YOLOv8-16bit/ultralytics/yolo/engine/exporter.py

# Ultralytics YOLO 🚀, AGPL-3.0 license
"""
Export a YOLOv8 PyTorch model to other formats. TensorFlow exports authored by https://github.com/zldrobit

Format                  | `format=argument`         | Model
---                     | ---                       | ---
PyTorch                 | -                         | yolov8n.pt
TorchScript             | `torchscript`             | yolov8n.torchscript
ONNX                    | `onnx`                    | yolov8n.onnx
OpenVINO                | `openvino`                | yolov8n_openvino_model/
TensorRT                | `engine`                  | yolov8n.engine
CoreML                  | `coreml`                  | yolov8n.mlmodel
TensorFlow SavedModel   | `saved_model`             | yolov8n_saved_model/
TensorFlow GraphDef     | `pb`                      | yolov8n.pb
TensorFlow Lite         | `tflite`                  | yolov8n.tflite
TensorFlow Edge TPU     | `edgetpu`                 | yolov8n_edgetpu.tflite
TensorFlow.js           | `tfjs`                    | yolov8n_web_model/
PaddlePaddle            | `paddle`                  | yolov8n_paddle_model/

Requirements:
    $ pip install ultralytics[export]

Python:
    from ultralytics import YOLO
    model = YOLO('yolov8n.pt')
    results = model.export(format='onnx')

CLI:
    $ yolo mode=export model=yolov8n.pt format=onnx

Inference:
    $ yolo predict model=yolov8n.pt                 # PyTorch
                         yolov8n.torchscript        # TorchScript
                         yolov8n.onnx               # ONNX Runtime or OpenCV DNN with --dnn
                         yolov8n_openvino_model     # OpenVINO
                         yolov8n.engine             # TensorRT
                         yolov8n.mlmodel            # CoreML (macOS-only)
                         yolov8n_saved_model        # TensorFlow SavedModel
                         yolov8n.pb                 # TensorFlow GraphDef
                         yolov8n.tflite             # TensorFlow Lite
                         yolov8n_edgetpu.tflite     # TensorFlow Edge TPU
                         yolov8n_paddle_model       # PaddlePaddle

TensorFlow.js:
    $ cd .. && git clone https://github.com/zldrobit/tfjs-yolov5-example.git && cd tfjs-yolov5-example
    $ npm install
    $ ln -s ../../yolov5/yolov8n_web_model public/yolov8n_web_model
    $ npm start
"""
import json
import os
import platform
import subprocess
import time
import warnings
from copy import deepcopy
from pathlib import Path

import torch

from ultralytics.nn.autobackend import check_class_names
from ultralytics.nn.modules import C2f, Detect, Segment
from ultralytics.nn.tasks import DetectionModel, SegmentationModel
from ultralytics.yolo.cfg import get_cfg
from ultralytics.yolo.utils import (DEFAULT_CFG, LINUX, LOGGER, MACOS, __version__, callbacks, colorstr,
                                    get_default_args, yaml_save)
from ultralytics.yolo.utils.checks import check_imgsz, check_requirements, check_version
from ultralytics.yolo.utils.files import file_size
from ultralytics.yolo.utils.ops import Profile
from ultralytics.yolo.utils.torch_utils import get_latest_opset, select_device, smart_inference_mode

ARM64 = platform.machine() in ('arm64', 'aarch64')


def export_formats():
    """YOLOv8 export formats"""
    import pandas
    x = [
        ['PyTorch', '-', '.pt', True, True],
        ['TorchScript', 'torchscript', '.torchscript', True, True],
        ['ONNX', 'onnx', '.onnx', True, True],
        ['OpenVINO', 'openvino', '_openvino_model', True, False],
        ['TensorRT', 'engine', '.engine', False, True],
        ['CoreML', 'coreml', '.mlmodel', True, False],
        ['TensorFlow SavedModel', 'saved_model', '_saved_model', True, True],
        ['TensorFlow GraphDef', 'pb', '.pb', True, True],
        ['TensorFlow Lite', 'tflite', '.tflite', True, False],
        ['TensorFlow Edge TPU', 'edgetpu', '_edgetpu.tflite', True, False],
        ['TensorFlow.js', 'tfjs', '_web_model', True, False],
        ['PaddlePaddle', 'paddle', '_paddle_model', True, True], ]
    return pandas.DataFrame(x, columns=['Format', 'Argument', 'Suffix', 'CPU', 'GPU'])


