| | 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() |
| |
|
