yolov5/export.py

# Ultralytics YOLOv5 🚀, AGPL-3.0 license
"""
Export a YOLOv5 PyTorch model to other formats. TensorFlow exports authored by https://github.com/zldrobit

Format                      | `export.py --include`         | Model
---                         | ---                           | ---
PyTorch                     | -                             | yolov5s.pt
TorchScript                 | `torchscript`                 | yolov5s.torchscript
ONNX                        | `onnx`                        | yolov5s.onnx
OpenVINO                    | `openvino`                    | yolov5s_openvino_model/
TensorRT                    | `engine`                      | yolov5s.engine
CoreML                      | `coreml`                      | yolov5s.mlmodel
TensorFlow SavedModel       | `saved_model`                 | yolov5s_saved_model/
TensorFlow GraphDef         | `pb`                          | yolov5s.pb
TensorFlow Lite             | `tflite`                      | yolov5s.tflite
TensorFlow Edge TPU         | `edgetpu`                     | yolov5s_edgetpu.tflite
TensorFlow.js               | `tfjs`                        | yolov5s_web_model/
PaddlePaddle                | `paddle`                      | yolov5s_paddle_model/

Requirements:
    $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime openvino-dev tensorflow-cpu  # CPU
    $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime-gpu openvino-dev tensorflow  # GPU

Usage:
    $ python export.py --weights yolov5s.pt --include torchscript onnx openvino engine coreml tflite ...

Inference:
    $ python detect.py --weights yolov5s.pt                 # PyTorch
                                 yolov5s.torchscript        # TorchScript
                                 yolov5s.onnx               # ONNX Runtime or OpenCV DNN with --dnn
                                 yolov5s_openvino_model     # OpenVINO
                                 yolov5s.engine             # TensorRT
                                 yolov5s.mlmodel            # CoreML (macOS-only)
                                 yolov5s_saved_model        # TensorFlow SavedModel
                                 yolov5s.pb                 # TensorFlow GraphDef
                                 yolov5s.tflite             # TensorFlow Lite
                                 yolov5s_edgetpu.tflite     # TensorFlow Edge TPU
                                 yolov5s_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/yolov5s_web_model public/yolov5s_web_model
    $ npm start
"""

import argparse
import contextlib
import json
import os
import platform
import re
import subprocess
import sys
import time
import warnings
from pathlib import Path

import pandas as pd
import torch
from torch.utils.mobile_optimizer import optimize_for_mobile

FILE = Path(__file__).resolve()
ROOT = FILE.parents[0]  # YOLOv5 root directory
if str(ROOT) not in sys.path:
    sys.path.append(str(ROOT))  # add ROOT to PATH
if platform.system() != "Windows":
    ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative

from models.experimental import attempt_load
from models.yolo import ClassificationModel, Detect, DetectionModel, SegmentationModel
from utils.dataloaders import LoadImages
from utils.general import (
    LOGGER,
    Profile,
    check_dataset,
    check_img_size,
    check_requirements,
    check_version,
    check_yaml,
    colorstr,
    file_size,
    get_default_args,
    print_args,
    url2file,
    yaml_save,
)
from utils.torch_utils import select_device, smart_inference_mode

MACOS = platform.system() == "Darwin"  # macOS environment


class iOSModel(torch.nn.Module):
    def __init__(self, model, im):
        """Initializes an iOS compatible model with normalization based on image dimensions."""
        super().__init__()
        b, c, h, w = im.shape  # batch, channel, height, width
        self.model = model
        self.nc = model.nc  # number of classes
        if w == h:
            self.normalize = 1.0 / w
        else:
            self.normalize = torch.tensor([1.0 / w, 1.0 / h, 1.0 / w, 1.0 / h])  # broadcast (slower, smaller)
            # np = model(im)[0].shape[1]  # number of points
            # self.normalize = torch.tensor([1. / w, 1. / h, 1. / w, 1. / h]).expand(np, 4)  # explicit (faster, larger)

    def forward(self, x):
        """Runs forward pass on the input tensor, returning class confidences and normalized coordinates."""
        xywh, conf, cls = self.model(x)[0].squeeze().split((4, 1, self.nc), 1)
        return cls * conf, xywh * self.normalize  # confidence (3780, 80), coordinates (3780, 4)


