`tlc.client.torch.metrics.metrics_collectors.segmentation_metrics_collector`¶

Collect metrics for segmentation problems.

Module Contents¶

Class	Description
`SegmentationMetricsCollector`	Collect predicted masks from model output.

Data	Description
`PREDICTED_MASK_METRIC_NAME`	Key for a column containing predicted masks (Deprecated: use `PREDICTED_MASK` instead)

tlc.client.torch.metrics.metrics_collectors.segmentation_metrics_collector.PREDICTED_MASK_METRIC_NAME = None¶: Key for a column containing predicted masks (Deprecated: use PREDICTED_MASK instead)

class tlc.client.torch.metrics.metrics_collectors.segmentation_metrics_collector.SegmentationMetricsCollector(label_map: dict[int, str], preprocess_fn: Callable[[tlc.core.builtins.types.SampleData, tlc.client.torch.metrics.predictor.PredictorOutput], tuple[Any, Any]] | None = None)¶

Collect predicted masks from model output.

Predicted masks are converted to PIL images, which can be written to the Run folder by a MetricsTableWriter.

Initialize the SegmentationMetricsCollector.

Parameters:

label_map – A dictionary mapping class ids to class labels.
preprocess_fn – A function that pre-processes the model output before computing metrics.

compute_metrics(batch: tlc.core.builtins.types.SampleData, predictor_output: tlc.client.torch.metrics.predictor.PredictorOutput) → dict[str, tlc.core.builtins.types.MetricData]¶

Convert predicted masks from model output to PIL images.

The result of preprocessing the model output is expected to be a list of tensors.

Parameters:

Returns:

A batch of metrics, where each metric is a PIL image corresponding to a mask.

preprocess(batch: tlc.core.builtins.types.SampleData, predictor_output: tlc.client.torch.metrics.predictor.PredictorOutput) → tuple[tlc.core.builtins.types.SampleData, torch.Tensor]¶

Default preprocessor for segmentation output.

By default just forwards the model predictions.

Parameters:

Returns:

A tuple containing the preprocessed batch and predictions.