source: https://chowdera.com/2022/01/202201030414249529.html

Yolov5Conversion.py

# yolov5 to coreml with additiona layer definition

import torch
import coremltools as ct
from pathlib import Path

# Add silu function for yolov5s v4 model: https://github.com/apple/coremltools/issues/1099
from coremltools.converters.mil import Builder as mb
from coremltools.converters.mil import register_torch_op
from coremltools.converters.mil.frontend.torch.ops import _get_inputs

# 自定义激活函数，因为在coremltools中不支持此操作
@register_torch_op
def silu(context, node):
    inputs = _get_inputs(context, node, expected=1)
    x = inputs[0]
    y = mb.sigmoid(x=x)
    z = mb.mul(x=x, y=y, name=node.name)
    context.add(z)

# 特征图大小，默认有三组，因为有三个下采样的步长
def featureMapDimensions(imgSize, strides):
    return [imgSize // stride for stride in strides]


def make_grid(nx, ny):
    yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)])
    return torch.stack((xv, yv), 2).view((ny, nx, 2)).float()

# 导出torchscript文件
def exportTorchscript(model, sampleInput, checkInputs, fileName):
    '''
    Traces a pytorch model and produces a TorchScript
    '''
    try:
        print(f'Starting TorchScript export with torch {torch.__version__}')
        ts = torch.jit.trace(model, sampleInput, check_inputs=checkInputs)
        ts.save(fileName)
        print(f'TorchScript export success, saved as {fileName}')
        return ts
    except Exception as e:
        print(f'TorchScript export failure: {e}')

# 获得coremltools可以操作的spec
def convertToCoremlSpec(torchScript, sampleInput):
    '''
    Converts a torchscript to a coreml model
    '''
    try:
        print(f'Starting CoreML conversion with coremltoola {ct.__version__}')
        nnSpec = ct.convert(torchScript, inputs=[ct.ImageType(
            name='image', shape=sampleInput.shape, scale=1 / 255.0, bias=[0, 0, 0])]).get_spec()

        print(f'CoreML conversion success')
    except Exception as e:
        print(f'CoreML conversion failure: {e}')
        return
    return nnSpec

# 设置输出的shape和数据类型
def addOutputMetaData(nnSpec,outputSize,featureMapDimensionss):
    '''
    Adds the correct output shapes and data types to the coreml model
    '''
    for i, featureMapDimension in enumerate(featureMapDimensionss):
        nnSpec.description.output[i].type.multiArrayType.shape.append(1)
        nnSpec.description.output[i].type.multiArrayType.shape.append(3)
        nnSpec.description.output[i].type.multiArrayType.shape.append(featureMapDimension)
        nnSpec.description.output[i].type.multiArrayType.shape.append(featureMapDimension)
        # pc, bx, by, bh, bw, c (no of class class labels)
        nnSpec.description.output[i].type.multiArrayType.shape.append(outputSize)
        nnSpec.description.output[i].type.multiArrayType.dataType = ct.proto.FeatureTypes_pb2.ArrayFeatureType.DOUBLE

# 添加输出层
def addExportLayerToCoreml(builder, numberOfClassLabels, imgSize, featureMapDimensionss, anchorGrid):
    '''
    Adds the yolov5 export layer to the coreml model
    '''
    outputNames = [output.name for output in builder.spec.description.output]

    for i, outputName in enumerate(outputNames):
        # formulas: https://github.com/ultralytics/yolov5/issues/471
        builder.add_activation(name=f"sigmoid_{outputName}", non_linearity="SIGMOID",
                               input_name=outputName, output_name=f"{outputName}_sigmoid")

