# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license """ Export a YOLO PyTorch model to other formats. TensorFlow exports authored by https://github.com/zldrobit. Format | `format=argument` | Model --- | --- | --- PyTorch | - | yolo26n.pt TorchScript | `torchscript` | yolo26n.torchscript ONNX | `onnx` | yolo26n.onnx OpenVINO | `openvino` | yolo26n_openvino_model/ TensorRT | `engine` | yolo26n.engine CoreML | `coreml` | yolo26n.mlpackage TensorFlow SavedModel | `saved_model` | yolo26n_saved_model/ TensorFlow GraphDef | `pb` | yolo26n.pb TensorFlow Lite | `tflite` | yolo26n.tflite TensorFlow Edge TPU | `edgetpu` | yolo26n_edgetpu.tflite TensorFlow.js | `tfjs` | yolo26n_web_model/ PaddlePaddle | `paddle` | yolo26n_paddle_model/ MNN | `mnn` | yolo26n.mnn NCNN | `ncnn` | yolo26n_ncnn_model/ IMX | `imx` | yolo26n_imx_model/ RKNN | `rknn` | yolo26n_rknn_model/ ExecuTorch | `executorch` | yolo26n_executorch_model/ Axelera | `axelera` | yolo26n_axelera_model/ Requirements: $ pip install "ultralytics[export]" Python: from ultralytics import YOLO model = YOLO('yolo26n.pt') results = model.export(format='onnx') CLI: $ yolo mode=export model=yolo26n.pt format=onnx Inference: $ yolo predict model=yolo26n.pt # PyTorch yolo26n.torchscript # TorchScript yolo26n.onnx # ONNX Runtime or OpenCV DNN with dnn=True yolo26n_openvino_model # OpenVINO yolo26n.engine # TensorRT yolo26n.mlpackage # CoreML (macOS-only) yolo26n_saved_model # TensorFlow SavedModel yolo26n.pb # TensorFlow GraphDef yolo26n.tflite # TensorFlow Lite yolo26n_edgetpu.tflite # TensorFlow Edge TPU yolo26n_paddle_model # PaddlePaddle yolo26n.mnn # MNN yolo26n_ncnn_model # NCNN yolo26n_imx_model # IMX yolo26n_rknn_model # RKNN yolo26n_executorch_model # ExecuTorch yolo26n_axelera_model # Axelera TensorFlow.js: $ cd .. && git clone https://github.com/zldrobit/tfjs-yolov5-example.git && cd tfjs-yolov5-example $ npm install $ ln -s ../../yolo26n_web_model public/yolo26n_web_model $ npm start """ from __future__ import annotations import json import os import re import shutil import subprocess import time from copy import deepcopy from datetime import datetime from pathlib import Path import numpy as np import torch from ultralytics import __version__ from ultralytics.cfg import TASK2DATA, get_cfg from ultralytics.data import build_dataloader from ultralytics.data.dataset import YOLODataset from ultralytics.data.utils import check_cls_dataset, check_det_dataset from ultralytics.nn.autobackend import check_class_names, default_class_names from ultralytics.nn.modules import C2f, Classify, Detect, RTDETRDecoder from ultralytics.nn.tasks import ClassificationModel, DetectionModel, SegmentationModel, WorldModel from ultralytics.utils import ( ARM64, DEFAULT_CFG, IS_COLAB, IS_DEBIAN_BOOKWORM, IS_DEBIAN_TRIXIE, IS_JETSON, IS_RASPBERRYPI, IS_UBUNTU, LINUX, LOGGER, MACOS, MACOS_VERSION, RKNN_CHIPS, SETTINGS, TORCH_VERSION, WINDOWS, YAML, callbacks, colorstr, get_default_args, is_dgx, is_jetson, ) from ultralytics.utils.checks import ( IS_PYTHON_3_10, IS_PYTHON_MINIMUM_3_9, check_apt_requirements, check_executorch_requirements, check_imgsz, check_requirements, check_tensorrt, check_version, is_intel, is_sudo_available, ) from ultralytics.utils.export import ( keras2pb, onnx2engine, onnx2saved_model, pb2tfjs, tflite2edgetpu, torch2executorch, torch2imx, torch2onnx, ) from ultralytics.utils.files import file_size from ultralytics.utils.metrics import batch_probiou from ultralytics.utils.nms import TorchNMS from ultralytics.utils.ops import Profile from ultralytics.utils.patches import arange_patch from ultralytics.utils.torch_utils import ( TORCH_1_10, TORCH_1_11, TORCH_1_13, TORCH_2_1, TORCH_2_3, TORCH_2_4, TORCH_2_9, select_device, ) def export_formats(): """Return a dictionary of Ultralytics YOLO export formats.""" x = [ ["PyTorch", "-", ".pt", True, True, []], ["TorchScript", "torchscript", ".torchscript", True, True, ["batch", "optimize", "half", "nms", "dynamic"]], ["ONNX", "onnx", ".onnx", True, True, ["batch", "dynamic", "half", "opset", "simplify", "nms"]], [ "OpenVINO", "openvino", "_openvino_model", True, False, ["batch", "dynamic", "half", "int8", "nms", "fraction"], ], [ "TensorRT", "engine", ".engine", False, True, ["batch", "dynamic", "half", "int8", "simplify", "nms", "fraction"], ], ["CoreML", "coreml", ".mlpackage", True, False, ["batch", "dynamic", "half", "int8", "nms"]], ["TensorFlow SavedModel", "saved_model", "_saved_model", True, True, ["batch", "int8", "keras", "nms"]], ["TensorFlow GraphDef", "pb", ".pb", True, True, ["batch"]], ["TensorFlow Lite", "tflite", ".tflite", True, False, ["batch", "half", "int8", "nms", "fraction"]], ["TensorFlow Edge TPU", "edgetpu", "_edgetpu.tflite", True, False, []], ["TensorFlow.js", "tfjs", "_web_model", True, False, ["batch", "half", "int8", "nms"]], ["PaddlePaddle", "paddle", "_paddle_model", True, True, ["batch"]], ["MNN", "mnn", ".mnn", True, True, ["batch", "half", "int8"]], ["NCNN", "ncnn", "_ncnn_model", True, True, ["batch", "half"]], ["IMX", "imx", "_imx_model", True, True, ["int8", "fraction", "nms"]], ["RKNN", "rknn", "_rknn_model", False, False, ["batch", "name"]], ["ExecuTorch", "executorch", "_executorch_model", True, False, ["batch"]], ["Axelera", "axelera", "_axelera_model", False, False, ["batch", "int8", "fraction"]], ] return dict(zip(["Format", "Argument", "Suffix", "CPU", "GPU", "Arguments"], zip(*x))) def best_onnx_opset(onnx, cuda=False) -> int: """Return max ONNX opset for this torch version with ONNX fallback.""" if TORCH_2_4: # _constants.ONNX_MAX_OPSET first defined in torch 1.13 opset = torch.onnx.utils._constants.ONNX_MAX_OPSET - 1 # use second-latest version for safety if TORCH_2_9: opset = min(opset, 20) # legacy TorchScript exporter caps at opset 20 in torch 2.9+ if cuda: opset -= 2 # fix CUDA ONNXRuntime NMS squeeze op errors else: version = ".".join(TORCH_VERSION.split(".")[:2]) opset = { "1.8": 12, "1.9": 12, "1.10": 13, "1.11": 14, "1.12": 15, "1.13": 17, "2.0": 17, # reduced from 18 to fix ONNX errors "2.1": 17, # reduced from 19 "2.2": 17, # reduced from 19 "2.3": 17, # reduced from 19 "2.4": 20, "2.5": 20, "2.6": 20, "2.7": 20, "2.8": 23, }.get(version, 12) return min(opset, onnx.defs.onnx_opset_version()) def validate_args(format, passed_args, valid_args): """Validate arguments based on the export format. Args: format (str): The export format. passed_args (SimpleNamespace): The arguments used during export. valid_args (list): List of valid arguments for the format. Raises: AssertionError: If an unsupported argument is used, or if the format lacks supported argument listings. """ export_args = ["half", "int8", "dynamic", "keras", "nms", "batch", "fraction"] assert valid_args is not None, f"ERROR ❌️ valid arguments for '{format}' not listed." custom = {"batch": 1, "data": None, "device": None} # exporter defaults default_args = get_cfg(DEFAULT_CFG, custom) for arg in export_args: not_default = getattr(passed_args, arg, None) != getattr(default_args, arg, None) if not_default: assert arg in valid_args, f"ERROR ❌️ argument '{arg}' is not supported for format='{format}'" def try_export(inner_func): """YOLO export decorator, i.e. @try_export.""" inner_args = get_default_args(inner_func) def outer_func(*args, **kwargs): """Export a model.""" prefix = inner_args["prefix"] dt = 0.0 try: with Profile() as dt: f = inner_func(*args, **kwargs) # exported file/dir or tuple of (file/dir, *) path = f if isinstance(f, (str, Path)) else f[0] mb = file_size(path) assert mb > 0.0, "0.0 MB output model size" LOGGER.info(f"{prefix} export success ✅ {dt.t:.1f}s, saved as '{path}' ({mb:.1f} MB)") return f except Exception as e: LOGGER.error(f"{prefix} export failure {dt.t:.1f}s: {e}") raise e return outer_func class Exporter: """A class for exporting YOLO models to various formats. This class provides functionality to export YOLO models to different formats including ONNX, TensorRT, CoreML, TensorFlow, and others. It handles format validation, device selection, model preparation, and the actual export process for each supported format. Attributes: args (SimpleNamespace): Configuration arguments for the exporter. callbacks (dict): Dictionary of callback functions for different export events. im (torch.Tensor): Input tensor for model inference during export. model (torch.nn.Module): The YOLO model to be exported. file (Path): Path to the model file being exported. output_shape (tuple): Shape of the model output tensor(s). pretty_name (str): Formatted model name for display purposes. metadata (dict): Model metadata including description, author, version, etc. device (torch.device): Device on which the model is loaded. imgsz (list): Input image size for the model. Methods: __call__: Main export method that handles the export process. get_int8_calibration_dataloader: Build dataloader for INT8 calibration. export_torchscript: Export model to TorchScript format. export_onnx: Export model to ONNX format. export_openvino: Export model to OpenVINO format. export_paddle: Export model to PaddlePaddle format. export_mnn: Export model to MNN format. export_ncnn: Export model to NCNN format. export_coreml: Export model to CoreML format. export_engine: Export model to TensorRT format. export_saved_model: Export model to TensorFlow SavedModel format. export_pb: Export model to TensorFlow GraphDef format. export_tflite: Export model to TensorFlow Lite format. export_edgetpu: Export model to Edge TPU format. export_tfjs: Export model to TensorFlow.js format. export_rknn: Export model to RKNN format. export_imx: Export model to IMX format. export_executorch: Export model to ExecuTorch format. export_axelera: Export model to Axelera format. Examples: Export a YOLO26 model to ONNX format >>> from ultralytics.engine.exporter import Exporter >>> exporter = Exporter() >>> exporter(model="yolo26n.pt") # exports to yolo26n.onnx Export with specific arguments >>> args = {"format": "onnx", "dynamic": True, "half": True} >>> exporter = Exporter(overrides=args) >>> exporter(model="yolo26n.pt") """ def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks: dict | None = None): """Initialize the Exporter class. Args: cfg (str | Path | dict | SimpleNamespace, optional): Configuration file path or configuration object. overrides (dict, optional): Configuration overrides. _callbacks (dict, optional): Dictionary of callback functions. """ self.args = get_cfg(cfg, overrides) self.callbacks = _callbacks or callbacks.get_default_callbacks() callbacks.add_integration_callbacks(self) def __call__(self, model=None) -> str: """Export a model and return the final exported path as a string. Returns: (str): Path to the exported file or directory (the last export artifact). """ t = time.time() fmt = self.args.format.lower() # to lowercase if fmt in {"tensorrt", "trt"}: # 'engine' aliases fmt = "engine" if fmt in {"mlmodel", "mlpackage", "mlprogram", "apple", "ios", "coreml"}: # 'coreml' aliases fmt = "coreml" fmts_dict = export_formats() fmts = tuple(fmts_dict["Argument"][1:]) # available export formats if fmt not in fmts: import difflib # Get the closest match if format is invalid matches = difflib.get_close_matches(fmt, fmts, n=1, cutoff=0.6) # 60% similarity required to match if not matches: msg = "Model is already in PyTorch format." if fmt == "pt" else f"Invalid export format='{fmt}'." raise ValueError(f"{msg} Valid formats are {fmts}") LOGGER.warning(f"Invalid export format='{fmt}', updating to format='{matches[0]}'") fmt = matches[0] flags = [x == fmt for x in fmts] if sum(flags) != 1: raise ValueError(f"Invalid export format='{fmt}'. Valid formats are {fmts}") ( jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle, mnn, ncnn, imx, rknn, executorch, axelera, ) = flags # export booleans is_tf_format = any((saved_model, pb, tflite, edgetpu, tfjs)) # Device dla = None if engine and self.args.device is None: LOGGER.warning("TensorRT requires GPU export, automatically assigning device=0") self.args.device = "0" if engine and "dla" in str(self.args.device): # convert int/list to str first device_str = str(self.args.device) dla = device_str.rsplit(":", 1)[-1] self.args.device = "0" # update device to "0" assert dla in {"0", "1"}, f"Expected device 'dla:0' or 'dla:1', but got {device_str}." if imx and self.args.device is None and torch.cuda.is_available(): LOGGER.warning("Exporting on CPU while CUDA is available, setting device=0 for faster export on GPU.") self.args.device = "0" # update device to "0" self.device = select_device("cpu" if self.args.device is None else self.args.device) # Argument compatibility checks fmt_keys = fmts_dict["Arguments"][flags.index(True) + 1] validate_args(fmt, self.args, fmt_keys) if axelera: if not IS_PYTHON_3_10: raise SystemError("Axelera export only supported on Python 3.10.") if not self.args.int8: LOGGER.warning("Setting int8=True for Axelera mixed-precision export.") self.args.int8 = True if model.task not in {"detect"}: raise ValueError("Axelera export only supported for detection models.") if not self.args.data: self.args.data = "coco128.yaml" # Axelera default to coco128.yaml if imx: if not self.args.int8: LOGGER.warning("IMX export requires int8=True, setting int8=True.") self.args.int8 = True if not self.args.nms and model.task in {"detect", "pose", "segment"}: LOGGER.warning("IMX export requires nms=True, setting nms=True.") self.args.nms = True if model.task not in {"detect", "pose", "classify", "segment"}: raise ValueError( "IMX export only supported for detection, pose estimation, classification, and segmentation models." ) if not hasattr(model, "names"): model.names = default_class_names() model.names = check_class_names(model.names) if hasattr(model, "end2end"): if self.args.end2end is not None: model.end2end = self.args.end2end if rknn or ncnn or executorch or paddle or imx or edgetpu: # Disable end2end branch for certain export formats as they does not support topk model.end2end = False LOGGER.warning(f"{fmt.upper()} export does not support end2end models, disabling end2end branch.") if engine and self.args.int8: # TensorRT<=10.3.0 with int8 has known end2end build issues # https://github.com/ultralytics/ultralytics/issues/23841 try: import tensorrt as trt if check_version(trt.