diff --git a/.gitignore b/.gitignore index 4ca1df788..90fd95f6e 100644 --- a/.gitignore +++ b/.gitignore @@ -10,3 +10,7 @@ TAGS /src/basis_factorization/tests/cxxtest_Test_LUFactorization.cxx /src/basis_factorization/tests/cxxtest_Test_ForrestTomlinFactorization.cxx /src/basis_factorization/tests/cxxtest_Test_PermutationMatrix.cxx +*.DS_Store +*-checkpoint.ipynb +*.so +*.pyc diff --git a/maraboupy/Makefile b/maraboupy/Makefile new file mode 100644 index 000000000..c1ba88d81 --- /dev/null +++ b/maraboupy/Makefile @@ -0,0 +1,93 @@ +ROOT_DIR = ../ + +SUBDIRS += \ + +PYBIND11_INCLUDES = $(shell python3 -m pybind11 --includes) + +LOCAL_INCLUDES += \ + .. \ + $(BASIS_FACTORIZATION_DIR) \ + $(CONFIGURATION_DIR) \ + $(ENGINE_DIR) \ + $(PYBIND11_INCLUDES) \ + +LINK_FLAGS += \ + +LOCAL_LIBRARIES += \ + +CFLAGS += \ + -DDEBUG_ON \ + +SUFFIX = $(shell python3-config --extension-suffix) +API_NAME = $(addprefix MarabouCore, $(SUFFIX)) + +SOURCES += \ + GlobalConfiguration.cpp \ + \ + Errno.cpp \ + Error.cpp \ + FloatUtils.cpp \ + MString.cpp \ + TimeUtils.cpp \ + \ + BasisFactorizationFactory.cpp \ + BlandsRule.cpp \ + ConstraintMatrixAnalyzer.cpp \ + CostFunctionManager.cpp \ + CostFunctionManagerFactory.cpp \ + DantzigsRule.cpp \ + DegradationChecker.cpp \ + Engine.cpp \ + EngineState.cpp \ + EntrySelectionStrategy.cpp \ + Equation.cpp \ + EtaMatrix.cpp \ + ForrestTomlinFactorization.cpp \ + FreshVariables.cpp \ + InputQuery.cpp \ + LPElement.cpp \ + LUFactorization.cpp \ + PermutationMatrix.cpp \ + PiecewiseLinearCaseSplit.cpp \ + PiecewiseLinearConstraint.cpp \ + PrecisionRestorer.cpp \ + Preprocessor.cpp \ + ProjectedSteepestEdge.cpp \ + ProjectedSteepestEdgeFactory.cpp \ + ReluConstraint.cpp \ + RowBoundTightener.cpp \ + RowBoundTightenerFactory.cpp \ + SmtCore.cpp \ + Statistics.cpp \ + Tableau.cpp \ + TableauFactory.cpp \ + TableauRow.cpp \ + TableauState.cpp \ + \ + MarabouCore.cpp \ + +TARGET = $(API_NAME) + +include ../Rules.mk + +COMPILE = c++ -std=c++11 -O3 -fPIC $(PYBIND11_INCLUDES) +LINK = c++ -std=c++11 -O3 -fPIC -shared -Wl,-undefined,dynamic_lookup $(PYBIND11_INCLUDES) + +$(TARGET): $(OBJECTS) + @echo "LD\t" $@ + @$(LINK) $(LINK_FLAGS) -o $@ $^ $(addprefix -l, $(SYSTEM_LIBRARIES)) $(addprefix -l, $(LOCAL_LIBRARIES)) + +vpath %.cpp $(BASIS_FACTORIZATION_DIR) +vpath %.cpp $(CONFIGURATION_DIR) +vpath %.cpp $(ENGINE_DIR) +vpath %.cpp $(ENGINE_REAL_DIR) +vpath %.cpp $(COMMON_DIR) +vpath %.cpp $(COMMON_REAL_DIR) + +# +# Local Variables: +# compile-command: "make -C ../../.. " +# tags-file-name: "../../../TAGS" +# c-basic-offset: 4 +# End: +# diff --git a/maraboupy/Marabou.py b/maraboupy/Marabou.py new file mode 100644 index 000000000..0c3efb474 --- /dev/null +++ b/maraboupy/Marabou.py @@ -0,0 +1,28 @@ +#Marabou File +from .MarabouNetworkNNet import * +from .MarabouNetworkTF import * + +def read_nnet(filename): + """ + Constructs a MarabouNetworkNnet object from a .nnet file + + Args: + filename: (string) path to the .nnet file. + Returns: + marabouNetworkNNet: (MarabouNetworkNNet) representing network + """ + return MarabouNetworkNNet(filename) + + +def read_tf(filename, inputName=None, outputName=None): + """ + Constructs a MarabouNetworkTF object from a frozen Tensorflow protobuf + + Args: + filename: (string) path to the .nnet file. + inputName: (string) optional, name of operation corresponding to input + outputName: (string) optional, name of operation corresponding to output + Returns: + marabouNetworkTF: (MarabouNetworkTF) representing network + """ + return MarabouNetworkTF(filename) \ No newline at end of file diff --git a/maraboupy/MarabouCore.cpp b/maraboupy/MarabouCore.cpp new file mode 100644 index 000000000..5547fe87d --- /dev/null +++ b/maraboupy/MarabouCore.cpp @@ -0,0 +1,106 @@ +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include "Engine.h" +#include "InputQuery.h" +#include "ReluplexError.h" +#include "FloatUtils.h" +#include "PiecewiseLinearConstraint.h" +#include "ReluConstraint.h" + +namespace py = pybind11; + +int redirectOutputToFile(std::string outputFilePath){ + // Redirect standard output to a file + int outputFile = open(outputFilePath.c_str(), O_WRONLY | O_CREAT | O_TRUNC, 0644); + if ( outputFile < 0 ) + { + printf( "Error redirecting output to file\n"); + exit( 1 ); + } + + int outputStream = dup( STDOUT_FILENO ); + if (outputStream < 0) + { + printf( "Error duplicating standard output\n" ); + exit(1); + } + + if ( dup2( outputFile, STDOUT_FILENO ) < 0 ) + { + printf("Error duplicating to standard output\n"); + exit(1); + } + + close( outputFile ); + return outputStream; +} + +void restoreOutputStream(int outputStream) +{ + // Restore standard output + fflush( stdout ); + if (dup2( outputStream, STDOUT_FILENO ) < 0){ + printf( "Error restoring output stream\n" ); + exit( 1 ); + } + close(outputStream); +} + +void addReluConstraint(InputQuery& ipq, unsigned var1, unsigned var2){ + PiecewiseLinearConstraint* r = new ReluConstraint(var1, var2); + ipq.addPiecewiseLinearConstraint(r); +} + +std::map solve(InputQuery inputQuery, std::string redirect=""){ + // Arguments: InputQuery object, filename to redirect output + // Returns: map from variable number to value + std::map ret; + int output=-1; + if(redirect.length()>0) + output=redirectOutputToFile(redirect); + try{ + Engine engine; + if(!engine.processInputQuery(inputQuery)) return ret; + + if(!engine.solve()) return ret; + + engine.extractSolution(inputQuery); + for(unsigned int i=0; i(m, "InputQuery") + .def(py::init()) + .def("setUpperBound", &InputQuery::setUpperBound) + .def("setLowerBound", &InputQuery::setLowerBound) + .def("getUpperBound", &InputQuery::getUpperBound) + .def("getLowerBound", &InputQuery::getLowerBound) + .def("setNumberOfVariables", &InputQuery::setNumberOfVariables) + .def("addEquation", &InputQuery::addEquation); + py::class_(m, "Equation") + .def(py::init()) + .def("addAddend", &Equation::addAddend) + .def("setScalar", &Equation::setScalar) + .def("markAuxiliaryVariable", &Equation::markAuxiliaryVariable); +} \ No newline at end of file diff --git a/maraboupy/MarabouNetwork.py b/maraboupy/MarabouNetwork.py new file mode 100644 index 000000000..74fcbdd5e --- /dev/null +++ b/maraboupy/MarabouNetwork.py @@ -0,0 +1,183 @@ +from . import MarabouCore +import numpy as np + +class MarabouNetwork: + """ + Abstract class representing general Marabou network + Defines functions common to MarabouNetworkNnet and MarabouNetworkTF + """ + def __init__(self): + """ + Constructs a MarabouNetwork object and calls function to initialize + """ + self.clear() + + def clear(self): + """ + Reset values to represent empty network + """ + self.numVars = 0 + self.equList = [] + self.reluList = [] + self.lowerBounds = dict() + self.upperBounds = dict() + self.inputVars = np.array([]) + self.outputVars = np.array([]) + + def getNewVariable(self): + """ + Function to request allocation of new variable + + Returns: + varnum: (int) representing new variable + """ + self.numVars += 1 + return self.numVars - 1 + + def addEquation(self, x): + """ + Function to add new equation to the network + Arguments: + x: (MarabouUtils.Equation) representing new equation + """ + self.equList += [x] + + def setLowerBound(self, x, v): + """ + Function to set lower bound for variable + Arguments: + x: (int) variable number to set + v: (float) value representing lower bound + """ + self.lowerBounds[x]=v + + def setUpperBound(self, x, v): + """ + Function to set upper bound for variable + Arguments: + x: (int) variable number to set + v: (float) value representing upper bound + """ + self.upperBounds[x]=v + + def addRelu(self, v1, v2): + """ + Function to add a new Relu constraint + Arguments: + v1: (int) variable representing input of Relu + v2: (int) variable representing output of Relu + """ + self.reluList += [(v1, v2)] + + def getMarabouQuery(self): + """ + Function to convert network into Marabou Query + Returns: + ipq: (MarabouCore.InputQuery) representing query + """ + ipq = MarabouCore.InputQuery() + ipq.setNumberOfVariables(self.numVars) + + for e in self.equList: + eq = MarabouCore.Equation() + for (c, v) in e.addendList: + assert v < self.numVars + eq.addAddend(c, v) + eq.markAuxiliaryVariable(e.auxVar) + eq.setScalar(e.scalar) + ipq.addEquation(eq) + + for r in self.reluList: + assert r[1] < self.numVars and r[0] < self.numVars + MarabouCore.addReluConstraint(ipq, r[0], r[1]) + + for l in self.lowerBounds: + assert l < self.numVars + ipq.setLowerBound(l, self.lowerBounds[l]) + + for u in self.upperBounds: + assert u < self.numVars + ipq.setUpperBound(u, self.upperBounds[u]) + + return ipq + + def solve(self, filename="", verbose=True): + """ + Function to solve query represented by this network + Arguments: + filename: (string) path to redirect output to + verbose: (bool) whether to print out solution + Returns: + vals: (dict: int->float) empty if UNSAT, else SATisfying solution + """ + ipq = self.getMarabouQuery() + vals = MarabouCore.solve(ipq, filename) + if verbose: + if len(vals)==0: + print("UNSAT") + else: + print("SAT") + #print(vals) + for i in range(self.inputVars.size): + print("input {} = {}".format(i, vals[self.inputVars.item(i)])) + + for i in range(self.outputVars.size): + print("output {} = {}".format(i, vals[self.outputVars.item(i)])) + + return vals + + def evaluateWithMarabou(self, inputValues, filename="evaluateWithMarabou.log"): + """ + Function to evaluate network at a given point using Marabou as solver + Arguments: + inputValues: (np array) representing input to network + filename: (string) path to redirect output + Returns: + outputValues: (np array) representing output of network + """ + inputVars = self.inputVars + outputVars = self.outputVars + + inputDict = dict() + assignList = zip(inputVars.reshape(-1), inputValues.reshape(-1)) + for x in assignList: + inputDict[x[0]] = x[1] + + ipq = self.getMarabouQuery() + for k in inputDict: + ipq.setLowerBound(k, inputDict[k]) + ipq.setUpperBound(k, inputDict[k]) + + outputDict = MarabouCore.solve(ipq, filename) + outputValues = outputVars.reshape(-1).astype(np.float64) + for i in range(len(outputValues)): + outputValues[i] = outputDict[outputValues[i]] + outputValues = outputValues.reshape(outputVars.shape) + return outputValues + + def evaluate(self, inputValues, useMarabou=True): + """ + Function to evaluate network at a given point + Arguments: + inputValues: (np array) representing input to network + useMarabou: (bool) whether to use Marabou solver or TF/NNet + Returns: + outputValues: (np array) representing output of network + """ + if useMarabou: + return self.evaluateWithMarabou(np.array(inputValues)) + if not useMarabou: + return self.evaluateWithoutMarabou(np.array(inputValues)) + + def findError(self, inputs): + """ + Function to find error between Marabou solver and TF/Nnet at a given point + Arguments: + inputs: (np array) representing input to network + Returns: + err: (np array) representing error in each output variable + """ + outMar = self.evaluate(inputs, useMarabou=True) + outNotMar = self.evaluate(inputs, useMarabou=False) + err = np.abs(outMar - outNotMar) + return err diff --git a/maraboupy/MarabouNetworkNNet.py b/maraboupy/MarabouNetworkNNet.py new file mode 100644 index 000000000..6b685120e --- /dev/null +++ b/maraboupy/MarabouNetworkNNet.py @@ -0,0 +1,314 @@ +from .MarabouUtils import * +from . import MarabouNetwork +import numpy as np + +class MarabouNetworkNNet(MarabouNetwork.MarabouNetwork): + """ + Class that implements a MarabouNetwork from an NNet file. + """ + def __init__ (self, filename): + """ + Constructs a MarabouNetworkNNet object from an .nnet file. + + Args: + filename: path to the .nnet file. + Attributes: + numLayers (int) The number of layers in the network + layerSizes (list of ints) Layer sizes. + inputSize (int) Size of the input. + outputSize (int) Size of the output. + maxLayersize (int) Size of largest layer. + inputMinimums (list of floats) Minimum value for each input. + inputMaximums (list of floats) Maximum value for each input. + inputMeans (list of floats) Mean value for each input. + inputRanges (list of floats) Range for each input + weights (list of list of lists) Outer index corresponds to layer + number. + biases (list of lists) Outer index corresponds to layer number. + """ + super().__init__() + + # read the file and load values + self.read_nnet(filename) + + # compute variable ranges + self.variableRanges() + + # identify variables involved in relu constraints + relus = self.findRelus() + + # build all equations that govern the network + equations = self.buildEquations() + for equation in equations: + e = Equation() + for term in equation[:-1]: + e.addAddend(term[1], term[0]) + e.setScalar(equation[-1]) + # aux variable is third to last to be added + e.markAuxiliaryVariable(equation[-3][0]) + + self.addEquation(e) + + + # add all the relu constraints + for relu in relus: + self.addRelu(relu[0], relu[1]) + + # Set all the bounds defined in the .nnet file + + # Set input variable bounds + for i, i_var in enumerate(self.inputVars): + self.setLowerBound(i_var, self.getInputMinimum(i_var)) + self.setUpperBound(i_var, self.getInputMaximum(i_var)) + + + # Set aux variable bounds (simplex technicality) + for aux_var in self.aux_variables: + self.setLowerBound(aux_var, 0.0) + self.setUpperBound(aux_var, 0.0) + + # Set bounds for forward facing variables + for f_var in self.f_variables: + self.setLowerBound(f_var, 0.0) + + # Set the number of variables + self.numVars = self.numberOfVariables() + + """ + Read the nnet file, load all the values and assign the class members. + + Args: + filename: path to the .nnet file. + """ + def read_nnet(self, file_name): + with open(file_name) as f: + line = f.readline() + cnt = 1 + while line[0:2] == "//": + line=f.readline() + cnt+= 1 + #numLayers does't include the input layer! + numLayers, inputSize, outputSize, maxLayersize = [int(x) for x in line.strip().split(",")[:-1]] + line=f.readline() + + #input layer size, layer1size, layer2size... + layerSizes = [int(x) for x in line.strip().split(",")[:-1]] + + line=f.readline() + symmetric = int(line.strip().split(",")[0]) + + line = f.readline() + inputMinimums = [float(x) for x in line.strip().split(",")[:-1]] + + line = f.readline() + inputMaximums = [float(x) for x in line.strip().split(",")[:-1]] + + line = f.readline() + inputMeans = [float(x) for x in line.strip().split(",")[:-1]] + + line = f.readline() + inputRanges = [float(x) for x in line.strip().split(",")[:-1]] + + weights=[] + biases = [] + for layernum in range(numLayers): + + previousLayerSize = layerSizes[layernum] + currentLayerSize = layerSizes[layernum+1] + # weights + weights.append([]) + biases.append([]) + #weights + for i in range(currentLayerSize): + line=f.readline() + aux = [float(x) for x in line.strip().split(",")[:-1]] + weights[layernum].append([]) + for j in range(previousLayerSize): + weights[layernum][i].append(aux[j]) + #biases + for i in range(currentLayerSize): + line=f.readline() + x = float(line.strip().split(",")[0]) + biases[layernum].append(x) + + self.numLayers = numLayers + self.layerSizes = layerSizes + self.inputSize = inputSize + self.outputSize = outputSize + self.maxLayersize = maxLayersize + self.inputMinimums = inputMinimums + self.inputMaximums = inputMaximums + self.inputMeans = inputMeans + self.inputRanges = inputRanges + self.weights = weights + self.biases = biases + + """ + Compute the variable number ranges for each type (b, f, aux) + + Args: + None + """ + def variableRanges(self): + input_variables = [] + b_variables = [] + f_variables = [] + aux_variables = [] + output_variables = [] + + input_variables = [i for i in range(self.layerSizes[0])] + + hidden_layers = self.layerSizes[1:-1] + + for layer, hidden_layer_length in enumerate(hidden_layers): + for i in range(hidden_layer_length): + offset = sum([x*3 for x in hidden_layers[:layer]]) + + b_variables.append(self.layerSizes[0] + offset + i) + aux_variables.append(self.layerSizes[0] + offset + i+hidden_layer_length) + f_variables.append(self.layerSizes[0] + offset + i+2*hidden_layer_length) + + #final layer + for i in range(self.layerSizes[-1]): + offset = sum([x*3 for x in hidden_layers[:len(hidden_layers) - 1]]) + output_variables.append(self.layerSizes[0] + offset + i + 3*hidden_layers[-1]) + aux_variables.append(self.layerSizes[0] + offset + i + 3*hidden_layers[-1] + self.layerSizes[-1]) + + self.inputVars = np.array(input_variables) + self.b_variables = b_variables + self.f_variables = f_variables + self.aux_variables = aux_variables + self.outputVars = np.array(output_variables) + + """ + Compute the variable number for the b variables in that correspond to the + layer, node argument. + + Args: + layer: (int) layer number. + node: (int) node number. + Returns: + variable number: (int) variable number that corresponds to the b variable + of the node defined by the layer, node indices. + """ + def nodeTo_b(self, layer, node): + assert(0 < layer) + assert(node < self.layerSizes[layer]) + + offset = self.layerSizes[0] + offset += sum([x*3 for x in self.layerSizes[1:layer]]) + + return offset + node + + + """ + Compute the variable number for the aux variables in that correspond to the + layer, node argument. + + Args: + layer: (int) layer number. + node: (int) node number. + Returns: + variable number: (int) variable number that corresponds to the aux variable + of the node defined by the layer, node indices. + """ + def nodeTo_aux(self, layer, node): + assert(0 < layer) + assert(node < self.layerSizes[layer]) + + offset = self.layerSizes[0] + offset += sum([x*3 for x in self.layerSizes[1:layer]]) + offset += self.layerSizes[layer] + + return offset + node + + """ + Compute the variable number for the f variables in that correspond to the + layer, node argument. + + Args: + layer: (int) layer number. + node: (int) node number. + Returns: + variable number: (int) variable number that corresponds to the f variable + of the node defined by the layer, node indices. + """ + def nodeTo_f(self, layer, node): + assert(layer < len(self.layerSizes)) + assert(node < self.layerSizes[layer]) + + if layer == 0: + return node + else: + offset = self.layerSizes[0] + offset += sum([x*3 for x in self.layerSizes[1:layer]]) + offset += 2*self.layerSizes[layer] + + return offset + node + """ + Constructs the equation representation from the class members + Arguments: + None + Returns: + equations_aux (list of lists) that represents all the equations in + the network. + """ + def buildEquations(self): + equations_aux = [] + equations_count = 0 + marabou_equations = [] + + for layer, size in enumerate(self.layerSizes): + if layer == 0: + continue + + for node in range(size): + #add marabou equation + + #equation = MarabouBindings.Equation() + equations_aux.append([]) + + #equations_aux[] + for previous_node in range(self.layerSizes[layer-1]): + #equation.addAddend(weights[layer-1][node][previous_node], self.nodeTo_f(layer-1, previous_node)) + equations_aux[equations_count].append([self.nodeTo_f(layer-1, previous_node), self.weights[layer-1][node][previous_node]]) + + #equation.addAddend(1.0, self.nodeTo_aux(layer, node)) + #equation.markAuxiliaryVariable(self.nodeTo_aux(layer, node)) + equations_aux[equations_count].append([self.nodeTo_aux(layer, node), 1.0]) + + #equation.addAddend(-1.0, self.nodeTo_b(layer, node)) + equations_aux[equations_count].append([self.nodeTo_b(layer, node), -1.0]) + + #equation.setScalar(-biases[layer-1][node]) + equations_aux[equations_count].append(-self.biases[layer-1][node]) + equations_count += 1 + + #marabou_equations.append(equation) + + return equations_aux + """ + Identify all relus and their associated variable numbers. + Arguments: + None + Returns: + relus (list of lists) that represents all the relus in + the network. + """ + def findRelus(self): + relus = [] + hidden_layers = self.layerSizes[1:-1] + for layer, size in enumerate(hidden_layers): + for node in range(size): + relus.append([self.nodeTo_b(layer+1, node), self.nodeTo_f(layer+1, node)]) + + return relus + + def numberOfVariables(self): + return self.layerSizes[0] + 3*sum(self.layerSizes[1:-1]) + 2*self.layerSizes[-1] + + def getInputMinimum(self, input): + return (self.inputMinimums[input] - self.inputMeans[input]) / self.inputRanges[input] + + def getInputMaximum(self, input): + return (self.inputMaximums[input] - self.inputMeans[input]) / self.inputRanges[input] diff --git a/maraboupy/MarabouNetworkTF.py b/maraboupy/MarabouNetworkTF.py new file mode 100644 index 000000000..fc6315e4e --- /dev/null +++ b/maraboupy/MarabouNetworkTF.py @@ -0,0 +1,393 @@ +import numpy as np +from tensorflow.python.framework import tensor_util +import os +os.environ['TF_CPP_MIN_LOG_LEVEL']='2' +os.environ["CUDA_VISIBLE_DEVICES"] = "-1" + +import tensorflow as tf +from . import MarabouUtils +from . import MarabouNetwork + +class MarabouNetworkTF(MarabouNetwork.MarabouNetwork): + def __init__(self, filename, inputName=None, outputName=None): + """ + Constructs a MarabouNetworkTF object from a frozen Tensorflow protobuf + + Args: + filename: (string) path to the .nnet file. + inputName: (string) optional, name of operation corresponding to input. + outputName: (string) optional, name of operation corresponding to output. + """ + super().__init__() + self.readFromPb(filename, inputName, outputName) + + def clear(self): + """ + Reset values to represent empty network + """ + super().clear() + self.varMap = dict() + self.shapeMap = dict() + self.inputOp = None + self.outputOp = None + self.sess = None + + def readFromPb(self, filename, inputName, outputName): + """ + Constructs a MarabouNetworkTF object from a frozen Tensorflow protobuf + + Args: + filename: (string) path to the .nnet file. + inputName: (string) optional, name of operation corresponding to input + outputName: (string) optional, name of operation corresponding to output + """ + ### Read protobuf file and begin session ### + with tf.gfile.GFile(filename, "rb") as f: + graph_def = tf.GraphDef() + graph_def.ParseFromString(f.read()) + with tf.Graph().as_default() as graph: + tf.import_graph_def(graph_def, name="") + self.sess = tf.Session(graph=graph) + ### END reading protobuf ### + + ### Find operations corresponding to input and output ### + if inputName: + inputOp = self.sess.graph.get_operation_by_name(inputName) + else: # If there is just one placeholder, use it as input + ops = self.sess.graph.get_operations() + placeholders = [x for x in ops if x.node_def.op == 'Placeholder'] + assert len(placeholders)==1 + inputOp = placeholders[0] + if outputName: + outputOp = self.sess.graph.get_operation_by_name(outputName) + else: # Assume that the last operation is the output + outputOp = self.sess.graph.get_operations()[-1] + self.setInputOp(inputOp) + self.setOutputOp(outputOp) + ### END finding input/output operations ### + + ### Generate equations corresponding to network ### + self.foundInputFlag = False + self.makeGraphEquations(self.outputOp) + assert self.foundInputFlag + ### END generating equations ### + + def setInputOp(self, op): + """ + Function to set input operation + Arguments: + op: (tf.op) Representing input + """ + shape = tuple(op.outputs[0].shape.as_list()) + self.shapeMap[op] = shape + self.inputOp = op + self.inputVars = self.opToVarArray(self.inputOp) + + def setOutputOp(self, op): + """ + Function to set output operation + Arguments: + op: (tf.op) Representing output + """ + shape = tuple(op.outputs[0].shape.as_list()) + self.shapeMap[op] = shape + self.outputOp = op + self.outputVars = self.opToVarArray(self.outputOp) + + def opToVarArray(self, x): + """ + Function to find variables corresponding to operation + Arguments: + x: (tf.op) the operation to find variables for + Returns: + v: (np array) of variable numbers, in same shape as x + """ + if x in self.varMap: + return self.varMap[x] + + ### Find number of new variables needed ### + if x in