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New ASSETS and trackers GMC cleanup (#4425)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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Glenn Jocher 2023-08-17 18:19:05 +02:00 • committed by GitHub
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commit 9d27e7ada4
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32 changed files with 222 additions and 201 deletions
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12
tests/test_cli.py
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@ -5,7 +5,7 @@ from pathlib import Path
import pytest
from ultralytics.utils import ROOT, SETTINGS
from ultralytics.utils import ASSETS, SETTINGS
WEIGHTS_DIR = Path(SETTINGS['weights_dir'])
TASK_ARGS = [
@ -40,12 +40,12 @@ def test_train(task, model, data):
@pytest.mark.parametrize('task,model,data', TASK_ARGS)
def test_val(task, model, data):
run(f'yolo val {task} model={WEIGHTS_DIR / model}.pt data={data} imgsz=32')
run(f'yolo val {task} model={WEIGHTS_DIR / model}.pt data={data} imgsz=32 save_txt save_json')
@pytest.mark.parametrize('task,model,data', TASK_ARGS)
def test_predict(task, model, data):
run(f"yolo predict model={WEIGHTS_DIR / model}.pt source={ROOT / 'assets'} imgsz=32 save save_crop save_txt")
run(f'yolo predict model={WEIGHTS_DIR / model}.pt source={ASSETS} imgsz=32 save save_crop save_txt')
@pytest.mark.parametrize('model,format', EXPORT_ARGS)
@ -56,11 +56,11 @@ def test_export(model, format):
def test_rtdetr(task='detect', model='yolov8n-rtdetr.yaml', data='coco8.yaml'):
# Warning: MUST use imgsz=640
run(f'yolo train {task} model={model} data={data} imgsz=640 epochs=1 cache=disk')
run(f"yolo predict {task} model={model} source={ROOT / 'assets/bus.jpg'} imgsz=640 save save_crop save_txt")
run(f"yolo predict {task} model={model} source={ASSETS / 'bus.jpg'} imgsz=640 save save_crop save_txt")
def test_fastsam(task='segment', model=WEIGHTS_DIR / 'FastSAM-s.pt', data='coco8-seg.yaml'):
source = ROOT / 'assets/bus.jpg'
source = ASSETS / 'bus.jpg'
run(f'yolo segment val {task} model={model} data={data} imgsz=32')
run(f'yolo segment predict model={model} source={source} imgsz=32 save save_crop save_txt')
@ -98,7 +98,7 @@ def test_mobilesam():
model = SAM(WEIGHTS_DIR / 'mobile_sam.pt')
# Source
source = ROOT / 'assets/zidane.jpg'
source = ASSETS / 'zidane.jpg'
# Predict a segment based on a point prompt
model.predict(source, points=[900, 370], labels=[1])