Add infer_shape for sparse Ops that are missing it.

theano/sparse/basic.py

@@ -655,6 +655,13 @@ class CSMGrad(gof.op.Op):
             grad = numpy.zeros_like(data)
             grad[self.kmap] = gout_data
             g_data[0] = grad
+
+    def infer_shape(self, node, shapes):
+        if self.kmap is None:
+            return [shapes[1]]
+        else:
+            return [shapes[0]]
+
 csm_grad = CSMGrad
 
 
@@ -936,6 +943,10 @@ class Transpose(gof.op.Op):
     def grad(self, (x,), (gz,)):
         assert _is_sparse_variable(x) and _is_sparse_variable(gz)
         return transpose(gz),
+
+    def infer_shape(self, node, shapes):
+        return [shapes[0][::-1]]
+
 transpose = Transpose()
 
 
@@ -957,6 +968,10 @@ class Neg(gof.op.Op):
     def grad(self, (x,), (gz,)):
         assert _is_sparse_variable(x) and _is_sparse_variable(gz)
         return -gz,
+
+    def infer_shape(self, node, shapes):
+        return [shapes[0]]
+
 neg = Neg()
 
 
@@ -989,6 +1004,10 @@ class AddSS(gof.op.Op):
         assert _is_sparse_variable(x) and _is_sparse_variable(y)
         assert _is_sparse_variable(gz)
         return gz, gz
+
+    def infer_shape(self, node, shapes):
+        return [shapes[0]]
+
 add_s_s = AddSS()
 
 
@@ -1023,6 +1042,10 @@ class AddSD(gof.op.Op):
         assert _is_sparse_variable(x) and _is_dense_variable(y)
         assert _is_dense_variable(gz)
         return sp_ones_like(x) * gz, gz
+
+    def infer_shape(self, node, shapes):
+        return [shapes[0]]
+
 add_s_d = AddSD()
 
 
@@ -1080,6 +1103,10 @@ class MulSS(gof.op.Op):
 
     def grad(self, (x, y), (gz,)):
         return y * gz, x * gz
+
+    def infer_shape(self, node, shapes):
+        return [shapes[0]]
+
 mul_s_s = MulSS()
 
 
@@ -1155,6 +1182,10 @@ class MulSD(gof.op.Op):
         assert _is_sparse_variable(x) and _is_dense_variable(y)
         assert _is_sparse_variable(gz)
         return y * gz, x * gz
+
+    def infer_shape(self, node, shapes):
+        return [shapes[0]]
+
 mul_s_d = MulSD()
 
 
@@ -1259,6 +1290,10 @@ class StructuredDot(gof.Op):
         # ga = g_out x b.T
         # gb = a.T x g_out
         return [structured_dot_grad(a, b, g_out), structured_dot(a.T, g_out)]
+
+    def infer_shape(self, node, shapes):
+        return [(shapes[0][0], shapes[1][1])]
+
 _structured_dot = StructuredDot()
 
 
@@ -1753,6 +1788,10 @@ class StructuredDotGradCSC(gof.Op):
         }
 
         """ % dict(locals(), **sub)
+
+    def infer_shape(self, node, shapes):
+        return [shapes[0]]
+
 sdg_csc = StructuredDotGradCSC()
 
 
@@ -1866,6 +1905,8 @@ class StructuredDotGradCSR(gof.Op):
 
         """ % dict(locals(), **sub)
 
+    def infer_shape(self, node, shapes):
+        return [shapes[0]]
 
 sdg_csr = StructuredDotGradCSR()