fix unit test and CI

CHANGELOG.md

@@ -1,6 +1,7 @@
 # Changelog
 
 ## Next Release
+  - Dropped support for parsing Tensorflow network format. Newest Marabou version that supports Tensorflow is at commit 190555573e4702.
 
 ## Version 2.0.0
 

maraboupy/Marabou.py

@@ -21,10 +21,6 @@
     from maraboupy.MarabouNetworkNNet import *
 except ImportError:
     warnings.warn("NNet parser is unavailable because the numpy package is not installed")
-try:
-    from maraboupy.MarabouNetworkTF import *
-except ImportError:
-    warnings.warn("Tensorflow parser is unavailable because tensorflow package is not installed")
 try:
     from maraboupy.MarabouNetworkONNX import *
 except ImportError:
@@ -43,24 +39,6 @@ def read_nnet(filename, normalize=False):
     """
     return MarabouNetworkNNet(filename, normalize=normalize)
 
-
-def read_tf(filename, inputNames=None, outputNames=None, modelType="frozen", savedModelTags=[]):
-    """Constructs a MarabouNetworkTF object from a frozen Tensorflow protobuf
-
-    Args:
-        filename (str): Path to tensorflow network
-        inputNames (list of str, optional): List of operation names corresponding to inputs
-        outputNames (list of str, optional): List of operation names corresponding to outputs
-        modelType (str, optional): Type of model to read. The default is "frozen" for a frozen graph.
-                            Can also use "savedModel_v1" or "savedModel_v2" for the SavedModel format
-                            created from either tensorflow versions 1.X or 2.X respectively.
-        savedModelTags (list of str, optional): If loading a SavedModel, the user must specify tags used, default is []
-
-    Returns:
-        :class:`~maraboupy.MarabouNetworkTF.MarabouNetworkTF`
-    """
-    return MarabouNetworkTF(filename, inputNames, outputNames, modelType, savedModelTags)
-
 def read_onnx(filename, inputNames=None, outputNames=None):
     """Constructs a MarabouNetworkONNX object from an ONNX file
 

maraboupy/MarabouNetwork.py

@@ -132,29 +132,17 @@ def evaluateLocalRobustness(self, input, epsilon, originalClass, verbose=True, o
                 - stats (:class:`~maraboupy.MarabouCore.Statistics`): A Statistics object to how Marabou performed
                 - maxClass (int): Output class which value is max within outputs if SAT.
         """
-        inputVars = None
-        if (type(self.inputVars) is list):
-            if (len(self.inputVars) != 1):
-                raise NotImplementedError("Operation for %d inputs is not implemented" % len(self.inputVars))
-            inputVars = self.inputVars[0][0]
-        elif (type(self.inputVars) is np.ndarray):
-            inputVars = self.inputVars[0]
-        else:
-            err_msg = "Unpexpected type of input vars."
-            raise RuntimeError(err_msg)
+        assert(type(self.inputVars) is list)
+        if (len(self.inputVars) != 1):
+            raise NotImplementedError("Operation for %d inputs is not implemented" % len(self.inputVars))
+        inputVars = self.inputVars[0][0]
 
         if inputVars.shape != input.shape:
             raise RuntimeError("Input shape of the model should be same as the input shape\n input shape of the model: {0}, shape of the input: {1}".format(inputVars.shape, input.shape))
 
-        if (type(self.outputVars) is list):
-            if (len(self.outputVars) != 1):
-                raise NotImplementedError("Operation for %d outputs is not implemented" % len(self.outputVars))
-        elif (type(self.outputVars) is np.ndarray):
-            if (len(self.outputVars) != 1):
-                raise NotImplementedError("Operation for %d outputs is not implemented" % len(self.outputVars))
-        else:
-            err_msg = "Unpexpected type of output vars."
-            raise RuntimeError(err_msg)
+        assert(type(self.outputVars) is list)
+        if (len(self.outputVars) != 1):
+            raise NotImplementedError("Operation for %d outputs is not implemented" % len(self.outputVars))
 
         if options == None:
             options = MarabouCore.Options()
@@ -165,7 +153,7 @@ def evaluateLocalRobustness(self, input, epsilon, originalClass, verbose=True, o
         for i in range(flattenInput.size):
             self.setLowerBound(flattenInputVars[i], flattenInput[i] - epsilon)
             self.setUpperBound(flattenInputVars[i], flattenInput[i] + epsilon)
-        
+
         maxClass = None
         outputStartIndex = self.outputVars[0][0][0]