工具docker封装完成

.gitignore

@@ -0,0 +1,4 @@
+/server/env/*.tar.gz
+/server/LEMON-master/origin_model/*.h5
+/server/LEMON-master/origin_model/*.hdf5
+/server/LEMON-master/dataset/*.npz

README.md

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+
+需要根据.gitignore 下载对应被忽略的大文件
+- env
+- model

docker-compose.yml

@@ -0,0 +1,23 @@
+version: '3.8'
+
+services:
+  backend:
+    build: ./server
+    restart: always
+    ports:
+      - 8080
+    volumes:
+      - ./server/output:/output
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: "nvidia"
+              count: "all"
+              capabilities: [ "gpu" ]
+    networks:
+      - ma-network
+    tty: true
+networks:
+  ma-network:
+    driver: bridge

server/Dockerfile

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+# 创建一个基础镜像 
+FROM nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04
+
+RUN apt-key del 7fa2af80
+RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub
+RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub
+
+# 重置apt-get
+RUN rm -rf /etc/apt/sources.list
+
+RUN echo "deb http://mirrors.tuna.tsinghua.edu.cn/ubuntu/ bionic main restricted universe multiverse" >> /etc/apt/sources.list
+
+RUN apt-get update
+
+RUN apt-get install -y default-jre
+
+RUN apt-get install -y cmake
+
+# 安装conda
+# yhyu13 : install additional packages
+# 设置apt的源为tsinghua镜像源
+RUN sed -i 's/archive.ubuntu.com/mirrors.tuna.tsinghua.edu.cn/g' /etc/apt/sources.list
+
+RUN apt-get update && apt-get install -y curl wget
+
+# 安装conda
+RUN curl -O https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh \
+    && bash Miniconda3-latest-Linux-x86_64.sh -b -p /opt/conda \
+    && rm Miniconda3-latest-Linux-x86_64.sh
+
+RUN /opt/conda/bin/conda init bash
+
+RUN mkdir env
+
+# 解压env中的mxnet tensorflow lemon
+COPY env/mxnet.tar.gz env/mxnet.tar.gz
+RUN mkdir /opt/conda/envs/mxnet
+RUN tar -zxvf env/mxnet.tar.gz -C /opt/conda/envs/mxnet
+
+COPY env/tensorflow.tar.gz env/tensorflow.tar.gz
+RUN mkdir /opt/conda/envs/tensorflow
+RUN tar -zxvf env/tensorflow.tar.gz -C /opt/conda/envs/tensorflow
+
+COPY env/lemon.tar.gz env/lemon.tar.gz
+RUN mkdir /opt/conda/envs/lemon
+RUN tar -zxvf env/lemon.tar.gz -C /opt/conda/envs/lemon
+
+RUN rm -rf env
+
+
+# 复制项目文件
+WORKDIR /app
+
+COPY LEMON-master /app/LEMON-master
+
+RUN echo "conda activate lemon" >> ~/.bashrc
+
+# RUN conda install -n lemon cudatoolkit=10.1
+# 随便启动一个进程空转
+CMD ["/bin/bash"]
+# CMD ["tail", "-f", "/dev/null"]
+
+

server/LEMON-master/.gitignore

@@ -0,0 +1,131 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+.idea/

server/LEMON-master/LICENSE

@@ -0,0 +1,201 @@
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server/LEMON-master/README.md

@@ -0,0 +1,222 @@
+# Deep Learning Library Testing via Effective Model Generation
+
+This is the implementation repository of our ESEC/FSE 2020 paper:  **Deep Learning Library Testing via Effective Model Generation.** 
+
+## Description
+
+`LEMON` is a novel approach to testing DL libraries by generating effective DL models via guided mutation. The goal is to test DL libraries as sufficiently as possible by exploring unused library code or different usage ways of library code. We further propose a heuristic strategy in `LEMON` to guide the process of model generation so as to generate models that can amplify the inconsistent degrees for real bugs. In this way, it is clearer to distinguish real bugs and uncertain impacts in DL libraries. We conducted an empirical study to evaluate the effectiveness of `LEMON` based on `20` release versions of `TensorFlow`,`Theano,` `CNTK`, and `MXNet`. `LEMON` detected `24` new bugs in the latest release versions of these libraries. The results also demonstrate that the models generated by `LEMON` outperform existing models and the models generated without guidance in terms of the number of unique bugs/inconsistencies and the achieved inconsistent degrees. 
+
+## Datasets/Models/Libraries
+
+### Datasets/Models
+
+We used `12` popular DL `models` based on `6` `datasets` including both images and sequential data, as the initial seed models in `LEMON`, which have been widely used in many existing studies.
+
+| Model       | Dataset              | Related link<sup>1</sup>                                     |
+| ----------- | -------------------- | ------------------------------------------------------------ |
+| AlexNet     | CIFAR-10             | [alexnet-cifar-10-keras-jupyter](https://github.com/toxtli/alexnet-cifar-10-keras-jupyter/blob/master/alexnet_test1.ipynb) |
+| LeNet5      | Fashion-MNIST        | [fashion_mnist_keras](https://colab.research.google.com/github/margaretmz/deep-learning/blob/master/fashion_mnist_keras.ipynb) |
+| LeNet5      | MNIST                | [lenet5-mnist](https://github.com/lucaaslb/lenet5-mnist)     |
+| LSTM-1      | Sine-Wave            | [LSTM-Neural-Network-for-Time-Series-Prediction](https://github.com/StevenZxy/CIS400/tree/f69489c0624157ae86b5d8ddb1fa99c89a927256/code/LSTM-Neural-Network-for-Time-Series-Prediction-master) |
+| LSTM-2      | Stock-Price          | [StockPricesPredictionProject](https://github.com/omerbsezer/LSTM_RNN_Tutorials_with_Demo/tree/master/StockPricesPredictionProject) |
+| ResNet50    | ImageNet<sup>2</sup> | Keras applications tutorial<sup>3</sup>                      |
+| MobileNetV1 | ImageNet<sup>2</sup> | Keras applications tutorial<sup>3</sup>                      |
+| InceptionV3 | ImageNet<sup>2</sup> | Keras applications tutorial<sup>3</sup>                      |
+| DenseNet121 | ImageNet<sup>2</sup> | Keras applications tutorial<sup>3</sup>                      |
+| VGG16       | ImageNet<sup>2</sup> | Keras applications tutorial<sup>3</sup>                      |
+| VGG19       | ImageNet<sup>2</sup> | Keras applications tutorial<sup>3</sup>                      |
+| Xception    | ImageNet<sup>2</sup> | Keras applications tutorial<sup>3</sup>                      |
+
+1:  The first 5 models are trained using existing repositories while the last 7 models in ImageNet are obtained directly using the API provided by Keras.
+
+2: We sampled 1500 images from ImageNet and you could obtain them from `sampled_imagenet-1500.npz`. You can also sample your own images from the [ImageNet validation dataset](http://www.image-net.org/challenges/LSVRC/2012/nonpub-downloads). 
+
+3: Keras applications tutorial can be found in: https://keras.io/api/applications/
+
+4: All model files and two regression dataset and ImageNet dataset we sampled can be access in [OneDrive](https://1drv.ms/u/s!Aj6dGBsJFcs0jnXVUfAtsEjdUW_T?e=ezo32C)
+
+**NOTE**: LEMON use 6 dataset, and 3 of them could be directly obtained from their homepages  ( [CIFAR-10](http://www.cs.toronto.edu/~kriz/cifar.html),  [Fashion-MNIST](https://github.com/zalandoresearch/fashion-mnist), [MNIST](http://yann.lecun.com/exdb/mnist/) ) or accessed by [Keras API](https://keras.io/api/datasets/). We only upload the ImageNet dataset sampled by LEMON and two datasets collected from  GitHub.
+
+In `dataset.zip`, there are 3 files:
+
+> sinewave.csv: dataset of LSTM-1
+> DIS.csv : dataset of LSTM-2
+> sampled_imagenet-1500.npz:  dataset  samples by LEMON from  ImageNet 
+
+In `origin_model.zip`, there are 12 models used in LEMON. 
+
+
+### Libraries
+
+We used `20` release versions of  `4` widely-used DL `libraries`, i.e., `TensorFlow`, `CNTK`,`Theano`, and `MXNet`, as subjects to constructed five experiments (indexed `E1` to `E5` in Table) to conduct differential testing.
+
+We share the link of each library and docker image used in `LEMON`. 
+
+| Experiment ID | Tensorflow                                                | Theano                                          | CNTK                                                         | MXNet                                                      | CUDA                                                         |
+| ------------- | --------------------------------------------------------- | ----------------------------------------------- | ------------------------------------------------------------ | ---------------------------------------------------------- | ------------------------------------------------------------ |
+| E1            | [1.14.0](https://pypi.org/project/tensorflow-gpu/1.14.0/) | [1.0.4](https://pypi.org/project/Theano/1.0.4/) | [2.7.0](https://pypi.org/project/cntk-gpu/2.7/)              | [1.5.1](https://pypi.org/project/mxnet-cu101/1.5.1.post0/) | [10.1](https://hub.docker.com/r/nvidia/cuda/tags?page=1&name=10.1-cudnn7-devel-ubuntu16.04) |
+| E2            | [1.13.1](https://pypi.org/project/tensorflow-gpu/1.13.1/) | [1.0.3](https://pypi.org/project/Theano/1.0.3/) | [2.6.0](https://pypi.org/project/cntk-gpu/2.6/)              | [1.4.1](https://pypi.org/project/mxnet-cu100/1.4.1/)       | [10.0](https://hub.docker.com/r/nvidia/cuda/tags?page=1&name=10.0-cudnn7-devel-ubuntu16.04) |
+| E3            | [1.12.0](https://pypi.org/project/tensorflow-gpu/1.12.0/) | [1.0.2](https://pypi.org/project/Theano/1.0.2/) | [2.5.1](https://pypi.org/project/cntk-gpu/2.5.1/)            | [1.3.1](https://pypi.org/project/mxnet-cu90/1.3.1/)        | [9.0](https://hub.docker.com/r/nvidia/cuda/tags?page=1&name=9.0-cudnn7-devel-ubuntu16.04) |
+| E4            | [1.11.0](https://pypi.org/project/tensorflow-gpu/1.11.0/) | [1.0.1](https://pypi.org/project/Theano/1.0.1/) | [2.4.0](https://docs.microsoft.com/en-us/cognitive-toolkit/Setup-Linux-Python?tabs=cntkpy24) | [1.2.1](https://pypi.org/project/mxnet-cu90/1.2.1.post1/)  | [9.0](https://hub.docker.com/r/nvidia/cuda/tags?page=1&name=9.0-cudnn7-devel-ubuntu16.04) |
+| E5            | [1.10.0](https://pypi.org/project/tensorflow-gpu/1.10.0/) | [1.0.0](https://pypi.org/project/Theano/1.0.0/) | [2.3.1](https://docs.microsoft.com/en-us/cognitive-toolkit/Setup-Linux-Python?tabs=cntkpy231) | [1.1.0](https://pypi.org/project/mxnet-cu90/1.1.0/)        | [9.0](https://hub.docker.com/r/nvidia/cuda/tags?page=1&name=9.0-cudnn7-devel-ubuntu16.04) |
+
+\* All libraries should be  `GPU-supported` version
+
+## Reproducibility
+
+### Environment 
+
+We conducted 5 experiments in `LEMON` of which the library and CUDA version information are as described above. In order to facilitate other researchers to reproduce `LEMON`, we provide a `docker`  image for the `E1` experiment. It can be easily obtained by the following command. (**Note: nvidia-docker2 is required!**) 
+
+**Step 0:** Please Install [nvidia-docker2](https://github.com/NVIDIA/nvidia-docker). You can use this [instruction](https://codepyre.com/2019/01/installing-nvidia-docker2-on-ubuntu-18.0.4/) to install it.
+
+**Step 1:** Clone the repository. Download the dataset and models from  [OneDrive](https://1drv.ms/u/s!Aj6dGBsJFcs0jnXVUfAtsEjdUW_T?e=ezo32C). Save the code and unzip datasets and models to `/your/local/path/` , e.g. `/your/local/path/origin_model` and `/your/local/path/dataset`. (`/your/local/path/` should be the absolute path on your server, e.g. `/home/user_xxx/`)
+
+**Step 2:** Using the following command to pull the docker image we released for `E1` and create a container for it. 
+
+```shell
+docker pull yenming1227/lemon:latest
+docker run --runtime=nvidia -it -v /your/local/path/:/data  yenming1227/lemon:latest /bin/bash
+```
+
+Then you will enter a container.(Remember to change `/your/local/path/` to the real path! ^_^)
+
+**Note: If your server is using http proxy, you should configure proxy in the container just as you did in your server before**
+
+**Step 3:** Create five virtual environments as shown below in your docker container.
+
+**Note: Please copy the installation command line by line to prevent some commands from being skipped.**
+
+ ```shell
+# tensorflow
+conda create -n tensorflow python=3.6
+source activate tensorflow
+pip install -r lemon_requirements.txt
+pip install keras==2.2.4
+pip install tensorflow-gpu==1.14.0
+source deactivate
+
+# theano
+conda create -n theano python=3.6
+source activate theano
+pip install -r lemon_requirements.txt
+conda install pygpu=0.7.6
+pip install keras==2.2.4
+pip install theano==1.0.4
+source deactivate
+
+# cntk
+conda create -n cntk python=3.6
+source activate cntk
+pip install -r lemon_requirements.txt
+pip install keras==2.2.4
+pip install cntk-gpu==2.7
+source deactivate
+
+# mxnet
+conda create -n mxnet python=3.6
+source activate mxnet
+pip install -r lemon_requirements.txt
+pip install keras-mxnet==2.2.4.2
+pip install mxnet-cu101==1.5.1.post0
+source deactivate
+
+# default lemon python
+conda create -n lemon python=3.6
+source activate lemon
+pip install -r lemon_requirements.txt
+pip install keras==2.2.4
+pip install tensorflow-gpu==1.14.0
+source deactivate
+ ```
+
+### Redis Startup
+
+LEMON uses redis to store intermediate outputs and exchange data between different processes. We have installed redis in our docker image, you can start it with the following command:
+
+```bash
+cd /root/redis-4.0.8/src
+./redis-server ../redis.conf
+```
+
+### Running LEMON
+
+The `LEMON` artifacts are well organized, and researchers can simply run `LEMON` with the following command. 
+
+**Note: We conducted five large scale experiments (generating 100 mutants for each of the 12 initial models and analyzing inconsistencies on 1500 inputs and locating bugs) and you can reproduce the bugs reported in `LEMON` by running tool with `experiments.conf`. However, it could not be completed within `48`  hours. Therefore, we provide a `demo run`, which can be completed within `1` hour if you running the tool with `demo.conf`.**
+
+```shell
+cd /LEMON
+git pull 
+source activate lemon
+```
+
+We really  recommend you use `git pull` to update `LEMON` to the latest version.
+
+**Mutation:**
+
+```shell
+python -u -m run.mutation_executor demo.conf
+```
+
+The above command shows how to generate mutants and calculating inconsistencies in `LEMON`. `demo.conf` is the configuration file we provided for `demo run`. 
+
+**Localization:**
+
+```shell
+python -u -m run.localization_executor demo.conf
+```
+
+This command shows the way to perform localization in `LEMON`. The final  bug reports will be stored in path `/data/lemon_outputs/bug_list.txt` 
+
+### Extension
+
+`LEMON` also supports researchers to switch to other models and datasets. You only need to focus on the code snippets of the data processing part in `DataUtils.get_data_by_exp` in `scripts/tools/utils.py`.
+
+```
+# TODO: Add your own data preprocessing here
+# Note: The returned inputs should be preprocessed and labels should decoded as one-hot vectors which could be directly feed in model. Both of them should be returned in batch, e.g. shape like (1500,28,28,1) and (1500,10)
+# 
+# elif 'xxx' in exp:
+#     x_test, y_test = get_your_data(dataset_dir)
+```
+
+Besides, you should name your model file in format ` NetworkName-DatasetName_origin.h5`, e.g.  `mobilenet.1.00.224-imagenet_origin.h5`. 
+
+Note: `_` and `-` can't be shown in `NetworkName`. You can replace them with `.` 
+
+For example , changing  `mobilenet_1.00_224-imagenet_origin.h5` to `mobilenet.1.00.224-imagenet_origin.h5`. 
+
+## Citation
+
+Please cite our paper if this work is helpful to you.
+```
+@inproceedings{DBLP:conf/sigsoft/WangYCLZ20,
+  author    = {Zan Wang and
+               Ming Yan and
+               Junjie Chen and
+               Shuang Liu and
+               Dongdi Zhang},
+  title     = {Deep learning library testing via effective model generation},
+  booktitle = {{ESEC/SIGSOFT} {FSE}},
+  pages     = {788--799},
+  publisher = {{ACM}},
+  year      = {2020}
+}
+```
+
+## Contact
+
+Authors information:
+
+| Name          | Email Address          | **Github id** |
+| ------------- | ---------------------- | ------------- |
+| Zan Wang      | wangzan@tju.edu.cn     | tjuwangzan    |
+| Ming Yan      | yanming@tju.edu.cn     | Jacob-yen     |
+| Junjie Chen * | junjiechen@tju.edu.cn  | JunjieChen    |
+| Shuang Liu    | shuang.liu@tju.edu.cn  | AbigailLiu    |
+| Dongdi Zhang  | zhangdongdi@tju.edu.cn | Dandy-John    |
+
+\* *corresponding author*
+
+

