Add assert in make_node

theano/sandbox/linalg/ops.py

@@ -381,6 +381,7 @@ class Cholesky(Op):
         assert imported_scipy, (
             "Scipy not available. Scipy is needed for the Cholesky op")
         x = as_tensor_variable(x)
+        assert x.ndim == 2
         return Apply(self, [x], [x.type()])
 
     def perform(self, node, inputs, outputs):
@@ -427,6 +428,9 @@ class CholeskyGrad(Op):
         x = as_tensor_variable(x)
         l = as_tensor_variable(l)
         dz = as_tensor_variable(dz)
+        assert x.ndim == 2
+        assert l.ndim == 2
+        assert dz.ndim == 2
         assert l.owner.op.lower == self.lower, (
             "lower/upper mismatch between Cholesky op and CholeskyGrad op"
         )
@@ -510,6 +514,7 @@ class MatrixPinv(Op):
 
     def make_node(self, x):
         x = as_tensor_variable(x)
+        assert x.ndim == 2
         return Apply(self, [x], [x.type()])
 
     def perform(self, node, (x,), (z, )):
@@ -558,6 +563,7 @@ class MatrixInverse(Op):
 
     def make_node(self, x):
         x = as_tensor_variable(x)
+        assert x.ndim == 2
         return Apply(self, [x], [x.type()])
 
     def perform(self, node, (x,), (z, )):
@@ -646,6 +652,8 @@ class Solve(Op):
             "Scipy not available. Scipy is needed for the Solve op")
         A = as_tensor_variable(A)
         b = as_tensor_variable(b)
+        assert A.ndim == 2
+        assert b.ndim in [1, 2]
         otype = tensor.tensor(
                 broadcastable=b.broadcastable,
                 dtype=(A * b).dtype)
@@ -788,6 +796,7 @@ class Det(Op):
     """
     def make_node(self, x):
         x = as_tensor_variable(x)
+        assert x.ndim == 2
         o = theano.tensor.scalar(dtype=x.dtype)
         return Apply(self, [x], [o])
 
@@ -852,8 +861,11 @@ class A_Xinv_b(Op):
         assert imported_scipy, (
             "Scipy not available. Scipy is needed for the A_Xinv_b op")
         a = as_tensor_variable(a)
-        b = as_tensor_variable(b)
         X = as_tensor_variable(X)
+        b = as_tensor_variable(b)
+        assert a.ndim == 2
+        assert X.ndim == 2
+        assert b.ndim == 2
         o = theano.tensor.matrix(dtype=x.dtype)
         return Apply(self, [a, X, b], [o])