Fix "Consider an iterator instead of materializing the list" issue

doc/hpcs2011_tutorial/logreg_example.py

@@ -36,9 +36,9 @@
 predict = theano.function(inputs=[x], outputs=prediction,
             name = "predict")
 
-if any( [x.op.__class__.__name__=='Gemv' for x in train.maker.fgraph.toposort()]):
+if any( x.op.__class__.__name__=='Gemv' for x in train.maker.fgraph.toposort()):
     print('Used the cpu')
-elif any( [x.op.__class__.__name__=='GpuGemm' for x in train.maker.fgraph.toposort()]):
+elif any( x.op.__class__.__name__=='GpuGemm' for x in train.maker.fgraph.toposort()):
     print('Used the gpu')
 else:
     print('ERROR, not able to tell if theano used the cpu or the gpu')

doc/tutorial/modes_solution_1.py

@@ -44,11 +44,11 @@
 predict = theano.function(inputs=[x], outputs=prediction,
             name="predict")
 
-if any([x.op.__class__.__name__ in ['Gemv', 'CGemv', 'Gemm', 'CGemm'] for x in
-train.maker.fgraph.toposort()]):
+if any( x.op.__class__.__name__ in ['Gemv', 'CGemv', 'Gemm', 'CGemm'] for x in
+train.maker.fgraph.toposort()):
     print('Used the cpu')
-elif any([x.op.__class__.__name__ in ['GpuGemm', 'GpuGemv'] for x in
-train.maker.fgraph.toposort()]):
+elif any( x.op.__class__.__name__ in ['GpuGemm', 'GpuGemv'] for x in
+train.maker.fgraph.toposort()):
     print('Used the gpu')
 else:
     print('ERROR, not able to tell if theano used the cpu or the gpu')

doc/tutorial/using_gpu_solution_1.py

@@ -48,11 +48,11 @@
 predict = theano.function(inputs=[], outputs=prediction,
             name="predict")
 
-if any([n.op.__class__.__name__ in ['Gemv', 'CGemv', 'Gemm', 'CGemm'] for n in
-train.maker.fgraph.toposort()]):
+if any( n.op.__class__.__name__ in ['Gemv', 'CGemv', 'Gemm', 'CGemm'] for n in
+train.maker.fgraph.toposort()):
     print('Used the cpu')
-elif any([n.op.__class__.__name__ in ['GpuGemm', 'GpuGemv'] for n in
-train.maker.fgraph.toposort()]):
+elif any( n.op.__class__.__name__ in ['GpuGemm', 'GpuGemv'] for n in
+train.maker.fgraph.toposort()):
     print('Used the gpu')
 else:
     print('ERROR, not able to tell if theano used the cpu or the gpu')

theano/compile/function.py

@@ -282,7 +282,7 @@ def opt_log1p(node):
                         "input.")
 
     # compute some features of the arguments:
-    uses_tuple = any([isinstance(i, (list, tuple)) for i in inputs])
+    uses_tuple = any( isinstance(i, (list, tuple)) for i in inputs)
     uses_updates = bool(updates)
     uses_givens = bool(givens)
 

theano/compile/profilemode.py

@@ -641,7 +641,7 @@ def exp_float32_op(op):
                 return False
             else:
                 l = list_scalar_op(op)
-                return any([s_op.__class__ in [scal.Exp] for s_op in l])
+                return any( s_op.__class__ in [scal.Exp] for s_op in l)
 
         printed_tip = False
         # tip 1
@@ -650,17 +650,17 @@ def exp_float32_op(op):
             printed_tip = True
 
         # tip 2
-        if not config.lib.amdlibm and any([amdlibm_speed_up(a.op) for i, a
-                                           in apply_time]):
+        if not config.lib.amdlibm and any( amdlibm_speed_up(a.op) for i, a
+                                           in apply_time):
             print("  - Try installing amdlibm and set the Theano flag "
                   "lib.amdlibm=True. This speeds up only some Elemwise "
                   "operation.")
             printed_tip = True
 