__version__, "<=10.3.0", hard=True): model.end2end = False LOGGER.warning( "TensorRT<=10.3.0 with int8 has known end2end build issues, disabling end2end branch." ) except ImportError: pass if self.args.half and self.args.int8: LOGGER.warning("half=True and int8=True are mutually exclusive, setting half=False.") self.args.half = False if self.args.half and jit and self.device.type == "cpu": LOGGER.warning( "half=True only compatible with GPU export for TorchScript, i.e. use device=0, setting half=False." ) self.args.half = False self.imgsz = check_imgsz(self.args.imgsz, stride=model.stride, min_dim=2) # check image size if self.args.optimize: assert not ncnn, "optimize=True not compatible with format='ncnn', i.e. use optimize=False" assert self.device.type == "cpu", "optimize=True not compatible with cuda devices, i.e. use device='cpu'" if rknn: if not self.args.name: LOGGER.warning( "Rockchip RKNN export requires a missing 'name' arg for processor type. " "Using default name='rk3588'." ) self.args.name = "rk3588" self.args.name = self.args.name.lower() assert self.args.name in RKNN_CHIPS, ( f"Invalid processor name '{self.args.name}' for Rockchip RKNN export. Valid names are {RKNN_CHIPS}." ) if self.args.nms: assert not isinstance(model, ClassificationModel), "'nms=True' is not valid for classification models." assert not tflite or not ARM64 or not LINUX, "TFLite export with NMS unsupported on ARM64 Linux" assert not is_tf_format or TORCH_1_13, "TensorFlow exports with NMS require torch>=1.13" assert not onnx or TORCH_1_13, "ONNX export with NMS requires torch>=1.13" if getattr(model, "end2end", False) or isinstance(model.model[-1], RTDETRDecoder): LOGGER.warning("'nms=True' is not available for end2end models. Forcing 'nms=False'.") self.args.nms = False self.args.conf = self.args.conf or 0.25 # set conf default value for nms export if (engine or coreml or self.args.nms) and self.args.dynamic and self.args.batch == 1: LOGGER.warning( f"'dynamic=True' model with '{'nms=True' if self.args.nms else f'format={self.args.format}'}' requires max batch size, i.e. 'batch=16'" ) if edgetpu: if not LINUX or ARM64: raise SystemError( "Edge TPU export only supported on non-aarch64 Linux. See https://coral.ai/docs/edgetpu/compiler" ) elif self.args.batch != 1: # see github.com/ultralytics/ultralytics/pull/13420 LOGGER.warning("Edge TPU export requires batch size 1, setting batch=1.") self.args.batch = 1 if isinstance(model, WorldModel): LOGGER.warning( "YOLOWorld (original version) export is not supported to any format. " "YOLOWorldv2 models (i.e. 'yolov8s-worldv2.pt') only support export to " "(torchscript, onnx, openvino, engine, coreml) formats. " "See https://docs.ultralytics.com/models/yolo-world for details." ) model.clip_model = None # openvino int8 export error: https://github.com/ultralytics/ultralytics/pull/18445 if self.args.int8 and not self.args.data: self.args.data = DEFAULT_CFG.data or TASK2DATA[getattr(model, "task", "detect")] # assign default data LOGGER.warning( f"INT8 export requires a missing 'data' arg for calibration. Using default 'data={self.args.data}'." ) if tfjs and (ARM64 and LINUX): raise SystemError("TF.js exports are not currently supported on ARM64 Linux") # Recommend OpenVINO if export and Intel CPU if SETTINGS.get("openvino_msg"): if is_intel(): LOGGER.info( "💡 ProTip: Export to OpenVINO format for best performance on Intel hardware." " Learn more at https://docs.ultralytics.com/integrations/openvino/" ) SETTINGS["openvino_msg"] = False # Input im = torch.zeros(self.args.batch, model.yaml.get("channels", 3), *self.imgsz).to(self.device) file = Path( getattr(model, "pt_path", None) or getattr(model, "yaml_file", None) or model.yaml.get("yaml_file", "") ) if file.suffix in {".yaml", ".yml"}: file = Path(file.name) # Update model model = deepcopy(model).to(self.device) for p in model.parameters(): p.requires_grad = False model.eval() model.float() model = model.fuse() if imx: from ultralytics.utils.export.imx import FXModel model = FXModel(model, self.imgsz) if tflite or edgetpu: from ultralytics.utils.export.tensorflow import tf_wrapper model = tf_wrapper(model) if executorch: from ultralytics.utils.export.executorch import executorch_wrapper model = executorch_wrapper(model) for m in model.modules(): if isinstance(m, Classify): m.export = True if isinstance(m, (Detect, RTDETRDecoder)): # includes all Detect subclasses like Segment, Pose, OBB m.dynamic = self.args.dynamic m.export = True m.format = self.args.format # Clamp max_det to anchor count for small image sizes (required for TensorRT compatibility) anchors = sum(int(self.imgsz[0] / s) * int(self.imgsz[1] / s) for s in model.stride.tolist()) m.max_det = min(self.args.max_det, anchors) m.agnostic_nms = self.args.agnostic_nms m.xyxy = self.args.nms and not coreml m.shape = None # reset cached shape for new export input size if hasattr(model, "pe") and hasattr(m, "fuse"): # for YOLOE models m.fuse(model.pe.to(self.device)) elif isinstance(m, C2f) and not is_tf_format: # EdgeTPU does not support FlexSplitV while split provides cleaner ONNX graph m.forward = m.forward_split y = None for _ in range(2): # dry runs y = NMSModel(model, self.args)(im) if self.args.nms and not coreml and not imx else model(im) if self.args.half and (onnx or jit) and self.device.type != "cpu": im, model = im.half(), model.half() # to FP16 # Assign self.im = im self.model = model self.file = file self.output_shape = ( tuple(y.shape) if isinstance(y, torch.Tensor) else tuple(tuple(x.shape if isinstance(x, torch.Tensor) else []) for x in y) ) self.pretty_name = Path(self.model.yaml.get("yaml_file", self.file)).stem.replace("yolo", "YOLO") data = model.args["data"] if hasattr(model, "args") and isinstance(model.args, dict) else "" description = f"Ultralytics {self.pretty_name} model {f'trained on {data}' if data else ''}" self.metadata = { "description": description, "author": "Ultralytics", "date": datetime.now().isoformat(), "version": __version__, "license": "AGPL-3.0 License (https://ultralytics.com/license)", "docs": "https://docs.ultralytics.com", "stride": int(max(model.stride)), "task": model.task, "batch": self.args.batch, "imgsz": self.imgsz, "names": model.names, "args": {k: v for k, v in self.args if k in fmt_keys}, "channels": model.yaml.get("channels", 3), "end2end": getattr(model, "end2end", False), } # model metadata if dla is not None: self.metadata["dla"] = dla # make sure `AutoBackend` uses correct dla device if it has one if model.task == "pose": self.metadata["kpt_shape"] = model.model[-1].kpt_shape if hasattr(model, "kpt_names"): self.metadata["kpt_names"] = model.kpt_names LOGGER.info( f"\n{colorstr('PyTorch:')} starting from '{file}' with input shape {tuple(im.shape)} BCHW and " f"output shape(s) {self.output_shape} ({file_size(file):.1f} MB)" ) self.run_callbacks("on_export_start") # Exports f = [""] * len(fmts) # exported filenames if jit: # TorchScript f[0] = self.export_torchscript() if engine: # TensorRT required before ONNX f[1] = self.export_engine(dla=dla) if onnx: # ONNX f[2] = self.export_onnx() if xml: # OpenVINO f[3] = self.export_openvino() if coreml: # CoreML f[4] = self.export_coreml() if is_tf_format: # TensorFlow formats self.args.int8 |= edgetpu f[5], keras_model = self.export_saved_model() if pb or tfjs: # pb prerequisite to tfjs f[6] = self.export_pb(keras_model=keras_model) if tflite: f[7] = self.export_tflite() if edgetpu: f[8] = self.export_edgetpu(tflite_model=Path(f[5]) / f"{self.file.stem}_full_integer_quant.tflite") if tfjs: f[9] = self.export_tfjs() if paddle: # PaddlePaddle f[10] = self.export_paddle() if mnn: # MNN f[11] = self.export_mnn() if ncnn: # NCNN f[12] = self.export_ncnn() if imx: f[13] = self.export_imx() if rknn: f[14] = self.export_rknn() if executorch: f[15] = self.export_executorch() if axelera: f[16] = self.export_axelera() # Finish f = [str(x) for x in f if x] # filter out '' and None if any(f): f = str(Path(f[-1])) square = self.imgsz[0] == self.imgsz[1] s = ( "" if square else f"WARNING ⚠️ non-PyTorch val requires square images, 'imgsz={self.imgsz}' will not " f"work. Use export 'imgsz={max(self.imgsz)}' if val is required." ) imgsz = self.imgsz[0] if square else str(self.imgsz)[1:-1].replace(" ", "") q = "int8" if self.args.int8 else "half" if self.args.half else "" # quantization LOGGER.info( f"\nExport complete ({time.time() - t:.1f}s)" f"\nResults saved to {colorstr('bold', file.parent.resolve())}" f"\nPredict: yolo predict task={model.task} model={f} imgsz={imgsz} {q}" f"\nValidate: yolo val task={model.task} model={f} imgsz={imgsz} data={data} {q} {s}" f"\nVisualize: https://netron.app" ) self.run_callbacks("on_export_end") return f # path to final export artifact def get_int8_calibration_dataloader(self, prefix=""): """Build and return a dataloader for calibration of INT8 models.""" LOGGER.info(f"{prefix} collecting INT8 calibration images from 'data={self.args.data}'") data = (check_cls_dataset if self.model.task == "classify" else check_det_dataset)(self.args.data) dataset = YOLODataset( data[self.args.split or "val"], data=data, fraction=self.args.fraction, task=self.model.task, imgsz=self.imgsz[0], augment=False, batch_size=self.args.batch, ) n = len(dataset) if n < 1: raise ValueError(f"The calibration dataset must have at least 1 image, but found {n} images.") batch = min(self.args.batch, n) if n < self.args.batch: LOGGER.warning( f"{prefix} calibration dataset has only {n} images, reducing calibration batch size to {batch}." ) if self.args.format == "axelera" and n < 100: LOGGER.warning(f"{prefix} >100 images required for Axelera calibration, found {n} images.") elif self.args.format != "axelera" and n < 300: LOGGER.warning(f"{prefix} >300 images recommended for INT8 calibration, found {n} images.") return build_dataloader(dataset, batch=batch, workers=0, drop_last=True) # required for batch loading @try_export def export_torchscript(self, prefix=colorstr("TorchScript:")): """Export YOLO model to TorchScript format.""" LOGGER.info(f"\n{prefix} starting export with torch {TORCH_VERSION}...") f = self.file.with_suffix(".torchscript") ts = torch.jit.trace(NMSModel(self.model, self.args) if self.args.nms else self.model, self.im, strict=False) extra_files = {"config.txt": json.dumps(self.metadata)} # torch._C.ExtraFilesMap() if self.args.optimize: # https://pytorch.org/tutorials/recipes/mobile_interpreter.html LOGGER.info(f"{prefix} optimizing for mobile...") from torch.utils.mobile_optimizer import optimize_for_mobile optimize_for_mobile(ts)._save_for_lite_interpreter(str(f), _extra_files=extra_files) else: ts.save(str(f), _extra_files=extra_files) return f @try_export def export_onnx(self, prefix=colorstr("ONNX:")): """Export YOLO model to ONNX format.""" requirements = ["onnx>=1.12.0,<2.0.0"] if self.args.simplify: requirements += ["onnxslim>=0.1.71", "onnxruntime" + ("-gpu" if torch.cuda.is_available() else "")] check_requirements(requirements) import onnx opset = self.args.opset or best_onnx_opset(onnx, cuda="cuda" in self.device.type) LOGGER.info(f"\n{prefix} starting export with onnx {onnx.__version__} opset {opset}...") if self.args.nms: assert TORCH_1_13, f"'nms=True' ONNX export requires torch>=1.13 (found torch=={TORCH_VERSION})" f = str(self.file.with_suffix(".onnx")) output_names = ["output0", "output1"] if self.model.task == "segment" else ["output0"] dynamic = self.args.dynamic if dynamic: dynamic = {"images": {0: "batch", 2: "height", 3: "width"}} # shape(1,3,640,640) if isinstance(self.model, SegmentationModel): dynamic["output0"] = {0: "batch", 2: "anchors"} # shape(1, 116, 8400) dynamic["output1"] = {0: "batch", 2: "mask_height", 3: "mask_width"} # shape(1,32,160,160) elif isinstance(self.model, DetectionModel): dynamic["output0"] = {0: "batch", 2: "anchors"} # shape(1, 84, 8400) if self.args.nms: # only batch size is dynamic with NMS dynamic["output0"].pop(2) if self.args.nms and self.model.task == "obb": self.args.opset = opset # for NMSModel self.args.simplify = True # fix OBB runtime error related to topk with arange_patch(self.args): torch2onnx( NMSModel(self.model, self.args) if self.args.nms else self.model, self.im, f, opset=opset, input_names=["images"], output_names=output_names, dynamic=dynamic or None, ) # Checks model_onnx = onnx.load(f) # load onnx model # Simplify if self.args.simplify: try: import onnxslim LOGGER.info(f"{prefix} slimming with onnxslim {onnxslim.__version__}...") model_onnx = onnxslim.slim(model_onnx) except Exception as e: LOGGER.warning(f"{prefix} simplifier failure: {e}") # Metadata for k, v in self.metadata.items(): meta = model_onnx.metadata_props.add() meta.key, meta.value = k, str(v) # IR version if getattr(model_onnx, "ir_version", 0) > 10: LOGGER.info(f"{prefix} limiting IR version {model_onnx.ir_version} to 10 for ONNXRuntime compatibility...") model_onnx.ir_version = 10 # FP16 conversion for CPU export (GPU exports are already FP16 from model.half() during tracing) if self.args.half and self.args.format == "onnx" and self.device.type == "cpu": try: from onnxruntime.transformers import float16 LOGGER.info(f"{prefix} converting to FP16...") model_onnx = float16.convert_float_to_float16(model_onnx, keep_io_types=True) except Exception as e: LOGGER.warning(f"{prefix} FP16 conversion failure: {e}") onnx.save(model_onnx, f) return f @try_export def export_openvino(self, prefix=colorstr("OpenVINO:")): """Export YOLO model to OpenVINO format.""" # OpenVINO <= 2025.1.0 error on macOS 15.4+: https://github.com/openvinotoolkit/openvino/issues/30023" check_requirements("openvino>=2025.2.0" if MACOS and MACOS_VERSION >= "15.4" else "openvino>=2024.0.0") import openvino as ov LOGGER.info(f"\n{prefix} starting export with openvino {ov.__version__}...") assert TORCH_2_1, f"OpenVINO export requires torch>=2.1 but torch=={TORCH_VERSION} is installed" ov_model = ov.convert_model( NMSModel(self.model, self.args) if self.args.nms else self.model, input=None if self.args.dynamic else [self.im.shape], example_input=self.im, ) def serialize(ov_model, file): """Set RT info, serialize, and save metadata YAML.""" ov_model.set_rt_info("YOLO", ["model_info", "model_type"]) ov_model.set_rt_info(True, ["model_info", "reverse_input_channels"]) ov_model.set_rt_info(114, ["model_info", "pad_value"]) ov_model.set_rt_info([255.0], ["model_info", "scale_values"]) ov_model.set_rt_info(self.args.iou, ["model_info", "iou_threshold"]) ov_model.set_rt_info([v.replace(" ", "_") for v in self.model.names.values()], ["model_info", "labels"]) if self.model.task != "classify": ov_model.set_rt_info("fit_to_window_letterbox", ["model_info", "resize_type"]) ov.save_model(ov_model, file, compress_to_fp16=self.args.half) YAML.save(Path(file).parent / "metadata.yaml", self.metadata) # add metadata.yaml if self.args.int8: fq = str(self.file).replace(self.file.suffix, f"_int8_openvino_model{os.sep}") fq_ov = str(Path(fq) / self.file.with_suffix(".xml").name) # INT8 requires nncf, nncf requires packaging>=23.2 https://github.com/openvinotoolkit/nncf/issues/3463 check_requirements("packaging>=23.2") # must be installed first to build nncf wheel check_requirements("nncf>=2.14.0,<3.0.0" if not TORCH_2_3 else "nncf>=2.14.0") import nncf # Generate calibration data for integer quantization ignored_scope = None if isinstance(self.model.model[-1], Detect): # Includes all Detect subclasses like Segment, Pose, OBB, WorldDetect, YOLOEDetect head_module_name = ".".join(list(self.model.named_modules())[-1][0].split(".")