def export_formats():
    """Returns a DataFrame of supported YOLOv5 model export formats and their properties."""
    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", False, False],
        ["TensorFlow.js", "tfjs", "_web_model", False, False],
        ["PaddlePaddle", "paddle", "_paddle_model", True, True],
    ]
    return pd.DataFrame(x, columns=["Format", "Argument", "Suffix", "CPU", "GPU"])


def try_export(inner_func):
    """Decorator @try_export for YOLOv5 model export functions that logs success/failure, time taken, and file size."""
    inner_args = get_default_args(inner_func)

    def outer_func(*args, **kwargs):
        """Logs success/failure and execution details of model export functions wrapped with @try_export decorator."""
        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


@try_export
def export_torchscript(model, im, file, optimize, prefix=colorstr("TorchScript:")):
    """Exports YOLOv5 model to TorchScript format, optionally optimized for mobile, with image shape and stride
    metadata.
    """
    LOGGER.info(f"\n{prefix} starting export with torch {torch.__version__}...")
    f = file.with_suffix(".torchscript")

    ts = torch.jit.trace(model, im, strict=False)
    d = {"shape": im.shape, "stride": int(max(model.stride)), "names": model.names}
    extra_files = {"config.txt": json.dumps(d)}  # torch._C.ExtraFilesMap()
    if optimize:  # https://pytorch.org/tutorials/recipes/mobile_interpreter.html
        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(model, im, file, opset, dynamic, simplify, prefix=colorstr("ONNX:")):
    """Exports a YOLOv5 model to ONNX format with dynamic axes and optional simplification."""
    check_requirements("onnx>=1.12.0")
    import onnx

    LOGGER.info(f"\n{prefix} starting export with onnx {onnx.__version__}...")
    f = str(file.with_suffix(".onnx"))

    output_names = ["output0", "output1"] if isinstance(model, SegmentationModel) else ["output0"]
    if dynamic:
        dynamic = {"images": {0: "batch", 2: "height", 3: "width"}}  # shape(1,3,640,640)
        if isinstance(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(model, DetectionModel):
            dynamic["output0"] = {0: "batch", 1: "anchors"}  # shape(1,25200,85)

    torch.onnx.export(
        model.cpu() if dynamic else model,  # --dynamic only compatible with cpu
        im.cpu() if dynamic else im,
        f,
        verbose=False,
        opset_version=opset,
        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

    # Metadata
    d = {"stride": int(max(model.stride)), "names": model.names}
    for k, v in d.items():
        meta = model_onnx.metadata_props.add()
        meta.key, meta.value = k, str(v)
    onnx.save(model_onnx, f)

    # Simplify
    if simplify:
        try:
            cuda = torch.cuda.is_available()
            check_requirements(("onnxruntime-gpu" if cuda else "onnxruntime", "onnx-simplifier>=0.4.1"))
            import onnxsim

            LOGGER.info(f"{prefix} simplifying with onnx-simplifier {onnxsim.__version__}...")
            model_onnx, check = onnxsim.simplify(model_onnx)
            assert check, "assert check failed"
            onnx.save(model_onnx, f)
        except Exception as e:
            LOGGER.info(f"{prefix} simplifier failure: {e}")
    return f, model_onnx


@try_export
def export_openvino(file, metadata, half, int8, data, prefix=colorstr("OpenVINO:")):
    """Exports a YOLOv5 model to OpenVINO format with optional FP16 and INT8 quantization; see https://pypi.org/project/openvino-dev/."""
    check_requirements("openvino-dev>=2023.0")  # 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(file).replace(file.suffix, f"_{'int8_' if int8 else ''}openvino_model{os.sep}")
    f_onnx = file.with_suffix(".onnx")
    f_ov = str(Path(f) / file.with_suffix(".xml").name)

    ov_model = mo.convert_model(f_onnx, model_name=file.stem, framework="onnx", compress_to_fp16=half)  # export

    if int8:
        check_requirements("nncf>=2.5.0")  # requires at least version 2.5.0 to use the post-training quantization
        import nncf
        import numpy as np

        from utils.dataloaders import create_dataloader

        def gen_dataloader(yaml_path, task="train", imgsz=640, workers=4):
            """Generates a DataLoader for model training or validation based on the given YAML dataset configuration."""
            data_yaml = check_yaml(yaml_path)
            data = check_dataset(data_yaml)
            dataloader = create_dataloader(
                data[task], imgsz=imgsz, batch_size=1, stride=32, pad=0.5, single_cls=False, rect=False, workers=workers
            )[0]
            return dataloader

