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ultralytics 8.2.9 OpenVINO INT8 fixes and tests (#10423)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Burhan 2024-05-05 09:11:17 -04:00 • committed by GitHub
parent 299797ff9e
commit 2583f842b8
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13 changed files with 250 additions and 206 deletions
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46
tests/test_cli.py
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@ -4,24 +4,14 @@ import subprocess
import pytest
from ultralytics.cfg import TASK2DATA, TASK2MODEL, TASKS
from ultralytics.utils import ASSETS, WEIGHTS_DIR, checks
CUDA_IS_AVAILABLE = checks.cuda_is_available()
CUDA_DEVICE_COUNT = checks.cuda_device_count()
TASK_ARGS = [
("detect", "yolov8n", "coco8.yaml"),
("segment", "yolov8n-seg", "coco8-seg.yaml"),
("classify", "yolov8n-cls", "imagenet10"),
("pose", "yolov8n-pose", "coco8-pose.yaml"),
("obb", "yolov8n-obb", "dota8.yaml"),
] # (task, model, data)
EXPORT_ARGS = [
("yolov8n", "torchscript"),
("yolov8n-seg", "torchscript"),
("yolov8n-cls", "torchscript"),
("yolov8n-pose", "torchscript"),
("yolov8n-obb", "torchscript"),
] # (model, format)
from . import CUDA_DEVICE_COUNT, CUDA_IS_AVAILABLE
# Constants
TASK_MODEL_DATA = [(task, WEIGHTS_DIR / TASK2MODEL[task], TASK2DATA[task]) for task in TASKS]
MODELS = [WEIGHTS_DIR / TASK2MODEL[task] for task in TASKS]
def run(cmd):
@ -38,28 +28,28 @@ def test_special_modes():
run("yolo cfg")
@pytest.mark.parametrize("task,model,data", TASK_ARGS)
@pytest.mark.parametrize("task,model,data", TASK_MODEL_DATA)
def test_train(task, model, data):
"""Test YOLO training for a given task, model, and data."""
run(f"yolo train {task} model={model}.yaml data={data} imgsz=32 epochs=1 cache=disk")
run(f"yolo train {task} model={model} data={data} imgsz=32 epochs=1 cache=disk")
@pytest.mark.parametrize("task,model,data", TASK_ARGS)
@pytest.mark.parametrize("task,model,data", TASK_MODEL_DATA)
def test_val(task, model, data):
"""Test YOLO validation for a given task, model, and data."""
run(f"yolo val {task} model={WEIGHTS_DIR / model}.pt data={data} imgsz=32 save_txt save_json")
run(f"yolo val {task} model={model} data={data} imgsz=32 save_txt save_json")
@pytest.mark.parametrize("task,model,data", TASK_ARGS)
@pytest.mark.parametrize("task,model,data", TASK_MODEL_DATA)
def test_predict(task, model, data):
"""Test YOLO prediction on sample assets for a given task and model."""
run(f"yolo predict model={WEIGHTS_DIR / model}.pt source={ASSETS} imgsz=32 save save_crop save_txt")
run(f"yolo predict model={model} source={ASSETS} imgsz=32 save save_crop save_txt")
@pytest.mark.parametrize("model,format", EXPORT_ARGS)
def test_export(model, format):
@pytest.mark.parametrize("model", MODELS)
def test_export(model):
"""Test exporting a YOLO model to different formats."""
run(f"yolo export model={WEIGHTS_DIR / model}.pt format={format} imgsz=32")
run(f"yolo export model={model} format=torchscript imgsz=32")
def test_rtdetr(task="detect", model="yolov8n-rtdetr.yaml", data="coco8.yaml"):
@ -129,10 +119,10 @@ def test_mobilesam():
# Slow Tests -----------------------------------------------------------------------------------------------------------
@pytest.mark.slow
@pytest.mark.parametrize("task,model,data", TASK_ARGS)
@pytest.mark.parametrize("task,model,data", TASK_MODEL_DATA)
@pytest.mark.skipif(not CUDA_IS_AVAILABLE, reason="CUDA is not available")
@pytest.mark.skipif(CUDA_DEVICE_COUNT < 2, reason="DDP is not available")
def test_train_gpu(task, model, data):
"""Test YOLO training on GPU(s) for various tasks and models."""
run(f"yolo train {task} model={model}.yaml data={data} imgsz=32 epochs=1 device=0") # single GPU
run(f"yolo train {task} model={model}.pt data={data} imgsz=32 epochs=1 device=0,1") # multi GPU
run(f"yolo train {task} model={model} data={data} imgsz=32 epochs=1 device=0") # single GPU
run(f"yolo train {task} model={model} data={data} imgsz=32 epochs=1 device=0,1") # multi GPU