ulralytics 8.0.199 *.npy image loading exception handling (#5683)
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>
This commit is contained in:
Glenn Jocher
GitHub
parent
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commit
cedce60f8c
16 changed files with 479 additions and 280 deletions
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@ -3,8 +3,8 @@
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import pytest
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import torch
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from ultralytics import YOLO, download
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from ultralytics.utils import ASSETS, DATASETS_DIR, WEIGHTS_DIR, checks
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from ultralytics import YOLO
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from ultralytics.utils import ASSETS, WEIGHTS_DIR, checks
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CUDA_IS_AVAILABLE = checks.cuda_is_available()
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CUDA_DEVICE_COUNT = checks.cuda_device_count()
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@ -27,6 +27,7 @@ def test_train():
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YOLO(MODEL).train(data=DATA, imgsz=64, epochs=1, device=device) # requires imgsz>=64
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@pytest.mark.slow
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@pytest.mark.skipif(not CUDA_IS_AVAILABLE, reason='CUDA is not available')
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def test_predict_multiple_devices():
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"""Validate model prediction on multiple devices."""
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@ -102,42 +103,3 @@ def test_predict_sam():
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# Reset image
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predictor.reset_image()
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@pytest.mark.skipif(not CUDA_IS_AVAILABLE, reason='CUDA is not available')
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def test_model_tune():
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"""Tune YOLO model for performance."""
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YOLO('yolov8n-pose.pt').tune(data='coco8-pose.yaml', plots=False, imgsz=32, epochs=1, iterations=2, device='cpu')
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YOLO('yolov8n-cls.pt').tune(data='imagenet10', plots=False, imgsz=32, epochs=1, iterations=2, device='cpu')
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@pytest.mark.skipif(not CUDA_IS_AVAILABLE, reason='CUDA is not available')
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def test_pycocotools():
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"""Validate model predictions using pycocotools."""
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from ultralytics.models.yolo.detect import DetectionValidator
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from ultralytics.models.yolo.pose import PoseValidator
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from ultralytics.models.yolo.segment import SegmentationValidator
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# Download annotations after each dataset downloads first
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url = 'https://github.com/ultralytics/assets/releases/download/v0.0.0/'
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args = {'model': 'yolov8n.pt', 'data': 'coco8.yaml', 'save_json': True, 'imgsz': 64}
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validator = DetectionValidator(args=args)
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validator()
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validator.is_coco = True
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download(f'{url}instances_val2017.json', dir=DATASETS_DIR / 'coco8/annotations')
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_ = validator.eval_json(validator.stats)
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args = {'model': 'yolov8n-seg.pt', 'data': 'coco8-seg.yaml', 'save_json': True, 'imgsz': 64}
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validator = SegmentationValidator(args=args)
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validator()
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validator.is_coco = True
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download(f'{url}instances_val2017.json', dir=DATASETS_DIR / 'coco8-seg/annotations')
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_ = validator.eval_json(validator.stats)
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args = {'model': 'yolov8n-pose.pt', 'data': 'coco8-pose.yaml', 'save_json': True, 'imgsz': 64}
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validator = PoseValidator(args=args)
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validator()
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validator.is_coco = True
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download(f'{url}person_keypoints_val2017.json', dir=DATASETS_DIR / 'coco8-pose/annotations')
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_ = validator.eval_json(validator.stats)
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@ -1,12 +1,20 @@
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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import contextlib
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from pathlib import Path
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import pytest
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from ultralytics import YOLO
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from ultralytics.utils import SETTINGS, checks
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from ultralytics import YOLO, download
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from ultralytics.utils import ASSETS, DATASETS_DIR, ROOT, SETTINGS, WEIGHTS_DIR
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from ultralytics.utils.checks import check_requirements
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MODEL = WEIGHTS_DIR / 'path with spaces' / 'yolov8n.pt' # test spaces in path
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CFG = 'yolov8n.yaml'
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SOURCE = ASSETS / 'bus.jpg'
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TMP = (ROOT / '../tests/tmp').resolve() # temp directory for test files
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@pytest.mark.skipif(not checks.check_requirements('ray', install=False), reason='RayTune not installed')
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@pytest.mark.skipif(not check_requirements('ray', install=False), reason='ray[tune] not installed')
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def test_model_ray_tune():
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"""Tune YOLO model with Ray optimization library."""
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YOLO('yolov8n-cls.yaml').tune(use_ray=True,
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@ -19,8 +27,90 @@ def test_model_ray_tune():
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device='cpu')
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@pytest.mark.skipif(not checks.check_requirements('mlflow', install=False), reason='MLflow not installed')
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@pytest.mark.skipif(not check_requirements('mlflow', install=False), reason='mlflow not installed')
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def test_mlflow():
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"""Test training with MLflow tracking enabled."""
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SETTINGS['mlflow'] = True
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YOLO('yolov8n-cls.yaml').train(data='imagenet10', imgsz=32, epochs=3, plots=False, device='cpu')
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@pytest.mark.skipif(not check_requirements('tritonclient', install=False), reason='tritonclient[all] not installed')
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def test_triton():
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"""Test NVIDIA Triton Server functionalities."""
