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ultralytics 8.2.10 add Classify and OBB Tasks to Results.summary() (#11653)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Laughing-q <1185102784@qq.com>
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Glenn Jocher 2024-05-06 12:26:01 +02:00 • committed by GitHub
parent f2f1afd269
commit 9d48190e6d
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5 changed files with 46 additions and 53 deletions
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1
tests/test_exports.py
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@ -18,6 +18,7 @@ from ultralytics.utils import (
checks,
)
from ultralytics.utils.torch_utils import TORCH_1_9, TORCH_1_13
from . import MODEL, SOURCE

57
tests/test_python.py
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@ -12,7 +12,7 @@ import yaml
from PIL import Image
from ultralytics import RTDETR, YOLO
from ultralytics.cfg import TASK2DATA
from ultralytics.cfg import MODELS, TASK2DATA
from ultralytics.data.build import load_inference_source
from ultralytics.utils import (
ASSETS,
@ -76,42 +76,27 @@ def test_predict_txt():
_ = YOLO(MODEL)(source=txt_file, imgsz=32)
def test_predict_img():
@pytest.mark.parametrize("model_name", MODELS)
def test_predict_img(model_name):
"""Test YOLO prediction on various types of image sources."""
model = YOLO(MODEL)
seg_model = YOLO(WEIGHTS_DIR / "yolov8n-seg.pt")
cls_model = YOLO(WEIGHTS_DIR / "yolov8n-cls.pt")
pose_model = YOLO(WEIGHTS_DIR / "yolov8n-pose.pt")
obb_model = YOLO(WEIGHTS_DIR / "yolov8n-obb.pt")
im = cv2.imread(str(SOURCE))
model = YOLO(WEIGHTS_DIR / model_name)
im = cv2.imread(str(SOURCE)) # uint8 numpy array
assert len(model(source=Image.open(SOURCE), save=True, verbose=True, imgsz=32)) == 1 # PIL
assert len(model(source=im, save=True, save_txt=True, imgsz=32)) == 1 # ndarray
assert len(model(torch.rand((2, 3, 32, 32)), imgsz=32)) == 2 # batch-size 2 Tensor, FP32 0.0-1.0 RGB order
assert len(model(source=[im, im], save=True, save_txt=True, imgsz=32)) == 2 # batch
assert len(list(model(source=[im, im], save=True, stream=True, imgsz=32))) == 2 # stream
assert len(model(torch.zeros(320, 640, 3).numpy(), imgsz=32)) == 1 # tensor to numpy
assert len(model(torch.zeros(320, 640, 3).numpy().astype(np.uint8), imgsz=32)) == 1 # tensor to numpy
batch = [
str(SOURCE), # filename
Path(SOURCE), # Path
"https://ultralytics.com/images/zidane.jpg" if ONLINE else SOURCE, # URI
cv2.imread(str(SOURCE)), # OpenCV
Image.open(SOURCE), # PIL
np.zeros((320, 640, 3)),
] # numpy
np.zeros((320, 640, 3), dtype=np.uint8), # numpy
]
assert len(model(batch, imgsz=32)) == len(batch) # multiple sources in a batch
# Test tensor inference
im = torch.rand((4, 3, 32, 32)) # batch-size 4, FP32 0.0-1.0 RGB order
results = model(im, imgsz=32)
assert len(results) == im.shape[0]
results = seg_model(im, imgsz=32)
assert len(results) == im.shape[0]
results = cls_model(im, imgsz=32)
assert len(results) == im.shape[0]
results = pose_model(im, imgsz=32)
assert len(results) == im.shape[0]
results = obb_model(im, imgsz=32)
assert len(results) == im.shape[0]
def test_predict_grey_and_4ch():
"""Test YOLO prediction on SOURCE converted to greyscale and 4-channel images."""
@ -236,19 +221,19 @@ def test_predict_callback_and_setup():
print(boxes)
def test_results():
@pytest.mark.parametrize("model", MODELS)
def test_results(model):
"""Test various result formats for the YOLO model."""
for m in "yolov8n-pose.pt", "yolov8n-seg.pt", "yolov8n.pt", "yolov8n-cls.pt":
results = YOLO(WEIGHTS_DIR / m)([SOURCE, SOURCE], imgsz=160)
for r in results:
r = r.cpu().numpy()
r = r.to(device="cpu", dtype=torch.float32)
r.save_txt(txt_file=TMP / "runs/tests/label.txt", save_conf=True)
r.save_crop(save_dir=TMP / "runs/tests/crops/")
r.tojson(normalize=True)
r.plot(pil=True)
r.plot(conf=True, boxes=True)
print(r, len(r), r.path)
results = YOLO(WEIGHTS_DIR / model)([SOURCE, SOURCE], imgsz=160)
for r in results:
r = r.cpu().numpy()
r = r.to(device="cpu", dtype=torch.float32)
r.save_txt(txt_file=TMP / "runs/tests/label.txt", save_conf=True)
r.save_crop(save_dir=TMP / "runs/tests/crops/")
r.tojson(normalize=True)
r.plot(pil=True)
r.plot(conf=True, boxes=True)
print(r, len(r), r.path)
def test_labels_and_crops():