autodl-projects/exps/LFNA/lfna-ttss-hpnet.py
2021-05-12 15:45:45 +08:00

135 lines
4.3 KiB
Python

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