Update baselines
This commit is contained in:
D-X-Y
302
exps/LFNA/basic.py
Normal file
302
exps/LFNA/basic.py
Normal file
@@ -0,0 +1,302 @@
|
|||||||
|
#####################################################
|
||||||
|
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
|
||||||
|
#####################################################
|
||||||
|
# python exps/LFNA/basic.py
|
||||||
|
#####################################################
|
||||||
|
import sys, time, torch, random, argparse
|
||||||
|
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 AverageMeter, time_string, convert_secs2time
|
||||||
|
|
||||||
|
from datasets.synthetic_core import get_synthetic_env
|
||||||
|
from models.xcore import get_model
|
||||||
|
|
||||||
|
def main(args):
|
||||||
|
torch.set_num_threads(args.workers)
|
||||||
|
prepare_seed(args.rand_seed)
|
||||||
|
logger = prepare_logger(args)
|
||||||
|
|
||||||
|
dynamic_env = get_synthetic_env()
|
||||||
|
for idx, (timestamp, (allx, ally)) in enumerate(dynamic_env):
|
||||||
|
import pdb
|
||||||
|
pdb.set_trace()
|
||||||
|
|
||||||
|
train_data, valid_data, xshape, class_num = get_datasets(
|
||||||
|
args.dataset, args.data_path, args.cutout_length
|
||||||
|
)
|
||||||
|
train_loader = torch.utils.data.DataLoader(
|
||||||
|
train_data,
|
||||||
|
batch_size=args.batch_size,
|
||||||
|
shuffle=True,
|
||||||
|
num_workers=args.workers,
|
||||||
|
pin_memory=True,
|
||||||
|
)
|
||||||
|
valid_loader = torch.utils.data.DataLoader(
|
||||||
|
valid_data,
|
||||||
|
batch_size=args.batch_size,
|
||||||
|
shuffle=False,
|
||||||
|
num_workers=args.workers,
|
||||||
|
pin_memory=True,
|
||||||
|
)
|
||||||
|
# get configures
|
||||||
|
model_config = load_config(args.model_config, {"class_num": class_num}, logger)
|
||||||
|
optim_config = load_config(args.optim_config, {"class_num": class_num}, logger)
|
||||||
|
|
||||||
|
if args.model_source == "normal":
|
||||||
|
base_model = obtain_model(model_config)
|
||||||
|
elif args.model_source == "nas":
|
||||||
|
base_model = obtain_nas_infer_model(model_config, args.extra_model_path)
|
||||||
|
elif args.model_source == "autodl-searched":
|
||||||
|
base_model = obtain_model(model_config, args.extra_model_path)
|
||||||
|
else:
|
||||||
|
raise ValueError("invalid model-source : {:}".format(args.model_source))
|
||||||
|
flop, param = get_model_infos(base_model, xshape)
|
||||||
|
logger.log("model ====>>>>:\n{:}".format(base_model))
|
||||||
|
logger.log("model information : {:}".format(base_model.get_message()))
|
||||||
|
logger.log("-" * 50)
|
||||||
|
logger.log(
|
||||||
|
"Params={:.2f} MB, FLOPs={:.2f} M ... = {:.2f} G".format(
|
||||||
|
param, flop, flop / 1e3
|
||||||
|
)
|
||||||
|
)
|
||||||
|
logger.log("-" * 50)
|
||||||
|
logger.log("train_data : {:}".format(train_data))
|
||||||
|
logger.log("valid_data : {:}".format(valid_data))
|
||||||
|
optimizer, scheduler, criterion = get_optim_scheduler(
|
||||||
|
base_model.parameters(), optim_config
|
||||||
|
)
|
||||||
|
logger.log("optimizer : {:}".format(optimizer))
|
||||||
|
logger.log("scheduler : {:}".format(scheduler))
|
||||||
|
logger.log("criterion : {:}".format(criterion))
