IA

2023-01-29 16:56:40 +01:00 · 2023-01-29 16:56:40 +01:00 · 0d48cc505a
commit 0d48cc505a
parent 19911a6eef
217 changed files with 6092 additions and 18 deletions
--- a/A4/A4C4-C2.pdf
+++ b/A4/A4C4-C2.pdf
--- a/D3/TP/Cours.ipynb
+++ b/D3/TP/Cours.ipynb
@ -100,7 +100,7 @@
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@ -114,12 +114,12 @@
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--- a/D3/TP/TP_SETI_SVM/SVM.ipynb
+++ b/D3/TP/TP_SETI_SVM/SVM.ipynb
--- a/D3/TP/TP_SETI_SVM/plot_digits_classification.ipynb
+++ b/D3/TP/TP_SETI_SVM/plot_digits_classification.ipynb
@ -571,7 +571,7 @@
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      "pygments_lexer": "ipython3",
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--- a/IA/seti_master-master/README.md
+++ b/IA/seti_master-master/README.md
@ -0,0 +1,91 @@
 This repository provide all the necessary material
 for the practical session of SETI Master - AI section.
 The main file is a jupyter notebook `code/tutorial.ipynb`, slides for the first
 part of the tutorial are under `doc/slides.pdf`.
 There are several dependancies to install.
 # Getting started
 * We suppose python3.9 is installed and pip
 * First we can move into code folder
  ```
  cd code
  ```
 * We recommend creating a dedicated environment for the tutorial, using for instance virtualenv. If virtualenv is not installed run:
  ```
  python3.9 -m pip install virtualenv
  ```
  Afterwards you can create the virtualenv using:
  ```
  virtualenv ~/.virtualenvs/tuto_seti
  ```
  consider adding `-p /usr/bin/python3.9` pointing to your python version if you have multiple python installed.
  Then activate the environment using 
  ```
  source ~/.virtualenvs/tuto_seti/bin/activate
  ```
 * Once in this venv, we can install python dependencies needed for this tutorial with :
  ```
  jupyter notebook tutorial.ipynb
  ```
 # Tools installation
 * **Z3**: your linux distribution should have a package (named z3):
  ```
  sudo apt install z3
  ```
  Otherwise check [here](https://github.com/Z3Prover/z3) for a detailed installation instruction 
 * **ISAIEH** can be found compiled in the `bin` folder.
 * **Marabou** can be found compiled in the `bin` folder.
 * **PyRAT** comes as a precompiled binary in this repository that requires Python 3.9
 When everything is installed, `jupyter notebook tutorial.ipynb` should open the tutorial notebook in your web browser.
 ## In case compiled version does not work
 * **ISAIEH**: The next is to install dependencies for ISAIEH, which we will use:
  * install the ocaml opam build system with:
  ```
  sudo apt install opam
  ```
  * Additional system dependencies with `m4` and `gmp` are needed at minimum.
  Install them using 
  ```
  sudo apt install m4 libgmp3-dev
  ```
  on a Debian-like.
  * Finally we can install ISAIEH with 
  ```
  git clone https://git.frama-c.com/pub/isaieh.git && cd isaieh && opam init --disable-sandboxing && opam install -y . && eval $(opam env) && dune build
  ```
 * **Marabou** can be compiled from [source](https://github.com/NeuralNetworkVerification/Marabou) using:
  ```
  sudo apt install cmake libboost-all-dev
  ```
  ```
  git clone https://github.com/NeuralNetworkVerification/Marabou
  cd Marabou
  mkdir build
  cd build
  cmake ..
  cmake --build .
  ```
  If this does not work an older version binary can be downloaded with
  ```
  wget https://aisafety.stanford.edu/marabou/marabou-1.0-x86_64-linux.zip
  ```
 ### Authors
 - Augustin Lemesle
 - Julien Girard-Satabin
 - Guillaume Charpiat
 - Zakaria Chihani
 - Marc Schoenauer
--- a/IA/seti_master-master/code/.ipynb_checkpoints/tutorial-checkpoint.ipynb
+++ b/IA/seti_master-master/code/.ipynb_checkpoints/tutorial-checkpoint.ipynb
--- a/IA/seti_master-master/code/pycache/formula_lang.cpython-39.pyc
+++ b/IA/seti_master-master/code/pycache/formula_lang.cpython-39.pyc
--- a/IA/seti_master-master/code/pycache/onnx2pytorch.cpython-39.pyc
+++ b/IA/seti_master-master/code/pycache/onnx2pytorch.cpython-39.pyc
--- a/IA/seti_master-master/code/pycache/pyrat_api.cpython-39.pyc
+++ b/IA/seti_master-master/code/pycache/pyrat_api.cpython-39.pyc
--- a/IA/seti_master-master/code/pycache/visualize_outputs.cpython-39.pyc
+++ b/IA/seti_master-master/code/pycache/visualize_outputs.cpython-39.pyc
--- a/IA/seti_master-master/code/acas.nnet
+++ b/IA/seti_master-master/code/acas.nnet
@ -0,0 +1,620 @@
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 2.91687e-02,1.42296e-02,3.36053e-02,4.55023e-02,-1.59656e-03,2.79376e-02,-1.48866e-02,-6.59574e-03,-5.59406e-01,8.73523e-03,-3.53054e-01,3.28847e-03,3.88876e-03,2.29865e-02,1.13683e-02,1.05610e-03,-4.53472e-05,1.91299e-02,2.67125e-02,1.31848e-02,-2.07126e-03,1.62074e-03,1.46194e-02,3.62973e-02,-1.99082e-02,-1.42614e-01,1.04892e-02,-8.19706e-05,1.39199e-02,-3.22181e-03,4.15122e-02,-5.53828e-03,6.03937e-03,5.90429e-03,9.06922e-03,1.51753e-03,-1.97379e-02,7.26115e-03,3.84611e-02,1.19480e-03,-1.36636e-02,2.46676e-02,1.65979e-02,1.20169e-02,5.64371e-03,1.17773e-02,3.39602e-02,1.98277e-02,-2.10452e-03,4.80108e-03,
 4.55802e-04,8.12854e-03,2.10374e-02,4.31362e-02,6.97595e-03,3.97354e-02,-7.91639e-03,1.09422e-02,2.37775e-01,-8.41754e-03,3.28521e-01,6.57417e-03,4.22466e-04,1.29686e-02,-7.76473e-03,3.65228e-02,1.07018e-02,1.53491e-03,2.22336e-02,2.23179e-02,4.63275e-03,5.43592e-03,6.84646e-04,2.29886e-03,-1.53913e-02,-1.11249e-01,-1.46463e-02,2.28915e-02,1.61600e-02,-7.59424e-04,3.22797e-02,8.34878e-03,3.66022e-03,6.44042e-03,1.02967e-02,2.16978e-03,7.54370e-02,2.50581e-02,3.27263e-02,1.06836e-02,3.73129e-03,6.17485e-03,-1.54975e-02,8.55993e-03,-1.06660e-02,-1.59203e-02,6.57542e-03,-4.99358e-03,1.15872e-02,-7.32343e-03,
