ObjectDetection3DLogger Class

ml_debugger.monitoring.object_detection_3d.object_detection_3d_torchlogger.ObjectDetection3DTorchLogger

Bases: MonitoringLogger, ObjectDetection3DTorchTracer

Base MonitoringLogger for 3D object detection tasks using PyTorch models.

Subclasses must implement the following abstract methods

• detect(model): Static method to check if this subclass handles the model.
• _get_n_class(model): Extract number of classes from the model.
• _register_model_hooks(model): Register forward hooks for logit capture.
• _parse_model_output(model_output): Convert model output to a standardized Detection3DOutput.

Optionally override

• _is_nms_free(): Return True if the model uses NMS-free inference.

__init__(model, model_name, version_name, result_name=None, n_epoch='latest', target_layers=None, additional_fields=None, auto_sync=True, force_table_recreate=False, api_endpoint=None, api_key=None, n_class=None, score_thresh=None, iou_thresh=None, max_detections_per_frame=None)

Initialize 3D object detection monitoring logger.

Parameters:

Name	Type	Description	Default
model	Module	PyTorch model to trace.	required
model_name	str	Name of the ML model.	required
version_name	str	Version identifier for the ML model.	required
result_name	Optional[str]	The name of the existing evaluation result to retrieve.	None
n_epoch	Union[str, Optional[int]]	Filter option for n_epoch value.	'latest'
target_layers	Optional[Dict[str, str]]	Mapping of layer aliases to module paths.	None
additional_fields	Optional[List[dict]]	Extra fields for database schema.	None
auto_sync	bool	Enable background syncing of logged data.	True
force_table_recreate	bool	Whether to drop and recreate existing tables.	False
api_endpoint	Optional[str]	URL of the service API for data upload.	None
api_key	Optional[str]	API key for authenticating with the service.	None
n_class	Optional[int]	Number of classes. Auto-detected from model if None.	None
score_thresh	Optional[float]	Minimum score threshold for user-visible NMS decisions.	None
iou_thresh	Optional[float]	IoU threshold for user-visible NMS decisions.	None
max_detections_per_frame	Optional[int]	Maximum detections per frame for user-visible output.	None

__call__(model_input, input_ids=None, dataset_type='deploying', **kwargs)

Invoke the logger on a single inference, recording I/O data.

Parameters:

Name	Type	Description	Default
model_input	Any	Input data for the model inference.	required
input_ids	Optional[List[str]]	Identifiers of each input data.	None
dataset_type	str	Identifier of input dataset. (e.g. 'train', 'val', 'test', 'deploying')	'deploying'
**kwargs	Any	Additional keyword arguments for parsing and saving I/O data.	{}

Returns:

Type	Description
Any	The raw output produced by the model.

get_hooked_features(layer_name)

Retrieve the captured output for a given layer alias.

Parameters:

Name	Type	Description	Default
layer_name	str	Alias of the layer whose activation was captured.	required

Returns:

Name	Type	Description
Any	Any	Activation data stored for the specified layer.

Raises:

Type	Description
KeyError	If no activation has been captured for layer_name.

export(output_path=None)

Export extracted features into a ZIP archive.

Uses the internal n_epoch resolved during validator setup to filter records, consistent with upload() and wait_for_save().

Parameters:

Name	Type	Description	Default
output_path	Optional[str]	Path or directory for saving the ZIP file. If no .zip extension, the default filename is appended. Defaults to cwd.	None

Returns:

Type	Description
Optional[Path]	Path to the created ZIP file, or None on non-primary distributed ranks.

wait_for_save(interval=3)

upload()