tlc.integration.super_gradients.callbacks.pose_callback

Callback for collecting predictions from SuperGradients detection models.

Module Contents

Classes

Class

Description

PoseEstimationMetricsCollectionCallback

Callback for collecting predictions from SuperGradients pose estimation models.

API

class PoseEstimationMetricsCollectionCallback(
project_name: str | None = None,
run_name: str | None = None,
run_description: str | None = None,
image_column_name: str = 'image',
label_column_name: str | None = None,
metrics_collection_epochs: list[int] | None = None,
collect_metrics_on_train_end: bool = True,
collect_val_only: bool = False,
batch_size: int | None = 32,
pipeline_params: PipelineParams | dict[str, Any] | None = None,
collect_predictions: bool | None = None,
collect_embeddings: bool = False,
embeddings_dim: int = 2,
embeddings_method: Literal[pacmap, umap] = 'pacmap',
inference_chunk_size: int = 5000,
)

Bases: tlc.integration.super_gradients.callbacks.base_callback.MetricsCollectionCallback

Callback for collecting predictions from SuperGradients pose estimation models.

Create a new metrics collection callback.

Parameters:

  • project_name – The name of the 3LC project to use if no active run exists.

  • run_name – The name of the 3LC run to use if no active run exists.

  • run_description – The description of the 3LC run to use if no active run exists.

  • image_column_name – The name of the column in the table that contains the images.

  • label_column_name – The name of the column in the table that contains the labels. If not provided, a task-specific default will be used.

  • metrics_collection_epochs – The zero-indexed epochs after which to collect metrics.

  • collect_metrics_on_train_end – Whether to collect metrics after training finishes.

  • collect_val_only – Whether to collect metrics only on the validation set.

  • metrics_collection_dataloader_args – Additional arguments to pass to the dataloaders used for metrics collection.

  • batch_size – The batch size to use for metrics collection.

  • pipeline_params – The pipeline parameters to use for metrics collection. Can be provided as a PipelineParams instance, a dictionary, or None (default) which will use a default PipelineParams instance.

  • collect_predictions – Whether to collect predictions. Default is None, which means predictions will be logged for all task-specific subclasses if not explicitly set to False. On using the base callback, no predictions will be logged by default.

  • collect_embeddings – Whether to collect embeddings. Is only applied if the model has a backbone attribute (Yolo-NAS models have this). Default is False because embeddings collection performs additional inference and therefore spends additional time.

  • embeddings_dim – The dimensionality to reduce the embeddings to, default is 2. 2 or 3 are recommended.

  • embeddings_method – The method to use for reducing the embeddings.

  • inference_chunk_size – SuperGradients Pipelines load all images in memory at once. This parameter effectively controls how many images to load in memory at once, and how many rows each metrics table will contain.

property label_column_name: str
compute_metrics(
images: list[str],
predictions: super_gradients.training.utils.predict.prediction_pose_estimation_results.ImagesPoseEstimationPrediction | super_gradients.training.utils.predict.prediction_pose_estimation_results.ImagePoseEstimationPrediction,
table: Table,
) → dict[str, Any]

Compute metrics from a batch of data and corresponding predictions.

metrics_column_schemas(
table: Table,
) → dict[str, Schema]

Return the column schemas for the metrics of this callback.