        ### Coordinates calculation ###
        # input (1, 3, nC, nC, cls+5), output (1, 3, nC, nC, 2) -> nC = 640 / strides[i]   cls为总的类别数量
        builder.add_slice(name=f"slice_coordinates_xy_{outputName}", input_name=f"{outputName}_sigmoid",
                          output_name=f"{outputName}_sliced_coordinates_xy", axis="width", start_index=0, end_index=2)
        # x,y * 2
        builder.add_elementwise(name=f"multiply_xy_by_two_{outputName}", input_names=[
                                f"{outputName}_sliced_coordinates_xy"], output_name=f"{outputName}_multiplied_xy_by_two", mode="MULTIPLY", alpha=2)
        # x,y * 2 - 0.5
        builder.add_elementwise(name=f"subtract_0_5_from_xy_{outputName}", input_names=[
                                f"{outputName}_multiplied_xy_by_two"], output_name=f"{outputName}_subtracted_0_5_from_xy", mode="ADD", alpha=-0.5)
        grid = make_grid(featureMapDimensionss[i], featureMapDimensionss[i]).numpy()
        # x,y * 2 - 0.5 + grid[i]
        builder.add_bias(name=f"add_grid_from_xy_{outputName}", input_name=f"{outputName}_subtracted_0_5_from_xy",
                         output_name=f"{outputName}_added_grid_xy", b=grid, shape_bias=grid.shape)
        # (x,y * 2 - 0.5 + grid[i]) * stride[i]
        builder.add_elementwise(name=f"multiply_xy_by_stride_{outputName}", input_names=[
                                f"{outputName}_added_grid_xy"], output_name=f"{outputName}_calculated_xy", mode="MULTIPLY", alpha=strides[i])

        # input (1, 3, nC, nC, cls+5), output (1, 3, nC, nC, 2)
        builder.add_slice(name=f"slice_coordinates_wh_{outputName}", input_name=f"{outputName}_sigmoid",
                          output_name=f"{outputName}_sliced_coordinates_wh", axis="width", start_index=2, end_index=4)
        # w,h * 2
        builder.add_elementwise(name=f"multiply_wh_by_two_{outputName}", input_names=[
                                f"{outputName}_sliced_coordinates_wh"], output_name=f"{outputName}_multiplied_wh_by_two", mode="MULTIPLY", alpha=2)
        # (w,h * 2) ** 2
        builder.add_unary(name=f"power_wh_{outputName}", input_name=f"{outputName}_multiplied_wh_by_two",
                          output_name=f"{outputName}_power_wh", mode="power", alpha=2)
        # (w,h * 2) ** 2 * anchor_grid[i]
        anchor = anchorGrid[i].expand(-1, featureMapDimensionss[i],
                                      featureMapDimensionss[i], -1).numpy()
        builder.add_load_constant_nd(
            name=f"anchors_{outputName}", output_name=f"{outputName}_anchors", constant_value=anchor, shape=anchor.shape)
        builder.add_elementwise(name=f"multiply_wh_with_achors_{outputName}", input_names=[
                                f"{outputName}_power_wh", f"{outputName}_anchors"], output_name=f"{outputName}_calculated_wh", mode="MULTIPLY")

        builder.add_concat_nd(name=f"concat_coordinates_{outputName}", input_names=[
                              f"{outputName}_calculated_xy", f"{outputName}_calculated_wh"], output_name=f"{outputName}_raw_coordinates", axis=-1)
        builder.add_scale(name=f"normalize_coordinates_{outputName}", input_name=f"{outputName}_raw_coordinates",
                          output_name=f"{outputName}_raw_normalized_coordinates", W=torch.tensor([1 / imgSize]).numpy(), b=0, has_bias=False)

        ### Confidence calculation ###
        builder.add_slice(name=f"slice_object_confidence_{outputName}", input_name=f"{outputName}_sigmoid",
                          output_name=f"{outputName}_object_confidence", axis="width", start_index=4, end_index=5)
        builder.add_slice(name=f"slice_label_confidence_{outputName}", input_name=f"{outputName}_sigmoid",
                          output_name=f"{outputName}_label_confidence", axis="width", start_index=5, end_index=0)
        # confidence = object_confidence * label_confidence
        builder.add_multiply_broadcastable(name=f"multiply_object_label_confidence_{outputName}", input_names=[
                                           f"{outputName}_label_confidence", f"{outputName}_object_confidence"], output_name=f"{outputName}_raw_confidence")

        # input: (1, 3, nC, nC, cls+5), output: (3 * nc^2, cls+5)
        builder.add_flatten_to_2d(
            name=f"flatten_confidence_{outputName}", input_name=f"{outputName}_raw_confidence", output_name=f"{outputName}_flatten_raw_confidence", axis=-1)
        builder.add_flatten_to_2d(
            name=f"flatten_coordinates_{outputName}", input_name=f"{outputName}_raw_normalized_coordinates", output_name=f"{outputName}_flatten_raw_coordinates", axis=-1)

    builder.add_concat_nd(name="concat_confidence", input_names=[
                          f"{outputName}_flatten_raw_confidence" for outputName in outputNames], output_name="raw_confidence", axis=-2)
    builder.add_concat_nd(name="concat_coordinates", input_names=[
                          f"{outputName}_flatten_raw_coordinates" for outputName in outputNames], output_name="raw_coordinates", axis=-2)

    builder.set_output(output_names=["raw_confidence", "raw_coordinates"], output_dims=[
                       (int(3*((imgSize/strides[0])**2+(imgSize/strides[1])**2+(imgSize/strides[2])**2)),numberOfClassLabels),
        (int(3*((imgSize/strides[0])**2+(imgSize/strides[1])**2+(imgSize/strides[2])**2)), 4)])