[:2]) ignored_scope = nncf.IgnoredScope( # ignore operations patterns=[ f".*{head_module_name}/.*/Add", f".*{head_module_name}/.*/Sub*", f".*{head_module_name}/.*/Mul*", f".*{head_module_name}/.*/Div*", ], types=["Sigmoid"], ) quantized_ov_model = nncf.quantize( model=ov_model, calibration_dataset=nncf.Dataset(self.get_int8_calibration_dataloader(prefix), self._transform_fn), preset=nncf.QuantizationPreset.MIXED, ignored_scope=ignored_scope, ) serialize(quantized_ov_model, fq_ov) return fq f = str(self.file).replace(self.file.suffix, f"_openvino_model{os.sep}") f_ov = str(Path(f) / self.file.with_suffix(".xml").name) serialize(ov_model, f_ov) return f @try_export def export_paddle(self, prefix=colorstr("PaddlePaddle:")): """Export YOLO model to PaddlePaddle format.""" assert not IS_JETSON, "Jetson Paddle exports not supported yet" check_requirements( ( "paddlepaddle-gpu>=3.0.0,<3.3.0" # pin <3.3.0 https://github.com/PaddlePaddle/Paddle/issues/77340 if torch.cuda.is_available() else "paddlepaddle==3.0.0" # pin 3.0.0 for ARM64 if ARM64 else "paddlepaddle>=3.0.0,<3.3.0", # pin <3.3.0 https://github.com/PaddlePaddle/Paddle/issues/77340 "x2paddle", ) ) import x2paddle from x2paddle.convert import pytorch2paddle LOGGER.info(f"\n{prefix} starting export with X2Paddle {x2paddle.__version__}...") f = str(self.file).replace(self.file.suffix, f"_paddle_model{os.sep}") pytorch2paddle(module=self.model, save_dir=f, jit_type="trace", input_examples=[self.im]) # export YAML.save(Path(f) / "metadata.yaml", self.metadata) # add metadata.yaml return f @try_export def export_mnn(self, prefix=colorstr("MNN:")): """Export YOLO model to MNN format using MNN https://github.com/alibaba/MNN.""" assert TORCH_1_10, "MNN export requires torch>=1.10.0 to avoid segmentation faults" f_onnx = self.export_onnx() # get onnx model first check_requirements("MNN>=2.9.6") import MNN from MNN.tools import mnnconvert # Setup and checks LOGGER.info(f"\n{prefix} starting export with MNN {MNN.version()}...") assert Path(f_onnx).exists(), f"failed to export ONNX file: {f_onnx}" f = str(self.file.with_suffix(".mnn")) # MNN model file args = ["", "-f", "ONNX", "--modelFile", f_onnx, "--MNNModel", f, "--bizCode", json.dumps(self.metadata)] if self.args.int8: args.extend(("--weightQuantBits", "8")) if self.args.half: args.append("--fp16") mnnconvert.convert(args) # remove scratch file for model convert optimize convert_scratch = Path(self.file.parent / ".__convert_external_data.bin") if convert_scratch.exists(): convert_scratch.unlink() return f @try_export def export_ncnn(self, prefix=colorstr("NCNN:")): """Export YOLO model to NCNN format using PNNX https://github.com/pnnx/pnnx.""" check_requirements("ncnn", cmds="--no-deps") # no deps to avoid installing opencv-python check_requirements("pnnx") import ncnn import pnnx LOGGER.info(f"\n{prefix} starting export with NCNN {ncnn.__version__} and PNNX {pnnx.__version__}...") f = Path(str(self.file).replace(self.file.suffix, f"_ncnn_model{os.sep}")) ncnn_args = dict( ncnnparam=(f / "model.ncnn.param").as_posix(), ncnnbin=(f / "model.ncnn.bin").as_posix(), ncnnpy=(f / "model_ncnn.py").as_posix(), ) pnnx_args = dict( ptpath=(f / "model.pt").as_posix(), pnnxparam=(f / "model.pnnx.param").as_posix(), pnnxbin=(f / "model.pnnx.bin").as_posix(), pnnxpy=(f / "model_pnnx.py").as_posix(), pnnxonnx=(f / "model.pnnx.onnx").as_posix(), ) f.mkdir(exist_ok=True) # make ncnn_model directory pnnx.export(self.model, inputs=self.im, **ncnn_args, **pnnx_args, fp16=self.args.half, device=self.device.type) for f_debug in ("debug.bin", "debug.param", "debug2.bin", "debug2.param", *pnnx_args.values()): Path(f_debug).unlink(missing_ok=True) YAML.save(f / "metadata.yaml", self.metadata) # add metadata.yaml return str(f) @try_export def export_coreml(self, prefix=colorstr("CoreML:")): """Export YOLO model to CoreML format.""" mlmodel = self.args.format.lower() == "mlmodel" # legacy *.mlmodel export format requested check_requirements( ["coremltools>=9.0", "numpy>=1.14.5,<=2.3.5"] ) # latest numpy 2.4.0rc1 breaks coremltools exports import coremltools as ct LOGGER.info(f"\n{prefix} starting export with coremltools {ct.__version__}...") assert not WINDOWS, "CoreML export is not supported on Windows, please run on macOS or Linux." assert TORCH_1_11, "CoreML export requires torch>=1.11" if self.args.batch > 1: assert self.args.dynamic, ( "batch sizes > 1 are not supported without 'dynamic=True' for CoreML export. Please retry at 'dynamic=True'." ) if self.args.dynamic: assert not self.args.nms, ( "'nms=True' cannot be used together with 'dynamic=True' for CoreML export. Please disable one of them." ) assert self.model.task != "classify", "'dynamic=True' is not supported for CoreML classification models." f = self.file.with_suffix(".mlmodel" if mlmodel else ".mlpackage") if f.is_dir(): shutil.rmtree(f) classifier_config = None if self.model.task == "classify": classifier_config = ct.ClassifierConfig(list(self.model.names.values())) model = self.model elif self.model.task == "detect": model = IOSDetectModel(self.model, self.im, mlprogram=not mlmodel) if self.args.nms else self.model else: if self.args.nms: LOGGER.warning(f"{prefix} 'nms=True' is only available for Detect models like 'yolo26n.pt'.") # TODO CoreML Segment and Pose model pipelining model = self.model ts = torch.jit.trace(model.eval(), self.im, strict=False) # TorchScript model if self.args.dynamic: input_shape = ct.Shape( shape=( ct.RangeDim(lower_bound=1, upper_bound=self.args.batch, default=1), self.im.shape[1], ct.RangeDim(lower_bound=32, upper_bound=self.imgsz[0] * 2, default=self.imgsz[0]), ct.RangeDim(lower_bound=32, upper_bound=self.imgsz[1] * 2, default=self.imgsz[1]), ) ) inputs = [ct.TensorType("image", shape=input_shape)] else: inputs = [ct.ImageType("image", shape=self.im.shape, scale=1 / 255, bias=[0.0, 0.0, 0.0])] # Based on apple's documentation it is better to leave out the minimum_deployment target and let that get set # Internally based on the model conversion and output type. # Setting minimum_deployment_target >= iOS16 will require setting compute_precision=ct.precision.FLOAT32. # iOS16 adds in better support for FP16, but none of the CoreML NMS specifications handle FP16 as input. ct_model = ct.convert( ts, inputs=inputs, classifier_config=classifier_config, convert_to="neuralnetwork" if mlmodel else "mlprogram", ) bits, mode = (8, "kmeans") if self.args.int8 else (16, "linear") if self.args.half else (32, None) if bits < 32: if "kmeans" in mode: check_requirements("scikit-learn") # scikit-learn package required for k-means quantization if mlmodel: ct_model = ct.models.neural_network.quantization_utils.quantize_weights(ct_model, bits, mode) elif bits == 8: # mlprogram already quantized to FP16 import coremltools.optimize.coreml as cto op_config = cto.OpPalettizerConfig(mode="kmeans", nbits=bits, weight_threshold=512) config = cto.OptimizationConfig(global_config=op_config) ct_model = cto.palettize_weights(ct_model, config=config) if self.args.nms and self.model.task == "detect": ct_model = self._pipeline_coreml(ct_model, weights_dir=None if mlmodel else ct_model.weights_dir) m = self.metadata # metadata dict ct_model.short_description = m.pop("description") ct_model.author = m.pop("author") ct_model.license = m.pop("license") ct_model.version = m.pop("version") ct_model.user_defined_metadata.update({k: str(v) for k, v in m.items()}) if self.model.task == "classify": ct_model.user_defined_metadata.update({"com.apple.coreml.model.preview.type": "imageClassifier"}) try: ct_model.save(str(f)) # save *.mlpackage except Exception as e: LOGGER.warning( f"{prefix} CoreML export to *.mlpackage failed ({e}), reverting to *.mlmodel export. " f"Known coremltools Python 3.11 and Windows bugs https://github.com/apple/coremltools/issues/1928." ) f = f.with_suffix(".mlmodel") ct_model.save(str(f)) return f @try_export def export_engine(self, dla=None, prefix=colorstr("TensorRT:")): """Export YOLO model to TensorRT format https://developer.nvidia.com/tensorrt.""" assert self.im.device.type != "cpu", "export running on CPU but must be on GPU, i.e. use 'device=0'" f_onnx = self.export_onnx() # run before TRT import https://github.com/ultralytics/ultralytics/issues/7016 # Force re-install TensorRT on CUDA 13 ARM devices to 10.15.x versions for RT-DETR exports # https://github.com/ultralytics/ultralytics/issues/22873 if is_jetson(jetpack=7) or is_dgx(): check_tensorrt("10.15") try: import tensorrt as trt except ImportError: check_tensorrt() import tensorrt as trt check_version(trt.__version__, ">=7.0.0", hard=True) check_version(trt.__version__, "!=10.1.0", msg="https://github.com/ultralytics/ultralytics/pull/14239") # Setup and checks LOGGER.info(f"\n{prefix} starting export with TensorRT {trt.__version__}...") assert Path(f_onnx).exists(), f"failed to export ONNX file: {f_onnx}" f = self.file.with_suffix(".engine") # TensorRT engine file onnx2engine( f_onnx, f, self.args.workspace, self.args.half, self.args.int8, self.args.dynamic, self.im.shape, dla=dla, dataset=self.get_int8_calibration_dataloader(prefix) if self.args.int8 else None, metadata=self.metadata, verbose=self.args.verbose, prefix=prefix, ) return f @try_export def export_saved_model(self, prefix=colorstr("TensorFlow SavedModel:")): """Export YOLO model to TensorFlow SavedModel format.""" cuda = torch.cuda.is_available() try: import tensorflow as tf except ImportError: check_requirements("tensorflow>=2.0.0,<=2.19.0") import tensorflow as tf check_requirements( ( "tf_keras<=2.19.0", # required by 'onnx2tf' package "sng4onnx>=1.0.1", # required by 'onnx2tf' package "onnx_graphsurgeon>=0.3.26", # required by 'onnx2tf' package "ai-edge-litert>=1.2.0" + (",<1.4.0" if MACOS else ""), # required by 'onnx2tf' package "onnx>=1.12.0,<2.0.0", "onnx2tf>=1.26.3,<1.29.0", # pin to avoid h5py build issues on aarch64 "onnxslim>=0.1.71", "onnxruntime-gpu" if cuda else "onnxruntime", "protobuf>=5", ), cmds="--extra-index-url https://pypi.ngc.nvidia.com", # onnx_graphsurgeon only on NVIDIA ) LOGGER.info(f"\n{prefix} starting export with tensorflow {tf.__version__}...") check_version( tf.__version__, ">=2.0.0", name="tensorflow", verbose=True, msg="https://github.com/ultralytics/ultralytics/issues/5161", ) f = Path(str(self.file).replace(self.file.suffix, "_saved_model")) if f.is_dir(): shutil.rmtree(f) # delete output folder # Export to TF images = None if self.args.int8 and self.args.data: images = [batch["img"] for batch in self.get_int8_calibration_dataloader(prefix)] images = ( torch.nn.functional.interpolate(torch.cat(images, 0).float(), size=self.imgsz) .permute(0, 2, 3, 1) .numpy() .astype(np.float32) ) # Export to ONNX if isinstance(self.model.model[-1], RTDETRDecoder): self.args.opset = self.args.opset or 19 assert 16 <= self.args.opset <= 19, "RTDETR export requires opset>=16;<=19" self.args.simplify = True f_onnx = self.export_onnx() # ensure ONNX is available keras_model = onnx2saved_model( f_onnx, f, int8=self.args.int8, images=images, disable_group_convolution=self.args.format in {"tfjs", "edgetpu"}, prefix=prefix, ) YAML.save(f / "metadata.yaml", self.metadata) # add metadata.yaml # Add TFLite metadata for file in f.rglob("*.tflite"): file.unlink() if "quant_with_int16_act.tflite" in str(file) else self._add_tflite_metadata(file) return str(f), keras_model # or keras_model = tf.saved_model.load(f, tags=None, options=None) @try_export def export_pb(self, keras_model, prefix=colorstr("TensorFlow GraphDef:")): """Export YOLO model to TensorFlow GraphDef *.pb format https://github.com/leimao/Frozen-Graph-TensorFlow.""" f = self.file.with_suffix(".pb") keras2pb(keras_model, f, prefix) return f @try_export def export_tflite(self, prefix=colorstr("TensorFlow Lite:")): """Export YOLO model to TensorFlow Lite format.""" # BUG https://github.com/ultralytics/ultralytics/issues/13436 import tensorflow as tf LOGGER.info(f"\n{prefix} starting export with tensorflow {tf.__version__}...") saved_model = Path(str(self.file).replace(self.file.suffix, "_saved_model")) if self.args.int8: f = saved_model / f"{self.file.stem}_int8.tflite" # fp32 in/out elif self.args.half: f = saved_model / f"{self.file.stem}_float16.tflite" # fp32 in/out else: f = saved_model / f"{self.file.stem}_float32.tflite" return str(f) @try_export def export_axelera(self, prefix=colorstr("Axelera:")): """Export YOLO model to Axelera format.""" os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python" try: from axelera import compiler except ImportError: check_apt_requirements( ["libllvm14", "libgirepository1.0-dev", "pkg-config", "libcairo2-dev", "build-essential", "cmake"] ) check_requirements( "axelera-voyager-sdk==1.5.2", cmds="--extra-index-url https://software.axelera.ai/artifactory/axelera-runtime-pypi " "--extra-index-url https://software.axelera.ai/artifactory/axelera-dev-pypi", ) from axelera import compiler from axelera.compiler import CompilerConfig self.args.opset = 17 # hardcode opset for Axelera onnx_path = self.export_onnx() model_name = Path(onnx_path).stem export_path = Path(f"{model_name}_axelera_model") export_path.mkdir(exist_ok=True) if "C2PSA" in self.model.