pep8

theano/sandbox/cuda/tests/test_dnn.py

@@ -12,7 +12,7 @@ from theano.tensor.signal.downsample import max_pool_2d
 
 # Skip test if cuda_ndarray is not available.
 import theano.sandbox.cuda as cuda
-if cuda.cuda_available == False:
+if not cuda.cuda_available:
     raise SkipTest('Optional package cuda disabled')
 
 if theano.config.mode == 'FAST_COMPILE':
@@ -24,13 +24,15 @@ else:
     mode_without_gpu = theano.compile.mode.get_default_mode().excluding('gpu')
 
 
-
-def pool_2d_i2n(input, ds=(2, 2), strides=None, pool_function=T.max, mode='ignore_borders'):
+def pool_2d_i2n(input, ds=(2, 2), strides=None,
+                pool_function=T.max, mode='ignore_borders'):
     if strides is None:
         strides = ds
 
     if strides[0] > ds[0] or strides[1] > ds[1]:
-        raise RuntimeError("strides should be smaller than or equal to ds, strides=(%d, %d) and ds=(%d, %d)" %
+        raise RuntimeError(
+            "strides should be smaller than or equal to ds,"
+            " strides=(%d, %d) and ds=(%d, %d)" %
             (strides + ds))
 
     shape = input.shape
@@ -40,7 +42,8 @@ def pool_2d_i2n(input, ds=(2, 2), strides=None, pool_function=T.max, mode='ignor
     output_width = (shape[2] - ds[0]) // strides[0] + 1
     output_height = (shape[3] - ds[1]) // strides[1] + 1
 
-    pooled_output = pooled_neibs.reshape((shape[0], shape[1], output_width, output_height))
+    pooled_output = pooled_neibs.reshape((shape[0], shape[1],
+                                          output_width, output_height))
     return pooled_output
 
 
@@ -53,20 +56,24 @@ def test_pooling():
     for func in (T.max, T.mean):
         for ws in (4, 5):
             for stride in (2, 3):
-                out1 = cuda.dnn.dnn_pool(x, ws=(ws, ws), stride=(stride, stride),
+                out1 = cuda.dnn.dnn_pool(
+                    x, ws=(ws, ws),
+                    stride=(stride, stride),
                     mode='max' if func is T.max else "average")
                 out2 = pool_2d_i2n(x, ds=(ws, ws), strides=(stride, stride),
-                    pool_function=func)
+                                   pool_function=func)
 
                 f1 = theano.function([x], out1, mode=mode_with_gpu)
                 f2 = theano.function([x], out2, mode=mode_with_gpu)
 
-                data = numpy.random.normal(0, 1, (1, 10, 100, 100)).astype("float32")
+                data = numpy.random.normal(
+                    0, 1, (1, 10, 100, 100)).astype("float32")
                 a = f1(data).__array__()
 
                 b = f2(data).__array__()
 
-                assert numpy.allclose(a, b, atol=numpy.finfo(numpy.float32).eps)
+                assert numpy.allclose(a, b,
+                                      atol=numpy.finfo(numpy.float32).eps)
 
 
 def test_pooling_opt():