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ultralytics 8.0.43 optimized Results class and fixes (#1069)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Alexander Duda <Alexander.Duda@me.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
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Glenn Jocher 2023-02-21 20:37:59 +01:00 • committed by GitHub
parent f2a7a29e53
commit fe61018975
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22 changed files with 180 additions and 102 deletions
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18
ultralytics/yolo/engine/model.py
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@ -9,7 +9,7 @@ from ultralytics.nn.tasks import (ClassificationModel, DetectionModel, Segmentat
guess_model_task, nn)
from ultralytics.yolo.cfg import get_cfg
from ultralytics.yolo.engine.exporter import Exporter
from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, RANK, callbacks, yaml_load
from ultralytics.yolo.utils import DEFAULT_CFG, DEFAULT_CFG_DICT, LOGGER, RANK, callbacks, yaml_load
from ultralytics.yolo.utils.checks import check_file, check_imgsz, check_yaml
from ultralytics.yolo.utils.downloads import GITHUB_ASSET_STEMS
from ultralytics.yolo.utils.torch_utils import smart_inference_mode
@ -203,7 +203,7 @@ class YOLO:
@smart_inference_mode()
def track(self, source=None, stream=False, **kwargs):
from ultralytics.tracker.track import register_tracker
from ultralytics.tracker import register_tracker
register_tracker(self)
# ByteTrack-based method needs low confidence predictions as input
conf = kwargs.get('conf') or 0.1
@ -237,6 +237,20 @@ class YOLO:
return validator.metrics
@smart_inference_mode()
def benchmark(self, **kwargs):
"""
Benchmark a model on all export formats.
Args:
**kwargs : Any other args accepted by the validators. To see all args check 'configuration' section in docs
"""
from ultralytics.yolo.utils.benchmarks import run_benchmarks
overrides = self.model.args.copy()
overrides.update(kwargs)
overrides = {**DEFAULT_CFG_DICT, **overrides} # fill in missing overrides keys with defaults
return run_benchmarks(model=self, imgsz=overrides['imgsz'], half=overrides['half'], device=overrides['device'])
def export(self, **kwargs):
"""
Export model.