         # tip 3
-        if not config.lib.amdlibm and any([exp_float32_op(a.op) and
+        if not config.lib.amdlibm and any( exp_float32_op(a.op) and
                                            a.inputs[0].dtype == 'float32'
-                                           for i, a in apply_time]):
+                                           for i, a in apply_time):
             print("  - With the default gcc libm, exp in float32 is slower "
                   "than in float64! Try Theano flag floatX=float64, or "
                   "install amdlibm and set the theano flags lib.amdlibm=True")
@@ -670,8 +670,9 @@ def exp_float32_op(op):
         for a, t in iteritems(apply_time):
             node = a[1]
             if (isinstance(node.op, T.Dot) and
-                    all([len(i.type.broadcastable) == 2
-                         for i in node.inputs])):
+                    all(
+                    len(i.type.broadcastable) == 2
+                         for i in node.inputs)):
                 print("  - You have a dot operation that was not optimized to"
                       " dot22 (which is faster). Make sure the inputs are "
                       "float32 or float64, and are the same for both inputs. "

theano/compile/profiling.py

@@ -838,8 +838,9 @@ def count_running_memory(order, fgraph, nodes_mem, ignore_dmap=False):
                     if (dependencies[ins] and
                             ins not in fgraph.outputs and
                             ins.owner and
-                            all([compute_map[v][0]
-                                 for v in dependencies[ins]])):
+                            all(
+                            compute_map[v][0]
+                                 for v in dependencies[ins])):
                         if ins not in view_of and not viewed_by.get(ins, []):
                             running_memory_size[cg] -= var_mem[ins]
                         elif ins in view_of:
@@ -971,8 +972,9 @@ def min_memory_generator(executable_nodes, viewed_by, view_of):
                         if (dependencies[ins] and
                                 ins not in fgraph.outputs and
                                 ins.owner and
-                                all([compute_map[v][0]
-                                     for v in dependencies[ins]])):
+                                all(
+                                compute_map[v][0]
+                                     for v in dependencies[ins])):
                             if (ins not in view_of and
                                     not viewed_by.get(ins, [])):
                                 mem_freed += var_mem[ins]
@@ -1192,8 +1194,8 @@ def print_stats(stats1, stats2):
                 code[out] = "v"
             shapes = str(fct_shapes[node.fgraph][node])
 
-            if all([hasattr(out.type, 'get_size')
-                    for out in node.outputs]):
+            if all( hasattr(out.type, 'get_size')
+                    for out in node.outputs):
                 size = "%9dB" % node_outputs_size
                 if node_outputs_size < config.profiling.min_memory_size:
                     N = idx
@@ -1311,7 +1313,7 @@ def exp_float32_op(op):
                 return False
             else:
                 l = list_scalar_op(op)
-                return any([s_op.__class__ in [scal.Exp] for s_op in l])
+                return any( s_op.__class__ in [scal.Exp] for s_op in l)
 
         printed_tip = False
         # tip 1
@@ -1320,17 +1322,17 @@ def exp_float32_op(op):
             printed_tip = True
 
         # tip 2
-        if not config.lib.amdlibm and any([amdlibm_speed_up(a.op) for a
-                                           in self.apply_time]):
+        if not config.lib.amdlibm and any( amdlibm_speed_up(a.op) for a
+                                           in self.apply_time):
             print("  - Try installing amdlibm and set the Theano flag "
                   "lib.amdlibm=True. This speeds up only some Elemwise "
                   "operation.", file = file)
             printed_tip = True
 
