verified tfx support
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0a03042bf7
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@ -373,7 +373,7 @@ class Trainer(TrainerIO):
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metrics.update(grad_norm_dic)
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metrics.update(grad_norm_dic)
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# log metrics
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# log metrics
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scalar_metrics = self.__metrics_to_scalars(metrics)
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scalar_metrics = self.__metrics_to_scalars(metrics, blacklist=self.__log_vals_blacklist())
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self.experiment.log(scalar_metrics, global_step=self.global_step)
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self.experiment.log(scalar_metrics, global_step=self.global_step)
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self.experiment.save()
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self.experiment.save()
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@ -401,7 +401,7 @@ class Trainer(TrainerIO):
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if stop:
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if stop:
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return
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return
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def __metrics_to_scalars(self, metrics):
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def __metrics_to_scalars(self, metrics, blacklist=[]):
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new_metrics = {}
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new_metrics = {}
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for k, v in metrics.items():
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for k, v in metrics.items():
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if type(v) is torch.Tensor:
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if type(v) is torch.Tensor:
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@ -410,10 +410,15 @@ class Trainer(TrainerIO):
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if type(v) is dict:
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if type(v) is dict:
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v = self.__metrics_to_scalars(v)
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v = self.__metrics_to_scalars(v)
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new_metrics[k] = float(v)
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if k not in blacklist:
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new_metrics[k] = float(v)
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return new_metrics
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return new_metrics
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def __log_vals_blacklist(self):
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"""avoid logging some vals lightning uses to maintain state"""
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blacklist = {'batch_nb', 'v_nb', 'epoch', 'gpu'}
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return blacklist
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def __run_tng_batch(self, data_batch, batch_nb):
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def __run_tng_batch(self, data_batch, batch_nb):
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if data_batch is None:
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if data_batch is None:
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