server/LEMON-master/config/demo.conf

@@ -0,0 +1,47 @@
+[parameters]
+mutate_ops=WS GF NEB NAI NS ARem ARep LA LC LR LS MLA
+metrics=D_MAD
+#metrics=deepgini
+exps=mobilenet.1.00.224-imagenet
+# lexnet-cifar10 lenet5-fashion-mnist fashion2 svhn  lenet5-mnist alexnet-cifar10 mobilenet.1.00.224-imagenet vgg16-imagenet
+# Path of the initial models
+# Name model file as 'alexnet-cifar10_origin.h5'
+
+origin_model_dir=origin_model
+
+# Path of the ImageNet and regression dataset
+dataset_dir=dataset
+
+# Modifying the backends is not recommended.
+# There is some hard-code in the program about the backends
+backend=tensorflow mxnet
+#..python_prefix = /root/anaconda3/envs/
+python_prefix=/opt/conda/envs/
+
+output_dir = lemon_outputs
+mutate_num=20 
+
+test_size=10
+pool_size=50
+mutate_ratio=0.3
+gpu_ids = 0,1
+threshold = 0.4
+# minutes
+time_limit = 3
+# use MCMC for mutator selection
+mutator_strategy = MCMC
+
+# use Roulette for mutant selection
+mutant_strategy = Roulette
+
+# use counter,timing
+stop_mode=counter
+
+
+[redis]
+# your-redis-server
+host= 127.0.0.1
+# redis port
+port= 6379
+# db number
+redis_db= 0

server/LEMON-master/config/experiments.conf

@@ -0,0 +1,43 @@
+[parameters]
+mutate_ops=WS GF NEB NAI NS ARem ARep LA LC LR LS MLA
+metrics=D_MAD
+exps=alexnet-cifar10 xception-imagenet lenet5-fashion-mnist lenet5-mnist resnet50-imagenet vgg16-imagenet vgg19-imagenet densenet121-imagenet mobilenet.1.00.224-imagenet inception.v3-imagenet lstm0-sinewave lstm2-price
+
+# Path of the initial models
+# Name model file as 'alexnet-cifar10_origin.h5'
+origin_model_dir=/data/origin_model
+
+# Path of the ImageNet and regression dataset
+dataset_dir=/data/dataset
+
+# Modifying the backends is not recommended.
+# There is some hard-code in the program about the backends
+backend=tensorflow theano cntk mxnet
+python_prefix = /root/anaconda3/envs/
+output_dir = /data/lemon_outputs
+mutate_num=2
+test_size=10
+pool_size=50
+mutate_ratio=0.3
+gpu_ids = 0,1
+threshold = 0.4
+# minutes
+time_limit = 60
+# use MCMC for mutator selection
+mutator_strategy = MCMC
+
+# use Roulette for mutant selection
+mutant_strategy = Roulette
+
+# use counter,timing
+stop_mode=timing
+
+
+[redis]
+# your-redis-server
+host= 127.0.0.1
+# redis port
+port= 6379 
+# db number
+redis_db= 0 
+

server/LEMON-master/dataset/DIS.csv

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server/LEMON-master/lemon_requirements.txt

@@ -0,0 +1,7 @@
+pandas==0.23.0
+Pillow==5.1.0
+redis==3.3.2
+scikit-image==0.13.1
+scikit-learn==0.19.1
+scipy==1.1.0
+h5py==2.10

server/LEMON-master/run/SVNH_DatasetUtil.py

@@ -0,0 +1,172 @@
+import numpy as np
+import scipy.io as sio
+import os.path
+
+train_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "data", "SVHN_train_32x32.mat")
+test_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "data", "SVHN_test_32x32.mat")
+extra_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "data", "SVHN_extra_32x32.mat")
+
+#default parameters for argparse
+# default_params = {
+#     "learning_rate": 0.001,
+#     "num_epochs": 25,
+#     "batch_size": 128,
+#     "train_data_file": "./assignment_httm_data/SVHN_train_32x32.mat",
+#     "test_data_file": "./assignment_httm_data/SVHN_test_32x32.mat",
+#     "extra_data_file": "./assignment_httm_data/SVHN_extra_32x32.mat",
+#     "load_extra": False,
+#     "model": "CNN1",
+#     "validation_percentage": 0.1,
+#     "data_shuffle": True,
+#     "preprocess": False,
+#     "mode": 'train',
+#     "runs_name": None,
+#     "tensorboard_dir": '~/tensorboard_runs'
+# }
+
+def load_raw_data(train_data_file, test_data_file, load_extra_data, extra_data_file):
+    """
+    Load RAW Google SVHN Digit Localization from .mat files
+    """
+    loading_information = "with Extra" if load_extra_data else "without Extra"
+    print("Loading SVHN dataset {}...".format(loading_information))
+    raw_train_data = sio.loadmat(train_data_file)
+    raw_test_data = sio.loadmat(test_data_file)
+    if load_extra_data:
+        raw_extra_data = sio.loadmat(extra_data_file)
+        print("Train size: {}, Test size: {}, Extra size: {}".format(raw_train_data['X'].shape[3],
+                                                                     raw_test_data['X'].shape[3],
+                                                                     raw_extra_data['X'].shape[3]))
+        return [raw_train_data, raw_test_data, raw_extra_data]
+    else:
+        print("Train size: {}, Test size: {}".format(raw_train_data['X'].shape[3],
+                                                     raw_test_data['X'].shape[3]))
+        return [raw_train_data, raw_test_data]
+
+
+def format_data(raw_data, number_of_examples):
+    """
+    Reshape RAW data to regular shape
+    """
+    old_shape = raw_data.shape
+    new_data = []
+    for i in range(number_of_examples):
+        new_data.append(raw_data[:, :, :, i])
+    new_data = np.asarray(new_data)
+    print("Data has been reshaped from {} to {}".format(raw_data.shape, new_data.shape))
+    return new_data / 255.
+
+
+def one_hot_encoder(data, number_of_labels):
+    """
+    One-hot encoder for labels
+    """
+    data_size = len(data)
+    one_hot_matrix = np.zeros(shape=(data_size, number_of_labels))
+    for i in range(data_size):
+        current_row = np.zeros(shape=(number_of_labels))
+        current_number = data[i][0]
+        if current_number == 10:
+            current_row[0] = 1
+        else:
+            current_row[current_number] = 1
+        one_hot_matrix[i] = current_row
+    return one_hot_matrix
+
+
+def load_svhn_data(train_path, test_path, extra_path, load_extra, eval_percentage):
+    """
+    Load SVHN Dataset
+    """
+    print("Loading SVHN dataset for classification...")
+    # Load raw dataset
+    if load_extra:
+
+        print("Found extra dataset, loading it...")
+        train, test, extra = load_raw_data(train_path, test_path, load_extra, extra_path)
+        train['X'] = np.concatenate((train['X'], extra['X']), axis=3)
+        train['y'] = np.concatenate((train['y'], extra['y']), axis=0)
+    else:
+        train, test = load_raw_data(train_path, test_path, load_extra, extra_path)
+
+    # get values and labels
+    train_all_values = format_data(train['X'], train['X'].shape[3])
+    train_all_labels = one_hot_encoder(train['y'], 10)
+    test_values = format_data(test['X'], test['X'].shape[3])
+    test_labels = one_hot_encoder(test['y'], 10)
+
+    np.random.seed(41)
+    shuffle_indices = np.random.permutation(np.arange(len(train_all_values)))
+    train_values_shuffled = train_all_values[shuffle_indices]
+    train_labels_shuffled = train_all_labels[shuffle_indices]
+
+    # Seperate into training and eval set
+    # Original setting split the data into training and validation samples
+    train_index = -1 * int(eval_percentage * float(len(train_values_shuffled)))
+    train_values, eval_values = train_values_shuffled[:train_index], train_values_shuffled[train_index:]
+    train_labels, eval_labels = train_labels_shuffled[:train_index], train_labels_shuffled[train_index:]
+    print("Train/Eval split: {:d}/{:d}".format(len(train_labels), len(eval_labels)))
+    print("Loading data completed")
+    return [train_values, train_labels, eval_values, eval_labels, test_values, test_labels]
+
+def my_load_svhn_data(train_path, test_path, extra_path, load_extra):
+    """
+    Load SVHN Dataset
+    """
+    print("Loading SVHN dataset for classification...")
+    # Load raw dataset
+    if load_extra:
+
+        print("Found extra dataset, loading it...")
+        train, test, extra = load_raw_data(train_path, test_path, load_extra, extra_path)
+        train['X'] = np.concatenate((train['X'], extra['X']), axis=3)
+        train['y'] = np.concatenate((train['y'], extra['y']), axis=0)
+    else:
+        train, test = load_raw_data(train_path, test_path, load_extra, extra_path)
+
+    # get values and labels
+    train_all_values = format_data(train['X'], train['X'].shape[3])
+    train_all_labels = one_hot_encoder(train['y'], 10)
+    test_values = format_data(test['X'], test['X'].shape[3])
+    test_labels = one_hot_encoder(test['y'], 10)
+
+    np.random.seed(41)
+    shuffle_indices = np.random.permutation(np.arange(len(train_all_values)))
+    train_values_shuffled = train_all_values[shuffle_indices]
+    train_labels_shuffled = train_all_labels[shuffle_indices]
+    print("Loading data completed")
+
+    return train_values_shuffled, train_labels_shuffled, test_values, test_labels
+
+    # Seperate into training and eval set
+    # # Original setting split the data into training and validation samples
+    # train_index = -1 * int(eval_percentage * float(len(train_values_shuffled)))
+    # train_values, eval_values = train_values_shuffled[:train_index], train_values_shuffled[train_index:]
+    # train_labels, eval_labels = train_labels_shuffled[:train_index], train_labels_shuffled[train_index:]
+    # print("Train/Eval split: {:d}/{:d}".format(len(train_labels), len(eval_labels)))
+    # print("Loading data completed")
+    # return [train_values, train_labels, eval_values, eval_labels, test_values, test_labels]
+
+def load_data():
+    train_X, train_Y, test_X, test_Y = my_load_svhn_data(train_path = train_path,
+                                                                      test_path = test_path,
+                                                                      extra_path = extra_path,
+                                                                      load_extra = False)
+
+    return (train_X, train_Y), (test_X, test_Y)
+
+
+if __name__ == "__main__":
+
+
+    # train_X, train_Y, eval_X, eval_Y, test_X, test_Y = load_svhn_data(train_path = train_path,
+    #                                                                   test_path = test_path,
+    #                                                                   extra_path = extra_path,
+    #                                                                   load_extra = True,
+    #                                                                   eval_percentage = 0.1
+    #                                                                  )
+    (train_X, train_Y), (test_X, test_Y) = load_data()
+    print(np.shape(train_X))
+    print(np.shape(train_Y))
+    print(np.shape(test_X))
+    print(np.shape(test_Y))

