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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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51
tests/test_python.py
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@ -495,50 +495,7 @@ def test_hub():
@pytest.mark.slow
@pytest.mark.skipif(not ONLINE, reason='environment is offline')
def test_triton():
"""Test NVIDIA Triton Server functionalities."""
checks.check_requirements('tritonclient[all]')
import subprocess
import time
from tritonclient.http import InferenceServerClient # noqa
# Create variables
model_name = 'yolo'
triton_repo_path = TMP / 'triton_repo'
triton_model_path = triton_repo_path / model_name
# Export model to ONNX
f = YOLO(MODEL).export(format='onnx', dynamic=True)
# Prepare Triton repo
(triton_model_path / '1').mkdir(parents=True, exist_ok=True)
Path(f).rename(triton_model_path / '1' / 'model.onnx')
(triton_model_path / 'config.pdtxt').touch()
# Define image https://catalog.ngc.nvidia.com/orgs/nvidia/containers/tritonserver
tag = 'nvcr.io/nvidia/tritonserver:23.09-py3' # 6.4 GB
# Pull the image
subprocess.call(f'docker pull {tag}', shell=True)
# Run the Triton server and capture the container ID
container_id = subprocess.check_output(
f'docker run -d --rm -v {triton_repo_path}:/models -p 8000:8000 {tag} tritonserver --model-repository=/models',
shell=True).decode('utf-8').strip()
# Wait for the Triton server to start
triton_client = InferenceServerClient(url='localhost:8000', verbose=False, ssl=False)
# Wait until model is ready
for _ in range(10):
with contextlib.suppress(Exception):
assert triton_client.is_model_ready(model_name)
break
time.sleep(1)
# Check Triton inference
YOLO(f'http://localhost:8000/{model_name}', 'detect')(SOURCE) # exported model inference
# Kill and remove the container at the end of the test
subprocess.call(f'docker kill {container_id}', shell=True)
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')