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ultralytics 8.2.9 OpenVINO INT8 fixes and tests (#10423)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Burhan 2024-05-05 09:11:17 -04:00 • committed by GitHub
parent 299797ff9e
commit 2583f842b8
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13 changed files with 250 additions and 206 deletions
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39
tests/test_engine.py
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@ -9,11 +9,7 @@ from ultralytics.engine.exporter import Exporter
from ultralytics.models.yolo import classify, detect, segment
from ultralytics.utils import ASSETS, DEFAULT_CFG, WEIGHTS_DIR
CFG_DET = "yolov8n.yaml"
CFG_SEG = "yolov8n-seg.yaml"
CFG_CLS = "yolov8n-cls.yaml" # or 'squeezenet1_0'
CFG = get_cfg(DEFAULT_CFG)
MODEL = WEIGHTS_DIR / "yolov8n"
from . import MODEL
def test_func(*args): # noqa
@ -26,15 +22,16 @@ def test_export():
exporter = Exporter()
exporter.add_callback("on_export_start", test_func)
assert test_func in exporter.callbacks["on_export_start"], "callback test failed"
f = exporter(model=YOLO(CFG_DET).model)
f = exporter(model=YOLO("yolov8n.yaml").model)
YOLO(f)(ASSETS) # exported model inference
def test_detect():
"""Test object detection functionality."""
overrides = {"data": "coco8.yaml", "model": CFG_DET, "imgsz": 32, "epochs": 1, "save": False}
CFG.data = "coco8.yaml"
CFG.imgsz = 32
overrides = {"data": "coco8.yaml", "model": "yolov8n.yaml", "imgsz": 32, "epochs": 1, "save": False}
cfg = get_cfg(DEFAULT_CFG)
cfg.data = "coco8.yaml"
cfg.imgsz = 32
# Trainer
trainer = detect.DetectionTrainer(overrides=overrides)
@ -43,7 +40,7 @@ def test_detect():
trainer.train()
# Validator
val = detect.DetectionValidator(args=CFG)
val = detect.DetectionValidator(args=cfg)
val.add_callback("on_val_start", test_func)
assert test_func in val.callbacks["on_val_start"], "callback test failed"
val(model=trainer.best) # validate best.pt
@ -54,7 +51,7 @@ def test_detect():
assert test_func in pred.callbacks["on_predict_start"], "callback test failed"
# Confirm there is no issue with sys.argv being empty.
with mock.patch.object(sys, "argv", []):
result = pred(source=ASSETS, model=f"{MODEL}.pt")
result = pred(source=ASSETS, model=MODEL)
assert len(result), "predictor test failed"
overrides["resume"] = trainer.last
@ -70,9 +67,10 @@ def test_detect():
def test_segment():
"""Test image segmentation functionality."""
overrides = {"data": "coco8-seg.yaml", "model": CFG_SEG, "imgsz": 32, "epochs": 1, "save": False}
CFG.data = "coco8-seg.yaml"
CFG.imgsz = 32
overrides = {"data": "coco8-seg.yaml", "model": "yolov8n-seg.yaml", "imgsz": 32, "epochs": 1, "save": False}
cfg = get_cfg(DEFAULT_CFG)
cfg.data = "coco8-seg.yaml"
cfg.imgsz = 32
# YOLO(CFG_SEG).train(**overrides) # works
# Trainer
@ -82,7 +80,7 @@ def test_segment():
trainer.train()
# Validator
val = segment.SegmentationValidator(args=CFG)
val = segment.SegmentationValidator(args=cfg)
val.add_callback("on_val_start", test_func)
assert test_func in val.callbacks["on_val_start"], "callback test failed"
val(model=trainer.best) # validate best.pt
@ -91,7 +89,7 @@ def test_segment():
pred = segment.SegmentationPredictor(overrides={"imgsz": [64, 64]})
pred.add_callback("on_predict_start", test_func)
assert test_func in pred.callbacks["on_predict_start"], "callback test failed"
result = pred(source=ASSETS, model=f"{MODEL}-seg.pt")
result = pred(source=ASSETS, model=WEIGHTS_DIR / "yolov8n-seg.pt")
assert len(result), "predictor test failed"
# Test resume
@ -108,9 +106,10 @@ def test_segment():
def test_classify():
"""Test image classification functionality."""
overrides = {"data": "imagenet10", "model": CFG_CLS, "imgsz": 32, "epochs": 1, "save": False}
CFG.data = "imagenet10"
CFG.imgsz = 32
overrides = {"data": "imagenet10", "model": "yolov8n-cls.yaml", "imgsz": 32, "epochs": 1, "save": False}
cfg = get_cfg(DEFAULT_CFG)
cfg.data = "imagenet10"
cfg.imgsz = 32
# YOLO(CFG_SEG).train(**overrides) # works
# Trainer
@ -120,7 +119,7 @@ def test_classify():
trainer.train()
# Validator
val = classify.ClassificationValidator(args=CFG)
val = classify.ClassificationValidator(args=cfg)
val.add_callback("on_val_start", test_func)
assert test_func in val.callbacks["on_val_start"], "callback test failed"
val(model=trainer.best)