server/LEMON-master/run/api_config_pool.json

@@ -0,0 +1,550 @@
+{
+  "Conv1D": {
+    "filters": [0],
+    "kernel_size": [0],
+    "strides": [0],
+    "padding": ["valid", "same", "causal"],
+    "data_format": ["channels_first", "channels_last"],
+    "dilation_rate": [0],
+    "activation": ["softmax", "elu", "selu", "softplus", "softsign", "relu", "tanh", "sigmoid", "hard_sigmoid", "exponential", "linear"],
+    "use_bias": [true, false],
+    "kernel_initializer": ["Zeros", "Ones", "Constant", "RandomNormal", "RandomUniform", "TruncatedNormal", "VarianceScaling",
+      "Orthogonal", "lecun_uniform", "glorot_normal",  "glorot_uniform", "he_normal", "lecun_normal", "Identity"],
+    "bias_initializer": ["Zeros", "Ones", "Constant", "RandomNormal", "RandomUniform", "TruncatedNormal", "VarianceScaling", "Orthogonal", "glorot_normal",  "glorot_uniform", "he_normal", "he_uniform", "lecun_normal", "lecun_uniform", "Identity"],
+    "kernel_regularizer": ["l1", "l2"],
+    "bias_regularizer": ["l1", "l2"],
+    "activity_regularizer": ["l1", "l2"],
+    "kernel_constraint": ["Constraint", "MaxNorm", "MinMaxNorm", "NonNeg", "UnitNorm"],
+    "bias_constraint": ["Constraint", "MaxNorm", "MinMaxNorm", "NonNeg", "UnitNorm"]
+  },
+  "Conv2D": {
+    "filters": [0],
+    "kernel_size": [0],
+    "strides": [0],
+    "padding": ["valid", "same"],
+    "data_format": ["channels_first", "channels_last"],
+    "dilation_rate": [0],
+    "activation": ["softmax", "elu", "selu", "softplus", "softsign", "relu", "tanh", "sigmoid", "hard_sigmoid", "exponential", "linear"],
+    "use_bias": [true, false],
+    "kernel_initializer": ["Zeros", "Ones", "Constant", "RandomNormal", "RandomUniform", "TruncatedNormal", "VarianceScaling",
+      "Orthogonal", "lecun_uniform", "glorot_normal",  "glorot_uniform", "he_normal", "lecun_normal", "Identity"],
+    "bias_initializer": ["Zeros", "Ones", "Constant", "RandomNormal", "RandomUniform", "TruncatedNormal", "VarianceScaling", "Orthogonal", "glorot_normal",  "glorot_uniform", "he_normal", "he_uniform", "lecun_normal", "lecun_uniform", "Identity"],
+    "kernel_regularizer": ["l1", "l2"],
+    "bias_regularizer": ["l1", "l2"],
+    "activity_regularizer": ["l1", "l2"],
+    "kernel_constraint": ["Constraint", "MaxNorm", "MinMaxNorm", "NonNeg", "UnitNorm"],
+    "bias_constraint": ["Constraint", "MaxNorm", "MinMaxNorm", "NonNeg", "UnitNorm"]
+  },
+  "Conv3D": {
+    "filters": [0],
+    "kernel_size": [0],
+    "strides": [0],
+    "padding": ["valid", "same"],
+    "data_format": ["channels_first", "channels_last"],
+    "dilation_rate": [0],
+    "activation": ["softmax", "elu", "selu", "softplus", "softsign", "relu", "tanh", "sigmoid", "hard_sigmoid", "exponential", "linear"],
+    "use_bias": [true, false],
+    "kernel_initializer": ["Zeros", "Ones", "Constant", "RandomNormal", "RandomUniform", "TruncatedNormal", "VarianceScaling",
+      "Orthogonal", "lecun_uniform", "glorot_normal",  "glorot_uniform", "he_normal", "lecun_normal", "Identity"],
+    "bias_initializer": ["Zeros", "Ones", "Constant", "RandomNormal", "RandomUniform", "TruncatedNormal", "VarianceScaling", "Orthogonal", "glorot_normal",  "glorot_uniform", "he_normal", "he_uniform", "lecun_normal", "lecun_uniform", "Identity"],
+    "kernel_regularizer": ["l1", "l2"],
+    "bias_regularizer": ["l1", "l2"],
+    "activity_regularizer": ["l1", "l2"],
+    "kernel_constraint": ["Constraint", "MaxNorm", "MinMaxNorm", "NonNeg", "UnitNorm"],
+    "bias_constraint": ["Constraint", "MaxNorm", "MinMaxNorm", "NonNeg", "UnitNorm"]
+  },
+  "SeparableConv1D": {
+    "filters": [0],
+    "kernel_size": [0],
+    "strides": [0],
+    "padding": ["valid", "same", "causal"],
+    "data_format": ["channels_first", "channels_last"],
+    "dilation_rate": [0],
+    "depth_multiplier": [0],
+    "activation": ["softmax", "elu", "selu", "softplus", "softsign", "relu", "tanh", "sigmoid", "hard_sigmoid", "exponential", "linear"],
+    "use_bias": [true, false],
+    "depthwise_initializer": ["Zeros", "Ones", "Constant", "RandomNormal", "RandomUniform", "TruncatedNormal", "VarianceScaling", "Orthogonal", "glorot_normal",  "glorot_uniform", "he_normal", "he_uniform", "lecun_normal", "lecun_uniform", "Identity"],
+    "pointwise_initializer": ["Zeros", "Ones", "Constant", "RandomNormal", "RandomUniform", "TruncatedNormal", "VarianceScaling", "Orthogonal", "glorot_normal",  "glorot_uniform", "he_normal", "he_uniform", "lecun_normal", "lecun_uniform", "Identity"],
+    "bias_initializer": ["Zeros", "Ones", "Constant", "RandomNormal", "RandomUniform", "TruncatedNormal", "VarianceScaling", "Orthogonal", "glorot_normal",  "glorot_uniform", "he_normal", "he_uniform", "lecun_normal", "lecun_uniform", "Identity"],
+    "depthwise_regularizer": ["l1", "l2"],
+    "pointwise_regularizer": ["l1", "l2"],
+    "bias_regularizer": ["l1", "l2"],
+    "activity_regularizer": ["l1", "l2"],
+    "depthwise_constraint": ["Constraint", "MaxNorm", "MinMaxNorm", "NonNeg", "UnitNorm"],
+    "pointwise_constraint": ["Constraint", "MaxNorm", "MinMaxNorm", "NonNeg", "UnitNorm"],
+    "bias_constraint": ["Constraint", "MaxNorm", "MinMaxNorm", "NonNeg", "UnitNorm"],
+    "trainable": [true, false]
+  },
+  "SeparableConv2D": {
+    "filters": [0],
+    "kernel_size": [0],
+    "strides": [0],
+    "padding": ["valid", "same"],
+    "data_format": ["channels_first", "channels_last"],
+    "dilation_rate": [0],
+    "depth_multiplier": [0],
+    "activation": ["softmax", "elu", "selu", "softplus", "softsign", "relu", "tanh", "sigmoid", "hard_sigmoid", "exponential", "linear"],
+    "use_bias": [true, false],
+    "depthwise_initializer": ["Zeros", "Ones", "Constant", "RandomNormal", "RandomUniform", "TruncatedNormal", "VarianceScaling", "Orthogonal", "glorot_normal",  "glorot_uniform", "he_normal", "he_uniform", "lecun_normal", "lecun_uniform", "Identity"],
+    "pointwise_initializer": ["Zeros", "Ones", "Constant", "RandomNormal", "RandomUniform", "TruncatedNormal", "VarianceScaling", "Orthogonal", "glorot_normal",  "glorot_uniform", "he_normal", "he_uniform", "lecun_normal", "lecun_uniform", "Identity"],
+    "bias_initializer": ["Zeros", "Ones", "Constant", "RandomNormal", "RandomUniform", "TruncatedNormal", "VarianceScaling", "Orthogonal", "glorot_normal",  "glorot_uniform", "he_normal", "he_uniform", "lecun_normal", "lecun_uniform", "Identity"],
+    "depthwise_regularizer": ["l1", "l2"],
+    "pointwise_regularizer": ["l1", "l2"],
+    "bias_regularizer": ["l1", "l2"],
+    "activity_regularizer": ["l1", "l2"],
+    "depthwise_constraint": ["Constraint", "MaxNorm", "MinMaxNorm", "NonNeg", "UnitNorm"],
+    "pointwise_constraint": ["Constraint", "MaxNorm", "MinMaxNorm", "NonNeg", "UnitNorm"],
+    "bias_constraint": ["Constraint", "MaxNorm", "MinMaxNorm", "NonNeg", "UnitNorm"]
+  },
+  "DepthwiseConv1D": {
+    "kernel_size": [0],
+    "strides": [0],
+    "padding": ["valid", "same"],
+    "depth_multiplier": [0],
+    "data_format": ["channels_first", "channels_last"],
+    "dilation_rate": [0],
+    "activation": ["softmax", "elu", "selu", "softplus", "softsign", "relu", "tanh", "sigmoid", "hard_sigmoid", "exponential", "linear"],
+    "use_bias": [true, false],
+    "depthwise_initializer": ["Zeros", "Ones", "Constant", "RandomNormal", "RandomUniform", "TruncatedNormal", "VarianceScaling", "Orthogonal", "glorot_normal",  "glorot_uniform", "he_normal", "he_uniform", "lecun_normal", "lecun_uniform", "Identity"],
+    "bias_initializer": ["Zeros", "Ones", "Constant", "RandomNormal", "RandomUniform", "TruncatedNormal", "VarianceScaling", "Orthogonal", "glorot_normal",  "glorot_uniform", "he_normal", "he_uniform", "lecun_normal", "lecun_uniform", "Identity"],
+    "depthwise_regularizer": ["l1", "l2"],
+    "bias_regularizer": ["l1", "l2"],
+    "activity_regularizer": ["l1", "l2"],
+    "depthwise_constraint": ["Constraint", "MaxNorm", "MinMaxNorm", "NonNeg", "UnitNorm"],
+    "bias_constraint": ["Constraint", "MaxNorm", "MinMaxNorm", "NonNeg", "UnitNorm"]
+  },
+  "DepthwiseConv2D": {
+    "kernel_size": [0],
+    "strides": [0],
+    "padding": ["valid", "same"],
+    "depth_multiplier": [0],
+    "data_format": ["channels_first", "channels_last"],
+    "dilation_rate": [0],
+    "activation": ["softmax", "elu", "selu", "softplus", "softsign", "relu", "tanh", "sigmoid", "hard_sigmoid", "exponential", "linear"],
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+    "use_bias": [true, false],
+    "kernel_initializer": ["Zeros", "Ones", "Constant", "RandomNormal", "RandomUniform", "TruncatedNormal", "VarianceScaling",
+      "Orthogonal", "lecun_uniform", "glorot_normal",  "glorot_uniform", "he_normal", "lecun_normal", "Identity"],
+    "bias_initializer": ["Zeros", "Ones", "Constant", "RandomNormal", "RandomUniform", "TruncatedNormal", "VarianceScaling", "Orthogonal", "glorot_normal",  "glorot_uniform", "he_normal", "he_uniform", "lecun_normal", "lecun_uniform", "Identity"],
+    "kernel_regularizer": ["l1", "l2"],
+    "bias_regularizer": ["l1", "l2"],
+    "activity_regularizer": ["l1", "l2"],
+    "kernel_constraint": ["Constraint", "MaxNorm", "MinMaxNorm", "NonNeg", "UnitNorm"],
+    "bias_constraint": ["Constraint", "MaxNorm", "MinMaxNorm", "NonNeg", "UnitNorm"]
+  },
+  "LocallyConnected2D": {
+    "filters": [0],
+    "kernel_size": [0],
+    "strides": [0],
+    "padding": ["valid", "same"],
+    "data_format": ["channels_first", "channels_last"],
+    "activation": ["softmax", "elu", "selu", "softplus", "softsign", "relu", "tanh", "sigmoid", "hard_sigmoid", "exponential", "linear"],
+    "use_bias": [true, false],
+    "kernel_initializer": ["Zeros", "Ones", "Constant", "RandomNormal", "RandomUniform", "TruncatedNormal", "VarianceScaling",
+      "Orthogonal", "lecun_uniform", "glorot_normal",  "glorot_uniform", "he_normal", "lecun_normal", "Identity"],
+    "bias_initializer": ["Zeros", "Ones", "Constant", "RandomNormal", "RandomUniform", "TruncatedNormal", "VarianceScaling", "Orthogonal", "glorot_normal",  "glorot_uniform", "he_normal", "he_uniform", "lecun_normal", "lecun_uniform", "Identity"],
+    "kernel_regularizer": ["l1", "l2"],
+    "bias_regularizer": ["l1", "l2"],
+    "activity_regularizer": ["l1", "l2"],
+    "kernel_constraint": ["Constraint", "MaxNorm", "MinMaxNorm", "NonNeg", "UnitNorm"],
+    "bias_constraint": ["Constraint", "MaxNorm", "MinMaxNorm", "NonNeg", "UnitNorm"]
+  }
+}

server/LEMON-master/run/localization_executor.py

@@ -0,0 +1,34 @@
+"""
+# Part  of localization phase
+"""
+import os
+import sys
+import configparser
+from datetime import datetime
+
+config_name = sys.argv[1]
+
+lemon_cfg = configparser.ConfigParser()
+lemon_cfg.read(f"./config/{config_name}")
+parameters = lemon_cfg['parameters']
+python_prefix = parameters['python_prefix'].rstrip("/")
+
+# 1. get unique inconsistency
+start_time = datetime.now()
+print("Localization Starts!")
+print("\n\nPhase1: Get Unique Inconsistency")
+get_unique_inconsistency = f"{python_prefix}\lemon\python -u -m scripts.localization.get_unique_inconsistency {config_name}"
+os.system(get_unique_inconsistency)
+
+# 2. localization
+print("\n\nPhase2: Localize")
+localize = f"{python_prefix}\lemon\python  -u -m run.localize_lemon {config_name}"
+os.system(localize)
+
+# 3. get suspected bugs
+print("\n\nPhase3: Suspected bugs analysis")
+get_suspecte_bugs = f"{python_prefix}\lemon\python  -u -m scripts.localization.suspected_bugs_detector {config_name}"
+os.system(get_suspecte_bugs)
+
+print("Localization finishes!")
+print(f"Localization time cost: {datetime.now() - start_time}")

server/LEMON-master/run/localize_lemon.py

@@ -0,0 +1,202 @@
+# -*-coding:UTF-8-*-
+"""
+# Part  of localization phase
+"""
+import argparse
+import sys
+import os
+import pickle
+import configparser
+from scripts.tools.utils import ModelUtils
+import keras
+from keras.engine.input_layer import InputLayer
+import warnings
+import datetime
+from scripts.logger.lemon_logger import Logger
+import shutil
+from itertools import combinations
+import keras.backend as K
+warnings.filterwarnings("ignore")
+
+
+def is_lstm_not_exists(exp_id,output_id):
+    if exp_id in ['lstm0-sinewave','lstm2-price'] and output_id in ['experiment4','experiment5']:
+        return True
+    else:
+        return False
+
+
+def get_HH_mm_ss(td):
+    days,seconds = td.days,td.seconds
+    hours = days * 24 + seconds // 3600
+    minutes = (seconds % 3600)//60
+    secs = seconds % 60
+    return hours,minutes,secs
+
+
+def generate_report(localize_res,savepath):
+    with open(savepath,"w+") as fw:
+        for localize_header, value in localize_res.items():
+            fw.write("current_layer, delta,Rl,previous_layer\n".format(localize_header))
+            for layer_res in value:
+                fw.write("{},{},{},{}\n".format(layer_res[0],layer_res[1],layer_res[2],layer_res[3]))
+
+
+def localize(mut_model_dir,select_idntfr, exp_name,localize_tmp_dir,report_dir,backends):
+    """
+    select_idntfrs: lenet5-mnist_origin0_input17
+    """
+    # get layer_output for all models coming from specific exp on all backends
+    identifier_split = select_idntfr.split("_")
+    data_index = int(identifier_split[-1][5:])
+    model_idntfr = "{}_{}".format(identifier_split[0], identifier_split[1])
+    if 'svhn' in model_idntfr or 'fashion2' in model_idntfr:
+        model_path = "{}/{}.hdf5".format(mut_model_dir, model_idntfr)
+    else:
+        model_path = "{}/{}.h5".format(mut_model_dir, model_idntfr)
+    #
+    # # check if indntfr hasn't been localized
+    # for bk1, bk2 in combinations(backends, 2):
+    #     report_path = os.path.join(report_dir, "{}_{}_{}_input{}.csv".format(model_idntfr, bk1, bk2, data_index))
+    #     # not exists; continue fo localize
+    #     if not os.path.exists(report_path):
+    #         break
+    # # all file exist; return
+    # else:
+    #     mylogger.logger.info(f"{select_idntfr} has been localized")
+    #     return
+
+    for bk in backends:
+        python_bin = f"{python_prefix}\{bk}\python"
+        return_stats = os.system(
+            f"{python_bin} -u -m run.patch_hidden_output_extractor --backend {bk} --output_dir {output_dir} --exp {exp_name}"
+            f" --model_idntfr {model_idntfr} --data_index {data_index} --config_name {config_name}")
+        # assert return_stats==0,"Getting hidden output failed!"
+        if return_stats != 0:
+            mylogger.logger.info("Getting hidden output failed!")
+            failed_list.append(select_idntfr)
+            return
+    mylogger.logger.info("Getting localization for {}".format(select_idntfr))
+    model = keras.models.load_model(model_path, custom_objects=ModelUtils.custom_objects())
+    for bk1, bk2 in combinations(backends, 2):
+        local_res = {}
+        local_res = get_outputs_divation_onbackends(model=model, backends=[bk1, bk2],
+                                                    model_idntfr=model_idntfr, local_res=local_res,
+                                                    data_index=data_index, localize_tmp_dir=localize_tmp_dir)
+        mylogger.logger.info("Generating localization report for {} on {}-{}!".format(model_idntfr,bk1,bk2))
+        report_path = os.path.join(report_dir, "{}_{}_{}_input{}.csv".format(model_idntfr,bk1,bk2, data_index))
+        generate_report(local_res, report_path)
+    del model
+    K.clear_session()
+
+
+def get_outputs_divation_onbackends(model,backends,model_idntfr,local_res,data_index,localize_tmp_dir):
+    backend1 = backends[0]
+    backend2 = backends[1]
+    with open(os.path.join(localize_tmp_dir, "{}_{}_{}".format(model_idntfr, backend1,data_index)), "rb") as fr:
+        model_layers_outputs_1 = pickle.load(fr)
+    with open(os.path.join(localize_tmp_dir, "{}_{}_{}".format(model_idntfr, backend2,data_index)), "rb") as fr:
+        model_layers_outputs_2 = pickle.load(fr)
+    divations = ModelUtils.layers_divation(model, model_layers_outputs_1, model_layers_outputs_2)
+    compare_res = []
+
+    for i, layer in enumerate(model.layers):
+        if isinstance(layer, InputLayer):
+            continue
+        delta, divation, inputlayers = divations[i]
+        layer_compare_res = [layer.name, delta[0], divation[0],",".join(inputlayers)]  # batch accepted default
+        compare_res.append(layer_compare_res)
+    identifier = "{}_{}_{}_input_{}".format(model_idntfr,backend1,backend2,data_index)
+    idntfr_localize = "{}_localize".format(identifier)
+    local_res[idntfr_localize] = compare_res
+    return local_res
+
+
+if __name__ == "__main__":
+
+    starttime = datetime.datetime.now()
+
+    # get id of experiments
+    config_name = sys.argv[1]
+    lemon_cfg = configparser.ConfigParser()
+    lemon_cfg.read(f"./config/{config_name}")
+    parameters = lemon_cfg['parameters']
+
+    output_dir = parameters['output_dir']
+    output_dir = output_dir[:-1] if output_dir.endswith("/") else output_dir
+    current_container = os.path.split(output_dir)[-1]
+    python_prefix = parameters['python_prefix'].rstrip("/")
+
+    """Initialization"""
+    mylogger = Logger()
+    backend_choices = [1,2,3]
+    exps = parameters['exps'].lstrip().rstrip().split(" ")
+    exps.sort(key=lambda x: x)
+    all_model_inputs = {e:set() for e in exps}
+    items_lists = list()
+    for backend_choice in backend_choices:
+        if backend_choice == 1:
+            pre_backends = ['tensorflow', 'theano', 'cntk']
+        elif backend_choice == 2:
+            pre_backends = ['tensorflow', 'theano', 'mxnet']
+        else:
+            pre_backends = ['tensorflow', 'cntk', 'mxnet']
+        backends_str = "-".join(pre_backends)
+        backend_pairs = [f"{pair[0]}_{pair[1]}" for pair in combinations(pre_backends, 2)]
+
+        with open(os.path.join(output_dir, f"localize_model_inputs-{backends_str}.pkl"), "rb") as fr:
+            localize_model_inputs = pickle.load(fr)
+            for exp_id,model_set in localize_model_inputs.items():
+                if exp_id in exps:
+                    for mi in model_set:
+                        all_model_inputs[exp_id].add(mi)
+
+    for exp,mi_set in all_model_inputs.items():
+        print(exp,len(mi_set))
+    failed_list = []
+    """Print result of inconsistency distribution"""
+    for exp_idntfr,model_inputs_set in all_model_inputs.items():
+        if len(model_inputs_set) > 0:
+            if exp_idntfr == 'inception.v3-imagenet' or exp_idntfr == 'densenet121-imagenet' or is_lstm_not_exists(exp_idntfr,current_container):
+                # inception and densenet can't run on mxnet.
+                # lstm can't run on mxnet before mxnet version 1.3.x
+                backends = ['tensorflow', 'theano', 'cntk']
+            else:
+                backends = ['tensorflow', 'theano', 'cntk','mxnet']
+            print("Localize for {} : {} left.".format(exp_idntfr,len(model_inputs_set)))
+            mut_dir = os.path.join(output_dir,exp_idntfr, "mut_model")
+            localization_dir = os.path.join(output_dir,exp_idntfr, "localization_result")
+            localize_output_dir = os.path.join(output_dir,exp_idntfr, "localize_tmp")
+
+            """make dir for hidden_output and localization dir """
+            if not os.path.exists(localize_output_dir):
+                os.makedirs(localize_output_dir)
+            if not os.path.exists(localization_dir):
+                os.makedirs(localization_dir)
+
+            """Localization"""
+            for idx,select_identifier in enumerate(model_inputs_set):
+                print("{} of {} {}".format(idx,len(model_inputs_set),select_identifier))
+                localize(mut_model_dir=mut_dir,select_idntfr=select_identifier,exp_name=exp_idntfr,
+                         localize_tmp_dir=localize_output_dir,report_dir=localization_dir
+                         ,backends=backends)
+
+            shutil.rmtree(localize_output_dir)
+
+    with open(os.path.join(output_dir, f"failed_idntfrs.txt"), "w") as fw:
+        if len(failed_list) > 0:
+            mylogger.logger.warning(f"{len(failed_list)} idntfrs fail to localize")
+            lists = [f"{line} \n" for line in failed_list]
+            fw.writelines(lists)
+        else:
+            mylogger.logger.info("all idntfrs localize successfully")
+
+    endtime = datetime.datetime.now()
+    time_delta = endtime - starttime
+    h,m,s = get_HH_mm_ss(time_delta)
+    mylogger.logger.info("Localization precess is done: Time used: {} hour,{} min,{} sec".format(h,m,s))
+
+
+
+
+

