Auto generate Op.{hash,eq,str}.

theano/gof/op.py

@@ -575,21 +575,24 @@ class Op(utils.object2, PureOp, CLinkerOp):
     def __init__(self, use_c_code=theano.config.cxx):
         self._op_use_c_code = use_c_code
 
+    def _props(self):
+        return (getattr(self, a) for a in self.__props__)
+
     def __hash__(self):
-        if hasattr(self, 'props'):
-            return hash((type(self), self.props()))
+        if hasattr(self, '__props__'):
+            return hash((type(self), self._props()))
         else:
             return super(Op, self).__hash__()
 
     def __str__(self):
-        if hasattr(self, 'props'):
-            return "%s{%s}" % (self.__class__.__name__, ", ".join(str(p) for p in self.props()))
+        if hasattr(self, '__props__'):
+            return "%s{%s}" % (self.__class__.__name__, ", ".join(str(p) for p in self._props()))
         else:
             return super(Op, self).__str__()
 
     def __eq__(self, other):
-        if hasattr(self, 'props'):
-            return (type(self) == type(other) and self.props() == other.props())
+        if hasattr(self, '__props__'):
+            return (type(self) == type(other) and self._props() == other._props())
         else:
             return NotImplemented
 

theano/tensor/nlinalg.py

@@ -34,14 +34,6 @@ class MatrixPinv(Op):
     def __init__(self):
         pass
 
-    def props(self):
-        """Function exposing different properties of each instance of the
-        op.
-
-        For the ``MatrixPinv`` op, there are no properties to be exposed.
-        """
-        return ()
-
     def make_node(self, x):
         x = as_tensor_variable(x)
         assert x.ndim == 2
@@ -67,14 +59,6 @@ class MatrixInverse(Op):
     def __init__(self):
         pass
 
-    def props(self):
-        """Function exposing different properties of each instance of the
-        op.
-
-        For the ``MatrixInverse`` op, there are no properties to be exposed.
-        """
-        return ()
-
     def make_node(self, x):
         x = as_tensor_variable(x)
         assert x.ndim == 2
@@ -298,14 +282,6 @@ class Eig(Op):
     """
     _numop = staticmethod(numpy.linalg.eig)
 
-    def props(self):
-        """Function exposing different properties of each instance of the
-        op.
-
-        For the ``Eig`` op, there are no properties to be exposed.
-        """
-        return ()
-
     def make_node(self, x):
         x = as_tensor_variable(x)
         assert x.ndim == 2
@@ -329,14 +305,12 @@ class Eigh(Eig):
 
     """
     _numop = staticmethod(numpy.linalg.eigh)
+    __props__ = ('UPLO',)
 
     def __init__(self, UPLO='L'):
         assert UPLO in ['L', 'U']
         self.UPLO = UPLO
 
-    def props(self):
-        return self.UPLO,
-
     def make_node(self, x):
         x = as_tensor_variable(x)
         assert x.ndim == 2
@@ -397,6 +371,8 @@ class EighGrad(Op):
     """Gradient of an eigensystem of a Hermitian matrix.
 
     """
+    __props__ = ('UPLO',)
+
     def __init__(self, UPLO='L'):
         assert UPLO in ['L', 'U']
         self.UPLO = UPLO
@@ -407,9 +383,6 @@ class EighGrad(Op):
             self.tri0 = numpy.triu
             self.tri1 = lambda a: numpy.tril(a, -1)
 
-    def props(self):
-        return (self.UPLO,)
-
     def make_node(self, x, w, v, gw, gv):
         x, w, v, gw, gv = map(as_tensor_variable, (x, w, v, gw, gv))
         assert x.ndim == 2
@@ -468,6 +441,7 @@ class QRFull(Op):
     and r is upper-triangular.
     """
     _numop = staticmethod(numpy.linalg.qr)
+    __props__ = ('mode',)
 
     def __init__(self, mode):
         self.mode = mode
@@ -479,9 +453,6 @@ class QRFull(Op):
         r = theano.tensor.matrix(dtype=x.dtype)
         return Apply(self, [x], [q, r])
 
-    def props(self):
-        return self.mode
-
     def perform(self, node, (x,), (q, r)):
         assert x.ndim == 2, "The input of qr function should be a matrix."
 
@@ -496,13 +467,11 @@ class QRIncomplete(Op):
     Factor the matrix a as qr and return a single matrix.
     """
     _numop = staticmethod(numpy.linalg.qr)
+    __props__ = ('mode',)
 
     def __init__(self, mode):
         self.mode = mode
 
-    def props(self):
-        return self.mode
-
     def make_node(self, x):
         x = as_tensor_variable(x)
         assert x.ndim == 2, "The input of qr function should be a matrix."
@@ -570,6 +539,7 @@ class SVD(Op):
 
     # See doc in the docstring of the function just after this class.
     _numop = staticmethod(numpy.linalg.svd)
+    __props__ = ('full_matrices', 'compute_uv')
 
     def __init__(self, full_matrices=True, compute_uv=True):
         """
@@ -587,9 +557,6 @@ class SVD(Op):
         self.full_matrices = full_matrices
         self.compute_uv = compute_uv
 
-    def props(self):
-        return self.full_matrices, self.compute_uv,
-
     def make_node(self, x):
         x = as_tensor_variable(x)
         assert x.ndim == 2, "The input of svd function should be a matrix."

theano/tensor/slinalg.py

@@ -42,14 +42,13 @@ class Cholesky(Op):
     #TODO: inplace
     #TODO: for specific dtypes
     #TODO: LAPACK wrapper with in-place behavior, for solve also
+
+    __props__ = ('lower', 'destructive')
+
     def __init__(self, lower=True):
         self.lower = lower
         self.destructive = False
 
-    def props(self):
-        return (self.lower,
-                self.destructive)
-
     def infer_shape(self, node, shapes):
         return [shapes[0]]
 
@@ -75,14 +74,13 @@ cholesky = Cholesky()
 class CholeskyGrad(Op):
     """
     """
+
+    __props__ = ('lower', 'destructive')
+
     def __init__(self, lower=True):
         self.lower = lower
         self.destructive = False
 
-    def props(self):
-        return (self.lower,
-                self.destructive)
-
     def make_node(self, x, l, dz):
         x = as_tensor_variable(x)
         l = as_tensor_variable(l)
@@ -141,6 +139,9 @@ class CholeskyGrad(Op):
 
 class Solve(Op):
     """Solve a system of linear equations"""
+
+    __props__ = ('A_structure', 'lower', 'overwrite_A', 'overwrite_b')
+
     def __init__(self,
                  A_structure='general',
                  lower=False,
@@ -153,12 +154,6 @@ class Solve(Op):
         self.overwrite_A = overwrite_A
         self.overwrite_b = overwrite_b
 
-    def props(self):
-        return (self.A_structure,
-                self.lower,
-                self.overwrite_A,
-                self.overwrite_b)
-
     def __repr__(self):
         return 'Solve{%s}' % str(self.props())
 
@@ -201,13 +196,12 @@ class Eigvalsh(Op):
     """Generalized eigenvalues of a Hermetian positive definite eigensystem
     """
 
+    __props__ = ('lower',)
+
     def __init__(self, lower=True):
         assert lower in [True, False]
         self.lower = lower
 
-    def props(self):
-        return (self.lower,)
-
     def make_node(self, a, b):
         assert imported_scipy, (
             "Scipy not  available. Scipy is needed for the Eigvalsh op")
@@ -258,6 +252,8 @@ class EigvalshGrad(Op):
     # discussion on github at
     # https://github.com/Theano/Theano/pull/1846#discussion-diff-12486764
 
+    __props__ = ('lower',)
+
     def __init__(self, lower=True):
         assert lower in [True, False]
         self.lower = lower
@@ -268,9 +264,6 @@ class EigvalshGrad(Op):
             self.tri0 = numpy.triu
             self.tri1 = lambda a: numpy.tril(a, -1)
 
-    def props(self):
-        return (self.lower,)
-
     def make_node(self, a, b, gw):
         assert imported_scipy, (
             "Scipy not available. Scipy is needed for the GEigvalsh op")