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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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tests/test_python.py
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@ -1,7 +1,6 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
import contextlib
import shutil
from copy import copy
from pathlib import Path
@ -15,7 +14,7 @@ from torchvision.transforms import ToTensor
from ultralytics import RTDETR, YOLO
from ultralytics.cfg import TASK2DATA
from ultralytics.data.build import load_inference_source
from ultralytics.utils import ASSETS, DEFAULT_CFG, LINUX, ONLINE, ROOT, SETTINGS, WINDOWS
from ultralytics.utils import ASSETS, DEFAULT_CFG, LINUX, MACOS, ONLINE, ROOT, SETTINGS, WINDOWS
from ultralytics.utils.downloads import download
from ultralytics.utils.torch_utils import TORCH_1_9
@ -50,14 +49,22 @@ def test_model_methods():
_ = model.task_map
def test_model_profile():
# Test profile=True model argument
from ultralytics.nn.tasks import DetectionModel
model = DetectionModel() # build model
im = torch.randn(1, 3, 64, 64) # requires min imgsz=64
_ = model.predict(im, profile=True)
def test_predict_txt():
# Write a list of sources (file, dir, glob, recursive glob) to a txt file
txt_file = TMP / 'sources.txt'
with open(txt_file, 'w') as f:
for x in [ASSETS / 'bus.jpg', ASSETS, ASSETS / '*', ASSETS / '**/*.jpg']:
f.write(f'{x}\n')
model = YOLO(MODEL)
model(source=txt_file, imgsz=32)
_ = YOLO(MODEL)(source=txt_file, imgsz=32)
def test_predict_img():
@ -143,8 +150,7 @@ def test_track_stream():
def test_val():
model = YOLO(MODEL)
model.val(data='coco8.yaml', imgsz=32, save_hybrid=True)
YOLO(MODEL).val(data='coco8.yaml', imgsz=32, save_hybrid=True)
def test_train_scratch():
@ -160,29 +166,25 @@ def test_train_pretrained():
def test_export_torchscript():
model = YOLO(MODEL)
f = model.export(format='torchscript', optimize=True)
f = YOLO(MODEL).export(format='torchscript', optimize=True)
YOLO(f)(SOURCE) # exported model inference
def test_export_onnx():
model = YOLO(MODEL)
f = model.export(format='onnx', dynamic=True)
f = YOLO(MODEL).export(format='onnx', dynamic=True)
YOLO(f)(SOURCE) # exported model inference
def test_export_openvino():
model = YOLO(MODEL)
f = model.export(format='openvino')
f = YOLO(MODEL).export(format='openvino')
YOLO(f)(SOURCE) # exported model inference
def test_export_coreml():
if not WINDOWS: # RuntimeError: BlobWriter not loaded with coremltools 7.0 on windows
model = YOLO(MODEL)
model.export(format='coreml', nms=True)
# if MACOS:
# YOLO(f)(SOURCE) # model prediction only supported on macOS
f = YOLO(MODEL).export(format='coreml', nms=True)
if MACOS:
YOLO(f)(SOURCE) # model prediction only supported on macOS
def test_export_tflite(enabled=False):
@ -204,13 +206,11 @@ def test_export_pb(enabled=False):
def test_export_paddle(enabled=False):
# Paddle protobuf requirements conflicting with onnx protobuf requirements
if enabled:
model = YOLO(MODEL)
model.export(format='paddle')
YOLO(MODEL).export(format='paddle')
def test_export_ncnn():
model = YOLO(MODEL)
f = model.export(format='ncnn')
f = YOLO(MODEL).export(format='ncnn')
YOLO(f)(SOURCE) # exported model inference
@ -218,14 +218,14 @@ def test_all_model_yamls():
for m in (ROOT / 'cfg' / 'models').rglob('*.yaml'):
if 'rtdetr' in m.name:
if TORCH_1_9: # torch<=1.8 issue - TypeError: __init__() got an unexpected keyword argument 'batch_first'
RTDETR(m.name)(SOURCE, imgsz=640) # must be 640
_ = RTDETR(m.name)(SOURCE, imgsz=640) # must be 640
else:
YOLO(m.name)
def test_workflow():
model = YOLO(MODEL)
model.train(data='coco8.yaml', epochs=1, imgsz=32)
model.train(data='coco8.yaml', epochs=1, imgsz=32, optimizer='SGD')
model.val(imgsz=32)
model.predict(SOURCE, imgsz=32)
model.export(format='onnx') # export a model to ONNX format
@ -254,8 +254,7 @@ def test_predict_callback_and_setup():
def test_results():
for m in 'yolov8n-pose.pt', 'yolov8n-seg.pt', 'yolov8n.pt', 'yolov8n-cls.pt':
model = YOLO(m)
results = model([SOURCE, SOURCE], imgsz=160)
results = YOLO(m)([SOURCE, SOURCE], imgsz=160)
for r in results:
r = r.cpu().numpy()
r = r.to(device='cpu', dtype=torch.float32)
@ -278,8 +277,7 @@ def test_data_utils():
for task in 'detect', 'segment', 'pose':
file = Path(TASK2DATA[task]).with_suffix('.zip') # i.e. coco8.zip
download(f'https://github.com/ultralytics/hub/raw/main/example_datasets/{file}', unzip=False)
shutil.move(str(file), TMP) # Python 3.8 requires string input to shutil.move()
download(f'https://github.com/ultralytics/hub/raw/main/example_datasets/{file}', unzip=False, dir=TMP)
stats = HUBDatasetStats(TMP / file, task=task)
stats.get_json(save=True)
stats.process_images()
@ -294,8 +292,7 @@ def test_data_converter():
from ultralytics.data.converter import coco80_to_coco91_class, convert_coco
file = 'instances_val2017.json'
download(f'https://github.com/ultralytics/yolov5/releases/download/v1.0/{file}')
shutil.move(file, TMP)
download(f'https://github.com/ultralytics/yolov5/releases/download/v1.0/{file}', dir=TMP)
convert_coco(labels_dir=TMP, use_segments=True, use_keypoints=False, cls91to80=True)
coco80_to_coco91_class()