Some flake8 fixes.

theano/sandbox/gpuarray/tests/test_conv_cuda_ndarray.py

@@ -6,32 +6,31 @@ import sys
 import time
 import unittest
 
-
 import numpy
 from six.moves import xrange
 
-from nose.plugins.skip import SkipTest
-imported_scipy_convolve2d = False
-try:
-    from scipy.signal import convolve2d
-    imported_scipy_convolve2d = True
-except ImportError:
-    pass
-
 import theano
 from theano import tensor
 from theano.tests.unittest_tools import seed_rng
 
 # We let that import do the init of the back-end if needed.
-from .test_basic_ops import (mode_with_gpu,
-                             mode_without_gpu)
+from .test_basic_ops import mode_with_gpu
 from ..type import GpuArrayType
 from ..conv import GpuConv
 from theano.sandbox.gpuarray import dnn
 
 import pygpu
+
+imported_scipy_convolve2d = False
+try:
+    from scipy.signal import convolve2d
+    imported_scipy_convolve2d = True
+except ImportError:
+    pass
+
 gftensor4 = GpuArrayType('float32', [False] * 4)
 
+
 def py_conv_valid_numpy(img, kern):
     assert img.shape[1] == kern.shape[1]
     outshp = (img.shape[0], kern.shape[0],
@@ -191,15 +190,17 @@ def _params_allgood(ishape, kshape, mode, subsample=(1, 1), img_stride=(1, 1),
         cpu_mflops = approx_fp / (t1 - t0)
         gpu_mflops = approx_fp / (t2 - t1)
         if verbose > 0:
-            print('%15s' % str(ishape), '%15s' % str(kshape), end=' ', file=sys.stdout)
-            print('%12.5f  %7.2f %7.2f %7.1f' % (approx_fp,
-                    cpu_mflops, gpu_mflops, (t1 - t0) / (t2 - t1)), file=sys.stdout)
+            print('%15s' % str(ishape), '%15s' % str(kshape), end=' ',
+                  file=sys.stdout)
+            print('%12.5f  %7.2f %7.2f %7.1f' %
+                  (approx_fp, cpu_mflops, gpu_mflops, (t1 - t0) / (t2 - t1)),
+                  file=sys.stdout)
     if not rval:
-        print(('test_' + mode + ' id=' + str(id) +
+        print('test_' + mode + ' id=' + str(id) +
               ' FAILED for ishape, kshape, mode, subsample,' +
               ' img_stride, kern_stride, version', ishape,
               kshape, mode, subsample, img_stride, kern_stride,
-                              version), file=sys.stdout)
+              version, file=sys.stdout)
         diff = cpuval - gpuval
         diffabs = numpy.absolute(diff)
         pr_diff = diffabs / numpy.absolute(cpuval)
@@ -210,7 +211,7 @@ def _params_allgood(ishape, kshape, mode, subsample=(1, 1), img_stride=(1, 1),
                                    nb_close, "/", diff.size))
         print("max relatif diff:", (pr_diff.max(), "avg rel diff:",
                                     numpy.average(pr_diff)))
-    if not rval and print_ != False:
+    if not rval and print_ is not False:
         if npy_img.shape[0] > 5:
             print("img", npy_img[0])
             print("kern", npy_kern[0])
@@ -242,7 +243,8 @@ def exec_conv(version, shapes, verbose, random, mode,
                  istride, kstride) in enumerate(shapes):
             ret = False
             try:
-                ret = _params_allgood(ishape,
+                ret = _params_allgood(
+                    ishape,
                     kshape,
                     mode,
                     subsample=subshape,
@@ -297,15 +299,15 @@ def get_shapes(imshp=(1, 1), kshp=(1, 1), subsample=(1, 1),
         ((3, 1) + imshp, (1, 1) + kshp, subsample, img_stride, kern_stride),
         # nkern only
         ((1, 1) + imshp, (2, 1) + kshp, subsample, img_stride, kern_stride),
-        #batch and nkern
+        # batch and nkern
         ((3, 1) + imshp, (2, 1) + kshp, subsample, img_stride, kern_stride),
-        #batch and stack
+        # batch and stack
         ((3, 2) + imshp, (1, 2) + kshp, subsample, img_stride, kern_stride),
-        #stack and nkern
+        # stack and nkern
         ((1, 2) + imshp, (2, 2) + kshp, subsample, img_stride, kern_stride),
-        #batch, nkern and stack
+        # batch, nkern and stack
         ((2, 2) + imshp, (2, 2) + kshp, subsample, img_stride, kern_stride),
-        #batch, nkern and stack
+        # batch, nkern and stack
         ((3, 2) + imshp, (4, 2) + kshp, subsample, img_stride, kern_stride)
         ]
 
