otx.core.model.hlabel_classification#

Class definition for classification model entity used in OTX.

Classes

OTXHlabelClsModel(label_info, ...)

H-label classification models used in OTX.

OVHlabelClassificationModel(model_name, ...)

Hierarchical classification model compatible for OpenVINO IR inference.
class otx.core.model.hlabel_classification.OTXHlabelClsModel(label_info: HLabelInfo, data_input_params: DataInputParams, model_name: str = 'hlabel_classification_model', optimizer: OptimizerCallable = <function _default_optimizer_callable>, scheduler: LRSchedulerCallable | LRSchedulerListCallable = <function _default_scheduler_callable>, metric: MetricCallable = <function _mixed_hlabel_accuracy>, torch_compile: bool = False)[source]#

Bases: OTXModel

H-label classification models used in OTX.

Args: label_info (HLabelInfo): Information about the hierarchical labels. data_input_params (DataInputParams): Parameters for data input. model_name (str, optional): Name of the model. Defaults to “hlabel_classification_model”. optimizer (OptimizerCallable, optional): Callable for the optimizer. Defaults to DefaultOptimizerCallable. scheduler (LRSchedulerCallable | LRSchedulerListCallable, optional): Callable for the learning rate scheduler. Defaults to DefaultSchedulerCallable. metric (MetricCallable, optional): Callable for the metric. Defaults to HLabelClsMetricCallable. torch_compile (bool, optional): Flag to indicate whether to use torch.compile. Defaults to False.

Initialize the base model with the given parameters.

Parameters:

  • label_info (LabelInfoTypes) – Information about the labels used in the model.

  • data_input_params (DataInputParams) – Parameters of the input data such as input size, mean, and std.

  • model_name (str, optional) – Name of the model. Defaults to “OTXModel”.

  • optimizer (OptimizerCallable, optional) – Callable for the optimizer. Defaults to DefaultOptimizerCallable.

  • scheduler (LRSchedulerCallable | LRSchedulerListCallable) – Callable for the learning rate scheduler. Defaults to DefaultSchedulerCallable.

  • metric (MetricCallable, optional) – Callable for the metric. Defaults to NullMetricCallable.

  • torch_compile (bool, optional) – Flag to indicate if torch.compile should be used. Defaults to False.

  • tile_config (TileConfig, optional) – Configuration for tiling. Defaults to TileConfig(enable_tiler=False).

Returns:

None

forward_explain(inputs: TorchDataBatch) → TorchPredBatch[source]#

Model forward explain function.

forward_for_tracing(image: Tensor) → Tensor | dict[str, Tensor][source]#

Model forward function used for the model tracing during model exportation.

get_dummy_input(batch_size: int = 1) → TorchDataBatch[source]#

Returns a dummy input for classification OV model.

class otx.core.model.hlabel_classification.OVHlabelClassificationModel(model_name: str, model_type: str = 'Classification', async_inference: bool = True, max_num_requests: int | None = None, use_throughput_mode: bool = True, model_api_configuration: dict[str, Any] | None = None, metric: MetricCallable = <function _mixed_hlabel_accuracy>, **kwargs)[source]#

Bases: OVModel

Hierarchical classification model compatible for OpenVINO IR inference.

It can consume OpenVINO IR model path or model name from Intel OMZ repository and create the OTX classification model compatible for OTX testing pipeline.

Initialize the base model with the given parameters.

Parameters:

  • label_info (LabelInfoTypes) – Information about the labels used in the model.

  • data_input_params (DataInputParams) – Parameters of the input data such as input size, mean, and std.

  • model_name (str, optional) – Name of the model. Defaults to “OTXModel”.

  • optimizer (OptimizerCallable, optional) – Callable for the optimizer. Defaults to DefaultOptimizerCallable.

  • scheduler (LRSchedulerCallable | LRSchedulerListCallable) – Callable for the learning rate scheduler. Defaults to DefaultSchedulerCallable.

  • metric (MetricCallable, optional) – Callable for the metric. Defaults to NullMetricCallable.

  • torch_compile (bool, optional) – Flag to indicate if torch.compile should be used. Defaults to False.

  • tile_config (TileConfig, optional) – Configuration for tiling. Defaults to TileConfig(enable_tiler=False).

Returns:

None