 3.12882e-02,7.73886e-03,1.97830e-02,4.86244e-02,1.44084e-02,3.67439e-02,-1.71598e-02,-1.32780e-02,-6.83744e-01,1.43570e-02,-1.77770e-01,7.81174e-03,-6.48117e-04,1.98732e-02,1.14211e-02,-1.06893e-04,1.46441e-03,2.19159e-02,2.66931e-02,7.07696e-03,8.55070e-04,-1.90231e-03,1.89910e-02,2.18761e-03,-1.48906e-02,2.50500e-01,1.08779e-02,3.12157e-03,1.45203e-02,3.87036e-03,3.78203e-02,6.54798e-03,8.52316e-03,-3.75273e-03,1.67625e-02,1.96232e-03,-3.15121e-02,-1.17148e-02,3.88476e-02,-8.53707e-03,-2.10246e-02,7.46594e-03,1.50692e-02,7.66760e-03,2.13937e-02,1.40507e-02,1.09046e-02,-3.06697e-03,-1.61312e-02,9.18950e-03,
 -1.37757e-02,
 -1.93597e-02,
 -1.97943e-02,
 -1.93225e-02,
 -1.89474e-02,
--- a/IA/seti_master-master/code/bin/aimos_compile/aimos/init.pyc
+++ b/IA/seti_master-master/code/bin/aimos_compile/aimos/init.pyc
--- a/IA/seti_master-master/code/bin/aimos_compile/aimos/aimos.pyc
+++ b/IA/seti_master-master/code/bin/aimos_compile/aimos/aimos.pyc
--- a/IA/seti_master-master/code/bin/aimos_compile/aimos/checks.pyc
+++ b/IA/seti_master-master/code/bin/aimos_compile/aimos/checks.pyc
--- a/IA/seti_master-master/code/bin/aimos_compile/aimos/core.pyc
+++ b/IA/seti_master-master/code/bin/aimos_compile/aimos/core.pyc
--- a/IA/seti_master-master/code/bin/aimos_compile/aimos/core_functions.pyc
+++ b/IA/seti_master-master/code/bin/aimos_compile/aimos/core_functions.pyc
--- a/IA/seti_master-master/code/bin/aimos_compile/aimos/core_transformations.pyc
+++ b/IA/seti_master-master/code/bin/aimos_compile/aimos/core_transformations.pyc
--- a/IA/seti_master-master/code/bin/aimos_compile/aimos/nnet.pyc
+++ b/IA/seti_master-master/code/bin/aimos_compile/aimos/nnet.pyc
--- a/IA/seti_master-master/code/bin/aimos_compile/environment.yml
+++ b/IA/seti_master-master/code/bin/aimos_compile/environment.yml
@ -0,0 +1,19 @@
 name: aimos
 channels:
  - conda-forge
  - defaults
 dependencies:
  - python=3.10
  - matplotlib
  - numpy
  - onnxruntime
  - pandas
  - pillow
  - pytorch
  - pyyaml
  - scikit-learn
  - tqdm
  - pip
  - pip:
      - opencv-python
      - tensorflow
--- a/IA/seti_master-master/code/bin/aimos_compile/requirements.txt
+++ b/IA/seti_master-master/code/bin/aimos_compile/requirements.txt
@ -0,0 +1,11 @@
 matplotlib
 numpy
 onnxruntime
 opencv-python
 pandas
 pillow
 pyyaml
 scikit-learn
 tensorflow
 torch
 tqdm
--- a/IA/seti_master-master/code/bin/aimos_compile/setup.py
+++ b/IA/seti_master-master/code/bin/aimos_compile/setup.py
@ -0,0 +1,20 @@
 """Steup module for AIMOS."""
 import sys
 from setuptools import setup
 if sys.version_info.minor < 9:
    raise Exception("AIMOS module only works for Python 3.9+")
 setup(
    name="AIMOS",
    version="1.0",
    description="AI Metamorphism Observing Software",
    author="Augustin Lemesle, Aymeric Varasse",
    author_email="augustin.lemesle@cea.fr",
    python_requires=">=3.9",
    packages=["aimos"],
    package_data={"aimos": ["*.pyc"]},
    entry_points={"console_scripts": ["aimos=aimos.aimos:main"]},
 )
--- a/IA/seti_master-master/code/bin/isaieh.exe
+++ b/IA/seti_master-master/code/bin/isaieh.exe
--- a/IA/seti_master-master/code/bin/marabou.elf
+++ b/IA/seti_master-master/code/bin/marabou.elf
--- a/IA/seti_master-master/code/danger.txt
+++ b/IA/seti_master-master/code/danger.txt
@ -0,0 +1,7 @@
 x0 >=  0 
 x0 <  0.2 
 x1 >=  0 
 x1 <  1 
 x2 >=  0 
 x2 <  1 
 y0 >=  0 
--- a/IA/seti_master-master/code/dataset/pycache/pytorch_dataset.cpython-38.pyc
+++ b/IA/seti_master-master/code/dataset/pycache/pytorch_dataset.cpython-38.pyc
--- a/IA/seti_master-master/code/dataset/data_ALPHA=0.5_BETA_=0.25_DELTA1=0.3_DELTA2=0.7_EPSILON=0.09.npy
+++ b/IA/seti_master-master/code/dataset/data_ALPHA=0.5_BETA_=0.25_DELTA1=0.3_DELTA2=0.7_EPSILON=0.09.npy
--- a/IA/seti_master-master/code/dataset/generate_dataset.py
+++ b/IA/seti_master-master/code/dataset/generate_dataset.py
@ -0,0 +1,88 @@
 """
 This script generates data for a classifier modelling
 a Guardian, opposing a Threat. The Guardian should
 output an alarm when the Threat in trespassing within a zone.
 The Guardian has access to three inputs: norm of the distance
 vector from Threat to Guardian, norm of speed vector of Threat, angle between
 the speed vector and the distance vector in radians.
 This script generates data in a csv file with the following columns,
 from left to right:
 NORM OF DISTANCE
 NORM OF SPEED VECTOR
 ANGLE
 OUTPUT
 Value in column OUTPUT is either 0 or 1.
 """
 # Imports
 from random import random as r
 import numpy as np
 import sys
 # Constants
 ALPHA = 0.5  # speed threshold for suspicious zone
 BETA = 0.25  # angle (radian) threshold for suspicious zone
 MAX_DIST = 1.  # maximum generated distance
 MAX_SPEED = 1.  # maximum speed
 MAX_ANGLE = 1.  # maximum angle (symmetry)
 DELTA_1 = 0.3  # limit of danger zone
 DELTA_2 = 0.7  # limit of suspicious zone
 EPSILON = 0.09  # threshold of frontier distance
 NUM_SAMPLING = int(sys.argv[1])
 def sample_data():
    """Returns a tuple (a,b,c)
    where a stands for the norm of distance vector,
    b stands for the norm of speed vector,
    c stands for the angle.
    We consider only data that are sufficiently far away from
    the limits DELTA_1 and DELTA_2
    """
    dist = r()*MAX_SPEED
    speed = r()*MAX_DIST
    angle = r()*MAX_ANGLE
    while abs(dist - DELTA_1) < EPSILON or abs(dist - DELTA_2) < EPSILON:
        dist = r() * MAX_SPEED
    while abs(speed - ALPHA) < EPSILON:
        speed = r()*MAX_DIST
    if abs(angle - BETA) < EPSILON:
        angle = r()*MAX_ANGLE
    return (dist, speed, angle)
 def is_safe(a, b, c):
    """Returns TRUE if (a,b,c) is a safe state,
    and FALSE otherwise.