#　添加设置nms
def createNmsModelSpec(nnSpec, numberOfClassLabels, classLabels):
    '''
    Create a coreml model with nms to filter the results of the model
    '''
    nmsSpec = ct.proto.Model_pb2.Model()
    nmsSpec.specificationVersion = 4

    # Define input and outputs of the model
    for i in range(2):
        nnOutput = nnSpec.description.output[i].SerializeToString()

        nmsSpec.description.input.add()
        nmsSpec.description.input[i].ParseFromString(nnOutput)

        nmsSpec.description.output.add()
        nmsSpec.description.output[i].ParseFromString(nnOutput)

    nmsSpec.description.output[0].name = "confidence"
    nmsSpec.description.output[1].name = "coordinates"

    # Define output shape of the model
    outputSizes = [numberOfClassLabels, 4]
    for i in range(len(outputSizes)):
        maType = nmsSpec.description.output[i].type.multiArrayType
        # First dimension of both output is the number of boxes, which should be flexible
        maType.shapeRange.sizeRanges.add()
        maType.shapeRange.sizeRanges[0].lowerBound = 0
        maType.shapeRange.sizeRanges[0].upperBound = -1
        # Second dimension is fixed, for "confidence" it's the number of classes, for coordinates it's position (x, y) and size (w, h)
        maType.shapeRange.sizeRanges.add()
        maType.shapeRange.sizeRanges[1].lowerBound = outputSizes[i]
        maType.shapeRange.sizeRanges[1].upperBound = outputSizes[i]
        del maType.shape[:]

    # Define the model type non maximum supression
    nms = nmsSpec.nonMaximumSuppression
    nms.confidenceInputFeatureName = "raw_confidence"
    nms.coordinatesInputFeatureName = "raw_coordinates"
    nms.confidenceOutputFeatureName = "confidence"
    nms.coordinatesOutputFeatureName = "coordinates"
    nms.iouThresholdInputFeatureName = "iouThreshold"
    nms.confidenceThresholdInputFeatureName = "confidenceThreshold"
    # Some good default values for the two additional inputs, can be overwritten when using the model
    nms.iouThreshold = 0.6
    nms.confidenceThreshold = 0.4
    nms.stringClassLabels.vector.extend(classLabels)

    return nmsSpec


# 导出mlmodel
def combineModelsAndExport(builderSpec, nmsSpec, fileName, imgSize,quantize):
    '''
    Combines the coreml model with export logic and the nms to one final model. Optionally save with different quantization (32, 16, 8) (Works only if on Mac Os)
    '''
    try:
        print(f'Combine CoreMl model with nms and export model')
        # Combine models to a single one
        pipeline = ct.models.pipeline.Pipeline(input_features=[("image", ct.models.datatypes.Array(3, imgSize, imgSize)),
                                                               ("iouThreshold", ct.models.datatypes.Double(
                                                               )),
                                                               ("confidenceThreshold", ct.models.datatypes.Double())], output_features=["confidence", "coordinates"])

        # Required version (>= ios13) in order for mns to work
        pipeline.spec.specificationVersion = 4
        pipeline.add_model(builderSpec)
        pipeline.add_model(nmsSpec)

        pipeline.spec.description.input[0].ParseFromString(builderSpec.description.input[0].SerializeToString())
        pipeline.spec.description.output[0].ParseFromString(nmsSpec.description.output[0].SerializeToString())
        pipeline.spec.description.output[1].ParseFromString(nmsSpec.description.output[1].SerializeToString())

        # Metadata for the model‚
        pipeline.spec.description.input[1].shortDescription = "(optional) IOU Threshold override (Default: 0.6)"
        pipeline.spec.description.input[2].shortDescription = "(optional) Confidence Threshold override (Default: 0.4)"
        pipeline.spec.description.output[0].shortDescription = u"Boxes \xd7 Class confidence"
        pipeline.spec.description.output[1].shortDescription = u"Boxes \xd7 [x, y, width, height] (relative to image size)"
        pipeline.spec.description.metadata.versionString = "1.0"
        pipeline.spec.description.metadata.shortDescription = "yolov5"
        pipeline.spec.description.metadata.author = "Leon De Andrade"
        pipeline.spec.description.metadata.license = ""

        model = ct.models.MLModel(pipeline.spec)
        model.save(fileName)

        if quantize:
            fileName16 = fileName.replace(".mlmodel", "_16.mlmodel")
            modelFp16 = ct.models.neural_network.quantization_utils.quantize_weights(
                model, nbits=16)
            modelFp16.save(fileName16)

            fileName8 = fileName.replace(".mlmodel", "_8.mlmodel")
            modelFp8 = ct.models.neural_network.quantization_utils.quantize_weights(
                model, nbits=8)
            modelFp8.save(fileName8)

        print(f'CoreML export success, saved as {fileName}')
    except Exception as e:
        print(f'CoreML export failure: {e}')

# 是否对步长和anchor顺序反转
def reversemodel(reverse, strides, anchors):
    if reverse:
        strides.reverse()
        anchors = anchors[::-1]
    return strides, anchors


def main(strides, anchors, reverseModel=False):

    strides,anchors = reversemodel(reverseModel, strides, anchors)
    
    anchorGrid = torch.tensor(anchors).float().view(3, -1, 1, 1, 2)

    numberOfClassLabels = len(classLabels)
    outputSize = numberOfClassLabels + 5
    featureMapDimensionss = featureMapDimensions(imgSize, strides)

    if not Path(model_input_path).exists():
        print("Error: Input model not found")
        return

    Path(model_output_directory).mkdir(parents=True, exist_ok=True)

    # 输入的shape
    sampleInput = torch.zeros((1, 3, imgSize, imgSize))

    checkInputs = [(torch.rand(1, 3, imgSize, imgSize),),(torch.rand(1, 3, imgSize, imgSize),)]

    # 加载pt文件
    model = torch.load(model_input_path, map_location=torch.device('cpu'))['model'].float()

    model.eval()
    model.model[-1].export = True
    # Dry run, necessary for correct tracing!
    model(sampleInput)

    # 导出为torchscript
    ts = exportTorchscript(model, sampleInput, checkInputs,f"{model_output_directory}/{model_output_name}.torchscript.pt")

    # 获得coremltools可以操纵的spec
    # Convert pytorch to raw coreml model
    modelSpec = convertToCoremlSpec(ts, sampleInput)
    addOutputMetaData(modelSpec, outputSize,featureMapDimensionss)

    # Add export logic to coreml model
    builder = ct.models.neural_network.NeuralNetworkBuilder(spec=modelSpec)
    addExportLayerToCoreml(builder, numberOfClassLabels,imgSize,featureMapDimensionss, anchorGrid)

    # Create nms logic
    nmsSpec = createNmsModelSpec(builder.spec, numberOfClassLabels, classLabels)

    # Combine model with export logic and nms logic
    combineModelsAndExport(builder.spec, nmsSpec, f"{model_output_directory}/{model_output_name}.mlmodel", imgSize, quantize)

if __name__ == '__main__':
    # ------------------------------------------------------------------------------#
    # ---------------------------------需要自己修改的常用参数--------------------------#
    
    # 图片的大小
    imgSize = 641
    # 标签的名称
    classLabels = ['a', 'b', 'c', 'd', 'e', '1', '2', '3', '4']  # 类别名称
    # 需要转换的pt文件
    model_input_path = './best.pt'
    # mlmodel输出的文件夹路径
    model_output_directory = "./"
    # 输出的mlmodel的命名(.mlmodel的前缀名称)
    model_output_name = 'yolov5-best-iOS'
    # 下采样的步长
    strides = [8, 16, 32]
    # anchor,与yolov5yaml中的文件相同
    anchors = ([10,13, 16,30, 33,23], [30,61, 62,45, 59,119], [116,90, 156,198, 373,326]) 
    # 步长的顺序是否相反
    modelreverse = True
    # 是否对mlmodel进行量化
    quantize = False   # WARNING! Unable to return a quantized MLModel instance sinceOS != macOS 10.14 or later
    
    # ---------------------------------需要自己修改的常用参数（完）--------------------------#
    # ----------------------------------------------------------------------------------- #
    main(strides,anchors,modelreverse)