        # noqa: F811

        def transform_fn(data_item):
            """
            Quantization transform function.

            Extracts and preprocess input data from dataloader item for quantization.
            Parameters:
               data_item: Tuple with data item produced by DataLoader during iteration
            Returns:
                input_tensor: Input data for quantization
            """
            assert data_item[0].dtype == torch.uint8, "input image must be uint8 for the quantization preprocessing"

            img = data_item[0].numpy().astype(np.float32)  # uint8 to fp16/32
            img /= 255.0  # 0 - 255 to 0.0 - 1.0
            return np.expand_dims(img, 0) if img.ndim == 3 else img

        ds = gen_dataloader(data)
        quantization_dataset = nncf.Dataset(ds, transform_fn)
        ov_model = nncf.quantize(ov_model, quantization_dataset, preset=nncf.QuantizationPreset.MIXED)

    ov.serialize(ov_model, f_ov)  # save
    yaml_save(Path(f) / file.with_suffix(".yaml").name, metadata)  # add metadata.yaml
    return f, None


@try_export
def export_paddle(model, im, file, metadata, prefix=colorstr("PaddlePaddle:")):
    """Exports a YOLOv5 model to PaddlePaddle format using X2Paddle, saving to `save_dir` and adding a metadata.yaml
    file.
    """
    check_requirements(("paddlepaddle", "x2paddle"))
    import x2paddle
    from x2paddle.convert import pytorch2paddle

    LOGGER.info(f"\n{prefix} starting export with X2Paddle {x2paddle.__version__}...")
    f = str(file).replace(".pt", f"_paddle_model{os.sep}")

    pytorch2paddle(module=model, save_dir=f, jit_type="trace", input_examples=[im])  # export
    yaml_save(Path(f) / file.with_suffix(".yaml").name, metadata)  # add metadata.yaml
    return f, None


@try_export
def export_coreml(model, im, file, int8, half, nms, prefix=colorstr("CoreML:")):
    """Exports YOLOv5 model to CoreML format with optional NMS, INT8, and FP16 support; requires coremltools."""
    check_requirements("coremltools")
    import coremltools as ct

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

    if nms:
        model = iOSModel(model, im)
    ts = torch.jit.trace(model, im, strict=False)  # TorchScript model
    ct_model = ct.convert(ts, inputs=[ct.ImageType("image", shape=im.shape, scale=1 / 255, bias=[0, 0, 0])])
    bits, mode = (8, "kmeans_lut") if int8 else (16, "linear") if half else (32, None)
    if bits < 32:
        if MACOS:  # quantization only supported on macOS
            with warnings.catch_warnings():
                warnings.filterwarnings("ignore", category=DeprecationWarning)  # suppress numpy==1.20 float warning
                ct_model = ct.models.neural_network.quantization_utils.quantize_weights(ct_model, bits, mode)
        else:
            print(f"{prefix} quantization only supported on macOS, skipping...")
    ct_model.save(f)
    return f, ct_model


@try_export
def export_engine(model, im, file, half, dynamic, simplify, workspace=4, verbose=False, prefix=colorstr("TensorRT:")):
    """
    Exports a YOLOv5 model to TensorRT engine format, requiring GPU and TensorRT>=7.0.0.