@@ -344,7 +346,6 @@ def get_shapes2(scales_img=(1, 1), scales_kern=(1, 1), subsample=(1, 1),
 
 
 def get_valid_shapes():
-
     #          img shape,     kern shape, subsample shape
 
     shapes = get_basic_shapes()
@@ -361,37 +362,34 @@ def get_valid_shapes():
 
     shapes += [
         # other test
-              ((2, 1, 2, 2), (1, 1, 2, 2), (1, 1), (1, 1), (1, 1))
-            , ((3, 2, 4, 4), (4, 2, 4, 4), (1, 1), (1, 1), (1, 1))
-            , ((4, 1, 10, 10), (1, 1, 2, 2), (1, 1), (1, 1), (1, 1))
-            , ((1, 1, 4, 4), (1, 1, 2, 3), (1, 1), (1, 1), (1, 1))
-            , ((4, 1, 10, 10), (1, 1, 2, 3), (1, 1), (1, 1), (1, 1))
-            , ((4, 1, 10, 10), (1, 1, 2, 10), (1, 1), (1, 1), (1, 1))
-            , ((4, 1, 20, 10), (1, 1, 2, 10), (1, 1), (1, 1), (1, 1))
-            , ((3, 2, 8, 8), (4, 2, 4, 4), (1, 1), (1, 1), (1, 1))  # stack, nkern, bsize
-            , ((3, 2, 8, 6), (4, 2, 4, 4), (1, 1), (1, 1), (1, 1))  # stack, nkern, bsize, non-square image
-            , ((3, 2, 8, 6), (4, 2, 4, 3), (1, 1), (1, 1), (1, 1))  # stack, nkern, bsize, non-square image, non-square kern
-            , ((3, 2, 8, 6), (4, 2, 4, 6), (1, 1), (1, 1), (1, 1))  # stack, nkern, bsize ,non-square image, non-square kern, kernsize==imgsize on one dim
-            , ((16, 5, 64, 64), (8, 5, 8, 8), (1, 1), (1, 1), (1, 1))  # a big one
-            , ((16, 1, 28, 28), (20, 1, 5, 5), (1, 1), (1, 1), (1, 1))  # MNIST LeNET layer 1
-            , ((20, 16, 32, 32), (1, 16, 28, 28), (1, 1), (1, 1), (1, 1))  # layer 1 backprop to weights
-            , ((60, 20, 28, 28), (10, 20, 5, 5), (1, 1), (2, 2), (1, 1))  # added a test case that fail from test_nnet.py.test_conv_nnet2
-            , ((10, 5, 28, 28), (10, 5, 5, 5), (1, 1), (2, 2), (1, 1))  # test precedent but reduced that triger the error
+        ((2, 1, 2, 2), (1, 1, 2, 2), (1, 1), (1, 1), (1, 1)),
+        ((3, 2, 4, 4), (4, 2, 4, 4), (1, 1), (1, 1), (1, 1)),
+        ((4, 1, 10, 10), (1, 1, 2, 2), (1, 1), (1, 1), (1, 1)),
+        ((1, 1, 4, 4), (1, 1, 2, 3), (1, 1), (1, 1), (1, 1)),
+        ((4, 1, 10, 10), (1, 1, 2, 3), (1, 1), (1, 1), (1, 1)),
+        ((4, 1, 10, 10), (1, 1, 2, 10), (1, 1), (1, 1), (1, 1)),
+        ((4, 1, 20, 10), (1, 1, 2, 10), (1, 1), (1, 1), (1, 1)),
+        ((3, 2, 8, 8), (4, 2, 4, 4), (1, 1), (1, 1), (1, 1)),  # stack, nkern, bsize
+        ((3, 2, 8, 6), (4, 2, 4, 4), (1, 1), (1, 1), (1, 1)),  # stack, nkern, bsize, non-square image
+        ((3, 2, 8, 6), (4, 2, 4, 3), (1, 1), (1, 1), (1, 1)),  # stack, nkern, bsize, non-square image, non-square kern
+        ((3, 2, 8, 6), (4, 2, 4, 6), (1, 1), (1, 1), (1, 1)),  # stack, nkern, bsize ,non-square image, non-square kern, kernsize==imgsize on one dim
+        ((16, 5, 64, 64), (8, 5, 8, 8), (1, 1), (1, 1), (1, 1)),  # a big one
+        ((16, 1, 28, 28), (20, 1, 5, 5), (1, 1), (1, 1), (1, 1)),  # MNIST LeNET layer 1
+        ((20, 16, 32, 32), (1, 16, 28, 28), (1, 1), (1, 1), (1, 1)),  # layer 1 backprop to weights
+        ((60, 20, 28, 28), (10, 20, 5, 5), (1, 1), (2, 2), (1, 1)),  # added a test case that fail from test_nnet.py.test_conv_nnet2
+        ((10, 5, 28, 28), (10, 5, 5, 5), (1, 1), (2, 2), (1, 1)),  # test precedent but reduced that triger the error
         # Test more than maxThreadsDim0
-            , ((2, 4, 13, 1050), (3, 4, 10, 11), (1, 1), (1, 1), (1, 1))
-            , ((2, 4, 1050, 13), (3, 4, 10, 11), (1, 1), (1, 1), (1, 1))
+        ((2, 4, 13, 1050), (3, 4, 10, 11), (1, 1), (1, 1), (1, 1)),
+        ((2, 4, 1050, 13), (3, 4, 10, 11), (1, 1), (1, 1), (1, 1)),
         ]
 
