowFlags() & ~Qt.WindowMaximizeButtonHint)
    window.show()
    app.exec_()