Uncontroversial/necessary list() additions around zip().

theano/compile/function_module.py

@@ -101,7 +101,7 @@ def fgraph_updated_vars(fgraph, expanded_inputs):
     potential_values = list(fgraph.outputs)  # copy the list
     if len(expanded_inputs) != len(fgraph.inputs):
         raise ValueError('expanded_inputs must match len(fgraph.inputs)')
-    for e_input, ivar in reversed(zip(expanded_inputs, fgraph.inputs)):
+    for e_input, ivar in reversed(list(zip(expanded_inputs, fgraph.inputs))):
         if e_input.update is not None:
             updated_vars[ivar] = potential_values.pop()
     return updated_vars
@@ -657,8 +657,8 @@ returned directly?"""
 
         if getattr(self.fn, 'need_update_inputs', True):
             # Update the inputs that have an update function
-            for input, storage in reversed(zip(self.maker.expanded_inputs,
-                                               self.input_storage)):
+            for input, storage in reversed(list(zip(self.maker.expanded_inputs,
+                                                    self.input_storage))):
                 if input.update is not None:
                     storage.data = outputs.pop()
         else:

theano/compile/io.py

@@ -150,7 +150,7 @@ class SymbolicInputKit(object):
         ret.sort()
         if not ret:
             return [[], []]
-        return zip(*ret)
+        return list(zip(*ret))
 
 
 class In(SymbolicInput):

theano/gof/link.py

@@ -7,7 +7,7 @@ import traceback
 import numpy
 
 import theano
-from theano.compat import PY3
+from theano.compat import PY3, izip
 from theano.compat.six import reraise
 from theano.compat.six.moves import StringIO
 from theano.gof import utils
@@ -354,7 +354,7 @@ class Linker(object):
                         % (takes, ['argument', 'arguments'][takes > 1], got)
             if (len(args) != len(inputs)):
                 raise TypeError(e_arity(len(inputs), len(args)))
-            for arg, variable in zip(args, inputs):
+            for arg, variable in izip(args, inputs):
                 variable.data = arg
             thunk()
             if unpack_single:
@@ -499,7 +499,7 @@ def map_storage(fgraph, order, input_storage, output_storage):
         assert len(fgraph.inputs) == len(input_storage)
 
     storage_map = {}
-    for r, storage in zip(fgraph.inputs, input_storage):
+    for r, storage in izip(fgraph.inputs, input_storage):
         storage_map[r] = storage
 #     for orphan in fgraph.orphans:
 #         if not isinstance(orphan, Constant):
@@ -508,7 +508,7 @@ def map_storage(fgraph, order, input_storage, output_storage):
 
     if output_storage is not None:
         assert len(fgraph.outputs) == len(output_storage)
-        for r, storage in zip(fgraph.outputs, output_storage):
+        for r, storage in izip(fgraph.outputs, output_storage):
             storage_map[r] = storage
 
     for node in order:
@@ -565,7 +565,7 @@ def streamline(fgraph, thunks, order, post_thunk_old_storage=None,
             for x in no_recycling:
                 x[0] = None
             try:
-                for thunk, node, old_storage in zip(thunks, order,
+                for thunk, node, old_storage in izip(thunks, order,
                                                      post_thunk_old_storage):
                     thunk()
                     for old_s in old_storage:
@@ -574,13 +574,11 @@ def streamline(fgraph, thunks, order, post_thunk_old_storage=None,
                 raise_with_op(node, thunk)
         f = streamline_default_f
     elif nice_errors:
-        thunk_node_list = zip(thunks, order)
-
         def streamline_nice_errors_f():
             for x in no_recycling:
                 x[0] = None
             try:
-                for thunk, node in thunk_node_list:
+                for thunk, node in izip(thunks, order):
                     thunk()
             except Exception:
                 raise_with_op(node, thunk)
@@ -743,9 +741,13 @@ class PerformLinker(LocalLinker):
         add_clear_storage(f, computed, storage_map)
         f.storage_map = storage_map
 
