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# Ultralytics YOLO 🚀, AGPL-3.0 license
import shutil
import uuid
from itertools import product
from pathlib import Path
import pytest
from ultralytics import YOLO
from ultralytics.cfg import TASK2DATA, TASK2MODEL, TASKS
from ultralytics.utils import (
IS_RASPBERRYPI,
LINUX,
MACOS,
WINDOWS,
Retry,
checks,
)
from ultralytics.utils.torch_utils import TORCH_1_9, TORCH_1_13
from tests import MODEL, SOURCE
def test_export_torchscript():
"""Test YOLO exports to TorchScript format."""
file = YOLO(MODEL).export(format="torchscript", optimize=False, imgsz=32)
YOLO(file)(SOURCE, imgsz=32) # exported model inference
def test_export_onnx():
"""Test YOLO exports to ONNX format."""
file = YOLO(MODEL).export(format="onnx", dynamic=True, imgsz=32)
YOLO(file)(SOURCE, imgsz=32) # exported model inference
@pytest.mark.skipif(checks.IS_PYTHON_3_12, reason="OpenVINO not supported in Python 3.12")
@pytest.mark.skipif(not TORCH_1_13, reason="OpenVINO requires torch>=1.13")
def test_export_openvino():
"""Test YOLO exports to OpenVINO format."""
file = YOLO(MODEL).export(format="openvino", imgsz=32)
YOLO(file)(SOURCE, imgsz=32) # exported model inference
@pytest.mark.slow
@pytest.mark.skipif(checks.IS_PYTHON_3_12, reason="OpenVINO not supported in Python 3.12")
@pytest.mark.skipif(not TORCH_1_13, reason="OpenVINO requires torch>=1.13")
@pytest.mark.parametrize(
"task, dynamic, int8, half, batch",
[ # generate all combinations but exclude those where both int8 and half are True
(task, dynamic, int8, half, batch)
for task, dynamic, int8, half, batch in product(TASKS, [True, False], [True, False], [True, False], [1, 2])
if not (int8 and half) # exclude cases where both int8 and half are True
],
)
def test_export_openvino_matrix(task, dynamic, int8, half, batch):
"""Test YOLO exports to OpenVINO format."""
file = YOLO(TASK2MODEL[task]).export(
format="openvino",
imgsz=32,
dynamic=dynamic,
int8=int8,
half=half,
batch=batch,
data=TASK2DATA[task],
)
if WINDOWS:
# Use unique filenames due to Windows file permissions bug possibly due to latent threaded use
# See https://github.com/ultralytics/ultralytics/actions/runs/8957949304/job/24601616830?pr=10423
file = Path(file)
file = file.rename(file.with_stem(f"{file.stem}-{uuid.uuid4()}"))
YOLO(file)([SOURCE] * batch, imgsz=64 if dynamic else 32) # exported model inference
with Retry(times=3, delay=1): # retry in case of potential lingering multi-threaded file usage errors
shutil.rmtree(file)
@pytest.mark.slow
@pytest.mark.parametrize("task, dynamic, int8, half, batch", product(TASKS, [True, False], [False], [False], [1, 2]))
def test_export_onnx_matrix(task, dynamic, int8, half, batch):
"""Test YOLO exports to ONNX format."""
file = YOLO(TASK2MODEL[task]).export(
format="onnx",
imgsz=32,
dynamic=dynamic,
int8=int8,
half=half,
batch=batch,
)
YOLO(file)([SOURCE] * batch, imgsz=64 if dynamic else 32) # exported model inference
Path(file).unlink() # cleanup
@pytest.mark.slow
@pytest.mark.parametrize("task, dynamic, int8, half, batch", product(TASKS, [False], [False], [False], [1, 2]))
def test_export_torchscript_matrix(task, dynamic, int8, half, batch):
"""Test YOLO exports to TorchScript format."""
file = YOLO(TASK2MODEL[task]).export(
format="torchscript",
imgsz=32,
dynamic=dynamic,
int8=int8,
half=half,
batch=batch,
)
YOLO(file)([SOURCE] * 3, imgsz=64 if dynamic else 32) # exported model inference at batch=3
Path(file).unlink() # cleanup
@pytest.mark.slow
@pytest.mark.skipif(not MACOS, reason="CoreML inference only supported on macOS")
@pytest.mark.skipif(not TORCH_1_9, reason="CoreML>=7.2 not supported with PyTorch<=1.8")
@pytest.mark.skipif(checks.IS_PYTHON_3_12, reason="CoreML not supported in Python 3.12")
@pytest.mark.parametrize(
"task, dynamic, int8, half, batch",
[ # generate all combinations but exclude those where both int8 and half are True
(task, dynamic, int8, half, batch)
for task, dynamic, int8, half, batch in product(TASKS, [False], [True, False], [True, False], [1])
if not (int8 and half) # exclude cases where both int8 and half are True
],
)
def test_export_coreml_matrix(task, dynamic, int8, half, batch):
"""Test YOLO exports to CoreML format."""
file = YOLO(TASK2MODEL[task]).export(
format="coreml",
imgsz=32,
dynamic=dynamic,
int8=int8,
half=half,
batch=batch,
)
YOLO(file)([SOURCE] * batch, imgsz=32) # exported model inference at batch=3
shutil.rmtree(file) # cleanup
@pytest.mark.skipif(not TORCH_1_9, reason="CoreML>=7.2 not supported with PyTorch<=1.8")
@pytest.mark.skipif(WINDOWS, reason="CoreML not supported on Windows") # RuntimeError: BlobWriter not loaded
@pytest.mark.skipif(IS_RASPBERRYPI, reason="CoreML not supported on Raspberry Pi")
@pytest.mark.skipif(checks.IS_PYTHON_3_12, reason="CoreML not supported in Python 3.12")
def test_export_coreml():
"""Test YOLO exports to CoreML format."""
if MACOS:
file = YOLO(MODEL).export(format="coreml", imgsz=32)
YOLO(file)(SOURCE, imgsz=32) # model prediction only supported on macOS for nms=False models
else:
YOLO(MODEL).export(format="coreml", nms=True, imgsz=32)
@pytest.mark.skipif(not LINUX, reason="Test disabled as TF suffers from install conflicts on Windows and macOS")
def test_export_tflite():
"""
Test YOLO exports to TFLite format.
Note TF suffers from install conflicts on Windows and macOS.
"""
model = YOLO(MODEL)
file = model.export(format="tflite", imgsz=32)
YOLO(file)(SOURCE, imgsz=32)
@pytest.mark.skipif(True, reason="Test disabled")
@pytest.mark.skipif(not LINUX, reason="TF suffers from install conflicts on Windows and macOS")
def test_export_pb():
"""
Test YOLO exports to *.pb format.
Note TF suffers from install conflicts on Windows and macOS.
"""
model = YOLO(MODEL)
file = model.export(format="pb", imgsz=32)
YOLO(file)(SOURCE, imgsz=32)
@pytest.mark.skipif(True, reason="Test disabled as Paddle protobuf and ONNX protobuf requirementsk conflict.")
def test_export_paddle():
"""
Test YOLO exports to Paddle format.
Note Paddle protobuf requirements conflicting with onnx protobuf requirements.
"""
YOLO(MODEL).export(format="paddle", imgsz=32)
@pytest.mark.slow
def test_export_ncnn():
"""Test YOLO exports to NCNN format."""
file = YOLO(MODEL).export(format="ncnn", imgsz=32)
YOLO(file)(SOURCE, imgsz=32) # exported model inference