self.shapeMap: + shape = self.shapeMap[x] + shape = [a if a is not None else 1 for a in shape] + else: + shape = [a if a is not None else 1 for a in x.outputs[0].get_shape().as_list()] + size = 1 + for a in shape: + size*=a + ### END finding number of new variables ### + + v = np.array([self.getNewVariable() for _ in range(size)]).reshape(shape) + self.numVars += size + self.varMap[x] = v + return v + + def getValues(self, op): + """ + Function to find underlying constants/variables representing operation + Arguments: + op: (tf.op) to get values of + Returns: + values: (np array) of scalars or variable numbers depending on op + """ + input_ops = [i.op for i in op.inputs] + + ### Operations not requiring new variables ### + if op.node_def.op == 'Identity': + return self.getValues(input_ops[0]) + if op.node_def.op in ['Reshape', 'Pack']: + prevValues = [tf.constant(self.getValues[i]) for i in input_ops] + names = [x.op.name for x in prevValues] + op.node_def.inputs = names + return self.sess.run(op) + if op.node_def.op == 'Const': + tproto = op.node_def.attr['value'].tensor + return tensor_util.MakeNdarray(tproto) + ### END operations not requiring new variables ### + + if op.node_def.op in ['MatMul', 'BiasAdd', 'Add', 'Relu', 'MaxPool', 'Conv2D', 'Placeholder']: + # need to create variables for these + return self.opToVarArray(op) + + raise NotImplementedError + + def matMulEquations(self, op): + """ + Function to generate equations corresponding to matrix multiplication + Arguments: + op: (tf.op) representing matrix multiplication operation + """ + + ### Get variables and constants of inputs ### + input_ops = [i.op for i in op.inputs] + prevValues = [self.getValues(i) for i in input_ops] + curValues = self.getValues(op) + aTranspose = op.node_def.attr['transpose_a'].b + bTranspose = op.node_def.attr['transpose_b'].b + A = prevValues[0] + B = prevValues[1] + if aTranspose: + A = np.transpose(A) + if bTranspose: + B = np.transpose(B) + assert (A.shape[0], B.shape[1]) == curValues.shape + assert A.shape[1] == B.shape[0] + m, n = curValues.shape + p = A.shape[1] + ### END getting inputs ### + + ### Generate actual equations ### + for i in range(m): + for j in range(n): + e = [] + e = MarabouUtils.Equation() + for k in range(p): + e.addAddend(B[k][j], A[i][k]) + e.addAddend(-1, curValues[i][j]) + e.setScalar(0.0) + aux = self.getNewVariable() + self.setLowerBound(aux, 0.0) + self.setUpperBound(aux, 0.0) + e.markAuxiliaryVariable(aux) + self.addEquation(e) + + def biasAddEquations(self, op): + """ + Function to generate equations corresponding to bias addition + Arguments: + op: (tf.op) representing bias add operation + """ + + ### Get variables and constants of inputs ### + input_ops = [i.op for i in op.inputs] + prevValues = [self.getValues(i) for i in input_ops] + curValues = self.getValues(op) + prevVars = prevValues[0].reshape(-1) + prevConsts = prevValues[1].reshape(-1) + # broadcasting + prevConsts = np.tile(prevConsts, len(prevVars)//len(prevConsts)) + curVars = curValues.reshape(-1) + assert len(prevVars)==len(curVars) and len(curVars)==len(prevConsts) + ### END getting inputs ### + + ### Generate actual equations ### + for i in range(len(curVars)): + e = MarabouUtils.Equation() + e.addAddend(1, prevVars[i]) + e.addAddend(-1, curVars[i]) + e.setScalar(-prevConsts[i]) + aux = self.getNewVariable() + self.setLowerBound(aux, 0.0) + self.setUpperBound(aux, 0.0) + e.markAuxiliaryVariable(aux) + self.addEquation(e) + + def conv2DEquations(self, op): + """ + Function to generate equations corresponding to 2D convolution operation + Arguments: + op: (tf.op) representing conv2D operation + """ + + ### Get variables and constants of inputs ### + input_ops = [i.op for i in op.inputs] + prevValues = [self.getValues(i) for i in input_ops] + curValues = self.getValues(op) + padding = op.node_def.attr['padding'].s.decode() + strides = list(op.node_def.attr['strides'].list.i) + prevValues, prevConsts = prevValues[0], prevValues[1] + _, out_height, out_width, out_channels = curValues.shape + _, in_height, in_width, in_channels = prevValues.shape + filter_height, filter_width, filter_channels, num_filters = prevConsts.shape + assert filter_channels == in_channels + assert out_channels == num_filters + # Use padding to determine top and left offsets + # See https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/quantized_conv_ops.cc#L51 + if padding=='SAME': + pad_top = ((out_height - 1) * strides[1] + filter_height - in_height) // 2 + pad_left = ((out_width - 1) * strides[2] + filter_width - in_width) // 2 + elif padding=='VALID': + pad_top = ((out_height - 1) * strides[1] + filter_height - in_height + 1) // 2 + pad_left = ((out_width - 1) * strides[2] + filter_width - in_width + 1) // 2 + else: + raise NotImplementedError + ### END getting inputs ### + + ### Generate actual equations ### + # There is one equation for every output variable + for i in range(out_height): + for j in range(out_width): + for k in range(out_channels): # Out_channel corresponds to filter number + e = MarabouUtils.Equation() + # The equation convolves the filter with the specified input region + # Iterate over the filter + for di in range(filter_height): + for dj in range(filter_width): + for dk in range(filter_channels): + + h_ind = int(strides[1]*i+di - pad_top) + w_ind = int(strides[2]*j+dj - pad_left) + if h_ind < in_height and h_ind>=0 and w_ind < in_width and w_ind >=0: + var = prevValues[0][h_ind][w_ind][dk] + c = prevConsts[di][dj][dk][k] + e.addAddend(c, var) + + # Add output variable + e.addAddend(-1, curValues[0][i][j][k]) + e.setScalar(0.0) + aux = self.getNewVariable() + self.setLowerBound(aux, 0.0) + self.setUpperBound(aux, 0.0) + e.markAuxiliaryVariable(aux) + self.addEquation(e) + + def reluEquations(self, op): + """ + Function to generate equations corresponding to pointwise Relu + Arguments: + op: (tf.op) representing Relu operation + """ + + ### Get variables and constants of inputs ### + input_ops = [i.op for i in op.inputs] + prevValues = [self.getValues(i) for i in input_ops] + curValues = self.getValues(op) + prev = prevValues[0].reshape(-1) + cur = curValues.reshape(-1) + assert len(prev) == len(cur) + ### END getting inputs ### + + ### Generate actual equations ### + for i in range(len(prev)): + self.addRelu(prev[i], cur[i]) + e = MarabouUtils.Equation() + e.addAddend(1.0, prev[i]) + e.addAddend(-1.0, cur[i]) + e.setScalar(0.0) + aux = self.getNewVariable() + e.addAddend(1.0, aux) + e.markAuxiliaryVariable(aux) + self.setLowerBound(aux, 0.0) + self.addEquation(e) + for f in cur: + self.setLowerBound(f, 0.0) + + def maxpoolEquations(self, op): + """ + Partially implemented function to generate maxpooling equations + Arguments: + op: (tf.op) representing maxpool operation + """ + raise NotImplementedError + + ### Get variables and constants of inputs ### + input_ops = [i.op for i in op.inputs] + prevValues = [self.getValues(i) for i in input_ops] + curValues = self.getValues(op) + validPadding = op.node_def.attr['padding'].s == b'VALID' + if not validPadding: + raise NotImplementedError + prevValues = prevValues[0] + strides = list(op.node_def.attr['strides'].list.i) + ksize = list(op.node_def.attr['ksize'].list.i) + for i in range(curValues.shape[1]): + for j in range(curValues.shape[2]): + for k in range(curValues.shape[3]): + maxVars = [] + for di in range(strides[1]*i, strides[1]*i + ksize[1]): + for dj in range(strides[2]*j, strides[2]*j + ksize[2]): + if di < prevValues.shape[1] and dj < prevValues.shape[2]: + maxVars += [prevValues[0][di][dj][k]] + self.maxList += [curValues[0][i][j][k], maxVars] + + def makeNeuronEquations(self, op): + """ + Function to generate equations corresponding to given operation + Arguments: + op: (tf.op) for which to generate equations + """ + if op.node_def.op in ['Identity', 'Reshape', 'Pack', 'Placeholder', 'Const']: + return + if op.node_def.op == 'MatMul': + self.matMulEquations(op) + elif op.node_def.op in ['BiasAdd', 