    A safe state is defined by the following:
    * a > DELTA_2
    * a > DELTA_1 and a < DELTA_2
      and (b < ALPHA or c > BETA)
    An unsafe state is defined by the following:
    * a < DELTA_1
    * a > DELTA_1 and a < DELTA_2
      and (b > ALPHA and c < BETA)
    """
    return(a > DELTA_2 or (DELTA_1 < a < DELTA_2 and
                           (b < ALPHA or c > BETA)))
 if __name__ == "__main__":
    FNAME = 'data_ALPHA='+str(ALPHA)+'_BETA_='+str(BETA) +\
            '_DELTA1='+str(DELTA_1) +\
            '_DELTA2=' + str(DELTA_2) + \
            '_EPSILON=' + str(EPSILON) + \
            '.npy'
    data = []
    for n in range(NUM_SAMPLING):
        (a, b, c) = sample_data()
        if is_safe(a, b, c):
            res = 0
        else:
            res = 1
        data.append([a, b, c, res])
    np.save(FNAME, np.asarray(data))
    print('Dataset generation completed')
--- a/IA/seti_master-master/code/dataset/pytorch_dataset.py
+++ b/IA/seti_master-master/code/dataset/pytorch_dataset.py
@ -0,0 +1,48 @@
 """Custom datasets. Must implement __len__ and __getitem__.
 The latter should return a dict with data and target vector
 """
 import os
 import sys
 from torch.utils.data import Dataset
 import torch
 import numpy as np
 # Don't forget to normalize inputs
 class CollisionDataset(Dataset):
    """Dataset wrapper for DFKI tutorial
    Rely on the data generated by generate_collisions.py
    """
    def __init__(self, fpath, train=True, transform=None):
        self.train = train
        self.transform = transform
        self.fpath = fpath
        datalist = []
        if os.path.exists(self.fpath):
            arr = np.load(self.fpath)
        else:
            sys.exit('Error: {} does not exist. Dataset not'.format(fpath) +
                     'created')
        # one data out of 10 is going on test data
        self.traindata = arr[np.arange(arr.shape[0]) % 10 != 0, :]
        self.testdata = arr[np.arange(arr.shape[0]) % 10 == 0, :]
    def __len__(self):
        if self.train:
            return len(self.traindata)
        else:
            return len(self.testdata)
    def __getitem__(self, idx):
        # Get data and objective
        if self.train:
            data = self.traindata
        else:
            data = self.testdata
        if self.transform:
            data = data.astype(np.float32)
            data = self.transform(data)
            data = data.squeeze()
        return data[idx, :-1], data[idx, -1].squeeze()
--- a/IA/seti_master-master/code/fmnist/adversarial.onnx
+++ b/IA/seti_master-master/code/fmnist/adversarial.onnx
--- a/IA/seti_master-master/code/fmnist/baseline.onnx
+++ b/IA/seti_master-master/code/fmnist/baseline.onnx
--- a/IA/seti_master-master/code/fmnist/certified.onnx
+++ b/IA/seti_master-master/code/fmnist/certified.onnx
--- a/IA/seti_master-master/code/fmnist/images/fmnist_1082.npy
+++ b/IA/seti_master-master/code/fmnist/images/fmnist_1082.npy
--- a/IA/seti_master-master/code/fmnist/images/fmnist_1463.npy
+++ b/IA/seti_master-master/code/fmnist/images/fmnist_1463.npy
--- a/IA/seti_master-master/code/fmnist/images/fmnist_152.npy
+++ b/IA/seti_master-master/code/fmnist/images/fmnist_152.npy
--- a/IA/seti_master-master/code/fmnist/images/fmnist_16.npy
+++ b/IA/seti_master-master/code/fmnist/images/fmnist_16.npy
--- a/IA/seti_master-master/code/fmnist/images/fmnist_1711.npy
+++ b/IA/seti_master-master/code/fmnist/images/fmnist_1711.npy
--- a/IA/seti_master-master/code/fmnist/images/fmnist_2107.npy
+++ b/IA/seti_master-master/code/fmnist/images/fmnist_2107.npy
--- a/IA/seti_master-master/code/fmnist/images/fmnist_2436.npy
+++ b/IA/seti_master-master/code/fmnist/images/fmnist_2436.npy
--- a/IA/seti_master-master/code/fmnist/images/fmnist_2505.npy
+++ b/IA/seti_master-master/code/fmnist/images/fmnist_2505.npy
--- a/IA/seti_master-master/code/fmnist/images/fmnist_2775.npy
+++ b/IA/seti_master-master/code/fmnist/images/fmnist_2775.npy
--- a/IA/seti_master-master/code/fmnist/images/fmnist_294.npy
+++ b/IA/seti_master-master/code/fmnist/images/fmnist_294.npy
--- a/IA/seti_master-master/code/fmnist/images/fmnist_3175.npy
+++ b/IA/seti_master-master/code/fmnist/images/fmnist_3175.npy
--- a/IA/seti_master-master/code/fmnist/images/fmnist_3242.npy
+++ b/IA/seti_master-master/code/fmnist/images/fmnist_3242.npy
--- a/IA/seti_master-master/code/fmnist/images/fmnist_3254.npy
+++ b/IA/seti_master-master/code/fmnist/images/fmnist_3254.npy
--- a/IA/seti_master-master/code/fmnist/images/fmnist_337.npy
+++ b/IA/seti_master-master/code/fmnist/images/fmnist_337.npy
--- a/IA/seti_master-master/code/fmnist/images/fmnist_3385.npy
+++ b/IA/seti_master-master/code/fmnist/images/fmnist_3385.npy
--- a/IA/seti_master-master/code/fmnist/images/fmnist_3641.npy
+++ b/IA/seti_master-master/code/fmnist/images/fmnist_3641.npy
--- a/IA/seti_master-master/code/fmnist/images/fmnist_3907.npy
+++ b/IA/seti_master-master/code/fmnist/images/fmnist_3907.npy
--- a/IA/seti_master-master/code/fmnist/images/fmnist_4069.npy
+++ b/IA/seti_master-master/code/fmnist/images/fmnist_4069.npy
--- a/IA/seti_master-master/code/fmnist/images/fmnist_4096.npy
+++ b/IA/seti_master-master/code/fmnist/images/fmnist_4096.npy
--- a/IA/seti_master-master/code/fmnist/images/fmnist_4158.npy
+++ b/IA/seti_master-master/code/fmnist/images/fmnist_4158.npy
--- a/IA/seti_master-master/code/fmnist/images/fmnist_4516.npy
+++ b/IA/seti_master-master/code/fmnist/images/fmnist_4516.npy
--- a/IA/seti_master-master/code/fmnist/images/fmnist_4651.npy
+++ b/IA/seti_master-master/code/fmnist/images/fmnist_4651.npy
--- a/IA/seti_master-master/code/fmnist/images/fmnist_5290.npy
+++ b/IA/seti_master-master/code/fmnist/images/fmnist_5290.npy
--- a/IA/seti_master-master/code/fmnist/images/fmnist_5400.npy
+++ b/IA/seti_master-master/code/fmnist/images/fmnist_5400.npy
--- a/IA/seti_master-master/code/fmnist/images/fmnist_5428.npy
+++ b/IA/seti_master-master/code/fmnist/images/fmnist_5428.npy
--- a/IA/seti_master-master/code/fmnist/images/fmnist_5744.npy
+++ b/IA/seti_master-master/code/fmnist/images/fmnist_5744.npy
--- a/IA/seti_master-master/code/fmnist/images/fmnist_5838.npy
+++ b/IA/seti_master-master/code/fmnist/images/fmnist_5838.npy