def gd_outputs(gd):
    """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):
    """YOLOv8 export decorator, i..e @try_export"""
    inner_args = get_default_args(inner_func)

    def outer_func(*args, **kwargs):
        prefix = inner_args['prefix']
        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.info(f'{prefix} export failure ❌ {dt.t:.1f}s: {e}')
            return None, None

    return outer_func


class iOSDetectModel(torch.nn.Module):
    """Wrap an Ultralytics YOLO model for iOS export"""

    def __init__(self, model, im):
        super().__init__()
        b, c, 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):
        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 Exporter:
    """
    A class for exporting a model.

    Attributes:
        args (SimpleNamespace): Configuration for the exporter.
        save_dir (Path): Directory to save results.
    """

    def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
        """
        Initializes the Exporter class.

        Args:
            cfg (str, optional): Path to a configuration file. Defaults to DEFAULT_CFG.
            overrides (dict, optional): Configuration overrides. Defaults to None.
            _callbacks (list, optional): List of callback functions. Defaults to None.
        """
        self.args = get_cfg(cfg, overrides)
        self.callbacks = _callbacks or callbacks.get_default_callbacks()
        callbacks.add_integration_callbacks(self)

    @smart_inference_mode()
    def __call__(self, model=None):
        self.run_callbacks('on_export_start')
        t = time.time()
        format = self.args.format.lower()  # to lowercase
        if format in ('tensorrt', 'trt'):  # engine aliases
            format = 'engine'
        fmts = tuple(export_formats()['Argument'][1:])  # available export formats
        flags = [x == format for x in fmts]
        if sum(flags) != 1:
            raise ValueError(f"Invalid export format='{format}'. Valid formats are {fmts}")
        jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle = flags  # export booleans

        # Load PyTorch model
        self.device = select_device('cpu' if self.args.device is None else self.args.device)
        if self.args.half and onnx and self.device.type == 'cpu':
            LOGGER.warning('WARNING ⚠️ half=True only compatible with GPU export, i.e. use device=0')
            self.args.half = False
            assert not self.args.dynamic, 'half=True not compatible with dynamic=True, i.e. use only one.'

        # Checks
        model.names = check_class_names(model.names)
        self.imgsz = check_imgsz(self.args.imgsz, stride=model.stride, min_dim=2)  # check image size
        if self.args.optimize:
            assert self.device.type == 'cpu', '--optimize not compatible with cuda devices, i.e. use --device cpu'
        if edgetpu and not LINUX:
            raise SystemError('Edge TPU export only supported on Linux. See https://coral.ai/docs/edgetpu/compiler/')

        # Input
        im = torch.zeros(self.args.batch, 3, *self.imgsz).to(self.device)
        file = Path(getattr(model, 'pt_path', None) or getattr(model, 'yaml_file', None) or model.yaml['yaml_file'])
        if file.suffix == '.yaml':
            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()
        for k, m in model.named_modules():
            if isinstance(m, (Detect, Segment)):
                m.dynamic = self.args.dynamic
                m.export = True
                m.format = self.args.format
            elif isinstance(m, C2f) and not any((saved_model, pb, tflite, edgetpu, tfjs)):
                # EdgeTPU does not support FlexSplitV while split provides cleaner ONNX graph
                m.forward = m.forward_split

        y = None
        for _ in range(2):
            y = model(im)  # dry runs
        if self.args.half and (engine or onnx) and self.device.type != 'cpu':
            im, model = im.half(), model.half()  # to FP16

