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ultralytics 8.1.32 fix CLIP backwards compatibility (#9253)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Glenn Jocher 2024-03-23 18:39:22 +01:00 committed by GitHub
parent 7d2e94bbe2
commit 6de99a29e6
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4 changed files with 10 additions and 3 deletions
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2
tests/test_cli.py
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@ -69,7 +69,7 @@ def test_rtdetr(task="detect", model="yolov8n-rtdetr.yaml", data="coco8.yaml"):
run(f"yolo predict {task} model={model} source={ASSETS / 'bus.jpg'} imgsz=160 save save_crop save_txt")
@pytest.mark.skipif(checks.IS_PYTHON_3_12, reason="MobileSAM Clip is not supported in Python 3.12")
@pytest.mark.skipif(checks.IS_PYTHON_3_12, reason="MobileSAM with CLIP is not supported in Python 3.12")
def test_fastsam(task="segment", model=WEIGHTS_DIR / "FastSAM-s.pt", data="coco8-seg.yaml"):
"""Test FastSAM segmentation functionality within Ultralytics."""
source = ASSETS / "bus.jpg"

7
tests/test_python.py
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@ -636,3 +636,10 @@ def test_model_embeddings():
for batch in [SOURCE], [SOURCE, SOURCE]: # test batch size 1 and 2
assert len(model_detect.embed(source=batch, imgsz=32)) == len(batch)
assert len(model_segment.embed(source=batch, imgsz=32)) == len(batch)
@pytest.mark.skipif(checks.IS_PYTHON_3_12, reason="YOLOWorld with CLIP is not supported in Python 3.12")
def test_yolo_world():
model = YOLO("yolov8s-world.pt") # no YOLOv8n-world model yet
model.set_classes(["tree", "window"])
model(ASSETS / "bus.jpg", conf=0.01)

2
ultralytics/__init__.py
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@ -1,6 +1,6 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
__version__ = "8.1.31"
__version__ = "8.1.32"
from ultralytics.data.explorer.explorer import Explorer
from ultralytics.models import RTDETR, SAM, YOLO, YOLOWorld

2
ultralytics/nn/tasks.py
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@ -572,7 +572,7 @@ class WorldModel(DetectionModel):
check_requirements("git+https://github.com/openai/CLIP.git")
import clip
if not self.clip_model:
if not getattr(self, "clip_model", None): # for backwards compatibility of models lacking clip_model attribute
self.clip_model = clip.load("ViT-B/32")[0]
device = next(self.clip_model.parameters()).device
text_token = clip.tokenize(text).to(device)