rpx_benchmark.tasks.tracking

Object tracking: per-frame MOT with stable track IDs.

 1"""Object tracking: per-frame MOT with stable track IDs."""
 2
 3from __future__ import annotations
 4
 5from dataclasses import dataclass
 6
 7from ..api import TaskType
 8from ._pipeline import PipelineResult, TaskRunConfig, run_pipeline
 9from .registry import TaskSpec, register_task
10
11PRIMARY_METRIC = "mota"
12
13
14@dataclass
15class ObjectTrackingRunConfig(TaskRunConfig):
16    pass
17
18
19def run_object_tracking(cfg: ObjectTrackingRunConfig) -> PipelineResult:
20    return run_pipeline(
21        task=TaskType.OBJECT_TRACKING,
22        primary_metric=PRIMARY_METRIC,
23        cfg=cfg,
24        compute_ts=False,
25    )
26
27
28TASK_SPEC = TaskSpec(
29    task=TaskType.OBJECT_TRACKING,
30    display_name="Object Tracking",
31    description="Per-frame multi-object tracking with persistent IDs.",
32    primary_metric=PRIMARY_METRIC,
33    required_modalities=["rgb", "mask"],
34    higher_is_better=True,
35    run=run_object_tracking,
36)
37
38register_task(TASK_SPEC)
PRIMARY_METRIC = 'mota'
@dataclass
class ObjectTrackingRunConfig(rpx_benchmark.tasks._pipeline.TaskRunConfig):
15@dataclass
16class ObjectTrackingRunConfig(TaskRunConfig):
17    pass

ObjectTrackingRunConfig(model: 'Optional[BenchmarkableModel]' = None, split: 'Difficulty | str' = , repo_id: 'Optional[str]' = None, cache_dir: 'Optional[str]' = None, revision: 'Optional[str]' = '51432e5d48895bbafb490ae7c5e34e0f6b7da7d6', batch_size: 'int' = 1, require_cuda: 'bool' = False, skip_flops: 'bool' = False, max_samples: 'Optional[int]' = None, manifest_path: 'Optional[str]' = None, device: 'str' = 'cuda', output_dir: 'Optional[str]' = None, progress: 'Optional[ProgressCallback]' = None, metric_suite: 'Optional[MetricSuite]' = None, upload_to_box: 'bool' = False, box_folder_id: 'Optional[str]' = None)

ObjectTrackingRunConfig( model: Optional[rpx_benchmark.adapters.BenchmarkableModel] = None, split: rpx_benchmark.api.Difficulty | str = <Difficulty.HARD: 'hard'>, repo_id: Optional[str] = None, cache_dir: Optional[str] = None, revision: Optional[str] = '51432e5d48895bbafb490ae7c5e34e0f6b7da7d6', batch_size: int = 1, require_cuda: bool = False, skip_flops: bool = False, max_samples: Optional[int] = None, manifest_path: Optional[str] = None, device: str = 'cuda', output_dir: Optional[str] = None, progress: Optional[Callable[[int, Optional[int], str], NoneType]] = None, metric_suite: Optional[rpx_benchmark.metrics.MetricSuite] = None, upload_to_box: bool = False, box_folder_id: Optional[str] = None)
def run_object_tracking( cfg: ObjectTrackingRunConfig) -> Tuple[rpx_benchmark.metrics.registry.BenchmarkResult, rpx_benchmark.deployment.DeploymentReadinessReport, Dict[str, pathlib.Path]]:
20def run_object_tracking(cfg: ObjectTrackingRunConfig) -> PipelineResult:
21    return run_pipeline(
22        task=TaskType.OBJECT_TRACKING,
23        primary_metric=PRIMARY_METRIC,
24        cfg=cfg,
25        compute_ts=False,
26    )
TASK_SPEC = TaskSpec(task=<TaskType.OBJECT_TRACKING: 'object_tracking'>, display_name='Object Tracking', primary_metric='mota', required_modalities=['rgb', 'mask'], run=<function run_object_tracking>, higher_is_better=True, description='Per-frame multi-object tracking with persistent IDs.', temporal_stability_fn=None, geometric_coherence_fn=None)