    https://developer.nvidia.com/tensorrt
    """
    assert im.device.type != "cpu", "export running on CPU but must be on GPU, i.e. `python export.py --device 0`"
    try:
        import tensorrt as trt
    except Exception:
        if platform.system() == "Linux":
            check_requirements("nvidia-tensorrt", cmds="-U --index-url https://pypi.ngc.nvidia.com")
        import tensorrt as trt

    if trt.__version__[0] == "7":  # TensorRT 7 handling https://github.com/ultralytics/yolov5/issues/6012
        grid = model.model[-1].anchor_grid
        model.model[-1].anchor_grid = [a[..., :1, :1, :] for a in grid]
        export_onnx(model, im, file, 12, dynamic, simplify)  # opset 12
        model.model[-1].anchor_grid = grid
    else:  # TensorRT >= 8
        check_version(trt.__version__, "8.0.0", hard=True)  # require tensorrt>=8.0.0
        export_onnx(model, im, file, 12, dynamic, simplify)  # opset 12
    onnx = file.with_suffix(".onnx")

    LOGGER.info(f"\n{prefix} starting export with TensorRT {trt.__version__}...")
    is_trt10 = int(trt.__version__.split(".")[0]) >= 10  # is TensorRT >= 10
    assert onnx.exists(), f"failed to export ONNX file: {onnx}"
    f = 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()
    if is_trt10:
        config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, workspace << 30)
    else:  # TensorRT versions 7, 8
        config.max_workspace_size = workspace * 1 << 30
    flag = 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
    network = builder.create_network(flag)
    parser = trt.OnnxParser(network, logger)
    if not parser.parse_from_file(str(onnx)):
        raise RuntimeError(f"failed to load ONNX file: {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 dynamic:
        if im.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, *im.shape[1:]), (max(1, im.shape[0] // 2), *im.shape[1:]), im.shape)
        config.add_optimization_profile(profile)

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

    build = builder.build_serialized_network if is_trt10 else builder.build_engine
    with build(network, config) as engine, open(f, "wb") as t:
        t.write(engine if is_trt10 else engine.serialize())
    return f, None


@try_export
def export_saved_model(
    model,
    im,
    file,
    dynamic,
    tf_nms=False,
    agnostic_nms=False,
    topk_per_class=100,
    topk_all=100,
    iou_thres=0.45,
    conf_thres=0.25,
    keras=False,
    prefix=colorstr("TensorFlow SavedModel:"),
):
    """Exports a YOLOv5 model to TensorFlow SavedModel format, supporting dynamic axes and non-maximum suppression
    (NMS).
    """
    # YOLOv5 TensorFlow SavedModel export
    try:
        import tensorflow as tf
    except Exception:
        check_requirements(f"tensorflow{'' if torch.cuda.is_available() else '-macos' if MACOS else '-cpu'}<=2.15.1")

        import tensorflow as tf
    from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2

    from models.tf import TFModel

    LOGGER.info(f"\n{prefix} starting export with tensorflow {tf.__version__}...")
    if tf.__version__ > "2.13.1":
        helper_url = "https://github.com/ultralytics/yolov5/issues/12489"
        LOGGER.info(
            f"WARNING ⚠️ using Tensorflow {tf.__version__} > 2.13.1 might cause issue when exporting the model to tflite {helper_url}"
        )  # handling issue https://github.com/ultralytics/yolov5/issues/12489
    f = str(file).replace(".pt", "_saved_model")
    batch_size, ch, *imgsz = list(im.shape)  # BCHW

    tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz)
    im = tf.zeros((batch_size, *imgsz, ch))  # BHWC order for TensorFlow
    _ = tf_model.predict(im, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)
    inputs = tf.keras.Input(shape=(*imgsz, ch), batch_size=None if dynamic else batch_size)
    outputs = tf_model.predict(inputs, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)
    keras_model = tf.keras.Model(inputs=inputs, outputs=outputs)
    keras_model.trainable = False
    keras_model.summary()
    if keras:
        keras_model.save(f, save_format="tf")
    else:
        spec = tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype)
        m = tf.function(lambda x: keras_model(x))  # full model
        m = m.get_concrete_function(spec)
        frozen_func = convert_variables_to_constants_v2(m)
        tfm = tf.Module()
        tfm.__call__ = tf.function(lambda x: frozen_func(x)[:4] if tf_nms else frozen_func(x), [spec])
        tfm.__call__(im)
        tf.saved_model.save(
            tfm,
            f,
            options=tf.saved_model.SaveOptions(experimental_custom_gradients=False)
            if check_version(tf.__version__, "2.6")
            else tf.saved_model.SaveOptions(),
        )
    return f, keras_model


