IA

D3/TP/Cours.ipynb

@@ -100,7 +100,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.8.10 64-bit",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -114,12 +114,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.9.4 (tags/v3.9.4:1f2e308, Apr  6 2021, 13:40:21) [MSC v.1928 64 bit (AMD64)]"
   },
   "orig_nbformat": 4,
   "vscode": {
    "interpreter": {
-    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+    "hash": "2ef431f6525756fa8a44688585fa332ef3b2e5fcfe8fe75df35bbf7028a8b511"
    }
   }
  },

D3/TP/TP_SETI_SVM/plot_digits_classification.ipynb

@@ -571,7 +571,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.9.4"
+      "version": "3.9.4 (tags/v3.9.4:1f2e308, Apr  6 2021, 13:40:21) [MSC v.1928 64 bit (AMD64)]"
     },
     "vscode": {
       "interpreter": {

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

IA/seti_master-master/code/acas.nnet

@@ -0,0 +1,620 @@
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+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,

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

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

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"]},
+)

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 

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')

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()

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

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 

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|))

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 

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

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 

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 

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,

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

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

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