__str__(): # YOLO11 config = CompilerConfig( quantization_scheme="per_tensor_min_max", ignore_weight_buffers=False, resources_used=0.25, aipu_cores_used=1, multicore_mode="batch", output_axm_format=True, model_name=model_name, ) else: # YOLOv8 config = CompilerConfig( tiling_depth=6, split_buffer_promotion=True, resources_used=0.25, aipu_cores_used=1, multicore_mode="batch", output_axm_format=True, model_name=model_name, ) qmodel = compiler.quantize( model=onnx_path, calibration_dataset=self.get_int8_calibration_dataloader(prefix), config=config, transform_fn=self._transform_fn, ) compiler.compile(model=qmodel, config=config, output_dir=export_path) axm_name = f"{model_name}.axm" axm_src = Path(axm_name) axm_dst = export_path / axm_name if axm_src.exists(): axm_src.replace(axm_dst) YAML.save(export_path / "metadata.yaml", self.metadata) return export_path @try_export def export_executorch(self, prefix=colorstr("ExecuTorch:")): """Export YOLO model to ExecuTorch *.pte format.""" assert TORCH_2_9, f"ExecuTorch requires torch>=2.9.0 but torch=={TORCH_VERSION} is installed" check_executorch_requirements() return torch2executorch(self.model, self.file, self.im, metadata=self.metadata, prefix=prefix) @try_export def export_edgetpu(self, tflite_model="", prefix=colorstr("Edge TPU:")): """Export YOLO model to Edge TPU format https://coral.ai/docs/edgetpu/models-intro/.""" cmd = "edgetpu_compiler --version" help_url = "https://coral.ai/docs/edgetpu/compiler/" assert LINUX, f"export only supported on Linux. See {help_url}" if subprocess.run(cmd, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL, shell=True).returncode != 0: LOGGER.info(f"\n{prefix} export requires Edge TPU compiler. Attempting install from {help_url}") sudo = "sudo " if is_sudo_available() else "" for c in ( f"{sudo}mkdir -p /etc/apt/keyrings", f"curl -fsSL https://packages.cloud.google.com/apt/doc/apt-key.gpg | {sudo}gpg --dearmor -o /etc/apt/keyrings/google.gpg", f'echo "deb [signed-by=/etc/apt/keyrings/google.gpg] https://packages.cloud.google.com/apt coral-edgetpu-stable main" | {sudo}tee /etc/apt/sources.list.d/coral-edgetpu.list', ): subprocess.run(c, shell=True, check=True) check_apt_requirements(["edgetpu-compiler"]) ver = subprocess.run(cmd, shell=True, capture_output=True, check=True).stdout.decode().rsplit(maxsplit=1)[-1] LOGGER.info(f"\n{prefix} starting export with Edge TPU compiler {ver}...") tflite2edgetpu(tflite_file=tflite_model, output_dir=tflite_model.parent, prefix=prefix) f = str(tflite_model).replace(".tflite", "_edgetpu.tflite") # Edge TPU model self._add_tflite_metadata(f) return f @try_export def export_tfjs(self, prefix=colorstr("TensorFlow.js:")): """Export YOLO model to TensorFlow.js format.""" check_requirements("tensorflowjs") f = str(self.file).replace(self.file.suffix, "_web_model") # js dir f_pb = str(self.file.with_suffix(".pb")) # *.pb path pb2tfjs(pb_file=f_pb, output_dir=f, half=self.args.half, int8=self.args.int8, prefix=prefix) # Add metadata YAML.save(Path(f) / "metadata.yaml", self.metadata) # add metadata.yaml return f @try_export def export_rknn(self, prefix=colorstr("RKNN:")): """Export YOLO model to RKNN format.""" LOGGER.info(f"\n{prefix} starting export with rknn-toolkit2...") check_requirements("rknn-toolkit2") check_requirements("onnx<1.19.0") # fix AttributeError: module 'onnx' has no attribute 'mapping' if IS_COLAB: # Prevent 'exit' from closing the notebook https://github.com/airockchip/rknn-toolkit2/issues/259 import builtins builtins.exit = lambda: None from rknn.api import RKNN self.args.opset = min(self.args.opset or 19, 19) # rknn-toolkit expects opset<=19 f = self.export_onnx() export_path = Path(f"{Path(f).stem}_rknn_model") export_path.mkdir(exist_ok=True) rknn = RKNN(verbose=False) rknn.config(mean_values=[[0, 0, 0]], std_values=[[255, 255, 255]], target_platform=self.args.name) rknn.load_onnx(model=f) rknn.build(do_quantization=False) # TODO: Add quantization support rknn.export_rknn(str(export_path / f"{Path(f).stem}-{self.args.name}.rknn")) YAML.save(export_path / "metadata.yaml", self.metadata) return export_path @try_export def export_imx(self, prefix=colorstr("IMX:")): """Export YOLO model to IMX format.""" assert LINUX, ( "Export only supported on Linux." "See https://developer.aitrios.sony-semicon.com/en/docs/raspberry-pi-ai-camera/imx500-converter?version=3.17.3&progLang=" ) assert IS_PYTHON_MINIMUM_3_9, "IMX export is only supported on Python 3.9 or above." if getattr(self.model, "end2end", False): raise ValueError("IMX export is not supported for end2end models.") check_requirements( ( "model-compression-toolkit>=2.4.1", "edge-mdt-cl<1.1.0", "edge-mdt-tpc>=1.2.0", "pydantic<=2.11.7", ) ) check_requirements("imx500-converter[pt]>=3.17.3") # Install Java>=17 try: java_output = subprocess.run(["java", "--version"], check=True, capture_output=True).stdout.decode() version_match = re.search(r"(?:openjdk|java) (\d+)", java_output) java_version = int(version_match.group(1)) if version_match else 0 assert java_version >= 17, "Java version too old" except (FileNotFoundError, subprocess.CalledProcessError, AssertionError): if IS_UBUNTU or IS_DEBIAN_TRIXIE: LOGGER.info(f"\n{prefix} installing Java 21 for Ubuntu...") check_apt_requirements(["openjdk-21-jre"]) elif IS_RASPBERRYPI or IS_DEBIAN_BOOKWORM: LOGGER.info(f"\n{prefix} installing Java 17 for Raspberry Pi or Debian ...") check_apt_requirements(["openjdk-17-jre"]) return torch2imx( self.model, self.file, self.args.conf, self.args.iou, self.args.max_det, metadata=self.metadata, dataset=self.get_int8_calibration_dataloader(prefix), prefix=prefix, ) def _add_tflite_metadata(self, file): """Add metadata to *.tflite models per https://ai.google.dev/edge/litert/models/metadata.""" import zipfile with zipfile.ZipFile(file, "a", zipfile.ZIP_DEFLATED) as zf: zf.writestr("metadata.json", json.dumps(self.metadata, indent=2)) def _pipeline_coreml(self, model, weights_dir=None, prefix=colorstr("CoreML Pipeline:")): """Create CoreML pipeline with NMS for YOLO detection models.""" import coremltools as ct LOGGER.info(f"{prefix} starting pipeline with coremltools {ct.__version__}...") # Output shapes spec = model.get_spec() outs = list(iter(spec.description.output)) if self.args.format == "mlmodel": # mlmodel doesn't infer shapes automatically outs[0].type.multiArrayType.shape[:] = self.output_shape[2], self.output_shape[1] - 4 outs[1].type.multiArrayType.shape[:] = self.output_shape[2], 4 # Checks names = self.metadata["names"] nx, ny = spec.description.input[0].type.imageType.width, spec.description.input[0].type.imageType.height nc = outs[0].type.multiArrayType.shape[-1] if len(names) != nc: # Hack fix for MLProgram NMS bug https://github.com/ultralytics/ultralytics/issues/22309 names = {**names, **{i: str(i) for i in range(len(names), nc)}} # Model from spec model = ct.models.MLModel(spec, weights_dir=weights_dir) # Create NMS protobuf nms_spec = ct.proto.Model_pb2.Model() nms_spec.specificationVersion = spec.specificationVersion for i in range(len(outs)): decoder_output = model._spec.description.output[i].SerializeToString() nms_spec.description.input.add() nms_spec.description.input[i].ParseFromString(decoder_output) nms_spec.description.output.add() nms_spec.description.output[i].ParseFromString(decoder_output) output_names = ["confidence", "coordinates"] for i, name in enumerate(output_names): nms_spec.description.output[i].name = name for i, out in enumerate(outs): ma_type = nms_spec.description.output[i].type.multiArrayType ma_type.shapeRange.sizeRanges.add() ma_type.shapeRange.sizeRanges[0].lowerBound = 0 ma_type.shapeRange.sizeRanges[0].upperBound = -1 ma_type.shapeRange.sizeRanges.add() ma_type.shapeRange.sizeRanges[1].lowerBound = out.type.multiArrayType.shape[-1] ma_type.shapeRange.sizeRanges[1].upperBound = out.type.multiArrayType.shape[-1] del ma_type.shape[:] nms = nms_spec.nonMaximumSuppression nms.confidenceInputFeatureName = outs[0].name # 1x507x80 nms.coordinatesInputFeatureName = outs[1].name # 1x507x4 nms.confidenceOutputFeatureName = output_names[0] nms.coordinatesOutputFeatureName = output_names[1] nms.iouThresholdInputFeatureName = "iouThreshold" nms.confidenceThresholdInputFeatureName = "confidenceThreshold" nms.iouThreshold = self.args.iou nms.confidenceThreshold = self.args.conf nms.pickTop.perClass = not self.args.agnostic_nms