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Merge pull request #38 from Megvii-BaseDetection/add-sche-exps
[Enhance]: Add sche exps.
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exps/bev_depth_lss_r50_256x704_128x128_20e_cbgs_2key_da_ema.py
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,93 @@ | ||
| # Copyright (c) Megvii Inc. All rights reserved. | ||
| """ | ||
| mAP: 0.3589 | ||
| mATE: 0.6119 | ||
| mASE: 0.2692 | ||
| mAOE: 0.5074 | ||
| mAVE: 0.4086 | ||
| mAAE: 0.2009 | ||
| NDS: 0.4797 | ||
| Eval time: 183.3s | ||
| Per-class results: | ||
| Object Class AP ATE ASE AOE AVE AAE | ||
| car 0.559 0.475 0.157 0.112 0.370 0.205 | ||
| truck 0.270 0.659 0.196 0.103 0.356 0.181 | ||
| bus 0.374 0.651 0.184 0.072 0.846 0.326 | ||
| trailer 0.179 0.963 0.227 0.512 0.294 0.127 | ||
| construction_vehicle 0.081 0.825 0.481 1.352 0.094 0.345 | ||
| pedestrian 0.363 0.690 0.297 0.831 0.491 0.244 | ||
| motorcycle 0.354 0.580 0.255 0.545 0.615 0.164 | ||
| bicycle 0.301 0.447 0.280 0.920 0.203 0.015 | ||
| traffic_cone 0.539 0.435 0.324 nan nan nan | ||
| barrier 0.569 0.394 0.293 0.120 nan nan | ||
| """ | ||
| from argparse import ArgumentParser, Namespace | ||
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| import pytorch_lightning as pl | ||
| import torch | ||
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| from callbacks.ema import EMACallback | ||
| from exps.bev_depth_lss_r50_256x704_128x128_20e_cbgs_2key_da import \ | ||
| BEVDepthLightningModel as BaseBEVDepthLightningModel | ||
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| class BEVDepthLightningModel(BaseBEVDepthLightningModel): | ||
| def __init__(self, **kwargs): | ||
| super().__init__(**kwargs) | ||
| self.data_use_cbgs = True | ||
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| 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-2) | ||
| return [optimizer] | ||
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| def main(args: Namespace) -> None: | ||
| if args.seed is not None: | ||
| pl.seed_everything(args.seed) | ||
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| model = BEVDepthLightningModel(**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) | ||
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| 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 = BEVDepthLightningModel.add_model_specific_args(parent_parser) | ||
| parser.set_defaults(profiler='simple', | ||
| deterministic=False, | ||
| max_epochs=20, | ||
| 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_depth_lss_r50_' | ||
| '256x704_128x128_20e_cbgs_2key_da_ema') | ||
| args = parser.parse_args() | ||
| main(args) | ||
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| if __name__ == '__main__': | ||
| run_cli() |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,96 @@ | ||
| # Copyright (c) Megvii Inc. All rights reserved. | ||
| """ | ||
| mAP: 0.3329 | ||
| mATE: 0.6832 | ||
| mASE: 0.2761 | ||
| mAOE: 0.5446 | ||
| mAVE: 0.5258 | ||
| mAAE: 0.2259 | ||
| NDS: 0.4409 | ||
| Per-class results: | ||
| Object Class AP ATE ASE AOE AVE AAE | ||
| car 0.505 0.531 0.165 0.189 0.618 0.234 | ||
| truck 0.274 0.731 0.206 0.211 0.546 0.223 | ||
| bus 0.394 0.673 0.219 0.148 1.061 0.274 | ||
| trailer 0.174 0.934 0.228 0.544 0.369 0.183 | ||
| construction_vehicle 0.079 1.043 0.528 1.162 0.112 0.376 | ||
| pedestrian 0.284 0.748 0.294 0.973 0.575 0.297 | ||
| motorcycle 0.345 0.633 0.256 0.719 0.667 0.214 | ||
| bicycle 0.314 0.544 0.252 0.778 0.259 0.007 | ||
| traffic_cone 0.453 0.519 0.335 nan nan nan | ||
| barrier 0.506 0.475 0.279 0.178 nan nan | ||
| """ | ||
| from argparse import ArgumentParser, Namespace | ||
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| import pytorch_lightning as pl | ||
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| from callbacks.ema import EMACallback | ||
| from exps.bev_depth_lss_r50_256x704_128x128_24e_ema import \ | ||
| BEVDepthLightningModel as BaseBEVDepthLightningModel | ||
| from models.bev_depth import BEVDepth | ||
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| class BEVDepthLightningModel(BaseBEVDepthLightningModel): | ||
| def __init__(self, **kwargs): | ||
| super().__init__(**kwargs) | ||
| self.key_idxes = [-1] | ||
| self.head_conf['bev_backbone_conf']['in_channels'] = 80 * ( | ||
| len(self.key_idxes) + 1) | ||
| self.head_conf['bev_neck_conf']['in_channels'] = [ | ||
| 80 * (len(self.key_idxes) + 1), 160, 320, 640 | ||
| ] | ||
| self.head_conf['train_cfg']['code_weight'] = [ | ||
| 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 | ||
| ] | ||
| self.model = BEVDepth(self.backbone_conf, | ||
| self.head_conf, | ||
| is_train_depth=True) | ||
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| def main(args: Namespace) -> None: | ||
| if args.seed is not None: | ||
| pl.seed_everything(args.seed) | ||
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| model = BEVDepthLightningModel(**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) | ||
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| 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 = BEVDepthLightningModel.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_depth_lss_r50_256x704_' | ||
| '128x128_24e_2key_ema') | ||
| args = parser.parse_args() | ||
| main(args) | ||
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| if __name__ == '__main__': | ||
| run_cli() |
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