# 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 | - | 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/ IMX | `imx` | yolo11n_imx_model/ RKNN | `rknn` | yolo11n_rknn_model/ Requirements: $ pip install "ultralytics[export]" Python: from ultralytics import YOLO model = YOLO('yolo11n.pt') results = model.export(format='onnx') CLI: $ yolo mode=export model=yolo11n.pt format=onnx Inference: $ yolo predict model=yolo11n.pt # PyTorch yolo11n.torchscript # TorchScript yolo11n.onnx # ONNX Runtime or OpenCV DNN with dnn=True yolo11n_openvino_model # OpenVINO yolo11n.engine # TensorRT yolo11n.mlpackage # CoreML (macOS-only) yolo11n_saved_model # TensorFlow SavedModel yolo11n.pb # TensorFlow GraphDef yolo11n.tflite # TensorFlow Lite yolo11n_edgetpu.tflite # TensorFlow Edge TPU yolo11n_paddle_model # PaddlePaddle yolo11n.mnn # MNN yolo11n_ncnn_model # NCNN yolo11n_imx_model # IMX TensorFlow.js: $ cd .. && git clone https://github.com/zldrobit/tfjs-yolov5-example.git && cd tfjs-yolov5-example $ npm install $ ln -s ../../yolo11n_web_model public/yolo11n_web_model $ npm start """ import json import os import re import shutil import subprocess import time import warnings 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_JETSON, LINUX, LOGGER, MACOS, MACOS_VERSION, RKNN_CHIPS, ROOT, SETTINGS, WINDOWS, YAML, callbacks, colorstr, get_default_args, ) from ultralytics.utils.checks import ( check_imgsz, check_is_path_safe, check_requirements, check_version, is_intel, is_sudo_available, ) from ultralytics.utils.downloads import attempt_download_asset, get_github_assets, safe_download from ultralytics.utils.export import export_engine, export_onnx from ultralytics.utils.files import file_size, spaces_in_path from ultralytics.utils.ops import Profile, nms_rotated from ultralytics.utils.patches import arange_patch from ultralytics.utils.torch_utils import TORCH_1_13, get_latest_opset, 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"]], ["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", "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"]], ["RKNN", "rknn", "_rknn_model", False, False, ["batch", "name"]], ] return dict(zip(["Format", "Argument", "Suffix", "CPU", "GPU", "Arguments"], zip(*x))) def validate_args(format, passed_args, valid_args): """ Validate arguments based on the export format. Args: format (str): The export format. passed_args (Namespace): 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 gd_outputs(gd): """Return TensorFlow GraphDef model output node names.""" name_list, input_list = [], [] for node in gd.node: # tensorflow.core.framework.node_def_pb2.NodeDef name_list.append(node.name) input_list.extend(node.input) return sorted(f"{x}:0" for x in list(set(name_list) - set(input_list)) if not x.startswith("NoOp")) 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, model = inner_func(*args, **kwargs) LOGGER.info(f"{prefix} export success ✅ {dt.t:.1f}s, saved as '{f}' ({file_size(f):.1f} MB)") return f, model 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 (tuple): 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. Examples: Export a YOLOv8 model to ONNX format >>> from ultralytics.engine.exporter import Exporter >>> exporter = Exporter() >>> exporter(model="yolov8n.pt") # exports to yolov8n.onnx Export with specific arguments >>> args = {"format": "onnx", "dynamic": True, "half": True} >>> exporter = Exporter(overrides=args) >>> exporter(model="yolov8n.pt") """ def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None): """ Initialize the Exporter class. Args: cfg (str, optional): Path to a configuration file. 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: """Return list of exported files/dirs after running callbacks.""" 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: raise ValueError(f"Invalid export format='{fmt}'. 