| import copy |
| from tqdm import tqdm |
|
|
| import torch |
| from trainer.build import TRAINER_REGISTRY |
| from trainer.build import BaseTrainer |
|
|
|
|
| @TRAINER_REGISTRY.register() |
| class DebugTrainer(BaseTrainer): |
| def __init__(self, cfg): |
| super().__init__(cfg) |
| self.best_metric = -1 |
|
|
| def forward(self, data_dict): |
| return self.model(data_dict) |
|
|
| def backward(self, loss): |
| self.optimizer.zero_grad() |
| 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() |
|
|
| def train_step(self, epoch): |
| self.model.train() |
| loader = self.data_loaders["train"] |
| pbar = tqdm(range(len(loader)), disable=(not self.accelerator.is_main_process), |
| desc=f"[Epoch {epoch + 1}/{self.epochs}]") |
| for i, data_dict in enumerate(loader): |
| with self.accelerator.accumulate(self.model): |
| data_dict['cur_step'] = epoch * len(loader) + i |
| data_dict['total_steps'] = self.total_steps |
| |
| pbar.update(1) |
|
|
| @torch.no_grad() |
| def eval_step(self, epoch): |
| 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): |
| pbar.update(1) |
| return |
|
|
| @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): |
| pbar.update(1) |
| return |
|
|
| def run(self): |
| 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: |
| self.eval_step(epoch) |
| break |
|
|
| self.test_step() |
| if self.mode == "train": |
| self.accelerator.end_training() |
|
|
