def build_SAE_network(config): nb_layers = 5 autoencoder, encoder, decoder = utilModelREDNet.build_REDNet( nb_layers, config.window, config.nb_filters, config.kernel, config.dropout, config.stride, config.every) autoencoder.compile(optimizer='adam', loss=util.micro_fm, metrics=['mse']) pkg_models = os.listdir( os.path.join(os.path.dirname(os.path.abspath(__file__)), 'MODELS')) if config.modelpath.replace('MODELS/', '') in pkg_models: config.modelpath = os.path.join( os.path.dirname(os.path.abspath(__file__)), config.modelpath) autoencoder.load_weights(config.modelpath) return autoencoder
def build_SAE_network(config, weights_filename): nb_layers = 5 autoencoder, encoder, decoder = utilModelREDNet.build_REDNet( nb_layers, config.window, config.nb_filters, config.kernel, config.dropout, config.stride, config.every) autoencoder.compile(optimizer='adam', loss=util.micro_fm, metrics=['mse']) print(autoencoder.summary()) if config.loadmodel != None: print('Loading initial weights from', config.loadmodel) autoencoder.load_weights(config.loadmodel) elif config.test == True: print('Loading test weights from', weights_filename) autoencoder.load_weights(weights_filename) return autoencoder
help='save the output image') args = parser.parse_args() if args.step == -1: args.step = args.window if args.demo == False and args.outFilename == None: util.print_error( "ERROR: no output mode selected\nPlease choose between -demo or -save options" ) parser.print_help() quit() nb_layers = 5 autoencoder, encoder, decoder = utilModelREDNet.build_REDNet( nb_layers, args.window, args.nb_filters, args.kernel, args.dropout, args.stride, args.every) autoencoder.compile(optimizer='adam', loss=util.micro_fm, metrics=['mse']) autoencoder.load_weights(args.modelpath) img = cv2.imread(args.imgpath, False) img = np.asarray(img) rows = img.shape[0] cols = img.shape[1] if img.shape[0] < args.window or img.shape[1] < args.window: # Scale approach new_rows = args.window if img.shape[0] < args.window else img.shape[0] new_cols = args.window if img.shape[1] < args.window else img.shape[1] img = cv2.resize(img, (new_cols, new_rows), interpolation=cv2.INTER_CUBIC)