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train.py
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import argparse
import os
import torch
import pytorch_lightning as pl
import pytorch_lightning.loggers as pl_loggers
from core.utils import load_cfg, load_weights
from core.distiller import Distiller
def build_logger(cfg):
return getattr(pl_loggers, cfg.type)(
**cfg.params
)
def main(args):
cfg = load_cfg(args.cfg)
distiller = Distiller(cfg)
if args.ckpt is not None:
ckpt = torch.load(args.ckpt, map_location="cpu")
load_weights(distiller, ckpt["state_dict"])
logger = build_logger(cfg.logger)
checkpoint_callback = pl.callbacks.ModelCheckpoint(
dirpath=os.getcwd() if args.checkpoint_dir is None else args.checkpoint_dir,
save_top_k=True,
save_last=True,
verbose=True,
monitor=cfg.trainer.monitor,
mode=cfg.trainer.monitor_mode
)
trainer = pl.Trainer(
gpus=args.gpus,
max_epochs=cfg.trainer.max_epochs,
accumulate_grad_batches=args.grad_batches,
distributed_backend=args.distributed_backend,
val_check_interval=args.val_check_interval,
logger=logger,
callbacks=[checkpoint_callback]
)
trainer.fit(distiller)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# pipeline configure
parser.add_argument("--gpus", type=int, default=0, help="number of available GPUs")
parser.add_argument('--distributed-backend', type=str, default="ddp", choices=('dp', 'ddp', 'ddp2'),
help='supports three options dp, ddp, ddp2')
parser.add_argument("--checkpoint_dir", type=str, default=None, help="path to checkpoint_dir")
parser.add_argument("--val-check-interval", type=int, default=500, help="validation check interval")
parser.add_argument("--grad_batches", type=int, default=1, help="number of batches to accumulate")
parser.add_argument("--ckpt", type=str, default=None, help="path to checkpoint")
parser.add_argument("--cfg", type=str, help="path to config file")
args = parser.parse_args()
main(args)