|
||||||
|
|
||||||
|
last_info, model_base_path, model_best_path = (
|
||||||
|
logger.path("info"),
|
||||||
|
logger.path("model"),
|
||||||
|
logger.path("best"),
|
||||||
|
)
|
||||||
|
network, criterion = torch.nn.DataParallel(base_model).cuda(), criterion.cuda()
|
||||||
|
|
||||||
|
if last_info.exists(): # automatically resume from previous checkpoint
|
||||||
|
logger.log(
|
||||||
|
"=> loading checkpoint of the last-info '{:}' start".format(last_info)
|
||||||
|
)
|
||||||
|
last_infox = torch.load(last_info)
|
||||||
|
start_epoch = last_infox["epoch"] + 1
|
||||||
|
last_checkpoint_path = last_infox["last_checkpoint"]
|
||||||
|
if not last_checkpoint_path.exists():
|
||||||
|
logger.log(
|
||||||
|
"Does not find {:}, try another path".format(last_checkpoint_path)
|
||||||
|
)
|
||||||
|
last_checkpoint_path = (
|
||||||
|
last_info.parent
|
||||||
|
/ last_checkpoint_path.parent.name
|
||||||
|
/ last_checkpoint_path.name
|
||||||
|
)
|
||||||
|
checkpoint = torch.load(last_checkpoint_path)
|
||||||
|
base_model.load_state_dict(checkpoint["base-model"])
|
||||||
|
scheduler.load_state_dict(checkpoint["scheduler"])
|
||||||
|
optimizer.load_state_dict(checkpoint["optimizer"])
|
||||||
|
valid_accuracies = checkpoint["valid_accuracies"]
|
||||||
|
max_bytes = checkpoint["max_bytes"]
|
||||||
|
logger.log(
|
||||||
|
"=> loading checkpoint of the last-info '{:}' start with {:}-th epoch.".format(
|
||||||
|
last_info, start_epoch
|
||||||
|
)
|
||||||
|
)
|
||||||
|
elif args.resume is not None:
|
||||||
|
assert Path(args.resume).exists(), "Can not find the resume file : {:}".format(
|
||||||
|
args.resume
|
||||||
|
)
|
||||||
|
checkpoint = torch.load(args.resume)
|
||||||
|
start_epoch = checkpoint["epoch"] + 1
|
||||||
|
base_model.load_state_dict(checkpoint["base-model"])
|
||||||
|
scheduler.load_state_dict(checkpoint["scheduler"])
|
||||||
|
optimizer.load_state_dict(checkpoint["optimizer"])
|
||||||
|
valid_accuracies = checkpoint["valid_accuracies"]
|
||||||
|
max_bytes = checkpoint["max_bytes"]
|
||||||
|
logger.log(
|
||||||
|
"=> loading checkpoint from '{:}' start with {:}-th epoch.".format(
|
||||||
|
args.resume, start_epoch
|
||||||
|
)
|
||||||
|
)
|
||||||
|
elif args.init_model is not None:
|
||||||
|
assert Path(
|
||||||
|
args.init_model
|
||||||
|
).exists(), "Can not find the initialization file : {:}".format(args.init_model)
|
||||||
|
checkpoint = torch.load(args.init_model)
|
||||||
|
base_model.load_state_dict(checkpoint["base-model"])
|
||||||
|
start_epoch, valid_accuracies, max_bytes = 0, {"best": -1}, {}
|
||||||
|
logger.log("=> initialize the model from {:}".format(args.init_model))
|
||||||
|
else:
|
||||||
|
logger.log("=> do not find the last-info file : {:}".format(last_info))
|
||||||
|
start_epoch, valid_accuracies, max_bytes = 0, {"best": -1}, {}
|
||||||
|
|
||||||
|
train_func, valid_func = get_procedures(args.procedure)
|
||||||
|
|
||||||
|
total_epoch = optim_config.epochs + optim_config.warmup
|
||||||
|
# Main Training and Evaluation Loop
|
||||||
|
start_time = time.time()
|
||||||
|
epoch_time = AverageMeter()
|
||||||
|
