| | from tqdm import tqdm |
| | import torch |
| | from trainer.build import TRAINER_REGISTRY, BaseTrainer |
| |
|
| | @TRAINER_REGISTRY.register() |
| | class OpenVocabTrainer(BaseTrainer): |
| | def __init__(self, cfg): |
| | super().__init__(cfg) |
| | self.best_metric = -1 |
| |
|
| | def forward(self, data_dict, mode): |
| | return self.model(data_dict, mode) |
| |
|
| | def backward(self, loss, mode=None): |
| | self.accelerator.backward(loss) |
| | |
| | if self.grad_norm is not None and self.accelerator.sync_gradients: |
| | self.accelerator.clip_grad_norm_(self.model.parameters(), self.grad_norm) |
| | |
| | self.optimizer.step() |
| | self.scheduler.step() |
| | self.optimizer.zero_grad() |
| |
|
| | def train_step(self, epoch, mode = None): |
| | self.model.train() |
| | loader = self.data_loaders[self.mode] |
| | is_main = self.accelerator.is_main_process |
| | |
| | pbar = tqdm(loader, disable=not is_main, desc=f"[Epoch {epoch + 1}/{self.epochs}]") |
| |
|
| | for data_dict in pbar: |
| | with self.accelerator.accumulate(self.model): |
| | |
| | data_dict = self.forward(data_dict, mode=mode) |
| |
|
| | |
| | loss, losses = self.loss(data_dict) |
| | |
| | |
| | self.backward(loss, mode=mode) |
| |
|
| | |
| | self.global_step += 1 |
| | log_dict = {'step': self.global_step, **losses} |
| |
|
| | if mode == 'qa': |
| | metrics = self.evaluator["train"].batch_metrics(data_dict) |
| | log_dict.update(metrics) |
| | self.log(log_dict, mode="train") |
| |
|
| | @torch.no_grad() |
| | def eval_step(self, epoch, mode): |
| | self.model.eval() |
| | loader = self.data_loaders["val"] |
| | pbar = tqdm(range(len(loader)), disable=(not self.accelerator.is_main_process)) |
| | for i, data_dict in enumerate(loader): |
| | data_dict = self.forward(data_dict, mode = mode) |
| | loss, losses = self.loss(data_dict) |
| | log_dict = {'epoch': epoch, **losses} |
| | pbar.update(1) |
| | self.log(log_dict, mode="val") |
| |
|
| | @torch.no_grad() |
| | def test_step(self): |
| | self.model.eval() |
| | loader = self.data_loaders["test"] |
| | pbar = tqdm(range(len(loader)), disable=(not self.accelerator.is_main_process)) |
| | for i, data_dict in enumerate(loader): |
| | data_dict = self.forward(data_dict) |
| | self.evaluator["val"].update(data_dict) |
| | pbar.update(1) |
| | is_best, results = self.evaluator["val"].record() |
| | self.log(results, mode="test") |
| | self.evaluator["val"].reset() |
| | return results |
| |
|
| | def run(self): |
| | num_trainable_params = 0 |
| | for name, param in self.model.named_parameters(): |
| | if param.requires_grad: |
| | num_trainable_params += param.numel() |
| | print(name) |
| |
|
| | print(f"Total number of trainable parameters: {num_trainable_params:,}") |
| | if self.mode in ['warmup', "pretrain"]: |
| | start_epoch = self.exp_tracker.epoch |
| | self.global_step = start_epoch * len(self.data_loaders[self.mode]) |
| | |
| | for epoch in range(start_epoch, self.epochs): |
| | self.exp_tracker.step() |
| | self.train_step(epoch, mode=self.mode) |
| | |
| | self.accelerator.wait_for_everyone() |
| | if self.accelerator.is_main_process: |
| | if self.epochs_per_save and (epoch + 1) % self.epochs_per_save == 0: |
| | self.save(f"ckpt_{epoch+1}.pth") |
| |
|
| | self.save(f"ckpt_{epoch+1}.pth") |
| | self.accelerator.end_training() |
| | else: |
| | if self.mode == "train": |
| | start_epoch = self.exp_tracker.epoch |
| | self.global_step = start_epoch * len(self.data_loaders["train"]) |
| | for epoch in range(start_epoch, self.epochs): |
| | self.exp_tracker.step() |
| | self.train_step(epoch) |
| | if self.epochs_per_eval and (epoch + 1) % self.epochs_per_eval == 0: |
| | is_best = self.eval_step(epoch) |
| | self.accelerator.print(f"[Epoch {epoch + 1}/{self.epochs}] finished eval, is_best: {is_best}") |
| | else: |
| | is_best = False |
| |
|
| | self.accelerator.wait_for_everyone() |
| | if self.accelerator.is_main_process: |
| | if is_best: |
| | self.save("best.pth") |
| | if self.epochs_per_save and (epoch + 1) % self.epochs_per_save == 0: |
| | self.save(f"ckpt_{epoch+1}.pth") |
| |
|
| | self.test_step() |
| | if self.mode == "train": |
| | self.accelerator.end_training() |
