merge; no conflicts

theano/gof/cc.py

@@ -36,11 +36,9 @@ AddConfigVar('gcc.cxxflags',
         StrParam(""))
 
 # gof imports
-from env import Env
 import graph
 import link
 import utils
-import op
 
 from compilelock import get_lock, release_lock
 
@@ -899,7 +897,7 @@ class CLinker(link.Linker):
                     isig = (i.signature(), topological_pos, i_idx)
                     try:
                         hash(isig)
-                    except: #generic constants don't have a hashable signature
+                    except Exception: #generic constants don't have a hashable signature
                         error_on_play[0] = True
                         return None
                     constant_ids[id(i)] = isig
@@ -1145,7 +1143,7 @@ def _execute(cthunk, init_tasks, tasks, error_storage):
                 else:
                     exc_value = exc_type(_exc_value, task)
                 exc_value.__thunk_trace__ = trace # this can be used to retrieve the location the Op was declared
-            except:
+            except Exception:
                 print >> sys.stderr, 'ERROR retrieving error_storage', error_storage
                 raise
 
@@ -1350,7 +1348,7 @@ class DualLinker(link.Linker):
                     thunk2()
                     for output1, output2 in zip(thunk1.outputs, thunk2.outputs):
                         self.checker(output1, output2)
-                except:
+                except Exception:
                     link.raise_with_op(node1)
 
         return f, i1, o1

theano/gof/cmodule.py

@@ -887,7 +887,7 @@ class ModuleCache(object):
                         # the same process.
                         self.module_hash_to_key_data[module_hash] = key_data
 
-                except:
+                except Exception:
                     # This may happen e.g. when an Op has no C implementation. In
                     # any case, we do not want to keep around the temporary work
                     # directory, as it may cause trouble if we create too many of

theano/gof/compilelock.py

@@ -270,9 +270,9 @@ class Unlocker(object):
         # not exist), we still want to try and remove the directory.
         try:
             self.os.remove(self.os.path.join(self.tmp_dir, 'lock'))
-        except:
+        except Exception:
             pass
         try:
             self.os.rmdir(self.tmp_dir)
-        except:
+        except Exception:
             pass

theano/gof/destroyhandler.py

@@ -226,7 +226,7 @@ class DestroyHandlerHelper2(toolbox.Bookkeeper):
             droot, impact, root_destroyer = self.refresh_droot_impact()
             try:
                 return [root_destroyer[droot[r]]]
-            except:
+            except Exception:
                 return []
 
         env.destroyers = get_destroyers_of

theano/gof/link.py

@@ -283,7 +283,7 @@ def streamline(env, thunks, order, post_thunk_old_storage = None, no_recycling =
                     thunk()
                     for old_s in old_storage:
                         old_s[0] = None
-            except:
+            except Exception:
                 raise_with_op(node)
         f = streamline_default_f
     elif nice_errors:
@@ -294,7 +294,7 @@ def streamline(env, thunks, order, post_thunk_old_storage = None, no_recycling =
             try:
                 for thunk, node in thunk_node_list:
                     thunk()
-            except:
+            except Exception:
                 raise_with_op(node)
         f = streamline_nice_errors_f
     else:
@@ -554,7 +554,7 @@ class WrapLinker(Linker):
             for i, (thunks, node) in enumerate(zip(thunk_groups, order)):
                 try:
                     wrapper(i, node, *thunks)
-                except:
+                except Exception:
                     raise_with_op(node)
         f.thunk_groups = thunk_groups
 

theano/gof/opt.py

@@ -211,7 +211,7 @@ class _metadict:
     def __setitem__(self, item, value):
         try:
             self.d[item] = value
-        except:
+        except Exception:
             for i, (key,val) in enumerate(self.l):
                 if key == item:
                     self.l[i] = (item, value)
@@ -220,14 +220,14 @@ class _metadict:
     def get(self, item, default):
         try:
             return self.d[item]
-        except:
+        except Exception:
             for item2, value in self.l:
                 try:
                     if item == item2:
                         return value
                     if item.equals(item2):
                         return value
-                except:
+                except Exception:
                     if item is item2:
                         return value
             else:
@@ -954,7 +954,7 @@ class TopoOptimizer(NavigatorOptimizer):
                     node = q.popleft()
                 current_node = node
                 self.process_node(env, node)
-        except:
+        except Exception:
             self.detach_updater(env, u)
             raise
         self.detach_updater(env, u)
@@ -988,7 +988,7 @@ class OpKeyOptimizer(NavigatorOptimizer):
                 node = q.pop()
                 current_node = node
                 self.process_node(env, node)
-        except:
+        except Exception:
             self.detach_updater(env, u)
             raise
         self.detach_updater(env, u)
@@ -1004,8 +1004,6 @@ class OpKeyOptimizer(NavigatorOptimizer):
 
