Merge pull request #799 from dwf/check_none_with_is

theano/compile/debugmode.py

@@ -589,7 +589,7 @@ def debugprint(r, prefix='', depth=-1, done=None, print_type=False,
 
         r_name = getattr(r, 'name', '')
         # normally if the name isn't set, it'll be None, so
-        # r_name == None here
+        # r_name is None here
         if r_name is None:
             r_name = ''
 

theano/compile/function_module.py

@@ -1112,10 +1112,13 @@ class FunctionMaker(object):
         self.accept_inplace = accept_inplace
         self.function_builder = function_builder
 
-        self.required = [(i.value == None) for i in self.inputs]
+        self.required = [(i.value is None) for i in self.inputs]
         self.refeed = [
-                (i.value != None and not isinstance(i.value, gof.Container) and i.update == None)
-                    for i in self.inputs]
+                (i.value is not None and
+                 not isinstance(i.value, gof.Container) and
+                 i.update is None)
+                for i in self.inputs
+        ]
 
     def _check_unused_inputs(self, inputs, outputs, on_unused_input):
         if on_unused_input is None:

theano/compile/tests/test_module.py

@@ -421,7 +421,7 @@ class T_module(unittest.TestCase):
         m = M.make()
         m.y = 77
         assert m.f(23) == 100
-        assert m.x == None
+        assert m.x is None
         m.x = 1000
         assert m.g(23) == 977
         assert m.y == 77

theano/gof/graph.py

@@ -249,7 +249,8 @@ class Variable(utils.object2):
 
     - `RandomVariable`
 
-    A Variable which is the output of a symbolic computation will have an owner != None.
+    A Variable which is the output of a symbolic computation will have an owner
+    not equal to None.
 
     Using the Variables' owner field and the Apply nodes' inputs fields, one can navigate a graph
     from an output all the way to the inputs. The opposite direction is not possible until an

theano/sandbox/cuda/GpuConvTransp3D.py

@@ -379,7 +379,7 @@ def computeR(W,b,d,H,Rshape = None):
         videoWidth = (outputWidth-1) * dc + filterWidth
         videoDur = (outputDur-1) * dt + filterDur
 
-        if Rshape != None and Rshape[0] != -1:
+        if Rshape is not None and Rshape[0] != -1:
             if Rshape[0] < videoHeight:
                 print (Rshape[0], videoHeight)
                 assert False

theano/sandbox/cuda/tests/test_mlp.py

@@ -59,7 +59,7 @@ def get_mode(use_gpu, check_isfinite=True):
 
 
 def print_mode(mode):
-    if mode != None and isinstance(mode, (theano.compile.ProfileMode,)):
+    if mode is not None and isinstance(mode, (theano.compile.ProfileMode,)):
         mode.print_summary()
 
 

theano/sandbox/cuda/type.py

@@ -62,7 +62,7 @@ class CudaNdarrayType(Type):
     """
 
     def __init__(self, broadcastable, name=None, dtype=None):
-        if dtype != None and dtype != 'float32':
+        if dtype is not None and dtype != 'float32':
             raise TypeError('%s only supports dtype float32 for now. Tried '
                             'using dtype %s for variable %s' %
                             (self.__class__.__name__, dtype, name))

theano/scan_module/scan_op.py

@@ -1293,7 +1293,7 @@ class Scan(PureOp):
         ## 8. Mask the outputs that are not differentiable
         # backwards pass
         for i in xrange(len(inner_gfn_outs)):
-            if inner_gfn_outs[i] == None:
+            if inner_gfn_outs[i] is None:
                 inner_gfn_outs[i] = tensor.zeros_like(diff_inputs[i])
 
         ## 9. Mask the g_outs that are Nones :

theano/scan_module/scan_opt.py

@@ -501,7 +501,7 @@ class ScanSaveMem(gof.Optimizer):
                     # 2.3.1 extract idx list of subtensor
                     this_slice = tensor.basic.get_idx_list(cl.inputs,
                                                      cl.op.idx_list)
-                    if this_slice == None:
+                    if this_slice is None:
                         # if unable to extract idx_list
                         #=> outputs needs all its intermediate values
                         global_nsteps = None
@@ -601,7 +601,7 @@ class ScanSaveMem(gof.Optimizer):
                 else:
                     this_slice = tensor.basic.get_idx_list(cl.inputs,
                                                          cl.op.idx_list)
-                    if this_slice == None:
+                    if this_slice is None:
                         store_steps[i] = 0
                         break
 

theano/tensor/basic.py

@@ -3175,10 +3175,10 @@ class Mean(elemwise.CAReduce):
         output[0] = numpy.mean(input, axis=self.axis)
 
     def c_code(self, node, name, inames, onames, sub):
-        if self.axis != None:
+        if self.axis is not None:
             return super(Op, self).c_code(node, name, inames, onames, sub)
         ret = elemwise.CAReduce.c_code(self, node, name, inames, onames, sub)
-        #TODO: c_code perform support only axis==None
+        #TODO: c_code perform support only axis is None
         return ret + """
   *((double *)PyArray_DATA(%s)) /= PyArray_SIZE(%s);
   """ % (onames[0], inames[0])

theano/tensor/elemwise.py

@@ -1312,7 +1312,7 @@ class CAReduce(Op):
             fail = sub["fail"]
             pattern = [0] * len(node.inputs[0].broadcastable)
             axis = self.axis
-            if axis == None:
+            if axis is None:
                 axis = range(len(pattern))
             for i in axis:
                 pattern[i] = 1

theano/tensor/extra_ops.py

@@ -163,7 +163,7 @@ class BinCountOp(theano.Op):
     def infer_shape(self, node, ins_shapes):
         x = node.inputs[0]
         m = basic.max(x) + 1
-        if self.minlength != None:
+        if self.minlength is not None:
             m = basic.maximum(m, self.minlength)
         return [[m]]
 