        # 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) for x in y)
        self.pretty_name = Path(self.model.yaml.get('yaml_file', self.file)).stem.replace('yolo', 'YOLO')
        trained_on = f'trained on {Path(self.args.data).name}' if self.args.data else '(untrained)'
        description = f'Ultralytics {self.pretty_name} model {trained_on}'
        self.metadata = {
            'description': description,
            'author': 'Ultralytics',
            'license': 'AGPL-3.0 https://ultralytics.com/license',
            'version': __version__,
            'stride': int(max(model.stride)),
            'task': model.task,
            'batch': self.args.batch,
            'imgsz': self.imgsz,
            'names': model.names}  # model metadata
        if model.task == 'pose':
            self.metadata['kpt_shape'] = model.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)')

        # 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()
        if onnx or xml:  # OpenVINO requires ONNX
            f[2], _ = self._export_onnx()
        if xml:  # OpenVINO
            f[3], _ = self._export_openvino()
        if coreml:  # CoreML
            f[4], _ = self._export_coreml()
        if any((saved_model, pb, tflite, edgetpu, tfjs)):  # TensorFlow formats
            self.args.int8 |= edgetpu
            f[5], s_model = self._export_saved_model()
            if pb or tfjs:  # pb prerequisite to tfjs
                f[6], _ = self._export_pb(s_model)
            if tflite:
                f[7], _ = self._export_tflite(s_model, nms=False, agnostic_nms=self.args.agnostic_nms)
            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()

        # 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(' ', '')
            data = f'data={self.args.data}' if model.task == 'segment' and format == 'pb' else ''
            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} {data}'
                f'\nValidate:        yolo val task={model.task} model={f} imgsz={imgsz} data={self.args.data} {s}'
                f'\nVisualize:       https://netron.app')

        self.run_callbacks('on_export_end')
        return f  # return list of exported files/dirs

    @try_export
    def _export_torchscript(self, prefix=colorstr('TorchScript:')):
        # YOLOv8 TorchScript model export
        LOGGER.info(f'\n{prefix} starting export with torch {torch.__version__}...')
        f = self.file.with_suffix('.torchscript')

        ts = torch.jit.trace(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:')):
        # YOLOv8 ONNX export
        requirements = ['onnx>=1.12.0']
        if self.args.simplify:
            requirements += ['onnxsim>=0.4.17', 'onnxruntime-gpu' if torch.cuda.is_available() else 'onnxruntime']
        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', 1: 'anchors'}  # shape(1,25200,85)
                dynamic['output1'] = {0: 'batch', 2: 'mask_height', 3: 'mask_width'}  # shape(1,32,160,160)
            elif isinstance(self.model, DetectionModel):
                dynamic['output0'] = {0: 'batch', 1: 'anchors'}  # shape(1,25200,85)

        torch.onnx.export(
            self.model.cpu() if dynamic else self.model,  # --dynamic only compatible with cpu
            self.im.cpu() if dynamic else self.im,
            f,
            verbose=False,
            opset_version=opset_version,
            do_constant_folding=True,  # WARNING: DNN inference with torch>=1.12 may require do_constant_folding=False
            input_names=['images'],
            output_names=output_names,
            dynamic_axes=dynamic or None)

        # Checks
        model_onnx = onnx.load(f)  # load onnx model
        # onnx.checker.check_model(model_onnx)  # check onnx model

        # Simplify
        if self.args.simplify:
            try:
                import onnxsim

                LOGGER.info(f'{prefix} simplifying with onnxsim {onnxsim.__version__}...')
                # subprocess.run(f'onnxsim {f} {f}', shell=True)
                model_onnx, check = onnxsim.simplify(model_onnx)
                assert check, 'Simplified ONNX model could not be validated'
            except Exception as e:
                LOGGER.info(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:')):
        # YOLOv8 OpenVINO export
        check_requirements('openvino-dev>=2022.3')  # requires openvino-dev: https://pypi.org/project/openvino-dev/
        import openvino.runtime as ov  # noqa
        from openvino.tools import mo  # noqa