         # tip 3
-        if not config.lib.amdlibm and any([exp_float32_op(a.op) and
+        if not config.lib.amdlibm and any( exp_float32_op(a.op) and
                                            a.inputs[0].dtype == 'float32'
-                                           for a in self.apply_time]):
+                                           for a in self.apply_time):
             print("  - With the default gcc libm, exp in float32 is slower "
                   "than in float64! Try Theano flag floatX=float64, or "
                   "install amdlibm and set the theano flags lib.amdlibm=True", file = file)
@@ -1340,8 +1342,9 @@ def exp_float32_op(op):
         for a in self.apply_time:
             node = a
             if (isinstance(node.op, T.Dot) and
-                    all([len(i.type.broadcastable) == 2
-                         for i in node.inputs])):
+                    all(
+                    len(i.type.broadcastable) == 2
+                         for i in node.inputs)):
                 print("  - You have a dot operation that was not optimized to"
                       " dot22 (which is faster). Make sure the inputs are "
                       "float32 or float64, and are the same for both inputs. "

theano/compile/tests/test_function_module.py

@@ -271,7 +271,7 @@ def test_copy_share_memory(self):
             l = [val for key, val in storage_map_cpy.items()
                  if key not in i_o_variables or isinstance(key, theano.tensor.Constant)]
             for storage in l:
-                self.assertTrue(any([storage is s for s in ori_storages]))
+                self.assertTrue(any(storage is s for s in ori_storages))
 
             # Assert storages of SharedVariable without updates are shared
             for (input, _1, _2), here, there in zip(ori.indices,
@@ -571,7 +571,7 @@ def test_free(self):
         for key, val in iteritems(func.fn.storage_map):
             if not isinstance(key, theano.gof.Constant):
                 check_list.append(val)
-        assert any([val[0] for val in check_list])
+        assert any( val[0] for val in check_list)
 
         func.free()
 
@@ -608,10 +608,10 @@ def test_deepcopy(self):
         self.assertTrue(len(f.defaults) == len(g.defaults))
         # print 'f.defaults = %s' % (f.defaults, )
         # print 'g.defaults = %s' % (g.defaults, )
-        self.assertTrue(all([f_req == g_req and f_feed == g_feed and
+        self.assertTrue(all(f_req == g_req and f_feed == g_feed and
                         f_val == g_val
                         for ((f_req, f_feed, f_val), (g_req, g_feed, g_val)) in zip(
-                            f.defaults, g.defaults)]))
+                            f.defaults, g.defaults)))
 
         self.assertFalse(g.value[1] is f.value[1])  # should not have been copied
         self.assertFalse(g.value[2] is f.value[2])  # should have been copied because it is mutable.

theano/configdefaults.py

@@ -196,7 +196,7 @@ def default_cuda_root():
 def filter_nvcc_flags(s):
     assert isinstance(s, str)
     flags = [flag for flag in s.split(' ') if flag]
-    if any([f for f in flags if not f.startswith("-")]):
+    if any( f for f in flags if not f.startswith("-")):
         raise ValueError(
             "Theano nvcc.flags support only parameter/value pairs without"
             " space between them. e.g.: '--machine 64' is not supported,"

theano/gof/cc.py

@@ -327,9 +327,9 @@ def get_c_declare(r, name, sub):
     # If some of these have `check_input=True` in their `.op`,
     # it means they need `r`'s dtype to be declared, so
     # we have to pass `check_input=True` to `c_declare`.
-    if ((any([getattr(c.op, 'check_input', config.check_input)
+    if ((any(getattr(c.op, 'check_input', config.check_input)
               for (c, _) in r.clients
-              if not isinstance(c, string_types)]) or
+              if not isinstance(c, string_types)) or
          (r.owner and
           getattr(r.owner.op, 'check_input', config.check_input)))):
         c_declare = r.type.c_declare(name, sub, True)
@@ -364,15 +364,15 @@ def get_c_extract(r, name, sub):
     # checks on the variable.
     # However that code is not used by C code of the apply node creating
     # this variable, so there is no need to check `r.owner.op.check_input`.
-    if any([getattr(c.op, 'check_input', config.check_input)
+    if any( getattr(c.op, 'check_input', config.check_input)
             for (c, _) in r.clients
-            if not isinstance(c, string_types)]):
+            if not isinstance(c, string_types)):
         # check_broadcast is just an hack to easily remove just the
         # broadcast check on the old GPU back-end. This check isn't
         # done in the new GPU back-end or on the CPU.
-        if any([getattr(c.op, 'check_broadcast', True)
+        if any( getattr(c.op, 'check_broadcast', True)
                 for (c, _) in r.clients
-                if not isinstance(c, string_types)]):
+                if not isinstance(c, string_types)):
             c_extract = r.type.c_extract(name, sub, True)
         else:
             try:

theano/gof/cmodule.py

@@ -1606,8 +1606,8 @@ def std_lib_dirs_and_libs():
                              r'EGG-INFO\mingw\usr\x86_64-w64-mingw32\lib')]
             for f, lib in [('libmsvcr90.a',
                             'mingw 4.5.2 or 4.8.1-2 (newer could work)')]:
-                if not any([os.path.exists(os.path.join(tmp_libdir, f))
-                            for tmp_libdir in libdirs]):
+                if not any( os.path.exists(os.path.join(tmp_libdir, f))
+                            for tmp_libdir in libdirs):
                     print(("Your Python version is from Canopy. " +
                            "You need to install the package '" + lib +
                            "' from Canopy package manager."
@@ -2118,7 +2118,7 @@ def join_options(init_part):
         # ARM (32-bit and 64-bit) architectures in order to make
         # Theano compatible with the Raspberry Pi, Raspberry Pi 2, or
         # other systems with ARM processors.
-        if (not any(['arm' in flag for flag in cxxflags]) and
+        if (not any( 'arm' in flag for flag in cxxflags) and
                 not any(arch in platform.machine() for arch in ['arm', 'aarch'])):
             n_bits = local_bitwidth()
             cxxflags.append('-m%d' % n_bits)

theano/gof/fg.py

@@ -350,9 +350,8 @@ def __remove_clients__(self, r, clients_to_remove,
         if variable in self.variables:
             # If the owner have other outputs still used,
             # then we must keep that variable in the graph.
-            if not variable.owner or not any(
-                [var for var in variable.owner.outputs
-                 if var.clients]):
+            if not variable.owner or not any( var for var in variable.owner.outputs
+                 if var.clients):
 
                 self.variables.remove(variable)
                 # This allow to quickly know if a var is still in the fgraph

theano/gof/tests/test_vm.py

@@ -70,7 +70,7 @@ def test_c_thunks():
         f(1, [2], [3, 2])
         from nose.tools import assert_raises
         assert_raises(ValueError, f, 0, [2], [3, 4])
-        assert any([hasattr(t, 'cthunk') for t in f.fn.thunks]) == c_thunks
+        assert any( hasattr(t, 'cthunk') for t in f.fn.thunks) == c_thunks
 
 
 def test_speed():

theano/gof/vm.py

@@ -67,8 +67,9 @@ def calculate_reallocate_info(order, fgraph, storage_map, compute_map_re,
             assert not (ins in view_of and viewed_by[ins])
             if (getattr(ins, 'ndim', None) == 0 and not storage_map[ins][0] and
                     ins not in fgraph.outputs and ins.owner and
-                    all([compute_map_re[v][0]
-                         for v in dependencies.get(ins, [])]) and
+                    all(
+                    compute_map_re[v][0]
+                         for v in dependencies.get(ins, [])) and
                     ins not in allocated):
                 # Constant Memory cannot be changed
                 # Constant and shared variables' storage_map value is not empty
@@ -969,7 +970,7 @@ def make_vm(self, nodes, thunks,
             if lazy is None:
                 lazy = config.vm.lazy
             if lazy is None:
-                lazy = not all([(not th.lazy) for th in thunks])
+                lazy = not all( (not th.lazy) for th in thunks)
             if not lazy:
                 # there is no conditional in the graph
                 if self.allow_gc:
@@ -1049,7 +1050,7 @@ def make_all(self, profiler=None, input_storage=None,
         if lazy is None:
             lazy = config.vm.lazy
         if lazy is None:
-            lazy = not all([(not th.lazy) for th in thunks])
+            lazy = not all( (not th.lazy) for th in thunks)
         if not (lazy or (config.profile and config.profile_memory) or
                 self.use_cloop or self.callback or self.callback_input):
             for pair in itervalues(reallocated_info):

