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Model coverage cleanup (#4585)

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Glenn Jocher 2023-08-27 04:19:41 +02:00 • committed by GitHub
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12 changed files with 132 additions and 175 deletions
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71
tests/test_cuda.py
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@ -1,16 +1,18 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
import contextlib
import subprocess
from pathlib import Path
import pytest
import torch
from ultralytics import YOLO
from ultralytics import YOLO, download
from ultralytics.utils import ASSETS, SETTINGS
CUDA_IS_AVAILABLE = torch.cuda.is_available()
CUDA_DEVICE_COUNT = torch.cuda.device_count()
DATASETS_DIR = Path(SETTINGS['datasets_dir'])
WEIGHTS_DIR = Path(SETTINGS['weights_dir'])
MODEL = WEIGHTS_DIR / 'path with spaces' / 'yolov8n.pt' # test spaces in path
DATA = 'coco8.yaml'
@ -37,13 +39,15 @@ def test_train_ddp():
def test_utils_benchmarks():
from ultralytics.utils.benchmarks import ProfileModels
YOLO(MODEL).export(format='engine', imgsz=32, dynamic=True, batch=1) # pre-export engine model, auto-device
# Pre-export a dynamic engine model to use dynamic inference
YOLO(MODEL).export(format='engine', imgsz=32, dynamic=True, batch=1)
ProfileModels([MODEL], imgsz=32, half=False, min_time=1, num_timed_runs=3, num_warmup_runs=1).profile()
@pytest.mark.skipif(not CUDA_IS_AVAILABLE, reason='CUDA is not available')
def test_predict_sam():
from ultralytics import SAM
from ultralytics.models.sam import Predictor as SAMPredictor
# Load a model
model = SAM(WEIGHTS_DIR / 'sam_b.pt')
@ -60,14 +64,63 @@ def test_predict_sam():
# Run inference with points prompt
model(ASSETS / 'zidane.jpg', points=[900, 370], labels=[1], device=0)
# Create SAMPredictor
overrides = dict(conf=0.25, task='segment', mode='predict', imgsz=1024, model='mobile_sam.pt')
predictor = SAMPredictor(overrides=overrides)
# Set image
predictor.set_image('ultralytics/assets/zidane.jpg') # set with image file
# predictor(bboxes=[439, 437, 524, 709])
# predictor(points=[900, 370], labels=[1])
# Reset image
predictor.reset_image()
@pytest.mark.skipif(not CUDA_IS_AVAILABLE, reason='CUDA is not available')
def test_model_tune():
subprocess.run('pip install ray[tune]'.split(), check=True)
YOLO('yolov8n-cls.yaml').tune(data='imagenet10',
grace_period=1,
max_samples=1,
imgsz=32,
epochs=1,
plots=False,
device='cpu')
with contextlib.suppress(RuntimeError): # RuntimeError may be caused by out-of-memory
YOLO('yolov8n-cls.yaml').tune(data='imagenet10',
grace_period=1,
max_samples=1,
imgsz=32,
epochs=1,
plots=False,
device='cpu')
@pytest.mark.skipif(not CUDA_IS_AVAILABLE, reason='CUDA is not available')
def test_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/'
validator = DetectionValidator(args={'model': 'yolov8n.pt', 'data': 'coco8.yaml', 'save_json': True, 'imgsz': 64})
validator()
validator.is_coco = True
download(f'{url}instances_val2017.json', dir=DATASETS_DIR / 'coco8/annotations')
_ = validator.eval_json(validator.stats)
validator = SegmentationValidator(args={
'model': 'yolov8n-seg.pt',
'data': 'coco8-seg.yaml',
'save_json': True,
'imgsz': 64})
validator()
validator.is_coco = True
download(f'{url}instances_val2017.json', dir=DATASETS_DIR / 'coco8-seg/annotations')
_ = validator.eval_json(validator.stats)
validator = PoseValidator(args={
'model': 'yolov8n-pose.pt',
'data': 'coco8-pose.yaml',
'save_json': True,
'imgsz': 64})
validator()
validator.is_coco = True
download(f'{url}person_keypoints_val2017.json', dir=DATASETS_DIR / 'coco8-pose/annotations')
_ = validator.eval_json(validator.stats)