optimized TensorType.filter because all inputs to function are passed through it

theano/tensor/basic.py

@@ -330,6 +330,7 @@ class TensorType(Type):
         self.broadcastable = tuple(broadcastable)
         self.dtype_specs() # error checking is done there
         self.name = name
+        self.numpy_dtype = numpy.dtype(self.dtype)
         if shape is None:
           #backport self.shape = tuple((1 if b else None) for b in self.broadcastable)
             l=[]
@@ -360,16 +361,16 @@ class TensorType(Type):
         This function is not meant to be called in user code.  It is for
         `Linker` instances to use when running a compiled graph.
         """
-        _data = data
-        if strict:
+        if (type(data) is numpy.ndarray) and (data.dtype is self.numpy_dtype):
+            pass # fall through to ndim check
+        elif strict:
+            # this is its own subcase that doesn't fall through to anything
             if not isinstance(data, numpy.ndarray):
                 raise TypeError("%s expected a ndarray object.", data, type(data))
             if not str(data.dtype) == self.dtype:
                 raise TypeError("%s expected a ndarray object with dtype = %s (got %s)." % (self, self.dtype, data.dtype))
             if not data.ndim == self.ndim:
                 raise TypeError("%s expected a ndarray object with %s dimensions (got %s)." % (self, self.ndim, data.ndim))
-            if self.filter_checks_isfinite and (not numpy.all(numpy.isfinite(data))):
-                raise TypeError("non-finite elements not allowed")
 
             if TensorType.use_shape:
                 for si, di in zip(self.shape, data.shape):
@@ -378,11 +379,17 @@ class TensorType(Type):
                             self, self.shape, data.shape))
             return data
         else:
-            data = theano._asarray(data, dtype = self.dtype)
-        if not self.ndim == data.ndim:
+            data = theano._asarray(data, dtype = self.dtype) #TODO - consider to pad shape with ones
+            # to make it consistent with self.broadcastable... like vector->row type thing
+        if self.ndim != data.ndim:
             raise TypeError("Wrong number of dimensions: expected %s, got %s with shape %s." % (self.ndim, data.ndim, data.shape), data)
-        if any(b and d != 1 for d, b in zip(data.shape, self.broadcastable)):
-            raise TypeError("Non-unit value on shape on a broadcastable dimension.", data.shape, self.broadcastable)
+        i = 0
+        for b in self.broadcastable:
+            if b and data.shape[i] != 1:
+                raise TypeError("Non-unit value on shape on a broadcastable dimension.", data.shape, self.broadcastable)
+            i+=1
+        if self.filter_checks_isfinite and (not numpy.all(numpy.isfinite(data))):
+            raise TypeError("non-finite elements not allowed")
         return data
 
     def dtype_specs(self):