theano/gpuarray/opt.py

@@ -615,9 +615,9 @@ def local_gpua_subtensor(node, context_name):
                 # And it is a shared var or an input of the graph.
                 not gpu_x.owner.inputs[0].owner):
             if len(x.clients) == 1:
-                if any([n == 'output' or any([isinstance(v.type, GpuArrayType)
-                                              for v in n.inputs + n.outputs])
-                        for n, _ in node.outputs[0].clients]):
+                if any( n == 'output' or any( isinstance(v.type, GpuArrayType)
+                                              for v in n.inputs + n.outputs)
+                        for n, _ in node.outputs[0].clients):
                     return
                 else:
                     return [host_from_gpu(gpu_x.owner.op(node.outputs[0]))]
@@ -988,10 +988,11 @@ def local_gpu_elemwise_careduce(node):
 
 @local_optimizer(None)
 def local_assert_no_cpu_op(node):
-    if (all([var.owner and isinstance(var.owner.op, HostFromGpu)
-             for var in node.inputs]) and
-        any([[c for c in var.clients if isinstance(c[0].op, GpuFromHost)]
-             for var in node.outputs])):
+    if (all(var.owner and isinstance(var.owner.op, HostFromGpu)
+             for var in node.inputs) and
+        any(
+        [c for c in var.clients if isinstance(c[0].op, GpuFromHost)]
+             for var in node.outputs)):
 
             if config.assert_no_cpu_op == "warn":
                 _logger.warning(("CPU Op %s is detected in the computation "

theano/gpuarray/tests/test_basic_ops.py

@@ -298,8 +298,8 @@ def test_gpu_contiguous():
     f = theano.function([a, i], gpu_contiguous(a.reshape((5, 4))[::i]),
                         mode=mode_with_gpu)
     topo = f.maker.fgraph.toposort()
-    assert any([isinstance(node.op, GpuSubtensor) for node in topo])
-    assert any([isinstance(node.op, GpuContiguous) for node in topo])
+    assert any( isinstance(node.op, GpuSubtensor) for node in topo)
+    assert any( isinstance(node.op, GpuContiguous) for node in topo)
     assert f(a_val, 1).flags.c_contiguous
     assert f(a_val, 2).flags.c_contiguous
     assert f(a_val, 2).flags.c_contiguous
@@ -349,8 +349,8 @@ def test_gpusplit_opt(self):
         m = self.shared(rng.rand(4, 6).astype(self.floatX))
         o = T.Split(2)(m, 0, [2, 2])
         f = theano.function([], o, mode=self.mode)
-        assert any([isinstance(node.op, self.split_op_class)
-                    for node in f.maker.fgraph.toposort()])
+        assert any( isinstance(node.op, self.split_op_class)
+                    for node in f.maker.fgraph.toposort())
         o1, o2 = f()
         assert numpy.allclose(o1, m.get_value(borrow=True)[:2])
         assert numpy.allclose(o2, m.get_value(borrow=True)[2:])
@@ -401,8 +401,8 @@ def check(dtype, N, M_=None):
         result = numpy.asarray(f(N, M))
         assert numpy.allclose(result, numpy.eye(N, M_, dtype=dtype))
         assert result.dtype == numpy.dtype(dtype)
-        assert any([isinstance(node.op, GpuEye)
-                    for node in f.maker.fgraph.toposort()])
+        assert any( isinstance(node.op, GpuEye)
+                    for node in f.maker.fgraph.toposort())
 
     for dtype in ['float32', 'int32', 'float16']:
         yield check, dtype, 3