server/LEMON-master/run/model2json.py

@@ -0,0 +1,438 @@
+import keras
+import json
+import os
+from itertools import product
+import sys
+sys.path.append("../")
+# from scripts.coverage import custom_objects
+import tensorflow as tf
+import psutil
+import scripts.tools.utils as utils_tools
+# folder_path = 'model_json'
+# api_config_pool_path = 'api_config_pool.json'
+PARAMETER_SPACE = 5
+import configparser
+
+def custom_objects():
+    def no_activation(x):
+        return x
+
+    def leakyrelu(x):
+        import keras.backend as K
+        return K.relu(x, alpha=0.01)
+
+    objects = {}
+    objects['no_activation'] = no_activation
+    objects['leakyrelu'] = leakyrelu
+    return objects
+
+
+def extract_edges(model):
+    layer_list = model.layers
+
+    existing_edges = []
+    for layer in layer_list:
+        start_layer_class = layer.__class__.__name__
+        if "Input" in start_layer_class:
+            continue
+        for node in layer._outbound_nodes:
+            end_layer_class = node.outbound_layer.__class__.__name__
+            edge = (start_layer_class, end_layer_class)  # edge should be direct
+            if edge not in existing_edges:
+                existing_edges.append(edge)
+    return existing_edges
+
+
+def extract_nodes(model):
+    """
+    existing_nodes: {"layer_name1": [layer_config1, layer_config2], "layer_name2": [], ...}
+    """
+    layer_list = model.layers
+    existing_nodes = {}
+
+    for layer in layer_list:
+        layer_config = layer.get_config()
+        layer_config.pop("name")
+        if "filters" in layer_config: layer_config.pop("filters")
+        if "units" in layer_config: layer_config.pop("units")
+        layer_class = layer.__class__.__name__
+        if 'Input' in layer_class:
+            continue
+        if layer_class not in existing_nodes:
+            existing_nodes[layer_class] = []
+        if layer_config not in existing_nodes[layer_class]:
+            existing_nodes[layer_class].append(layer_config)
+    return existing_nodes
+
+
+def extract_inputs(model):
+    """
+    existing_inputs: {"layer_class": {"input_dims": [], "dtype": [], "shape": []}}
+    layer_dims: {"layer_class": {"input_dims": [], "output_dims": []}}
+    """
+    # if model.__class__.__name__ == 'Sequential':
+    #     layer_list = model.layers
+    # else:
+    #     layer_list = model.layers[1:]  # ignore the first input layer
+    layer_list = model.layers
+    existing_inputs = {}
+    layer_dims = {}
+    for layer in layer_list:
+        layer_class = layer.__class__.__name__
+        if 'Input' in layer_class:
+            continue
+        if layer_class not in existing_inputs:
+            existing_inputs[layer_class] = {"input_dims": [], "dtype": [], "shape": []}
+            layer_dims[layer_class] = {"input_dims": [], "output_dims": []}
+        input_dims = len(layer.input.shape)
+        output_dims = len(layer.output.shape)
+        dtype = str(layer.input.dtype.name)
+        shape = str(list(layer.input.shape))
+        if input_dims not in existing_inputs[layer_class]['input_dims']:
+            existing_inputs[layer_class]['input_dims'].append(input_dims)
+        if input_dims not in layer_dims[layer_class]['input_dims']:
+            layer_dims[layer_class]['input_dims'].append(input_dims)
+        if output_dims not in layer_dims[layer_class]['output_dims']:
+            layer_dims[layer_class]['output_dims'].append(output_dims)
+        if dtype not in existing_inputs[layer_class]['dtype']:
+            existing_inputs[layer_class]['dtype'].append(dtype)
+        if shape not in existing_inputs[layer_class]['shape']:
+            existing_inputs[layer_class]['shape'].append(shape)
+    return existing_inputs, layer_dims
+
+
+def model_to_json(model_path, folder_path):
+    cur_model = keras.models.load_model(model_path, custom_objects=custom_objects())
+    cur_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
+
+    existing_edges = extract_edges(cur_model)
+    existing_node = extract_nodes(cur_model)
+    existing_inputs, layer_dims = extract_inputs(cur_model)
+    layer_dict = {}
+    cur_edge_num = 0
+    for layer in cur_model.layers:
+        layer_name = layer.__class__.__name__
+        if 'Input' in layer_name:
+            continue
+        layer_dict[layer_name] = layer_dict[layer_name] + 1 if layer_name in layer_dict else 1
+        inbound_nodes = layer._inbound_nodes
+        if inbound_nodes:
+            if isinstance(inbound_nodes[0].inbound_layers, list):
+                cur_edge_num += len(inbound_nodes[0].inbound_layers)
+            else:
+                if inbound_nodes:
+                    cur_edge_num += 1
+
+    layer_num = sum(layer_dict.values())
+    layer_type = list(layer_dict.keys())
+    print(f'layer_num:{layer_num}')
+    print(f'layer_type_num:{layer_type}')
+    print(f'cur_edge_num:{cur_edge_num}')
+    print(existing_edges)
+    print(existing_node)
+    print(existing_inputs)
+    print(layer_dims)
+
+    cur_model_info = {}
+    cur_model_info['edges'] = existing_edges
+    cur_model_info['layer_config'] = existing_node
+    cur_model_info['layer_input_info'] = existing_inputs
+    cur_model_info['layer_num'] = layer_num
+    cur_model_info['layer_type'] = layer_type
+    cur_model_info['cur_edge_num'] = cur_edge_num
+    cur_model_info['layer_dims'] = layer_dims
+    if 'svhn' in model_path or 'fashion2' in model_path:
+        json_path = model_path.replace("hdf5","json")+".json"
+    else:
+        json_path = os.path.join(folder_path, model_path.split("\\")[-1][:-3] + '.json')
+    # if not os.path.exists(json_path):
+    for op in cur_model_info['layer_config']:
+        for config in cur_model_info['layer_config'][op]:
+            for config_key, config_value in config.items():
+                if not (isinstance(config_value, int) or isinstance(config_value, str) or isinstance(config_value,
+                                                                                                     list) or isinstance(
+                    config_value, float) or isinstance(config_value, dict) or isinstance(config_value,
+                                                                                         set) or isinstance(
+                    config_value, tuple)):
+                    config[config_key] = str(config_value)
+
+    try:
+        with open(json_path, 'w') as json_file:
+            json.dump(cur_model_info, json_file, indent=4)
+    except Exception as e:
+        print("!!!!!!!")
+        print(cur_model_info)
+        raise e
+
+
+def union_json(single_json_path, all_json_path):
+    """
+    single_json_path:单个json文件的路径
+    all_json_path:分母json文件的路径
+    """
+    with open(single_json_path, 'r') as json_file:
+        model_info = json.load(json_file)
+    if not os.path.exists(all_json_path):
+        all_layer_info = {}
+    else:
+        with open(all_json_path, 'r') as all_json_file:
+            all_layer_info = json.load(all_json_file)
+
+    if 'layer_config' not in all_layer_info.keys():
+        all_layer_info['layer_config'] = {}
+    for layer_class, layer_configs in model_info['layer_config'].items():
+        if layer_class not in all_layer_info['layer_config'].keys():
+            all_layer_info['layer_config'][layer_class] = layer_configs
+        else:
+            for layer_config in layer_configs:
+                if layer_config not in all_layer_info['layer_config'][layer_class]:
+                    all_layer_info['layer_config'][layer_class].append(layer_config)
+
+    if 'layer_input_info' not in all_layer_info.keys():
+        all_layer_info['layer_input_info'] = {}
+    for layer_class, layer_input_info in model_info['layer_input_info'].items():
+        if layer_class not in all_layer_info['layer_input_info'].keys():
+            all_layer_info['layer_input_info'][layer_class] = layer_input_info
+        else:
+            for attr in ["input_dims", "dtype", "shape"]:
+                if attr not in all_layer_info['layer_input_info'][layer_class].keys():
+                    all_layer_info['layer_input_info'][layer_class][attr] = layer_input_info[attr]
+                else:
+                    all_layer_info['layer_input_info'][layer_class][attr] = list(
+                        set(layer_input_info[attr]).union(set(all_layer_info['layer_input_info'][layer_class][attr])))
+
+    if 'layer_dims' not in all_layer_info.keys():
+        all_layer_info['layer_dims'] = {}
+    for layer_class, layer_dims in model_info['layer_dims'].items():
+        if layer_class not in all_layer_info['layer_dims'].keys():
+            all_layer_info['layer_dims'][layer_class] = layer_dims
+        else:
+            for attr in ["input_dims", "output_dims"]:
+                if attr not in all_layer_info['layer_dims'][layer_class].keys():
+                    all_layer_info['layer_dims'][layer_class][attr] = layer_dims[attr]
+                else:
+                    all_layer_info['layer_dims'][layer_class][attr] = list(
+                        set(layer_dims[attr]).union(set(all_layer_info['layer_dims'][layer_class][attr])))
+
+    if 'layer_type' not in all_layer_info.keys():
+        all_layer_info['layer_type'] = model_info['layer_type']
+    else:
+        all_layer_info['layer_type'] = list(set(model_info['layer_type']).union(set(all_layer_info['layer_type'])))
+
+    if 'max_edge_num' not in all_layer_info.keys():
+        all_layer_info['max_edge_num'] = model_info['cur_edge_num']
+    else:
+        all_layer_info['max_edge_num'] = max(all_layer_info['max_edge_num'], model_info['cur_edge_num'])
+
+    if 'max_layer_num' not in all_layer_info.keys():
+        all_layer_info['max_layer_num'] = model_info['layer_num']
+    else:
+        all_layer_info['max_layer_num'] = max(all_layer_info['max_layer_num'], model_info['layer_num'])
+
+    with open(all_json_path, 'w') as json_file:
+        json.dump(all_layer_info, json_file, indent=4)
+
+
+class CoverageCalculatornew:
+
+    # init里只能是和具体模型无关的数值的初始化
+    def __init__(self, all_json_path, api_config_pool_path):
+        self.all_layer_info = {}
+        self.edges = []
+        self.all_edges = []
+        self.layer_config = {}
+        self.layer_input_info = {}
+        self.POSSIBLE_DTYPE = {'bfloat16', 'double', 'float16', 'float32', 'float64', 'half'}
+
+        with open(api_config_pool_path, "r") as pool_file:
+            self.api_config_pool = json.load(pool_file)
+        with open(all_json_path, 'r') as json_file:
+            self.all_layer_info = json.load(json_file)
+
+        self.total_dtype_num = len(self.all_layer_info["layer_input_info"]) * len(self.POSSIBLE_DTYPE)
+        self.total_shape_num = len(self.all_layer_info["layer_input_info"]) * PARAMETER_SPACE
+        self.total_ndims_num = 0
+        for layer_class in self.all_layer_info["layer_input_info"]:
+            ndims_list = self.all_layer_info["layer_input_info"][layer_class]["input_dims"]
+            self.total_ndims_num += len(ndims_list)
+        self.total_input_num = self.total_ndims_num + self.total_dtype_num + self.total_shape_num
+
+        self.total_param = {}
+        # self.total_param_list = {}
+        self.total_param_num = 0
+        for layer_class in self.api_config_pool:
+            self.total_param[layer_class] = 0
+            # self.total_param_list[layer_class] = {}
+            for config in self.api_config_pool[layer_class]:
+                # self.total_param_list[layer_class][config] = []
+                if self.api_config_pool[layer_class][config] == [0]:
+                    self.total_param[layer_class] += PARAMETER_SPACE
+                else:
+                    self.total_param[layer_class] += len(self.api_config_pool[layer_class][config])
+            self.total_param_num += self.total_param[layer_class]
+
+        for pre_layer, next_layer in product(self.all_layer_info["layer_dims"].keys(), repeat=2):
+            if set(self.all_layer_info["layer_dims"][pre_layer]["output_dims"]).intersection(
+                    set(self.all_layer_info["layer_dims"][next_layer]["input_dims"])) != 0:
+                self.all_edges.append([pre_layer, next_layer])
+
+        self.max_edge_num = self.all_layer_info['max_edge_num']
+        self.max_layer_num = self.all_layer_info['max_layer_num']
+        self.layer_type = len(self.all_layer_info["layer_type"])
+
+        self.cur_edge_num = 0
+        self.cur_layer_num = 0
+        self.cur_layer_type = 0
+
+    def load_json(self, json_path):
+        with open(json_path, 'r') as json_file:
+            model_info = json.load(json_file)
+
+        self.cur_edge_num = model_info["cur_edge_num"]
+        self.cur_layer_num = model_info['layer_num']
+        self.cur_layer_type = len(model_info['layer_type'])
+        self.edges = []
+        self.layer_config = {}
+        self.layer_input_info = {}
+
+        for edge in model_info['edges']:
+            if edge not in self.edges:
+                self.edges.append(edge)
+
+        for class_type, configs in model_info["layer_config"].items():
+            if class_type not in self.layer_config:
+                self.layer_config[class_type] = configs
+            else:
+                for config in configs:
+                    if config not in self.layer_config[class_type]:
+                        self.layer_config[class_type].append(config)
+        print(self.layer_config)
+
+        for layer_class, layer_input_info in model_info['layer_input_info'].items():
+            if layer_class not in self.layer_input_info:
+                self.layer_input_info[layer_class] = layer_input_info
+            else:
+                for attr in ["input_dims", "dtype", "shape"]:
+                    if attr not in self.layer_input_info[layer_class].keys():
+                        self.layer_input_info[layer_class][attr] = layer_input_info[attr]
+                    else:
+                        self.layer_input_info[layer_class][attr] = list(
+                            set(layer_input_info[attr]).union(
+                                set(self.layer_input_info[layer_class][attr])))
+
+    def api_pair_coverage(self):
+        # print(f"The API Pair Coverage Is: {len(self.edges)}/{len(self.all_edges)}")
+        return len(self.edges) / len(self.all_edges)
+
+    def _layer_config_coverage(self, layer_config_list, layer_class):
+        """
+            hp: count of param_value.
+            param_list: {param1: [value1, value2], ...}
+        """
+        config_pool = self.api_config_pool[layer_class]
+        param_list = {}
+        for param in config_pool:
+            param_list[param] = []
+        hp = 0
+        # Journal Submitted Version is Below.
+        for layer_config in layer_config_list:
+            for param in layer_config:
+                if param not in param_list:
+                    continue
+                if config_pool[param] == [0]:
+                    if layer_config[param] not in param_list[param] and len(param_list[param]) <= PARAMETER_SPACE:
+                        param_list[param].append(layer_config[param])
+                        hp += 1
+                else:
+                    if layer_config[param] not in param_list[param]:
+                        param_list[param].append(layer_config[param])
+                        hp += 1
+        return hp, param_list
+
+    def config_coverage(self):
+        total_hp = 0
+        for layer_class in self.layer_config:
+            if layer_class in self.api_config_pool:
+                layer_config_list = self.layer_config[layer_class]
+                hp, param_list = self._layer_config_coverage(layer_config_list, layer_class)
+                total_hp += hp
+        # print(f"The Configuration Coverage is: {total_hp}/{self.total_param_num}")
+        return total_hp / self.total_param_num
+
+    def ndims_coverage(self):
+        """
+        ndims_cov
+        """
+        covered_ndims_num = 0
+        for layer_class in self.layer_input_info:
+            ndims_list = self.layer_input_info[layer_class]["input_dims"]
+            covered_ndims_num += len(ndims_list)
+        return covered_ndims_num
+
+    def dtype_coverage(self):
+        covered_dtype_num = 0
+        for layer_class in self.layer_input_info:
+            dtype_list = self.layer_input_info[layer_class]["dtype"]
+            covered_dtype_num += len(dtype_list)
+        return covered_dtype_num
+
+    def shape_coverage(self):
+        covered_shape_num = 0
+        for layer_class in self.layer_input_info:
+            shape_list = self.layer_input_info[layer_class]["shape"]
+            covered_shape_num += min(len(shape_list),
+                                     PARAMETER_SPACE)  # if the total number of shape is larger that SHAPE_SPACE, we set it as 100%
+        return covered_shape_num
+
+    def input_coverage(self):
+        """
+        input_cov = ndim_cov + dtype_cov + shape_cov
+        """
+        covered_ndims = self.ndims_coverage()
+        covered_dtype = self.dtype_coverage()
+        covered_shape = self.shape_coverage()
+        print(f"The NDims Coverage Is: {covered_ndims}/{self.total_ndims_num}")
+        print(f"The DType Coverage Is: {covered_dtype}/{self.total_dtype_num}")
+        print(f"The Shape Coverage Is: {covered_shape}/{self.total_shape_num}")
+        print(f"The Input Coverage Is: {covered_ndims + covered_dtype + covered_shape}/{self.total_input_num}")
+        input_cov = (covered_ndims + covered_dtype + covered_shape) / self.total_input_num
+        ndims_cov = covered_ndims / self.total_ndims_num
+        dtype_cov = covered_dtype / self.total_dtype_num
+        shape_cov = covered_shape / self.total_shape_num
+        return input_cov, ndims_cov, dtype_cov, shape_cov
+
+    def op_type_cover(self):
+        print(f'op_type_cover is: {self.cur_layer_type}/{self.layer_type}')
+        return self.cur_layer_type / self.layer_type
+
+    def op_num_cover(self):
+        print(f'op_num_cover is: {self.cur_layer_num}/{self.max_layer_num}')
+        return self.cur_layer_num / self.max_layer_num
+
+    def edge_cover(self):
+        print(f'edge_cover is: {self.cur_edge_num}/{self.max_edge_num}')
+        return self.cur_edge_num / self.max_edge_num
+
+    def cal_coverage(self):
+        input_cov, ndims_cov, dtype_cov, shape_cov = self.input_coverage()
+        config_cov = self.config_coverage()
+        api_cov = self.api_pair_coverage()
+        op_type_cov = self.op_type_cover()
+        op_num_cov = self.op_num_cover()
+        edge_cov = self.edge_cover()
+        return input_cov, config_cov, api_cov, op_type_cov, op_num_cov, edge_cov
+
+# if __name__ == '__main__':
+#     model_path1 = 'data/mnist_output/000005/models/tensorflow.h5'
+#     model_path2 = 'data/mnist_output/000004/models/tensorflow.h5'
+#     all_json_path = os.path.join(folder_path, "all_layer_info.json")
+#     model_to_json(model_path1)
+#     model_to_json(model_path2)
+#     for file in os.listdir(folder_path):
+#         if file != 'all_layer_info.json':
+#             file_path = os.path.join(folder_path, file)
+#             union_json(file_path, all_json_path)
+#
+#     cal_cov = CoverageCalculator(all_json_path)
+#     cal_cov.load_json('model_json/000004.json')
+#     cal_cov.cal_coverage()

