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Fix error with exporting quantized saved model when labels cache not present (#4174)

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Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This commit is contained in:
Colin Wong
2023-08-05 08:35:14 -05:00
committed by GitHub
parent f0ad1a7f5a
commit 730af8390b
2 changed files with 4 additions and 3 deletions
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4
ultralytics/data/annotator.py
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@ -24,13 +24,13 @@ def auto_annotate(data, det_model='yolov8x.pt', sam_model='sam_b.pt', device='',
det_results = det_model(data, stream=True, device=device) det_results = det_model(data, stream=True, device=device)
for result in det_results: for result in det_results:
boxes = result.boxes.xyxy # Boxes object for bbox outputs
class_ids = result.boxes.cls.int().tolist() # noqa class_ids = result.boxes.cls.int().tolist() # noqa
if len(class_ids): if len(class_ids):
boxes = result.boxes.xyxy # Boxes object for bbox outputs
sam_results = sam_model(result.orig_img, bboxes=boxes, verbose=False, save=False, device=device) sam_results = sam_model(result.orig_img, bboxes=boxes, verbose=False, save=False, device=device)
segments = sam_results[0].masks.xyn # noqa segments = sam_results[0].masks.xyn # noqa
with open(str(Path(output_dir) / Path(result.path).stem) + '.txt', 'w') as f: with open(f'{str(Path(output_dir) / Path(result.path).stem)}.txt', 'w') as f:
for i in range(len(segments)): for i in range(len(segments)):
s = segments[i] s = segments[i]
if len(s) == 0: if len(s) == 0:

3
ultralytics/engine/exporter.py
View File

@ -605,7 +605,8 @@ class Exporter:
# Generate calibration data for integer quantization # Generate calibration data for integer quantization
LOGGER.info(f"{prefix} collecting INT8 calibration images from 'data={self.args.data}'") LOGGER.info(f"{prefix} collecting INT8 calibration images from 'data={self.args.data}'")
dataset = YOLODataset(check_det_dataset(self.args.data)['val'], imgsz=self.imgsz[0], augment=False) data = check_det_dataset(self.args.data)
dataset = YOLODataset(data['val'], data=data, imgsz=self.imgsz[0], augment=False)
images = [] images = []
n_images = 100 # maximum number of images n_images = 100 # maximum number of images
for n, batch in enumerate(dataset): for n, batch in enumerate(dataset):