ultralytics 8.3.65 Rockchip RKNN Integration for Ultralytics YOLO models (#16308)
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>
This commit is contained in:
Ivor Zhu
GitHub
parent
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commit
b5e0cee943
41 changed files with 390 additions and 118 deletions
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@ -51,6 +51,20 @@ PYTHON_VERSION = platform.python_version()
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TORCH_VERSION = torch.__version__
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TORCHVISION_VERSION = importlib.metadata.version("torchvision") # faster than importing torchvision
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IS_VSCODE = os.environ.get("TERM_PROGRAM", False) == "vscode"
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RKNN_CHIPS = frozenset(
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{
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"rk3588",
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"rk3576",
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"rk3566",
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"rk3568",
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"rk3562",
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"rv1103",
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"rv1106",
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"rv1103b",
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"rv1106b",
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"rk2118",
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}
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) # Rockchip processors available for export
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HELP_MSG = """
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Examples for running Ultralytics:
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@ -4,25 +4,26 @@ Benchmark a YOLO model formats for speed and accuracy.
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Usage:
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from ultralytics.utils.benchmarks import ProfileModels, benchmark
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ProfileModels(['yolov8n.yaml', 'yolov8s.yaml']).profile()
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benchmark(model='yolov8n.pt', imgsz=160)
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ProfileModels(['yolo11n.yaml', 'yolov8s.yaml']).profile()
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benchmark(model='yolo11n.pt', imgsz=160)
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Format | `format=argument` | Model
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--- | --- | ---
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PyTorch | - | yolov8n.pt
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TorchScript | `torchscript` | yolov8n.torchscript
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ONNX | `onnx` | yolov8n.onnx
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OpenVINO | `openvino` | yolov8n_openvino_model/
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TensorRT | `engine` | yolov8n.engine
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CoreML | `coreml` | yolov8n.mlpackage
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TensorFlow SavedModel | `saved_model` | yolov8n_saved_model/
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TensorFlow GraphDef | `pb` | yolov8n.pb
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TensorFlow Lite | `tflite` | yolov8n.tflite
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TensorFlow Edge TPU | `edgetpu` | yolov8n_edgetpu.tflite
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TensorFlow.js | `tfjs` | yolov8n_web_model/
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PaddlePaddle | `paddle` | yolov8n_paddle_model/
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MNN | `mnn` | yolov8n.mnn
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NCNN | `ncnn` | yolov8n_ncnn_model/
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PyTorch | - | yolo11n.pt
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TorchScript | `torchscript` | yolo11n.torchscript
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ONNX | `onnx` | yolo11n.onnx
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OpenVINO | `openvino` | yolo11n_openvino_model/
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TensorRT | `engine` | yolo11n.engine
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CoreML | `coreml` | yolo11n.mlpackage
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TensorFlow SavedModel | `saved_model` | yolo11n_saved_model/
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TensorFlow GraphDef | `pb` | yolo11n.pb
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TensorFlow Lite | `tflite` | yolo11n.tflite
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TensorFlow Edge TPU | `edgetpu` | yolo11n_edgetpu.tflite
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TensorFlow.js | `tfjs` | yolo11n_web_model/
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PaddlePaddle | `paddle` | yolo11n_paddle_model/
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MNN | `mnn` | yolo11n.mnn
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NCNN | `ncnn` | yolo11n_ncnn_model/
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RKNN | `rknn` | yolo11n_rknn_model/
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"""
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import glob
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@ -41,7 +42,7 @@ from ultralytics import YOLO, YOLOWorld
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from ultralytics.cfg import TASK2DATA, TASK2METRIC
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from ultralytics.engine.exporter import export_formats
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from ultralytics.utils import ARM64, ASSETS, IS_JETSON, IS_RASPBERRYPI, LINUX, LOGGER, MACOS, TQDM, WEIGHTS_DIR
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from ultralytics.utils.checks import IS_PYTHON_3_12, check_requirements, check_yolo
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from ultralytics.utils.checks import IS_PYTHON_3_12, check_requirements, check_yolo, is_rockchip
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from ultralytics.utils.downloads import safe_download
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from ultralytics.utils.files import file_size
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from ultralytics.utils.torch_utils import get_cpu_info, select_device
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@ -121,6 +122,11 @@ def benchmark(
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assert not isinstance(model, YOLOWorld), "YOLOWorldv2 IMX exports not supported"
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assert model.task == "detect", "IMX only supported for detection task"
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assert "C2f" in model.__str__(), "IMX only supported for YOLOv8"
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if i == 15: # RKNN
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assert not isinstance(model, YOLOWorld), "YOLOWorldv2 RKNN exports not supported yet"
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assert not is_end2end, "End-to-end models not supported by RKNN yet"
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assert LINUX, "RKNN only supported on Linux"
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assert not is_rockchip(), "RKNN Inference only supported on Rockchip devices"
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if "cpu" in device.type:
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assert cpu, "inference not supported on CPU"
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if "cuda" in device.type:
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@ -334,7 +340,7 @@ class ProfileModels:
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Examples:
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Profile models and print results
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>>> from ultralytics.utils.benchmarks import ProfileModels
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>>> profiler = ProfileModels(["yolov8n.yaml", "yolov8s.yaml"], imgsz=640)
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>>> profiler = ProfileModels(["yolo11n.yaml", "yolov8s.yaml"], imgsz=640)
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>>> profiler.profile()
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"""
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@ -368,7 +374,7 @@ class ProfileModels:
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Examples:
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Initialize and profile models
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>>> from ultralytics.utils.benchmarks import ProfileModels
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>>> profiler = ProfileModels(["yolov8n.yaml", "yolov8s.yaml"], imgsz=640)
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>>> profiler = ProfileModels(["yolo11n.yaml", "yolov8s.yaml"], imgsz=640)
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>>> profiler.profile()
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"""
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self.paths = paths
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@ -19,6 +19,7 @@ import requests
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import torch
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from ultralytics.utils import (
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ARM64,
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ASSETS,
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AUTOINSTALL,
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IS_COLAB,
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@ -30,6 +31,7 @@ from ultralytics.utils import (
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MACOS,
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ONLINE,
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PYTHON_VERSION,
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RKNN_CHIPS,
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ROOT,
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TORCHVISION_VERSION,
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USER_CONFIG_DIR,
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@ -487,10 +489,10 @@ def check_yolov5u_filename(file: str, verbose: bool = True):
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return file
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def check_model_file_from_stem(model="yolov8n"):
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def check_model_file_from_stem(model="yolo11n"):
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"""Return a model filename from a valid model stem."""