--- a/IA/seti_master-master/code/fmnist/images/fmnist_6092.npy
+++ b/IA/seti_master-master/code/fmnist/images/fmnist_6092.npy
--- a/IA/seti_master-master/code/fmnist/images/fmnist_6182.npy
+++ b/IA/seti_master-master/code/fmnist/images/fmnist_6182.npy
--- a/IA/seti_master-master/code/fmnist/images/fmnist_6536.npy
+++ b/IA/seti_master-master/code/fmnist/images/fmnist_6536.npy
--- a/IA/seti_master-master/code/fmnist/images/fmnist_6757.npy
+++ b/IA/seti_master-master/code/fmnist/images/fmnist_6757.npy
--- a/IA/seti_master-master/code/fmnist/images/fmnist_6833.npy
+++ b/IA/seti_master-master/code/fmnist/images/fmnist_6833.npy
--- a/IA/seti_master-master/code/fmnist/images/fmnist_7152.npy
+++ b/IA/seti_master-master/code/fmnist/images/fmnist_7152.npy
--- a/IA/seti_master-master/code/fmnist/images/fmnist_7341.npy
+++ b/IA/seti_master-master/code/fmnist/images/fmnist_7341.npy
--- a/IA/seti_master-master/code/fmnist/images/fmnist_7390.npy
+++ b/IA/seti_master-master/code/fmnist/images/fmnist_7390.npy
--- a/IA/seti_master-master/code/fmnist/images/fmnist_7523.npy
+++ b/IA/seti_master-master/code/fmnist/images/fmnist_7523.npy
--- a/IA/seti_master-master/code/fmnist/images/fmnist_7539.npy
+++ b/IA/seti_master-master/code/fmnist/images/fmnist_7539.npy
--- a/IA/seti_master-master/code/fmnist/images/fmnist_7978.npy
+++ b/IA/seti_master-master/code/fmnist/images/fmnist_7978.npy
--- a/IA/seti_master-master/code/fmnist/images/fmnist_808.npy
+++ b/IA/seti_master-master/code/fmnist/images/fmnist_808.npy
--- a/IA/seti_master-master/code/fmnist/images/fmnist_8228.npy
+++ b/IA/seti_master-master/code/fmnist/images/fmnist_8228.npy
--- a/IA/seti_master-master/code/fmnist/images/fmnist_8427.npy
+++ b/IA/seti_master-master/code/fmnist/images/fmnist_8427.npy
--- a/IA/seti_master-master/code/fmnist/images/fmnist_8734.npy
+++ b/IA/seti_master-master/code/fmnist/images/fmnist_8734.npy
--- a/IA/seti_master-master/code/fmnist/images/fmnist_8841.npy
+++ b/IA/seti_master-master/code/fmnist/images/fmnist_8841.npy
--- a/IA/seti_master-master/code/fmnist/images/fmnist_9031.npy
+++ b/IA/seti_master-master/code/fmnist/images/fmnist_9031.npy
--- a/IA/seti_master-master/code/fmnist/images/fmnist_9038.npy
+++ b/IA/seti_master-master/code/fmnist/images/fmnist_9038.npy
--- a/IA/seti_master-master/code/fmnist/images/fmnist_9079.npy
+++ b/IA/seti_master-master/code/fmnist/images/fmnist_9079.npy
--- a/IA/seti_master-master/code/fmnist/images/fmnist_9209.npy
+++ b/IA/seti_master-master/code/fmnist/images/fmnist_9209.npy
--- a/IA/seti_master-master/code/fmnist/images/fmnist_9266.npy
+++ b/IA/seti_master-master/code/fmnist/images/fmnist_9266.npy
--- a/IA/seti_master-master/code/fmnist/images/fmnist_9335.npy
+++ b/IA/seti_master-master/code/fmnist/images/fmnist_9335.npy
--- a/IA/seti_master-master/code/fmnist/images/fmnist_940.npy
+++ b/IA/seti_master-master/code/fmnist/images/fmnist_940.npy
--- a/IA/seti_master-master/code/fmnist/img_labels.csv
+++ b/IA/seti_master-master/code/fmnist/img_labels.csv
@ -0,0 +1,50 @@
 fmnist_4069.npy, 7
 fmnist_6182.npy, 3
 fmnist_7152.npy, 8
 fmnist_7390.npy, 2
 fmnist_8841.npy, 3
 fmnist_5744.npy, 1
 fmnist_4158.npy, 1
 fmnist_3175.npy, 1
 fmnist_5290.npy, 5
 fmnist_9038.npy, 0
 fmnist_3242.npy, 8
 fmnist_9335.npy, 5
 fmnist_2505.npy, 2
 fmnist_2775.npy, 9
 fmnist_9079.npy, 1
 fmnist_8228.npy, 1
 fmnist_6092.npy, 5
 fmnist_3385.npy, 9
 fmnist_4516.npy, 7
 fmnist_3907.npy, 9
 fmnist_7978.npy, 1
 fmnist_8427.npy, 9
 fmnist_6536.npy, 8
 fmnist_1711.npy, 9
 fmnist_152.npy, 1
 fmnist_940.npy, 9
 fmnist_2107.npy, 4
 fmnist_5428.npy, 1
 fmnist_7341.npy, 9
 fmnist_4651.npy, 8
 fmnist_3641.npy, 9
 fmnist_6757.npy, 0
 fmnist_2436.npy, 1
 fmnist_7523.npy, 8
 fmnist_9266.npy, 7
 fmnist_16.npy, 2
 fmnist_5838.npy, 8
 fmnist_9031.npy, 0
 fmnist_7539.npy, 9
 fmnist_5400.npy, 0
 fmnist_4096.npy, 4
 fmnist_8734.npy, 8
 fmnist_337.npy, 2
 fmnist_6833.npy, 3
 fmnist_1082.npy, 1
 fmnist_294.npy, 5
 fmnist_808.npy, 1
 fmnist_1463.npy, 0
 fmnist_9209.npy, 1
 fmnist_3254.npy, 5
--- a/IA/seti_master-master/code/fmnist/pruned.onnx
+++ b/IA/seti_master-master/code/fmnist/pruned.onnx
--- a/IA/seti_master-master/code/fmnist/pruned_certified.onnx
+++ b/IA/seti_master-master/code/fmnist/pruned_certified.onnx
--- a/IA/seti_master-master/code/formula.marabou
+++ b/IA/seti_master-master/code/formula.marabou
@ -0,0 +1,6 @@
 x0 >=  0.7 
 x0 <=  0.3 
 x1 >=  0.4 
 x1 <=  1 
 x2 <=  0.25 
 x2 >=  0 
--- a/IA/seti_master-master/code/formula.smt2
+++ b/IA/seti_master-master/code/formula.smt2
@ -0,0 +1,11 @@
 (assert (>= |CELL_actual_input_0_0_0| (/ 3152519739159347 4503599627370496)))
 (assert (< |CELL_actual_input_0_0_0| (/ 5404319552844595 18014398509481984)))
 (assert (>= |CELL_actual_input_0_0_1| (/ 3602879701896397 9007199254740992)))
 (assert (< |CELL_actual_input_0_0_1| (/ 1 1)))
 (assert (< |CELL_actual_input_0_0_2| (/ 1 4)))
 (assert (>= |CELL_actual_input_0_0_2| (/ 0 1)))
 (check-sat)
 (get-value (|CELL_actual_input_0_0_0|))
 (get-value (|CELL_actual_input_0_0_1|))
 (get-value (|CELL_actual_input_0_0_2|))
 (get-value (|CELL_actual_output_0_0_0|))
--- a/IA/seti_master-master/code/formula.txt
+++ b/IA/seti_master-master/code/formula.txt
@ -0,0 +1,11 @@
 x0 >=  0.6698755476125 
 x0 <  0.6798577687 
 x1 >=  0.375 
 x1 <  0.5 
 x2 >=  0.375 
 x2 <  0.5 
 x3 >=  0.49375 
 x3 <  0.5 
 x4 >=  0.33125000000000004 
 x4 <  0.45 
 y0 <  3.9911256459 
--- a/IA/seti_master-master/code/formula_lang.py
+++ b/IA/seti_master-master/code/formula_lang.py
@ -0,0 +1,275 @@
 """A small module to express formulas and write them in SMTLIB2,
 Marabou input format, PyRAT input format. Conjunction of linear constraints,
 with logical operators if necessary"""
 from fractions import Fraction
 import re
 DIST = 'x0'
 SPEED = 'x1'
 ANGLE = 'x2'
 OUTPUT = 'y0'
 GEQ = '>='
 GQ = '>'
 LT = '<'
 AND = 'and'
 OR = 'or'
 class Atom():
    """A basic atom for a formula"""
    def __init__(self):
        self.var = None
        self.real = None
    def is_real(self):
        return self.real is not None
    def is_var(self):
        return self.var is not None
 class Real(Atom):
    """A real number"""
    def __init__(self, x):