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check_requirements('tritonclient[all]')
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import subprocess
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import time
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from tritonclient.http import InferenceServerClient # noqa
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# Create variables
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model_name = 'yolo'
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triton_repo_path = TMP / 'triton_repo'
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triton_model_path = triton_repo_path / model_name
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# Export model to ONNX
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f = YOLO(MODEL).export(format='onnx', dynamic=True)
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# Prepare Triton repo
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(triton_model_path / '1').mkdir(parents=True, exist_ok=True)
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Path(f).rename(triton_model_path / '1' / 'model.onnx')
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(triton_model_path / 'config.pdtxt').touch()
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# Define image https://catalog.ngc.nvidia.com/orgs/nvidia/containers/tritonserver
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tag = 'nvcr.io/nvidia/tritonserver:23.09-py3' # 6.4 GB
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# Pull the image
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subprocess.call(f'docker pull {tag}', shell=True)
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# Run the Triton server and capture the container ID
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container_id = subprocess.check_output(
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f'docker run -d --rm -v {triton_repo_path}:/models -p 8000:8000 {tag} tritonserver --model-repository=/models',
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shell=True).decode('utf-8').strip()
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# Wait for the Triton server to start
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triton_client = InferenceServerClient(url='localhost:8000', verbose=False, ssl=False)
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# Wait until model is ready
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for _ in range(10):
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with contextlib.suppress(Exception):
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assert triton_client.is_model_ready(model_name)
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break
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time.sleep(1)
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# Check Triton inference
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YOLO(f'http://localhost:8000/{model_name}', 'detect')(SOURCE) # exported model inference
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# Kill and remove the container at the end of the test
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subprocess.call(f'docker kill {container_id}', shell=True)
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@pytest.mark.skipif(not check_requirements('pycocotools', install=False), reason='pycocotools not installed')
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def test_pycocotools():
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"""Validate model predictions using pycocotools."""
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from ultralytics.models.yolo.detect import DetectionValidator
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from ultralytics.models.yolo.pose import PoseValidator
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from ultralytics.models.yolo.segment import SegmentationValidator
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# Download annotations after each dataset downloads first
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url = 'https://github.com/ultralytics/assets/releases/download/v0.0.0/'
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args = {'model': 'yolov8n.pt', 'data': 'coco8.yaml', 'save_json': True, 'imgsz': 64}
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validator = DetectionValidator(args=args)
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validator()
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validator.is_coco = True
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download(f'{url}instances_val2017.json', dir=DATASETS_DIR / 'coco8/annotations')
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_ = validator.eval_json(validator.stats)
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args = {'model': 'yolov8n-seg.pt', 'data': 'coco8-seg.yaml', 'save_json': True, 'imgsz': 64}
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validator = SegmentationValidator(args=args)
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validator()
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validator.is_coco = True
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download(f'{url}instances_val2017.json', dir=DATASETS_DIR / 'coco8-seg/annotations')
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_ = validator.eval_json(validator.stats)
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args = {'model': 'yolov8n-pose.pt', 'data': 'coco8-pose.yaml', 'save_json': True, 'imgsz': 64}
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validator = PoseValidator(args=args)
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validator()
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validator.is_coco = True
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download(f'{url}person_keypoints_val2017.json', dir=DATASETS_DIR / 'coco8-pose/annotations')
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_ = validator.eval_json(validator.stats)
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@ -495,50 +495,7 @@ def test_hub():
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@pytest.mark.slow
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@pytest.mark.skipif(not ONLINE, reason='environment is offline')
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def test_triton():
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"""Test NVIDIA Triton Server functionalities."""
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checks.check_requirements('tritonclient[all]')
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import subprocess
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import time
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from tritonclient.http import InferenceServerClient # noqa
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# Create variables
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model_name = 'yolo'
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triton_repo_path = TMP / 'triton_repo'
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triton_model_path = triton_repo_path / model_name
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# Export model to ONNX
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f = YOLO(MODEL).export(format='onnx', dynamic=True)
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# Prepare Triton repo
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(triton_model_path / '1').mkdir(parents=True, exist_ok=True)
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Path(f).rename(triton_model_path / '1' / 'model.onnx')
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(triton_model_path / 'config.pdtxt').touch()
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# Define image https://catalog.ngc.nvidia.com/orgs/nvidia/containers/tritonserver
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tag = 'nvcr.io/nvidia/tritonserver:23.09-py3' # 6.4 GB
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# Pull the image
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subprocess.call(f'docker pull {tag}', shell=True)
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# Run the Triton server and capture the container ID
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container_id = subprocess.check_output(
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f'docker run -d --rm -v {triton_repo_path}:/models -p 8000:8000 {tag} tritonserver --model-repository=/models',
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shell=True).decode('utf-8').strip()
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# Wait for the Triton server to start
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triton_client = InferenceServerClient(url='localhost:8000', verbose=False, ssl=False)
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# Wait until model is ready
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for _ in range(10):
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with contextlib.suppress(Exception):
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assert triton_client.is_model_ready(model_name)
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break
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time.sleep(1)
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# Check Triton inference
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YOLO(f'http://localhost:8000/{model_name}', 'detect')(SOURCE) # exported model inference
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# Kill and remove the container at the end of the test
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subprocess.call(f'docker kill {container_id}', shell=True)
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def test_model_tune():
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"""Tune YOLO model for performance."""
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YOLO('yolov8n-pose.pt').tune(data='coco8-pose.yaml', plots=False, imgsz=32, epochs=1, iterations=2, device='cpu')
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YOLO('yolov8n-cls.pt').tune(data='imagenet10', plots=False, imgsz=32, epochs=1, iterations=2, device='cpu')
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