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if model and not Path(model).suffix and Path(model).stem in downloads.GITHUB_ASSETS_STEMS:
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return Path(model).with_suffix(".pt") # add suffix, i.e. yolov8n -> yolov8n.pt
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return Path(model).with_suffix(".pt") # add suffix, i.e. yolo11n -> yolo11n.pt
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else:
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return model
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@ -782,6 +784,21 @@ def cuda_is_available() -> bool:
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return cuda_device_count() > 0
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def is_rockchip():
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"""Check if the current environment is running on a Rockchip SoC."""
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if LINUX and ARM64:
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try:
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with open("/proc/device-tree/compatible") as f:
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dev_str = f.read()
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*_, soc = dev_str.split(",")
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if soc.replace("\x00", "") in RKNN_CHIPS:
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return True
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except OSError:
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return False
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else:
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return False
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def is_sudo_available() -> bool:
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"""
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Check if the sudo command is available in the environment.
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@ -798,5 +815,7 @@ def is_sudo_available() -> bool:
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# Run checks and define constants
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check_python("3.8", hard=False, verbose=True) # check python version
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check_torchvision() # check torch-torchvision compatibility
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# Define constants
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IS_PYTHON_MINIMUM_3_10 = check_python("3.10", hard=False)
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IS_PYTHON_3_12 = PYTHON_VERSION.startswith("3.12")
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@ -405,7 +405,7 @@ def get_github_assets(repo="ultralytics/assets", version="latest", retry=False):
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LOGGER.warning(f"⚠️ GitHub assets check failure for {url}: {r.status_code} {r.reason}")
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return "", []
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data = r.json()
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return data["tag_name"], [x["name"] for x in data["assets"]] # tag, assets i.e. ['yolov8n.pt', 'yolov8s.pt', ...]
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return data["tag_name"], [x["name"] for x in data["assets"]] # tag, assets i.e. ['yolo11n.pt', 'yolov8s.pt', ...]
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def attempt_download_asset(file, repo="ultralytics/assets", release="v8.3.0", **kwargs):
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@ -297,7 +297,7 @@ class v8SegmentationLoss(v8DetectionLoss):
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raise TypeError(
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"ERROR ❌ segment dataset incorrectly formatted or not a segment dataset.\n"
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"This error can occur when incorrectly training a 'segment' model on a 'detect' dataset, "
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"i.e. 'yolo train model=yolov8n-seg.pt data=coco8.yaml'.\nVerify your dataset is a "
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"i.e. 'yolo train model=yolo11n-seg.pt data=coco8.yaml'.\nVerify your dataset is a "
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"correctly formatted 'segment' dataset using 'data=coco8-seg.yaml' "
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"as an example.\nSee https://docs.ultralytics.com/datasets/segment/ for help."
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) from e
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@ -666,7 +666,7 @@ class v8OBBLoss(v8DetectionLoss):
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raise TypeError(
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"ERROR ❌ OBB dataset incorrectly formatted or not a OBB dataset.\n"
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"This error can occur when incorrectly training a 'OBB' model on a 'detect' dataset, "
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"i.e. 'yolo train model=yolov8n-obb.pt data=dota8.yaml'.\nVerify your dataset is a "
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"i.e. 'yolo train model=yolo11n-obb.pt data=dota8.yaml'.\nVerify your dataset is a "
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"correctly formatted 'OBB' dataset using 'data=dota8.yaml' "
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"as an example.\nSee https://docs.ultralytics.com/datasets/obb/ for help."
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) from e
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@ -30,10 +30,10 @@ def run_ray_tune(
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```python
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from ultralytics import YOLO
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# Load a YOLOv8n model
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# Load a YOLO11n model
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model = YOLO("yolo11n.pt")
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# Start tuning hyperparameters for YOLOv8n training on the COCO8 dataset
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# Start tuning hyperparameters for YOLO11n training on the COCO8 dataset
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result_grid = model.tune(data="coco8.yaml", use_ray=True)
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```
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"""
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