Update codes
This commit is contained in:
D-X-Y
206
exps/GeMOSA/basic-his.py
Normal file
206
exps/GeMOSA/basic-his.py
Normal file
@@ -0,0 +1,206 @@
|
||||
#####################################################
|
||||
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
|
||||
#####################################################
|
||||
# python exps/LFNA/basic-his.py --srange 1-999 --env_version v1 --hidden_dim 16
|
||||
#####################################################
|
||||
import sys, time, copy, torch, random, argparse
|
||||
from tqdm import tqdm
|
||||
from copy import deepcopy
|
||||
|
||||
from xautodl.procedures import (
|
||||
prepare_seed,
|
||||
prepare_logger,
|
||||
save_checkpoint,
|
||||
copy_checkpoint,
|
||||
)
|
||||
from xautodl.log_utils import time_string
|
||||
from xautodl.log_utils import AverageMeter, convert_secs2time
|
||||
|
||||
from xautodl.utils import split_str2indexes
|
||||
|
||||
from xautodl.procedures.advanced_main import basic_train_fn, basic_eval_fn
|
||||
from xautodl.procedures.metric_utils import SaveMetric, MSEMetric, ComposeMetric
|
||||
from xautodl.datasets.synthetic_core import get_synthetic_env
|
||||
from xautodl.models.xcore import get_model
|
||||
|
||||
|
||||
from lfna_utils import lfna_setup
|
||||
|
||||
|
||||
def subsample(historical_x, historical_y, maxn=10000):
|
||||
total = historical_x.size(0)
|
||||
if total <= maxn:
|
||||
return historical_x, historical_y
|
||||
else:
|
||||
indexes = torch.randint(low=0, high=total, size=[maxn])
|
||||
return historical_x[indexes], historical_y[indexes]
|
||||
|
||||
|
||||
def main(args):
|
||||
logger, env_info, model_kwargs = lfna_setup(args)
|
||||
|
||||
# check indexes to be evaluated
|
||||
to_evaluate_indexes = split_str2indexes(args.srange, env_info["total"], None)
|
||||
logger.log(
|
||||
"Evaluate {:}, which has {:} timestamps in total.".format(
|
||||
args.srange, len(to_evaluate_indexes)
|
||||
)
|
||||
)
|
||||
|
||||
w_container_per_epoch = dict()
|
||||
|
||||
per_timestamp_time, start_time = AverageMeter(), time.time()
|
||||
for i, idx in enumerate(to_evaluate_indexes):
|
||||
|
||||
need_time = "Time Left: {:}".format(
|
||||
convert_secs2time(
|
||||
per_timestamp_time.avg * (len(to_evaluate_indexes) - i), True
|
||||
)
|
||||
)
|
||||
logger.log(
|
||||
"[{:}]".format(time_string())
|
||||
+ " [{:04d}/{:04d}][{:04d}]".format(i, len(to_evaluate_indexes), idx)
|
||||
+ " "
|
||||
+ need_time
|
||||
)
|
||||
# train the same data
|
||||
assert idx != 0
|
||||
historical_x, historical_y = [], []
|
||||
for past_i in range(idx):
|
||||
historical_x.append(env_info["{:}-x".format(past_i)])
|
||||
historical_y.append(env_info["{:}-y".format(past_i)])
|
||||
historical_x, historical_y = torch.cat(historical_x), torch.cat(historical_y)
|
||||
historical_x, historical_y = subsample(historical_x, historical_y)
|
||||
# build model
|
||||
model = get_model(dict(model_type="simple_mlp"), **model_kwargs)
|
||||
# build optimizer
|
||||
optimizer = torch.optim.Adam(model.parameters(), lr=args.init_lr, amsgrad=True)
|
||||
criterion = torch.nn.MSELoss()
|
||||
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
|
||||
optimizer,
|
||||
milestones=[
|
||||
int(args.epochs * 0.25),
|
||||
int(args.epochs * 0.5),
|
||||
int(args.epochs * 0.75),
|
||||
],
|
||||
gamma=0.3,
|
||||
)
|
||||
train_metric = MSEMetric()
|
||||
best_loss, best_param = None, None
|
||||
for _iepoch in range(args.epochs):
|
||||
preds = model(historical_x)
|
||||
optimizer.zero_grad()
|
||||
loss = criterion(preds, historical_y)
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
lr_scheduler.step()
|
||||
# save best
|
||||
if best_loss is None or best_loss > loss.item():
|
||||
best_loss = loss.item()
|
||||
best_param = copy.deepcopy(model.state_dict())
|
||||
model.load_state_dict(best_param)
|
||||
with torch.no_grad():
|
||||
train_metric(preds, historical_y)
|
||||
train_results = train_metric.get_info()
|
||||
|
||||
metric = ComposeMetric(MSEMetric(), SaveMetric())
|
||||
eval_dataset = torch.utils.data.TensorDataset(
|
||||
env_info["{:}-x".format(idx)], env_info["{:}-y".format(idx)]
|
||||
)
|
||||
eval_loader = torch.utils.data.DataLoader(
|
||||
eval_dataset, batch_size=args.batch_size, shuffle=False, num_workers=0
|
||||
)
|
||||
results = basic_eval_fn(eval_loader, model, metric, logger)
|
||||
log_str = (
|
||||
"[{:}]".format(time_string())
|
||||
+ " [{:04d}/{:04d}]".format(idx, env_info["total"])
|
||||
+ " train-mse: {:.5f}, eval-mse: {:.5f}".format(
|
||||
train_results["mse"], results["mse"]
|
||||
)
|
||||
)
|
||||
logger.log(log_str)
|
||||
|
||||
save_path = logger.path(None) / "{:04d}-{:04d}.pth".format(
|
||||
idx, env_info["total"]
|
||||
)
|
||||
w_container_per_epoch[idx] = model.get_w_container().no_grad_clone()
|
||||
save_checkpoint(
|
||||
{
|
||||
"model_state_dict": model.state_dict(),
|
||||
"model": model,
|
||||
"index": idx,
|
||||
"timestamp": env_info["{:}-timestamp".format(idx)],
|
||||
},
|
||||
save_path,
|
||||
logger,
|
||||
)
|
||||
logger.log("")
|
||||
per_timestamp_time.update(time.time() - start_time)
|
||||
start_time = time.time()
|
||||
|
||||
save_checkpoint(
|
||||
{"w_container_per_epoch": w_container_per_epoch},
|
||||
logger.path(None) / "final-ckp.pth",
|
||||
logger,
|
||||
)
|
||||
logger.log("-" * 200 + "\n")
|
||||
logger.close()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser("Use all the past data to train.")
|
||||
parser.add_argument(
|
||||
"--save_dir",
|
||||
type=str,
|
||||
default="./outputs/lfna-synthetic/use-all-past-data",
|
||||
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(
|
||||
"--batch_size",
|
||||
type=int,
|
||||
default=512,
|
||||
help="The batch size",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--epochs",
|
||||
type=int,
|
||||
default=1000,
|
||||
help="The total number of epochs.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--srange", type=str, required=True, help="The range of models to be evaluated"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--workers",
|
||||
type=int,
|
||||
default=4,
|
||||
help="The number of data loading workers (default: 4)",
|
||||
)
|
||||
# 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)
|
||||
Reference in New Issue
Block a user