记录异常检测AnomalyCLIP运行自己构建的数据集

原创已于 2025-05-20 14:40:11 修改 · 640 阅读

4 ·

CC 4.0 BY-SA版权

文章标签：

#python #开发语言

于 2025-04-29 14:43:21 首次发布

测试结果

第一列为原始图片，第二列为直接调用官网提供的checkpoint的测试结果，第三列为在自己数据集上训练然后测试的结果

下一步训练计划：

1.去除背景，直接把背景设置成全白。2.训练去除背景的数据

下载代码

https://github.com/zqhang/AnomalyCLIP

创建环境

#1.安装基础环境
conda create -n anomalyclip python=3.8
conda activate anomalyclip
pip install -r requirements.txt

#2.安装代码各种所需的库，如thop、ftfy、regex、tabulate、cv2等，需要的下载就行
pip install thop
pip install ftfy
pip install regex
pip install tabulate
pip install opencv-python

#3.根据自己的驱动调整torch，我是cuda11.6，但torch2.0.0最低支持11.7，所以默认安装torch2.0.0不行，卸载torch2.0.0，安装torch1.13.1，也可以去torch官网看看自己适合什么版本
pip uninstall torch torchvision torchaudio
pip install torch==1.13.1+cu116 torchvision==0.14.1+cu116 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu116

构建自己的数据

1.我的目标是构建单类异常，不管他什么类型异常，都检测成异常即可

cd generate_dataset_json
python SDD.py

./data
    └── SDD
        ├── fengjiyepian
        │   ├── ground_truth
        │   │   └── bad
        │   │       ├── bad1.png
        │   │       └── bad2.png
        │   ├── test
        │   │   ├── bad
        │   │   │   ├── bad1.png
        │   │   │   └── bad2.png
        │   │   └── good
        │   │       ├── good1.png
        │   │       └── good2.png
        │   └── train
        │       └── good
        │           ├── good1.png
        │           └── good2.png
        └── meta.json

2.生成meta.json文件，就是你训练图片和测试图片文件路径：

①修改自己的数据集类型名CLSNAMES = ['fengjiyepian']

②修改要生成数据集的路径runner = SDDSolver(root='./data/SDD')

import os
import json


class SDDSolver(object):
    CLSNAMES = [
        'fengjiyepian',
    ]

    def __init__(self, root='data/mvtec'):
        self.root = root
        self.meta_path = f'{root}/meta.json'

    def run(self):
        info = dict(train={}, test={})
        anomaly_samples = 0
        normal_samples = 0
        for cls_name in self.CLSNAMES:
            cls_dir = f'{self.root}/{cls_name}'
            for phase in ['train', 'test']:
                cls_info = []
                species = os.listdir(f'{cls_dir}/{phase}')
                for specie in species:
                    is_abnormal = True if specie not in ['good'] else False
                    img_names = os.listdir(f'{cls_dir}/{phase}/{specie}')
                    mask_names = os.listdir(f'{cls_dir}/ground_truth/{specie}') if is_abnormal else None
                    img_names.sort()
                    mask_names.sort() if mask_names is not None else None
                    for idx, img_name in enumerate(img_names):
                        info_img = dict(
                            img_path=f'{cls_name}/{phase}/{specie}/{img_name}',
                            mask_path=f'{cls_name}/ground_truth/{specie}/{mask_names[idx]}' if is_abnormal else '',
                            cls_name=cls_name,
                            specie_name=specie,
                            anomaly=1 if is_abnormal else 0,
                        )
                        cls_info.append(info_img)
                        if phase == 'test':
                            if is_abnormal:
                                anomaly_samples = anomaly_samples + 1
                            else:
                                normal_samples = normal_samples + 1
                info[phase][cls_name] = cls_info
        with open(self.meta_path, 'w') as f:
            f.write(json.dumps(info, indent=4) + "\n")
        print('normal_samples', normal_samples, 'anomaly_samples', anomaly_samples)


if __name__ == '__main__':
    runner = SDDSolver(root='./data/SDD')
    runner.run()

3.生成的meta.json示例如下

{
    "train": {
        "fengjiyepian": [
            {
                "img_path": "fengjiyepian/train/good/DJI_20240606121233_0019_2336_3384.png",
                "mask_path": "",
                "cls_name": "fengjiyepian",
                "specie_name": "good",
                "anomaly": 0
            }
        ]
    },
    "test": {
        "fengjiyepian": [
            {
                "img_path": "fengjiyepian/test/bad/video-20220926-Tang5_3792_1091_1466.png",
                "mask_path": "fengjiyepian/ground_truth/bad/video-20220926-Tang5_3792_1091_1466.png",
                "cls_name": "fengjiyepian",
                "specie_name": "bad",
                "anomaly": 1
            },
            {
                "img_path": "fengjiyepian/test/good/1459_1003_944.png",
                "mask_path": "",
                "cls_name": "fengjiyepian",
                "specie_name": "good",
                "anomaly": 0
            }
        ]
    }
}