 
 
-from utils import D
-
 class ChangeTracker:
     def __init__(self):
         self.changed = False

theano/printing.py

@@ -702,7 +702,7 @@ def pydotprint_variables(vars,
                                config.device + '.' + format)
     try:
         import pydot as pd
-    except:
+    except ImportError:
         print "failed to import pydot. Yous must install pydot for this function to work."
         return
     g=pd.Dot()

theano/sandbox/cuda/nvcc_compiler.py

@@ -264,7 +264,7 @@ def nvcc_module_compile_str(
                     continue
                 if l[l.index(':'):].startswith(': warning: label'):
                     continue
-            except:
+            except Exception:
                 pass
             print >> sys.stderr, l
         print >> sys.stderr, '==============================='

theano/sandbox/cuda/tests/walltime.py

 import sys, time
-from theano.compile.sharedvalue import shared
 from theano.compile.pfunc import pfunc
 from theano import tensor
 
 import numpy
 
 import theano.sandbox.cuda as tcn
-from theano.sandbox.cuda.basic_ops import host_from_gpu, gpu_from_host
 
 def compare_fns(fns, input, reps=10):
     times = {}
@@ -15,7 +13,7 @@ def compare_fns(fns, input, reps=10):
             print 'TOPOSORT', implname
             for i, n in enumerate(impl.maker.env.toposort()):
                 print i, n
-        except:
+        except Exception:
             pass
         t0 = time.time()
         for i in xrange(reps):

theano/sandbox/linalg/ops.py

@@ -12,7 +12,7 @@ from theano.gof.opt import Optimizer
 
 try:
     import scipy.linalg
-except:
+except ImportError:
     pass # some ops (e.g. Cholesky) won't work
 
 class Hint(Op):
@@ -342,7 +342,7 @@ class MatrixInverse(Op):
     def perform(self, node, (x,), (z, )):
         try:
             z[0] = numpy.linalg.inv(x).astype(x.dtype)
-        except:
+        except Exception:
             print 'Failed to invert', node.inputs[0]
             raise
     def grad(self, inputs, g_outputs):
@@ -470,7 +470,7 @@ class Det(Op):
     def perform(self, node, (x,), (z, )):
         try:
             z[0] = numpy.asarray(scipy.linalg.det(x), dtype=x.dtype)
-        except:
+        except Exception:
             print 'Failed to compute determinant', x
             raise
     def grad(self, inputs, g_outputs):

theano/sandbox/symbolic_module.py

@@ -40,14 +40,14 @@ class InitGraph(type):
 
 class SymbolicModule(object):
     #installs class attributes from build_graph after declaration
-    __metaclass__ = InitGraph 
+    __metaclass__ = InitGraph
 
     #if we call this function, it will return a new SymbolicModule
     def __new__(self, **kwargs):
         class SymMod(SymbolicModule):
             @staticmethod
             def build_graph(*bg_args, **bg_kwargs):
-                #this one is like self.build_graph, 
+                #this one is like self.build_graph,
                 #except that the kwargs are automatically inserted
                 kwcopy = copy.copy(kwargs)
                 kwcopy.update(bg_kwargs)
@@ -55,13 +55,13 @@ class SymbolicModule(object):
         setattr(SymMod, '__name__', self.__name__ + '_derived')
         return SymMod
     @staticmethod
-    def build_graph(): 
+    def build_graph():
         return {}
 
 def issymbolicmodule(thing):
     try:
         return issubclass(thing, SymbolicModule)
-    except:
+    except Exception:
         return False
 
 def issymbolicmethod(thing):
@@ -87,7 +87,7 @@ def compile_fn(f, path_locals, common_inputs):
     #make new inputs for the vars named in args
     # this has the effect of creating new storage for these arguments
     # The common storage doesn't get messed with.
-    inputs = [In(path_locals.get(name,name)) for name in args] 
+    inputs = [In(path_locals.get(name,name)) for name in args]
     inputs.extend([v for k,v in common_inputs.items() if k not in args])
     outputs = f()
     #print 'inputs', inputs
@@ -136,7 +136,7 @@ def compile(smod, initial_values={}):
     inputs = {}
     for path_locals, val in walker(smod):
         if isinstance(val, theano.Variable) and (val.owner is None) and (val not in inputs):
-            inputs[val] = theano.In(val, value=theano.gof.Container(val, ['a'])) 
+            inputs[val] = theano.In(val, value=theano.gof.Container(val, ['a']))
 
     assert len(inputs) == len([v for v in inputs.items()])
 