server/LEMON-master/run/model_to_txt.py

@@ -0,0 +1,143 @@
+import os
+import keras
+import psutil
+import configparser
+import os
+import sys
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+import scripts.tools.utils as tools_utils
+import argparse
+import json
+class LossHistory(keras.callbacks.Callback):
+    def on_train_begin(self, logs=None):
+        self.losses = []
+        self.acc = []
+        self.mem_info_list = []
+
+    def on_batch_end(self, batch, logs=None):
+        process = psutil.Process()
+        self.losses.append(logs.get('loss'))
+        self.acc.append(logs.get('acc'))
+        self.mem_info_list.append(process.memory_info().rss / (1024**3))
+
+def save_log_txt(model, path, name, bk, x_train, y_train):
+    history_loss = LossHistory()
+    model.fit(x_train, y_train, epochs=2, batch_size=1024, validation_split=0.2, verbose=1, callbacks=[history_loss])
+
+    model_name = name.split("/")[-1].split("_")[0]
+    method_name = name.split("/")[-1].split("_")[1]
+    valid_acc = [acc for acc in history_loss.acc if acc is not None]
+    result_data = {
+        "model": model_name,
+        "method": method_name,
+        "result": {
+            "Losses": sum(history_loss.losses)/len(history_loss.losses),
+            "Accuracy": sum(valid_acc)/len(valid_acc),
+            "MemoryInfoList": sum(history_loss.mem_info_list)/len(history_loss.mem_info_list)
+        }
+    }
+
+    if os.path.exists(path):
+        with open(path, 'r') as json_file:
+            data = json.load(json_file)
+    else:
+        data = []
+    data.append(result_data)
+    
+    with open(path, 'w') as json_file:
+        json.dump(data, json_file, indent=4)
+        
+
+def custom_objects():
+    def no_activation(x):
+        return x
+    def leakyrelu(x):
+        import keras.backend as K
+        return K.relu(x, alpha=0.01)
+    objects = {}
+    objects['no_activation'] = no_activation
+    objects['leakyrelu'] = leakyrelu
+    return objects
+
+def model_to_txt1(model_path, bk):
+    
+    cur_model = keras.models.load_model(model_path, custom_objects=custom_objects())
+    cur_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
+
+    data = tools_utils.DataUtils
+    
+    
+    if 'svhn' in model_path or 'fashion2' in model_path:
+        txt_path = model_path.split("\\")[-1][:-5].split("mut_model")[0]+bk+".json"
+    else:
+        txt_path = model_path.split("\\")[-1][:-3].split("mut_model")[0]+bk+".json"
+    if 'svhn' in model_path or 'fashion2' in model_path:
+        model_path = model_path.split("\\")[-1][:-5]
+    else:
+        model_path = model_path.split("\\")[-1][:-3]
+    data_path = model_path.split("/")[-1]
+    x_test, y_test = data.get_data_by_exp(data_path)
+    save_log_txt(cur_model,txt_path,model_path,bk,x_test, y_test)
+
+
+if __name__ == "__main__":
+
+    """Parser of command args"""
+    parse = argparse.ArgumentParser()
+    parse.add_argument("--backend", type=str, help="name of backends")
+    parse.add_argument("--model_path", type=str, help="redis db port")
+    parse.add_argument("--root_dir", type=str, help="redis db port")
+    flags, unparsed = parse.parse_known_args(sys.argv[1:])
+
+    """Load Configuration"""
+    lemon_cfg = configparser.ConfigParser()
+    # lemon_cfg.read(f"./config/{flags.config_name}")
+    
+    conf_path = os.path.join(os.path.dirname(os.getcwd()), "config", "demo.conf")
+    lemon_cfg.read(conf_path)
+    parameters = lemon_cfg['parameters']
+    gpu_ids = parameters['gpu_ids']
+    gpu_list = parameters['gpu_ids'].split(",")
+
+    """Init cuda"""
+    os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+    os.environ["CUDA_VISIBLE_DEVICES"] = gpu_ids
+
+    """Switch backend"""
+    bk_list = ['tensorflow', 'theano', 'cntk', 'mxnet']
+    bk = flags.backend
+    print('.........................',type(bk))
+    os.environ['KERAS_BACKEND'] = bk
+    os.environ['PYTHONHASHSEED'] = '0'
+
+    if bk == 'tensorflow':
+        os.environ["TF_CPP_MIN_LOG_LEVEL"] = '2'  # 只显示 warning 和 Error
+        import tensorflow as tf
+
+    if bk == 'theano':
+        if len(gpu_list) == 2:
+            os.environ[
+                'THEANO_FLAGS'] = f"device=cuda,contexts=dev{gpu_list[0]}->cuda{gpu_list[0]};dev{gpu_list[1]}->cuda{gpu_list[1]}," \
+                                  f"force_device=True,floatX=float32,lib.cnmem=1"
+        else:
+            os.environ['THEANO_FLAGS'] = f"device=cuda,contexts=dev{gpu_list[0]}->cuda{gpu_list[0]}," \
+                                         f"force_device=True,floatX=float32,lib.cnmem=1"
+        batch_size = 32
+        import theano as th
+
+        mylogger.info(th.__version__)
+    if bk == "cntk":
+        batch_size = 32
+        from cntk.device import try_set_default_device, gpu
+        try_set_default_device(gpu(int(gpu_list[0])))
+        import cntk as ck
+
+    if bk == "mxnet":
+        batch_size = 32
+        import mxnet as mxnet
+
+    from keras import backend as K
+    import keras
+    print("Using {} as backend for states extraction| {} is wanted".format(K.backend(),bk))
+    """Get model hidden output on selected_index data on specific backend"""
+    model_to_txt1(flags.model_path, bk)