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) = ( flags # export booleans ) is_tf_format = any((saved_model, pb, tflite, edgetpu, tfjs)) # Device dla = None if fmt == "engine" and self.args.device is None: LOGGER.warning("TensorRT requires GPU export, automatically assigning device=0") self.args.device = "0" if fmt == "engine" and "dla" in str(self.args.device): # convert int/list to str first dla = self.args.device.rsplit(":", 1)[-1] self.args.device = "0" # update device to "0" assert dla in {"0", "1"}, f"Expected self.args.device='dla:0' or 'dla:1, but got {self.args.device}." 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 imx: if not self.args.int8: LOGGER.warning("IMX export requires int8=True, setting int8=True.") self.args.int8 = True if model.task != "detect": raise ValueError("IMX export only supported for detection models.") if not hasattr(model, "names"): model.names = default_class_names() model.names = check_class_names(model.names) 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 onnx and self.device.type == "cpu": LOGGER.warning("half=True only compatible with GPU export, i.e. use device=0") 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.int8 and tflite: assert not getattr(model, "end2end", False), "TFLite INT8 export not supported for end2end models." if self.args.nms: assert not isinstance(model, ClassificationModel), "'nms=True' is not valid for classification models." assert not (tflite and ARM64 and LINUX), "TFLite export with NMS unsupported on ARM64 Linux" if getattr(model, "end2end", False): 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 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.torch_utils import FXModel model = FXModel(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 m.max_det = self.args.max_det m.xyxy = self.args.nms and not coreml 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 if isinstance(m, Detect) and imx: from ultralytics.utils.tal import make_anchors m.anchors, m.strides = ( x.transpose(0, 1) for x in make_anchors( torch.cat([s / m.stride.unsqueeze(-1) for s in self.imgsz], dim=1), m.stride, 0.5 ) ) y = None for _ in range(2): # dry runs y = NMSModel(model, self.args)(im) if self.args.nms and not coreml else model(im) if self.args.half and onnx and self.device.type != "cpu": im, model = im.half(), model.half() # to FP16 # Filter warnings warnings.filterwarnings("ignore", category=torch.jit.TracerWarning) # suppress TracerWarning warnings.filterwarnings("ignore", category=UserWarning) # suppress shape prim::Constant missing ONNX warning warnings.filterwarnings("ignore", category=DeprecationWarning) # suppress CoreML np.bool deprecation warning # 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), } # 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 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 or ncnn: # 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() # 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(" ", "") predict_data = f"data={data}" if model.task == "segment" and fmt == "pb" else "" 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} {predict_data}" 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 # return list of exported files/dirs 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 < self.args.batch: raise ValueError( f"The calibration dataset ({n} images) must have at least as many images as the batch size " f"('batch={self.args.batch}')." ) elif n < 300: LOGGER.warning(f"{prefix} >300 images recommended for INT8 calibration, found {n} images.") return build_dataloader(dataset, batch=self.args.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, None @try_export def export_onnx(self, prefix=colorstr("ONNX:")): """Export YOLO model to ONNX format.""" requirements = ["onnx>=1.12.0,<1.18.0"] if self.args.simplify: requirements += ["onnxslim>=0.1.56", "onnxruntime" + ("-gpu" if torch.cuda.is_available() else "")] check_requirements(requirements) import onnx # noqa opset_version = self.args.opset or get_latest_opset() LOGGER.info(f"\n{prefix} starting export with onnx {onnx.