        super(Real, self).__init__()
        if isinstance(x, (float, int)):
            self.real = x
        else:
            print('Error, Real can only be of int or float')
            raise ValueError
    def __str__(self):
        return str(self.real)
    def to_smtlib(self):
        frac = Fraction(self.real)
        return '(/ {} {})'.format(frac.numerator, frac.denominator)
    def to_marabou(self):
        return ' {} '.format(self.real)
    def to_pyrat(self):
        return ' {} '.format(self.real)
 class Var(Atom):
    """A variable with a name"""
    def __init__(self, x):
        super(Var, self).__init__()
        if isinstance(x, str):
            if re.search("^[xy][0-9]$", x):
                self.var = x
            else:
                raise ValueError("Input should be of the form x[0-9] or y[0-9]")
        else:
            raise ValueError("Input should be of the form x[0-9] or y[0-9]")
    def __str__(self):
        return str(self.var)
    def to_smtlib(self):
        if self.var == DIST:
            return '|CELL_actual_input_0_0_0|'
        elif self.var == SPEED:
            return'|CELL_actual_input_0_0_1|'
        elif self.var == ANGLE:
            return'|CELL_actual_input_0_0_2|'
        else:  # only remaining is Output
            return '|CELL_actual_output_0_0_0|'
    def to_marabou(self):
        return self.var
    def to_pyrat(self):
        return self.var
 class Constr():
    """A linear constraint"""
    def __init__(self, a, bop, b):
        """a and b are atoms, bop is either '>=' or '<'"""
        if not (isinstance(a, Atom) and isinstance(b, Atom)):
            print('Error, either a or b are not atoms. \
                    Create atoms using Var or Real constructors.')
            raise ValueError
        else:
            if bop == GEQ or bop == LT:
                self.constr = (a, bop, b)
            else:
                print('Error, second argument should be either >= or <')
                raise ValueError
    def __str__(self):
        a, bop, b = self.constr
        return '{} {} {}'.format(str(a), bop, str(b))
    def to_smtlib(self):
        """write a constraint to SMTLIB format"""
        a, bop, b = self.constr
        if "y" in str(a) or "y" in str(b):
            bop = GEQ if bop == LT else LT
        return '({} {} {})'.format(bop, a.to_smtlib(), b.to_smtlib())
    def to_marabou(self):
        """write a constraint to marabou format"""
        a, bop, b = self.constr
        if "y" in str(a) or "y" in str(b):
            bop = GEQ if bop == LT else LT
        if bop == GEQ:
            return '{} {} {}'.format(a.to_marabou(), bop, b.to_marabou())
        else:
            # print('Warning: Marabou does not support strict inequality, \
            #         using Lower or Equal ( <= ) instead')
            return '{} {} {}'.format(a.to_marabou(), '<=', b.to_marabou())
    def to_pyrat(self):
        a, bop, b = self.constr
        return '{} {} {}'.format(a.to_pyrat(), bop, b.to_pyrat())
 def well_formed(c):
    """Check if c is a well formed clause"""
    if len(c) == 3:
        cx = c[0]
        lop = c[1]
        cy = c[2]
        if lop in (AND, OR):
            return isinstance(cx, Constr) and isinstance(cy, Constr)
        else:
            print('Error, second argument should be AND | OR')
            return False
 class Formula():
    """A formula composed of a conjunction of constraints"""
    def __init__(self):
        """A formula is empty at the beginning"""
        self.storage = []
    def __str__(self):
        to_display = ''
        for clause in self.storage:
            if isinstance(clause, Constr):
                to_display += '{}; '.format(str(clause))
            else:
                cx, lop, cy = clause
                to_display += '({} {} {}); '.format(str(cx), str(lop),
                                                    str(cy))
        return to_display
    def add(self, c):
        """Add a disjunction to the formula with the constr"""
        if isinstance(c, Constr):
            self.storage.append(c)
        elif well_formed(c):
            self.storage.append((c[0], c[1], c[2]))
        else:
            print('Error, input must either be an instance of Constr or \
                    a tuple (formula, AND|OR, constr)')
    def to_smtlib(self):
        """Write down the formula to SMTLIB format"""
        to_write = ''
        for clause in self.storage:
            if isinstance(clause, Constr):
                tmp = clause.to_smtlib()
            else:
                cx, lop, cy = clause
                tmp = '({} {} {})'.format(
                    str(lop), cx.to_smtlib(), cy.to_smtlib())
            to_write += '(assert {})\n'.format(tmp)
        return to_write
    def to_marabou(self):
        """Write down the formula to Marabou format"""
        to_write = ''
        for clause in self.storage:
            if isinstance(clause, Constr):
                to_write += '{}\n'.format(clause.to_marabou())
            else:
                # Marabou only deal with AND logical operators
                cx, lop, cy = clause
                if lop == AND:
                    to_write += '{}\n'.format(cx.to_marabou())
                    to_write += '{}\n'.format(cy.to_marabou())
                else:
                    print('Error, Marabou output format does not support \
                          OR operator. Aborting conversion.')
                    raise ValueError
        return to_write
    def to_pyrat(self):
        """Write down the formula to PyRAT format"""
        to_write = ''
        for clause in self.storage:
            if isinstance(clause, Constr):
                to_write += '{}\n'.format(clause.to_pyrat())
            else:
                # PyRAT only deal with AND logical operators
                cx, lop, cy = clause
                if lop == AND:
                    to_write += '{}\n'.format(cx.to_pyrat())
                    to_write += '{}\n'.format(cy.to_pyrat())
                else:
                    print('Error, PyRAT output format does not support \
                          OR operator. Aborting conversion.')