server/LEMON-master/run/mutate_lemon.py

@@ -0,0 +1,640 @@
+# -*-coding:UTF-8-*-
+import csv
+from itertools import *
+import keras
+import json
+import networkx as nx
+import sys
+
+# sys.path.append("../")
+import os
+
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
+
+from scripts.logger.lemon_logger import Logger
+from scripts.tools.mutator_selection_logic import Roulette, MCMC
+from scripts.mutation.model_mutation_generators import *
+
+import argparse
+
+import ast
+
+import numpy as np
+from scripts.mutation.mutation_utils import *
+import pickle
+from scripts.tools import utils
+from scripts.tools.utils import ModelUtils
+import shutil
+import re
+import datetime
+import configparser
+import warnings
+import math
+
+lines = 0
+# np.random.seed(20200501)
+warnings.filterwarnings("ignore")
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
+os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+os.environ["CUDA_VISIBLE_DEVICES"] = ""
+import psutil
+
+
+def partially_nan_or_inf(predictions, bk_num):  # 检查是否无穷大或空
+    """
+    Check if there is NAN in the result
+    """
+
+    def get_nan_num(nds):
+        _nan_num = 0
+        for nd in nds:
+            if np.isnan(nd).any() or np.isinf(nd).any():
+                _nan_num += 1
+        return _nan_num
+
+    if len(predictions) == bk_num:
+        for input_predict in zip(*predictions):
+            nan_num = get_nan_num(input_predict)
+            if 0 < nan_num < bk_num:
+                return True
+            else:
+                continue
+        return False
+    else:
+        raise Exception("wrong backend amounts")
+
+
+def get_selector_by_startegy_name(mutator_s, mutant_s):
+    mutant_strategy_dict = {"ROULETTE": Roulette}
+    mutator_strategy_dict = {"MCMC": MCMC}
+    return mutator_strategy_dict[mutator_s], mutant_strategy_dict[mutant_s]
+
+
+def save_mutate_history(selector, invalid_history: dict, mutant_history: list):
+    mutator_history_path = os.path.join(experiment_dir, "mutator_history.csv")
+    mutant_history_path = os.path.join(experiment_dir, "mutant_history.txt")
+    with open(mutator_history_path, "w+") as fw:
+        fw.write("Name,Success,Invalid,Total\n")
+        for op in invalid_history.keys():
+            mtrs = selector.mutators[op]
+            invalid_cnt = invalid_history[op]
+            fw.write(
+                "{},{},{},{}\n".format(
+                    op, mtrs.delta_bigger_than_zero, invalid_cnt, mtrs.total
+                )
+            )
+    with open(mutant_history_path, "w+") as fw:
+        for mutant in mutant_history:
+            fw.write("{}\n".format(mutant))
+
+
+def is_nan_or_inf(t):
+    if math.isnan(t) or math.isinf(t):
+        return True
+    else:
+        return False
+
+
+def continue_checker(**run_stat):  # 判断算法是否满足退出条件
+    start_time = run_stat["start_time"]
+    time_limitation = run_stat["time_limit"]
+    cur_counters = run_stat["cur_counters"]
+    counters_limit = run_stat["counters_limit"]
+    s_mode = run_stat["stop_mode"]
+
+    # if timing
+    # 时间限制
+    if s_mode == "TIMING":
+        hours, minutes, seconds = utils.ToolUtils.get_HH_mm_ss(
+            datetime.datetime.now() - start_time
+        )
+        total_minutes = hours * 60 + minutes
+        mutate_logger.info(
+            f"INFO: Mutation progress: {total_minutes}/{time_limitation} Minutes!"
+        )
+        if total_minutes < time_limitation:
+            return True
+        else:
+            return False
+    # if counters
+    # 次数限制,size(models)<N
+    elif s_mode == "COUNTER":
+        if cur_counters < counters_limit:
+            mutate_logger.info(
+                "INFO: Mutation progress {}/{}".format(cur_counters + 1, counters_limit)
+            )
+            return True
+        else:
+            return False
+    else:
+        raise Exception(f"Error! Stop Mode {s_mode} not Found!")
+
+
+def calc_inner_div(model):
+    graph = nx.DiGraph()
+    for layer in model.layers:
+        graph.add_node(layer.name)
+        for inbound_node in layer._inbound_nodes:
+            if inbound_node.inbound_layers:
+                for parent_layer in inbound_node.inbound_layers:
+                    graph.add_edge(parent_layer.name, layer.name)
+    longest_path = nx.dag_longest_path(graph)
+    return len(longest_path) / len(graph)
+
+
+def _generate_and_predict(
+    res_dict, filename, mutate_num, mutate_ops, test_size, exp, backends
+):
+    # 主算法函数
+    """
+    Generate models using mutate operators and store them
+    """
+    mutate_op_history = {k: 0 for k in mutate_ops}
+    mutate_op_invalid_history = {k: 0 for k in mutate_ops}
+    mutant_history = []
+    # get mutator selection strategy
+    if "svhn" in exp or "fashion2" in exp:
+        origin_model_name = "{}_origin0.hdf5".format(exp)
+    else:
+        origin_model_name = "{}_origin0.h5".format(exp)
+    # 初始种子模型列表Ms初始时只有这一个模型
+    root_dir = os.path.dirname(os.getcwd())
+
+    origin_save_path = os.path.join(mut_dir, origin_model_name)
+    mutator_selector_func, mutant_selector_func = get_selector_by_startegy_name(
+        mutator_strategy, mutant_strategy
+    )
+    # [origin_model_name] means seed pool only contains initial model at beginning.
+    mutator_selector, mutant_selector = mutator_selector_func(
+        mutate_ops
+    ), mutant_selector_func([origin_model_name], capacity=mutate_num + 1)
+    # MCMC,Roulette
+    shutil.copy(src=filename, dst=origin_save_path)
+    origin_model_status, res_dict, accumulative_inconsistency, _ = get_model_prediction(
+        res_dict, origin_save_path, origin_model_name, exp, test_size, backends
+    )
+
+    if not origin_model_status:
+        mutate_logger.error(
+            f"Origin model {exp} crashed on some backends! LEMON would skip it"
+        )
+        sys.exit(-1)
+
+    last_used_mutator = None
+    last_inconsistency = accumulative_inconsistency  # ACC
+    mutant_counter = 0
+
+    start_time = datetime.datetime.now()
+    order_inconsistency_dict = {}
+    run_stat = {
+        "start_time": start_time,
+        "time_limit": time_limit,
+        "cur_counters": mutant_counter,
+        "counters_limit": mutate_num,
+        "stop_mode": stop_mode,
+    }
+
+    # 满足限制条件就继续循环
+    while continue_checker(**run_stat):
+        global model_num
+        if model_num == mutate_num:
+            break
+        picked_seed = utils.ToolUtils.select_mutant(
+            mutant_selector
+        )  # 轮盘赌选择种子模型(伪代码3-14行)
+        selected_op = utils.ToolUtils.select_mutator(
+            mutator_selector, last_used_mutator=last_used_mutator
+        )  # 蒙特卡洛选择变异算子(伪代码15-20行)
+        mutate_op_history[selected_op] += 1
+        last_used_mutator = selected_op
+        mutator = mutator_selector.mutators[selected_op]  # 变异算子对象
+        mutant = mutant_selector.mutants[picked_seed]  # 种子模型对象
+
+        if "svhn" in picked_seed or "fashion2" in picked_seed:
+            new_seed_name = "{}-{}{}.hdf5".format(
+                picked_seed[:-5], selected_op, mutate_op_history[selected_op]
+            )
+
+        else:
+            new_seed_name = "{}-{}{}.h5".format(
+                picked_seed[:-3], selected_op, mutate_op_history[selected_op]
+            )  # 生成新模型
+        # seed name would not be duplicate
+        if new_seed_name not in mutant_selector.mutants.keys():
+            # 对应伪代码22行，因为种子模型是以当前选择的种子模型和变异算子命名的，所以重名就表示这个模型已经存在了
+            new_seed_path = os.path.join(mut_dir, new_seed_name)
+            picked_seed_path = os.path.join(mut_dir, picked_seed)
+            mutate_st = datetime.datetime.now()
+
+            model_mutation_generators = (
+                root_dir + "/scripts/mutation/model_mutation_generators.py"
+            )
+            mutate_status = os.system(
+                "{}/lemon/bin/python -u {} --model {} "
+                "--mutate_op {} --save_path {} --mutate_ratio {}".format(
+                    python_prefix,
+                    model_mutation_generators,
+                    picked_seed_path,
+                    selected_op,
+                    new_seed_path,
+                    flags.mutate_ratio,
+                )
+            )
+            # 使用变异算子进行变异(伪代码21行)
+
+            mutate_et = datetime.datetime.now()
+            mutate_dt = mutate_et - mutate_st
+            h, m, s = utils.ToolUtils.get_HH_mm_ss(mutate_dt)
+            mutate_logger.info(
+                "INFO:Mutate Time Used on {} : {}h, {}m, {}s".format(
+                    selected_op, h, m, s
+                )
+            )
+            # mutation status code is successful
+
+            if mutate_status == 0:  # 变异执行完成
+                mutant.selected += 1
+                mutator.total += 1
+                # execute this model on all platforms
+                predict_status, res_dict, accumulative_inconsistency, model_outputs = (
+                    get_model_prediction(
+                        res_dict, new_seed_path, new_seed_name, exp, test_size, backends
+                    )
+                )
+                # 计算ACC(m)
+
+                if predict_status:
+                    mutant_history.append(new_seed_name)
+                    # 伪代码23-25行
+                    print("type:", type(model_outputs))
+                    print("model_outputs:", model_outputs)
+
+                    if utils.ModelUtils.is_valid_model(
+                        inputs_backends=model_outputs, backends_nums=len(backends)
+                    ):
+
+                        delta = (
+                            accumulative_inconsistency - last_inconsistency
+                        )  # 也就是ACC(m)-ACC(s)
+                        # 下面两个if好像没什么用，因为mutator字典里只有MCMC，mutant字典里只有ROULETTE
+
+                        if mutator_strategy == "MCMC":
+                            mutator.delta_bigger_than_zero = (
+                                mutator.delta_bigger_than_zero + 1
+                                if delta > 0
+                                else mutator.delta_bigger_than_zero
+                            )
+
+                        if mutant_strategy == "ROULETTE" and delta > 0:
+                            # when size >= capacity:
+                            # random_mutant & Roulette would drop one and add new one
+                            if mutant_selector.is_full():
+                                mutant_selector.pop_one_mutant()
+                            mutant_selector.add_mutant(
+                                new_seed_name
+                            )  # 如果放大了不一致程度，即ACC(m)>=ACC(s),就加入到种子模型集合里
+                            last_inconsistency = accumulative_inconsistency  # 29行
+
+                        mutate_logger.info(
+                            "SUCCESS:{} pass testing!".format(new_seed_name)
+                        )
+                        mutant_counter += 1
+                    else:
+                        mutate_op_invalid_history[selected_op] += 1
+                        mutate_logger.error("Invalid model Found!")
+                else:
+                    mutate_logger.error("Crashed or NaN model Found!")
+            else:
+                mutate_logger.error(
+                    "Exception raised when mutate {} with {}".format(
+                        picked_seed, selected_op
+                    )
+                )
+
+            mutate_logger.info("Mutated op used history:")
+            mutate_logger.info(mutate_op_history)
+
+            mutate_logger.info("Invalid mutant generated history:")
+            mutate_logger.info(mutate_op_invalid_history)
+
+        run_stat["cur_counters"] = mutant_counter
+
+    save_mutate_history(mutator_selector, mutate_op_invalid_history, mutant_history)
+
+    # calc_cov = CoverageCalculatornew(all_json_path, api_config_pool_path)
+    # lines = 0
+    # for file in os.listdir(folder_path):
+    #     if file == 'total.json': continue
+    #     file_path = os.path.join(folder_path, file)
+    #     calc_cov.load_json(file_path)
+    #     with open(file_path, 'r') as sub_json:
+    #         sub_info = json.load(sub_json)
+    #     outer_div = len(tar_set - set(sub_info['layer_type']))
+    #     input_cov, config_cov, api_cov, op_type_cov, op_num_cov, edge_cov = calc_cov.cal_coverage()
+    #     with open(output_path, 'a+', newline='') as fi:
+    #         writer = csv.writer(fi)
+    #         head = ['Layer Input Coverage', 'Layer Parameter Diversity', 'Layer Sequence Diversity',
+    #                 'Operator Type Coverage', 'Operator Num Coverage', 'Edge Coverage', 'Accumulative inconsistency']
+    #         if not lines:
+    #             writer.writerow(head)
+    #         lines += 1
+    #         printlist = [input_cov, config_cov, api_cov, op_type_cov, op_num_cov, edge_cov,
+    #                      acc[lines]]
+    #         writer.writerow(printlist)
+
+    return res_dict
+
+
+def generate_metrics_result(res_dict, predict_output, model_idntfr):  # 计算ACC
+    mutate_logger.info("Generating Metrics Result")
+    accumulative_incons = 0
+    backends_pairs_num = 0
+    # Compare results pair by pair
+    for pair in combinations(predict_output.items(), 2):  # 每一对库
+        backends_pairs_num += 1
+        backend1, backend2 = pair
+        bk_name1, prediction1 = backend1
+        bk_name2, prediction2 = backend2
+        bk_pair = "{}_{}".format(bk_name1, bk_name2)
+        for metrics_name, metrics_result_dict in res_dict.items():
+            metrics_func = utils.MetricsUtils.get_metrics_by_name(metrics_name)  # 计算
+            # metrics_results in list type
+            metrics_results = metrics_func(
+                prediction1, prediction2, y_test[: flags.test_size]
+            )
+            # 一共test_size个数据集，所以metrics_result是长度为test_size的预测结果列表
+            # ACC -> float: The sum of all inputs under all backends
+            accumulative_incons += sum(metrics_results)  # ACC=∑
+            for input_idx, delta in enumerate(metrics_results):
+                delta_key = "{}_{}_{}_input{}".format(
+                    model_idntfr, bk_name1, bk_name2, input_idx
+                )
+                metrics_result_dict[delta_key] = delta
+
+    mutate_logger.info(f"Accumulative Inconsistency: {accumulative_incons}")
+    return res_dict, accumulative_incons
+
+
+def generate_gini_result(predict_output, backends):
+    gini_res = {bk: 0 for bk in backends}
+    for pair in predict_output.items():
+        bk_name, prediction = pair
+        gini_res[bk_name] = utils.MetricsUtils.get_gini_mean(prediction)
+    return gini_res
+
+
+def generate_theta(predict_output, backends):
+    theta_res = {bk: 0 for bk in backends}
+    for pair in predict_output.items():
+        bk_name, prediction = pair
+        theta_res[bk_name] = utils.MetricsUtils.get_theta_mean(
+            prediction, y_test[: flags.test_size]
+        )
+    return theta_res
+
+
+SHAPE_SPACE = 5
+model_num = 0
+
+
+def get_model_prediction(res_dict, model_path, model_name, exp, test_size, backends):
+    # 计算ACC
+    """
+    Get model prediction on different backends and calculate distance by metrics
+    """
+
+    root_dir = model_path.split("origin_model")[0]
+
+    npy_path = (
+        root_dir + "res.npy"
+    )  # 保存模型预测结果的路径,patch_prediction_extractor.py中的44行改成一样的路径
+
+    predict_output = {b: [] for b in backends}
+    model_idntfr = model_name[:-3]
+    all_backends_predict_status = True
+    for bk in backends:
+        python_bin = f"{python_prefix}/{bk}/bin/python"
+        predict_st = datetime.datetime.now()
+        # 使用不同的库进行预测
+        pre_status_bk = os.system(
+            f"{python_bin} -u -m patch_prediction_extractor --backend {bk} "
+            f"--exp {exp} --test_size {test_size} --model {model_path} "
+            f"--config_name {flags.config_name}"
+        )
+
+        predict_et = datetime.datetime.now()
+        predict_td = predict_et - predict_st
+        h, m, s = utils.ToolUtils.get_HH_mm_ss(predict_td)
+        mutate_logger.info(
+            "Prediction Time Used on {} : {}h, {}m, {}s".format(bk, h, m, s)
+        )
+
+        # If no exception is thrown,save prediction result
+        if pre_status_bk == 0:  # 预测执行成功，保存结果
+            # data = pickle.loads(redis_conn.hget("prediction_{}".format(model_name), bk))
+            data = np.load(npy_path)
+            predict_output[bk] = data
+            # print(data)
+        # record the crashed backend
+        else:
+            all_backends_predict_status = False
+            mutate_logger.error(
+                "{} crash on backend {} when predicting ".format(model_name, bk)
+            )
+
+    status = False
+    accumulative_incons = None
+
+    # run ok on all platforms
+    if (
+        all_backends_predict_status
+    ):  # 所有的库都执行成功且保存了结果，判断结果中是否有错误
+        predictions = list(predict_output.values())
+        res_dict, accumulative_incons = generate_metrics_result(
+            res_dict=res_dict, predict_output=predict_output, model_idntfr=model_idntfr
+        )
+        # 计算ACC(用于衡量预测结果的不一致程度)
+        # gini_res = generate_gini_result(predict_output=predict_output, backends=backends)
+        # theta = generate_theta(predict_output=predict_output, backends=backends)
+        # import csv
+        # csvfile = open(r"D:\lemon_outputs\result\mobilenet.1.00.224-imagenet\tensorflow\5.csv", 'a+',newline='')
+        # write=csv.writer(csvfile)
+        # write.writerow([accumulative_incons, gini_res['tensorflow'], theta['tensorflow']])
+        # csvfile.close()
+        #
+        # csvfile = open(r"D:\lemon_outputs\result\mobilenet.1.00.224-imagenet\mxnet\5.csv", 'a+',newline='')
+        # write=csv.writer(csvfile)
+        # write.writerow([accumulative_incons, gini_res['mxnet'], theta['mxnet']])
+        # csvfile.close()
+        # 计算gini
+        # If all backends are working fine, check if there exists NAN or INF in the result
+        # `accumulative_incons` is nan or inf --> NaN or INF in results
+        if is_nan_or_inf(accumulative_incons):
+            # has NaN on partial backends
+            if partially_nan_or_inf(predictions, len(backends)):
+                nan_model_path = os.path.join(nan_dir, f"{model_idntfr}_NaN_bug.h5")
+                mutate_logger.error("Error: Found one NaN bug. move NAN model")
+
+            # has NaN on all backends --> not a NaN bug
+            else:
+                nan_model_path = os.path.join(
+                    nan_dir, f"{model_idntfr}_NaN_on_all_backends.h5"
+                )
+                mutate_logger.error(
+                    "Error: Found one NaN Model on all libraries. move NAN model"
+                )
+            shutil.move(model_path, nan_model_path)
+
+        else:  # No NaN or INF on any backend
+            print(model_path)
+            for bk in backends:
+                python_bin = f"{python_prefix}/{bk}/bin/python"
+                os.system(
+                    f"{python_bin} -u -m model_to_txt --backend {bk} --model_path {model_path} --root_dir {root_dir}"
+                )
+            # if 'svhn' in model_name or 'fashion2' in model_name:
+            #     file_path = os.path.join(folder_path, model_path.split("\\")[-1][:-5] + '.json')
+            # else:
+            #     file_path = os.path.join(folder_path, model_path.split("\\")[-1][:-3] + '.json')
+            # union_json(file_path, all_json_path)
+            # model_now = keras.models.load_model(model_path, custom_objects=custom_objects())
+            # inner_div[model_num] = calc_inner_div(model_now)
+            # with open(file_path, 'r') as sub_json:
+            #    sub_info = json.load(sub_json)
+            # if len(set(sub_info['layer_type'])) > len(tar_set):
+            #     tar_set = set(sub_info['layer_type'])
+
+            mutate_logger.info("Saving prediction")
+            with open(
+                "{}/prediction_{}.pkl".format(inner_output_dir, model_idntfr), "wb+"
+            ) as f:
+                pickle.dump(predict_output, file=f)
+            status = True
+
+    # save crashed model
+    else:
+        mutate_logger.error("Error: move crash model")
+        crash_model_path = os.path.join(crash_dir, model_name)
+        shutil.move(model_path, crash_model_path)
+
+    return status, res_dict, accumulative_incons, predict_output
+
+
+if __name__ == "__main__":
+
+    starttime = datetime.datetime.now()
+    """
+    
+    Parser of command args. 
+    It could make mutate_lemon.py run independently without relying on mutation_executor.py
+    """
+    parse = argparse.ArgumentParser()
+    parse.add_argument(
+        "--is_mutate",
+        type=ast.literal_eval,
+        default=False,
+        help="parameter to determine mutation option",
+    )
+    parse.add_argument(
+        "--mutate_op",
+        type=str,
+        nargs="+",
+        choices=[
+            "WS",
+            "GF",
+            "NEB",
+            "NAI",
+            "NS",
+            "ARem",
+            "ARep",
+            "LA",
+            "LC",
+            "LR",
+            "LS",
+            "MLA",
+        ],
+        help="parameter to determine mutation option",
+    )
+    parse.add_argument(
+        "--model", type=str, help="relative path of model file(from root dir)"
+    )
+    parse.add_argument(
+        "--output_dir", type=str, help="relative path of output dir(from root dir)"
+    )
+    parse.add_argument("--backends", type=str, nargs="+", help="list of backends")
+    parse.add_argument(
+        "--mutate_num",
+        type=int,
+        help="number of variant models generated by each mutation operator",
+    )
+    parse.add_argument("--mutate_ratio", type=float, help="ratio of mutation")
+    parse.add_argument("--exp", type=str, help="experiments identifiers")
+    parse.add_argument("--test_size", type=int, help="amount of testing image")
+    parse.add_argument("--config_name", type=str, help="config name")
+    flags, unparsed = parse.parse_known_args(sys.argv[1:])
+    warnings.filterwarnings("ignore")
+    lemon_cfg = configparser.ConfigParser()
+    # lemon_cfg.read(f".\config\{flags.config_name}")
+    cfg_path = os.path.join(os.path.dirname(os.getcwd()), "config", flags.config_name)
+    lemon_cfg.read(cfg_path)
+    # lemon_cfg.read(f"config/demo.conf")
+    time_limit = lemon_cfg["parameters"].getint("time_limit")
+    mutator_strategy = lemon_cfg["parameters"].get("mutator_strategy").upper()
+    mutant_strategy = lemon_cfg["parameters"].get("mutant_strategy").upper()
+    stop_mode = lemon_cfg["parameters"].get("stop_mode").upper()
+    alpha = lemon_cfg["parameters"].getfloat("alpha")
+
+    mutate_logger = Logger()
+    #    pool = redis.ConnectionPool(host=lemon_cfg['redis']['host'], port=lemon_cfg['redis']['port'],
+    #                                db=lemon_cfg['redis'].getint('redis_db'))
+    #    redis_conn = redis.Redis(connection_pool=pool)
+
+    # for k in redis_conn.keys():
+    #     if flags.exp in k.decode("utf-8"):
+    #         redis_conn.delete(k)
+
+    # exp : like lenet5-mnist
+    experiment_dir = os.path.join(flags.output_dir, flags.exp)
+    mut_dir = os.path.join(experiment_dir, "mut_model")
+    crash_dir = os.path.join(experiment_dir, "crash")
+    nan_dir = os.path.join(experiment_dir, "nan")
+    inner_output_dir = os.path.join(experiment_dir, "inner_output")
+    metrics_result_dir = os.path.join(experiment_dir, "metrics_result")
+
+    x, y = utils.DataUtils.get_data_by_exp(flags.exp)  # 从conf文件中读取数据并转换形式
+    x_test, y_test = x[: flags.test_size], y[: flags.test_size]
+    pool_size = lemon_cfg["parameters"].getint("pool_size")
+    python_prefix = lemon_cfg["parameters"]["python_prefix"].rstrip("\\")
+    try:  # 执行算法
+        metrics_list = lemon_cfg["parameters"]["metrics"].split(" ")  # D_MAD
+        lemon_results = {k: dict() for k in metrics_list}
+        lemon_results = _generate_and_predict(
+            lemon_results,
+            flags.model,
+            flags.mutate_num,
+            flags.mutate_op,
+            flags.test_size,
+            flags.exp,
+            flags.backends,
+        )
+        with open(
+            "{}/{}_lemon_results.pkl".format(experiment_dir, flags.exp), "wb+"
+        ) as f:
+            pickle.dump(lemon_results, file=f)
+        utils.MetricsUtils.generate_result_by_metrics(
+            metrics_list, lemon_results, metrics_result_dir, flags.exp
+        )
+
+    except Exception as e:
+        mutate_logger.exception(sys.exc_info())
+
+    from keras import backend as K
+
+    K.clear_session()
+
+    endtime = datetime.datetime.now()
+    time_delta = endtime - starttime
+    h, m, s = utils.ToolUtils.get_HH_mm_ss(time_delta)
+    mutate_logger.info(
+        "Mutation process is done: Time used: {} hour,{} min,{} sec".format(h, m, s)
+    )

server/LEMON-master/run/mutation_executor.py

@@ -0,0 +1,146 @@
+# -*-coding:UTF-8-*-
+import argparse
+import sys
+import os
+
+sys.path.append("../")
+from scripts.logger.lemon_logger import Logger
+import warnings
+import datetime
+import configparser
+from scripts.tools import utils
+
+"""Init cuda"""
+warnings.filterwarnings("ignore")
+os.environ["KERAS_BACKEND"] = "tensorflow"
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"  # 只显示 warning 和 Error
+os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+os.environ["CUDA_VISIBLE_DEVICES"] = ""
+"""Setting random seed"""
+
+if __name__ == "__main__":
+
+    # config_name = sys.argv[1]
+    config_name = "demo.conf"
+
+    parse = argparse.ArgumentParser()
+    parse.add_argument(
+        "--exp",
+        type=str,
+        choices=[
+            "lexnet-cifar10",
+            "lenet5-fashion-mnist",
+            "fashion2",
+            "svhn",
+            "lenet5-mnist",
+            "alexnet-cifar10",
+            "mobilenet.1.00.224-imagenet",
+            "vgg16-imagenet",
+        ],
+        help="experiments identifiers",
+    )
+    parse.add_argument("--root_dir", type=str, help="root dir")
+    parse.add_argument(
+        "--output_dir", type=str, help="relative path of output dir(from root dir)"
+    )
+    parse.add_argument(
+        "--mutate_num",
+        type=int,
+        help="number of variant models generated by each mutation operator",
+    )
+    parse.add_argument("--config_name", type=str, help="config name,like demo.conf")
+    flags_input, unparsed = parse.parse_known_args(sys.argv[1:])
+
+    lemon_cfg = configparser.ConfigParser()
+    cfg_path = os.path.join(flags_input.root_dir, "config", flags_input.config_name)
+    lemon_cfg.read(cfg_path)
+    parameters = lemon_cfg["parameters"]
+    flags = argparse.Namespace(
+        mutate_ops=parameters["mutate_ops"],
+        exps=flags_input.exp.lstrip().rstrip().split(" "),
+        origin_model_dir=os.path.join(
+            flags_input.root_dir, parameters["origin_model_dir"]
+        ),
+        output_dir=flags_input.output_dir,
+        backend=parameters["backend"],
+        mutate_num=flags_input.mutate_num,
+        mutate_ratio=parameters.getfloat("mutate_ratio"),
+        test_size=parameters.getint("test_size"),
+        threshold=parameters.getfloat("threshold"),
+        redis_db=lemon_cfg["parameters"].getint("redis_db"),
+        python_prefix=parameters["python_prefix"].rstrip("/"),
+    )
+
+    parse = argparse.ArgumentParser()
+    if not os.path.exists(flags.output_dir):
+        os.makedirs(flags.output_dir)
+
+    main_log = Logger()
+
+    """Lemon process"""
+    main_log.logger.info("Success: Lemon start successfully!")
+    start_time = datetime.datetime.now()
+    for exp_identifier in flags.exps:
+
+        """Make directory"""
+        experiment_dir = os.path.join(
+            flags.output_dir, exp_identifier
+        )  # exp : like lenet5-mnist
+        mut_dir = os.path.join(experiment_dir, "mut_model")
+        crash_dir = os.path.join(experiment_dir, "crash")
+        nan_dir = os.path.join(experiment_dir, "nan")
+        inner_output_dir = os.path.join(experiment_dir, "inner_output")
+        metrics_result_dir = os.path.join(experiment_dir, "metrics_result")
+        print(experiment_dir)
+        if not os.path.exists(experiment_dir):
+            os.makedirs(experiment_dir)
+        if not os.path.exists(mut_dir):
+            os.makedirs(mut_dir)
+        if not os.path.exists(crash_dir):
+            os.makedirs(crash_dir)
+        if not os.path.exists(nan_dir):
+            os.makedirs(nan_dir)
+        if not os.path.exists(inner_output_dir):
+            os.makedirs(inner_output_dir)
+        if not os.path.exists(metrics_result_dir):
+            os.makedirs(metrics_result_dir)
+
+        try:
+            """Mutate and get output of different backends"""
+            main_log.info("INFO:Lemon mutation starting!")
+            main_log.info("INFO:Lemon for exp: {}".format(exp_identifier))
+            if "svhn" in exp_identifier or "fashion2" in exp_identifier:
+                origin_model_name = "{}_origin.hdf5".format(exp_identifier)
+            else:
+                origin_model_name = "{}_origin.h5".format(exp_identifier)
+            origin_model_file = os.path.join(flags.origin_model_dir, origin_model_name)
+            mutate_lemon = (
+                "{}/lemon/bin/python -u -m mutate_lemon --mutate_op {} --model {} --output_dir {}"
+                " --backends {} --mutate_num {} --mutate_ratio {} --exp {} --test_size {} --redis_db {} --config_name {}".format(
+                    flags.python_prefix,
+                    flags.mutate_ops,
+                    origin_model_file,
+                    flags.output_dir,
+                    flags.backend,
+                    flags.mutate_num,
+                    flags.mutate_ratio,
+                    exp_identifier,
+                    flags.test_size,
+                    flags.redis_db,
+                    config_name,
+                )
+            )
+            os.system(mutate_lemon)
+            print(mutate_lemon)
+
+        except Exception:
+            main_log.error("Error: Lemon for exp:{} Failed!".format(exp_identifier))
+
+            main_log.exception(sys.exc_info())
+
+    end_time = datetime.datetime.now()
+    time_delta = end_time - start_time
+    h, m, s = utils.ToolUtils.get_HH_mm_ss(time_delta)
+    main_log.info(
+        "INFO:Lemon is done: Time used: {} hour,{} min,{} sec".format(h, m, s)
+    )

server/LEMON-master/run/patch_hidden_output_extractor.py

@@ -0,0 +1,123 @@
+# -*-coding:UTF-8-*-
+"""
+# Part  of localization phase
+# get prediction for each backend
+"""
+import sys
+sys.path.append("../")
+import os
+import pickle
+import argparse
+from scripts.tools.utils import DataUtils,ModelUtils
+from scripts.logger.lemon_logger import Logger
+import configparser
+import warnings
+import traceback
+import numpy as np
+
+#np.random.seed(20200501)
+warnings.filterwarnings("ignore")
+
+
+def _get_hidden_output(test_data,backend,select_model,model_dir,data_index):
+    """
+        layers_output: list of ndarray which store outputs in each layer
+        The result stored in redis like:
+        (lenet5-mnist_origin0_theano,layers_output)
+    """
+    if 'svhn' in select_model or 'fashion2' in select_model:
+        model_pathname = os.path.join(model_dir, "{}.hdf5".format(select_model))
+    else:
+        model_pathname = os.path.join(model_dir, "{}.h5".format(select_model))
+    model = keras.models.load_model(model_pathname,custom_objects=ModelUtils.custom_objects())
+
+    model_idntfr_backend = "{}_{}_{}".format(select_model, backend, data_index)
+    select_data = np.expand_dims(test_data[data_index], axis=0)
+    layers_output = ModelUtils.layers_output(model, select_data)
+    with open(os.path.join(localize_output_dir,model_idntfr_backend),"wb") as fw:
+        pickle.dump(layers_output,fw)
+
+
+if __name__ == "__main__":
+
+    """Parser of command args"""
+    parse = argparse.ArgumentParser()
+    parse.add_argument("--backend", type=str, help="name of backends")
+    parse.add_argument("--exp", type=str, help="experiments identifiers")
+    parse.add_argument("--output_dir", type=str, help="relative path of output dir(from root dir)")
+    parse.add_argument("--data_index", type=int, help="redis db port")
+    parse.add_argument("--config_name", type=str, help="config name")
+    parse.add_argument("--model_idntfr", type=str, help="redis db port")
+    flags, unparsed = parse.parse_known_args(sys.argv[1:])
+    mylogger = Logger()
+
+    """Load Configuration"""
+    warnings.filterwarnings("ignore")
+    lemon_cfg = configparser.ConfigParser()
+    # lemon_cfg.read(f"./config/{flags.config_name}")
+    grandparent_directory = os.path.dirname(os.getcwd())
+    conf_path = grandparent_directory + "/config/demo.conf"
+    lemon_cfg.read(conf_path)
+    parameters = lemon_cfg['parameters']
+    gpu_ids = parameters['gpu_ids']
+    gpu_list = parameters['gpu_ids'].split(",")
+
+    """Init cuda"""
+    os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+    os.environ["CUDA_VISIBLE_DEVICES"] = gpu_ids
+    warnings.filterwarnings("ignore")
+
+    batch_size = 64
+    """Switch backend"""
+    bk_list = ['tensorflow', 'theano', 'cntk', 'mxnet']
+    bk = flags.backend
+    print('.........................',type(bk))
+    os.environ['KERAS_BACKEND'] = bk
+    os.environ['PYTHONHASHSEED'] = '0'
+
+    if bk == 'tensorflow':
+        os.environ["TF_CPP_MIN_LOG_LEVEL"] = '2'  # 只显示 warning 和 Error
+        import tensorflow as tf
+        mylogger.info(tf.__version__)
+
+    if bk == 'theano':
+        if len(gpu_list) == 2:
+            os.environ[
+                'THEANO_FLAGS'] = f"device=cuda,contexts=dev{gpu_list[0]}->cuda{gpu_list[0]};dev{gpu_list[1]}->cuda{gpu_list[1]}," \
+                                  f"force_device=True,floatX=float32,lib.cnmem=1"
+        else:
+            os.environ['THEANO_FLAGS'] = f"device=cuda,contexts=dev{gpu_list[0]}->cuda{gpu_list[0]}," \
+                                         f"force_device=True,floatX=float32,lib.cnmem=1"
+        batch_size = 32
+        import theano as th
+
+        mylogger.info(th.__version__)
+    if bk == "cntk":
+        batch_size = 32
+        from cntk.device import try_set_default_device, gpu
+        try_set_default_device(gpu(int(gpu_list[0])))
+        import cntk as ck
+        mylogger.info(ck.__version__)
+
+    if bk == "mxnet":
+        batch_size = 32
+        import mxnet as mxnet
+        mylogger.info(mxnet.__version__)
+
+    from keras import backend as K
+    import keras
+    mylogger.logger.info("Using {} as backend for states extraction| {} is wanted".format(K.backend(),bk))
+
+    """Get model hidden output on selected_index data on specific backend"""
+    try:
+        backend_input_dict = {}
+        localize_output_dir = os.path.join(flags.output_dir,flags.exp,"localize_tmp")
+        x, y = DataUtils.get_data_by_exp(flags.exp)
+        mut_dir = os.path.join(flags.output_dir,flags.exp,"mut_model")
+        _get_hidden_output(test_data=x, backend=bk,select_model=flags.model_idntfr,model_dir=mut_dir,data_index=flags.data_index)
+        mylogger.logger.info("Hidden output extracting done!")
+    except:
+        traceback.print_exc()
+        sys.exit(-1)
+
+

server/LEMON-master/run/patch_prediction_extractor.py

@@ -0,0 +1,134 @@
+# -*-coding:UTF-8-*-
+"""get prediction for each backend
+"""
+import sys
+import os
+sys.path.append("../")
+import numpy as np
+import pickle
+import argparse
+import configparser
+from scripts.tools.utils import DataUtils
+from scripts.logger.lemon_logger import Logger
+import warnings
+
+main_logger = Logger()
+
+
+def custom_objects():
+
+    def no_activation(x):
+        return x
+
+    def leakyrelu(x):
+        import keras.backend as K
+        return K.relu(x, alpha=0.01)
+
+    objects = {}
+    objects['no_activation'] = no_activation
+    objects['leakyrelu'] = leakyrelu
+    return objects
+
+
+def _get_prediction(bk, x, y, model_path,batch_size):
+    """
+    Get prediction of models on different backends
+    """
+    test_x, test_y = x[:flags.test_size],y[:flags.test_size]
+    predict_model = keras.models.load_model(model_path,custom_objects=custom_objects())
+    # predict_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
+    main_logger.info("INFO:load model and compile done!")
+    res = predict_model.predict(test_x,batch_size=batch_size)
+    root_dir = model_path.split("origin_model")[0]
+    
+    npy_path = root_dir + 'res.npy'  # 保存模型预测结果的路径,patch_prediction_extractor.py中的44行改成一样的路径
+
+    #test_x:测试集
+    #batch_size:单次训练数据样本大小
+    np.save(npy_path,res)
+    #把预测结果保存到本地
+    main_logger.info("SUCCESS:Get prediction for {} successfully on {}!".format(mut_model_name,bk))
+    """Store prediction result to redis"""
+#    redis_conn.hset("prediction_{}".format(mut_model_name),bk,pickle.dumps(res))
+
+
+if __name__ == "__main__":
+
+    """Parser of command args"""
+    parse = argparse.ArgumentParser()
+    parse.add_argument("--backend", type=str, help="name of backends")
+    parse.add_argument("--exp", type=str, help="experiments identifiers")
+    parse.add_argument("--test_size", type=int, help="amount of testing image")
+    parse.add_argument("--model", type=str, help="path of the model to predict")
+    #parse.add_argument("--redis_db", type=int)
+    parse.add_argument("--config_name", type=str)
+    flags, unparsed = parse.parse_known_args(sys.argv[1:])
+    
+    """Load Configuration"""
+    warnings.filterwarnings("ignore")
+    lemon_cfg = configparser.ConfigParser()
+    # lemon_cfg.read(f"./config/{flags.config_name}")
+    root_dir = flags.model.split("origin_model")[0]
+    cfg_path = os.path.join(os.path.dirname(os.getcwd()), "config", flags.config_name)
+    lemon_cfg.read(cfg_path)
+    #pool = redis.ConnectionPool(host=lemon_cfg['redis']['host'], port=lemon_cfg['redis']['port'],db=flags.redis_db)
+    #redis_conn = redis.Redis(connection_pool=pool)
+
+    parameters = lemon_cfg['parameters']
+   # gpu_ids = parameters['gpu_ids']
+   # gpu_list = parameters['gpu_ids'].split(",")
+
+    """Init cuda"""
+    #os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+    #os.environ["CUDA_VISIBLE_DEVICES"] = gpu_ids
+    warnings.filterwarnings("ignore")
+
+    batch_size= 32
+    """Switch backend"""
+    bk_list = ['tensorflow', 'mxnet']
+    bk = flags.backend
+    os.environ['KERAS_BACKEND'] = bk
+    os.environ['PYTHONHASHSEED'] = '0'
+    if bk == 'tensorflow':
+        os.environ["TF_CPP_MIN_LOG_LEVEL"] = '2'  # 只显示 warning 和 Error
+        import tensorflow as tf
+        main_logger.info(tf.__version__)
+        batch_size = 128
+        import keras
+    if bk == 'theano':
+        # if len(gpu_list) == 2:
+        #     os.environ['THEANO_FLAGS'] = f"device=cuda,contexts=dev{gpu_list[0]}->cuda{gpu_list[0]};dev{gpu_list[1]}->cuda{gpu_list[1]}," \
+        #                                  f"force_device=True,floatX=float32,lib.cnmem=1"
+        # else:
+        #     os.environ['THEANO_FLAGS'] = f"device=cuda,contexts=dev{gpu_list[0]}->cuda{gpu_list[0]}," \
+        #                                  f"force_device=True,floatX=float32,lib.cnmem=1"
+        import theano as th
+        import keras
+        main_logger.info(th.__version__)
+    if bk == "cntk":
+        #from cntk.device import try_set_default_device,gpu
+        #try_set_default_device(gpu(int(gpu_list[0])))
+        import cntk as ck
+        main_logger.info(ck.__version__)
+        import keras
+
+    if bk == "mxnet":
+        import mxnet as mxnet
+        main_logger.info(f"mxnet_version {mxnet.__version__}")
+        import keras
+
+        batch_size = 16
+    from keras import backend as K
+
+
+    try:
+        """Get model prediction"""
+        main_logger.info("INFO:Using {} as backend for states extraction| {} is wanted".format(K.backend(),bk))
+        x, y = DataUtils.get_data_by_exp(flags.exp)#读取数据集并做转换
+        mut_model_name = os.path.split(flags.model)[-1]
+        print(flags.model)
+        _get_prediction(bk=bk, x=x, y=y, model_path=flags.model,batch_size=batch_size)
+    except Exception:
+        import traceback
+        traceback.print_exc()
+        sys.exit(-1)

server/LEMON-master/run/sub_model/svhn_origin0-ARem1.json

@@ -0,0 +1,280 @@
+{
+    "edges": [
+        [
+            "Conv2D",
+            "Activation"
+        ],
+        [
+            "Activation",
+            "MaxPooling2D"
+        ],
+        [
+            "MaxPooling2D",
+            "Conv2D"
+        ],
+        [
+            "MaxPooling2D",
+            "Flatten"
+        ],
+        [
+            "Flatten",
+            "Dense"
+        ],
+        [
+            "Dense",
+            "Dense"
+        ]
+    ],
+    "layer_config": {
+        "Conv2D": [
+            {
+                "trainable": true,
+                "kernel_size": [
+                    5,
+                    5
+                ],
+                "strides": [
+                    1,
+                    1
+                ],
+                "padding": "valid",
+                "data_format": "channels_last",
+                "dilation_rate": [
+                    1,
+                    1
+                ],
+                "activation": "linear",
+                "use_bias": false,
+                "kernel_initializer": {
+                    "class_name": "VarianceScaling",
+                    "config": {
+                        "scale": 1.0,
+                        "mode": "fan_avg",
+                        "distribution": "uniform",
+                        "seed": null
+                    }
+                },
+                "bias_initializer": {
+                    "class_name": "Zeros",
+                    "config": {}
+                },
+                "kernel_regularizer": "None",
+                "bias_regularizer": "None",
+                "activity_regularizer": "None",
+                "kernel_constraint": "None",
+                "bias_constraint": "None"
+            }
+        ],
+        "Activation": [
+            {
+                "trainable": true,
+                "activation": "relu"
+            }
+        ],
+        "MaxPooling2D": [
+            {
+                "trainable": true,
+                "pool_size": [
+                    2,
+                    2
+                ],
+                "padding": "valid",
+                "strides": [
+                    2,
+                    2
+                ],
+                "data_format": "channels_last"
+            }
+        ],
+        "Flatten": [
+            {
+                "trainable": true,
+                "data_format": "channels_last"
+            }
+        ],
+        "Dense": [
+            {
+                "trainable": true,
+                "activation": "relu",
+                "use_bias": true,
+                "kernel_initializer": {
+                    "class_name": "VarianceScaling",
+                    "config": {
+                        "scale": 1.0,
+                        "mode": "fan_avg",
+                        "distribution": "uniform",
+                        "seed": null
+                    }
+                },
+                "bias_initializer": {
+                    "class_name": "Zeros",
+                    "config": {}
+                },
+                "kernel_regularizer": "None",
+                "bias_regularizer": "None",
+                "activity_regularizer": "None",
+                "kernel_constraint": "None",
+                "bias_constraint": "None"
+            },
+            {
+                "trainable": true,
+                "activation": "no_activation",
+                "use_bias": true,
+                "kernel_initializer": {
+                    "class_name": "VarianceScaling",
+                    "config": {
+                        "scale": 1.0,
+                        "mode": "fan_avg",
+                        "distribution": "uniform",
+                        "seed": null
+                    }
+                },
+                "bias_initializer": {
+                    "class_name": "Zeros",
+                    "config": {}
+                },
+                "kernel_regularizer": "None",
+                "bias_regularizer": "None",
+                "activity_regularizer": "None",
+                "kernel_constraint": "None",
+                "bias_constraint": "None"
+            },
+            {
+                "trainable": true,
+                "activation": "softmax",
+                "use_bias": true,
+                "kernel_initializer": {
+                    "class_name": "VarianceScaling",
+                    "config": {
+                        "scale": 1.0,
+                        "mode": "fan_avg",
+                        "distribution": "uniform",
+                        "seed": null
+                    }
+                },
+                "bias_initializer": {
+                    "class_name": "Zeros",
+                    "config": {}
+                },
+                "kernel_regularizer": "None",
+                "bias_regularizer": "None",
+                "activity_regularizer": "None",
+                "kernel_constraint": "None",
+                "bias_constraint": "None"
+            }
+        ]
+    },
+    "layer_input_info": {
+        "Conv2D": {
+            "input_dims": [
+                4
+            ],
+            "dtype": [
+                "float32"
+            ],
+            "shape": [
+                "[Dimension(None), Dimension(32), Dimension(32), Dimension(3)]",
+                "[Dimension(None), Dimension(14), Dimension(14), Dimension(6)]"
+            ]
+        },
+        "Activation": {
+            "input_dims": [
+                4
+            ],
+            "dtype": [
+                "float32"
+            ],
+            "shape": [
+                "[Dimension(None), Dimension(28), Dimension(28), Dimension(6)]",
+                "[Dimension(None), Dimension(10), Dimension(10), Dimension(16)]"
+            ]
+        },
+        "MaxPooling2D": {
+            "input_dims": [
+                4
+            ],
+            "dtype": [
+                "float32"
+            ],
+            "shape": [
+                "[Dimension(None), Dimension(28), Dimension(28), Dimension(6)]",
+                "[Dimension(None), Dimension(10), Dimension(10), Dimension(16)]"
+            ]
+        },
+        "Flatten": {
+            "input_dims": [
+                4
+            ],
+            "dtype": [
+                "float32"
+            ],
+            "shape": [
+                "[Dimension(None), Dimension(5), Dimension(5), Dimension(16)]"
+            ]
+        },
+        "Dense": {
+            "input_dims": [
+                2
+            ],
+            "dtype": [
+                "float32"
+            ],
+            "shape": [
+                "[Dimension(None), Dimension(None)]",
+                "[Dimension(None), Dimension(120)]",
+                "[Dimension(None), Dimension(84)]"
+            ]
+        }
+    },
+    "layer_num": 10,
+    "layer_type": [
+        "Conv2D",
+        "Activation",
+        "MaxPooling2D",
+        "Flatten",
+        "Dense"
+    ],
+    "cur_edge_num": 10,
+    "layer_dims": {
+        "Conv2D": {
+            "input_dims": [
+                4
+            ],
+            "output_dims": [
+                4
+            ]
+        },
+        "Activation": {
+            "input_dims": [
+                4
+            ],
+            "output_dims": [
+                4
+            ]
+        },
+        "MaxPooling2D": {
+            "input_dims": [
+                4
+            ],
+            "output_dims": [
+                4
+            ]
+        },
+        "Flatten": {
+            "input_dims": [
+                4
+            ],
+            "output_dims": [
+                2
+            ]
+        },
+        "Dense": {
+            "input_dims": [
+                2
+            ],
+            "output_dims": [
+                2
+            ]
+        }
+    }
+}

