backported import and any to python 2.4

theano/sandbox/cuda/__init__.py

-from .type import CudaNdarrayType
+from theano.sandbox.cuda.type import CudaNdarrayType
 
-from .var import (CudaNdarrayVariable,
+from theano.sandbox.cuda.var import (CudaNdarrayVariable,
     CudaNdarrayConstant,
     CudaNdarraySharedVariable,
     shared_constructor)

theano/sandbox/cuda/basic_ops.py

@@ -4,10 +4,10 @@ import numpy
 from theano import Op, Type, Apply, Variable, Constant
 from theano import tensor, scalar
 
-from .type import CudaNdarrayType
-from .type_support import filter as type_support_filter
+from theano.sandbox.cuda.type import CudaNdarrayType
+from theano.sandbox.cuda.type_support import filter as type_support_filter
 
-from .elemwise import NaiveAlgo
+from theano.sandbox.cuda.elemwise import NaiveAlgo
 
 import logging, copy
 _logger_name = 'theano_cuda_ndarray.basic_ops'

theano/sandbox/cuda/blas.py

@@ -3,7 +3,7 @@ from theano import tensor, scalar
 import StringIO
 
 import cuda_ndarray
-from .type import CudaNdarrayType
+from theano.sandbox.cuda.type import CudaNdarrayType
 
 class GpuDot22(Op):
     def __str__(self):

theano/sandbox/cuda/nnet.py

@@ -3,9 +3,9 @@ from theano import tensor, scalar
 import StringIO
 
 import cuda_ndarray
-from .type import CudaNdarrayType
+from theano.sandbox.cuda.type import CudaNdarrayType
 
-from .kernel_codegen import nvcc_kernel, inline_reduce_max, inline_reduce_sum, inline_softmax
+from theano.sandbox.cuda.kernel_codegen import nvcc_kernel, inline_reduce_max, inline_reduce_sum, inline_softmax
 
 class GpuCrossentropySoftmaxArgmax1HotWithBias (Op):
     nin=3

theano/sandbox/cuda/opt.py

 import sys
 import theano
+import numpy
 from theano import tensor, scalar, compile
 from theano.gof import local_optimizer, EquilibriumDB, SequenceDB
 
@@ -47,8 +48,8 @@ gpu_cut_copies.register('cut_gpu_constant_transfers', tensor.opt.constant_foldin
 @local_optimizer([])
 def local_gpu_elemwise_0(node):
     if isinstance(node.op, tensor.Elemwise):
-        if any(hasattr(i.owner, 'op') and isinstance(i.owner.op, HostFromGpu) for i in node.inputs):
-            if any(o.type.dtype == 'float64' for o in node.outputs):
+        if numpy.any(hasattr(i.owner, 'op') and isinstance(i.owner.op, HostFromGpu) for i in node.inputs):
+            if numpy.any(o.type.dtype == 'float64' for o in node.outputs):
                 print 'WARNING: THERE ARE STILL float64s in your graph local_gpu_elemwise_0', node
             else:
                 # move the add to a GpuAdd
@@ -110,7 +111,7 @@ def local_gpu_dot(node):
             x, y = host_input.owner.inputs
             return [gpu_dot22(gpu_from_host(x), gpu_from_host(y))]
     if node.op == tensor.blas._dot22:
-        if any((i.owner and i.owner.op == host_from_gpu) for i in node.inputs):
+        if numpy.any((i.owner and i.owner.op == host_from_gpu) for i in node.inputs):
             x, y = node.inputs
             return [host_from_gpu(gpu_dot22(gpu_from_host(x), gpu_from_host(y)))]
     return False

theano/sandbox/cuda/type.py

@@ -9,9 +9,9 @@ import theano.config as config
 
 import cuda_ndarray
 
-from .type_support import filter as type_support_filter
+from theano.sandbox.cuda.type_support import filter as type_support_filter
 
-from .nvcc_compiler import nvcc_module_compile_str
+from theano.sandbox.cuda.nvcc_compiler import nvcc_module_compile_str
 
 class CudaNdarrayType(Type):
 

theano/sandbox/cuda/var.py

@@ -4,10 +4,10 @@ from theano import Op, Type, Apply, Variable, Constant
 from theano import tensor
 from theano.compile.sandbox.sharedvalue import shared, SharedVariable, shared_constructor
 
-from .type import CudaNdarrayType
-from .type_support import filter as type_support_filter
+from theano.sandbox.cuda.type import CudaNdarrayType
+from theano.sandbox.cuda.type_support import filter as type_support_filter
 
-from .basic_ops import HostFromGpu, GpuFromHost
+from theano.sandbox.cuda.basic_ops import HostFromGpu, GpuFromHost
 
 class _operators(tensor.basic._tensor_py_operators):
     """Define a few properties and conversion methods for CudaNdarray Variables.