nms.stringClassLabels.vector.extend(names.values()) nms_model = ct.models.MLModel(nms_spec) # Pipeline models together pipeline = ct.models.pipeline.Pipeline( input_features=[ ("image", ct.models.datatypes.Array(3, ny, nx)), ("iouThreshold", ct.models.datatypes.Double()), ("confidenceThreshold", ct.models.datatypes.Double()), ], output_features=output_names, ) pipeline.add_model(model) pipeline.add_model(nms_model) # Correct datatypes pipeline.spec.description.input[0].ParseFromString(model._spec.description.input[0].SerializeToString()) pipeline.spec.description.output[0].ParseFromString(nms_model._spec.description.output[0].SerializeToString()) pipeline.spec.description.output[1].ParseFromString(nms_model._spec.description.output[1].SerializeToString()) # Update metadata pipeline.spec.specificationVersion = spec.specificationVersion pipeline.spec.description.metadata.userDefined.update( {"IoU threshold": str(nms.iouThreshold), "Confidence threshold": str(nms.confidenceThreshold)} ) # Save the model model = ct.models.MLModel(pipeline.spec, weights_dir=weights_dir) model.input_description["image"] = "Input image" model.input_description["iouThreshold"] = f"(optional) IoU threshold override (default: {nms.iouThreshold})" model.input_description["confidenceThreshold"] = ( f"(optional) Confidence threshold override (default: {nms.confidenceThreshold})" ) model.output_description["confidence"] = 'Boxes × Class confidence (see user-defined metadata "classes")' model.output_description["coordinates"] = "Boxes × [x, y, width, height] (relative to image size)" LOGGER.info(f"{prefix} pipeline success") return model @staticmethod def _transform_fn(data_item) -> np.ndarray: """The transformation function for Axelera/OpenVINO quantization preprocessing.""" data_item: torch.Tensor = data_item["img"] if isinstance(data_item, dict) else data_item assert data_item.dtype == torch.uint8, "Input image must be uint8 for the quantization preprocessing" im = data_item.numpy().astype(np.float32) / 255.0 # uint8 to fp16/32 and 0 - 255 to 0.0 - 1.0 return im[None] if im.ndim == 3 else im def add_callback(self, event: str, callback): """Append the given callback to the specified event.""" self.callbacks[event].append(callback) def run_callbacks(self, event: str): """Execute all callbacks for a given event.""" for callback in self.callbacks.get(event, []): callback(self) class IOSDetectModel(torch.nn.Module): """Wrap an Ultralytics YOLO model for Apple iOS CoreML export.""" def __init__(self, model, im, mlprogram=True): """Initialize the IOSDetectModel class with a YOLO model and example image. Args: model (torch.nn.Module): The YOLO model to wrap. im (torch.Tensor): Example input tensor with shape (B, C, H, W). mlprogram (bool): Whether exporting to MLProgram format. """ super().__init__() _, _, h, w = im.shape # batch, channel, height, width self.model = model self.nc = len(model.names) # number of classes self.mlprogram = mlprogram if w == h: self.normalize = 1.0 / w # scalar else: self.normalize = torch.tensor( [1.0 / w, 1.0 / h, 1.0 / w, 1.0 / h], # broadcast (slower, smaller) device=next(model.parameters()).device, ) def forward(self, x): """Normalize predictions of object detection model with input size-dependent factors.""" xywh, cls = self.model(x)[0].transpose(0, 1).split((4, self.nc), 1) if self.mlprogram and self.nc % 80 != 0: # NMS bug https://github.com/ultralytics/ultralytics/issues/22309 pad_length = int(((self.nc + 79) // 80) * 80) - self.nc # pad class length to multiple of 80 cls = torch.nn.functional.pad(cls, (0, pad_length, 0, 0), "constant", 0) return cls, xywh * self.normalize class NMSModel(torch.nn.Module): """Model wrapper with embedded NMS for Detect, Segment, Pose and OBB.""" def __init__(self, model, args): """Initialize the NMSModel. Args: model (torch.nn.Module): The model to wrap with NMS postprocessing. args (SimpleNamespace): The export arguments. """ super().__init__() self.model = model self.args = args self.obb = model.task == "obb" self.is_tf = self.args.format in frozenset({"saved_model", "tflite", "tfjs"}) def forward(self, x): """Perform inference with NMS post-processing. Supports Detect, Segment, OBB and Pose. Args: x (torch.Tensor): The preprocessed tensor with shape (B, C, H, W). Returns: (torch.Tensor | tuple): Tensor of shape (B, max_det, 4 + 2 + extra_shape) where B is the batch size, or a tuple of (detections, proto) for segmentation models. """ from functools import partial from torchvision.ops import nms preds = self.model(x) pred = preds[0] if isinstance(preds, tuple) else preds kwargs = dict(device=pred.device, dtype=pred.dtype) bs = pred.shape[0] pred = pred.transpose(-1, -2) # shape(1,84,6300) to shape(1,6300,84) extra_shape = pred.shape[-1] - (4 + len(self.model.names)) # extras from Segment, OBB, Pose if self.args.dynamic and self.args.batch > 1: # batch size needs to always be same due to loop unroll pad = torch.zeros(torch.max(torch.tensor(self.args.batch - bs), torch.tensor(0)), *pred.shape[1:], **kwargs) pred = torch.cat((pred, pad)) boxes, scores, extras = pred.split([4, len(self.model.names), extra_shape], dim=2) scores, classes = scores.max(dim=-1) self.args.max_det = min(pred.shape[1], self.args.max_det) # in case num_anchors < max_det # (N, max_det, 4 coords + 1 class score + 1 class label + extra_shape). out = torch.zeros(pred.shape[0], self.args.max_det, boxes.shape[-1] + 2 + extra_shape, **kwargs) for i in range(bs): box, cls, score, extra = boxes[i], classes[i], scores[i], extras[i] mask = score > self.args.conf if self.is_tf or (self.args.format == "onnx" and self.obb): # TFLite GatherND error if mask is empty score *= mask # Explicit length otherwise reshape error, hardcoded to `self.args.max_det * 5` mask = score.topk(min(self.args.max_det * 5, score.shape[0])).indices box, score, cls, extra = box[mask], score[mask], cls[mask], extra[mask] nmsbox = box.clone() # `8` is the minimum value experimented to get correct NMS results for obb multiplier = 8 if self.obb else 1 / max(len(self.model.names), 1) # Normalize boxes for NMS since large values for class offset causes issue with int8 quantization if self.args.format == "tflite": # TFLite is already normalized nmsbox *= multiplier else: nmsbox = multiplier * (nmsbox / torch.tensor(x.shape[2:], **kwargs).max()) if not self.args.agnostic_nms: # class-wise NMS end = 2 if self.obb else 4 # fully explicit expansion otherwise reshape error cls_offset = cls.view(cls.shape[0], 1).expand(cls.shape[0], end) offbox = nmsbox[:, :end] + cls_offset * multiplier nmsbox = torch.cat((offbox, nmsbox[:, end:]), dim=-1) nms_fn = ( partial( TorchNMS.fast_nms, use_triu=not ( self.is_tf or (self.args.opset or 14) < 14 or (self.args.format == "openvino" and self.args.int8) # OpenVINO int8 error with triu ), iou_func=batch_probiou, exit_early=False, ) if self.obb else nms ) keep = nms_fn( torch.cat([nmsbox, extra], dim=-1) if self.obb else nmsbox, score, self.args.iou, )[: self.args.max_det] dets = torch.cat( [box[keep], score[keep].view(-1, 1), cls[keep].view(-1, 1).to(out.dtype), extra[keep]], dim=-1 ) # Zero-pad to max_det size to avoid reshape error pad = (0, 0, 0, self.args.max_det - dets.shape[0]) out[i] = torch.nn.functional.pad(dets, pad) return (out[:bs], preds[1]) if self.model.task == "segment" else out[:bs]