__version__} opset {opset_version}...") f = str(self.file.with_suffix(".onnx")) output_names = ["output0", "output1"] if isinstance(self.model, SegmentationModel) 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_version # for NMSModel with arange_patch(self.args): export_onnx( NMSModel(self.model, self.args) if self.args.nms else self.model, self.im, f, opset=opset_version, 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) onnx.save(model_onnx, f) return f, model_onnx @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_1_13, f"OpenVINO export requires torch>=1.13.0 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") import nncf def transform_fn(data_item) -> np.ndarray: """Quantization transform function.""" 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 np.expand_dims(im, 0) if im.ndim == 3 else im # 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*", f".*{head_module_name}\\.dfl.*", ], types=["Sigmoid"], ) quantized_ov_model = nncf.quantize( model=ov_model, calibration_dataset=nncf.Dataset(self.get_int8_calibration_dataloader(prefix), transform_fn), preset=nncf.QuantizationPreset.MIXED, ignored_scope=ignored_scope, ) serialize(quantized_ov_model, fq_ov) return fq, None 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, None @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" if torch.cuda.is_available() else "paddlepaddle==3.0.0" # pin 3.0.0 for ARM64 if ARM64 else "paddlepaddle>=3.0.0", "x2paddle", ) ) import x2paddle # noqa from x2paddle.convert import pytorch2paddle # noqa 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, None @try_export def export_mnn(self, prefix=colorstr("MNN:")): """Export YOLO model to MNN format using MNN https://github.com/alibaba/MNN.""" f_onnx, _ = self.export_onnx() # get onnx model first check_requirements("MNN>=2.9.6") import MNN # noqa 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, None @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") import ncnn # noqa LOGGER.info(f"\n{prefix} starting export with NCNN {ncnn.__version__}...") f = Path(str(self.file).replace(self.file.suffix, f"_ncnn_model{os.sep}")) f_ts = self.file.with_suffix(".torchscript") name = Path("pnnx.exe" if WINDOWS else "pnnx") # PNNX filename pnnx = name if name.is_file() else (ROOT / name) if not pnnx.is_file(): LOGGER.warning( f"{prefix} PNNX not found. Attempting to download binary file from " "https://github.com/pnnx/pnnx/.\nNote PNNX Binary file must be placed in current working directory " f"or in {ROOT}. See PNNX repo for full installation instructions." ) system = "macos" if MACOS else "windows" if WINDOWS else "linux-aarch64" if ARM64 else "linux" try: release, assets = get_github_assets(repo="pnnx/pnnx") asset = [x for x in assets if f"{system}.zip" in x][0] assert isinstance(asset, str), "Unable to retrieve PNNX repo assets" # i.e. pnnx-20240410-macos.zip LOGGER.info(f"{prefix} successfully found latest PNNX asset file {asset}") except Exception as e: release = "20240410" asset = f"pnnx-{release}-{system}.zip" LOGGER.warning(f"{prefix} PNNX GitHub assets not found: {e}, using default {asset}") unzip_dir = safe_download(f"https://github.com/pnnx/pnnx/releases/download/{release}/{asset}", delete=True) if check_is_path_safe(Path.cwd(), unzip_dir): # avoid path traversal security vulnerability shutil.move(src=unzip_dir / name, dst=pnnx) # move binary to ROOT pnnx.chmod(0o777) # set read, write, and execute permissions for everyone shutil.rmtree(unzip_dir) # delete unzip dir ncnn_args = [ f"ncnnparam={f / 'model.ncnn.param'}", f"ncnnbin={f / 'model.ncnn.bin'}", f"ncnnpy={f / 'model_ncnn.py'}", ] pnnx_args = [ f"pnnxparam={f / 'model.pnnx.param'}", f"pnnxbin={f / 'model.pnnx.bin'}", f"pnnxpy={f / 'model_pnnx.py'}", f"pnnxonnx={f / 'model.pnnx.onnx'}", ] cmd = [ str(pnnx), str(f_ts), *ncnn_args, *pnnx_args, f"fp16={int(self.args.half)}", f"device={self.device.type}", f'inputshape="{[self.args.batch, 3, *self.imgsz]}"', ] f.mkdir(exist_ok=True) # make ncnn_model directory LOGGER.info(f"{prefix} running '{' '.join(cmd)}'") subprocess.run(cmd, check=True) # Remove debug files pnnx_files = [x.rsplit("=", 1)[-1] for