#!/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,), )