135 lines
4.3 KiB
Python
135 lines
4.3 KiB
Python
#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
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#####################################################
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# python exps/LFNA/lfna-ttss-hpnet.py --env_version v1 --hidden_dim 16
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#####################################################
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import sys, time, copy, torch, random, argparse
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from tqdm import tqdm
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from copy import deepcopy
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from pathlib import Path
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lib_dir = (Path(__file__).parent / ".." / ".." / "lib").resolve()
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if str(lib_dir) not in sys.path:
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sys.path.insert(0, str(lib_dir))
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from procedures import prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint
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from log_utils import time_string
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from log_utils import AverageMeter, convert_secs2time
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from utils import split_str2indexes
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from procedures.advanced_main import basic_train_fn, basic_eval_fn
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from procedures.metric_utils import SaveMetric, MSEMetric, ComposeMetric
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from datasets.synthetic_core import get_synthetic_env
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from models.xcore import get_model
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from xlayers import super_core
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from lfna_utils import lfna_setup, train_model, TimeData
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from lfna_models import HyperNet_VX as HyperNet
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def main(args):
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logger, env_info, model_kwargs = lfna_setup(args)
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dynamic_env = env_info["dynamic_env"]
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model = get_model(dict(model_type="simple_mlp"), **model_kwargs)
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total_time = env_info["total"]
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for i in range(total_time):
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for xkey in ("timestamp", "x", "y"):
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nkey = "{:}-{:}".format(i, xkey)
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assert nkey in env_info, "{:} no in {:}".format(nkey, list(env_info.keys()))
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train_time_bar = total_time // 2
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criterion = torch.nn.MSELoss()
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logger.log("There are {:} weights.".format(model.get_w_container().numel()))
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# pre-train the model
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dataset = init_dataset = TimeData(0, env_info["0-x"], env_info["0-y"])
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shape_container = model.get_w_container().to_shape_container()
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hypernet = HyperNet(shape_container, 16)
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print(hypernet)
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optimizer = torch.optim.Adam(hypernet.parameters(), lr=args.init_lr, amsgrad=True)
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best_loss, best_param = None, None
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for _iepoch in range(args.epochs):
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container = hypernet(None)
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preds = model.forward_with_container(dataset.x, container)
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optimizer.zero_grad()
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loss = criterion(preds, dataset.y)
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loss.backward()
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optimizer.step()
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# save best
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if best_loss is None or best_loss > loss.item():
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best_loss = loss.item()
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best_param = copy.deepcopy(model.state_dict())
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print("hyper-net : best={:.4f}".format(best_loss))
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init_loss = train_model(model, init_dataset, args.init_lr, args.epochs)
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logger.log("The pre-training loss is {:.4f}".format(init_loss))
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print(model)
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print(hypernet)
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logger.log("-" * 200 + "\n")
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logger.close()
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if __name__ == "__main__":
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parser = argparse.ArgumentParser("Use the data in the past.")
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parser.add_argument(
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"--save_dir",
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type=str,
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default="./outputs/lfna-synthetic/lfna-debug",
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help="The checkpoint directory.",
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)
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parser.add_argument(
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"--env_version",
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type=str,
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required=True,
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help="The synthetic enviornment version.",
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)
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parser.add_argument(
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"--hidden_dim",
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type=int,
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required=True,
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help="The hidden dimension.",
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)
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#####
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parser.add_argument(
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"--init_lr",
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type=float,
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default=0.1,
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help="The initial learning rate for the optimizer (default is Adam)",
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)
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parser.add_argument(
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"--meta_batch",
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type=int,
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default=32,
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help="The batch size for the meta-model",
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)
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parser.add_argument(
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"--meta_seq",
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type=int,
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default=10,
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help="The length of the sequence for meta-model.",
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)
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parser.add_argument(
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"--epochs",
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type=int,
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default=2000,
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help="The total number of epochs.",
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)
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# Random Seed
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parser.add_argument("--rand_seed", type=int, default=-1, help="manual seed")
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args = parser.parse_args()
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if args.rand_seed is None or args.rand_seed < 0:
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args.rand_seed = random.randint(1, 100000)
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assert args.save_dir is not None, "The save dir argument can not be None"
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args.save_dir = "{:}-{:}-d{:}".format(
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args.save_dir, args.env_version, args.hidden_dim
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)
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main(args)
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