x in pnnx_args] for f_debug in ("debug.bin", "debug.param", "debug2.bin", "debug2.param", *pnnx_files): Path(f_debug).unlink(missing_ok=True) YAML.save(f / "metadata.yaml", self.metadata) # add metadata.yaml return str(f), None @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>=8.0") import coremltools as ct # noqa 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 self.args.batch == 1, "CoreML batch sizes > 1 are not supported. Please retry at 'batch=1'." f = self.file.with_suffix(".mlmodel" if mlmodel else ".mlpackage") if f.is_dir(): shutil.rmtree(f) bias = [0.0, 0.0, 0.0] scale = 1 / 255 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) 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 'yolo11n.pt'.") # TODO CoreML Segment and Pose model pipelining model = self.model ts = torch.jit.trace(model.eval(), self.im, strict=False) # TorchScript model # 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_depoloyment_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=[ct.ImageType("image", shape=self.im.shape, scale=scale, bias=bias)], # expects ct.TensorType 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": if mlmodel: weights_dir = None else: ct_model.save(str(f)) # save otherwise weights_dir does not exist weights_dir = str(f / "Data/com.apple.CoreML/weights") ct_model = self._pipeline_coreml(ct_model, weights_dir=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, ct_model @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 try: import tensorrt as trt # noqa except ImportError: if LINUX: check_requirements("tensorrt>7.0.0,!=10.1.0") import tensorrt as trt # noqa 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 export_engine( 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, None @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 # noqa except ImportError: check_requirements("tensorflow>=2.0.0") import tensorflow as tf # noqa check_requirements( ( "tf_keras", # 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", # required by 'onnx2tf' package "onnx>=1.12.0,<1.18.0", "onnx2tf>=1.26.3", "onnxslim>=0.1.56", "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 # Pre-download calibration file to fix https://github.com/PINTO0309/onnx2tf/issues/545 onnx2tf_file = Path("calibration_image_sample_data_20x128x128x3_float32.npy") if not onnx2tf_file.exists(): attempt_download_asset(f"{onnx2tf_file}.zip", unzip=True, delete=True) # Export to ONNX self.args.simplify = True f_onnx, _ = self.export_onnx() # Export to TF np_data = None if self.args.int8: tmp_file = f / "tmp_tflite_int8_calibration_images.npy" # int8 calibration images file if self.args.data: f.mkdir() 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 ) np.save(str(tmp_file), images.numpy().astype(np.float32)) # BHWC np_data = [["images", tmp_file, [[[[0, 0, 0]]]], [[[[255, 255, 255]]]]]] import onnx2tf # scoped for after ONNX export for reduced conflict during import LOGGER.info(f"{prefix} starting TFLite export with onnx2tf {onnx2tf.__version__}...") keras_model = onnx2tf.convert( input_onnx_file_path=f_onnx, output_folder_path=str(f), not_use_onnxsim=True, verbosity="error", # note INT8-FP16 activation bug https://github.com/ultralytics/ultralytics/issues/15873 output_integer_quantized_tflite=self.args.int8, quant_type="per-tensor", # "per-tensor" (faster) or "per-channel" (slower but more accurate) custom_input_op_name_np_data_path=np_data, enable_batchmatmul_unfold=True, # fix lower no. of detected objects on GPU delegate output_signaturedefs=True, # fix error with Attention block group convolution disable_group_convolution=self.args.format in {"tfjs", "edgetpu"}, # fix error with group convolution optimization_for_gpu_delegate=True, ) YAML.save(f / "metadata.yaml", self.metadata) # add metadata.yaml # Remove/rename TFLite models if self.args.int8: tmp_file.unlink(missing_ok=True) for file in f.rglob("*_dynamic_range_quant.tflite"): file.rename(file.with_name(file.stem.replace("_dynamic_range_quant", "_int8") + file.suffix)) for file in f.rglob("*_integer_quant_with_int16_act.tflite"): file.unlink() # delete extra fp16 activation TFLite files # Add TFLite metadata for file in f.rglob("*.tflite"): f.unlink() if "quant_with_int16_act.tflite" in str(f) 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.""" import tensorflow as tf # noqa from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2 # noqa LOGGER.info(f"\n{prefix} starting export with tensorflow {tf.