Call as_tensor_variable() before checking the attribute on it.

theano/tensor/signal/pool.py

@@ -261,10 +261,10 @@ class Pool(Op):
         self.mode = mode
 
     def make_node(self, x):
-        if x.type.ndim != 4:
-            raise TypeError()
         # TODO: consider restricting the dtype?
         x = tensor.as_tensor_variable(x)
+        if x.type.ndim != 4:
+            raise TypeError()
         # If the input shape are broadcastable we can have 0 in the output shape
         broad = x.broadcastable[:2] + (False, False)
         out = tensor.TensorType(x.dtype, broad)
@@ -641,12 +641,12 @@ class MaxPoolGrad(PoolGrad):
     def make_node(self, x, maxout, gz):
         # make_node should only be called by the grad function of
         # Pool, so these asserts should not fail.
-        assert isinstance(x, Variable) and x.ndim == 4
-        assert isinstance(maxout, Variable) and maxout.ndim == 4
-        assert isinstance(gz, Variable) and gz.ndim == 4
         x = tensor.as_tensor_variable(x)
         maxout = tensor.as_tensor_variable(maxout)
         gz = tensor.as_tensor_variable(gz)
+        assert isinstance(x, Variable) and x.ndim == 4
+        assert isinstance(maxout, Variable) and maxout.ndim == 4
+        assert isinstance(gz, Variable) and gz.ndim == 4
 
         return Apply(self, [x, maxout, gz], [x.type()])
 
@@ -823,10 +823,10 @@ class AveragePoolGrad(PoolGrad):
     def make_node(self, x, gz, dummy=None):
         # make_node should only be called by the grad function of
         # Pool, so these asserts should not fail.
-        assert isinstance(x, Variable) and x.ndim == 4
-        assert isinstance(gz, Variable) and gz.ndim == 4
         x = tensor.as_tensor_variable(x)
         gz = tensor.as_tensor_variable(gz)
+        assert isinstance(x, Variable) and x.ndim == 4
+        assert isinstance(gz, Variable) and gz.ndim == 4
 
         return Apply(self, [x, gz], [x.type()])