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* release better dino result * refine * fix deta config * add dino better hyper * add DINO pretrained weights links * bump to v0.4.0
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,274 @@ | ||
| #!/usr/bin/env python | ||
| # Copyright (c) Facebook, Inc. and its affiliates. | ||
| """ | ||
| Training script using the new "LazyConfig" python config files. | ||
| This scripts reads a given python config file and runs the training or evaluation. | ||
| It can be used to train any models or dataset as long as they can be | ||
| instantiated by the recursive construction defined in the given config file. | ||
| Besides lazy construction of models, dataloader, etc., this scripts expects a | ||
| few common configuration parameters currently defined in "configs/common/train.py". | ||
| To add more complicated training logic, you can easily add other configs | ||
| in the config file and implement a new train_net.py to handle them. | ||
| """ | ||
| import logging | ||
| import os | ||
| import sys | ||
| import time | ||
| import torch | ||
| from torch.nn.parallel import DataParallel, DistributedDataParallel | ||
|
|
||
| from detectron2.checkpoint import DetectionCheckpointer | ||
| from detectron2.config import LazyConfig, instantiate | ||
| from detectron2.engine import ( | ||
| SimpleTrainer, | ||
| default_argument_parser, | ||
| default_setup, | ||
| default_writers, | ||
| hooks, | ||
| launch, | ||
| ) | ||
| from detectron2.engine.defaults import create_ddp_model | ||
| from detectron2.evaluation import inference_on_dataset, print_csv_format | ||
| from detectron2.utils import comm | ||
|
|
||
| sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir))) | ||
|
|
||
| logger = logging.getLogger("detrex") | ||
|
|
||
|
|
||
| def match_name_keywords(n, name_keywords): | ||
| out = False | ||
| for b in name_keywords: | ||
| if b in n: | ||
| out = True | ||
| break | ||
| return out | ||
|
|
||
|
|
||
| class Trainer(SimpleTrainer): | ||
| """ | ||
| We've combine Simple and AMP Trainer together. | ||
| """ | ||
|
|
||
| def __init__( | ||
| self, | ||
| model, | ||
| dataloader, | ||
| optimizer, | ||
| amp=False, | ||
| clip_grad_params=None, | ||
| grad_scaler=None, | ||
| ): | ||
| super().__init__(model=model, data_loader=dataloader, optimizer=optimizer) | ||
|
|
||
| unsupported = "AMPTrainer does not support single-process multi-device training!" | ||
| if isinstance(model, DistributedDataParallel): | ||
| assert not (model.device_ids and len(model.device_ids) > 1), unsupported | ||
| assert not isinstance(model, DataParallel), unsupported | ||
|
|
||
| if amp: | ||
| if grad_scaler is None: | ||
| from torch.cuda.amp import GradScaler | ||
|
|
||
| grad_scaler = GradScaler() | ||
| self.grad_scaler = grad_scaler | ||
|
|
||
| # set True to use amp training | ||
| self.amp = amp | ||
|
|
||
| # gradient clip hyper-params | ||
| self.clip_grad_params = clip_grad_params | ||
|
|
||
| def run_step(self): | ||
| """ | ||
| Implement the standard training logic described above. | ||
| """ | ||
| assert self.model.training, "[Trainer] model was changed to eval mode!" | ||
| assert torch.cuda.is_available(), "[Trainer] CUDA is required for AMP training!" | ||
| from torch.cuda.amp import autocast | ||
|
|
||
| start = time.perf_counter() | ||
| """ | ||
| If you want to do something with the data, you can wrap the dataloader. | ||
| """ | ||
| data = next(self._data_loader_iter) | ||
| data_time = time.perf_counter() - start | ||
|
|
||
| """ | ||
| If you want to do something with the losses, you can wrap the model. | ||
| """ | ||
| loss_dict = self.model(data) | ||
| with autocast(enabled=self.amp): | ||
| if isinstance(loss_dict, torch.Tensor): | ||
| losses = loss_dict | ||
| loss_dict = {"total_loss": loss_dict} | ||
| else: | ||
| losses = sum(loss_dict.values()) | ||
|
|
||
| """ | ||
| If you need to accumulate gradients or do something similar, you can | ||
| wrap the optimizer with your custom `zero_grad()` method. | ||
| """ | ||
| self.optimizer.zero_grad() | ||
|
|
||
| if self.amp: | ||
| self.grad_scaler.scale(losses).backward() | ||
| if self.clip_grad_params is not None: | ||
| self.grad_scaler.unscale_(self.optimizer) | ||
| self.clip_grads(self.model.parameters()) | ||
| self.grad_scaler.step(self.optimizer) | ||
| self.grad_scaler.update() | ||
| else: | ||
| losses.backward() | ||
| if self.clip_grad_params is not None: | ||
| self.clip_grads(self.model.parameters()) | ||
| self.optimizer.step() | ||
|
|
||
| self._write_metrics(loss_dict, data_time) | ||
|
|
||
| def clip_grads(self, params): | ||
| params = list(filter(lambda p: p.requires_grad and p.grad is not None, params)) | ||
| if len(params) > 0: | ||
| return torch.nn.utils.clip_grad_norm_( | ||
| parameters=params, | ||
| **self.clip_grad_params, | ||
| ) | ||
|
|
||
|
|
||
| def do_test(cfg, model): | ||
| if "evaluator" in cfg.dataloader: | ||
| ret = inference_on_dataset( | ||
| model, instantiate(cfg.dataloader.test), instantiate(cfg.dataloader.evaluator) | ||
| ) | ||
| print_csv_format(ret) | ||
| return ret | ||
|
|
||
|
|
||
| def do_train(args, cfg): | ||
| """ | ||
| Args: | ||
| cfg: an object with the following attributes: | ||
| model: instantiate to a module | ||
| dataloader.{train,test}: instantiate to dataloaders | ||
| dataloader.evaluator: instantiate to evaluator for test set | ||
| optimizer: instantaite to an optimizer | ||
| lr_multiplier: instantiate to a fvcore scheduler | ||
| train: other misc config defined in `configs/common/train.py`, including: | ||
| output_dir (str) | ||
| init_checkpoint (str) | ||
| amp.enabled (bool) | ||
| max_iter (int) | ||
| eval_period, log_period (int) | ||
| device (str) | ||
| checkpointer (dict) | ||
| ddp (dict) | ||
| """ | ||
| model = instantiate(cfg.model) | ||
| logger = logging.getLogger("detectron2") | ||
| logger.info("Model:\n{}".format(model)) | ||
| model.to(cfg.train.device) | ||
|
|
||
| # this is an hack of train_net | ||
| param_dicts = [ | ||
| { | ||
| "params": [ | ||
| p | ||
| for n, p in model.named_parameters() | ||
| if not match_name_keywords(n, ["backbone"]) | ||
| and not match_name_keywords(n, ["reference_points", "sampling_offsets"]) | ||
| and p.requires_grad | ||
| ], | ||
| "lr": 2e-4, | ||
| }, | ||
| { | ||
| "params": [ | ||
| p | ||
| for n, p in model.named_parameters() | ||
| if match_name_keywords(n, ["backbone"]) and p.requires_grad | ||
| ], | ||
| "lr": 2e-5, | ||
| }, | ||
| { | ||
| "params": [ | ||
| p | ||
| for n, p in model.named_parameters() | ||
| if match_name_keywords(n, ["reference_points", "sampling_offsets"]) | ||
| and p.requires_grad | ||
| ], | ||
| "lr": 2e-5, | ||
| }, | ||
| ] | ||
| optim = torch.optim.AdamW(param_dicts, 2e-4, weight_decay=1e-4) | ||
|
|
||
| train_loader = instantiate(cfg.dataloader.train) | ||
|
|
||
| model = create_ddp_model(model, **cfg.train.ddp) | ||
|
|
||
| trainer = Trainer( | ||
| model=model, | ||
| dataloader=train_loader, | ||
| optimizer=optim, | ||
| amp=cfg.train.amp.enabled, | ||
| clip_grad_params=cfg.train.clip_grad.params if cfg.train.clip_grad.enabled else None, | ||
| ) | ||
|
|
||
| checkpointer = DetectionCheckpointer( | ||
| model, | ||
| cfg.train.output_dir, | ||
| trainer=trainer, | ||
| ) | ||
|
|
||
| trainer.register_hooks( | ||
| [ | ||
| hooks.IterationTimer(), | ||
| hooks.LRScheduler(scheduler=instantiate(cfg.lr_multiplier)), | ||
| hooks.PeriodicCheckpointer(checkpointer, **cfg.train.checkpointer) | ||
| if comm.is_main_process() | ||
| else None, | ||
| hooks.EvalHook(cfg.train.eval_period, lambda: do_test(cfg, model)), | ||
| hooks.PeriodicWriter( | ||
| default_writers(cfg.train.output_dir, cfg.train.max_iter), | ||
| period=cfg.train.log_period, | ||
| ) | ||
| if comm.is_main_process() | ||
| else None, | ||
| ] | ||
| ) | ||
|
|
||
| checkpointer.resume_or_load(cfg.train.init_checkpoint, resume=args.resume) | ||
| if args.resume and checkpointer.has_checkpoint(): | ||
| # The checkpoint stores the training iteration that just finished, thus we start | ||
| # at the next iteration | ||
| start_iter = trainer.iter + 1 | ||
| else: | ||
| start_iter = 0 | ||
| trainer.train(start_iter, cfg.train.max_iter) | ||
|
|
||
|
|
||
| def main(args): | ||
| cfg = LazyConfig.load(args.config_file) | ||
| cfg = LazyConfig.apply_overrides(cfg, args.opts) | ||
| default_setup(cfg, args) | ||
|
|
||
| if args.eval_only: | ||
| model = instantiate(cfg.model) | ||
| model.to(cfg.train.device) | ||
| model = create_ddp_model(model) | ||
| DetectionCheckpointer(model).load(cfg.train.init_checkpoint) | ||
| print(do_test(cfg, model)) | ||
| else: | ||
| do_train(args, cfg) | ||
|
|
||
|
|
||
| if __name__ == "__main__": | ||
| args = default_argument_parser().parse_args() | ||
| launch( | ||
| main, | ||
| args.num_gpus, | ||
| num_machines=args.num_machines, | ||
| machine_rank=args.machine_rank, | ||
| dist_url=args.dist_url, | ||
| args=(args,), | ||
| ) |
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