                    raise ValueError
        return to_write
    def write_smtlib(self, fpath='formula.smt2'):
        with open(fpath, 'w') as f:
            to_write = self.to_smtlib()
            to_write += '(check-sat)\n'
            to_write += '(get-value (|CELL_actual_input_0_0_0|))\n'
            to_write += '(get-value (|CELL_actual_input_0_0_1|))\n'
            to_write += '(get-value (|CELL_actual_input_0_0_2|))\n'
            to_write += '(get-value (|CELL_actual_output_0_0_0|))\n'
            f.write(to_write)
            print('Wrote SMT formula in file {}'.format(fpath))
    def write_marabou(self, fpath='formula.marabou'):
        with open(fpath, 'w') as f:
            to_write = self.to_marabou()
            f.write(to_write)
            print('Wrote marabou formula in file {}'.format(fpath))
    def write_pyrat(self, fpath='formula.txt'):
        with open(fpath, 'w') as f:
            to_write = self.to_pyrat()
            f.write(to_write)
            print('Wrote pyrat formula in file {}'.format(fpath))
 def formula_p1():
    distance = Var('x0')
    angle1 = Var('x1')
    angle2 = Var('x2')
    speed1 = Var('x3')
    speed2 = Var('x4')
    output = Var('y0')
    constrs = []
    constrs.append(Constr(distance, '>=', Real(0.6)))
    constrs.append(Constr(distance, '<', Real(0.6798577687)))
    constrs.append(Constr(angle1, '>=', Real(-0.5)))
    constrs.append(Constr(angle1, '<', Real(0.5)))
    constrs.append(Constr(angle2, '>=', Real(-0.5)))
    constrs.append(Constr(angle2, '<', Real(0.5)))
    constrs.append(Constr(speed1, '>=', Real(0.45)))
    constrs.append(Constr(speed1, '<', Real(0.5)))
    constrs.append(Constr(speed2, '>=', Real(-0.5)))
    constrs.append(Constr(speed2, '<', Real(-0.45)))
    constrs.append(Constr(output, '<', Real(3.9911256459)))
    formula = Formula()
    for c in constrs:
        formula.add(c)
    return formula
--- a/IA/seti_master-master/code/formula_p1.marabou
+++ b/IA/seti_master-master/code/formula_p1.marabou
@ -0,0 +1,11 @@
 x0 >=  0.6 
 x0 <=  0.6798577687 
 x1 >=  -0.5 
 x1 <=  0.5 
 x2 >=  -0.5 
 x2 <=  0.5 
 x3 >=  0.45 
 x3 <=  0.5 
 x4 >=  -0.5 
 x4 <=  -0.45 
 y0 >=  3.9911256459 
--- a/IA/seti_master-master/code/formula_p1.txt
+++ b/IA/seti_master-master/code/formula_p1.txt
@ -0,0 +1,11 @@
 x0 >=  0.6 
 x0 <  0.6798577687 
 x1 >=  -0.5 
 x1 <  0.5 
 x2 >=  -0.5 
 x2 <  0.5 
 x3 >=  0.45 
 x3 <  0.5 
 x4 >=  -0.5 
 x4 <  -0.45 
 y0 <  3.9911256459 
--- a/IA/seti_master-master/code/imgs/acas.png
+++ b/IA/seti_master-master/code/imgs/acas.png
--- a/IA/seti_master-master/code/imgs/fashion_mnist.png
+++ b/IA/seti_master-master/code/imgs/fashion_mnist.png
--- a/IA/seti_master-master/code/imgs/network.png
+++ b/IA/seti_master-master/code/imgs/network.png
--- a/IA/seti_master-master/code/imgs/problem_small.png
+++ b/IA/seti_master-master/code/imgs/problem_small.png
--- a/IA/seti_master-master/code/network.nnet
+++ b/IA/seti_master-master/code/network.nnet
@ -0,0 +1,64 @@
 // Neural Network File Format by Kyle Julian, Stanford 2016
 5,3,1,10,
 3,10,10,5,2,1,
 0,
 -1,-1,-1,
 1,1,1,
 0.0,0.0,0.0,0.0,
 1.0,1.0,1.0,1.0,
 -1.14708e+00,2.46452e-01,-2.32174e-01,
 -7.61497e-01,2.73782e-01,-4.27904e-01,
 -6.80755e-01,6.63188e-02,5.86105e-01,
 5.98529e-01,-1.33719e-01,-5.05083e-01,
 6.31086e-01,-1.39754e-01,-2.67506e-01,
 2.35380e-01,-4.44053e-01,3.20437e-01,
 6.32453e-01,-2.90920e-01,-4.45502e-01,
 3.53347e-01,6.45762e-01,-2.06620e-02,
 -1.43721e-01,5.84932e-01,-1.13164e+00,
 -4.68328e-01,-4.39698e-01,4.51289e-01,
 7.70765e-01,
 6.28211e-01,
 2.91335e-01,
 -5.18309e-01,
 -5.32540e-01,
 -3.62792e-01,
 5.60616e-01,
 -6.89317e-02,
 1.10183e-01,
 -3.52598e-01,
 7.06974e-01,4.06502e-02,1.16121e-01,2.32606e-01,6.13336e-02,-2.62563e-01,5.55975e-02,1.67173e-01,2.27252e-01,-5.94368e-02,
 7.57920e-01,6.94863e-01,-5.62161e-02,5.11224e-01,1.28189e-01,-3.47042e-01,2.47291e-02,-5.01331e-01,8.40055e-01,-3.27839e-01,
 1.30464e-01,1.38840e-01,8.53394e-02,4.33917e-01,-2.17293e-01,-2.20861e-01,-4.53556e-01,-2.68264e-02,-2.48756e-01,2.09623e-02,
 -2.61459e-01,2.63584e-02,4.27502e-01,1.54376e-01,-1.68515e-01,-2.48366e-01,-4.57562e-01,6.29832e-01,-2.09967e-01,9.70081e-02,
 2.54692e-01,-1.72164e-01,4.81198e-01,2.93183e-01,2.63407e-02,-3.04535e-01,-3.73956e-01,-3.37929e-01,-2.98805e-01,-2.73287e-01,
 -5.62678e-02,-1.16400e-01,1.15052e-01,-1.37510e-01,-6.93518e-02,-1.93212e-01,-5.16142e-01,3.97659e-01,1.00357e-01,4.76279e-01,
 -1.39871e-01,-1.05592e-01,-6.37275e-01,-1.98621e-01,3.43004e-01,-5.12464e-01,-1.19070e-01,4.31925e-01,5.00866e-01,1.93172e-01,
 -1.04033e-01,-4.00014e-02,-1.01434e-01,-1.92857e-01,2.18325e-01,-1.78187e-01,-2.92106e-01,1.20725e-01,-3.78140e-01,-1.11649e-01,
 6.58711e-01,5.91526e-01,4.98885e-01,-3.56871e-01,4.43454e-03,-1.71262e-01,-4.43739e-01,-4.10895e-01,6.37532e-01,7.72243e-02,
 -2.67042e-01,-3.68756e-01,-4.33448e-01,5.12721e-01,-8.91776e-02,-1.20972e-01,-1.92482e-01,-2.61665e-01,-4.78430e-01,-3.07123e-01,
 -2.38114e-01,
 7.51102e-03,
 -2.23299e-01,
 6.05565e-02,
 1.58353e-01,
 -1.19271e-01,
 7.16477e-02,
 9.81083e-02,
 1.95617e-01,
 2.03306e-01,
 5.18320e-01,7.15371e-01,-2.05711e-01,-4.03320e-01,5.47083e-01,4.20018e-01,7.35758e-01,1.75688e-01,9.05056e-01,-4.85407e-02,
 -3.53168e-01,-5.91812e-01,3.67945e-01,-8.78026e-02,2.48187e-01,2.97342e-02,-5.63803e-01,-2.96360e-01,1.69950e-01,-1.22268e-01,
 -6.05207e-01,9.87874e-02,2.19470e-02,-5.88511e-02,1.84518e-01,1.30692e-01,-2.73866e-01,5.43046e-01,2.04818e-01,7.58518e-02,
 -1.16016e-01,-5.11686e-01,4.66400e-01,-1.55879e-01,-3.90377e-01,3.87312e-01,2.14543e-01,1.49492e-01,-1.19779e-01,-1.88518e-01,
 2.08475e-01,8.79257e-01,8.90662e-02,-4.83591e-01,7.59465e-01,1.06837e-03,6.20371e-01,1.46788e-01,9.59736e-01,3.48543e-01,
 -2.14665e-01,
 -2.23339e-01,
 8.03788e-01,
 -1.17165e-01,
