# Copyright (C) 2023-2025 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
"""Class definition for classification model entity used in OTX."""
from __future__ import annotations
from typing import TYPE_CHECKING, Any
import torch
from otx.backend.openvino.models.base import OVModel
from otx.data.entity.torch import OTXDataBatch, OTXPredBatch
from otx.metrics import MetricInput
from otx.metrics.accuracy import (
MultiLabelClsMetricCallable,
)
from otx.types.task import OTXTaskType
if TYPE_CHECKING:
from model_api.models.result import ClassificationResult
from otx.metrics import MetricCallable
from otx.types import PathLike
[docs]
class OVMultilabelClassificationModel(OVModel):
"""Multilabel 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.
"""
def __init__(
self,
model_path: PathLike,
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 = MultiLabelClsMetricCallable,
**kwargs,
) -> None:
"""Initialize the multilabel classification model.
Args:
model_path (PathLike): Path to the OpenVINO IR model or model name from Intel OMZ.
model_type (str): Type of the model. Defaults to "Classification".
async_inference (bool): Whether to use asynchronous inference. Defaults to True.
max_num_requests (int | None): Maximum number of inference requests. Defaults to None.
use_throughput_mode (bool): Whether to use throughput mode. Defaults to True.
model_api_configuration (dict[str, Any] | None): Configuration for the model API. Defaults to None.
metric (MetricCallable): Metric callable for evaluation. Defaults to MultiLabelClsMetricCallable.
**kwargs: Additional keyword arguments.
"""
model_api_configuration = model_api_configuration if model_api_configuration else {}
model_api_configuration.update({"multilabel": True, "confidence_threshold": 0.0})
super().__init__(
model_path=model_path,
model_type=model_type,
async_inference=async_inference,
max_num_requests=max_num_requests,
use_throughput_mode=use_throughput_mode,
model_api_configuration=model_api_configuration,
metric=metric,
)
self._task = OTXTaskType.MULTI_LABEL_CLS
def _customize_outputs(
self,
outputs: list[ClassificationResult],
inputs: OTXDataBatch,
) -> OTXPredBatch:
"""Customize the outputs of the model for OTX compatibility.
Args:
outputs (list[ClassificationResult]): List of classification results from the model.
inputs (OTXDataBatch): Input batch containing images and metadata.
Returns:
OTXPredBatch: Customized prediction batch containing scores, saliency maps, and feature vectors.
"""
pred_scores = [torch.tensor([top_label.confidence for top_label in out.top_labels]) for out in outputs]
if outputs and outputs[0].saliency_map.size != 0:
# Squeeze dim 4D => 3D, (1, num_classes, H, W) => (num_classes, H, W)
predicted_s_maps = [out.saliency_map[0] for out in outputs]
# Squeeze dim 2D => 1D, (1, internal_dim) => (internal_dim)
predicted_f_vectors = [out.feature_vector[0] for out in outputs]
return OTXPredBatch(
batch_size=len(outputs),
images=inputs.images,
imgs_info=inputs.imgs_info,
scores=pred_scores,
labels=[],
saliency_map=predicted_s_maps,
feature_vector=predicted_f_vectors,
)
return OTXPredBatch(
batch_size=len(outputs),
images=inputs.images,
imgs_info=inputs.imgs_info,
scores=pred_scores,
labels=[],
)