🔀 [Merge] branch 'TRAIN'

yolo/utils/model_utils.py

@@ -37,31 +37,31 @@ def lerp(start: float, end: float, step: Union[int, float], total: int = 1):
 
 
 class EMA(Callback):
-    def __init__(self, decay: float = 0.9999, tau: float = 500):
+    def __init__(self, decay: float = 0.9999, tau: float = 2000):
         super().__init__()
         logger.info(":chart_with_upwards_trend: Enable Model EMA")
         self.decay = decay
         self.tau = tau
         self.step = 0
+        self.ema_state_dict = None
 
     def setup(self, trainer, pl_module, stage):
         pl_module.ema = deepcopy(pl_module.model)
-        self.ema_parameters = [param.clone().detach().to(pl_module.device) for param in pl_module.parameters()]
+        self.tau /= trainer.world_size
         for param in pl_module.ema.parameters():
             param.requires_grad = False
 
     def on_validation_start(self, trainer: "Trainer", pl_module: "LightningModule"):
-        for param, ema_param in zip(pl_module.ema.parameters(), self.ema_parameters):
-            param.data.copy_(ema_param)
-            trainer.strategy.broadcast(param)
+        if self.ema_state_dict is None:
+            self.ema_state_dict = deepcopy(pl_module.model.state_dict())
+        pl_module.ema.load_state_dict(self.ema_state_dict)
 
-    @rank_zero_only
     @no_grad()
     def on_train_batch_end(self, trainer: "Trainer", pl_module: "LightningModule", *args, **kwargs) -> None:
         self.step += 1
         decay_factor = self.decay * (1 - exp(-self.step / self.tau))
-        for param, ema_param in zip(pl_module.parameters(), self.ema_parameters):
-            ema_param.data.copy_(lerp(param.detach(), ema_param, decay_factor))
+        for key, param in pl_module.model.state_dict().items():
+            self.ema_state_dict[key] = lerp(param.detach(), self.ema_state_dict[key], decay_factor)
 
 
 def create_optimizer(model: YOLO, optim_cfg: OptimizerConfig) -> Optimizer: