export-nms.py

# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# NOTE: this file was checked out from https://github.com/mshamash/yolov5/tree/fix/coreml_export_nms_layer
# NOTE: full credit goes to https://github.com/mshamash and https://github.com/pocketpixels
# NOTE: a PR for adding NMS support for Core ML export is pending merge: https://github.com/ultralytics/yolov5/pull/7263
"""
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/

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 path/to/export.py --weights yolov5s.pt --include torchscript onnx openvino engine coreml tflite ...

Inference:
    $ python path/to/detect.py --weights yolov5s.pt                 # PyTorch
                                         yolov5s.torchscript        # TorchScript
                                         yolov5s.onnx               # ONNX Runtime or OpenCV DNN with --dnn
                                         yolov5s.xml                # 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

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
"""

from utils.torch_utils import select_device
from utils.general import (LOGGER, check_dataset, check_img_size, check_requirements, check_version, colorstr,
                           file_size, print_args, url2file)
from utils.dataloaders import LoadImages
from models.yolo import Detect
from models.experimental import attempt_load
import argparse
import json
import os
import platform
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


def export_formats():
    # YOLOv5 export formats
    x = [
        ['PyTorch', '-', '.pt', True],
        ['TorchScript', 'torchscript', '.torchscript', True],
        ['ONNX', 'onnx', '.onnx', True],
        ['OpenVINO', 'openvino', '_openvino_model', False],
        ['TensorRT', 'engine', '.engine', True],
        ['CoreML', 'coreml', '.mlmodel', False],
        ['TensorFlow SavedModel', 'saved_model', '_saved_model', True],
        ['TensorFlow GraphDef', 'pb', '.pb', True],
        ['TensorFlow Lite', 'tflite', '.tflite', False],
        ['TensorFlow Edge TPU', 'edgetpu', '_edgetpu.tflite', False],
        ['TensorFlow.js', 'tfjs', '_web_model', False], ]
    return pd.DataFrame(x, columns=['Format', 'Argument', 'Suffix', 'GPU'])


def export_torchscript(model, im, file, optimize, prefix=colorstr('TorchScript:')):
    # YOLOv5 TorchScript model export
    try:
        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)

        LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
        return f
    except Exception as e:
        LOGGER.info(f'{prefix} export failure: {e}')


def export_onnx(model, im, file, opset, train, dynamic, simplify, prefix=colorstr('ONNX:')):
    # YOLOv5 ONNX export
    try:
        check_requirements(('onnx',))
        import onnx

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

        torch.onnx.export(
            model,
            im,
            f,
            verbose=False,
            opset_version=opset,
            training=torch.onnx.TrainingMode.TRAINING if train else torch.onnx.TrainingMode.EVAL,
            do_constant_folding=not train,
            input_names=['images'],
            output_names=['output'],
            dynamic_axes={
                'images': {
                    0: 'batch',
                    2: 'height',
                    3: 'width'},  # shape(1,3,640,640)
                'output': {
                    0: 'batch',
                    1: 'anchors'}  # shape(1,25200,85)
            } if dynamic else 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:
                check_requirements(('onnx-simplifier',))
                import onnxsim

                LOGGER.info(f'{prefix} simplifying with onnx-simplifier {onnxsim.__version__}...')
                model_onnx, check = onnxsim.simplify(model_onnx,
                                                     dynamic_input_shape=dynamic,
                                                     input_shapes={'images': list(im.shape)} if dynamic else None)
                assert check, 'assert check failed'
                onnx.save(model_onnx, f)
            except Exception as e:
                LOGGER.info(f'{prefix} simplifier failure: {e}')
        LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
        return f
    except Exception as e:
        LOGGER.info(f'{prefix} export failure: {e}')


def export_openvino(model, im, file, prefix=colorstr('OpenVINO:')):
    # YOLOv5 OpenVINO export
    try:
        check_requirements(('openvino-dev',))  # requires openvino-dev: https://pypi.org/project/openvino-dev/
        import openvino.inference_engine as ie

        LOGGER.info(f'\n{prefix} starting export with openvino {ie.__version__}...')
        f = str(file).replace('.pt', '_openvino_model' + os.sep)

        cmd = f"mo --input_model {file.with_suffix('.onnx')} --output_dir {f}"
        subprocess.check_output(cmd, shell=True)

        LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
        return f
    except Exception as e:
        LOGGER.info(f'\n{prefix} export failure: {e}')


class CoreMLExportModel(torch.nn.Module):

    def __init__(self, base_model, img_size):
        super().__init__()
        self.base_model = base_model
        self.img_size = img_size

    def forward(self, x):
        x = self.base_model(x)[0]
        x = x.squeeze(0)
        # Convert box coords to normalized coordinates [0 ... 1]
        w = self.img_size[0]
        h = self.img_size[1]
        objectness = x[:, 4:5]
        class_probs = x[:, 5:] * objectness
        boxes = x[:, :4] * torch.tensor([1. / w, 1. / h, 1. / w, 1. / h])
        return class_probs, boxes


def export_coreml(model, im, file, num_boxes, num_classes, labels, conf_thres, iou_thres, prefix=colorstr('CoreML:')):
    # YOLOv5 CoreML export
    try:
        check_requirements(('coremltools',))
        import coremltools as ct

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

        export_model = CoreMLExportModel(model, img_size=opt.imgsz)

        ts = torch.jit.trace(export_model, im, strict=False)  # TorchScript model
        orig_model = ct.convert(ts, inputs=[ct.ImageType('image', shape=im.shape, scale=1 / 255, bias=[0, 0, 0])])

        spec = orig_model.get_spec()
        old_box_output_name = spec.description.output[1].name
        old_scores_output_name = spec.description.output[0].name
        ct.utils.rename_feature(spec, old_scores_output_name, "raw_confidence")
        ct.utils.rename_feature(spec, old_box_output_name, "raw_coordinates")
        spec.description.output[0].type.multiArrayType.shape.extend([num_boxes, num_classes])
        spec.description.output[1].type.multiArrayType.shape.extend([num_boxes, 4])
        spec.description.output[0].type.multiArrayType.dataType = ct.proto.FeatureTypes_pb2.ArrayFeatureType.DOUBLE
        spec.description.output[1].type.multiArrayType.dataType = ct.proto.FeatureTypes_pb2.ArrayFeatureType.DOUBLE

        yolo_model = ct.models.MLModel(spec)

        # Build Non Maximum Suppression model
        nms_spec = ct.proto.Model_pb2.Model()
        nms_spec.specificationVersion = 3

        for i in range(2):
            decoder_output = 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 = [num_classes, 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 = "raw_confidence"
        nms.coordinatesInputFeatureName = "raw_coordinates"
        nms.confidenceOutputFeatureName = "confidence"
        nms.coordinatesOutputFeatureName = "coordinates"
        nms.iouThresholdInputFeatureName = "iouThreshold"
        nms.confidenceThresholdInputFeatureName = "confidenceThreshold"

        nms.iouThreshold = iou_thres
        nms.confidenceThreshold = conf_thres
        nms.pickTop.perClass = False
        nms.stringClassLabels.vector.extend(labels)

        nms_model = ct.models.MLModel(nms_spec)

        # Assembling a pipeline model from the two models
        input_features = [("image", ct.models.datatypes.Array(3, 300, 300)),
                          ("iouThreshold", ct.models.datatypes.Double()),
                          ("confidenceThreshold", ct.models.datatypes.Double())]

        output_features = ["confidence", "coordinates"]

        pipeline = ct.models.pipeline.Pipeline(input_features, output_features)

        pipeline.add_model(yolo_model)
        pipeline.add_model(nms_model)

        # The "image" input should really be an image, not a multi-array
        pipeline.spec.description.input[0].ParseFromString(spec.description.input[0].SerializeToString())

        # Copy the declarations of the "confidence" and "coordinates" outputs
        # The Pipeline makes these strings by default
        pipeline.spec.description.output[0].ParseFromString(nms_spec.description.output[0].SerializeToString())
        pipeline.spec.description.output[1].ParseFromString(nms_spec.description.output[1].SerializeToString())

        # Add descriptions to the inputs and outputs
        pipeline.spec.description.input[1].shortDescription = "(optional) IOU Threshold override"
        pipeline.spec.description.input[2].shortDescription = "(optional) Confidence Threshold override"
        pipeline.spec.description.output[0].shortDescription = "Boxes Class confidence"
        pipeline.spec.description.output[1].shortDescription = "Boxes [x, y, width, height] (normalized to [0...1])"

        # Add metadata to the model
        pipeline.spec.description.metadata.shortDescription = "YOLOv5 object detector"
        pipeline.spec.description.metadata.author = "Ultralytics"

        # Add the default threshold values and list of class labels
        user_defined_metadata = {
            "iou_threshold": str(iou_thres),
            "confidence_threshold": str(conf_thres),
            "classes": ", ".join(labels)}
        pipeline.spec.description.metadata.userDefined.update(user_defined_metadata)