server/LEMON-master/run/sub_model/svhn_origin0-ARep1-ARem5.json

@@ -0,0 +1,294 @@
+{
+    "edges": [
+        [
+            "Conv2D",
+            "Activation"
+        ],
+        [
+            "Activation",
+            "MaxPooling2D"
+        ],
+        [
+            "MaxPooling2D",
+            "Conv2D"
+        ],
+        [
+            "MaxPooling2D",
+            "Flatten"
+        ],
+        [
+            "Flatten",
+            "Dense"
+        ],
+        [
+            "Dense",
+            "Dense"
+        ]
+    ],
+    "layer_config": {
+        "Conv2D": [
+            {
+                "trainable": true,
+                "kernel_size": [
+                    5,
+                    5
+                ],
+                "strides": [
+                    1,
+                    1
+                ],
+                "padding": "valid",
+                "data_format": "channels_last",
+                "dilation_rate": [
+                    1,
+                    1
+                ],
+                "activation": "tanh",
+                "use_bias": false,
+                "kernel_initializer": {
+                    "class_name": "VarianceScaling",
+                    "config": {
+                        "scale": 1.0,
+                        "mode": "fan_avg",
+                        "distribution": "uniform",
+                        "seed": null
+                    }
+                },
+                "bias_initializer": {
+                    "class_name": "Zeros",
+                    "config": {}
+                },
+                "kernel_regularizer": "None",
+                "bias_regularizer": "None",
+                "activity_regularizer": "None",
+                "kernel_constraint": "None",
+                "bias_constraint": "None"
+            },
+            {
+                "trainable": true,
+                "kernel_size": [
+                    5,
+                    5
+                ],
+                "strides": [
+                    1,
+                    1
+                ],
+                "padding": "valid",
+                "data_format": "channels_last",
+                "dilation_rate": [
+                    1,
+                    1
+                ],
+                "activation": "no_activation",
+                "use_bias": false,
+                "kernel_initializer": {
+                    "class_name": "VarianceScaling",
+                    "config": {
+                        "scale": 1.0,
+                        "mode": "fan_avg",
+                        "distribution": "uniform",
+                        "seed": null
+                    }
+                },
+                "bias_initializer": {
+                    "class_name": "Zeros",
+                    "config": {}
+                },
+                "kernel_regularizer": "None",
+                "bias_regularizer": "None",
+                "activity_regularizer": "None",
+                "kernel_constraint": "None",
+                "bias_constraint": "None"
+            }
+        ],
+        "Activation": [
+            {
+                "trainable": true,
+                "activation": "relu"
+            }
+        ],
+        "MaxPooling2D": [
+            {
+                "trainable": true,
+                "pool_size": [
+                    2,
+                    2
+                ],
+                "padding": "valid",
+                "strides": [
+                    2,
+                    2
+                ],
+                "data_format": "channels_last"
+            }
+        ],
+        "Flatten": [
+            {
+                "trainable": true,
+                "data_format": "channels_last"
+            }
+        ],
+        "Dense": [
+            {
+                "trainable": true,
+                "activation": "relu",
+                "use_bias": true,
+                "kernel_initializer": {
+                    "class_name": "VarianceScaling",
+                    "config": {
+                        "scale": 1.0,
+                        "mode": "fan_avg",
+                        "distribution": "uniform",
+                        "seed": null
+                    }
+                },
+                "bias_initializer": {
+                    "class_name": "Zeros",
+                    "config": {}
+                },
+                "kernel_regularizer": "None",
+                "bias_regularizer": "None",
+                "activity_regularizer": "None",
+                "kernel_constraint": "None",
+                "bias_constraint": "None"
+            },
+            {
+                "trainable": true,
+                "activation": "softmax",
+                "use_bias": true,
+                "kernel_initializer": {
+                    "class_name": "VarianceScaling",
+                    "config": {
+                        "scale": 1.0,
+                        "mode": "fan_avg",
+                        "distribution": "uniform",
+                        "seed": null
+                    }
+                },
+                "bias_initializer": {
+                    "class_name": "Zeros",
+                    "config": {}
+                },
+                "kernel_regularizer": "None",
+                "bias_regularizer": "None",
+                "activity_regularizer": "None",
+                "kernel_constraint": "None",
+                "bias_constraint": "None"
+            }
+        ]
+    },
+    "layer_input_info": {
+        "Conv2D": {
+            "input_dims": [
+                4
+            ],
+            "dtype": [
+                "float32"
+            ],
+            "shape": [
+                "[Dimension(None), Dimension(32), Dimension(32), Dimension(3)]",
+                "[Dimension(None), Dimension(14), Dimension(14), Dimension(6)]"
+            ]
+        },
+        "Activation": {
+            "input_dims": [
+                4
+            ],
+            "dtype": [
+                "float32"
+            ],
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server/LEMON-master/run/sub_model/svhn_origin0-ARep1-ARep14.json

@@ -0,0 +1,294 @@
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server/LEMON-master/run/sub_model/svhn_origin0-ARep1-ARep2.json

@@ -0,0 +1,317 @@
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server/LEMON-master/run/sub_model/svhn_origin0-ARep1-ARep8.json

@@ -0,0 +1,294 @@
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server/LEMON-master/run/sub_model/svhn_origin0-ARep1-GF1.json

@@ -0,0 +1,294 @@
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server/LEMON-master/run/sub_model/svhn_origin0-ARep1-GF10.json

@@ -0,0 +1,294 @@
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server/LEMON-master/run/sub_model/svhn_origin0-ARep1-GF11.json

@@ -0,0 +1,294 @@
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server/LEMON-master/run/sub_model/svhn_origin0-ARep1-GF2.json

@@ -0,0 +1,294 @@
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server/LEMON-master/run/sub_model/svhn_origin0-ARep1-GF9.json

@@ -0,0 +1,294 @@
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+}

server/LEMON-master/run/sub_model/svhn_origin0-ARep1-LA1.json

@@ -0,0 +1,335 @@
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+}

server/LEMON-master/run/sub_model/svhn_origin0-ARep1-LC1.json

@@ -0,0 +1,298 @@
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server/LEMON-master/run/sub_model/svhn_origin0-ARep1-LC4.json

@@ -0,0 +1,298 @@
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+}

server/LEMON-master/run/sub_model/svhn_origin0-ARep1-LC6.json

@@ -0,0 +1,298 @@
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+}

server/LEMON-master/run/sub_model/svhn_origin0-ARep1-MLA3.json

@@ -0,0 +1,376 @@
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server/LEMON-master/run/sub_model/svhn_origin0-ARep1-NAI1.json

@@ -0,0 +1,294 @@
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server/LEMON-master/run/sub_model/svhn_origin0-ARep1-NAI2.json

@@ -0,0 +1,294 @@
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+}

server/LEMON-master/run/sub_model/svhn_origin0-ARep1-NAI9.json

@@ -0,0 +1,294 @@
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server/LEMON-master/run/sub_model/svhn_origin0-ARep1-NEB3.json

@@ -0,0 +1,294 @@
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server/LEMON-master/run/sub_model/svhn_origin0-ARep1-NS4.json

@@ -0,0 +1,294 @@
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+}

server/LEMON-master/run/sub_model/svhn_origin0-ARep1-WS1.json

@@ -0,0 +1,294 @@
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+}

server/LEMON-master/run/sub_model/svhn_origin0-ARep1.json

@@ -0,0 +1,294 @@
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+}

server/LEMON-master/run/sub_model/svhn_origin0-ARep10.json

@@ -0,0 +1,280 @@
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+}

server/LEMON-master/run/sub_model/svhn_origin0-GF4.json

@@ -0,0 +1,257 @@
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+                "[Dimension(None), Dimension(84)]"
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+        }
+    }
+}

server/LEMON-master/run/sub_model/svhn_origin0-LA2.json

@@ -0,0 +1,298 @@
+{
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+                    2
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+                "[Dimension(None), Dimension(84)]"
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+            ]
+        }
+    }
+}

server/LEMON-master/run/sub_model/svhn_origin0-LA3.json

@@ -0,0 +1,303 @@
+{
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+                "[Dimension(None), Dimension(84)]"
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+        }
+    }
+}

server/LEMON-master/run/sub_model/svhn_origin0-LA5.json

@@ -0,0 +1,294 @@
+{
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+                "[Dimension(None), Dimension(84)]"
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+}

server/LEMON-master/run/sub_model/svhn_origin0-LA7.json

@@ -0,0 +1,292 @@
+{
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+        }
+    }
+}

server/LEMON-master/run/sub_model/svhn_origin0-LC3.json

@@ -0,0 +1,261 @@
+{
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+        }
+    }
+}

server/LEMON-master/run/sub_model/svhn_origin0-LC8.json

@@ -0,0 +1,261 @@
+{
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+                "[Dimension(None), Dimension(120)]",
+                "[Dimension(None), Dimension(84)]"
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+}

server/LEMON-master/run/sub_model/svhn_origin0-LR1-ARem3.json

@@ -0,0 +1,297 @@
+{
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server/LEMON-master/run/sub_model/svhn_origin0-LR1-ARep11.json

@@ -0,0 +1,297 @@
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server/LEMON-master/run/sub_model/svhn_origin0-LR1-ARep16.json

@@ -0,0 +1,297 @@
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server/LEMON-master/run/sub_model/svhn_origin0-LR1-GF12-ARem8.json

@@ -0,0 +1,297 @@
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server/LEMON-master/run/sub_model/svhn_origin0-LR1-GF12-ARep15.json

@@ -0,0 +1,260 @@
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server/LEMON-master/run/sub_model/svhn_origin0-LR1-GF12-GF13.json

@@ -0,0 +1,260 @@
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server/LEMON-master/run/sub_model/svhn_origin0-LR1-GF12-GF14.json

@@ -0,0 +1,260 @@
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server/LEMON-master/run/sub_model/svhn_origin0-LR1-GF12-LR10.json

@@ -0,0 +1,226 @@
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server/LEMON-master/run/sub_model/svhn_origin0-LR1-GF12-MLA9.json

@@ -0,0 +1,350 @@
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+}

server/LEMON-master/run/sub_model/svhn_origin0-LR1-GF12-WS8.json

@@ -0,0 +1,260 @@
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server/LEMON-master/run/sub_model/svhn_origin0-LR1-GF12.json

@@ -0,0 +1,260 @@
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server/LEMON-master/run/sub_model/svhn_origin0-LR1-GF3.json

@@ -0,0 +1,260 @@
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server/LEMON-master/run/sub_model/svhn_origin0-LR1-LA4.json

@@ -0,0 +1,335 @@
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+}

server/LEMON-master/run/sub_model/svhn_origin0-LR1-LA9.json

@@ -0,0 +1,301 @@
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+}

server/LEMON-master/run/sub_model/svhn_origin0-LR1-LC5.json

@@ -0,0 +1,264 @@
+{
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+}

server/LEMON-master/run/sub_model/svhn_origin0-LR1-LC7.json

@@ -0,0 +1,264 @@
+{
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+}

server/LEMON-master/run/sub_model/svhn_origin0-LR1-LC9.json

@@ -0,0 +1,264 @@
+{
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+            "MaxPooling2D"
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+                "activation": "relu"
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+                "[Dimension(None), Dimension(120)]",
+                "[Dimension(None), Dimension(84)]"
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+}

server/LEMON-master/run/sub_model/svhn_origin0-LR1-LR2-ARem7.json

@@ -0,0 +1,263 @@
+{
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+}

server/LEMON-master/run/sub_model/svhn_origin0-LR1-LR2-ARep12.json

@@ -0,0 +1,249 @@
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server/LEMON-master/run/sub_model/svhn_origin0-LR1-LR2-ARep13.json

@@ -0,0 +1,249 @@
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server/LEMON-master/run/sub_model/svhn_origin0-LR1-LR2-ARep3-ARem2.json

@@ -0,0 +1,286 @@
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+}

server/LEMON-master/run/sub_model/svhn_origin0-LR1-LR2-ARep3-ARep4.json

@@ -0,0 +1,249 @@
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+}

server/LEMON-master/run/sub_model/svhn_origin0-LR1-LR2-ARep3-ARep5.json

@@ -0,0 +1,286 @@
+{
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+}

server/LEMON-master/run/sub_model/svhn_origin0-LR1-LR2-ARep3-ARep6.json

@@ -0,0 +1,286 @@
+{
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+}

server/LEMON-master/run/sub_model/svhn_origin0-LR1-LR2-ARep3-ARep7.json

@@ -0,0 +1,249 @@
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+}

server/LEMON-master/run/sub_model/svhn_origin0-LR1-LR2-ARep3-GF15.json

@@ -0,0 +1,249 @@
+{
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+}

server/LEMON-master/run/sub_model/svhn_origin0-LR1-LR2-ARep3-GF7.json

@@ -0,0 +1,249 @@
+{
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+}

server/LEMON-master/run/sub_model/svhn_origin0-LR1-LR2-ARep3-LA6.json

@@ -0,0 +1,298 @@
+{
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server/LEMON-master/run/sub_model/svhn_origin0-LR1-LR2-ARep3-MLA7.json

@@ -0,0 +1,365 @@
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+}

server/LEMON-master/run/sub_model/svhn_origin0-LR1-LR2-ARep3-NAI11.json

@@ -0,0 +1,249 @@
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server/LEMON-master/run/sub_model/svhn_origin0-LR1-LR2-ARep3-NAI6.json

@@ -0,0 +1,249 @@
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+}

server/LEMON-master/run/sub_model/svhn_origin0-LR1-LR2-ARep3-NEB1.json

@@ -0,0 +1,249 @@
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+}

server/LEMON-master/run/sub_model/svhn_origin0-LR1-LR2-ARep3-NEB2.json

@@ -0,0 +1,249 @@
+{
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+}

server/LEMON-master/run/sub_model/svhn_origin0-LR1-LR2-ARep3-NS5.json

@@ -0,0 +1,249 @@
+{
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+                "[Dimension(None), Dimension(120)]",
+                "[Dimension(None), Dimension(84)]"
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+}

server/LEMON-master/run/sub_model/svhn_origin0-LR1-LR2-ARep3-NS6.json

@@ -0,0 +1,249 @@
+{
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+}

server/LEMON-master/run/sub_model/svhn_origin0-LR1-LR2-ARep3-WS4.json

@@ -0,0 +1,249 @@
+{
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+            "MaxPooling2D"
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+            "Conv2D"
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+                    2
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+                    2
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+                "data_format": "channels_last"
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+                "bias_regularizer": "None",
+                "activity_regularizer": "None",
+                "kernel_constraint": "None",
+                "bias_constraint": "None"
+            }
+        ]
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+            "dtype": [
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+                "[Dimension(None), Dimension(120)]",
+                "[Dimension(None), Dimension(84)]"
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+}