Cleanup #1

theano/sandbox/cuda/basic_ops.py

@@ -130,7 +130,6 @@ class GpuFromHost(GpuOp):
     def __str__(self):
         return 'GpuFromHost'
 
-
     def make_node(self, x):
         if not isinstance(x.type, tensor.TensorType):
             raise TypeError("Expected a Theano variable with type "
@@ -583,6 +582,14 @@ class GpuCAReduce(GpuOp):
         if not hasattr(self, "pre_scalar_op"):
             self.pre_scalar_op = None
 
+    def __str__(self):
+        pre = ""
+        if self.pre_scalar_op:
+            pre = "pre=%s,red=" % str(self.pre_scalar_op)
+        return "GpuCAReduce{%s%s}{%s}" % (
+            pre, str(self.scalar_op),
+            ','.join(str(i) for i in self.reduce_mask))
+
     def make_node(self, x):
         x = as_cuda_ndarray_variable(x)
         if (x.type.ndim != len(self.reduce_mask)):
@@ -3634,6 +3641,7 @@ class GpuAllocEmpty(GpuOp):
     Implement Alloc on the gpu, but without initializing memory.
 
     """
+    __props__ = ()
 
     @staticmethod
     def validate_shape(shape):
@@ -3832,6 +3840,7 @@ class CopyOnNegativeStrides(GpuOp):
     If it does, returns a c contiguous copy.
 
     """
+    __props__ = ()
 
     view_map = {0: [0]}
     check_input = False

theano/sandbox/cuda/blas.py

@@ -898,7 +898,6 @@ class BaseGpuCorrMM(GpuOp):
             return self.border_mode
         return (0, 0)
 
-
     def flops(self, inp, outp):
         """
         Useful with the hack in profilemode to print the MFlops.
@@ -2194,14 +2193,12 @@ class GpuDownsampleFactorMax(GpuOp):
     Implement downsample with max on the gpu.
 
     """
-
     __props__ = ('ds', 'ignore_border')
 
     def __init__(self, ds, ignore_border=False):
         self.ds = tuple(ds)
         self.ignore_border = ignore_border
 
-
     def make_node(self, x):
         if not isinstance(x.type, CudaNdarrayType):
             raise TypeError()

theano/sandbox/cuda/opt.py

@@ -1127,9 +1127,9 @@ def local_gpu_advanced_incsubtensor1(node):
             compute_capability = device_properties(active_device_no)['major']
             if (compute_capability < 2 or y.ndim != 2 or x.ndim != 2):
 
-                gpu_op = GpuAdvancedIncSubtensor1(set_instead_of_inc=set_instead_of_inc)
+                gpu_op = GpuAdvancedIncSubtensor1(**node.op._props_dict())
             else:
-                gpu_op = theano.sandbox.cuda.basic_ops.GPUAdvancedIncSubtensor1_dev20(**node.op._props_dict())
+                gpu_op = GpuAdvancedIncSubtensor1_dev20(**node.op._props_dict())
             return [gpu_op(as_cuda_ndarray_variable(x),
                            as_cuda_ndarray_variable(y), *coords)]
 
@@ -1912,7 +1912,7 @@ def local_gpu_downsample_factor_max(node):
         if (pad) != (0, 0) or node.op.mode != 'max' or stride != ws:
             return
         if (x.owner and isinstance(x.owner.op, HostFromGpu)):
-            gpu_ds = GpuDownsampleFactorMax(node.op.ds, node.op.ignore_border)
+            gpu_ds = GpuDownsampleFactorMax(**node.op._props_dict())
             return [host_from_gpu(gpu_ds(x.owner.inputs[0]))]
 
 
@@ -1929,7 +1929,7 @@ def local_gpu_downsample_factor_max_grad(node):
         if pad != (0, 0) or node.op.mode != 'max' or stride != ws:
             return
         if (x.owner and isinstance(x.owner.op, HostFromGpu)):
-            gpu_ds_grad = GpuDownsampleFactorMaxGrad(ws, node.op.ignore_border)
+            gpu_ds_grad = GpuDownsampleFactorMaxGrad(**node.op._props_dict())
             return [host_from_gpu(gpu_ds_grad(x.owner.inputs[0],
                                               as_cuda_ndarray_variable(z),
                                               as_cuda_ndarray_variable(gz)))]