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bev_stereo_lss_r50_256x704_128x128_24e_2key_ema.py
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bev_stereo_lss_r50_256x704_128x128_24e_2key_ema.py
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# Copyright (c) Megvii Inc. All rights reserved.
"""
mAP: 0.3494
mATE: 0.6672
mASE: 0.2785
mAOE: 0.5607
mAVE: 0.4687
mAAE: 0.2295
NDS: 0.4542
Eval time: 166.7s
Per-class results:
Object Class AP ATE ASE AOE AVE AAE
car 0.509 0.522 0.163 0.187 0.507 0.228
truck 0.287 0.694 0.213 0.202 0.449 0.229
bus 0.390 0.681 0.207 0.152 0.902 0.261
trailer 0.167 0.945 0.248 0.491 0.340 0.185
construction_vehicle 0.087 1.057 0.515 1.199 0.104 0.377
pedestrian 0.351 0.729 0.299 0.987 0.575 0.321
motorcycle 0.368 0.581 0.262 0.721 0.663 0.226
bicycle 0.338 0.494 0.258 0.921 0.209 0.008
traffic_cone 0.494 0.502 0.341 nan nan nan
barrier 0.502 0.467 0.278 0.185 nan nan
"""
from argparse import ArgumentParser, Namespace
import pytorch_lightning as pl
import torch
import torch.nn.parallel
import torch.utils.data
import torch.utils.data.distributed
from callbacks.ema import EMACallback
from exps.bev_stereo_lss_r50_256x704_128x128_24e_2key import \
BEVStereoLightningModel as BaseBEVStereoLightningModel
class BEVStereoLightningModel(BaseBEVStereoLightningModel):
def configure_optimizers(self):
lr = self.basic_lr_per_img * \
self.batch_size_per_device * self.gpus
optimizer = torch.optim.AdamW(self.model.parameters(),
lr=lr,
weight_decay=1e-7)
return [optimizer]
def main(args: Namespace) -> None:
if args.seed is not None:
pl.seed_everything(args.seed)
model = BEVStereoLightningModel(**vars(args))
train_dataloader = model.train_dataloader()
ema_callback = EMACallback(len(train_dataloader.dataset) * args.max_epochs)
trainer = pl.Trainer.from_argparse_args(args, callbacks=[ema_callback])
if args.evaluate:
trainer.test(model, ckpt_path=args.ckpt_path)
else:
trainer.fit(model)
def run_cli():
parent_parser = ArgumentParser(add_help=False)
parent_parser = pl.Trainer.add_argparse_args(parent_parser)
parent_parser.add_argument('-e',
'--evaluate',
dest='evaluate',
action='store_true',
help='evaluate model on validation set')
parent_parser.add_argument('-b', '--batch_size_per_device', type=int)
parent_parser.add_argument('--seed',
type=int,
default=0,
help='seed for initializing training.')
parent_parser.add_argument('--ckpt_path', type=str)
parser = BEVStereoLightningModel.add_model_specific_args(parent_parser)
parser.set_defaults(
profiler='simple',
deterministic=False,
max_epochs=24,
accelerator='ddp',
num_sanity_val_steps=0,
gradient_clip_val=5,
limit_val_batches=0,
enable_checkpointing=False,
precision=16,
default_root_dir='./outputs/bev_stereo_lss_r50_256x704_'
'128x128_24e_2key_ema')
args = parser.parse_args()
main(args)
if __name__ == '__main__':
run_cli()