model_watermark_detect/tests/verify_tool_accuracy_test.py

"""
支持所有待测模型，对指定文件夹下所有模型文件进行水印检测，并进行模型水印准确率验证
"""

import argparse
import os
import sys
rootpath = str(os.path.abspath(os.path.join(os.path.dirname(__file__), '../')))
sys.path.append(rootpath)

from watermark_verify import verify_tool

model_types = {
    "classification": [
        "alexnet","alexnet_keras", "vgg16", "vgg16_tensorflow", "googlenet", "resnet"
    ],
    "object_detection": [
        "ssd", "yolox", "faster-rcnn"
    ],
}

def find_onnx_files(root_dir):
    onnx_files = []
    # 遍历根目录及其子目录
    for dirpath, _, filenames in os.walk(root_dir):
        # 查找所有以 .onnx 结尾的文件
        for filename in filenames:
            if filename.endswith('.onnx'):
                # 获取完整路径并添加到列表
                onnx_files.append(os.path.join(dirpath, filename))
    return onnx_files

def filter_model_dirs(model_dir, targets):
    for target in targets:
        if target in model_dir:
            return True
    return False

if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='模型标签验证准确率验证脚本')
    parser.add_argument('--target_dir', default="origin_models", type=str, help='模型文件存放根目录，支持子文件夹递归处理')
    # parser.add_argument('--model_type', default=None, type=str, help='按照模型分类过滤，用于区分是目标检测模型还是图像分类模型，可选参数：classification、objection_detect')
    # parser.add_argument('--model_value', default=None, type=str, help='按照模型名称过滤，可选参数：alexnet、googlenet、resnet、vgg16、ssd、yolox、rcnn')
    parser.add_argument('--model_file_filter', default=None, type=str, help='按照模型文件名过滤, 比如剪枝模型文件名存在prune。默认为None')
    parser.add_argument('--except_result', default=None, type=str, help='模型推理预期结果。默认为None')

    args, _ = parser.parse_known_args()
    if args.target_dir is None:
        raise Exception("模型目录参数不可为空")
    # if args.model_type is None:
    #     raise Exception("模型类型参数不可为空")
    if args.except_result is None:
        raise Exception("模型推理预期结果不可为空")

    # 获取所有模型目录信息
    # model_dirs = [item for item in os.listdir(args.target_dir) if os.path.isdir(os.path.join(args.target_dir, item))]
    # if args.model_type:
    #     filter_models = model_types[args.model_type]
    #     model_dirs = [item for item in model_dirs if filter_model_dirs(item, filter_models)]
    # if args.model_value:
    #     model_dirs = [item for item in model_dirs if args.model_value.lower() in item.lower()]
    model_dirs = [args.target_dir]

    # 遍历符合条件的模型目录列表，进行标签提取检测，并记录准确率
    for model_dir in model_dirs:
        total = 0
        correct = 0
        onnx_files = find_onnx_files(os.path.join(args.target_dir, model_dir))
        onnx_files = [os.path.abspath(item) for item in onnx_files]
        if args.model_file_filter:
            onnx_files = [item for item in onnx_files if args.model_file_filter in item]
        else:
            onnx_files = [item for item in onnx_files if "pruned" not in item]
        print(f"model_name: {model_dir}\nonnx_files:")
        print(*onnx_files, sep='\n')
        for onnx_file in onnx_files:
            verify_result = verify_tool.label_verification(onnx_file)
            total += 1
            if str(verify_result) == args.except_result:
                correct += 1
        print(f"model_name: {model_dir}, accuracy: {correct * 100.0 / total}%")