__version__}...") f = self.file.with_suffix(".pb") m = tf.function(lambda x: keras_model(x)) # full model m = m.get_concrete_function(tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype)) frozen_func = convert_variables_to_constants_v2(m) frozen_func.graph.as_graph_def() tf.io.write_graph(graph_or_graph_def=frozen_func.graph, logdir=str(f.parent), name=f.name, as_text=False) return f, None @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 # noqa 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), None @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}") for c in ( "curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -", 'echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" | ' "sudo tee /etc/apt/sources.list.d/coral-edgetpu.list", "sudo apt-get update", "sudo apt-get install edgetpu-compiler", ): subprocess.run(c if is_sudo_available() else c.replace("sudo ", ""), shell=True, check=True) 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}...") f = str(tflite_model).replace(".tflite", "_edgetpu.tflite") # Edge TPU model cmd = ( "edgetpu_compiler " f'--out_dir "{Path(f).parent}" ' "--show_operations " "--search_delegate " "--delegate_search_step 30 " "--timeout_sec 180 " f'"{tflite_model}"' ) LOGGER.info(f"{prefix} running '{cmd}'") subprocess.run(cmd, shell=True) self._add_tflite_metadata(f) return f, None @try_export def export_tfjs(self, prefix=colorstr("TensorFlow.js:")): """Export YOLO model to TensorFlow.js format.""" check_requirements("tensorflowjs") import tensorflow as tf import tensorflowjs as tfjs # noqa LOGGER.info(f"\n{prefix} starting export with tensorflowjs {tfjs.__version__}...") f = str(self.file).replace(self.file.suffix, "_web_model") # js dir f_pb = str(self.file.with_suffix(".pb")) # *.pb path gd = tf.Graph().as_graph_def() # TF GraphDef with open(f_pb, "rb") as file: gd.ParseFromString(file.read()) outputs = ",".join(gd_outputs(gd)) LOGGER.info(f"\n{prefix} output node names: {outputs}") quantization = "--quantize_float16" if self.args.half else "--quantize_uint8" if self.args.int8 else "" with spaces_in_path(f_pb) as fpb_, spaces_in_path(f) as f_: # exporter can not handle spaces in path cmd = ( "tensorflowjs_converter " f'--input_format=tf_frozen_model {quantization} --output_node_names={outputs} "{fpb_}" "{f_}"' ) LOGGER.info(f"{prefix} running '{cmd}'") subprocess.run(cmd, shell=True) if " " in f: LOGGER.warning(f"{prefix} your model may not work correctly with spaces in path '{f}'.") # Add metadata YAML.save(Path(f) / "metadata.yaml", self.metadata) # add metadata.yaml return f, None @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") 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 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 f = f.replace(".onnx", f"-{self.args.name}.rknn") rknn.export_rknn(f"{export_path / f}") YAML.save(export_path / "metadata.yaml", self.metadata) return export_path, None @try_export def export_imx(self, prefix=colorstr("IMX:")): """Export YOLO model to IMX format.""" gptq = False assert LINUX, ( "export only supported on Linux. " "See https://developer.aitrios.sony-semicon.com/en/raspberrypi-ai-camera/documentation/imx500-converter" ) if getattr(self.model, "end2end", False): raise ValueError("IMX export is not supported for end2end models.") check_requirements( ("model-compression-toolkit>=2.3.0,<2.4.1", "sony-custom-layers>=0.3.0", "edge-mdt-tpc>=1.1.0") ) check_requirements("imx500-converter[pt]>=3.16.1") # Separate requirements for imx500-converter import model_compression_toolkit as mct import onnx from edgemdt_tpc import get_target_platform_capabilities from sony_custom_layers.pytorch import multiclass_nms LOGGER.info(f"\n{prefix} starting export with model_compression_toolkit {mct.