@try_export
def export_pb(keras_model, file, prefix=colorstr("TensorFlow GraphDef:")):
    """Exports YOLOv5 model to TensorFlow GraphDef *.pb format; see https://github.com/leimao/Frozen_Graph_TensorFlow for details."""
    import tensorflow as tf
    from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2

    LOGGER.info(f"\n{prefix} starting export with tensorflow {tf.__version__}...")
    f = 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(
    keras_model, im, file, int8, per_tensor, data, nms, agnostic_nms, prefix=colorstr("TensorFlow Lite:")
):
    # YOLOv5 TensorFlow Lite export
    """Exports YOLOv5 model to TensorFlow Lite format with optional FP16, INT8, and NMS support."""
    import tensorflow as tf

    LOGGER.info(f"\n{prefix} starting export with tensorflow {tf.__version__}...")
    batch_size, ch, *imgsz = list(im.shape)  # BCHW
    f = str(file).replace(".pt", "-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 int8:
        from models.tf import representative_dataset_gen

        dataset = LoadImages(check_dataset(check_yaml(data))["train"], img_size=imgsz, auto=False)
        converter.representative_dataset = lambda: representative_dataset_gen(dataset, ncalib=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
        if per_tensor:
            converter._experimental_disable_per_channel = True
        f = str(file).replace(".pt", "-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(file, prefix=colorstr("Edge TPU:")):
    """
    Exports a YOLOv5 model to Edge TPU compatible TFLite format; requires Linux and Edge TPU compiler.

    https://coral.ai/docs/edgetpu/models-intro/
    """
    cmd = "edgetpu_compiler --version"
    help_url = "https://coral.ai/docs/edgetpu/compiler/"
    assert platform.system() == "Linux", f"export only supported on Linux. See {help_url}"
    if subprocess.run(f"{cmd} > /dev/null 2>&1", 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(file).replace(".pt", "-int8_edgetpu.tflite")  # Edge TPU model
    f_tfl = str(file).replace(".pt", "-int8.tflite")  # TFLite model

    subprocess.run(
        [
            "edgetpu_compiler",
            "-s",
            "-d",
            "-k",
            "10",
            "--out_dir",
            str(file.parent),
            f_tfl,
        ],
        check=True,
    )
    return f, None


@try_export
def export_tfjs(file, int8, prefix=colorstr("TensorFlow.js:")):
    """Exports a YOLOv5 model to TensorFlow.js format, optionally with uint8 quantization."""
    check_requirements("tensorflowjs")
    import tensorflowjs as tfjs

    LOGGER.info(f"\n{prefix} starting export with tensorflowjs {tfjs.__version__}...")
    f = str(file).replace(".pt", "_web_model")  # js dir
    f_pb = file.with_suffix(".pb")  # *.pb path
    f_json = f"{f}/model.json"  # *.json path

    args = [
        "tensorflowjs_converter",
        "--input_format=tf_frozen_model",
        "--quantize_uint8" if int8 else "",
        "--output_node_names=Identity,Identity_1,Identity_2,Identity_3",
        str(f_pb),
        f,
    ]
    subprocess.run([arg for arg in args if arg], check=True)

    json = Path(f_json).read_text()
    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"}}}',
            json,
        )
        j.write(subst)
    return f, None


def add_tflite_metadata(file, metadata, num_outputs):
    """
    Adds TFLite metadata to a model file, supporting multiple outputs, as specified by TensorFlow guidelines.