        LOGGER.info(f'\n{prefix} starting export with openvino {ov.__version__}...')
        f = str(self.file).replace(self.file.suffix, f'_openvino_model{os.sep}')
        f_onnx = self.file.with_suffix('.onnx')
        f_ov = str(Path(f) / self.file.with_suffix('.xml').name)

        ov_model = mo.convert_model(f_onnx,
                                    model_name=self.pretty_name,
                                    framework='onnx',
                                    compress_to_fp16=self.args.half)  # export
        ov.serialize(ov_model, f_ov)  # save
        yaml_save(Path(f) / 'metadata.yaml', self.metadata)  # add metadata.yaml
        return f, None

    @try_export
    def _export_paddle(self, prefix=colorstr('PaddlePaddle:')):
        # YOLOv8 Paddle export
        check_requirements(('paddlepaddle', '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_coreml(self, prefix=colorstr('CoreML:')):
        # YOLOv8 CoreML export
        check_requirements('coremltools>=6.0')
        import coremltools as ct  # noqa

        LOGGER.info(f'\n{prefix} starting export with coremltools {ct.__version__}...')
        f = self.file.with_suffix('.mlmodel')

        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())) if self.args.nms else None
            model = self.model
        elif self.model.task == 'detect':
            model = iOSDetectModel(self.model, self.im) if self.args.nms else self.model
        else:
            # TODO CoreML Segment and Pose model pipelining
            model = self.model

        ts = torch.jit.trace(model.eval(), self.im, strict=False)  # TorchScript model
        ct_model = ct.convert(ts,
                              inputs=[ct.ImageType('image', shape=self.im.shape, scale=scale, bias=bias)],
                              classifier_config=classifier_config)
        bits, mode = (8, 'kmeans_lut') 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
            ct_model = ct.models.neural_network.quantization_utils.quantize_weights(ct_model, bits, mode)
        if self.args.nms and self.model.task == 'detect':
            ct_model = self._pipeline_coreml(ct_model)

        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()})
        ct_model.save(str(f))
        return f, ct_model

    @try_export
    def _export_engine(self, workspace=4, verbose=False, prefix=colorstr('TensorRT:')):
        # YOLOv8 TensorRT export 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'"
        try:
            import tensorrt as trt  # noqa
        except ImportError:
            if LINUX:
                check_requirements('nvidia-tensorrt', cmds='-U --index-url https://pypi.ngc.nvidia.com')
            import tensorrt as trt  # noqa

        check_version(trt.__version__, '7.0.0', hard=True)  # require tensorrt>=8.0.0
        self.args.simplify = True
        f_onnx, _ = self._export_onnx()

        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
        logger = trt.Logger(trt.Logger.INFO)
        if verbose:
            logger.min_severity = trt.Logger.Severity.VERBOSE

        builder = trt.Builder(logger)
        config = builder.create_builder_config()
        config.max_workspace_size = workspace * 1 << 30
        # config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, workspace << 30)  # fix TRT 8.4 deprecation notice

        flag = (1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
        network = builder.create_network(flag)
        parser = trt.OnnxParser(network, logger)
        if not parser.parse_from_file(f_onnx):
            raise RuntimeError(f'failed to load ONNX file: {f_onnx}')

        inputs = [network.get_input(i) for i in range(network.num_inputs)]
        outputs = [network.get_output(i) for i in range(network.num_outputs)]
        for inp in inputs:
            LOGGER.info(f'{prefix} input "{inp.name}" with shape{inp.shape} {inp.dtype}')
        for out in outputs:
            LOGGER.info(f'{prefix} output "{out.name}" with shape{out.shape} {out.dtype}')

        if self.args.dynamic:
            shape = self.im.shape
            if shape[0] <= 1:
                LOGGER.warning(f'{prefix} WARNING ⚠️ --dynamic model requires maximum --batch-size argument')
            profile = builder.create_optimization_profile()
            for inp in inputs:
                profile.set_shape(inp.name, (1, *shape[1:]), (max(1, shape[0] // 2), *shape[1:]), shape)
            config.add_optimization_profile(profile)