4.数据集整理如下

无训练的测试过程

1.在dataset.py的generate_class_info添加自己数据集的内容，我是添加obj_list = ['fengjiyepian']

def generate_class_info(dataset_name):
    class_name_map_class_id = {}
    if dataset_name == 'mvtec':
        obj_list = ['carpet', 'bottle', 'hazelnut', 'leather', 'cable', 'capsule', 'grid', 'pill',
                    'transistor', 'metal_nut', 'screw', 'toothbrush', 'zipper', 'tile', 'wood']
    elif dataset_name == 'visa':
        obj_list = ['candle', 'capsules', 'cashew', 'chewinggum', 'fryum', 'macaroni1', 'macaroni2',
                    'pcb1', 'pcb2', 'pcb3', 'pcb4', 'pipe_fryum']
    elif dataset_name == 'mpdd':
        obj_list = ['bracket_black', 'bracket_brown', 'bracket_white', 'connector', 'metal_plate', 'tubes']
    elif dataset_name == 'btad':
        obj_list = ['01', '02', '03']
    elif dataset_name == 'DAGM_KaggleUpload':
        obj_list = ['Class1','Class2','Class3','Class4','Class5','Class6','Class7','Class8','Class9','Class10']
    elif dataset_name == 'SDD':
        obj_list = ['electrical commutators']
    elif dataset_name == 'DTD':
        obj_list = ['Woven_001', 'Woven_127', 'Woven_104', 'Stratified_154', 'Blotchy_099', 'Woven_068', 'Woven_125', 'Marbled_078', 'Perforated_037', 'Mesh_114', 'Fibrous_183', 'Matted_069']
    elif dataset_name == 'colon':
        obj_list = ['colon']
    elif dataset_name == 'ISBI':
        obj_list = ['skin']
    elif dataset_name == 'Chest':
        obj_list = ['chest']
    elif dataset_name == 'thyroid':
        obj_list = ['thyroid']
    elif dataset_name == 'fengjiyepian':
        obj_list = ['fengjiyepian'] 
    for k, index in zip(obj_list, range(len(obj_list))):
        class_name_map_class_id[k] = index

    return obj_list, class_name_map_class_id

2.在test.py中修改对应的超参数，

if __name__ == '__main__':
    parser = argparse.ArgumentParser("AnomalyCLIP", add_help=True)
    # paths
    parser.add_argument("--data_path", type=str, default="./data/SDD", help="path to test dataset")
    parser.add_argument("--save_path", type=str, default='./results/', help='path to save results')
    parser.add_argument("--checkpoint_path", type=str, default='./checkpoints/9_12_4_multiscale/epoch_15.pth', help='path to checkpoint')
    # model
    parser.add_argument("--dataset", type=str, default='fengjiyepian')
    parser.add_argument("--features_list", type=int, nargs="+", default=[6, 12, 18, 24], help="features used")
    parser.add_argument("--image_size", type=int, default=518, help="image size")
    parser.add_argument("--depth", type=int, default=9, help="image size")
    parser.add_argument("--n_ctx", type=int, default=12, help="zero shot")
    parser.add_argument("--t_n_ctx", type=int, default=4, help="zero shot")
    parser.add_argument("--feature_map_layer", type=int,  nargs="+", default=[0, 1, 2, 3], help="zero shot")
    parser.add_argument("--metrics", type=str, default='image-pixel-level')
    parser.add_argument("--seed", type=int, default=111, help="random seed")
    parser.add_argument("--sigma", type=int, default=4, help="zero shot")
    
    args = parser.parse_args()
    print(args)
    setup_seed(args.seed)
    test(args)

3.在test.py启动可视化

#在test.py将注释的这行启动即可，可视化结果宝成在results中
visualizer(items['img_path'], anomaly_map.detach().cpu().numpy(), args.image_size, args.save_path, cls_name)

4.运行test.py即可，得到结果

在自己的数据上训练并测试

1.修改train.py中的超参

if __name__ == '__main__':
    parser = argparse.ArgumentParser("AnomalyCLIP", add_help=True)
    parser.add_argument("--train_data_path", type=str, default="./data/SDD", help="train dataset path")
    parser.add_argument("--save_path", type=str, default='./checkpoints', help='path to save results')


    parser.add_argument("--dataset", type=str, default='fengjiyepian', help="train dataset name")

    parser.add_argument("--depth", type=int, default=9, help="image size")
    parser.add_argument("--n_ctx", type=int, default=12, help="zero shot")
    parser.add_argument("--t_n_ctx", type=int, default=4, help="zero shot")
    parser.add_argument("--feature_map_layer", type=int, nargs="+", default=[0, 1, 2, 3], help="zero shot")
    parser.add_argument("--features_list", type=int, nargs="+", default=[6, 12, 18, 24], help="features used")

    parser.add_argument("--epoch", type=int, default=15, help="epochs")
    parser.add_argument("--learning_rate", type=float, default=0.001, help="learning rate")
    parser.add_argument("--batch_size", type=int, default=8, help="batch size")
    parser.add_argument("--image_size", type=int, default=518, help="image size")
    parser.add_argument("--print_freq", type=int, default=1, help="print frequency")
    parser.add_argument("--save_freq", type=int, default=1, help="save frequency")
    parser.add_argument("--seed", type=int, default=111, help="random seed")
    args = parser.parse_args()
    setup_seed(args.seed)
    train(args)

2.运行train.py进行训练。

3.用训练好的checkponit测试