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"Test Acc": correct.detach().cpu().numpy(),
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"Best Valid": best_valid_acc.detach().cpu().numpy(),
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"Loss": L.cpu().data.item(),
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}
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)
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checkpoint(f, replay_buffer, "last_ckpt.pt", args)
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logger.dump_tabular()
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if __name__ == "__main__":
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parser = argparse.ArgumentParser("Hybrid training via contrastive learning")
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parser.add_argument("--dataset", type=str, default="cifar10", choices=["cifar10", "svhn", "cifar100"])
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parser.add_argument("--data_root", type=str, default="/data/lhao/")
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# optimization
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parser.add_argument("--lr", type=float, default=1e-4)
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parser.add_argument("--decay_epochs", nargs="+", type=int, default=[160, 180], help="decay learning rate by decay_rate at these epochs")
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parser.add_argument("--decay_rate", type=float, default=0.3, help="learning rate decay multiplier")
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parser.add_argument("--clf_only", action="store_true", help="If set, then only train the classifier")
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parser.add_argument("--start_epoch", type=int, default=0, help="helpful for reloading")
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parser.add_argument("--labels_per_class", type=int, default=-1, help="number of labeled examples per class, if zero then use all labels")
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parser.add_argument("--optimizer", choices=["adam", "sgd"], default="adam")
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parser.add_argument("--batch_size", type=int, default=64)
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parser.add_argument("--sgld_batch_size", type=int, default=64)
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parser.add_argument("--n_epochs", type=int, default=200)
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parser.add_argument("--warmup_iters", type=int, default=-1, help="number of iters to linearly increase learning rate, if -1 then no warmmup")
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# loss weighting
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parser.add_argument("--pyxce", type=float, default=0.0)
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parser.add_argument("--pxcontrast", type=float, default=0.0)
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parser.add_argument("--pxsgld", type=float, default=0.0)
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parser.add_argument("--pxycontrast", type=float, default=0.0)
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parser.add_argument("--pxysgld", type=float, default=0.0)
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# regularization
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parser.add_argument("--dropout_rate", type=float, default=0.0)
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parser.add_argument("--sigma", type=float, default=3e-2, help="stddev of gaussian noise to add to input, .03 works but .1 is more stable")
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parser.add_argument("--weight_decay", type=float, default=0.0)
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# network
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parser.add_argument(
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"--norm", type=str, default=None, choices=[None, "norm", "batch", "instance", "layer", "act"], help="norm to add to weights, none works fine"
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)
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# EBM specific
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parser.add_argument("--n_steps", type=int, default=20, help="number of steps of SGLD per iteration, 100 works for short-run, 20 works for PCD")
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parser.add_argument("--width", type=int, default=10, help="WRN width parameter")
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parser.add_argument("--depth", type=int, default=28, help="WRN depth parameter")
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parser.add_argument("--uncond", action="store_true", help="If set, then the EBM is unconditional")
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parser.add_argument(
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"--class_cond_p_x_sample",
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action="store_true",
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help="If set we sample from p(y)p(x|y), othewise sample from p(x),"
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"Sample quality higher if set, but classification accuracy better if not.",
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)
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parser.add_argument("--buffer_size", type=int, default=10000)
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parser.add_argument("--reinit_freq", type=float, default=0.05)
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parser.add_argument("--sgld_lr", type=float, default=1.0)
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parser.add_argument("--sgld_std", type=float, default=1e-2)
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parser.add_argument("--contrast_k", type=int, default=65536, help="number of negative samples")
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parser.add_argument("--contrast_t", default=0.1, type=float, help="softmax temperature (default: 0.1)")
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parser.add_argument("--smoothing", default=0.0, type=float)
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parser.add_argument("--workers", default=4, type=int, metavar="N", help="number of data loading workers")
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parser.add_argument("--seed", default=None, type=int, help="seed for initializing training. ")
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# logging + evaluation
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parser.add_argument("--log_dir", type=str, default="./save/tmp")
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parser.add_argument("--id", default="testofhybridshit", type=str)
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parser.add_argument("--ckpt_every", type=int, default=-1, help="Epochs between checkpoint save")
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parser.add_argument("--plot_every", type=int, default=40, help="Epochs between plot")
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parser.add_argument("--eval_every", type=int, default=1, help="Epochs between evaluation")
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parser.add_argument("--print_every", type=int, default=100, help="Iterations between print")
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parser.add_argument("--load_path", type=str, default=None)
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parser.add_argument("--plot_uncond", choices=[0, 1], type=float, default=0)
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parser.add_argument("--plot_cond", choices=[0, 1], type=float, default=0)
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parser.add_argument("--plot_contrast", choices=[0, 1], type=float, default=0)
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parser.add_argument("--n_valid", type=int, default=5000)
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args = parser.parse_args()
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if args.load_path is not None:
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with open("{}/variant.json".format(dirname(abspath(args.load_path)))) as json_file:
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configs = json.load(json_file)
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loaded = SimpleNamespace(**configs)
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overwrite = copy.deepcopy(args)
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overwrite = vars(overwrite)
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overwrite.update(vars(loaded))
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overwrite = SimpleNamespace(**overwrite)
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exp_prefix = f"{args.id}-{overwrite.exp_prefix}"
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overwrite.exp_prefix = exp_prefix
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overwrite.log_dir = f"{dirname(dirname(abspath(args.load_path)))}/{exp_prefix}"
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# You may want to change seed, SGLD steps when restart after crashing
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# helpful to stablize log q(y|x) + log q(x) and log q(y|x) + log q(x|y) + log q(x)
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overwrite.seed = args.seed
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overwrite.n_steps = args.n_steps
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overwrite.start_epoch = args.start_epoch if args.start_epoch > 0 else overwrite.start_epoch
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overwrite.warmup_iters = args.warmup_iters
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overwrite.workers = args.workers
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args = overwrite
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else:
|
exp_prefix = f"{args.id}-{uuid.uuid4().hex}"
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args.exp_prefix = exp_prefix
|
args.log_dir = f"{args.log_dir}/{exp_prefix}"
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args.plot_contrast = 1 if (args.pxycontrast > 0 and args.plot_contrast) else 0
|
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