Fix import paths

theano/sandbox/cuda/opt.py

@@ -373,11 +373,11 @@ def local_gpu_conv(node):
             gpu_conv = GpuConvOp_from_ConvOp(node.op)
             return [host_from_gpu(gpu_conv(gpu_from_host(img), gpu_from_host(kern)))]
 
-import tensor.signal.downsample
+import theano.tensor.signal.downsample as downsample
 @register_opt()
 @local_optimizer([])
 def local_gpu_downsample_factor_max(node):
-    if isinstance(node.op, tensor.signal.downsample.DownsampleFactorMax):
+    if isinstance(node.op, downsample.DownsampleFactorMax):
         x, = node.inputs
         if (x.owner and x.owner.op == host_from_gpu):
             gpu_ds = GpuDownsampleFactorMax(node.op.ds, node.op.ignore_border)
@@ -386,7 +386,7 @@ def local_gpu_downsample_factor_max(node):
 @register_opt()
 @local_optimizer([])
 def local_gpu_downsample_factor_max_grad(node):
-    if isinstance(node.op, tensor.signal.downsample.DownsampleFactorMaxGrad):
+    if isinstance(node.op, downsample.DownsampleFactorMaxGrad):
         x,z,gz = node.inputs
         if (x.owner and x.owner.op == host_from_gpu):
             gpu_ds_grad = GpuDownsampleFactorMaxGrad(node.op.ds, node.op.ignore_border)

theano/sandbox/cuda/tests/test_blas.py

@@ -13,7 +13,7 @@ if cuda_ndarray.enable_cuda == False:
 
 import theano.sandbox.cuda as tcn
 
-from tensor.signal.downsample import DownsampleFactorMax
+from theano.tensor.signal.downsample import DownsampleFactorMax
 
 import theano.compile.mode
 

theano/sandbox/cuda/tests/test_nnet.py

@@ -7,7 +7,7 @@ from theano import tensor
 import theano.tensor.nnet
 
 import theano.sandbox.conv
-import tensor.signal.downsample
+import theano.tensor.signal.downsample as downsample
 
 import numpy
 
@@ -307,7 +307,7 @@ def run_conv_nnet2_classif(use_gpu, isize, ksize, n_batch, n_iter,
     conv_op.set_flops()
     conv_op1.set_flops()
 
-    ds_op = tensor.signal.downsample.DownsampleFactorMax((2,2), ignore_border=False)
+    ds_op = downsample.DownsampleFactorMax((2,2), ignore_border=False)
     if downsample_ops:
         hid = tensor.tanh(ds_op(conv_op(x, w0)+b0.dimshuffle((0,'x','x'))))
     else: