Merge pull request #3801 from nouiz/doc

theano/misc/tests/test_pycuda_example.py

@@ -14,7 +14,6 @@ if not cuda_ndarray.cuda_available:
     from nose.plugins.skip import SkipTest
     raise SkipTest('Optional package cuda disabled')
 
-import theano
 import theano.tensor as T
 from theano.misc.pycuda_example import (PycudaElemwiseSourceModuleOp,
 #                                        PycudaElemwiseKernelOp,

theano/sandbox/cuda/opt.py

@@ -368,8 +368,8 @@ def local_gpu_split(node):
         outs_clients = reduce(list.__add__,
                               [out.clients for out in node.outputs])
         if (input.owner and isinstance(input.owner.op, HostFromGpu) or
-            any([c != 'output' and isinstance(c.op, GpuFromHost) for c, idx
-                 in outs_clients])):
+            any(c != 'output' and isinstance(c.op, GpuFromHost) for c, idx
+                in outs_clients)):
             new_op = GpuSplit(node.op.len_splits)
             split_res = new_op(as_cuda_ndarray_variable(input),
                                *node.inputs[1:], return_list=True)
@@ -1253,9 +1253,9 @@ def local_gpu_pdbbreakpoint_op(node):
 
             input_is_from_gpu = (inp.owner and
                                  isinstance(inp.owner.op, HostFromGpu))
-            output_goes_to_gpu = any([c[0] != "output" and
-                                      isinstance(c[0].op, GpuFromHost)
-                                      for c in out.clients])
+            output_goes_to_gpu = any(c[0] != "output" and
+                                     isinstance(c[0].op, GpuFromHost)
+                                     for c in out.clients)
 
             if input_is_from_gpu:
                 # The op should be applied on the GPU version of the input
@@ -2154,9 +2154,9 @@ def local_gpualloc(node):
             replace = True
         elif all([c != 'output' and
                   c.op == tensor.join and
-                  all([i.owner and
-                       i.owner.op in [host_from_gpu, tensor.alloc]
-                       for i in c.inputs[1:]])
+                  all(i.owner and
+                      i.owner.op in [host_from_gpu, tensor.alloc]
+                      for i in c.inputs[1:])
                   for c, idx in node.outputs[0].clients]):
             # if the client is on gpu or alloc
             replace = True

theano/sandbox/cuda/tests/test_abstractconv.py

@@ -35,7 +35,8 @@ class TestConv2d(unittest.TestCase):
         self.border_modes = ["valid", "full", (0, 0), (1, 1), (5, 5), (5, 2)]
         self.filter_flip = [True, False]
 
-    def get_output_shape(self, inputs_shape, filters_shape, subsample, border_mode):
+    def get_output_shape(self, inputs_shape, filters_shape,
+                         subsample, border_mode):
         if border_mode == "valid":
             border_mode = (0, 0)
         if border_mode == "full":
@@ -139,8 +140,10 @@ class TestConv2d(unittest.TestCase):
             utt.verify_grad(abstract_conv2d_gradweight, [inputs_val, output_val],
                             mode=mode, eps=1)
 
-    def run_gradinput(self, inputs_shape, filters_shape, output_shape, ref=dnn_gradinput,
-                      subsample=(1, 1), filter_flip=True, verify_grad=True, mode=mode_without_gpu,
+    def run_gradinput(self, inputs_shape, filters_shape,
+                      output_shape, ref=dnn_gradinput,
+                      subsample=(1, 1), filter_flip=True,
+                      verify_grad=True, mode=mode_without_gpu,
                       border_mode='valid', device='cpu', provide_shape=False):
 
         output_val = numpy.random.random(output_shape).astype('float32')
@@ -188,7 +191,6 @@ class TestConv2d(unittest.TestCase):
         mode = mode_with_gpu
         # provide_shape is not used by the CuDNN impementation
         provide_shape = False
-
         for (i, f), s, b, flip in itertools.product(
                 zip(self.inputs_shapes, self.filters_shapes),
                 self.subsamples,
@@ -210,7 +212,7 @@ class TestConv2d(unittest.TestCase):
                                provide_shape=provide_shape, border_mode=b,
                                filter_flip=flip)
 
-    def test_gpucormm_conv(self):
+    def test_gpucorrmm_conv(self):
         if not dnn_available():
             raise SkipTest(cuda.dnn.dnn_available.msg)
 

theano/sandbox/cuda/tests/test_basic_ops.py

@@ -873,8 +873,6 @@ def test_hostfromgpu_shape_i():
 
 # -----------------------------------------------------------------------
 
-import theano.sandbox.cuda as cuda_ndarray
-
 
 def test_gpujoin_assert_cndas():
     # this will end up being an ndarray, as it's float64

theano/scan_module/scan_perform_ext.py

 import errno
 import logging
 import os
-from six.moves import reload_module as reload
 import sys
 import warnings
 

theano/tensor/nnet/corr.py

@@ -179,13 +179,13 @@ class BaseCorrMM(gof.Op):
                 raise ValueError("height must be given for backprop with vertical sampling or border_mode='half'")
             height = '(*(npy_int*)(PyArray_DATA(%s)))' % height
         else:
-            height = 'NULL'
+            height = '-1'
         if ((direction != 0) and (dW != 1)) or ((direction == 1) and (padW == -1)):
             if not width:
                 raise ValueError("width must be given for backprop with horizontal sampling or border_mode='half'")
             width = '(*(npy_int*)(PyArray_DATA(%s)))' % width
         else:
-            width = 'NULL'
+            width = '-1'
         sub = sub.copy()
         sub.update(locals())
 

theano/tensor/signal/downsample.py

@@ -63,7 +63,7 @@ def max_pool_2d(input, ds, ignore_border=None, st=None, padding=(0, 0),
     ignore_border : bool (default None, will print a warning and set to False)
         When True, (5,5) input with ds=(2,2) will generate a (2,2) output.
         (3,3) otherwise.
-    st : tuple of lenght 2
+    st : tuple of two ints
         Stride size, which is the number of shifts over rows/cols to get the
         next pool region. If st is None, it is considered equal to ds
         (no overlap on pooling regions).
@@ -80,13 +80,17 @@ def max_pool_2d(input, ds, ignore_border=None, st=None, padding=(0, 0),
     if input.ndim < 2:
         raise NotImplementedError('max_pool_2d requires a dimension >= 2')
     if ignore_border is None:
-        warnings.warn("max_pool_2d() will have the parameter ignore_border"
-                      " default value changed to True (currently"
-                      " False). To have consistent behavior with all Theano"
-                      " version, explicitly add the parameter"
-                      "  ignore_border=True. (this is also faster than"
-                      " ignore_border=False)",
-                      stacklevel=2)
+        warnings.warn(
+            "max_pool_2d() will have the parameter ignore_border"
+            " default value changed to True (currently"
+            " False). To have consistent behavior with all Theano"
+            " version, explicitly add the parameter ignore_border=True."
+            " On the GPU, using ignore_border=False is needed to use CuDNN."
+            " When using ignore_border=False and not using CuDNN, the only"
+            " GPU combination supported is when"
+            " `ds == st and padding == (0, 0) and mode == 'max'`."
+            " Otherwise, the convolution will be executed on CPU.",
+            stacklevel=2)
         ignore_border = False
     if input.ndim == 4:
         op = DownsampleFactorMax(ds, ignore_border, st=st, padding=padding,

theano/tensor/tests/test_basic.py

@@ -5020,7 +5020,7 @@ class T_reshape(utt.InferShapeTester, utt.TestOptimizationMixin):
         # The tag canonicalize is needed for the shape test in FAST_COMPILE
         self.mode = mode
         self.ignore_topo = ignore_topo
-        return super(T_reshape, self).__init__(name)
+        super(T_reshape, self).__init__(name)
 
     def function(self, inputs, outputs):
         f = function(inputs, outputs, mode=self.mode)

theano/tensor/tests/test_blas_c.py

@@ -12,7 +12,6 @@ from theano.tensor.blas_scipy import ScipyGer
 from theano.tensor.blas import Ger
 
 from theano.tensor.blas_c import CGemv
-from theano.tensor.blas_scipy import ScipyGer
 from theano.tensor.blas import Gemv
 
 from theano.tensor.blas_c import check_force_gemv_init

theano/tensor/tests/test_subtensor.py

@@ -1264,7 +1264,7 @@ class TestAdvancedSubtensor(unittest.TestCase):
         self.mode = mode
         self.dtype = dtype
         self.ignore_topo = ignore_topo
-        return super(TestAdvancedSubtensor, self).__init__(name)
+        super(TestAdvancedSubtensor, self).__init__(name)
 
     def setUp(self):
         self.s = iscalar()

theano/tests/test_flake8.py

@@ -183,7 +183,7 @@ whitelist_flake8 = [
 ]
 
 
-def list_files(dir_path=theano.__path__[0], pattern='*.py'):
+def list_files(dir_path=theano.__path__[0], pattern='*.py', no_match=".#"):
     """
     List all files under theano's path.
     """
@@ -192,7 +192,8 @@ def list_files(dir_path=theano.__path__[0], pattern='*.py'):
         for f in files:
             if fnmatch(f, pattern):
                 path = os.path.join(dir, f)
-                files_list.append(path)
+                if not f.startswith(no_match):
+                    files_list.append(path)
     return files_list