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ultralytics 8.3.65 Rockchip RKNN Integration for Ultralytics YOLO models (#16308)

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Signed-off-by: Francesco Mattioli <Francesco.mttl@gmail.com>
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com>
Co-authored-by: Lakshantha Dissanayake <lakshantha@ultralytics.com>
Co-authored-by: Burhan <Burhan-Q@users.noreply.github.com>
Co-authored-by: Laughing-q <1185102784@qq.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
Co-authored-by: Ultralytics Assistant <135830346+UltralyticsAssistant@users.noreply.github.com>
Co-authored-by: Lakshantha Dissanayake <lakshanthad@yahoo.com>
Co-authored-by: Francesco Mattioli <Francesco.mttl@gmail.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Ivor Zhu 2025-01-20 20:25:54 -05:00 • committed by GitHub
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44
ultralytics/utils/benchmarks.py
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@ -4,25 +4,26 @@ Benchmark a YOLO model formats for speed and accuracy.
Usage:
from ultralytics.utils.benchmarks import ProfileModels, benchmark
ProfileModels(['yolov8n.yaml', 'yolov8s.yaml']).profile()
benchmark(model='yolov8n.pt', imgsz=160)
ProfileModels(['yolo11n.yaml', 'yolov8s.yaml']).profile()
benchmark(model='yolo11n.pt', imgsz=160)
Format | `format=argument` | Model
--- | --- | ---
PyTorch | - | yolov8n.pt
TorchScript | `torchscript` | yolov8n.torchscript
ONNX | `onnx` | yolov8n.onnx
OpenVINO | `openvino` | yolov8n_openvino_model/
TensorRT | `engine` | yolov8n.engine
CoreML | `coreml` | yolov8n.mlpackage
TensorFlow SavedModel | `saved_model` | yolov8n_saved_model/
TensorFlow GraphDef | `pb` | yolov8n.pb
TensorFlow Lite | `tflite` | yolov8n.tflite
TensorFlow Edge TPU | `edgetpu` | yolov8n_edgetpu.tflite
TensorFlow.js | `tfjs` | yolov8n_web_model/
PaddlePaddle | `paddle` | yolov8n_paddle_model/
MNN | `mnn` | yolov8n.mnn
NCNN | `ncnn` | yolov8n_ncnn_model/
PyTorch | - | yolo11n.pt
TorchScript | `torchscript` | yolo11n.torchscript
ONNX | `onnx` | yolo11n.onnx
OpenVINO | `openvino` | yolo11n_openvino_model/
TensorRT | `engine` | yolo11n.engine
CoreML | `coreml` | yolo11n.mlpackage
TensorFlow SavedModel | `saved_model` | yolo11n_saved_model/
TensorFlow GraphDef | `pb` | yolo11n.pb
TensorFlow Lite | `tflite` | yolo11n.tflite
TensorFlow Edge TPU | `edgetpu` | yolo11n_edgetpu.tflite
TensorFlow.js | `tfjs` | yolo11n_web_model/
PaddlePaddle | `paddle` | yolo11n_paddle_model/
MNN | `mnn` | yolo11n.mnn
NCNN | `ncnn` | yolo11n_ncnn_model/
RKNN | `rknn` | yolo11n_rknn_model/
"""
import glob
@ -41,7 +42,7 @@ from ultralytics import YOLO, YOLOWorld
from ultralytics.cfg import TASK2DATA, TASK2METRIC
from ultralytics.engine.exporter import export_formats
from ultralytics.utils import ARM64, ASSETS, IS_JETSON, IS_RASPBERRYPI, LINUX, LOGGER, MACOS, TQDM, WEIGHTS_DIR
from ultralytics.utils.checks import IS_PYTHON_3_12, check_requirements, check_yolo
from ultralytics.utils.checks import IS_PYTHON_3_12, check_requirements, check_yolo, is_rockchip
from ultralytics.utils.downloads import safe_download
from ultralytics.utils.files import file_size
from ultralytics.utils.torch_utils import get_cpu_info, select_device
@ -121,6 +122,11 @@ def benchmark(
assert not isinstance(model, YOLOWorld), "YOLOWorldv2 IMX exports not supported"
assert model.task == "detect", "IMX only supported for detection task"
assert "C2f" in model.__str__(), "IMX only supported for YOLOv8"
if i == 15: # RKNN
assert not isinstance(model, YOLOWorld), "YOLOWorldv2 RKNN exports not supported yet"
assert not is_end2end, "End-to-end models not supported by RKNN yet"
assert LINUX, "RKNN only supported on Linux"
assert not is_rockchip(), "RKNN Inference only supported on Rockchip devices"
if "cpu" in device.type:
assert cpu, "inference not supported on CPU"
if "cuda" in device.type:
@ -334,7 +340,7 @@ class ProfileModels:
Examples:
Profile models and print results
>>> from ultralytics.utils.benchmarks import ProfileModels
>>> profiler = ProfileModels(["yolov8n.yaml", "yolov8s.yaml"], imgsz=640)
>>> profiler = ProfileModels(["yolo11n.yaml", "yolov8s.yaml"], imgsz=640)
>>> profiler.profile()
"""
@ -368,7 +374,7 @@ class ProfileModels:
Examples:
Initialize and profile models
>>> from ultralytics.utils.benchmarks import ProfileModels
>>> profiler = ProfileModels(["yolov8n.yaml", "yolov8s.yaml"], imgsz=640)
>>> profiler = ProfileModels(["yolo11n.yaml", "yolov8s.yaml"], imgsz=640)
>>> profiler.profile()
"""
self.paths = paths