 -2.03201e-01,
 -1.43015e+00,6.21176e-01,6.64389e-01,-7.09840e-01,-1.33294e+00,
 -8.00502e-01,-4.58154e-01,-6.62400e-01,6.04809e-01,-2.02863e-01,
 2.34882e+00,
 2.01511e-01,
 -3.07737e+00,6.45394e-01,
 2.76908e+00,
--- a/IA/seti_master-master/code/network.onnx
+++ b/IA/seti_master-master/code/network.onnx
--- a/IA/seti_master-master/code/onnx2pytorch.py
+++ b/IA/seti_master-master/code/onnx2pytorch.py
@ -0,0 +1,328 @@
 #stolen from https://gist.github.com/qinjian623/6aa777037534c1c1dccbb66f832e93b8
 import onnx
 import struct
 import torch
 import torch.nn as nn
 import warnings
 from collections import OrderedDict
 data_type_tab = {
    1: ['f', 4],
    2: ['B', 1],
    3: ['b', 1],
    4: ['H', 2],
    5: ['h', 2],
    6: ['i', 4],
    7: ['q', 8],
    10: ['e', 2],
    11: ['d', 8],
    12: ['I', 4],
    13: ['Q', 8]
 }
 def empty(x):
    return x
 def _slim422(l4):
    assert len(l4) == 4
    p0, p1 = l4[::2]
    if l4[0] == 0:  # TODO bad code
        p0 = l4[2] // 2
        if l4[2] == 1:
            p0 = 1
    if l4[1] == 0:  # TODO bad code
        p1 = l4[3] // 2
        if l4[3] == 1:
            p1 = 1
    return p0, p1
 def _check_attr(attrs, map):
    for attr in attrs:
        if attr.name not in map:
            warnings.warn("Missing {} in parser's attr_map.".format(attr.name))
 def unpack_weights(initializer):
    ret = {}
    for i in initializer:
        name = i.name
        dtype = i.data_type
        shape = list(i.dims)
        if dtype not in data_type_tab:
            warnings("This data type {} is not supported yet.".format(dtype))
        fmt, size = data_type_tab[dtype]
        if len(i.raw_data) == 0:
            if dtype == 1:
                data_list = i.float_data
            elif dtype == 7:
                data_list = i.int64_data
            else:
                warnings.warn("No-raw-data type {} not supported yet.".format(dtype))
        else:
            data_list = struct.unpack('<' + fmt * (len(i.raw_data) // size), i.raw_data)
        t = torch.tensor(data_list)
        if len(shape) != 0:
            t = t.view(*shape)
        ret[name] = t
    return ret
 def rebuild_conv(node, weights):
    rebuild_conv.conv_attr_map = {
        "pads": "padding",
        "strides": "stride",
        "kernel_shape": "kernel_size",
        "group": "groups",
        "dilations": "dilation"
    }
    assert len(node.output) == 1
    with_bias = False
    if len(node.input) == 3:
        with_bias = True
        bias_name = node.input[2]
        bias = weights[bias_name]
    weight_name = node.input[1]
    weight = weights[weight_name]
    in_channels = weight.shape[1]
    out_channels = weight.shape[0]
    kwargs = {}
    for att in node.attribute:
        kwargs[rebuild_conv.conv_attr_map[att.name]] = list(att.ints) if att.name != 'group' else att.i
    if 'padding' in kwargs:
        kwargs["padding"] = _slim422(kwargs["padding"])
    groups = 1 if 'groups' not in kwargs else kwargs['groups']
    in_channels *= groups
    conv = nn.Conv2d(in_channels, out_channels, **kwargs, bias=with_bias)
    conv.weight.data = weight
    if with_bias:
        conv.bias.data = bias
    return conv, node.input[:1], node.output
 def rebuild_dropout(node, weights):
    ratio = node.attribute[0].f
    return nn.Dropout2d(p=ratio), node.input, node.output
 def rebuild_relu(node, weights, nodes):
    return nn.ReLU(), node.input, node.output
 def rebuild_maxpool(node, weights):
    rebuild_maxpool.mp_attr_map = {
        "pads": "padding",
        "strides": "stride",
        "kernel_shape": "kernel_size",
    }
    kwargs = {}
    for att in node.attribute:
        kwargs[rebuild_maxpool.mp_attr_map[att.name]] = list(att.ints)
    if 'padding' in kwargs:
        kwargs["padding"] = _slim422(kwargs["padding"])
    mp = nn.MaxPool2d(**kwargs)
    return mp, node.input, node.output
 def rebuild_add(node, weights, graph):
    def add(a, b):
        return a + b
    return add, node.input, node.output
 def rebuild_transpose(node, weights, graph):
    perm = node.attribute[0].ints
    def transpose(x):
        x = x.permute(*perm)
        return x
    return transpose, node.input, node.output
 def rebuild_flatten(node, weights):
    if len(node.attribute) == 0:
        d = 1
    else:
        d = node.attribute[0].i
    def flatten(x):
        o_shape = []
        for i in range(d):
            o_shape.append(x.shape[i])
        o_shape.append(-1)
        return x.view(*o_shape)
    return flatten, node.input, node.output
 def rebuild_gemm(node, weights):
    weight = weights[node.input[1]]
    bias = weights[node.input[2]]
    in_feats = weight.shape[1]
    out_feats = weight.shape[0]
    linear = nn.Linear(in_features=in_feats, out_features=out_feats)
    linear.weight.data = weight
    linear.bias.data = bias
    return linear, node.input[:1], node.output
 #TODO: rebuild matmul, find a way to include the transpose weights
 def rebuild_matmul(node, weights, nodes):
    weight = weights[node.input[1]]
    for next_node in nodes:
        if next_node.input[0] == node.output[0]:
            bias = weights[next_node.input[1]]
    in_feats = weight.shape[0]
    out_feats = weight.shape[1]
    linear = nn.Linear(in_features=in_feats,
            out_features=out_feats)
    linear.weight.data = weight.transpose(1,0)
    linear.bias.data = bias
    return linear, node.input[:1], node.output
 def rebuild_pad(node, weights):
    mode = node.attribute[0].s
    pads = list(node.attribute[1].ints)
    value = node.attribute[2].f
    assert mode == b'constant'  # TODO constant only
    assert sum(pads[:4]) == 0  # TODO pad2d only
    pad = nn.ConstantPad2d(pads[4:], value)
    return pad, node.input, node.output
 def rebuild_constant(node, weights, nodes):