@@ -286,7 +286,7 @@ class RepeatOp(theano.Op):
     def make_node(self, x, repeats):
         x = basic.as_tensor_variable(x)
         repeats = basic.as_tensor_variable(repeats)
-        if self.axis == None:
+        if self.axis is None:
             out_type = theano.tensor.TensorType(dtype=x.dtype,
                                                 broadcastable=[False])
         else:
@@ -326,7 +326,7 @@ class RepeatOp(theano.Op):
         repeats = node.inputs[1]
         out_shape = list(i0_shapes)
 
-        if self.axis == None:
+        if self.axis is None:
             if repeats.ndim == 0:
                 if len(i0_shapes) == 0:
                     out_shape = [repeats]

theano/tensor/nnet/conv.py

@@ -361,7 +361,7 @@ class ConvOp(Op):
         self.dy=dy
         self.verbose=verbose
         self.version=version
-        if openmp == None:
+        if openmp is None:
             openmp = config.openmp
         self.openmp = openmp
 

theano/tensor/nnet/nnet.py

@@ -1648,7 +1648,7 @@ class Prepend_scalar_constant_to_each_row(gof.Op):
         mat, = inp
         output, = out
         new_shape = (mat.shape[0], mat.shape[1] + 1)
-        if output[0] == None:
+        if output[0] is None:
             output[0] = numpy.empty(new_shape, dtype=mat.dtype)
             out = output[0]
         else:
@@ -1665,7 +1665,7 @@ class Prepend_scalar_constant_to_each_row(gof.Op):
     def infer_shape(self, node, in_shapes):
         shp = (in_shapes[0][0], in_shapes[0][1] + 1)
         return [shp]
-        
+
 
     def grad(self, inp, grads):
         mat, = inp
@@ -1703,7 +1703,7 @@ class Prepend_scalar_to_each_row(gof.Op):
         val, mat = inp
         output, = out
         new_shape = (mat.shape[0], mat.shape[1] + 1)
-        if output[0] == None:
+        if output[0] is None:
             output[0] = numpy.empty(new_shape, dtype=mat.dtype)
             out = output[0]
         else:

theano/tensor/raw_random.py

@@ -454,7 +454,7 @@ def normal(random_state, size=None, avg=0.0, std=1.0, ndim=None, dtype=None):
     """
     avg = tensor.as_tensor_variable(avg)
     std = tensor.as_tensor_variable(std)
-    if dtype == None:
+    if dtype is None:
         dtype = tensor.scal.upcast(theano.config.floatX, avg.dtype, std.dtype)
     ndim, size, bcast = _infer_ndim_bcast(ndim, size, avg, std)
     op = RandomFunction('normal',

theano/tensor/tests/test_basic.py

@@ -5888,7 +5888,7 @@ def test_transpose():
     assert tensor.transpose(x1).name == 'x1.T'
     assert tensor.transpose(x2).name == 'x2.T'
     assert tensor.transpose(x3).name == 'x3.T'
-    assert tensor.transpose(tensor.dmatrix()).name == None
+    assert tensor.transpose(tensor.dmatrix()).name is None
 
 
 if __name__ == '__main__':

theano/tensor/tests/test_blas_c.py

@@ -65,8 +65,8 @@ class TestCGer(TestCase, TestOptimizationMixin):
         self.assert_(CGer(False) != Ger(False))
 
         # assert that eq works for non-CGer instances
-        self.assert_(CGer(False) != None)
-        self.assert_(CGer(True) != None)
+        self.assert_(CGer(False) is not None)
+        self.assert_(CGer(True) is not None)
 
     def test_hash(self):
         self.assert_(hash(CGer(True)) == hash(CGer(True)))

theano/tensor/tests/test_opt.py

@@ -909,7 +909,7 @@ class test_fusion(unittest.TestCase):
                 continue
             print "new cases", id
 
-            if shared_fn == None:
+            if shared_fn is None:
                 assert gpu==False
                 f = compile.function(list(sym_inputs), g,mode=mode)
                 for x in range(nb_repeat):
@@ -2398,7 +2398,7 @@ class test_assert(utt.InferShapeTester):
 
     def setUp(self):
         super(test_assert, self).setUp()
-    
+
     def test0(self):
         x=T.scalar()
         y=T.scalar()
@@ -2471,7 +2471,7 @@ class test_assert(utt.InferShapeTester):
         self._compile_and_check([admat, adscal, bdscal], [out],
                         [admat_val, adscal_val, bdscal_val], Assert)
 
-        
+
 def test_local_mul_specialize():
     mode = theano.config.mode
     if mode == 'FAST_COMPILE':