        LOGGER.info(
            f'{prefix} building FP{16 if builder.platform_has_fast_fp16 and self.args.half else 32} engine as {f}')
        if builder.platform_has_fast_fp16 and self.args.half:
            config.set_flag(trt.BuilderFlag.FP16)

        # Write file
        with builder.build_engine(network, config) as engine, open(f, 'wb') as t:
            # Metadata
            meta = json.dumps(self.metadata)
            t.write(len(meta).to_bytes(4, byteorder='little', signed=True))
            t.write(meta.encode())
            # Model
            t.write(engine.serialize())

        return f, None

    @try_export
    def _export_saved_model(self, prefix=colorstr('TensorFlow SavedModel:')):

        # YOLOv8 TensorFlow SavedModel export
        try:
            import tensorflow as tf  # noqa
        except ImportError:
            cuda = torch.cuda.is_available()
            check_requirements(f"tensorflow{'-macos' if MACOS else '-aarch64' if ARM64 else '' if cuda else '-cpu'}")
            import tensorflow as tf  # noqa
        check_requirements(('onnx', 'onnx2tf>=1.7.7', 'sng4onnx>=1.0.1', 'onnxsim>=0.4.17', 'onnx_graphsurgeon>=0.3.26',
                            'tflite_support', 'onnxruntime-gpu' if torch.cuda.is_available() else 'onnxruntime'),
                           cmds='--extra-index-url https://pypi.ngc.nvidia.com')

        LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
        f = Path(str(self.file).replace(self.file.suffix, '_saved_model'))
        if f.is_dir():
            import shutil
            shutil.rmtree(f)  # delete output folder

        # Export to ONNX
        self.args.simplify = True
        f_onnx, _ = self._export_onnx()

        # Export to TF
        int8 = '-oiqt -qt per-tensor' if self.args.int8 else ''
        cmd = f'onnx2tf -i {f_onnx} -o {f} -nuo --non_verbose {int8}'
        LOGGER.info(f"\n{prefix} running '{cmd.strip()}'")
        subprocess.run(cmd, shell=True)
        yaml_save(f / 'metadata.yaml', self.metadata)  # add metadata.yaml

        # Remove/rename TFLite models
        if self.args.int8:
            for file in f.rglob('*_dynamic_range_quant.tflite'):
                file.rename(file.with_stem(file.stem.replace('_dynamic_range_quant', '_int8')))
            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)

        # Load saved_model
        keras_model = tf.saved_model.load(f, tags=None, options=None)

        return str(f), keras_model

    @try_export
    def _export_pb(self, keras_model, prefix=colorstr('TensorFlow GraphDef:')):
        # YOLOv8 TensorFlow GraphDef *.pb export 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, keras_model, nms, agnostic_nms, prefix=colorstr('TensorFlow Lite:')):
        # YOLOv8 TensorFlow Lite export
        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

        # # OLD TFLITE EXPORT CODE BELOW -------------------------------------------------------------------------------
        # batch_size, ch, *imgsz = list(self.im.shape)  # BCHW
        # f = str(self.file).replace(self.file.suffix, '-fp16.tflite')
        #
        # converter = tf.lite.TFLiteConverter.from_keras_model(keras_model)
        # converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS]
        # converter.target_spec.supported_types = [tf.float16]
        # converter.optimizations = [tf.lite.Optimize.DEFAULT]
        # if self.args.int8:
        #
        #     def representative_dataset_gen(dataset, n_images=100):
        #         # Dataset generator for use with converter.representative_dataset, returns a generator of np arrays
        #         for n, (path, img, im0s, vid_cap, string) in enumerate(dataset):
        #             im = np.transpose(img, [1, 2, 0])
        #             im = np.expand_dims(im, axis=0).astype(np.float32)
        #             im /= 255
        #             yield [im]
        #             if n >= n_images:
        #                 break
        #
        #     dataset = LoadImages(check_det_dataset(self.args.data)['train'], imgsz=imgsz, auto=False)
        #     converter.representative_dataset = lambda: representative_dataset_gen(dataset, n_images=100)
        #     converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
        #     converter.target_spec.supported_types = []
        #     converter.inference_input_type = tf.uint8  # or tf.int8
        #     converter.inference_output_type = tf.uint8  # or tf.int8
        #     converter.experimental_new_quantizer = True
        #     f = str(self.file).replace(self.file.suffix, '-int8.tflite')
        # if nms or agnostic_nms:
        #     converter.target_spec.supported_ops.append(tf.lite.OpsSet.SELECT_TF_OPS)
        #
        # tflite_model = converter.convert()
        # open(f, 'wb').write(tflite_model)
        # return f, None

    @try_export
    def _export_edgetpu(self, tflite_model='', prefix=colorstr('Edge TPU:')):
        # YOLOv8 Edge TPU export https://coral.ai/docs/edgetpu/models-intro/
        LOGGER.warning(f'{prefix} WARNING ⚠️ Edge TPU known bug https://github.com/ultralytics/ultralytics/issues/1185')

        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 = subprocess.run('sudo --version >/dev/null', shell=True).returncode == 0  # sudo installed on system
            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 sudo else c.replace('sudo ', ''), shell=True, check=True)
        ver = subprocess.run(cmd, shell=True, capture_output=True, check=True).stdout.decode().split()[-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 = f'edgetpu_compiler -s -d -k 10 --out_dir {Path(f).parent} {tflite_model}'
        LOGGER.info(f"{prefix} running '{cmd}'")
        subprocess.run(cmd.split(), check=True)
        self._add_tflite_metadata(f)
        return f, None

    @try_export
    def _export_tfjs(self, prefix=colorstr('TensorFlow.js:')):
        # YOLOv8 TensorFlow.js export
        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 = 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}')

        cmd = f'tensorflowjs_converter --input_format=tf_frozen_model --output_node_names={outputs} {f_pb} {f}'
        subprocess.run(cmd.split(), check=True)

        # f_json = Path(f) / 'model.json'  # *.json path
        # with open(f_json, 'w') as j:  # sort JSON Identity_* in ascending order
        #     subst = re.sub(
        #         r'{"outputs": {"Identity.?.?": {"name": "Identity.?.?"}, '
        #         r'"Identity.?.?": {"name": "Identity.?.?"}, '
        #         r'"Identity.?.?": {"name": "Identity.?.?"}, '
        #         r'"Identity.?.?": {"name": "Identity.?.?"}}}',
        #         r'{"outputs": {"Identity": {"name": "Identity"}, '
        #         r'"Identity_1": {"name": "Identity_1"}, '
        #         r'"Identity_2": {"name": "Identity_2"}, '
        #         r'"Identity_3": {"name": "Identity_3"}}}',
        #         f_json.read_text(),
        #     )
        #     j.write(subst)
        yaml_save(Path(f) / 'metadata.yaml', self.metadata)  # add metadata.yaml
        return f, None

    def _add_tflite_metadata(self, file):
        # Add metadata to *.tflite models per https://www.tensorflow.org/lite/models/convert/metadata
        from tflite_support import flatbuffers  # noqa
        from tflite_support import metadata as _metadata  # noqa
        from tflite_support import metadata_schema_py_generated as _metadata_fb  # noqa

        # Create model info
        model_meta = _metadata_fb.ModelMetadataT()
        model_meta.name = self.metadata['description']
        model_meta.version = self.metadata['version']
        model_meta.author = self.metadata['author']
        model_meta.license = self.metadata['license']

        # Label file
        tmp_file = Path(file).parent / 'temp_meta.txt'
        with open(tmp_file, 'w') as f:
            f.write(str(self.metadata))

        label_file = _metadata_fb.AssociatedFileT()
        label_file.name = tmp_file.name
        label_file.type = _metadata_fb.AssociatedFileType.TENSOR_AXIS_LABELS

