medkit.training.utils#

Classes:

`BatchData`	A BatchData pack data allowing both column and row access
`MetricsComputer`(args, *kwargs)	A MetricsComputer is the base protocol to compute metrics in training

class BatchData[source]#

A BatchData pack data allowing both column and row access

Methods:

to_device(device)

Ensure that Tensors in the BatchData object are on the specified device

to_device(device)[source]#

Ensure that Tensors in the BatchData object are on the specified device

Parameters: device (device) – A torch.device object representing the device on which tensors will be allocated.
Return type: BatchData
Returns: BatchData – A new object with the tensors on the proper device.

class MetricsComputer(*args, **kwargs)[source]#

A MetricsComputer is the base protocol to compute metrics in training

Methods:

`compute`(all_data)	Compute metrics using 'all_data'
`prepare_batch`(model_output, input_batch)	Prepare a batch of data to compute the metrics

prepare_batch(model_output, input_batch)[source]#

Prepare a batch of data to compute the metrics

Parameters

Return type

Dict[str, List[Any]]

Returns

Dict[str, List[Any]] – A dictionary with the required data to calculate the metric

compute(all_data)[source]#

Compute metrics using ‘all_data’

Parameters: all_data (Dict[str, List[Any]]) – A dictionary to compute the metrics. i.e. A dictionary with a list of ‘references’ and a list of ‘predictions’.
Return type: Dict[str, float]
Returns: Dict[str, float] – A dictionary with the results