Add super/norm layers in xcore
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D-X-Y
212
exps/LFNA/lfna-v1.py
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212
exps/LFNA/lfna-v1.py
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#####################################################
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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-v1.py
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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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class Population:
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def __init__(self):
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self._time2model = dict()
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def append(self, timestamp, model):
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if timestamp in self._time2model:
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raise ValueError("This timestamp has been added.")
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self._time2model[timestamp] = model
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def main(args):
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prepare_seed(args.rand_seed)
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logger = prepare_logger(args)
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cache_path = (logger.path(None) / ".." / "env-info.pth").resolve()
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if cache_path.exists():
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env_info = torch.load(cache_path)
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else:
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env_info = dict()
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dynamic_env = get_synthetic_env()
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env_info["total"] = len(dynamic_env)
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for idx, (timestamp, (_allx, _ally)) in enumerate(tqdm(dynamic_env)):
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env_info["{:}-timestamp".format(idx)] = timestamp
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env_info["{:}-x".format(idx)] = _allx
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env_info["{:}-y".format(idx)] = _ally
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env_info["dynamic_env"] = dynamic_env
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torch.save(env_info, cache_path)
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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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base_model = get_model(
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dict(model_type="simple_mlp"),
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act_cls="leaky_relu",
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norm_cls="simple_learn_norm",
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mean=0,
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std=1,
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input_dim=1,
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output_dim=1,
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)
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w_container = base_model.named_parameters_buffers()
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print("There are {:} weights.".format(w_container.numel()))
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pool = Population()
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pool.append(0, w_container)
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# LFNA meta-training
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per_epoch_time, start_time = AverageMeter(), time.time()
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for iepoch in range(args.epochs):
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import pdb
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pdb.set_trace()
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print("-")
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for i, idx in enumerate(to_evaluate_indexes):
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need_time = "Time Left: {:}".format(
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convert_secs2time(
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per_timestamp_time.avg * (len(to_evaluate_indexes) - i), True
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)
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)
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logger.log(
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"[{:}]".format(time_string())
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+ " [{:04d}/{:04d}][{:04d}]".format(i, len(to_evaluate_indexes), idx)
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+ " "
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+ need_time
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)
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# train the same data
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assert idx != 0
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historical_x = env_info["{:}-x".format(idx)]
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historical_y = env_info["{:}-y".format(idx)]
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# build model
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mean, std = historical_x.mean().item(), historical_x.std().item()
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model_kwargs = dict(input_dim=1, output_dim=1, mean=mean, std=std)
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model = get_model(dict(model_type="simple_mlp"), **model_kwargs)
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# build optimizer
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optimizer = torch.optim.Adam(model.parameters(), lr=args.init_lr, amsgrad=True)
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criterion = torch.nn.MSELoss()
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lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
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optimizer,
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milestones=[
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int(args.epochs * 0.25),
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int(args.epochs * 0.5),
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int(args.epochs * 0.75),
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],
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gamma=0.3,
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)
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train_metric = MSEMetric()
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best_loss, best_param = None, None
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for _iepoch in range(args.epochs):
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preds = model(historical_x)
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optimizer.zero_grad()
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loss = criterion(preds, historical_y)
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loss.backward()
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optimizer.step()
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lr_scheduler.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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model.load_state_dict(best_param)
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with torch.no_grad():
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train_metric(preds, historical_y)
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train_results = train_metric.get_info()
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metric = ComposeMetric(MSEMetric(), SaveMetric())
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eval_dataset = torch.utils.data.TensorDataset(
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env_info["{:}-x".format(idx)], env_info["{:}-y".format(idx)]
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)
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eval_loader = torch.utils.data.DataLoader(
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eval_dataset, batch_size=args.batch_size, shuffle=False, num_workers=0
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)
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results = basic_eval_fn(eval_loader, model, metric, logger)
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log_str = (
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"[{:}]".format(time_string())
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+ " [{:04d}/{:04d}]".format(idx, env_info["total"])
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+ " train-mse: {:.5f}, eval-mse: {:.5f}".format(
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train_results["mse"], results["mse"]
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)
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)
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logger.log(log_str)
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save_path = logger.path(None) / "{:04d}-{:04d}.pth".format(
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idx, env_info["total"]
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)
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save_checkpoint(
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{
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"model_state_dict": model.state_dict(),
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"model": model,
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"index": idx,
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"timestamp": env_info["{:}-timestamp".format(idx)],
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},
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save_path,
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logger,
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)
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logger.log("")
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per_timestamp_time.update(time.time() - start_time)
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start_time = time.time()
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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-v1",
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help="The checkpoint directory.",
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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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"--batch_size",
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type=int,
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default=512,
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help="The batch size",
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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=1000,
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help="The total number of epochs.",
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)
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parser.add_argument(
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"--workers",
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type=int,
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default=4,
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help="The number of data loading workers (default: 4)",
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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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main(args)
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