-    shapes += [ ((60, 1, 28, 28), (20, 1, 5, 5), (1, 1), (1, 1), (1, 1))  # test_lenet_28 1 layers
-            , ((60, 20, 12, 12), (30, 20, 5, 5), (1, 1), (1, 1), (1, 1))  # test_lenet_28 2 layers
-            , ((60, 30, 8, 8), (20, 30, 5, 5), (1, 1), (1, 1), (1, 1))  # test_lenet_28 bprop 1 full
-            , ((20, 60, 12, 12), (30, 60, 8, 8), (1, 1), (1, 1), (1, 1))  # test_lenet_28 bprop 2 valid
-#            , ((1,60,28,28),(20,60,24,24), (1, 1), (1, 1), (1, 1))#test_lenet_28 bprop 2 valid
-            , ((10, 1, 64, 64), (20, 1, 7, 7), (1, 1), (1, 1), (1, 1))  # test_lenet_64 1 layers
-            , ((10, 20, 29, 29), (30, 20, 7, 7), (1, 1), (1, 1), (1, 1))  # test_lenet_64 2 layers
-            , ((10, 30, 23, 23), (20, 30, 7, 7), (1, 1), (1, 1), (1, 1))  # test_lenet_64 full
-#            , ((20,10,29,29),(30,10,23,23), (1, 1), (1, 1), (1, 1))#test_lenet_64 bprop 1
-#            , ((1,10,64,64),(20,10,58,58), (1, 1), (1, 1), (1, 1))#test_lenet_64 bprop 2
+    shapes += [((60, 1, 28, 28), (20, 1, 5, 5), (1, 1), (1, 1), (1, 1)),  # test_lenet_28 1 layers
+               ((60, 20, 12, 12), (30, 20, 5, 5), (1, 1), (1, 1), (1, 1)),  # test_lenet_28 2 layers
+               ((60, 30, 8, 8), (20, 30, 5, 5), (1, 1), (1, 1), (1, 1)),  # test_lenet_28 bprop 1 full
+               ((20, 60, 12, 12), (30, 60, 8, 8), (1, 1), (1, 1), (1, 1)),  # test_lenet_28 bprop 2 valid
+               ((10, 1, 64, 64), (20, 1, 7, 7), (1, 1), (1, 1), (1, 1)),  # test_lenet_64 1 layers
+               ((10, 20, 29, 29), (30, 20, 7, 7), (1, 1), (1, 1), (1, 1)),  # test_lenet_64 2 layers
+               ((10, 30, 23, 23), (20, 30, 7, 7), (1, 1), (1, 1), (1, 1)),  # test_lenet_64 full
                ]
     return shapes
 
@@ -428,42 +426,34 @@ def test_full():
 
     shapes += [
         # other test
-              ((2, 1, 2, 2), (1, 1, 2, 2), (1, 1), (1, 1), (1, 1))
-            , ((3, 2, 4, 4), (4, 2, 4, 4), (1, 1), (1, 1), (1, 1))
-            , ((4, 1, 10, 10), (1, 1, 2, 2), (1, 1), (1, 1), (1, 1))
-            , ((1, 1, 4, 4), (1, 1, 2, 3), (1, 1), (1, 1), (1, 1))
-            , ((4, 1, 10, 10), (1, 1, 2, 3), (1, 1), (1, 1), (1, 1))
-            , ((4, 1, 10, 10), (1, 1, 2, 10), (1, 1), (1, 1), (1, 1))
-            , ((4, 1, 20, 10), (1, 1, 2, 10), (1, 1), (1, 1), (1, 1))
-            , ((3, 2, 8, 8), (4, 2, 4, 4), (1, 1), (1, 1), (1, 1))  # stack, nkern, bsize
-            , ((3, 2, 8, 6), (4, 2, 4, 4), (1, 1), (1, 1), (1, 1))  # stack, nkern, bsize, non-square image
-            , ((3, 2, 8, 6), (4, 2, 4, 3), (1, 1), (1, 1), (1, 1))  # stack, nkern, bsize, non-square image, non-square kern
-            , ((3, 2, 8, 6), (4, 2, 4, 6), (1, 1), (1, 1), (1, 1))  # stack, nkern, bsize ,non-square image, non-square kern, kernsize==imgsize on one dim
-            , ((16, 5, 64, 64), (8, 5, 8, 8), (1, 1), (1, 1), (1, 1))  # a big one
-            , ((16, 1, 28, 28), (20, 1, 5, 5), (1, 1), (1, 1), (1, 1))  # MNIST LeNET layer 1
-            , ((20, 16, 32, 32), (1, 16, 28, 28), (1, 1), (1, 1), (1, 1))  # layer 1 backprop to weights
+        ((2, 1, 2, 2), (1, 1, 2, 2), (1, 1), (1, 1), (1, 1)),
+        ((3, 2, 4, 4), (4, 2, 4, 4), (1, 1), (1, 1), (1, 1)),
+        ((4, 1, 10, 10), (1, 1, 2, 2), (1, 1), (1, 1), (1, 1)),
+        ((1, 1, 4, 4), (1, 1, 2, 3), (1, 1), (1, 1), (1, 1)),
+        ((4, 1, 10, 10), (1, 1, 2, 3), (1, 1), (1, 1), (1, 1)),
+        ((4, 1, 10, 10), (1, 1, 2, 10), (1, 1), (1, 1), (1, 1)),
+        ((4, 1, 20, 10), (1, 1, 2, 10), (1, 1), (1, 1), (1, 1)),
+        ((3, 2, 8, 8), (4, 2, 4, 4), (1, 1), (1, 1), (1, 1)),  # stack, nkern, bsize
+        ((3, 2, 8, 6), (4, 2, 4, 4), (1, 1), (1, 1), (1, 1)),  # stack, nkern, bsize, non-square image
+        ((3, 2, 8, 6), (4, 2, 4, 3), (1, 1), (1, 1), (1, 1)),  # stack, nkern, bsize, non-square image, non-square kern
+        ((3, 2, 8, 6), (4, 2, 4, 6), (1, 1), (1, 1), (1, 1)),  # stack, nkern, bsize ,non-square image, non-square kern, kernsize==imgsize on one dim
+        ((16, 5, 64, 64), (8, 5, 8, 8), (1, 1), (1, 1), (1, 1)),  # a big one
+        ((16, 1, 28, 28), (20, 1, 5, 5), (1, 1), (1, 1), (1, 1)),  # MNIST LeNET layer 1
+        ((20, 16, 32, 32), (1, 16, 28, 28), (1, 1), (1, 1), (1, 1)),  # layer 1 backprop to weights
 
         # other test
-            , ((3, 1, 1, 1), (2, 1, 5, 3), (1, 1), (1, 1), (1, 1))  # kernel bigger then image
-            , ((3, 2, 1, 1), (4, 2, 1, 1), (1, 1), (1, 1), (1, 1))
-            , ((3, 2, 4, 4), (4, 2, 2, 6), (1, 1), (1, 1), (1, 1))
-            , ((3, 2, 4, 4), (4, 2, 8, 6), (1, 1), (1, 1), (1, 1))  # kernel bigger then image
-            , ((4, 2, 10, 10), (3, 2, 2, 12), (1, 1), (1, 1), (1, 1))
+        ((3, 1, 1, 1), (2, 1, 5, 3), (1, 1), (1, 1), (1, 1)),  # kernel bigger then image
+        ((3, 2, 1, 1), (4, 2, 1, 1), (1, 1), (1, 1), (1, 1)),
+        ((3, 2, 4, 4), (4, 2, 2, 6), (1, 1), (1, 1), (1, 1)),
+        ((3, 2, 4, 4), (4, 2, 8, 6), (1, 1), (1, 1), (1, 1)),  # kernel bigger then image
+        ((4, 2, 10, 10), (3, 2, 2, 12), (1, 1), (1, 1), (1, 1)),
         ]
     shapes += [
-#        ((60,1,28,28),(20,1,5,5), (1, 1), (1, 1), (1, 1))#test_lenet_28 1 layers
-#            , ((60,20,12,12),(30,20,5,5), (1, 1), (1, 1), (1, 1))#test_lenet_28 2 layers
-             ((60, 30, 8, 8), (20, 30, 5, 5), (1, 1), (1, 1), (1, 1))  # test_lenet_28 bprop 1 full
-#            , ((20,60,12,12),(30,60,8,8), (1, 1), (1, 1), (1, 1))#test_lenet_28 bprop 2 valid
-#            , ((1,60,28,28),(20,60,24,24), (1, 1), (1, 1), (1, 1))#test_lenet_28 bprop 2 valid
-#            , ((10,1,64,64),(20,1,7,7), (1, 1), (1, 1), (1, 1))#test_lenet_64 1 layers
-#            , ((10,20,29,29),(30,20,7,7), (1, 1), (1, 1), (1, 1))#test_lenet_64 2 layers
-            , ((10, 30, 23, 23), (20, 30, 7, 7), (1, 1), (1, 1), (1, 1))  # test_lenet_64 full
-#            , ((20,10,29,29),(30,10,23,23), (1, 1), (1, 1), (1, 1))#test_lenet_64 bprop 1
-#            , ((1,10,64,64),(20,10,58,58), (1, 1), (1, 1), (1, 1))#test_lenet_64 bprop 2
+        ((60, 30, 8, 8), (20, 30, 5, 5), (1, 1), (1, 1), (1, 1)),  # test_lenet_28 bprop 1 full
+        ((10, 30, 23, 23), (20, 30, 7, 7), (1, 1), (1, 1), (1, 1)),  # test_lenet_64 full
         # Test more than maxThreadsDim0
-            , ((2, 4, 13, 1050), (3, 4, 10, 11), (1, 1), (1, 1), (1, 1))
-            , ((2, 4, 1050, 13), (3, 4, 10, 11), (1, 1), (1, 1), (1, 1))
+        ((2, 4, 13, 1050), (3, 4, 10, 11), (1, 1), (1, 1), (1, 1)),
+        ((2, 4, 1050, 13), (3, 4, 10, 11), (1, 1), (1, 1), (1, 1)),
         ]
 
     version = [-1]
@@ -562,7 +552,6 @@ class TestConv2DGPU(unittest.TestCase):
                 for mode in ['valid', 'full']:
                     for shapes in [((3, 2, 8, 8), (4, 2, 5, 5), (8, 8)),
                                    ((3, 2, 8, 8), (4, 2, 5, 5), (5, 8)),
-                                   #((3, 2, 8, 8), (4, 2, 5, 5), (8, 5)),
                                    # We use only the number of columns.
                                    ]:
 
@@ -580,47 +569,45 @@ def benchmark():
 
     shapes_valid = [
         # test_lenet_28 shape
-        ((20, 60, 12, 12), (30, 60, 8, 8), (1, 1), (1, 1), (1, 1))  # valid
-        , ((60, 20, 12, 12), (30, 20, 5, 5), (1, 1), (1, 1), (1, 1))  # valid
-        , ((60, 1, 28, 28), (20, 1, 5, 5), (1, 1), (1, 1), (1, 1))  # valid
-        , ((1, 60, 28, 28), (20, 60, 24, 24), (1, 1), (1, 1), (1, 1))  # valid
+        ((20, 60, 12, 12), (30, 60, 8, 8), (1, 1), (1, 1), (1, 1)),  # valid
+        ((60, 20, 12, 12), (30, 20, 5, 5), (1, 1), (1, 1), (1, 1)),  # valid
+        ((60, 1, 28, 28), (20, 1, 5, 5), (1, 1), (1, 1), (1, 1)),  # valid
+        ((1, 60, 28, 28), (20, 60, 24, 24), (1, 1), (1, 1), (1, 1)),  # valid
         # test_lenet_32 shape
-        , ((20, 60, 14, 14), (30, 60, 10, 10), (1, 1), (1, 1), (1, 1))  # valid
-        , ((60, 20, 14, 14), (30, 20, 5, 5), (1, 1), (1, 1), (1, 1))  # valid
-        , ((60, 1, 32, 32), (20, 1, 5, 5), (1, 1), (1, 1), (1, 1))  # valid
-        , ((1, 60, 32, 32), (20, 60, 28, 28), (1, 1), (1, 1), (1, 1))  # valid
+        ((20, 60, 14, 14), (30, 60, 10, 10), (1, 1), (1, 1), (1, 1)),  # valid
+        ((60, 20, 14, 14), (30, 20, 5, 5), (1, 1), (1, 1), (1, 1)),  # valid
+        ((60, 1, 32, 32), (20, 1, 5, 5), (1, 1), (1, 1), (1, 1)),  # valid
+        ((1, 60, 32, 32), (20, 60, 28, 28), (1, 1), (1, 1), (1, 1)),  # valid
         # test_lenet_64 shape
-        , ((10, 20, 29, 29), (30, 20, 7, 7), (1, 1), (1, 1), (1, 1))  # valid
-        , ((20, 10, 29, 29), (30, 10, 23, 23), (1, 1), (1, 1), (1, 1))  # valid
-        , ((10, 1, 64, 64), (20, 1, 7, 7), (1, 1), (1, 1), (1, 1))  # valid
-        , ((1, 10, 64, 64), (20, 10, 58, 58), (1, 1), (1, 1), (1, 1))  # valid
+        ((10, 20, 29, 29), (30, 20, 7, 7), (1, 1), (1, 1), (1, 1)),  # valid
+        ((20, 10, 29, 29), (30, 10, 23, 23), (1, 1), (1, 1), (1, 1)),  # valid
+        ((10, 1, 64, 64), (20, 1, 7, 7), (1, 1), (1, 1), (1, 1)),  # valid
+        ((1, 10, 64, 64), (20, 10, 58, 58), (1, 1), (1, 1), (1, 1)),  # valid
         # test_lenet_108 shape
-        , ((10, 20, 51, 51), (30, 20, 7, 7), (1, 1), (1, 1), (1, 1))  # valid
-        , ((20, 10, 51, 51), (30, 10, 45, 45), (1, 1), (1, 1), (1, 1))  # valid
-        , ((10, 1, 108, 108), (20, 1, 7, 7), (1, 1), (1, 1), (1, 1))  # valid
-        , ((1, 10, 108, 108), (20, 10, 102, 102), (1, 1), (1, 1), (1, 1))  # valid
+        ((10, 20, 51, 51), (30, 20, 7, 7), (1, 1), (1, 1), (1, 1)),  # valid
+        ((20, 10, 51, 51), (30, 10, 45, 45), (1, 1), (1, 1), (1, 1)),  # valid
+        ((10, 1, 108, 108), (20, 1, 7, 7), (1, 1), (1, 1), (1, 1)),  # valid
+        ((1, 10, 108, 108), (20, 10, 102, 102), (1, 1), (1, 1), (1, 1)),  # valid
         # test_lenet_256 shape
-        , ((2, 20, 124, 124), (30, 20, 9, 9), (1, 1), (1, 1), (1, 1))  # valid
-        , ((20, 2, 124, 124), (30, 2, 116, 116), (1, 1), (1, 1), (1, 1))  # valid
-        , ((2, 1, 256, 256), (20, 1, 9, 9), (1, 1), (1, 1), (1, 1))  # valid
-        , ((1, 2, 256, 256), (20, 2, 248, 248), (1, 1), (1, 1), (1, 1))  # valid
+        ((2, 20, 124, 124), (30, 20, 9, 9), (1, 1), (1, 1), (1, 1)),  # valid
+        ((20, 2, 124, 124), (30, 2, 116, 116), (1, 1), (1, 1), (1, 1)),  # valid
+        ((2, 1, 256, 256), (20, 1, 9, 9), (1, 1), (1, 1), (1, 1)),  # valid
+        ((1, 2, 256, 256), (20, 2, 248, 248), (1, 1), (1, 1), (1, 1)),  # valid
         ]
 
     shapes_full = [
         # test_lenet_28 shape
-         ((60, 30, 8, 8), (20, 30, 5, 5), (1, 1), (1, 1), (1, 1))  # full
+        ((60, 30, 8, 8), (20, 30, 5, 5), (1, 1), (1, 1), (1, 1)),  # full
         # test_lenet_32 shape
-         , ((60, 30, 10, 10), (20, 30, 5, 5), (1, 1), (1, 1), (1, 1))  # full conv_full_patch_stack_padded' N=1
+        ((60, 30, 10, 10), (20, 30, 5, 5), (1, 1), (1, 1), (1, 1)),  # full conv_full_patch_stack_padded' N=1
         # test_lenet_64 shape
-         , ((10, 30, 23, 23), (20, 30, 7, 7), (1, 1), (1, 1), (1, 1))  # full conv_full_patch_stack_padded' N=3
+        ((10, 30, 23, 23), (20, 30, 7, 7), (1, 1), (1, 1), (1, 1)),  # full conv_full_patch_stack_padded' N=3
         # test_lenet_108 shape
-         , ((10, 30, 45, 45), (20, 30, 7, 7), (1, 1), (1, 1), (1, 1))  # full 'conv_full_patch_stack_padded' N=9
+        ((10, 30, 45, 45), (20, 30, 7, 7), (1, 1), (1, 1), (1, 1)),  # full 'conv_full_patch_stack_padded' N=9
         # test_lenet_256 shape
-         , ((2, 30, 116, 116), (20, 30, 9, 9), (1, 1), (1, 1), (1, 1))  # full conv_reference_full
+        ((2, 30, 116, 116), (20, 30, 9, 9), (1, 1), (1, 1), (1, 1)),  # full conv_reference_full
         ]
 
-#    shapes_valid=shapes_valid[-1:]
-#    shapes_full=shapes_full[-1:]
     version = [-1]
     verbose = 1
     random = True

theano/sandbox/gpuarray/tests/test_neighbours.py

-import unittest
 
 from theano.tensor.nnet.tests import test_neighbours
 # We let that import do the init of the back-end if needed.
-from .test_basic_ops import (mode_with_gpu,
-                             mode_without_gpu)
+from .test_basic_ops import mode_with_gpu
 
 from ..neighbours import GpuImages2Neibs
 

theano/sandbox/gpuarray/tests/test_nnet.py

 from __future__ import print_function
-from nose.plugins.skip import SkipTest
+
 import numpy
 import unittest
 
@@ -7,8 +7,6 @@ import theano
 import theano.tensor as T
 import theano.tests.unittest_tools as utt
 
-from theano.sandbox import gpuarray
-
 # We let that import do the init of the back-end if needed.
 from .test_basic_ops import (mode_with_gpu,
                              mode_without_gpu)
@@ -36,15 +34,13 @@ def test_GpuCrossentropySoftmaxArgmax1HotWithBias():
         n_in = 4098
         n_out = 4099
 
-    x = T.fmatrix('x')
     y = T.lvector('y')
 
     b = T.fvector('b')
-    #W = T.fmatrix('W')
 
     # we precompute the dot with big shape before to allow the test of
     # GpuCrossentropySoftmax1HotWithBiasDx to don't fail with the error
-    #(the launch timed out and was terminated) on GPU card not
+    # (the launch timed out and was terminated) on GPU card not
     # powerful enough. We need the big shape to check for corner
     # case.
     dot_result = T.fmatrix('dot_result')
@@ -54,7 +50,6 @@ def test_GpuCrossentropySoftmaxArgmax1HotWithBias():
 
     xx = numpy.asarray(numpy.random.rand(batch_size, n_in),
                        dtype=numpy.float32)
-    #?????yy = numpy.ones((batch_size,),dtype='float32')
     yy = numpy.ones((batch_size,), dtype='int32')
     b_values = numpy.zeros((n_out,), dtype='float32')
     W_values = numpy.asarray(numpy.random.rand(n_in, n_out), dtype='float32')
@@ -71,8 +66,6 @@ def test_GpuCrossentropySoftmaxArgmax1HotWithBias():
     classify_gpu = theano.function(inputs=[y, b, dot_result],
                                    outputs=[loss, y_pred, dW],
                                    mode=mode_with_gpu)
-    # theano.printing.debugprint(classify)
-    # theano.printing.debugprint(classify_gpu)
 
     assert any([isinstance(node.op,
                            T.nnet.CrossentropySoftmaxArgmax1HotWithBias)
@@ -97,12 +90,10 @@ def test_GpuCrossentropySoftmax1HotWithBiasDx():
     We check that we loop when their is too much threads
 
