232 lines
7.6 KiB
Python
232 lines
7.6 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-test-hpnet.py --env_version v1 --hidden_dim 16 --layer_dim 32 --epochs 500000 --init_lr 0.02
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# python exps/LFNA/lfna-test-hpnet.py --env_version v1 --hidden_dim 16 --layer_dim 32 --epochs 500000 --init_lr 0.02 --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 / ".." / ".." / "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, trunc_normal_
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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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from lfna_models import 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(**model_kwargs)
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model = model.to(args.device)
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criterion = torch.nn.MSELoss()
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shape_container = model.get_w_container().to_shape_container()
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hypernet = HyperNet(shape_container, args.layer_dim, args.task_dim)
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hypernet = hypernet.to(args.device)
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logger.log(
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"{:} There are {:} weights in the base-model.".format(
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time_string(), model.numel()
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)
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)
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logger.log(
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"{:} There are {:} weights in the meta-model.".format(
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time_string(), hypernet.numel()
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)
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)
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# task_embed = torch.nn.Parameter(torch.Tensor(env_info["total"], args.task_dim))
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total_bar = 100
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task_embeds = []
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for i in range(total_bar):
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tensor = torch.Tensor(1, args.task_dim).to(args.device)
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task_embeds.append(torch.nn.Parameter(tensor))
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for task_embed in task_embeds:
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trunc_normal_(task_embed, std=0.02)
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for i in range(total_bar):
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env_info["{:}-x".format(i)] = env_info["{:}-x".format(i)].to(args.device)
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env_info["{:}-y".format(i)] = env_info["{:}-y".format(i)].to(args.device)
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model.train()
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hypernet.train()
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parameters = list(hypernet.parameters()) + task_embeds
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# optimizer = torch.optim.Adam(parameters, lr=args.init_lr, amsgrad=True)
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optimizer = torch.optim.Adam(parameters, lr=args.init_lr, weight_decay=1e-5)
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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 * 0.8),
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int(args.epochs * 0.9),
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],
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gamma=0.1,
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)
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# total_bar = env_info["total"] - 1
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# LFNA meta-training
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loss_meter = AverageMeter()
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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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need_time = "Time Left: {:}".format(
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convert_secs2time(per_epoch_time.avg * (args.epochs - iepoch), True)
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)
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head_str = (
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"[{:}] [{:04d}/{:04d}] ".format(time_string(), iepoch, args.epochs)
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+ need_time
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)
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losses = []
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# for ibatch in range(args.meta_batch):
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for cur_time in range(total_bar):
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# cur_time = random.randint(0, total_bar)
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cur_task_embed = task_embeds[cur_time]
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cur_container = hypernet(cur_task_embed)
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cur_x = env_info["{:}-x".format(cur_time)].to(args.device)
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cur_y = env_info["{:}-y".format(cur_time)].to(args.device)
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cur_dataset = TimeData(cur_time, cur_x, cur_y)
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preds = model.forward_with_container(cur_dataset.x, cur_container)
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optimizer.zero_grad()
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loss = criterion(preds, cur_dataset.y)
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losses.append(loss)
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final_loss = torch.stack(losses).mean()
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final_loss.backward()
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optimizer.step()
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lr_scheduler.step()
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loss_meter.update(final_loss.item())
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if iepoch % 100 == 0:
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logger.log(
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head_str
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+ " meta-loss: {:.4f} ({:.4f}) :: lr={:.5f}, batch={:}".format(
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loss_meter.avg,
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loss_meter.val,
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min(lr_scheduler.get_last_lr()),
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len(losses),
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)
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)
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save_checkpoint(
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{
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"hypernet": hypernet.state_dict(),
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"task_embed": task_embed,
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"lr_scheduler": lr_scheduler.state_dict(),
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"iepoch": iepoch,
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},
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logger.path("model"),
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logger,
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)
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loss_meter.reset()
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per_epoch_time.update(time.time() - start_time)
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start_time = time.time()
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print(model)
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print(hypernet)
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w_container_per_epoch = dict()
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for idx in range(0, total_bar):
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future_time = env_info["{:}-timestamp".format(idx)]
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future_x = env_info["{:}-x".format(idx)]
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future_y = env_info["{:}-y".format(idx)]
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future_container = hypernet(task_embeds[idx])
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w_container_per_epoch[idx] = future_container.no_grad_clone()
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with torch.no_grad():
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future_y_hat = model.forward_with_container(
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future_x, w_container_per_epoch[idx]
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)
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future_loss = criterion(future_y_hat, future_y)
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logger.log("meta-test: [{:03d}] -> loss={:.4f}".format(idx, future_loss.item()))
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save_checkpoint(
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{"w_container_per_epoch": w_container_per_epoch},
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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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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-test-hpnet",
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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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"--layer_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=64,
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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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"--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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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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# 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.task_dim = args.layer_dim
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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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