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ulralytics 8.0.199 *.npy image loading exception handling (#5683)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: snyk-bot <snyk-bot@snyk.io>
Co-authored-by: Yonghye Kwon <developer.0hye@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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Glenn Jocher 2023-10-15 18:24:06 +02:00 • committed by GitHub
parent 5b3c4cfc0e
commit cedce60f8c
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16 changed files with 479 additions and 280 deletions
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44
tests/test_cuda.py
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@ -3,8 +3,8 @@
import pytest
import torch
from ultralytics import YOLO, download
from ultralytics.utils import ASSETS, DATASETS_DIR, WEIGHTS_DIR, checks
from ultralytics import YOLO
from ultralytics.utils import ASSETS, WEIGHTS_DIR, checks
CUDA_IS_AVAILABLE = checks.cuda_is_available()
CUDA_DEVICE_COUNT = checks.cuda_device_count()
@ -27,6 +27,7 @@ def test_train():
YOLO(MODEL).train(data=DATA, imgsz=64, epochs=1, device=device) # requires imgsz>=64
@pytest.mark.slow
@pytest.mark.skipif(not CUDA_IS_AVAILABLE, reason='CUDA is not available')
def test_predict_multiple_devices():
"""Validate model prediction on multiple devices."""
@ -102,42 +103,3 @@ def test_predict_sam():
# Reset image
predictor.reset_image()
@pytest.mark.skipif(not CUDA_IS_AVAILABLE, reason='CUDA is not available')
def test_model_tune():
"""Tune YOLO model for performance."""
YOLO('yolov8n-pose.pt').tune(data='coco8-pose.yaml', plots=False, imgsz=32, epochs=1, iterations=2, device='cpu')
YOLO('yolov8n-cls.pt').tune(data='imagenet10', plots=False, imgsz=32, epochs=1, iterations=2, device='cpu')
@pytest.mark.skipif(not CUDA_IS_AVAILABLE, reason='CUDA is not available')
def test_pycocotools():
"""Validate model predictions using pycocotools."""
from ultralytics.models.yolo.detect import DetectionValidator
from ultralytics.models.yolo.pose import PoseValidator
from ultralytics.models.yolo.segment import SegmentationValidator
# Download annotations after each dataset downloads first
url = 'https://github.com/ultralytics/assets/releases/download/v0.0.0/'
args = {'model': 'yolov8n.pt', 'data': 'coco8.yaml', 'save_json': True, 'imgsz': 64}
validator = DetectionValidator(args=args)
validator()
validator.is_coco = True
download(f'{url}instances_val2017.json', dir=DATASETS_DIR / 'coco8/annotations')
_ = validator.eval_json(validator.stats)
args = {'model': 'yolov8n-seg.pt', 'data': 'coco8-seg.yaml', 'save_json': True, 'imgsz': 64}
validator = SegmentationValidator(args=args)
validator()
validator.is_coco = True
download(f'{url}instances_val2017.json', dir=DATASETS_DIR / 'coco8-seg/annotations')
_ = validator.eval_json(validator.stats)
args = {'model': 'yolov8n-pose.pt', 'data': 'coco8-pose.yaml', 'save_json': True, 'imgsz': 64}
validator = PoseValidator(args=args)
validator()
validator.is_coco = True
download(f'{url}person_keypoints_val2017.json', dir=DATASETS_DIR / 'coco8-pose/annotations')
_ = validator.eval_json(validator.stats)