     """
-    n_in = 1000
     batch_size = 4097
     n_out = 1250
 
     if not isinstance(mode_with_gpu, theano.compile.DebugMode):
-        n_in = 4098
         n_out = 4099
 
     # Seed numpy.random with config.unittests.rseed
@@ -137,25 +128,7 @@ def test_GpuCrossentropySoftmax1HotWithBiasDx():
 
     rtol = 1e-5
     atol = 1e-6
-    if not numpy.allclose(cpu_out, gpu_out, rtol=rtol, atol=atol):
-        abs_err, rel_err = T.numeric_grad.abs_rel_err(cpu_out, gpu_out)
-        scaled_err = numpy.minimum(abs_err / atol, rel_err / rtol)
-        max_i = scaled_err.argmax()
-
-        print('max err index:', max_i, max_i / batch_size, end=' ')
-        print(max_i % batch_size, max_i / n_out, max_i & n_out)
-        print('At that index:')
-        print('err:', scaled_err.flatten()[max_i])
-        print('absolute error:', abs_err.flatten()[max_i])
-        print('relative error:', rel_err.flatten()[max_i])
-        print('cpu_out:', cpu_out.flatten()[max_i])
-        print('gpu_out:', gpu_out.flatten()[max_i])
-        print('softmax_output_value:', softmax_output_value.flatten()[max_i])
-        print('dnll_value:', dnll_value[max_i / n_out])
-        print('y_idx_value:', y_idx_value[max_i / n_out])
-
-        assert False, "numpy.allclose(cpu_out, gpu_out, rtol=%s, atol=%s)" % (
-            rtol, atol)
+    utt.assert_allclose(cpu_out, gpu_out, rtol=rtol, atol=atol)
 
 
 def test_softmax_with_bias_float16():
@@ -166,6 +139,7 @@ def test_softmax_with_bias_float16():
     softmax_with_bias_unittest_template(dtypeInput='float32',
                                         dtypeBias='float16')
 
+
 def test_softmax_with_bias_float32():
     softmax_with_bias_unittest_template(dtypeInput='float32',
                                         dtypeBias='float32')
@@ -188,6 +162,7 @@ def softmax_with_bias_unittest_template(dtypeInput, dtypeBias):
 
     TODO: check that we loop when there are too many threads. (THIS IS
     NOT IMPLEMENTED)
+
     """
     x = T.matrix('x', dtype=dtypeInput)
     b = T.vector('b', dtype=dtypeBias)
@@ -228,9 +203,11 @@ def softmax_with_bias_unittest_template(dtypeInput, dtypeBias):
 def test_softmax_float16():
     softmax_unittest_template('float16')
 
+
 def test_softmax_float32():
     softmax_unittest_template('float32')
 
+
 def test_softmax_float64():
     softmax_unittest_template('float64')
 

theano/sandbox/gpuarray/tests/test_type.py

-import operator
-
 import numpy
 
 import theano
@@ -25,7 +23,6 @@ def test_deep_copy():
 
 def test_values_eq_approx():
     a = rand_gpuarray(20, dtype='float32')
-    g = GpuArrayType(dtype='float32', broadcastable=(False,))('g')
     assert GpuArrayType.values_eq_approx(a, a)
     b = a.copy()
     b[0] = numpy.asarray(b[0]) + 1.

theano/tests/test_flake8.py

@@ -160,12 +160,8 @@ whitelist_flake8 = [
     "sandbox/gpuarray/__init__.py",
     "sandbox/gpuarray/tests/test_subtensor.py",
     "sandbox/gpuarray/tests/test_scan.py",
-    "sandbox/gpuarray/tests/test_neighbours.py",
-    "sandbox/gpuarray/tests/test_conv_cuda_ndarray.py",
-    "sandbox/gpuarray/tests/test_type.py",
     "sandbox/gpuarray/tests/test_opt.py",
     "sandbox/gpuarray/tests/test_elemwise.py",
-    "sandbox/gpuarray/tests/test_nnet.py",
     "scan_module/scan_utils.py",
     "scan_module/scan_views.py",
     "scan_module/scan.py",