| import torch | |
| from loguru import logger | |
| from torch import Tensor | |
| from torch.cuda.amp import GradScaler, autocast | |
| from tqdm import tqdm | |
| from yolo.config.config import Config, TrainConfig | |
| from yolo.model.yolo import YOLO | |
| from yolo.tools.model_helper import EMA, get_optimizer, get_scheduler | |
| from yolo.utils.loss import get_loss_function | |
| class Trainer: | |
| def __init__(self, model: YOLO, cfg: Config, device): | |
| train_cfg: TrainConfig = cfg.hyper.train | |
| self.model = model.to(device) | |
| self.device = device | |
| self.optimizer = get_optimizer(model.parameters(), train_cfg.optimizer) | |
| self.scheduler = get_scheduler(self.optimizer, train_cfg.scheduler) | |
| self.loss_fn = get_loss_function(cfg) | |
| if train_cfg.ema.get("enabled", False): | |
| self.ema = EMA(model, decay=train_cfg.ema.decay) | |
| else: | |
| self.ema = None | |
| self.scaler = GradScaler() | |
| def train_one_batch(self, data: Tensor, targets: Tensor, progress: tqdm): | |
| data, targets = data.to(self.device), targets.to(self.device) | |
| self.optimizer.zero_grad() | |
| with autocast(): | |
| outputs = self.model(data) | |
| loss, loss_item = self.loss_fn(outputs, targets) | |
| loss_iou, loss_dfl, loss_cls = loss_item | |
| progress.set_description(f"Loss IoU: {loss_iou:.5f}, DFL: {loss_dfl:.5f}, CLS: {loss_cls:.5f}") | |
| self.scaler.scale(loss).backward() | |
| self.scaler.step(self.optimizer) | |
| self.scaler.update() | |
| if self.ema: | |
| self.ema.update() | |
| return loss.item() | |
| def train_one_epoch(self, dataloader): | |
| self.model.train() | |
| total_loss = 0 | |
| with tqdm(dataloader, desc="Training") as progress: | |
| for data, targets in progress: | |
| loss = self.train_one_batch(data, targets, progress) | |
| total_loss += loss | |
| if self.scheduler: | |
| self.scheduler.step() | |
| return total_loss / len(dataloader) | |
| def save_checkpoint(self, epoch: int, filename="checkpoint.pt"): | |
| checkpoint = { | |
| "epoch": epoch, | |
| "model_state_dict": self.model.state_dict(), | |
| "optimizer_state_dict": self.optimizer.state_dict(), | |
| } | |
| if self.ema: | |
| self.ema.apply_shadow() | |
| checkpoint["model_state_dict_ema"] = self.model.state_dict() | |
| self.ema.restore() | |
| torch.save(checkpoint, filename) | |
| def train(self, dataloader, num_epochs): | |
| logger.info("start train") | |
| for epoch in range(num_epochs): | |
| epoch_loss = self.train_one_epoch(dataloader) | |
| logger.info(f"Epoch {epoch+1}/{num_epochs}, Loss: {epoch_loss:.4f}") | |
| if (epoch + 1) % 5 == 0: | |
| self.save_checkpoint(epoch, f"checkpoint_epoch_{epoch+1}.pth") | |