yolov8n.pt模型 -->.onnx-->kmodel在01SudioK230运行问题

[shenhuxi-pen]

用yolov8n训练的模型，转换成best.onnx后，再用附件代码转成test.kmodel.下载到01 Sutio K230板上运行，识别不到任何东西，问题帮忙看看是出在那个环节？ best.onnx模型： best.zip test.kmode模型： test.zip

onnx在PC上推理：

01k230镜像：CanMV-K230_micropython_v1.1-0-g5a6fc54_nncase_v2.9.0.img

PC端环境：nncase==2.9.0 /nncase_kpu==2.9.0

转换代码（onnx-->kmodel): 转换代码.txt

01 Sudio k230 运行代码： 运行代码.txt

[curioyang]

跑真实场景的数据你配置随机数做校正集啊

import os
import argparse
import numpy as np
from PIL import Image
import onnxsim
import onnx
import nncase

def parse_model_input_output(model_file):
    onnx_model = onnx.load(model_file)
    input_all = [node.name for node in onnx_model.graph.input]
    input_initializer = [node.name for node in onnx_model.graph.initializer]
    input_names = list(set(input_all) - set(input_initializer))
    input_tensors = [
        node for node in onnx_model.graph.input if node.name in input_names]

    # input
    inputs = []
    for _, e in enumerate(input_tensors):
        onnx_type = e.type.tensor_type
        input_dict = {}
        input_dict['name'] = e.name
        input_dict['dtype'] = onnx.mapping.TENSOR_TYPE_TO_NP_TYPE[onnx_type.elem_type]
        input_dict['shape'] = [(i.dim_value if i.dim_value != 0 else d) for i, d in zip(
            onnx_type.shape.dim, [1, 3, 640, 640])]
        inputs.append(input_dict)

    return onnx_model, inputs

def onnx_simplify(model_file, dump_dir):
    onnx_model, inputs = parse_model_input_output(model_file)
    onnx_model = onnx.shape_inference.infer_shapes(onnx_model)
    input_shapes = {}
    for input in inputs:
        input_shapes[input['name']] = input['shape']

    onnx_model, check = onnxsim.simplify(onnx_model, input_shapes=input_shapes)
    assert check, "Simplified ONNX model could not be validated"

    model_file = os.path.join(dump_dir, 'simplified.onnx')
    onnx.save_model(onnx_model, model_file)
    return model_file

def read_model_file(model_file):
    with open(model_file, 'rb') as f:
        model_content = f.read()
    return model_content

def generate_data_ramdom(shape, batch):
    data = []
    for i in range(batch):
        data.append([np.random.randint(0, 256, shape).astype(np.uint8)])
    return data

def generate_data(shape, batch, calib_dir):
    img_paths = [os.path.join(calib_dir, p) for p in os.listdir(calib_dir)]
    data = []
    for i in range(batch):
        assert i < len(img_paths), "calibration images not enough."
        img_data = Image.open(img_paths[i]).convert('RGB')
        img_data = img_data.resize((shape[3], shape[2]), Image.BILINEAR)
        img_data = np.asarray(img_data, dtype=np.uint8)
        img_data = np.transpose(img_data, (2, 0, 1))
        data.append([img_data[np.newaxis, ...]])
    return data

def main():
    parser = argparse.ArgumentParser(prog="nncase")
    parser.add_argument("--target", default="k230",type=str, help='target to run')
    parser.add_argument("--model",type=str, help='model file')
    parser.add_argument("--dataset", type=str, help='calibration_dataset')

    args = parser.parse_args()

    input_shape = [1, 3, 640, 640]

    dump_dir = 'tmp/object_det'
    if not os.path.exists(dump_dir):
        os.makedirs(dump_dir)

    # onnx simplify
    model_file = onnx_simplify(args.model, dump_dir)

    # compile_options
    compile_options = nncase.CompileOptions()
    compile_options.target = args.target
    compile_options.preprocess = True
    compile_options.swapRB = False
    compile_options.input_shape = input_shape
    compile_options.input_type = 'uint8'

    compile_options.input_range = [0, 1]
    compile_options.mean = [
        0,
        0,
        0
    ]
    compile_options.std = [
        1,
        1,
        1
    ]
    compile_options.input_layout = 'NCHW'
    #compile_options.output_layout = 'NHWC'

    compile_options.dump_ir = True
    compile_options.dump_asm = True
    compile_options.dump_dir = dump_dir
    compile_options.quant_type = 'uint8'

    # compiler
    compiler = nncase.Compiler(compile_options)

    # import
    model_content = read_model_file(model_file)
    import_options = nncase.ImportOptions()
    compiler.import_onnx(model_content, import_options)

    # ptq_options
    ptq_options = nncase.PTQTensorOptions()
    ptq_options.samples_count = 10
    ptq_options.calibrate_method = 'NoClip'
    ptq_options.quant_type = 'int16'
    #ptq_options.set_tensor_data(generate_data_ramdom(input_shape, ptq_options.samples_count))
    ptq_options.set_tensor_data(generate_data(input_shape, ptq_options.samples_count, args.dataset))
    compiler.use_ptq(ptq_options)

    # compile
    compiler.compile()

    # kmodel
    kmodel = compiler.gencode_tobytes()
    with open(os.path.join('./onnx/yolov8n_640.kmodel'), 'wb') as f:
        f.write(kmodel)

if __name__ == '__main__':
    main()

参考一下这个

[shenhuxi-pen]