'Add']: + self.biasAddEquations(op) + elif op.node_def.op == 'Conv2D': + self.conv2DEquations(op) + elif op.node_def.op == 'Relu': + self.reluEquations(op) + elif op.node_def.op == 'MaxPool': + self.maxpoolEquations(op) + else: + raise NotImplementedError + + def makeGraphEquations(self, op): + """ + Function to generate equations for network necessary to calculate op + Arguments: + op: (tf.op) representing operation until which we want to generate network equations + """ + in_ops = [x.op for x in op.inputs] + for x in in_ops: + if not x.name == self.inputOp.name: + self.makeGraphEquations(x) + else: + self.foundInputFlag = True + self.makeNeuronEquations(op) + + def evaluateWithoutMarabou(self, inputValues): + """ + Function to evaluate network at a given point using Tensorflow + Arguments: + inputValues: (np array) representing input to network + Returns: + outputValues: (np array) representing output of network + """ + inputShape = self.shapeMap[self.inputOp] + inputShape = [i if i is not None else 1 for i in inputShape] + # Try to reshape given input to correct shape + inputValues = inputValues.reshape(inputShape) + + inputName = self.inputOp.name + outputName = self.outputOp.name + out = self.sess.run(outputName + ":0", feed_dict={inputName + ":0":inputValues}) + return out[0] diff --git a/maraboupy/MarabouUtils.py b/maraboupy/MarabouUtils.py new file mode 100644 index 000000000..a707a52f7 --- /dev/null +++ b/maraboupy/MarabouUtils.py @@ -0,0 +1,78 @@ +class Equation: + """ + Python class to conveniently represent MarabouCore.Equation + """ + def __init__(self): + """ + Construct empty equation + """ + self.addendList = [] + self.auxVar = None + self.scalar = None + + def setScalar(self, x): + """ + Set scalar of equation + Arguments: + x: (float) scalar RHS of equation + """ + self.scalar = x + + def addAddend(self, c, x): + """ + Add addend to equation + Arguments: + c: (float) coefficient of addend + x: (int) variable number of variable in addend + """ + self.addendList += [(c, x)] + + def markAuxiliaryVariable(self, aux): + """ + Mark variable as auxiliary for this equation + Arguments: + aux: (int) variable number of variable to mark + """ + self.auxVar = aux + +def addEquality(network, vars, coeffs, scalar): + """ + Function to conveniently add equality constraint to network + \sum_i vars_i*coeffs_i = scalar + Arguments: + network: (MarabouNetwork) to which to add constraint + vars: (list) of variable numbers + coeffs: (list) of coefficients + scalar: (float) representing RHS of equation + """ + assert len(vars)==len(coeffs) + e = Equation() + for i in range(len(vars)): + e.addAddend(coeffs[i], vars[i]) + e.setScalar(scalar) + aux = network.getNewVariable() + network.setLowerBound(aux, 0.0) + network.setUpperBound(aux, 0.0) + e.markAuxiliaryVariable(aux) + network.addEquation(e) + +def addInequality(network, vars, coeffs, scalar): + """ + Function to conveniently add inequality constraint to network + \sum_i vars_i*coeffs_i <= scalar + Arguments: + network: (MarabouNetwork) to which to add constraint + vars: (list) of variable numbers + coeffs: (list) of coefficients + scalar: (float) representing RHS of equation + """ + assert len(vars)==len(coeffs) + e = Equation() + for i in range(len(vars)): + e.addAddend(coeffs[i], vars[i]) + e.setScalar(scalar) + aux = network.getNewVariable() + network.setLowerBound(aux, 0.0) + e.markAuxiliaryVariable(aux) + e.addAddend(1.0, aux) + network.addEquation(e) \ No newline at end of file diff --git a/maraboupy/__init__.py b/maraboupy/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/maraboupy/examples/AcasUtils.py b/maraboupy/examples/AcasUtils.py new file mode 100644 index 000000000..99021eee3 --- /dev/null +++ b/maraboupy/examples/AcasUtils.py @@ -0,0 +1,94 @@ +import numpy as np +import math + +# These discrete points define a grid that spans the 5D input space. +# This grid can be made more refined if you want to make the region more precise, but this will result in more points +ranges = [499, 800, 2000, 3038, 5316, 6450, 7200, 7950, 8725, 10633, 13671, 16709, 19747, + 22785, 25823, 28862, 31900, 34938, 37976, 41014, 48608, 60760] +thetas = np.linspace(-np.pi,np.pi,41) +psis = np.linspace(-np.pi,np.pi,41) +sos = [100, 136, 186, 254, 346, 472, 645, 880, 1200] +sis = [0, 100, 142, 203, 290, 414, 590, 841, 1200] + +numStates = len(ranges)*len(thetas)*len(psis)*len(sos)*len(sis) +numActions = 5 + +def rescaleInput(x): + inputRanges = [66287.1, 6.28318530718, 6.28318530718, 1100.0, 1200.0] + inputMeans = [21770.2, 0.0, 0.0, 468.777778, 420.0] + ret = [] + for i in range(len(x)): + ret += [(x[i]-inputMeans[i])/inputRanges[i]] + return ret + +def rescaleOutput(x): + outputRange = 399.086622997 + outputMean = 8.2091785953 + return [y*outputRange+outputMean for y in x] + +def getInputLowerBound(i): + inputMins = [-0.320142, -0.5, -0.5, -0.3352525, -0.35] + return inputMins[i] + +def getInputUpperBound(i): + inputMaxes = [0.679858, 0.5, 0.5, 0.6647475, 0.65] + return inputMaxes[i] + +def collisionSoon(r,th,psi,v_own,v_int,timeLimit=50.0,rangeLimit=4000.0): + ''' + Function that determines if the encounter geometry will result in a collision soon if + both aircraft fly in the current heading direction and at the same speed. + + "Collision soon" is defined by the distance between aircraft at closest point of approach (CPA) + being under some limit and at some time under some limit. Nominally, the limits are defined as 4000 ft + and 50.0 seconds. + + The property to be checked: If there is a "collision soon", the network should never + alert COC (in other words, output_0 should never be the lower cost output). + This applies to the tau=0, pra=1 network. + + The first five inputs describe the encounter geometry. + The output is True if a collision will occur soon, or False otherwise. + ''' + relx = r*np.cos(th) # x-coordinate of intruder relative to ownship + rely = r*np.sin(th) # y-coordinate of intruder relative to ownship + dx = v_int * np.cos(psi) - v_own # x-velocity of intruder relative to ownship + dy = v_int * np.sin(psi) # y-velocity of intruder relative to ownship + + timeToCPA = -1 + rangeCPA = r + if dx*dx+dy*dy > 0.0: # Make sure relative speed is not 0 + + timeToCPA = (-relx * dx - rely * dy) / (dx * dx + dy * dy) # Time to intruder's closest point of approach + time = np.minimum(timeLimit,timeToCPA) # Cap the time to CPA to the timeLimit + xT = relx + dx * time # x-coordinate at time of closest point of approach + yT = rely + dy * time # y-coordinate at time of closest point of appraoch + rangeCPA = np.sqrt(xT * xT + yT * yT) # distance to intruder at closest point of approach + + # If timeToCPA is negative, than the aircraft are moving apart. Therefore, the minimum separation occurs now + if timeToCPA < 0: + rangeCPA = r + + if rangeCPA 1.0]) + + # Rescale inputs for network + collisionList = [rescaleInput(x) for x in collisionList] + return collisionList diff --git a/maraboupy/examples/ConvexHullExample.py b/maraboupy/examples/ConvexHullExample.py new file mode 100644 index 000000000..a66a894cc --- /dev/null +++ b/maraboupy/examples/ConvexHullExample.py @@ -0,0 +1,46 @@ +from AcasUtils import * +from scipy.spatial import ConvexHull +from maraboupy import MarabouUtils +from maraboupy import Marabou + +print("Finding points to verify...") +collisionList = getCollisionList() + +print("Finding convex hull...") +hull = ConvexHull(collisionList) +hyperplanes = hull.equations + +### Load network +filename = "./networks/ACASXU_TF12_run3_DS_Minus15_Online_tau0_pra1_200Epochs.pb" +network = Marabou.read_tf(filename) + +# first dimension is batch size +inputVars = network.inputVars[0] +outputVars = network.outputVars[0] + +# set bounds for inputs +for i in range(len(inputVars)): + network.setLowerBound(inputVars[i], getInputLowerBound(i)) + network.setUpperBound(inputVars[i], getInputUpperBound(i)) + +# set constraints according to convex hull +for hyperplane in hyperplanes: + MarabouUtils.addInequality(network, inputVars, hyperplane[:-1], -hyperplane[-1]) + +# property to be UNSAT: output[0] is least +for i in range(1, len(outputVars)): + MarabouUtils.addInequality(network, [outputVars[0], outputVars[i]], [1, -1],0) + +print("Solving...") +vals = network.solve("") +if len(vals)==0: + print("UNSAT") +else: + print("SAT") + print("Input Values:") + for i in range(inputVars.size): + print("%d: %.4e" % (i, vals[inputVars[i]])) + + print("\nOutput Values:") + for i in range(outputVars.size): + print("%d: %.4e" % (i, vals[outputVars[i]])) diff --git a/maraboupy/examples/Example Notebook.ipynb b/maraboupy/examples/Example Notebook.ipynb new file mode 100644 index 000000000..85eaacb64 --- /dev/null +++ b/maraboupy/examples/Example Notebook.ipynb @@ -0,0 +1,304 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### import" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from maraboupy import Marabou" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### file names" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "nnet_file_name = \"../../src/input_parsers/acas_example/ACASXU_run2a_1_1_tiny_2.nnet\"\n", + "proto_file_name = \"./networks/ACASXU_TF12_run3_DS_Minus15_Online_tau0_pra1_200Epochs.pb\"\n", + "#proto_file_name = \"../examples/networks/ACASXU_TF12_run3_DS_Minus15_Online_tau1_pra1_200Epochs.pb\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### load the network from NNet file" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SAT\n", + "input 0 = -0.32842287715105956\n", + "input 1 = 0.4093292458896232\n", + "input 2 = -0.01737928983483918\n", + "input 3 = -0.27476839800416775\n", + "input 4 = -0.306281330264657\n", + "output 0 = 0.5\n", + "output 1 = -0.18876336089104007\n", + "output 2 = 0.8081545469315792\n", + "output 3 = -2.7642138441457913\n", + "output 4 = -0.1299298407079034\n" + ] + } + ], + "source": [ + "net1 = Marabou.read_nnet(nnet_file_name)\n", + "net1.setLowerBound(net1.outputVars[0], .5)\n", + "vals1 = net1.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Aux functions for proto net" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def getInputLowerBound(i):\n", + " inputMins = [-0.320142, -0.5, -0.5, -0.3352525, -0.35]\n", + " return inputMins[i]\n", + "\n", + "def getInputUpperBound(i):\n", + " inputMaxes = [0.679858, 0.5, 0.5, 0.6647475, 0.65]\n", + " return inputMaxes[i]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "UNSAT\n" + ] + } + ], + "source": [ + "net2 = Marabou.read_tf(proto_file_name)\n", + "\n", + "for inputVar, i in enumerate(net2.inputVars[0]):\n", + " net2.setLowerBound(inputVar, getInputLowerBound(i))\n", + " net2.setUpperBound(inputVar, getInputUpperBound(i))\n", + "\n", + "vals2 = net2.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Eval Example" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SAT\n", + "input 0 = -0.32842287715105956\n", + "input 1 = 0.4093292458896232\n", + "input 2 = -0.01737928983483918\n", + "input 3 = -0.27476839800416775\n", + "input 4 = -0.306281330264657\n", + "output 0 = 0.5\n", + "output 1 = -0.18876336089104007\n", + "output 2 = 0.8081545469315792\n", + "output 3 = -2.7642138441457913\n", + "output 4 = -0.1299298407079034\n" + ] + } + ], + "source": [ + "net3 = Marabou.read_nnet(nnet_file_name)\n", + "net3.setLowerBound(net1.outputVars[0], .5)\n", + "vals1 = net3.solve()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "input0 = -0.328422874212265\n", + "input1 = 0.40932923555374146\n", + "input2 = -0.017379289492964745\n", + "input3 = -0.2747684121131897\n", + "input4 = -0.30628132820129395\n", + "\n", + "output0 = 0.5\n", + "output1 = -0.18876336514949799\n", + "output2 = 0.8081545233726501\n", + "output3 = -2.764213800430298\n", + "output4 = -0.12992984056472778" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SAT\n", + "input 0 = -0.328422874212265\n", + "input 1 = 0.40932923555374146\n", + "input 2 = -0.017379289492964745\n", + "input 3 = -0.2747684121131897\n", + "input 4 = -0.30628132820129395\n", + "output 0 = 0.49999677515649005\n", + "output 1 = -0.18876658957388143\n", + "output 2 = 0.807785545376377\n", + "output 3 = -2.7642226430352106\n", + "output 4 = -0.12984317469803675\n" + ] + } + ], + "source": [ + "### Eval Example:\n", + "net3 = Marabou.read_nnet(nnet_file_name)\n", + "\n", + "net3.setLowerBound(net3.inputVars[0], input0)\n", + "net3.setUpperBound(net3.inputVars[0], input0)\n", + "\n", + "net3.setLowerBound(net3.inputVars[1], input1)\n", + "net3.setUpperBound(net3.inputVars[1], input1)\n", + "\n", + "net3.setLowerBound(net3.inputVars[2], input2)\n", + "net3.setUpperBound(net3.inputVars[2], input2)\n", + "\n", + "net3.setLowerBound(net3.inputVars[3], input3)\n", + "net3.setUpperBound(net3.inputVars[3], input3)\n", + "\n", + "net3.setLowerBound(net3.inputVars[4], input4)\n", + "net3.setUpperBound(net3.inputVars[4], input4)\n", + "\n", + "\n", + "vals3 = net3.solve()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DIFF0 = 3.2248435099524464e-06\n", + "DIFF1 = 3.2244243834411712e-06\n", + "DIFF2 = 0.0003689779962731121\n", + "DIFF3 = 8.842604912739915e-06\n", + "DIFF4 = -8.666586669103804e-05\n", + "NORM OF ERROR: 0.0003791499998324417\n" + ] + } + ], + "source": [ + "#for i, var in enumerate(input_variables):\n", + "# print(\"input\" + str(i) + \" = {}\".format(vals[var]) )\n", + "\n", + "#for i, var in enumerate(output_variables):\n", + "# print(\"output\" + str(i) + \" = {}\".format(vals[var]) )\n", + "\n", + "print(\"DIFF0 = {}\".format(output0 - vals3[net3.outputVars[0]]))\n", + "print(\"DIFF1 = {}\".format(output1 - vals3[net3.outputVars[1]]))\n", + "print(\"DIFF2 = {}\".format(output2 - vals3[net3.outputVars[2]]))\n", + "print(\"DIFF3 = {}\".format(output3 - vals3[net3.outputVars[3]]))\n", + "print(\"DIFF4 = {}\".format(output4 - vals3[net3.outputVars[4]]))\n", + "\n", + "error = np.array([ output0 - vals3[net3.outputVars[0]],output1 - vals3[net3.outputVars[1]], output2 - vals3[net3.outputVars[2]], output3 - vals3[net3.outputVars[3]], output4 - vals3[net3.outputVars[4]], ])\n", + "\n", + "print(\"NORM OF ERROR: {}\".format(np.linalg.norm(error)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/maraboupy/examples/MarabouCoreExample.py b/maraboupy/examples/MarabouCoreExample.py new file mode 100644 index 000000000..48fbf4b93 --- /dev/null +++ b/maraboupy/examples/MarabouCoreExample.py @@ -0,0 +1,87 @@ +from maraboupy import MarabouCore + +# The example from the CAV'17 paper: +# 0 <= x0 <= 1 +# 0 <= x1f +# 0 <= x2f +# 1/2 <= x3 <= 1 +# +# x0 - x1b = 0 --> x0 - x1b + x6 = 0 +# x0 + x2b = 0 --> x0 + x2b + x7 = 0 +# x1f + x2f - x3 = 0 --> x1f + x2f - x3 + x8 = 0 +# +# x2f = Relu(x2b) +# x3f = Relu(x3b) +# +# x0: x0 +# x1: x1b +# x2: x1f +# x3: x2b +# x4: x2f +# x5: x3 + +large = 10.0 + +inputQuery = MarabouCore.InputQuery() + +inputQuery.setNumberOfVariables(9) + +inputQuery.setLowerBound(0, 0) +inputQuery.setUpperBound(0, 1) + +inputQuery.setLowerBound(1, -large) +inputQuery.setUpperBound(1, large) + +inputQuery.setLowerBound(2, 0) +inputQuery.setUpperBound(2, large) + +inputQuery.setLowerBound(3, -large) +inputQuery.setUpperBound(3, large) + +inputQuery.setLowerBound(4, 0) +inputQuery.setUpperBound(4, large) + +inputQuery.setLowerBound(5, 0.5) +inputQuery.setUpperBound(5, 1) + +inputQuery.setLowerBound(6, 0) +inputQuery.setUpperBound(6, 0) +inputQuery.setLowerBound(7, 0) +inputQuery.setUpperBound(7, 0) +inputQuery.setLowerBound(8, 0) +inputQuery.setUpperBound(8, 0) + +equation1 = MarabouCore.Equation() +equation1.addAddend(1, 0) +equation1.addAddend(-1, 1) +equation1.addAddend(1, 6) +equation1.setScalar(0) +equation1.markAuxiliaryVariable(6) +inputQuery.addEquation(equation1) + +equation2 = MarabouCore.Equation() +equation2.addAddend(1, 0) +equation2.addAddend(1, 3) +equation2.addAddend(1, 7) +equation2.setScalar(0) +equation2.markAuxiliaryVariable(7) +inputQuery.addEquation(equation2) + +equation3 = MarabouCore.Equation() +equation3.addAddend(1, 2) +equation3.addAddend(1, 4) +equation3.addAddend(-1, 