__version__}...") # 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): cmd = (["sudo"] if is_sudo_available() else []) + ["apt", "install", "-y", "openjdk-21-jre"] subprocess.run(cmd, check=True) def representative_dataset_gen(dataloader=self.get_int8_calibration_dataloader(prefix)): for batch in dataloader: img = batch["img"] img = img / 255.0 yield [img] tpc = get_target_platform_capabilities(tpc_version="4.0", device_type="imx500") bit_cfg = mct.core.BitWidthConfig() if "C2PSA" in self.model.__str__(): # YOLO11 layer_names = ["sub", "mul_2", "add_14", "cat_21"] weights_memory = 2585350.2439 n_layers = 238 # 238 layers for fused YOLO11n else: # YOLOv8 layer_names = ["sub", "mul", "add_6", "cat_17"] weights_memory = 2550540.8 n_layers = 168 # 168 layers for fused YOLOv8n # Check if the model has the expected number of layers if len(list(self.model.modules())) != n_layers: raise ValueError("IMX export only supported for YOLOv8n and YOLO11n models.") for layer_name in layer_names: bit_cfg.set_manual_activation_bit_width([mct.core.common.network_editors.NodeNameFilter(layer_name)], 16) config = mct.core.CoreConfig( mixed_precision_config=mct.core.MixedPrecisionQuantizationConfig(num_of_images=10), quantization_config=mct.core.QuantizationConfig(concat_threshold_update=True), bit_width_config=bit_cfg, ) resource_utilization = mct.core.ResourceUtilization(weights_memory=weights_memory) quant_model = ( mct.gptq.pytorch_gradient_post_training_quantization( # Perform Gradient-Based Post Training Quantization model=self.model, representative_data_gen=representative_dataset_gen, target_resource_utilization=resource_utilization, gptq_config=mct.gptq.get_pytorch_gptq_config( n_epochs=1000, use_hessian_based_weights=False, use_hessian_sample_attention=False ), core_config=config, target_platform_capabilities=tpc, )[0] if gptq else mct.ptq.pytorch_post_training_quantization( # Perform post training quantization in_module=self.model, representative_data_gen=representative_dataset_gen, target_resource_utilization=resource_utilization, core_config=config, target_platform_capabilities=tpc, )[0] ) class NMSWrapper(torch.nn.Module): """Wrap PyTorch Module with multiclass_nms layer from sony_custom_layers.""" def __init__( self, model: torch.nn.Module, score_threshold: float = 0.001, iou_threshold: float = 0.7, max_detections: int = 300, ): """ Initialize NMSWrapper with PyTorch Module and NMS parameters. Args: model (torch.nn.Module): Model instance. score_threshold (float): Score threshold for non-maximum suppression. iou_threshold (float): Intersection over union threshold for non-maximum suppression. max_detections (int): The number of detections to return. """ super().__init__() self.model = model self.score_threshold = score_threshold self.iou_threshold = iou_threshold self.max_detections = max_detections def forward(self, images): """Forward pass with model inference and NMS post-processing.""" # model inference outputs = self.model(images) boxes = outputs[0] scores = outputs[1] nms = multiclass_nms( boxes=boxes, scores=scores, score_threshold=self.score_threshold, iou_threshold=self.iou_threshold, max_detections=self.max_detections, ) return nms quant_model = NMSWrapper( model=quant_model, score_threshold=self.args.conf or 0.001, iou_threshold=self.args.iou, max_detections=self.args.max_det, ).to(self.device) f = Path(str(self.file).replace(self.file.suffix, "_imx_model")) f.mkdir(exist_ok=True) onnx_model = f / Path(str(self.file.name).replace(self.file.suffix, "_imx.onnx")) # js dir mct.exporter.pytorch_export_model( model=quant_model, save_model_path=onnx_model, repr_dataset=representative_dataset_gen ) model_onnx = onnx.load(onnx_model) # load onnx model for k, v in self.metadata.items(): meta = model_onnx.metadata_props.add() meta.key, meta.value = k, str(v) onnx.save(model_onnx, onnx_model) subprocess.run( ["imxconv-pt", "-i", str(onnx_model), "-o", str(f), "--no-input-persistency", "--overwrite-output"], check=True, ) # Needed for imx models. with open(f / "labels.txt", "w", encoding="utf-8") as file: file.writelines([f"{name}\n" for _, name in self.model.names.items()]) return f, None 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 # noqa LOGGER.info(f"{prefix} starting pipeline with coremltools {ct.