def get_model(args, n_dim, r, from_ckpt=False, train=True, grocery='false'): """Create a model based on arguments""" if train: opt_params = { 'alg' : args.alg, 'lr' : args.lr, 'b1' : 0.9, 'b2' : 0.999, 'batch_size': args.batch_size, 'layers': args.layers } else: opt_params = default_opt print(args.model) # create model if args.model == 'audiounet': model = models.AudioUNet(from_ckpt=from_ckpt, n_dim=n_dim, r=r, opt_params=opt_params, log_prefix=args.logname) elif args.model == 'audiotfilm': model = models.AudioTfilm(from_ckpt=from_ckpt, n_dim=n_dim, r=r, pool_size = args.pool_size, strides=args.strides, opt_params=opt_params, log_prefix=args.logname) elif args.model == 'dnn': model = models.DNN(from_ckpt=from_ckpt, n_dim=n_dim, r=r, opt_params=opt_params, log_prefix=args.logname) elif args.model == 'spline': model = models.Spline(from_ckpt=from_ckpt, n_dim=n_dim, r=r, opt_params=opt_params, log_prefix=args.logname) else: raise ValueError('Invalid model') return model
def get_model(args, n_dim, r, from_ckpt=False, train=True): """Create a model based on arguments""" if train: opt_params = { 'alg' : args.alg, 'lr' : args.lr, 'b1' : 0.9, 'b2' : 0.999, 'batch_size': args.batch_size, 'layers': args.layers } else: opt_params = default_opt # create model model = models.AudioUNet(from_ckpt=from_ckpt, n_dim=n_dim, r=r, opt_params=opt_params, log_prefix=args.logname) return model
def get_model(args, n_dim, r, from_ckpt=False, train=True): """Create a model based on arguments""" if train: opt_params = { "alg": args.alg, "lr": args.lr, "b1": 0.9, "b2": 0.999, "batch_size": args.batch_size, "layers": args.layers, } else: opt_params = default_opt # create model model = models.AudioUNet( from_ckpt=from_ckpt, n_dim=n_dim, r=r, opt_params=opt_params, log_prefix=args.logname, ) return model