        # Don't forget this or Core ML might attempt to run the model on an unsupported operating system version!
        pipeline.spec.specificationVersion = 3

        ct_model = ct.models.MLModel(pipeline.spec)

        f = str(file).replace('.pt', '.mlmodel')
        ct_model.save(f)

        LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
        return ct_model, f
    except Exception as e:
        LOGGER.info(f'\n{prefix} export failure: {e}')
        return None, None


def export_engine(model, im, file, train, half, simplify, workspace=4, verbose=False, prefix=colorstr('TensorRT:')):
    # YOLOv5 TensorRT export https://developer.nvidia.com/tensorrt
    try:
        check_requirements(('tensorrt',))
        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, train, False, 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, 13, train, False, simplify)  # opset 13
        onnx = file.with_suffix('.onnx')

        LOGGER.info(f'\n{prefix} starting export with TensorRT {trt.__version__}...')
        assert im.device.type != 'cpu', 'export running on CPU but must be on GPU, i.e. `python export.py --device 0`'
        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()
        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(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)]
        LOGGER.info(f'{prefix} Network Description:')
        for inp in inputs:
            LOGGER.info(f'{prefix}\tinput "{inp.name}" with shape {inp.shape} and dtype {inp.dtype}')
        for out in outputs:
            LOGGER.info(f'{prefix}\toutput "{out.name}" with shape {out.shape} and dtype {out.dtype}')

        LOGGER.info(f'{prefix} building FP{16 if builder.platform_has_fast_fp16 else 32} engine in {f}')
        if builder.platform_has_fast_fp16:
            config.set_flag(trt.BuilderFlag.FP16)
        with builder.build_engine(network, config) as engine, open(f, 'wb') as t:
            t.write(engine.serialize())
        LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
        return f
    except Exception as e:
        LOGGER.info(f'\n{prefix} export failure: {e}')


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:')):
    # YOLOv5 TensorFlow SavedModel export
    try:
        import tensorflow as tf
        from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2

        from models.tf import TFDetect, TFModel

        LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
        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)[0], [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())
        LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
        return keras_model, f
    except Exception as e:
        LOGGER.info(f'\n{prefix} export failure: {e}')
        return None, None


def export_pb(keras_model, im, file, prefix=colorstr('TensorFlow GraphDef:')):
    # YOLOv5 TensorFlow GraphDef *.pb export https://github.com/leimao/Frozen_Graph_TensorFlow
    try:
        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)

        LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
        return f
    except Exception as e:
        LOGGER.info(f'\n{prefix} export failure: {e}')


def export_tflite(keras_model, im, file, int8, data, nms, agnostic_nms, prefix=colorstr('TensorFlow Lite:')):
    # YOLOv5 TensorFlow Lite export
    try:
        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(data)['train'], img_size=imgsz, auto=False)  # representative data
            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
            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)
        LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
        return f
    except Exception as e:
        LOGGER.info(f'\n{prefix} export failure: {e}')


def export_edgetpu(keras_model, im, file, prefix=colorstr('Edge TPU:')):
    # YOLOv5 Edge TPU export https://coral.ai/docs/edgetpu/models-intro/
    try:
        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(cmd + ' >/dev/null', 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

        cmd = f"edgetpu_compiler -s {f_tfl}"
        subprocess.run(cmd, shell=True, check=True)

        LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
        return f
    except Exception as e:
        LOGGER.info(f'\n{prefix} export failure: {e}')


def export_tfjs(keras_model, im, file, prefix=colorstr('TensorFlow.js:')):
    # YOLOv5 TensorFlow.js export
    try:
        check_requirements(('tensorflowjs',))
        import re

        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 + '/model.json'  # *.json path

        cmd = f'tensorflowjs_converter --input_format=tf_frozen_model ' \
              f'--output_node_names="Identity,Identity_1,Identity_2,Identity_3" {f_pb} {f}'
        subprocess.run(cmd, shell=True)

        with open(f_json) as j:
            json = j.read()
        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)

        LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
        return f
    except Exception as e:
        LOGGER.info(f'\n{prefix} export failure: {e}')


@torch.no_grad()
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
        train=False,  # model.train() mode
        optimize=False,  # TorchScript: optimize for mobile
        int8=False,  # CoreML/TF INT8 quantization
        dynamic=False,  # ONNX/TF: 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
):
    t = time.time()
    include = [x.lower() for x in include]  # to lowercase
    formats = tuple(export_formats()['Argument'][1:])  # --include arguments
    flags = [x in include for x in formats]
    assert sum(flags) == len(include), f'ERROR: Invalid --include {include}, valid --include arguments are {formats}'
    jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs = flags  # export booleans
    file = Path(url2file(weights) if str(weights).startswith(('http:/', 'https:/')) else weights)  # PyTorch weights

