228 lines
7.2 KiB
Python
228 lines
7.2 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/GeMOSA/baselines/slbm-nof.py --env_version v1 --hidden_dim 16 --epochs 500 --init_lr 0.1 --device cuda
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# python exps/GeMOSA/baselines/slbm-nof.py --env_version v2 --hidden_dim 16 --epochs 500 --init_lr 0.1 --device cuda
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# python exps/GeMOSA/baselines/slbm-nof.py --env_version v3 --hidden_dim 32 --epochs 1000 --init_lr 0.05 --device cuda
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# python exps/GeMOSA/baselines/slbm-nof.py --env_version v4 --hidden_dim 32 --epochs 1000 --init_lr 0.05 --device cuda
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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 / ".." / ".." / "..").resolve()
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print("LIB-DIR: {:}".format(lib_dir))
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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 xautodl.procedures import (
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prepare_seed,
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prepare_logger,
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save_checkpoint,
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copy_checkpoint,
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)
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from xautodl.log_utils import time_string
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from xautodl.log_utils import AverageMeter, convert_secs2time
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from xautodl.procedures.metric_utils import (
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SaveMetric,
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MSEMetric,
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Top1AccMetric,
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ComposeMetric,
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)
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from xautodl.datasets.synthetic_core import get_synthetic_env
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from xautodl.models.xcore import get_model
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from xautodl.utils import show_mean_var
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def subsample(historical_x, historical_y, maxn=10000):
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total = historical_x.size(0)
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if total <= maxn:
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return historical_x, historical_y
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else:
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indexes = torch.randint(low=0, high=total, size=[maxn])
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return historical_x[indexes], historical_y[indexes]
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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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env = get_synthetic_env(mode="test", version=args.env_version)
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model_kwargs = dict(
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config=dict(model_type="norm_mlp"),
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input_dim=env.meta_info["input_dim"],
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output_dim=env.meta_info["output_dim"],
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hidden_dims=[args.hidden_dim] * 2,
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act_cls="relu",
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norm_cls="layer_norm_1d",
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)
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logger.log("The total enviornment: {:}".format(env))
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w_containers = dict()
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if env.meta_info["task"] == "regression":
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criterion = torch.nn.MSELoss()
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metric_cls = MSEMetric
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elif env.meta_info["task"] == "classification":
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criterion = torch.nn.CrossEntropyLoss()
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metric_cls = Top1AccMetric
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else:
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raise ValueError(
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"This task ({:}) is not supported.".format(all_env.meta_info["task"])
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)
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seq_times = env.get_seq_times(0, args.seq_length)
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_, (allxs, allys) = env.seq_call(seq_times)
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allxs, allys = allxs.view(-1, allxs.shape[-1]), allys.view(-1, 1)
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if env.meta_info["task"] == "classification":
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allys = allys.view(-1)
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historical_x, historical_y = allxs.to(args.device), allys.to(args.device)
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model = get_model(**model_kwargs)
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model = model.to(args.device)
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optimizer = torch.optim.Adam(model.parameters(), lr=args.init_lr, amsgrad=True)
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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 = metric_cls(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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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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model.analyze_weights()
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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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print(train_results)
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metric = metric_cls(True)
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per_timestamp_time, start_time = AverageMeter(), time.time()
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for idx, (future_time, (future_x, future_y)) in enumerate(env):
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need_time = "Time Left: {:}".format(
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convert_secs2time(per_timestamp_time.avg * (len(env) - idx), True)
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)
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logger.log(
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"[{:}]".format(time_string())
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+ " [{:04d}/{:04d}]".format(idx, len(env))
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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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# build optimizer
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xmetric = ComposeMetric(metric_cls(True), SaveMetric())
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future_x, future_y = future_x.to(args.device), future_y.to(args.device)
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future_y_hat = model(future_x)
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future_loss = criterion(future_y_hat, future_y)
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metric(future_y_hat, future_y)
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log_str = (
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"[{:}]".format(time_string())
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+ " [{:04d}/{:04d}]".format(idx, len(env))
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+ " train-score: {:.5f}, eval-score: {:.5f}".format(
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train_results["score"], metric.get_info()["score"]
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)
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)
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logger.log(log_str)
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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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save_checkpoint(
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{"w_containers": w_containers},
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logger.path(None) / "final-ckp.pth",
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logger,
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)
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logger.log("-" * 200 + "\n")
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logger.close()
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return metric.get_info()["score"]
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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/GeMOSA-synthetic/use-same-nof-timestamp",
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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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parser.add_argument(
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"--seq_length", type=int, default=20, help="The sequence length."
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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=300,
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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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"--device",
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type=str,
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default="cpu",
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help="",
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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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args.save_dir = "{:}-d{:}_e{:}_lr{:}-env{:}".format(
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args.save_dir, args.hidden_dim, args.epochs, args.init_lr, args.env_version
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)
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if args.rand_seed is None or args.rand_seed < 0:
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results = []
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for iseed in range(3):
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args.rand_seed = random.randint(1, 100000)
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result = main(args)
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results.append(result)
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show_mean_var(results)
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else:
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main(args)
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