    raw_data = node.attribute[0].t.raw_data
    data_type = node.attribute[0].t.data_type
    fmt, size = data_type_tab[data_type]
    data = struct.unpack('<' + fmt * (len(raw_data) // size), raw_data)
    if len(data) == 1:
        data = data[0]
    def constant():
        return torch.tensor(data)
    return constant, [], node.output
 def rebuild_sum(node, weights):
    def sum(*inputs):
        ret = inputs[0]
        for i in inputs[1:]:
            ret += i
        return ret
    return sum, node.input, node.output
 def rebuild_shape(node, weights):
    def shape(x):
        return torch.tensor(list(x.shape))
    return shape, node.input, node.output
 def rebuild_gather(node, weights):
    axis = node.attribute[0].i
    def gather(x, idx):
        return torch.gather(x, axis, idx)
    return gather, node.input, node.output
 def _nd_unsqueeze(x, dims):
    dims = sorted(dims)
    for d in dims:
        x = torch.unsqueeze(x, dim=d)
    return x
 def rebuild_unsqueeze(node, weights):
    axes = node.attribute[0].ints
    def unsqueeze(x):
        return _nd_unsqueeze(x, axes)
    return unsqueeze, node.input, node.output
 def rebuild_mul(node, weights):
    def mul(a, b):
        return a * b
    return mul, node.input, node.output
 def rebuild_softmax(node, weights):
    def f_softmax(x):
        return x.softmax(dim=1, dtype=torch.double).float()
    return f_softmax, node.input, node.output
 def rebuild_reshape(node, weights, nodes):
    def reshape(x, s):
        data_shape = x.shape
        onnx_shape = s.tolist()
        pt_shape = []
        for idx, d in enumerate(onnx_shape):
            if d == 0:
                pt_shape.append(data_shape[idx])
            else:
                pt_shape.append(d)
        return torch.reshape(x, pt_shape)
    return reshape, node.input, node.output
 def rebuild_op(node, weights, nodes):
    op_type = node.op_type
    return globals()['rebuild_'+op_type.lower()](node, weights, nodes)
 def construct_pytorch_nodes(graph, weights):
    ret = []
    for single_node in graph.node:
        ret.append(rebuild_op(single_node, weights, graph.node))
    return ret
 def resolve_deps(name, deps, inter_tensors):
    if name in inter_tensors:
        return
    else:
        op, deps_names = deps[name]
        args = []
        for deps_name in deps_names:
            resolve_deps(deps_name, deps, inter_tensors)
            args.append(inter_tensors[deps_name])
        result = op(*args)
        inter_tensors[name] = result
 class DependencyModule(nn.Module):
    #TODO: add a layers and convert everything to a dict
    def __init__(self, onnx_model, input_name=None):
        super(DependencyModule, self).__init__()
        self.deps = {}
        self.inter_tensors = dict()
        self.weights = unpack_weights(onnx_model.graph.initializer)
        nodes = construct_pytorch_nodes(onnx_model.graph, self.weights)
        #TODO: ModuleDict with propernames
        layers = OrderedDict({})
        for idx, (node, inputs, outputs) in enumerate(nodes):
            if isinstance(node, nn.Module):
                layers['layer'+str(idx)] = node
            for output_name in outputs:
                self.deps[output_name] = (node, inputs)
        self.input_name = onnx_model.graph.input[0].name    # TODO only you
        self.output_name = onnx_model.graph.output[0].name  # TODO only you
        if input_name is not None:
            self.input_name = input_name
        self.layers = nn.ModuleDict(layers)
    def forward(self, input):
        self.inter_tensors = self.weights.copy()
        self.inter_tensors[self.input_name] = input
        resolve_deps(self.output_name, self.deps, self.inter_tensors)
        return self.inter_tensors[self.output_name]
 def convert(onnx_path):
    onnx_model = onnx.load(onnx_path)
    reconstruct_model = DependencyModule(onnx_model)
    return reconstruct_model
--- a/IA/seti_master-master/code/pyrat39/analysis_param.pyc
+++ b/IA/seti_master-master/code/pyrat39/analysis_param.pyc
--- a/IA/seti_master-master/code/pyrat39/benchmarks/1.csv
+++ b/IA/seti_master-master/code/pyrat39/benchmarks/1.csv
@ -0,0 +1,46 @@
 network, prop
 1_1, 1
 1_2, 1
 1_3, 1
 1_4, 1
 1_5, 1
 1_6, 1
 1_7, 1
 1_8, 1
 1_9, 1
 2_1, 1
 2_2, 1
 2_3, 1
 2_4, 1
 2_5, 1
 2_6, 1
 2_7, 1
 2_8, 1
 2_9, 1
 3_1, 1
 3_2, 1
 3_3, 1
 3_4, 1
 3_5, 1
 3_6, 1
 3_7, 1
 3_8, 1
 3_9, 1
 4_1, 1
 4_2, 1
 4_3, 1
 4_4, 1
 4_5, 1
 4_6, 1
 4_7, 1
 4_8, 1
 4_9, 1
 5_1, 1
 5_2, 1
 5_3, 1
 5_4, 1
 5_5, 1
 5_6, 1
 5_7, 1
 5_8, 1
 5_9, 1
--- a/IA/seti_master-master/code/pyrat39/benchmarks/1_3_4.csv
+++ b/IA/seti_master-master/code/pyrat39/benchmarks/1_3_4.csv
@ -0,0 +1,136 @@
 network, prop
 1_1, 1
 1_2, 1
 1_3, 1
 1_4, 1
 1_5, 1
 1_6, 1
 1_7, 1
 1_8, 1
 1_9, 1
 2_1, 1
 2_2, 1
 2_3, 1
 2_4, 1
 2_5, 1
 2_6, 1
 2_7, 1
 2_8, 1
 2_9, 1
 3_1, 1
 3_2, 1
 3_3, 1
 3_4, 1
 3_5, 1
 3_6, 1
 3_7, 1
 3_8, 1
 3_9, 1
 4_1, 1
 4_2, 1
 4_3, 1
 4_4, 1
 4_5, 1
 4_6, 1
 4_7, 1
 4_8, 1
 4_9, 1
 5_1, 1
 5_2, 1
 5_3, 1
 5_4, 1
 5_5, 1
 5_6, 1
 5_7, 1
 5_8, 1
 5_9, 1
 1_1, 3
 1_2, 3
 1_3, 3
 1_4, 3
 1_5, 3
 1_6, 3
 1_7, 3
 1_8, 3
 1_9, 3
 2_1, 3
 2_2, 3
 2_3, 3
 2_4, 3
 2_5, 3
 2_6, 3
 2_7, 3
 2_8, 3
 2_9, 3
 3_1, 3
 3_2, 3
 3_3, 3
 3_4, 3
 3_5, 3
 3_6, 3
 3_7, 3
 3_8, 3
 3_9, 3
 4_1, 3
 4_2, 3
 4_3, 3
 4_4, 3
 4_5, 3
 4_6, 3
 4_7, 3
 4_8, 3
 4_9, 3
 5_1, 3
 5_2, 3
 5_3, 3
 5_4, 3
 5_5, 3
 5_6, 3
 5_7, 3
 5_8, 3
 5_9, 3
 1_1, 4
 1_2, 4
 1_3, 4
 1_4, 4
 1_5, 4
 1_6, 4
 1_7, 4
 1_8, 4
 1_9, 4
 2_1, 4
 2_2, 4
 2_3, 4
 2_4, 4
 2_5, 4
 2_6, 4
 2_7, 4
 2_8, 4
 2_9, 4
 3_1, 4
 3_2, 4
 3_3, 4
 3_4, 4
 3_5, 4
 3_6, 4
 3_7, 4
 3_8, 4
 3_9, 4
 4_1, 4
 4_2, 4
 4_3, 4
 4_4, 4
 4_5, 4
 4_6, 4
 4_7, 4
 4_8, 4
 4_9, 4
 5_1, 4
 5_2, 4
 5_3, 4
 5_4, 4
 5_5, 4
 5_6, 4
 5_7, 4
 5_8, 4
 5_9, 4
--- a/Show more
+++ b/Show more