import theano/tensor/nnet/conv by default. fix import problem to allow this.

theano/tensor/nnet/__init__.py

 from nnet import *
-#from conv import conv2d, ConvOp # causes circular import
+from conv import conv2d, ConvOp
 from Conv3D import *
 from ConvGrad3D import *
 from ConvTransp3D import *

theano/tensor/nnet/conv.py

@@ -15,8 +15,8 @@ import logging
 import numpy
 
 import theano
-import theano.tensor as tensor
-from theano import gof, Op, tensor, config
+from theano.tensor import get_constant_value, blas, as_tensor_variable
+from theano import Op, config
 from theano.gof.apply_shape import Apply
 from theano.gof.python25 import any
 
@@ -29,7 +29,6 @@ try:
 except ImportError:
     pass
 
-
 _logger=logging.getLogger("theano.signal.conv")
 def _debug(*msg):
     _logger.debug(' '.join([ str(x) for x in msg]))
@@ -79,14 +78,14 @@ def conv2d(input, filters, image_shape=None, filter_shape=None,
         image_shape = list(image_shape)
         for i in range(len(image_shape)):
             if image_shape[i] is not None:
-                image_shape[i] = tensor.get_constant_value(tensor.as_tensor_variable(image_shape[i]))
+                image_shape[i] = get_constant_value(as_tensor_variable(image_shape[i]))
                 assert str(image_shape[i].dtype).startswith('int')
                 image_shape[i] = int(image_shape[i])
     if filter_shape is not None:
         filter_shape = list(filter_shape)
         for i in range(len(filter_shape)):
             if filter_shape[i] is not None:
-                filter_shape[i] = tensor.get_constant_value(tensor.as_tensor_variable(filter_shape[i]))
+                filter_shape[i] = get_constant_value(as_tensor_variable(filter_shape[i]))
                 assert str(filter_shape[i].dtype).startswith('int')
                 filter_shape[i] = int(filter_shape[i])
 
@@ -537,8 +536,8 @@ class ConvOp(Op):
         kerns - 4 dim: nkern x stackidx x rows x cols
         """
         outdim = kerns.ndim
-        _inputs = tensor.as_tensor_variable(inputs)
-        _kerns = tensor.as_tensor_variable(kerns)
+        _inputs = as_tensor_variable(inputs)
+        _kerns = as_tensor_variable(kerns)
         # TODO: lift this restriction by upcasting either inputs or kerns
         if _inputs.ndim != 4:
             raise TypeError('make_node requires 4D tensor of inputs')
@@ -551,7 +550,7 @@ class ConvOp(Op):
             bcastable23 = [self.outshp[0]==1, self.outshp[1]==1]
         else:
             bcastable23 = [False, False]
-        output = tensor.tensor(dtype=_inputs.type.dtype,
+        output = theano.tensor.tensor(dtype=_inputs.type.dtype,
                                       broadcastable=[_inputs.broadcastable[0],
                                                      _kerns.broadcastable[0]]+bcastable23);
 
@@ -857,7 +856,7 @@ class ConvOp(Op):
 #define VALID 0
 #define MOD %
 using namespace std;
-""" + tensor.blas.blas_header_text()
+""" + blas.blas_header_text()
 
     def use_blas(self):
         """ Return True if we will generate code that use gemm.
@@ -874,22 +873,22 @@ using namespace std;
 
     def c_libraries(self):
         if self.use_blas():
-            return tensor.blas.ldflags()
+            return blas.ldflags()
         return []
 
     def c_compile_args(self):
         if self.use_blas():
-            return tensor.blas.ldflags(libs=False, flags=True)
+            return blas.ldflags(libs=False, flags=True)
         return []
 
     def c_lib_dirs(self):
         if self.use_blas():
-            return tensor.blas.ldflags(libs=False, libs_dir=True)
+            return blas.ldflags(libs=False, libs_dir=True)
         return []
 
     def c_header_dirs(self):
         if self.use_blas():
-            return tensor.blas.ldflags(libs=False, include_dir=True)
+            return blas.ldflags(libs=False, include_dir=True)
         return []
 
     def c_code(self, node, name, (img2d, filtersflipped), (z, ), sub):