这意味着你的数据将始终与你的metrics 放在相同的设备上。 在Lightning中支持使用原生的self.log,Lightning会根据on_step和on_epoch标志来记录metric,如果on_epoch=True,logger 会在epoch结束的时候自动调用.compute()。 metric 的.reset()方法的度量在一个epoch结束后自动被调用。 Lightning的转换 已经熟悉Lightning的...
这意味着你的数据将始终与你的metrics 放在相同的设备上。 在Lightning中支持使用原生的self.log,Lightning会根据on_step 和on_epoch标志来记录metric,如果on_epoch=True,logger 会在epoch结束的时候自动调用.compute()。 metric 的.reset()方法的度量在一个epoch结束后自动被调用。 Lightning的转换 已经熟悉Lightning...
compute_on_step: bool = True, dist_sync_on_step: bool = False, process_group: Optional[Any] = None, dist_sync_fn: Callable = None, ): super().__init__( compute_on_step=compute_on_step, dist_sync_on_step=dist_sync_on_step, ...
_stat_scores_updatefromtorchmetrics.utilities.enumsimportAverageMethod,MDMCAverageMethoddef_my_sensitivity_compute(tp:Tensor,fp:Tensor,tn:Tensor,fn:Tensor,average:str,mdmc_average:Optional[str],)
train_acc, on_step=True, on_epoch=True, prog_bar=True) return loss ... Expected behavior The training run shouldn't see a "graph break" warning. Environment TorchMetrics version (and how you installed TM, e.g. conda, pip, build from source): using pip, version 0.11.4 Python & PyTo...
我通过使用f1_score.compute().item()解决了这个问题。我了解到，当我们使用torchmetrics时，有一种方法使用自定义累加计算所有批次的指标。因此，它不需要使用AverageMeter来保存值并计算得分的平均值。
def training_epoch_end(self, training_step_outputs): # compute metrics train_accuracy = self.train_acc.compute() train_f1 = self.train_f1.compute() train_auroc = self.train_auroc.compute() # log metrics self.log("epoch_train_accuracy", train_accuracy) ...
(result_metric, on_step) return elem_type({k: apply_to_collection(v, dtype, function, *args, **kwargs) for k, v in data.items()}) File "E:\vfrancais\sources\python\pylayermonitoring\.venv\lib\site-packages\lightning\pytorch\trainer\connectors\logger_connector\result.py", line 438, ...
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self.log_dict({'IoU' : intersect['iou']}, on_step=False, on_epoch=True, prog_bar=True, batch_size=4) def on_validation_end(self): self.print(self.mAP.compute()) def configure_optimizers(self): return optim.Adam(self.parameters(), lr=self.lr, weight_decay=self.decay) ...