Remove badly implemented infer_shape as they are not needed and generated error when run.

theano/sparse/basic.py

@@ -1563,6 +1563,8 @@ class Usmm(gof.op.Op):
     x or y are sparse matrix(the other can be sparse or dense)
     z is a dense matrix
     alpha is a scalar
+
+    :note: We don't implement the infer_shape as it is inserted by optimization only
     """
     def __eq__(self, other):
         return type(self) == type(other)
@@ -1576,19 +1578,6 @@ class Usmm(gof.op.Op):
     def __str__(self):
         return 'Usmm{no_inplace}'
 
-    def infer_shape(self, node, shapes):
-        xshp, yshp = shapes
-        x, y = node.inputs
-        if x.ndim == 2 and y.ndim == 2:
-            return [(xshp[0], yshp[1])]
-        if x.ndim == 1 and y.ndim == 2:
-            return [(yshp[1],)]
-        if x.ndim == 2 and y.ndim == 1:
-            return [(xshp[0],)]
-        if x.ndim == 1 and y.ndim == 1:
-            return [()]
-        raise NotImplementedError()
-
     def make_node(self, alpha, x, y, z):
         if not _is_sparse_variable(x) and not _is_sparse_variable(y):
             # If x and y are tensor, we don't want to use this class
@@ -1644,6 +1633,8 @@ class UsmmCscDense(gof.Op):
     x are sparse matrix
     y, z is a dense matrix
     alpha is a scalar
+
+    :note: We don't implement the infer_shape as it is inserted by optimization only
     """
     def __init__(self, inplace):
         self.inplace = inplace
@@ -1662,19 +1653,6 @@ class UsmmCscDense(gof.Op):
     def __hash__(self):
         return hash(type(self)) ^ self.inplace
 
-    def infer_shape(self, node, shapes):
-        xshp, yshp = shapes
-        x, y = node.inputs
-        if x.ndim == 2 and y.ndim == 2:
-            return [(xshp[0], yshp[1])]
-        if x.ndim == 1 and y.ndim == 2:
-            return [(yshp[1],)]
-        if x.ndim == 2 and y.ndim == 1:
-            return [(xshp[0],)]
-        if x.ndim == 1 and y.ndim == 1:
-            return [()]
-        raise NotImplementedError()
-
     def make_node(self, alpha, x_val, x_ind, x_ptr, x_nrows, y, z):
         alpha = tensor.as_tensor_variable(alpha)
         x_val = tensor.as_tensor_variable(x_val)