__version__}...") _, _, h, w = list(self.im.shape) # BCHW # Output shapes spec = model.get_spec() out0, out1 = iter(spec.description.output) if MACOS: from PIL import Image img = Image.new("RGB", (w, h)) # w=192, h=320 out = model.predict({"image": img}) out0_shape = out[out0.name].shape # (3780, 80) out1_shape = out[out1.name].shape # (3780, 4) else: # linux and windows can not run model.predict(), get sizes from PyTorch model output y out0_shape = self.output_shape[2], self.output_shape[1] - 4 # (3780, 80) out1_shape = self.output_shape[2], 4 # (3780, 4) # Checks names = self.metadata["names"] nx, ny = spec.description.input[0].type.imageType.width, spec.description.input[0].type.imageType.height _, nc = out0_shape # number of anchors, number of classes assert len(names) == nc, f"{len(names)} names found for nc={nc}" # check # Define output shapes (missing) out0.type.multiArrayType.shape[:] = out0_shape # (3780, 80) out1.type.multiArrayType.shape[:] = out1_shape # (3780, 4) # Model from spec model = ct.models.MLModel(spec, weights_dir=weights_dir) # 3. Create NMS protobuf nms_spec = ct.proto.Model_pb2.Model() nms_spec.specificationVersion = spec.specificationVersion for i in range(2): 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) nms_spec.description.output[0].name = "confidence" nms_spec.description.output[1].name = "coordinates" output_sizes = [nc, 4] for i in range(2): 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 = output_sizes[i] ma_type.shapeRange.sizeRanges[1].upperBound = output_sizes[i] del ma_type.shape[:] nms = nms_spec.nonMaximumSuppression nms.confidenceInputFeatureName = out0.name # 1x507x80 nms.coordinatesInputFeatureName = out1.name # 1x507x4 nms.confidenceOutputFeatureName = "confidence" nms.coordinatesOutputFeatureName = "coordinates" nms.iouThresholdInputFeatureName = "iouThreshold" nms.confidenceThresholdInputFeatureName = "confidenceThreshold" nms.iouThreshold = self.args.iou nms.confidenceThreshold = self.args.conf nms.pickTop.perClass = True nms.stringClassLabels.vector.extend(names.values()) nms_model = ct.models.MLModel(nms_spec) # 4. 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=["confidence", "coordinates"], ) 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 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): """ 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). """ super().__init__() _, _, h, w = im.shape # batch, channel, height, width self.model = model self.nc = len(model.names) # number of classes 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) 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) return cls, xywh * self.normalize # confidence (3780, 80), coordinates (3780, 4) 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 (Namespace): 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 (N, 3, H, W). Returns: (torch.Tensor): List of detections, each an (N, max_det, 4 + 2 + extra_shape) Tensor where N is the number of detections after NMS. """ 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: # 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 # 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-specific NMS end = 2 if self.obb else 4 # fully explicit expansion otherwise reshape error # large max_wh causes issues when quantizing cls_offset = cls.reshape(-1, 1).expand(nmsbox.shape[0], end) offbox = nmsbox[:, :end] + cls_offset * multiplier nmsbox = torch.cat((offbox, nmsbox[:, end:]), dim=-1) nms_fn = ( partial( nms_rotated, 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 ), ) 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]