5) +equation3.addAddend(1, 8) +equation3.setScalar(0) +equation3.markAuxiliaryVariable(8) +inputQuery.addEquation(equation3) + +inputQuery.addReluConstraint(1, 2) +inputQuery.addReluConstraint(3, 4) + +results = MarabouCore.solve(inputQuery, "") +if len(results)>0: + print("SAT") + print(results) +else: + print("UNSAT") \ No newline at end of file diff --git a/maraboupy/examples/MarabouNetworkTFExample.py b/maraboupy/examples/MarabouNetworkTFExample.py new file mode 100644 index 000000000..c6eac61fc --- /dev/null +++ b/maraboupy/examples/MarabouNetworkTFExample.py @@ -0,0 +1,53 @@ +from maraboupy import Marabou +import numpy as np + + +# Network corresponds to inputs x0, x1 +# Outputs: y0 = |x0| + |x1|, y1 = x0^2 + x1^2 +filename = './networks/graph_test_medium.pb' +network = Marabou.read_tf(filename) + +## Or, you can specify the operation names of the input and output operations +## By default chooses the only placeholder as input, last op as output +#inputName = 'Placeholder' +#outputName = 'y_out' +#network = MarabouNetworkTF(filename=filename, inputName=inputName, outputName = outputName) + +# Get the input and output variable numbers; [0] since first dimension is batch size +inputVars = network.inputVars[0] +outputVars = network.outputVars[0] + +# Set input bounds +network.setLowerBound(inputVars[0],-10.0) +network.setUpperBound(inputVars[0], -10.0) +network.setLowerBound(inputVars[1],-10.0) +network.setUpperBound(inputVars[1], -10.0) + +# Set output bounds +network.setLowerBound(outputVars[1], 194.0) +network.setUpperBound(outputVars[1], 210.0) + +# Call to C++ Marabou solver +vals = network.solve("marabou.log") + +# Print results +if len(vals)==0: + print("UNSAT") +else: + print("SAT") + print("Input Values:") + for i in range(inputVars.size): + print("%d: %.4e" % (i, vals[inputVars[i]])) + + print("\nOutput Values:") + for i in range(outputVars.size): + print("%d: %.4e" % (i, vals[outputVars[i]])) + + inputValues = [vals[x] for x in inputVars] + outputValues = [vals[x] for x in outputVars] + error = network.findError(inputValues) + print("\nError norm:") + print(np.linalg.norm(error)) + + + diff --git a/maraboupy/examples/networks/ACASXU_TF12_run3_DS_Minus15_Online_tau0_pra1_200Epochs.nnet b/maraboupy/examples/networks/ACASXU_TF12_run3_DS_Minus15_Online_tau0_pra1_200Epochs.nnet new file mode 100644 index 000000000..d05b3e95b --- /dev/null +++ b/maraboupy/examples/networks/ACASXU_TF12_run3_DS_Minus15_Online_tau0_pra1_200Epochs.nnet @@ -0,0 +1,558 @@ +// Neural Network File Format by Kyle Julian, Stanford 2016 +7,5,5,45, +5,45,45,45,45,45,45,5, +15, +499.0,-3.141593,-3.141593,100.0,0.0, +60760.0,3.141593,3.141593,1200.0,1200.0, +1.9485976744e+04,0.0,0.0,468.777778,420.0,8.2091785953, +60261.0,6.28318530718,6.28318530718,1100.0,1200.0,399.086622997, +1.44730e-01,-1.42178e-03,-1.28327e-03,-7.89436e-01,-4.21580e-01, +-7.56187e-01,-2.93059e-01,3.12114e-01,-2.60486e-02,-2.48499e-02, 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These networks were trained to represent different discrete combinations of state variables tau and previous RA (pra). Tau has 9 discrete values while pra has 5: + +Tau: [0, 1, 5, 10, 20, 40, 60, 80, 100] +Pra: [1, 2, 3, 4, 5] + +In previous versions of the ACAS Xu networks, the network names were marked with "1_1" .... "5_9" to reprent the different pra and tau combination indices. Now, the networks names are more descriptive and specify the actual tau and pra combination used to train that network. + + + +These networks have five inputs and produce five outputs, but the training data was normalized by subtracting by the mean and dividing by the range. Therefore, when passing inputs, use the following normalization constants: + + Input_1 Input_2 Input_3 Input_4 Input_5 +Means: 21770.2, 0.0, 0.0, 468.777778, 420.0 +Ranges: 66287.1, 6.28318530718, 6.28318530718, 1100.0, 1200.0 + + + +In addition, the target data was normalized for all networks using a single normalization constant for all outputs. To get the unnormalized network outputs, multiply by the output range and then add the output mean: + + Output +Mean: 8.2091785953 +Range: 399.086622997 + + + +The inputs to the network should never go outside the bounds of the training data. Unnormalized, those bounds are: + + Input_1 Input_2 Input_3 Input_4 Input_5 +Mins: 548.9, -3.141593, -3.141593, 100.0, 0.0 +Maxes: 66836.0, 3.141593, 3.141593, 1200.0, 1200.0 + +For convenience, the normalized bounds are: + + Input_1 Input_2 Input_3 Input_4 Input_5 +Mins: -0.320142, -0.5, -0.5, -0.3352525, -0.35 +Maxes: 0.679858, 0.5, 0.5, 0.6647475, 0.65 + + + +Lastly, an additional cost of 15.0 (unnormalized) is added to the first network output (Clear-of-Conflict) when the previous RA is not COC (networks with pra > 1), and the distance between the two aircraft at closest point of approach (CPA) is less than 4000 ft. The distance at CPA (dCPA) can be calculated as a function of the unnormalized input variables: + +r = input_1 +theta = input_2 +psi = input_3 +v_own = input_4 +v_int = input_5 + +dx = v_int*cos(psi) - v_own +dy = v_int*sin(psi) +tCPA = (r*cos(theta)*dx - r*sin(theta)*dy)/(dx^2 + dy^2) +xCPA = r*cos(theta) + dx*tCPA +yCPA = r*sin(theta) + dy*tCPA +dCPA = sqrt(xCPA^2 + yCPA^2) + + +This additional cost was present in the training data, but represents a large discontinuity that the neural network regressed poorly. The solution was to remove the cost from the training data and then re-apply the cost to the network outputs. This resulted in neural networks that trained more accurately, but now an additional cost must be added to the network outputs in some cases. \ No newline at end of file diff --git a/maraboupy/examples/networks/frozen_lisa_model.pb b/maraboupy/examples/networks/frozen_lisa_model.pb new file mode 100644 index 000000000..5ec352495 Binary files /dev/null and b/maraboupy/examples/networks/frozen_lisa_model.pb differ diff --git a/maraboupy/examples/networks/graph_test.pb b/maraboupy/examples/networks/graph_test.pb new file mode 100644 index 000000000..7fd1140c4 Binary files /dev/null and b/maraboupy/examples/networks/graph_test.pb differ diff --git a/maraboupy/examples/networks/graph_test_cnn.pb b/maraboupy/examples/networks/graph_test_cnn.pb new file mode 100644 index 000000000..fb0c700fa Binary files /dev/null and b/maraboupy/examples/networks/graph_test_cnn.pb differ diff --git a/maraboupy/examples/networks/graph_test_cnn_noMP.pb b/maraboupy/examples/networks/graph_test_cnn_noMP.pb new file mode 100644 index 000000000..83b75d95e Binary files /dev/null and b/maraboupy/examples/networks/graph_test_cnn_noMP.pb differ diff --git a/maraboupy/examples/networks/graph_test_medium.pb b/maraboupy/examples/networks/graph_test_medium.pb new file mode 100644 index 000000000..9a1c163d0 Binary files /dev/null and b/maraboupy/examples/networks/graph_test_medium.pb differ diff --git a/maraboupy/examples/test_evaluation_methods.py b/maraboupy/examples/test_evaluation_methods.py new file mode 100644 index 000000000..1e7d0f9a3 --- /dev/null +++ b/maraboupy/examples/test_evaluation_methods.py @@ -0,0 +1,312 @@ +import numpy as np +from maraboupy import MarabouCore + +def read_nnet(file_name): + with open(file_name) as f: + line = f.readline() + cnt = 1 + while line[0:2] == "//": + line=f.readline() + cnt+= 1 + #numLayers does't include the input layer! + numLayers, inputSize, outputSize, maxLayersize = [int(x) for x in line.strip().split(",")[:-1]] + line=f.readline() + + #input layer size, layer1size, layer2size... + layerSizes = [int(x) for x in line.strip().split(",")[:-1]] + + line=f.readline() + symmetric = int(line.strip().split(",")[0]) + + line = f.readline() + inputMinimums = [float(x) for x in line.strip().split(",")[:-1]] + + line = f.readline() + inputMaximums = [float(x) for x in line.strip().split(",")[:-1]] + + line = f.readline() + inputMeans = [float(x) for x in line.strip().split(",")[:-1]] + + line = f.readline() + inputRanges = [float(x) for x in line.strip().split(",")[:-1]] + + weights=[] + biases = [] + for layernum in range(numLayers): + + previousLayerSize = layerSizes[layernum] + currentLayerSize = layerSizes[layernum+1] + # weights + weights.append([]) + biases.append([]) + #weights + for i in range(currentLayerSize): + line=f.readline() + aux = [float(x) for x in line.strip().split(",")[:-1]] + weights[layernum].append([]) + for j in range(previousLayerSize): + weights[layernum][i].append(aux[j]) + #biases + for i in range(currentLayerSize): + line=f.readline() + x = float(line.strip().split(",")[0]) + biases[layernum].append(x) + return numLayers, layerSizes, inputSize, outputSize, maxLayersize, inputMinimums, inputMaximums, inputMeans, inputRanges, weights, biases + +def variableRanges(layerSizes): + input_variables = [] + b_variables = [] + f_variables = [] + aux_variables = [] + output_variables = [] + + input_variables = [i for i in range(layerSizes[0])] + + hidden_layers = layerSizes[1:-1] + + for layer, hidden_layer_length in enumerate(hidden_layers): + for i in range(hidden_layer_length): + offset = sum([x*3 for x in hidden_layers[:layer]]) + + b_variables.append(layerSizes[0] + offset + i) + aux_variables.append(layerSizes[0] + offset + i+hidden_layer_length) + f_variables.append(layerSizes[0] + offset + i+2*hidden_layer_length) + + #final layer + for i in range(layerSizes[-1]): + offset = sum([x*3 for x in hidden_layers[:len(hidden_layers) - 1]]) + output_variables.append(layerSizes[0] + offset + i + 3*hidden_layers[-1]) + aux_variables.append(layerSizes[0] + offset + i + 3*hidden_layers[-1] + layerSizes[-1]) + return input_variables, b_variables, f_variables, aux_variables, output_variables + + +def nodeTo_b(layer, node): + assert(0 < layer) + assert(node < layerSizes[layer]) + + offset = layerSizes[0] + offset += sum([x*3 for x in layerSizes[1:layer]]) + + return offset + node + +def nodeTo_aux(layer, node): + assert(0 < layer) + assert(node < layerSizes[layer]) + + offset = layerSizes[0] + offset += sum([x*3 for x in layerSizes[1:layer]]) + offset += layerSizes[layer] + + return offset + node + +def nodeTo_f(layer, node): + assert(layer < len(layerSizes)) + assert(node < layerSizes[layer]) + + if layer == 0: + return node + else: + offset = layerSizes[0] + offset += sum([x*3 for x in layerSizes[1:layer]]) + offset += 2*layerSizes[layer] + + return offset + node + +def buildEquations(layerSizes): + equations_aux = [] + equations_count = 0 + marabou_equations = [] + + for layer, size in enumerate(layerSizes): + if layer == 0: + continue + + for node in range(size): + #add marabou equation + + equation = MarabouCore.Equation() + equations_aux.append([]) + + #equations_aux[] + for previous_node in range(layerSizes[layer-1]): + equation.addAddend(weights[layer-1][node][previous_node], nodeTo_f(layer-1, previous_node)) + equations_aux[equations_count].append([nodeTo_f(layer-1, previous_node), weights[layer-1][node][previous_node]]) + + equation.addAddend(1.0, nodeTo_aux(layer, node)) + equation.markAuxiliaryVariable(nodeTo_aux(layer, node)) + equations_aux[equations_count].append([nodeTo_aux(layer, node), 1.0]) + + equation.addAddend(-1.0, nodeTo_b(layer, node)) + equations_aux[equations_count].append([nodeTo_b(layer, node), -1.0]) + + equation.setScalar(-biases[layer-1][node]) + equations_aux[equations_count].append(-biases[layer-1][node]) + equations_count += 1 + + marabou_equations.append(equation) + + return marabou_equations + +def findRelus(layerSizes): + relus = [] + hidden_layers = layerSizes[1:-1] + for layer, size in enumerate(hidden_layers): + for node in range(size): + relus.append([nodeTo_b(layer+1, node), nodeTo_f(layer+1, node)]) + + return relus + +def numberOfVariables(layerSizes): + return layerSizes[0] + 3*sum(layerSizes[1:-1]) + 2*layerSizes[-1] + +def getInputMinimum(input): + return (inputMinimums[input] - inputMeans[input]) / inputRanges[input] + +def getInputMaximum(input): + return (inputMaximums[input] - inputMeans[input]) / inputRanges[input] + +# 5-5-5 +#file_name = ../../src/input_parsers/acas_example/ACASXU_run2a_1_1_tiny.nnet" + +#5-50-5 +file_name = "../../src/input_parsers/acas_example/ACASXU_run2a_1_1_tiny_2.nnet" + +#5-50-50-5 +#file_name = "../../src/input_parsers/acas_example/ACASXU_run2a_1_1_tiny_3.nnet" + + +#5-50-50-50-5, 150 Relus +#file_name = "../../src/input_parsers/acas_example/ACASXU_run2a_1_1_tiny_4.nnet" + +numLayers, layerSizes, inputSize,\ + outputSize, maxLayersize, inputMinimums,\ + inputMaximums, inputMeans, inputRanges,\ + weights, biases =read_nnet(file_name) + +print(layerSizes) + +equation_objects = buildEquations(layerSizes) + +input_variables, b_variables, f_variables,\ + aux_variables, output_variables = variableRanges(layerSizes) + +relus = findRelus(layerSizes) + +ipq = MarabouCore.InputQuery() + +#add the equations + +#print("EQUATION: {}".format(len(equation_objects))) + + +for eq in equation_objects: + ipq.addEquation(eq) + +#mark aux vars + + +#add the relus +for relu in relus: + ipq.addReluConstraint(relu[0], relu[1]) + +#add the network bounds + +#input minimums and maximums +print("INPUT VARS: {}".format(input_variables)) + +for i, i_var in enumerate(input_variables): + ipq.setLowerBound(i_var, getInputMinimum(i_var)) + print("INPUT MINIMUMS: {}".format(getInputMinimum(i_var))) + ipq.setUpperBound(i_var, getInputMaximum(i_var)) + print("INPUT MAXIMUMS: {}".format(getInputMaximum(i_var))) + +#aux variables +for aux_var in aux_variables: + ipq.setLowerBound(aux_var, 0.0) + ipq.setUpperBound(aux_var, 0.0) + + +for f_var in f_variables: + ipq.setLowerBound(f_var, 0.0) + + +#add the property boudns + +#ipq.setLowerBound( output_variables[0], 0.5 ); +#print("OUTPUT VARIABLE FOR BOUND: {}".format(output_variables[0])) + +#CHECK THE EVAL + +input0 = -0.328422874212265 +input1 = 0.40932923555374146 +input2 = -0.017379289492964745 +input3 = -0.2747684121131897 +input4 = -0.30628132820129395 + +output0 = 0.5 +output1 = -0.18876336514949799 +output2 = 0.8081545233726501 +output3 = -2.764213800430298 +output4 = -0.12992984056472778 + +ipq.setLowerBound(input_variables[0], input0) +ipq.setUpperBound(input_variables[0], input0) + +ipq.setLowerBound(input_variables[1], input1) +ipq.setUpperBound(input_variables[1], input1) + +ipq.setLowerBound(input_variables[2], input2) +ipq.setUpperBound(input_variables[2], input2) + +ipq.setLowerBound(input_variables[3], input3) +ipq.setUpperBound(input_variables[3], input3) + +ipq.setLowerBound(input_variables[4], input4) +ipq.setUpperBound(input_variables[4], input4) + + +#Input[0] = -0.328422877151060 +#Input[1] = 0.413358209042742 +#Input[2] = -0.013607140388665 +#Input[3] = -0.304152412046139 +#Input[4] = -0.300504199489204 + + +input0 = -0.328422874212265 +input1 = 0.40932923555374146 +input2 = -0.017379289492964745 +input3 = -0.2747684121131897 +input4 = -0.30628132820129395 + +output0 = 0.5 +output1 = -0.18876336514949799 +output2 = 0.8081545233726501 +output3 = -2.764213800430298 +output4 = -0.12992984056472778 + +numvars = numberOfVariables(layerSizes) + +ipq.setNumberOfVariables(numvars) + +vals = MarabouCore.solve(ipq, "dump") +if len(vals)==0: + print("UNSAT") +else: + print("SAT") + print(vals) + +for i, var in enumerate(input_variables): + print("input" + str(i) + " = {}".format(vals[var]) ) + +for i, var in enumerate(output_variables): + print("output" + str(i) + " = {}".format(vals[var]) ) + +print("DIFF0 = {}".format(output0 - vals[output_variables[0]])) +print("DIFF1 = {}".format(output1 - vals[output_variables[1]])) +print("DIFF2 = {}".format(output2 - vals[output_variables[2]])) +print("DIFF3 = {}".format(output3 - vals[output_variables[3]])) +print("DIFF4 = {}".format(output4 - vals[output_variables[4]])) + +error = np.array([ output0 - vals[output_variables[0]],output1 - vals[output_variables[1]], output2 - vals[output_variables[2]], output3 - vals[output_variables[3]], output4 - vals[output_variables[4]], ]) + +print("NORM OF ERROR: {}".format(np.linalg.norm(error)))