    https://www.tensorflow.org/lite/models/convert/metadata
    """
    with contextlib.suppress(ImportError):
        # check_requirements('tflite_support')
        from tflite_support import flatbuffers
        from tflite_support import metadata as _metadata
        from tflite_support import metadata_schema_py_generated as _metadata_fb

        tmp_file = Path("/tmp/meta.txt")
        with open(tmp_file, "w") as meta_f:
            meta_f.write(str(metadata))

        model_meta = _metadata_fb.ModelMetadataT()
        label_file = _metadata_fb.AssociatedFileT()
        label_file.name = tmp_file.name
        model_meta.associatedFiles = [label_file]

        subgraph = _metadata_fb.SubGraphMetadataT()
        subgraph.inputTensorMetadata = [_metadata_fb.TensorMetadataT()]
        subgraph.outputTensorMetadata = [_metadata_fb.TensorMetadataT()] * num_outputs
        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(file)
        populator.load_metadata_buffer(metadata_buf)
        populator.load_associated_files([str(tmp_file)])
        populator.populate()
        tmp_file.unlink()


def pipeline_coreml(model, im, file, names, y, prefix=colorstr("CoreML Pipeline:")):
    """Converts a PyTorch YOLOv5 model to CoreML format with NMS, handling different input/output shapes and saving the
    model.
    """
    import coremltools as ct
    from PIL import Image

    print(f"{prefix} starting pipeline with coremltools {ct.__version__}...")
    batch_size, ch, h, w = list(im.shape)  # BCHW
    t = time.time()

    # YOLOv5 Output shapes
    spec = model.get_spec()
    out0, out1 = iter(spec.description.output)
    if platform.system() == "Darwin":
        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, out1_shape = out[out0.name].shape, out[out1.name].shape
    else:  # linux and windows can not run model.predict(), get sizes from pytorch output y
        s = tuple(y[0].shape)
        out0_shape, out1_shape = (s[1], s[2] - 5), (s[1], 4)  # (3780, 80), (3780, 4)

    # Checks
    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.versionString = "https://github.com/ultralytics/yolov5"
    pipeline.spec.description.metadata.shortDescription = "https://github.com/ultralytics/yolov5"
    pipeline.spec.description.metadata.author = "[email protected]"
    pipeline.spec.description.metadata.license = "https://github.com/ultralytics/yolov5/blob/master/LICENSE"
    pipeline.spec.description.metadata.userDefined.update(
        {
            "classes": ",".join(names.values()),
            "iou_threshold": str(nms.iouThreshold),
            "confidence_threshold": str(nms.confidenceThreshold),
        }
    )

    # Save the model
    f = file.with_suffix(".mlmodel")  # filename
    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)"
    model.save(f)  # pipelined
    print(f"{prefix} pipeline success ({time.time() - t:.2f}s), saved as {f} ({file_size(f):.1f} MB)")


@smart_inference_mode()
def run(
    data=ROOT / "data/coco128.yaml",  # 'dataset.yaml path'
    weights=ROOT / "yolov5s.pt",  # weights path
    imgsz=(640, 640),  # image (height, width)
    batch_size=1,  # batch size
    device="cpu",  # cuda device, i.e. 0 or 0,1,2,3 or cpu
    include=("torchscript", "onnx"),  # include formats
    half=False,  # FP16 half-precision export
    inplace=False,  # set YOLOv5 Detect() inplace=True
    keras=False,  # use Keras
    optimize=False,  # TorchScript: optimize for mobile
    int8=False,  # CoreML/TF INT8 quantization
    per_tensor=False,  # TF per tensor quantization
    dynamic=False,  # ONNX/TF/TensorRT: dynamic axes
    simplify=False,  # ONNX: simplify model
    opset=12,  # ONNX: opset version
    verbose=False,  # TensorRT: verbose log
    workspace=4,  # TensorRT: workspace size (GB)
    nms=False,  # TF: add NMS to model
    agnostic_nms=False,  # TF: add agnostic NMS to model
    topk_per_class=100,  # TF.js NMS: topk per class to keep
    topk_all=100,  # TF.js NMS: topk for all classes to keep
    iou_thres=0.45,  # TF.js NMS: IoU threshold
    conf_thres=0.25,  # TF.js NMS: confidence threshold
):
    """Exports YOLOv5 model to specified formats including ONNX, TensorRT, CoreML, and TensorFlow; see https://github.com/ultralytics/yolov5."""
    t = time.time()
    include = [x.lower() for x in include]  # to lowercase
    fmts = tuple(export_formats()["Argument"][1:])  # --include arguments
    flags = [x in include for x in fmts]
    assert sum(flags) == len(include), f"ERROR: Invalid --include {include}, valid --include arguments are {fmts}"
    jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle = flags  # export booleans
    file = Path(url2file(weights) if str(weights).startswith(("http:/", "https:/")) else weights)  # PyTorch weights