    # Load PyTorch model
    device = select_device(device)
    assert not (device.type == 'cpu' and half), '--half only compatible with GPU export, i.e. use --device 0'
    model = attempt_load(weights, device=device, inplace=True, fuse=True)  # load FP32 model
    nc, names = model.nc, model.names  # number of classes, class names

    # Checks
    imgsz *= 2 if len(imgsz) == 1 else 1  # expand
    opset = 12 if ('openvino' in include) else opset  # OpenVINO requires opset <= 12
    assert nc == len(names), f'Model class count {nc} != len(names) {len(names)}'

    # 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
    if half:
        im, model = im.half(), model.half()  # to FP16
    model.train() if train else model.eval()  # training mode = no Detect() layer grid construction
    for k, m in model.named_modules():
        if isinstance(m, Detect):
            m.inplace = inplace
            m.onnx_dynamic = dynamic
            m.export = True

    for _ in range(2):
        y = model(im)  # dry runs
    shape = tuple(y[0].shape)  # model output shape
    LOGGER.info(f"\n{colorstr('PyTorch:')} starting from {file} with output shape {shape} ({file_size(file):.1f} MB)")

    # Exports
    f = [''] * 10  # exported filenames
    warnings.filterwarnings(action='ignore', category=torch.jit.TracerWarning)  # suppress TracerWarning
    if jit:
        f[0] = export_torchscript(model, im, file, optimize)
    if engine:  # TensorRT required before ONNX
        f[1] = export_engine(model, im, file, train, half, simplify, workspace, verbose)
    if onnx or xml:  # OpenVINO requires ONNX
        f[2] = export_onnx(model, im, file, opset, train, dynamic, simplify)
    if xml:  # OpenVINO
        f[3] = export_openvino(model, im, file)
    if coreml:
        nb = shape[1]
        _, f[4] = export_coreml(model, im, file, nb, nc, names.values(), conf_thres, iou_thres)

    # TensorFlow Exports
    if any((saved_model, pb, tflite, edgetpu, tfjs)):
        if int8 or edgetpu:  # TFLite --int8 bug https://github.com/ultralytics/yolov5/issues/5707
            check_requirements(('flatbuffers==1.12',))  # required before `import tensorflow`
        assert not (tflite and tfjs), 'TFLite and TF.js models must be exported separately, please pass only one type.'
        model, f[5] = 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,
                                         conf_thres=conf_thres,
                                         iou_thres=iou_thres)  # keras model
        if pb or tfjs:  # pb prerequisite to tfjs
            f[6] = export_pb(model, im, file)
        if tflite or edgetpu:
            f[7] = export_tflite(model, im, file, int8=int8 or edgetpu, data=data, nms=nms, agnostic_nms=agnostic_nms)
        if edgetpu:
            f[8] = export_edgetpu(model, im, file)
        if tfjs:
            f[9] = export_tfjs(model, im, file)

    # Finish
    f = [str(x) for x in f if x]  # filter out '' and None
    if any(f):
        LOGGER.info(f'\nExport complete ({time.time() - t:.2f}s)'
                    f"\nResults saved to {colorstr('bold', file.parent.resolve())}"
                    f"\nDetect:          python detect.py --weights {f[-1]}"
                    f"\nPyTorch Hub:     model = torch.hub.load('ultralytics/yolov5', 'custom', '{f[-1]}')"
                    f"\nValidate:        python val.py --weights {f[-1]}"
                    f"\nVisualize:       https://netron.app")
    return f  # return list of exported files/dirs


def parse_opt():
    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('--train', action='store_true', help='model.train() mode')
    parser.add_argument('--optimize', action='store_true', help='TorchScript: optimize for mobile')
    parser.add_argument('--int8', action='store_true', help='CoreML/TF INT8 quantization')
    parser.add_argument('--dynamic', action='store_true', help='ONNX/TF: dynamic axes')
    parser.add_argument('--simplify', action='store_true', help='ONNX: simplify model')
    parser.add_argument('--opset', type=int, default=12, 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', 'onnx'],
                        help='torchscript, onnx, openvino, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs')
    opt = parser.parse_args()
    print_args(vars(opt))
    return opt


def main(opt):
    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)