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import copy |
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from tqdm import tqdm |
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import torch |
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from trainer.build import TRAINER_REGISTRY |
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from trainer.build import BaseTrainer |
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@TRAINER_REGISTRY.register() |
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class DebugTrainer(BaseTrainer): |
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def __init__(self, cfg): |
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super().__init__(cfg) |
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self.best_metric = -1 |
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def forward(self, data_dict): |
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return self.model(data_dict) |
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def backward(self, loss): |
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self.optimizer.zero_grad() |
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self.accelerator.backward(loss) |
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if self.grad_norm is not None and self.accelerator.sync_gradients: |
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self.accelerator.clip_grad_norm_(self.model.parameters(), self.grad_norm) |
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self.optimizer.step() |
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self.scheduler.step() |
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def train_step(self, epoch): |
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self.model.train() |
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loader = self.data_loaders["train"] |
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pbar = tqdm(range(len(loader)), disable=(not self.accelerator.is_main_process), |
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desc=f"[Epoch {epoch + 1}/{self.epochs}]") |
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for i, data_dict in enumerate(loader): |
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with self.accelerator.accumulate(self.model): |
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data_dict['cur_step'] = epoch * len(loader) + i |
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data_dict['total_steps'] = self.total_steps |
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pbar.update(1) |
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@torch.no_grad() |
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def eval_step(self, epoch): |
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self.model.eval() |
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loader = self.data_loaders["val"] |
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pbar = tqdm(range(len(loader)), disable=(not self.accelerator.is_main_process)) |
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for i, data_dict in enumerate(loader): |
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pbar.update(1) |
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return |
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@torch.no_grad() |
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def test_step(self): |
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self.model.eval() |
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loader = self.data_loaders["test"] |
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pbar = tqdm(range(len(loader)), disable=(not self.accelerator.is_main_process)) |
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for i, data_dict in enumerate(loader): |
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pbar.update(1) |
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return |
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def run(self): |
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if self.mode == "train": |
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start_epoch = self.exp_tracker.epoch |
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self.global_step = start_epoch * len(self.data_loaders["train"]) |
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for epoch in range(start_epoch, self.epochs): |
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self.exp_tracker.step() |
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self.train_step(epoch) |
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if self.epochs_per_eval and (epoch + 1) % self.epochs_per_eval == 0: |
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self.eval_step(epoch) |
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break |
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self.test_step() |
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if self.mode == "train": |
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self.accelerator.end_training() |
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