    # Load PyTorch model
    device = select_device(device)
    if half:
        assert device.type != "cpu" or coreml, "--half only compatible with GPU export, i.e. use --device 0"
        assert not dynamic, "--half not compatible with --dynamic, i.e. use either --half or --dynamic but not both"
    model = attempt_load(weights, device=device, inplace=True, fuse=True)  # load FP32 model

    # Checks
    imgsz *= 2 if len(imgsz) == 1 else 1  # expand
    if optimize:
        assert device.type == "cpu", "--optimize not compatible with cuda devices, i.e. use --device cpu"

    # Input
    gs = int(max(model.stride))  # grid size (max stride)
    imgsz = [check_img_size(x, gs) for x in imgsz]  # verify img_size are gs-multiples
    im = torch.zeros(batch_size, 3, *imgsz).to(device)  # image size(1,3,320,192) BCHW iDetection

    # Update model
    model.eval()
    for k, m in model.named_modules():
        if isinstance(m, Detect):
            m.inplace = inplace
            m.dynamic = dynamic
            m.export = True

    for _ in range(2):
        y = model(im)  # dry runs
    if half and not coreml:
        im, model = im.half(), model.half()  # to FP16
    shape = tuple((y[0] if isinstance(y, tuple) else y).shape)  # model output shape
    metadata = {"stride": int(max(model.stride)), "names": model.names}  # model metadata
    LOGGER.info(f"\n{colorstr('PyTorch:')} starting from {file} with output shape {shape} ({file_size(file):.1f} MB)")

    # Exports
    f = [""] * len(fmts)  # exported filenames
    warnings.filterwarnings(action="ignore", category=torch.jit.TracerWarning)  # suppress TracerWarning
    if jit:  # TorchScript
        f[0], _ = export_torchscript(model, im, file, optimize)
    if engine:  # TensorRT required before ONNX
        f[1], _ = export_engine(model, im, file, half, dynamic, simplify, workspace, verbose)
    if onnx or xml:  # OpenVINO requires ONNX
        f[2], _ = export_onnx(model, im, file, opset, dynamic, simplify)
    if xml:  # OpenVINO
        f[3], _ = export_openvino(file, metadata, half, int8, data)
    if coreml:  # CoreML
        f[4], ct_model = export_coreml(model, im, file, int8, half, nms)
        if nms:
            pipeline_coreml(ct_model, im, file, model.names, y)
    if any((saved_model, pb, tflite, edgetpu, tfjs)):  # TensorFlow formats
        assert not tflite or not tfjs, "TFLite and TF.js models must be exported separately, please pass only one type."
        assert not isinstance(model, ClassificationModel), "ClassificationModel export to TF formats not yet supported."
        f[5], s_model = export_saved_model(
            model.cpu(),
            im,
            file,
            dynamic,
            tf_nms=nms or agnostic_nms or tfjs,
            agnostic_nms=agnostic_nms or tfjs,
            topk_per_class=topk_per_class,
            topk_all=topk_all,
            iou_thres=iou_thres,
            conf_thres=conf_thres,
            keras=keras,
        )
        if pb or tfjs:  # pb prerequisite to tfjs
            f[6], _ = export_pb(s_model, file)
        if tflite or edgetpu:
            f[7], _ = export_tflite(
                s_model, im, file, int8 or edgetpu, per_tensor, data=data, nms=nms, agnostic_nms=agnostic_nms
            )
            if edgetpu:
                f[8], _ = export_edgetpu(file)
            add_tflite_metadata(f[8] or f[7], metadata, num_outputs=len(s_model.outputs))
        if tfjs:
            f[9], _ = export_tfjs(file, int8)
    if paddle:  # PaddlePaddle
        f[10], _ = export_paddle(model, im, file, metadata)