@@ -188,7 +188,7 @@ def compile(smod, initial_values={}):
             else :
                 # check for weird objects that we would like to disallow
                 # not all objects can be transfered by the clone mechanism below
-                raise TypeError('reflecting not supported for', 
+                raise TypeError('reflecting not supported for',
                         (thing, type(thing), getattr(thing, '__name__', None)))
         return reflected[thing]
     rval = reflect(smod)
@@ -278,7 +278,7 @@ if 0:
             if isinstance(val, theano.Variable):
                 try:
                     kres = klass.KlassMember(val)
-                except:
+                except Exception:
                     kres = klass.KlassVariable(val)
                 setattr(SymMod, key, kres)
             elif callable(val) and getattr(val, '__is_symbolic'):
@@ -320,7 +320,7 @@ if 0:
         else:
             def params(): return [w, b]
         return just_symbolic(locals())
-        
+
     if 0:
         print 'logistic_regression', logistic_regression
         print 'tanh_layer', tanh_layer
@@ -349,7 +349,7 @@ if 0:
             name = symbolic_module.name if name is None else name
 
         def __init__(self, constructor_fn=None):
-            """ A constructor fn builds 
+            """ A constructor fn builds
             - a graph on top of the variable table, and
             - compilable methods.
             """
@@ -387,7 +387,7 @@ if 0:
 
         return locals()
 
-    #at this point there is a neural_net module all built and compiled, 
+    #at this point there is a neural_net module all built and compiled,
     # there is also a neural_net.symbolic_module which can be imported.
 
     @SymbolicModule_fromFn
@@ -404,7 +404,7 @@ if 0:
         transform = d[:npc,:].T / v[:npc]
         return locals()
 
-    #at this point there is a neural_net module all built and compiled, 
+    #at this point there is a neural_net module all built and compiled,
     # there is also a neural_net.symbolic_module which can be imported.
 
 
@@ -431,4 +431,3 @@ if 0:
         """stats_collector(nnet_on_pca.x, 'mean')
         """
         return mean_collector(x=r)
-

theano/scan_module/scan_op.py

@@ -1104,7 +1104,7 @@ class Scan(PureOp):
                     # states) it is more of a safety check ( all random
                     # states should be after n_outs_not_shared ...
                     g_outs[i] = tensor.zeros_like(scan_outputs[i])
-                except:
+                except Exception:
                     g_outs[i] = theano.tensor.constant(
                         numpy.array(0, theano.config.floatX))
 

theano/tensor/blas.py

@@ -341,7 +341,7 @@ def ldflags(libs=True, flags=False, libs_dir=False, include_dir=False):
         try:
             t0, t1, t2 = t[0:3]
             assert t0 == '-'
-        except:
+        except Exception:
             raise ValueError('invalid token in config.blas.ldflags', t)
         if libs_dir and t1 == 'L':
             rval.append(t[2:])
@@ -997,7 +997,7 @@ def _gemm_canonicalize(r, scale, rval, maxclients):
             return scale*thing
     try:
         r.type.broadcastable
-    except:
+    except Exception:
         return None
 
     if ((r.type.ndim not in (1, 2)) or
@@ -1083,7 +1083,7 @@ def _factor_canonicalized(lst):
     while i < len(lst)-1:
         try:
             s_i,M_i = lst[i]
-        except:
+        except Exception:
             i += 1
             continue
 
@@ -1091,7 +1091,7 @@ def _factor_canonicalized(lst):
         while j < len(lst):
             try:
                 s_j,M_j = lst[j]
-            except:
+            except Exception:
                 j += 1
                 continue
 
@@ -1113,7 +1113,7 @@ def _gemm_from_factored_list(lst):
         try:
             s, M = sM
             return True
-        except:
+        except Exception:
             return False
     lst = [(T.cast(sM[0],sM[1].type.dtype), sM[1])
             for sM in lst if is_pair(sM)]
@@ -1135,7 +1135,7 @@ def _gemm_from_factored_list(lst):
             if gemm_of_sM_list:
                 def item_to_var(t):
                     try: s,M = t
-                    except: return t
+                    except Exception: return t
                     if s == 1: return M
                     if s == -1: return -M
                     return s*M
@@ -1608,4 +1608,3 @@ from opt import register_specialize, register_canonicalize
 def local_print_as_we_go_along(node):
     if node.op in (T.sub, T.add):
         debugprint(node)
-

theano/tensor/nnet/conv.py

@@ -89,7 +89,7 @@ def conv2d(input, filters, image_shape=None, filter_shape=None,
     if image_shape and filter_shape:
         try:
             assert image_shape[1]==filter_shape[1]
-        except:
+        except Exception:
             print 'image ', image_shape, ' filters ', filter_shape
             raise
 

theano/tensor/nnet/nnet.py

@@ -406,7 +406,7 @@ def local_softmax_with_bias(node):
 
                 try:
                     sm_bias = softmax_with_bias(non_vector_sum, vector_sum)
-                except:
+                except Exception:
                     #if our arguments have the wrong types, then forget about it
                     return
 