        # Create input info
        input_meta = _metadata_fb.TensorMetadataT()
        input_meta.name = 'image'
        input_meta.description = 'Input image to be detected.'
        input_meta.content = _metadata_fb.ContentT()
        input_meta.content.contentProperties = _metadata_fb.ImagePropertiesT()
        input_meta.content.contentProperties.colorSpace = _metadata_fb.ColorSpaceType.RGB
        input_meta.content.contentPropertiesType = _metadata_fb.ContentProperties.ImageProperties

        # Create output info
        output1 = _metadata_fb.TensorMetadataT()
        output1.name = 'output'
        output1.description = 'Coordinates of detected objects, class labels, and confidence score'
        output1.associatedFiles = [label_file]
        if self.model.task == 'segment':
            output2 = _metadata_fb.TensorMetadataT()
            output2.name = 'output'
            output2.description = 'Mask protos'
            output2.associatedFiles = [label_file]

        # Create subgraph info
        subgraph = _metadata_fb.SubGraphMetadataT()
        subgraph.inputTensorMetadata = [input_meta]
        subgraph.outputTensorMetadata = [output1, output2] if self.model.task == 'segment' else [output1]
        model_meta.subgraphMetadata = [subgraph]

        b = flatbuffers.Builder(0)
        b.Finish(model_meta.Pack(b), _metadata.MetadataPopulator.METADATA_FILE_IDENTIFIER)
        metadata_buf = b.Output()

        populator = _metadata.MetadataPopulator.with_model_file(str(file))
        populator.load_metadata_buffer(metadata_buf)
        populator.load_associated_files([str(tmp_file)])
        populator.populate()
        tmp_file.unlink()

    def _pipeline_coreml(self, model, prefix=colorstr('CoreML Pipeline:')):
        # YOLOv8 CoreML pipeline
        import coremltools as ct  # noqa

        LOGGER.info(f'{prefix} starting pipeline with coremltools {ct.__version__}...')
        batch_size, ch, 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))  # img(192 width, 320 height)
            # img = torch.zeros((*opt.img_size, 3)).numpy()  # img size(320,192,3) iDetection
            out = model.predict({'image': img})
            out0_shape = out[out0.name].shape
            out1_shape = out[out1.name].shape
        else:  # linux and windows can not run model.predict(), get sizes from pytorch 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
        na, nc = out0_shape
        # na, nc = out0.type.multiArrayType.shape  # number anchors, 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)
        # spec.neuralNetwork.preprocessing[0].featureName = '0'

        # Flexible input shapes
        # from coremltools.models.neural_network import flexible_shape_utils
        # s = [] # shapes
        # s.append(flexible_shape_utils.NeuralNetworkImageSize(320, 192))
        # s.append(flexible_shape_utils.NeuralNetworkImageSize(640, 384))  # (height, width)
        # flexible_shape_utils.add_enumerated_image_sizes(spec, feature_name='image', sizes=s)
        # r = flexible_shape_utils.NeuralNetworkImageSizeRange()  # shape ranges
        # r.add_height_range((192, 640))
        # r.add_width_range((192, 640))
        # flexible_shape_utils.update_image_size_range(spec, feature_name='image', size_range=r)

        # Print
        # print(spec.description)

        # Model from spec
        model = ct.models.MLModel(spec)

        # 3. Create NMS protobuf
        nms_spec = ct.proto.Model_pb2.Model()
        nms_spec.specificationVersion = 5
        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 = 0.45
        nms.confidenceThreshold = 0.25
        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 = 5
        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)
        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):
        """
        Appends the given callback.
        """
        self.callbacks[event].append(callback)

    def run_callbacks(self, event: str):
        for callback in self.callbacks.get(event, []):
            callback(self)


def export(cfg=DEFAULT_CFG):
    cfg.model = cfg.model or 'yolov8n.yaml'
    cfg.format = cfg.format or 'torchscript'

    from ultralytics import YOLO
    model = YOLO(cfg.model)
    model.export(**vars(cfg))


if __name__ == '__main__':
    """
    CLI:
    yolo mode=export model=yolov8n.yaml format=onnx
    """
    export()