    # Finish
    f = [str(x) for x in f if x]  # filter out '' and None
    if any(f):
        cls, det, seg = (isinstance(model, x) for x in (ClassificationModel, DetectionModel, SegmentationModel))  # type
        det &= not seg  # segmentation models inherit from SegmentationModel(DetectionModel)
        dir = Path("segment" if seg else "classify" if cls else "")
        h = "--half" if half else ""  # --half FP16 inference arg
        s = (
            "# WARNING ⚠️ ClassificationModel not yet supported for PyTorch Hub AutoShape inference"
            if cls
            else "# WARNING ⚠️ SegmentationModel not yet supported for PyTorch Hub AutoShape inference"
            if seg
            else ""
        )
        LOGGER.info(
            f'\nExport complete ({time.time() - t:.1f}s)'
            f"\nResults saved to {colorstr('bold', file.parent.resolve())}"
            f"\nDetect:          python {dir / ('detect.py' if det else 'predict.py')} --weights {f[-1]} {h}"
            f"\nValidate:        python {dir / 'val.py'} --weights {f[-1]} {h}"
            f"\nPyTorch Hub:     model = torch.hub.load('ultralytics/yolov5', 'custom', '{f[-1]}')  {s}"
            f'\nVisualize:       https://netron.app'
        )
    return f  # return list of exported files/dirs


def parse_opt(known=False):
    """Parses command-line arguments for YOLOv5 model export configurations, returning the parsed options."""
    parser = argparse.ArgumentParser()
    parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="dataset.yaml path")
    parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov5s.pt", help="model.pt path(s)")
    parser.add_argument("--imgsz", "--img", "--img-size", nargs="+", type=int, default=[640, 640], help="image (h, w)")
    parser.add_argument("--batch-size", type=int, default=1, help="batch size")
    parser.add_argument("--device", default="cpu", help="cuda device, i.e. 0 or 0,1,2,3 or cpu")
    parser.add_argument("--half", action="store_true", help="FP16 half-precision export")
    parser.add_argument("--inplace", action="store_true", help="set YOLOv5 Detect() inplace=True")
    parser.add_argument("--keras", action="store_true", help="TF: use Keras")
    parser.add_argument("--optimize", action="store_true", help="TorchScript: optimize for mobile")
    parser.add_argument("--int8", action="store_true", help="CoreML/TF/OpenVINO INT8 quantization")
    parser.add_argument("--per-tensor", action="store_true", help="TF per-tensor quantization")
    parser.add_argument("--dynamic", action="store_true", help="ONNX/TF/TensorRT: dynamic axes")
    parser.add_argument("--simplify", action="store_true", help="ONNX: simplify model")
    parser.add_argument("--opset", type=int, default=17, help="ONNX: opset version")
    parser.add_argument("--verbose", action="store_true", help="TensorRT: verbose log")
    parser.add_argument("--workspace", type=int, default=4, help="TensorRT: workspace size (GB)")
    parser.add_argument("--nms", action="store_true", help="TF: add NMS to model")
    parser.add_argument("--agnostic-nms", action="store_true", help="TF: add agnostic NMS to model")
    parser.add_argument("--topk-per-class", type=int, default=100, help="TF.js NMS: topk per class to keep")
    parser.add_argument("--topk-all", type=int, default=100, help="TF.js NMS: topk for all classes to keep")
    parser.add_argument("--iou-thres", type=float, default=0.45, help="TF.js NMS: IoU threshold")
    parser.add_argument("--conf-thres", type=float, default=0.25, help="TF.js NMS: confidence threshold")
    parser.add_argument(
        "--include",
        nargs="+",
        default=["torchscript"],
        help="torchscript, onnx, openvino, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle",
    )
    opt = parser.parse_known_args()[0] if known else parser.parse_args()
    print_args(vars(opt))
    return opt


def main(opt):
    """Executes the YOLOv5 model inference or export with specified weights and options."""
    for opt.weights in opt.weights if isinstance(opt.weights, list) else [opt.weights]:
        run(**vars(opt))


if __name__ == "__main__":
    opt = parse_opt()
    main(opt)