flake8

theano/sandbox/cuda/dnn.py

@@ -1530,8 +1530,8 @@ if (err != CUDNN_STATUS_SUCCESS) {
 """ % dict(out=out, fail=sub['fail'],
            name=name, input=inputs[0],
            ws=ws, pad=pad, str=stride,
-           nd=node.inputs[0].ndim-2, input_desc="input"+name,
-           output_desc="output"+name,
+           nd=node.inputs[0].ndim - 2, input_desc="input" + name,
+           output_desc="output" + name,
            mode_flag=mode_flag)
 
     def grad(self, inp, grads):
@@ -1550,8 +1550,8 @@ if (err != CUDNN_STATUS_SUCCESS) {
         # not connected to desc
         return [[1], [0], [0], [0]]
 
-    #def c_code_cache_version(self):
-    #    return (8, version())
+    def c_code_cache_version(self):
+        return (8, version())
 
 
 class GpuDnnPoolGrad(DnnBase):
@@ -1662,12 +1662,7 @@ if (output_grad%(name)s != NULL) { cudnnDestroyTensorDescriptor(output_grad%(nam
 if (pool%(name)s != NULL) { cudnnDestroyPoolingDescriptor(pool%(name)s); }
 """ % dict(name=name)
 
-#    def perform(self, node, inputs_storage, output_storage):
-#        output_storage[0][0] = inputs_storage[0].copy()
-#        return
-    
     def c_code(self, node, name, inputs, outputs, sub):
-#       raise NotImplementedError()
         # Here the name out and inp are based on the cudnn definition.
         # Not the definition of this class.
         # This make it complicated.
@@ -1770,16 +1765,15 @@ if (err%(name)s != CUDNN_STATUS_SUCCESS) {
 """ % dict(output_grad=out_grad,
            fail=sub['fail'], name=name,
            input=inp, input_grad=inp_grad, output=out,
-           input_desc="input"+name,
-           input_grad_desc="input_grad"+name,
-           output_desc="output"+name,
-           output_grad_desc="output_grad"+name,
+           input_desc="input" + name,
+           input_grad_desc="input_grad" + name,
+           output_desc="output" + name,
+           output_grad_desc="output_grad" + name,
            mode_flag=mode_flag, nd=node.inputs[0].ndim - 2,
            ws=ws, pad=pad, str=stride)
 
     def c_code_cache_version(self):
-        return 
-        #return (7, version())
+        return (8, version())
 
     def infer_shape(self, node, shape):
         return [shape[0]]
@@ -2351,7 +2345,6 @@ if True:
                                                 ds, st, pad)
                 return [host_from_gpu(ret)]
 
-
     @register_opt('cudnn')
     @local_optimizer([GpuSoftmax])
     def local_softmax_dnn(node):

theano/sandbox/cuda/tests/test_dnn.py

@@ -300,7 +300,8 @@ def test_pooling():
                 # Not implemented
                 continue
             pad_ = theano.shared(numpy.array(pad))
-            ## This test the CPU grad + opt + GPU implemtentation
+
+            # This test the CPU grad + opt + GPU implemtentation
             def fn(x):
                 return pool_2d(x, (2, 2), ignore_border=True,
                                padding=pad, mode=mode)
@@ -1005,7 +1006,8 @@ class TestDnnInferShapes(utt.InferShapeTester):
         ):
             self._compile_and_check(
                 [img],
-                [dnn.GpuDnnPool(mode=params[2])(img, params[0], params[1], (0,0))],
+                [dnn.GpuDnnPool(mode=params[2])
+                               (img, params[0], params[1], (0, 0))],
                 [img_val],
                 dnn.GpuDnnPool
             )