tabensemb.model.AbstractNN.log_dict#

method

AbstractNN.log_dict(dictionary: Mapping[str, Metric | Tensor | int | float | Mapping[str, Metric | Tensor | int | float]], prog_bar: bool = False, logger: bool | None = None, on_step: bool | None = None, on_epoch: bool | None = None, reduce_fx: str | Callable = 'mean', enable_graph: bool = False, sync_dist: bool = False, sync_dist_group: Any | None = None, add_dataloader_idx: bool = True, batch_size: int | None = None, rank_zero_only: bool = False) → None#

Log a dictionary of values at once.

Example:

values = {'loss': loss, 'acc': acc, ..., 'metric_n': metric_n}
self.log_dict(values)
Parameters:

  • dictionary – key value pairs. The values can be a float, Tensor, Metric, a dictionary of the former or a MetricCollection.

  • prog_bar – if True logs to the progress base.

  • logger – if True logs to the logger.

  • on_step – if True logs at this step. None auto-logs for training_step but not validation/test_step. The default value is determined by the hook. See extensions/logging:Automatic Logging for details.

  • on_epoch – if True logs epoch accumulated metrics. None auto-logs for val/test step but not training_step. The default value is determined by the hook. See extensions/logging:Automatic Logging for details.

  • reduce_fx – reduction function over step values for end of epoch. torch.mean() by default.

  • enable_graph – if True, will not auto-detach the graph

  • sync_dist – if True, reduces the metric across GPUs/TPUs. Use with care as this may lead to a significant communication overhead.

  • sync_dist_group – the ddp group to sync across.

  • add_dataloader_idx – if True, appends the index of the current dataloader to the name (when using multiple). If False, user needs to give unique names for each dataloader to not mix values.

  • batch_size – Current batch size. This will be directly inferred from the loaded batch, but some data structures might need to explicitly provide it.

  • rank_zero_only – Whether the value will be logged only on rank 0. This will prevent synchronization which would produce a deadlock as not all processes would perform this log call.