xautodl/exps/basic/xmain.py
2021-06-10 21:53:22 +08:00

260 lines
9.7 KiB
Python

#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.06 #
#####################################################
# python exps/basic/xmain.py --save_dir outputs/x #
#####################################################
import os, sys, time, torch, random, argparse
from copy import deepcopy
from pathlib import Path
lib_dir = (Path(__file__).parent / ".." / "..").resolve()
print("LIB-DIR: {:}".format(lib_dir))
if str(lib_dir) not in sys.path:
sys.path.insert(0, str(lib_dir))
from xautodl import xmisc
def main(args):
train_data = xmisc.nested_call_by_yaml(args.train_data_config, args.data_path)
valid_data = xmisc.nested_call_by_yaml(args.valid_data_config, args.data_path)
logger = xmisc.Logger(args.save_dir, prefix="seed-{:}-".format(args.rand_seed))
logger.log("Create the logger: {:}".format(logger))
logger.log("Arguments : -------------------------------")
for name, value in args._get_kwargs():
logger.log("{:16} : {:}".format(name, value))
logger.log("Python Version : {:}".format(sys.version.replace("\n", " ")))
logger.log("PyTorch Version : {:}".format(torch.__version__))
logger.log("cuDNN Version : {:}".format(torch.backends.cudnn.version()))
logger.log("CUDA available : {:}".format(torch.cuda.is_available()))
logger.log("CUDA GPU numbers : {:}".format(torch.cuda.device_count()))
logger.log(
"CUDA_VISIBLE_DEVICES : {:}".format(
os.environ["CUDA_VISIBLE_DEVICES"]
if "CUDA_VISIBLE_DEVICES" in os.environ
else "None"
)
)
logger.log("The training data is:\n{:}".format(train_data))
logger.log("The validation data is:\n{:}".format(valid_data))
model = xmisc.nested_call_by_yaml(args.model_config)
logger.log("The model is:\n{:}".format(model))
logger.log("The model size is {:.4f} M".format(xmisc.count_parameters(model)))
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,
)
logger.log("The training loader: {:}".format(train_loader))
logger.log("The validation loader: {:}".format(valid_loader))
optimizer = xmisc.nested_call_by_yaml(
args.optim_config,
model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay,
)
loss = xmisc.nested_call_by_yaml(args.loss_config)
logger.log("The optimizer is:\n{:}".format(optimizer))
logger.log("The loss is {:}".format(loss))
model, loss = torch.nn.DataParallel(model).cuda(), loss.cuda()
import pdb
pdb.set_trace()
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(
description="Train a classification model with a loss function.",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
parser.add_argument(
"--save_dir", type=str, help="Folder to save checkpoints and log."
)
parser.add_argument("--resume", type=str, help="Resume path.")
parser.add_argument("--init_model", type=str, help="The initialization model path.")
parser.add_argument("--model_config", type=str, help="The path to the model config")
parser.add_argument("--optim_config", type=str, help="The optimizer config file.")
parser.add_argument("--loss_config", type=str, help="The loss config file.")
parser.add_argument(
"--train_data_config", type=str, help="The training dataset config path."
)
parser.add_argument(
"--valid_data_config", type=str, help="The validation dataset config path."
)
parser.add_argument("--data_path", type=str, help="The path to the dataset.")
parser.add_argument("--algorithm", type=str, help="The algorithm.")
# Optimization options
parser.add_argument("--lr", type=float, help="The learning rate")
parser.add_argument("--weight_decay", type=float, help="The weight decay")
parser.add_argument("--batch_size", type=int, default=2, help="The batch size.")
parser.add_argument("--workers", type=int, default=4, help="The number of workers")
# 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)
if args.save_dir is None:
raise ValueError("The save-path argument can not be None")
main(args)