for epoch in range(start_epoch, total_epoch):
|
||||||
|
scheduler.update(epoch, 0.0)
|
||||||
|
need_time = "Time Left: {:}".format(
|
||||||
|
convert_secs2time(epoch_time.avg * (total_epoch - epoch), True)
|
||||||
|
)
|
||||||
|
epoch_str = "epoch={:03d}/{:03d}".format(epoch, total_epoch)
|
||||||
|
LRs = scheduler.get_lr()
|
||||||
|
find_best = False
|
||||||
|
# set-up drop-out ratio
|
||||||
|
if hasattr(base_model, "update_drop_path"):
|
||||||
|
base_model.update_drop_path(
|
||||||
|
model_config.drop_path_prob * epoch / total_epoch
|
||||||
|
)
|
||||||
|
logger.log(
|
||||||
|
"\n***{:s}*** start {:s} {:s}, LR=[{:.6f} ~ {:.6f}], scheduler={:}".format(
|
||||||
|
time_string(), epoch_str, need_time, min(LRs), max(LRs), scheduler
|
||||||
|
)
|
||||||
|
)
|
||||||
|
|
||||||
|
# train for one epoch
|
||||||
|
train_loss, train_acc1, train_acc5 = train_func(
|
||||||
|
train_loader,
|
||||||
|
network,
|
||||||
|
criterion,
|
||||||
|
scheduler,
|
||||||
|
optimizer,
|
||||||
|
optim_config,
|
||||||
|
epoch_str,
|
||||||
|
args.print_freq,
|
||||||
|
logger,
|
||||||
|
)
|
||||||
|
# log the results
|
||||||
|
logger.log(
|
||||||
|
"***{:s}*** TRAIN [{:}] loss = {:.6f}, accuracy-1 = {:.2f}, accuracy-5 = {:.2f}".format(
|
||||||
|
time_string(), epoch_str, train_loss, train_acc1, train_acc5
|
||||||
|
)
|
||||||
|
)
|
||||||
|
|
||||||
|
# evaluate the performance
|
||||||
|
if (epoch % args.eval_frequency == 0) or (epoch + 1 == total_epoch):
|
||||||
|
logger.log("-" * 150)
|
||||||
|
valid_loss, valid_acc1, valid_acc5 = valid_func(
|
||||||
|
valid_loader,
|
||||||
|
network,
|
||||||
|
criterion,
|
||||||
|
optim_config,
|
||||||
|
epoch_str,
|
||||||
|
args.print_freq_eval,
|
||||||
|
logger,
|
||||||
|
)
|
||||||
|
valid_accuracies[epoch] = valid_acc1
|
||||||
|
logger.log(
|
||||||
|
"***{:s}*** VALID [{:}] loss = {:.6f}, accuracy@1 = {:.2f}, accuracy@5 = {:.2f} | Best-Valid-Acc@1={:.2f}, Error@1={:.2f}".format(
|
||||||
|
time_string(),
|
||||||
|
epoch_str,
|
||||||
|
valid_loss,
|
||||||
|
valid_acc1,
|
||||||
|
valid_acc5,
|
||||||
|
valid_accuracies["best"],
|
||||||
|
100 - valid_accuracies["best"],
|
||||||
|
)
|
||||||
|
)
|
||||||
|
if valid_acc1 > valid_accuracies["best"]:
|
||||||
|
valid_accuracies["best"] = valid_acc1
|
||||||
|
find_best = True
|
||||||
|
logger.log(
|
||||||
|
"Currently, the best validation accuracy found at {:03d}-epoch :: acc@1={:.2f}, acc@5={:.2f}, error@1={:.2f}, error@5={:.2f}, save into {:}.".format(
|
||||||
|
epoch,
|
||||||
|
valid_acc1,
|
||||||
|
valid_acc5,
|
||||||
|
100 - valid_acc1,
|
||||||
|
100 - valid_acc5,
|
||||||
|
model_best_path,
|
||||||
|
)
|
||||||
|
)
|
||||||
|
num_bytes = (
|
||||||
|
torch.cuda.max_memory_cached(next(network.parameters()).device) * 1.0
|
||||||
|
)
|
||||||
|
logger.log(
|
||||||
|
"[GPU-Memory-Usage on {:} is {:} bytes, {:.2f} KB, {:.2f} MB, {:.2f} GB.]".format(
|
||||||
|
next(network.parameters()).device,
|
||||||
|
int(num_bytes),
|
||||||
|
num_bytes / 1e3,
|
||||||
|
num_bytes / 1e6,
|
||||||
|
num_bytes / 1e9,
|
||||||
|
)
|
||||||
|
)
|
||||||
|
max_bytes[epoch] = num_bytes
|
||||||
|
if epoch % 10 == 0:
|
||||||
|
