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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
# forward
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()
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