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@@ -62,8 +62,7 @@ class YoloTrainer(object):
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self.scaler = torch.cuda.amp.GradScaler(enabled=args.fp16)
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# ---------------------------- Build Optimizer ----------------------------
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- self.grad_accumulate = max(64 // args.batch_size, 1)
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- cfg.base_lr = cfg.per_image_lr * args.batch_size * self.grad_accumulate
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+ cfg.base_lr = cfg.per_image_lr * args.batch_size
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cfg.min_lr = cfg.base_lr * cfg.min_lr_ratio
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self.optimizer, self.start_epoch = build_yolo_optimizer(cfg, model, args.resume)
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@@ -209,25 +208,22 @@ class YoloTrainer(object):
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# Compute loss
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loss_dict = self.criterion(outputs=outputs, targets=targets)
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losses = loss_dict['losses']
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- losses /= self.grad_accumulate
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loss_dict_reduced = distributed_utils.reduce_dict(loss_dict)
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# Backward
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self.scaler.scale(losses).backward()
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- gnorm = get_total_grad_norm(model.parameters())
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# Optimize
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- if (iter_i + 1) % self.grad_accumulate == 0:
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- if self.cfg.clip_max_norm > 0:
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- self.scaler.unscale_(self.optimizer)
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- gnorm = torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=self.cfg.clip_max_norm)
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- self.scaler.step(self.optimizer)
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- self.scaler.update()
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- self.optimizer.zero_grad()
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-
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- # ModelEMA
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- if self.model_ema is not None:
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- self.model_ema.update(model)
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+ if self.cfg.clip_max_norm > 0:
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+ self.scaler.unscale_(self.optimizer)
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+ gnorm = torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=self.cfg.clip_max_norm)
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+ self.scaler.step(self.optimizer)
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+ self.scaler.update()
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+ self.optimizer.zero_grad()
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+
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+ # ModelEMA
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+ if self.model_ema is not None:
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+ self.model_ema.update(model)
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# Update log
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metric_logger.update(**loss_dict_reduced)
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