torch.cuda.empty_cache()
|
||||||
|
|
||||||
|
# save checkpoint
|
||||||
|
save_path = save_checkpoint(
|
||||||
|
{
|
||||||
|
"epoch": epoch,
|
||||||
|
"args": deepcopy(args),
|
||||||
|
"max_bytes": deepcopy(max_bytes),
|
||||||
|
"FLOP": flop,
|
||||||
|
"PARAM": param,
|
||||||
|
"valid_accuracies": deepcopy(valid_accuracies),
|
||||||
|
"model-config": model_config._asdict(),
|
||||||
|
"optim-config": optim_config._asdict(),
|
||||||
|
"base-model": base_model.state_dict(),
|
||||||
|
"scheduler": scheduler.state_dict(),
|
||||||
|
"optimizer": optimizer.state_dict(),
|
||||||
|
},
|
||||||
|
model_base_path,
|
||||||
|
logger,
|
||||||
|
)
|
||||||
|
if find_best:
|
||||||
|
copy_checkpoint(model_base_path, model_best_path, logger)
|
||||||
|
last_info = save_checkpoint(
|
||||||
|
{
|
||||||
|
"epoch": epoch,
|
||||||
|
"args": deepcopy(args),
|
||||||
|
"last_checkpoint": save_path,
|
||||||
|
},
|
||||||
|
logger.path("info"),
|
||||||
|
logger,
|
||||||
|
)
|
||||||
|
|
||||||
|
# measure elapsed time
|
||||||
|
epoch_time.update(time.time() - start_time)
|
||||||
|
start_time = time.time()
|
||||||
|
|
||||||
|
logger.log("\n" + "-" * 200)
|
||||||
|
logger.log(
|
||||||
|
"Finish training/validation in {:} with Max-GPU-Memory of {:.2f} MB, and save final checkpoint into {:}".format(
|
||||||
|
convert_secs2time(epoch_time.sum, True),
|
||||||
|
max(v for k, v in max_bytes.items()) / 1e6,
|
||||||
|
logger.path("info"),
|
||||||
|
)
|
||||||
|
)
|
||||||
|
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(
|
||||||
|
"--workers",
|
||||||
|
type=int,
|
||||||
|
default=8,
|
||||||
|
help="number of data loading workers (default: 8)",
|
||||||
|
)
|
||||||
|
# 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"
|
||||||
|
main(args)
|
||||||
29
lib/models/xcore.py
Normal file
29
lib/models/xcore.py
Normal file
@@ -0,0 +1,29 @@
|
|||||||
|
#####################################################
|
||||||
|
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
|
||||||
|
#######################################################
|
||||||
|
# Use module in xlayers to construct different models #
|
||||||
|
#######################################################
|
||||||
|
from typing import List, Text, Dict, Any
|
||||||
|
|
||||||
|
__all__ = ["get_model"]
|
||||||
|
|
||||||
|
|
||||||
|
from xlayers.super_core import SuperSequential, SuperMLPv1
|
||||||
|
from xlayers.super_core import SuperSimpleNorm
|
||||||
|
from xlayers.super_core import SuperLinear
|
||||||
|
|
||||||
|
|
||||||
|
def get_model(config: Dict[Text, Any], **kwargs):
|
||||||
|
model_type = config.get("model_type", "simple_mlp")
|
||||||
|
if model_type == "simple_mlp":
|
||||||
|
model = SuperSequential(
|
||||||
|
SuperSimpleNorm(kwargs["mean"], kwargs["std"]),
|
||||||
|
SuperLinear(kwargs["input_dim"], 200),
|
||||||
|
torch.nn.LeakyReLU(),
|
||||||
|
SuperLinear(200, 100),
|
||||||
|
torch.nn.LeakyReLU(),
|
||||||
|
SuperLinear(100, kwargs["output_dim"]),
|
||||||
|
)
|
||||||
|
else:
|
||||||
|
raise TypeError("Unkonwn model type: {:}".format(model_type))
|
||||||
|
return model
|
||||||
Reference in New Issue
Block a user