Pep8 / flake8

theano/sandbox/cuda/opt.py

@@ -8,15 +8,14 @@ import warnings
 import numpy
 
 import theano
-from theano.scan_module import scan_utils, scan_op, scan_opt
 from theano import scalar as scal
-from theano import tensor, compile, gof
+from theano import tensor, gof
 import theano.ifelse
 
 from theano.compile import optdb
 from theano.gof import (local_optimizer, EquilibriumDB, SequenceDB, ProxyDB,
                         Optimizer, toolbox, DestroyHandler,
-                        InconsistencyError, EquilibriumOptimizer)
+                        EquilibriumOptimizer)
 from theano.gof.python25 import all, any
 from theano.sandbox.cuda.basic_ops import *
 from theano.sandbox.cuda.type import CudaNdarrayType
@@ -34,9 +33,9 @@ from theano.sandbox.cuda.nnet import (
         GpuSoftmax, GpuSoftmaxWithBias)
 from theano.sandbox.cuda.elemwise import SupportCodeError
 from theano.scalar.basic_scipy import Erfinv
-from theano.sandbox.cuda.elemwise import ErfinvGPU, erfinv_gpu
+from theano.sandbox.cuda.elemwise import erfinv_gpu
 from theano.sandbox.cuda.var import CudaNdarrayConstant
-from theano.scan_module import scan_utils, scan_op
+from theano.scan_module import scan_utils, scan_op, scan_opt
 from theano.tensor.blas import _is_real_vector, _is_real_matrix
 
 #optdb.print_summary()  # shows what is currently registered
@@ -622,8 +621,8 @@ def local_gpu_careduce(node):
                     # Try to make a simpler pattern based on reshaping
                     # The principle is that if two adjacent dimensions have
                     # the same value in the reduce_mask, then we can reshape
-                    # to make them a single dimension, do the reduction, and then
-                    # reshape to get them back.
+                    # to make them a single dimension, do the reduction, and
+                    # then reshape to get them back.
 
                     shape_of = node.fgraph.shape_feature.shape_of
 
@@ -641,7 +640,7 @@ def local_gpu_careduce(node):
                     new_greduce = GpuCAReduce(new_mask, scalar_op)
                     reshaped_x = x.reshape(tensor.stack(*new_in_shp))
                     gpu_reshaped_x = gpu_from_host(reshaped_x)
-                    reshaped_gpu_inputs = [ gpu_reshaped_x ]
+                    reshaped_gpu_inputs = [gpu_reshaped_x]
                     if new_greduce.supports_c_code(reshaped_gpu_inputs):
                         reduce_reshaped_x = host_from_gpu(
                             new_greduce(gpu_reshaped_x))
@@ -1020,6 +1019,7 @@ def local_gpu_conv(node):
                                          float(op.imshp[1])))
                 cstride = int(numpy.ceil(op.imshp_logical[2] /
                                          float(op.imshp[2])))
+
                 def make_graph(img, kern):
                     buf = tensor.alloc(numpy.asarray(0, dtype=img.dtype),
                                        img.shape[0], *op.imshp_logical)
@@ -1027,6 +1027,7 @@ def local_gpu_conv(node):
                                                img)
                     img = gpu_from_host(img)
                     return ret(img, kern)
+
                 return make_graph
         return ret
 
@@ -1344,7 +1345,6 @@ def local_gpualloc(node):
 @register_opt()
 @local_optimizer([tensor.Alloc])
 def local_gpualloc_memset_0(node):
-    replace = False
     if isinstance(node.op, GpuAlloc) and not node.op.memset_0:
         inp = node.inputs[0]
         if (isinstance(inp, CudaNdarrayConstant) and
@@ -1522,9 +1522,11 @@ def gpuScanOptimization(node):
             local_fgraph = gof.FunctionGraph(tmp_in, tmp_out)
             _cmodule_key = gof.CLinker().cmodule_key_(local_fgraph, [])
             info['gpu_hash'] = hash(_cmodule_key)
+
             def typeConstructor(broadcastable, dtype):
                 assert dtype == 'float32'
                 return CudaNdarrayType(broadcastable=broadcastable)
+
             _outputs = scan_op.Scan(
                 scan_ins,
                 scan_outs,