这个代码可以不？？ import os import argparse import numpy as np from PIL import Image import onnxsim import onnx import nncase

def parse_model_input_output(model_file): onnx_model = onnx.load(model_file) input_all = [node.name for node in onnx_model.graph.input] input_initializer = [node.name for node in onnx_model.graph.initializer] input_names = list(set(input_all) - set(input_initializer)) input_tensors = [ node for node in onnx_model.graph.input if node.name in input_names]

# input
inputs = []
for _, e in enumerate(input_tensors):
    onnx_type = e.type.tensor_type
    input_dict = {}
    input_dict['name'] = e.name
    input_dict['dtype'] = onnx.helper.tensor_dtype_to_np_dtype(onnx_type.elem_type)
    input_dict['shape'] = [(i.dim_value if i.dim_value != 0 else d) for i, d in zip(
        onnx_type.shape.dim, [1, 3, 640, 640])]
    inputs.append(input_dict)

return onnx_model, inputs

def model_simplify(model_file): """ simplify model """ if model_file.split('.')[-1] == "onnx": onnx_model, inputs = parse_model_input_output(model_file) onnx_model = onnx.shape_inference.infer_shapes(onnx_model) input_shapes = {} for input in inputs: input_shapes[input['name']] = input['shape']

    onnx_model, check = onnxsim.simplify(onnx_model, test_input_shapes=input_shapes)
    assert check, "Simplified ONNX model could not be validated"

    model_file = os.path.join(os.path.dirname(model_file), 'simplified.onnx')
    onnx.save_model(onnx_model, model_file)
    print("[ onnx done ]")
elif model_file.split('.')[-1] == "tflite":
    print("[ tflite skip ]")
else:
    raise Exception(f"Unsupport type {model_file.split('.')[-1]}")

return model_file

def read_model_file(model_file): with open(model_file, 'rb') as f: model_content = f.read() return model_content

def generate_data_ramdom(shape, batch): data = [] for i in range(batch): data.append([np.random.randint(0, 256, shape).astype(np.uint8)]) return data

def generate_data(shape, batch, calib_dir): img_paths = [os.path.join(calib_dir, p) for p in os.listdir(calib_dir)] data = [] for i in range(batch): assert i < len(img_paths), "calibration images not enough." img_data = Image.open(img_paths[i]).convert('RGB') img_data = img_data.resize((shape[3], shape[2]), Image.BILINEAR) img_data = np.asarray(img_data, dtype=np.uint8) img_data = np.transpose(img_data, (2, 0, 1)) data.append([img_data[np.newaxis, ...]]) return np.array(data)

def main(): parser = argparse.ArgumentParser(prog="nncase") parser.add_argument("--target",default="k230",type=str, help='target to run') parser.add_argument("--model",type=str, help='model file') parser.add_argument("--dataset", type=str, help='calibration_dataset')

args = parser.parse_args()

#临时文件目录
dump_dir = 'tmp/mobile_retinaface'
if not os.path.exists(dump_dir):
    os.makedirs(dump_dir)

############################设置参数#########################
# 1. 设置编译参数，compile_options
compile_options = nncase.CompileOptions()
# 指定编译目标, 如'cpu', 'k230',cpu生成cpu上推理的kmodel,k230生成在k230(kpu)上推理的kmodel

compile_options.target = "k230"
# 预处理
compile_options.preprocess = True
# （1）预处理---Transpose相关参数
# 输入数据的shape，默认为[]。当 preprocess为 True时，必须指定
input_shape = [1, 3, 640, 640]
compile_options.input_shape = input_shape
# 输入数据的layout，默认为""
compile_options.input_layout = "NCHW"
#compile_options.input_layout = "0,1,2,3"

# （2）预处理---SwapRB相关参数
compile_options.swapRB = False

# （3）预处理---Dequantize（反量化）相关参数
# 开启预处理时指定输入数据类型，默认为"float"；当 preprocess为 True时，必须指定为"uint8"或者"float32"
compile_options.input_type = 'uint8'            
# input_type=‘uint8’时反量化有效，反量化之后的数据范围
compile_options.input_range = [0, 1]

# （4）预处理---Normalization相关参数
compile_options.mean = [ 0,0,0]
compile_options.std = [1, 1, 1]

# 后处理
compile_options.output_layout = "NCHW"
#Compiler类, 根据编译参数配置Compiler，用于编译神经网络模型
compiler = nncase.Compiler(compile_options)

# 2. 设置导入参数，import_options（一般默认即可）
import_options = nncase.ImportOptions()
model_path = "C:/Users/Administrator/Desktop/ultralytics-main/tmp1/best.onnx"
model_file = model_simplify(model_path)
model_content = read_model_file(model_file)
compiler.import_onnx(model_content, import_options)

# 3. 设置量化参数，ptq_options
ptq_options = nncase.PTQTensorOptions()
ptq_options.samples_count = 100
calib_dir2 = "C:/Users/Administrator/Desktop/ultralytics-main/datasets/bvn2/images/calibdata"
ptq_options.set_tensor_data(generate_data(input_shape, ptq_options.samples_count, calib_dir2))
compiler.use_ptq(ptq_options)
############################设置参数#########################

# 4.编译神经网络模型（kmodel）
compiler.compile()

# 5.保存kmodel
kmodel = compiler.gencode_tobytes()
dump_path = "C:/Users/Administrator/Desktop/ultralytics-main/tmp1"
kmodel_path = "C:/Users/Administrator/Desktop/ultralytics-main/tmp1/test.kmodel"
kmodel_path = os.path.join(dump_path, "test.kmodel")
with open(kmodel_path, 'wb') as f:
    f.write(kmodel)
print("----------------end-----------------")
return kmodel_path

if name == 'main': main()

[Javachenlianghong]

我试了还是什么都识别不到

[curioyang]

@shenhuxi-pen 目前整个流程没问题了是吧