@@ -1149,7 +1149,7 @@ def local_advanced_indexing_crossentropy_onehot(node):
     if isinstance(node.op, tensor.AdvancedSubtensor):
         try:
             log, rows, labels = node.inputs
-        except:
+        except Exception:
             pass
         if log and log.owner and log.owner.op == tensor.log:
             sm = log.owner.inputs[0]
@@ -1160,7 +1160,7 @@ def local_advanced_indexing_crossentropy_onehot(node):
         if pre_log and isinstance(pre_log.op, tensor.AdvancedSubtensor):
             try:
                 sm, rows, labels = pre_log.inputs
-            except:
+            except Exception:
                 pass
 
 
@@ -1187,7 +1187,7 @@ def local_advanced_indexing_crossentropy_onehot_grad(node):
     sm = None
     try:
         d_sm, sm = node.inputs
-    except:
+    except Exception:
         return
 
     if (sm is not None) and sm.owner and (sm.owner.op in (softmax, softmax_with_bias)):
@@ -1245,7 +1245,7 @@ def local_advanced_indexing_crossentropy_onehot_grad(node):
     if d_sm.owner and isinstance(d_sm.owner.op, tensor.AdvancedIncSubtensor):
         try:
             z, incr, rows, labels = d_sm.owner.inputs
-        except:
+        except Exception:
             return
         # Check that z == zeros_like(softmax(x))
         # We know z has the right size because z has the same size as d_sm,
@@ -1313,7 +1313,7 @@ def local_advanced_indexing_crossentropy_onehot_grad(node):
             if adv_subtensor is not None:
                 try:
                     maybe_sm, maybe_rows, maybe_labels = adv_subtensor.owner.inputs
-                except:
+                except Exception:
                     return
 
                 if not (maybe_sm is sm and maybe_rows is rows and maybe_labels is labels):
@@ -1336,7 +1336,7 @@ def local_advanced_indexing_crossentropy_onehot_grad(node):
         # AdvIncSubtensor(zeros, grad_nll, arange(len(y)), y) / softmax
         try:
             num, denom = d_sm.owner.inputs
-        except:
+        except Exception:
             return
 
         if denom != sm:
@@ -1346,7 +1346,7 @@ def local_advanced_indexing_crossentropy_onehot_grad(node):
         if num.owner and isinstance(num.owner.op, tensor.AdvancedIncSubtensor):
             try:
                 z, incr, rows, labels = num.owner.inputs
-            except:
+            except Exception:
                 return
 
             # Check z is zeros_like(log(sm))
@@ -1499,7 +1499,7 @@ class Prepend_scalar_constant_to_each_row(gof.Op):
             if output[0].shape!=new_shape:
                 try:
                     output[0].resize(new_shape)
-                except:
+                except Exception:
                     output[0]=numpy.empty(new_shape, dtype=mat.dtype)
             out=output[0]
 
@@ -1544,7 +1544,7 @@ class Prepend_scalar_to_each_row(gof.Op):
             if output[0].shape!=new_shape:
                 try:
                     output[0].resize(new_shape)
-                except:
+                except Exception:
                     output[0]=numpy.empty(new_shape, dtype=mat.dtype)
             out=output[0]
         out[:,0].fill(val)

theano/tensor/opt.py

@@ -78,7 +78,7 @@ def scalarconsts_rest(inputs):
             v = get_constant_value(i)
             consts.append(v)
             origconsts.append(i)
-        except:
+        except Exception:
             nonconsts.append(i)
     return consts, origconsts, nonconsts
 
@@ -937,7 +937,7 @@ def local_subtensor_make_vector(node):
         if x.owner and x.owner.op == make_vector:
             try:
                 idx, = node.op.idx_list
-            except:
+            except Exception:
                 #'how can you have multiple indexes into a shape?'
                 raise
 
@@ -954,7 +954,7 @@ def local_subtensor_make_vector(node):
                 try:
                     v = get_constant_value(idx)
                     return [x.owner.inputs[v]]
-                except:
+                except Exception:
                     pass
             else:
                 # it is a slice of ints and/or Variables
@@ -964,7 +964,7 @@ def local_subtensor_make_vector(node):
                     return [make_vector(*x.owner.inputs.__getitem__(idx))]
                 except TypeError:
                     pass
-                except:
+                except Exception:
                     _logger.error('failed to index with "%s"' % str(idx))
                     raise
 
@@ -1291,7 +1291,7 @@ def local_useless_subtensor(node):
                 # tracker keep. Subtensor accept any scalar int{8,16,32,64}
                 # as index type.
                 assert str(length_pos.type.dtype) == "int64"
-                assert str(length_pos_shape_i.type.dtype) in ["int8", "int16", 
+                assert str(length_pos_shape_i.type.dtype) in ["int8", "int16",
                                                               "int32", "int64"]
                 # We already know that start and step are not variables
                 # and so they don't appear in the input of the node