config, reuse=True) if loss_type == 'g': return losses['g_loss'] elif loss_type == 'd': return losses['d_loss'] else: raise ValueError('loss type is not supported.') if __name__ == "__main__": config = ng.Config('inpaint.yml') if config.GPU_ID != -1: ng.set_gpus(config.GPU_ID) else: ng.get_gpus(config.NUM_GPUS) # training data with open(config.DATA_FLIST[config.DATASET][0]) as f: fnames = f.read().splitlines() data = ng.data.DataFromFNames(fnames, config.IMG_SHAPES, random_crop=config.RANDOM_CROP) images = data.data_pipeline(config.BATCH_SIZE) # main model model = InpaintCAModel() g_vars, d_vars, losses = model.build_graph_with_losses(images, config=config) # validation images if config.VAL: with open(config.DATA_FLIST[config.DATASET][1]) as f: val_fnames = f.read().splitlines()
default=None, type=str, help='filename list to use as dataset') parser.add_argument('--height', default=256, type=int, help='height of images in data flist') parser.add_argument('--width', default=256, type=int, help='width of images in data flist') parser.add_argument('--blend', action='store_true') if __name__ == "__main__": ng.get_gpus(1, dedicated=False) args = parser.parse_args() config = ng.Config(args.config) logger = logging.getLogger() invert_mask = str2bool(args.invert_mask) model = InpaintCAModel() if args.image is not None: image = cv2.imread(args.image) h, w, _ = image.shape assert image.shape == mask.shape else: if args.flist is None: flist = config.DATA_FLIST[config.DATASET][1] shapes = config.IMG_SHAPES else:
help='The height of images should be defined, otherwise batch mode is not' ' supported.') parser.add_argument( '--image_width', default=-1, type=int, help='The width of images should be defined, otherwise batch mode is not' ' supported.') parser.add_argument('--checkpoint_dir', default='', type=str, help='The directory of tensorflow checkpoint.') if __name__ == "__main__": FLAGS = ng.Config('inpaint.yml') ng.get_gpus(1) # os.environ['CUDA_VISIBLE_DEVICES'] ='' args = parser.parse_args() sess_config = tf.ConfigProto() sess_config.gpu_options.allow_growth = True sess = tf.Session(config=sess_config) model = InpaintCAModel() input_image_ph = tf.placeholder(tf.float32, shape=(1, args.image_height, args.image_width * 2, 3)) output = model.build_server_graph(FLAGS, input_image_ph) output = (output + 1.) * 127.5 output = tf.reverse(output, [-1]) output = tf.saturate_cast(output, tf.uint8)
core.seg_limit = 4000000 #// 10 # 보통 이게 더 큼 core.compl_limit = 1000000 #// 10 # segnet_yml = 'segnet/seg48_4[553].yml' # segnet configuration segnet_model_path = 'segnet/seg48_4[553].h5' # saved segnet model complnet_ckpt_dir = 'v2_180923' # saved complnets directory #-------------------------------------------- # for segnet with open(segnet_yml, 'r') as f: config = yaml.load(f) # for complnet complnet = InpaintCAModel('v2') ng.get_gpus(1, False) #TODO: 단 한 번만 호출할 것. #-------------------------------------------- dilate_kernel = core.rect5 if not os.path.isdir('./cleaned'): os.mkdir('./cleaned') app = QtWidgets.QApplication(sys.argv) screen = app.primaryScreen() print('Screen: %s' % screen.name()) size = screen.size() print('Size: %d x %d' % (size.width(), size.height())) rect = screen.availableGeometry() print('Available: %d x %d' % (rect.width(), rect.height())) MainWindow = MyMainScreen() MainWindow.show() #app.exec_()