-        return f, [Container(input, storage) for input, storage in zip(fgraph.inputs, input_storage)], \
-            [Container(output, storage, True) for output, storage in zip(fgraph.outputs, output_storage)], \
-            thunks, order
+        return (f,
+                [Container(input, storage)
+                 for input, storage in izip(fgraph.inputs, input_storage)],
+                [Container(output, storage, True)
+                 for output, storage in izip(fgraph.outputs, output_storage)],
+                thunks,
+                order)
 
 
 def add_clear_storage(f, computed, storage_map):
@@ -864,11 +866,11 @@ class WrapLinker(Linker):
         inputs0 = input_lists[0]
         outputs0 = output_lists[0]
 
-        thunk_groups = zip(*thunk_lists)
+        thunk_groups = list(zip(*thunk_lists))
         order = [x[0] for x in zip(*order_lists)]
 
         to_reset = []
-        for thunks, node in zip(thunk_groups, order):
+        for thunks, node in izip(thunk_groups, order):
             for j, output in enumerate(node.outputs):
                 if output in no_recycling:
                     for thunk in thunks:
@@ -879,13 +881,13 @@ class WrapLinker(Linker):
 
         def f():
             for inputs in input_lists[1:]:
-                for input1, input2 in zip(inputs0, inputs):
+                for input1, input2 in izip(inputs0, inputs):
                     input2.storage[0] = copy(input1.storage[0])
             for x in to_reset:
                 x[0] = None
             pre(self, [input.data for input in input_lists[0]],
                 order, thunk_groups)
-            for i, (thunks, node) in enumerate(zip(thunk_groups, order)):
+            for i, (thunks, node) in enumerate(izip(thunk_groups, order)):
                 try:
                     wrapper(i, node, *thunks)
                 except Exception:

theano/gof/opt.py

@@ -253,7 +253,7 @@ class SeqOptimizer(Optimizer, list):
             else:
                 ll.append((opt.name, opt.__class__.__name__,
                            opts.index(opt)))
-        lll = zip(prof, ll)
+        lll = list(zip(prof, ll))
 
         def cmp(a, b):
             if a[0] == b[0]:
@@ -539,7 +539,7 @@ class MergeFeature(object):
                     continue
 
                 # Schedule transfer of clients from node to candidate
-                pairs = zip(node.outputs, candidate.outputs)
+                pairs = izip(node.outputs, candidate.outputs)
 
                 # transfer names
                 for node_output, cand_output in pairs:

theano/ifelse.py

@@ -595,7 +595,7 @@ class CondMerge(gof.Optimizer):
                     old_outs += [proposal.outputs]
                 else:
                     old_outs += proposal.outputs
-                pairs = zip(old_outs, new_outs)
+                pairs = list(zip(old_outs, new_outs))
                 fgraph.replace_all_validate(pairs, reason='cond_merge')
 
 
@@ -699,7 +699,7 @@ def cond_merge_random_op(main_node):
                 old_outs += [proposal.outputs]
             else:
                 old_outs += proposal.outputs
-            pairs = zip(old_outs, new_outs)
+            pairs = list(zip(old_outs, new_outs))
             main_outs = clone(main_node.outputs, replace=pairs)
             return main_outs
 

theano/misc/pycuda_example.py

@@ -235,9 +235,9 @@ class PycudaElemwiseSourceModuleOp(GpuOp):
                                        tuple([n + "[i]" for n in in_name]),
                                        tuple(n + "[i]" for n in out_name), {})
         c_code_param = ", ".join([_replace_npy_types(var.type.dtype_specs()[1]) + " *" + name
-                                  for var, name in (zip(inputs, in_name) +
-                                                    zip(out_node.outputs,
-                                                        out_name))] +
+                                  for var, name in (list(zip(inputs, in_name)) +
+                                                    list(zip(out_node.outputs,
+                                                        out_name)))] +
                                  ["int size"])
         mod = SourceModule("""
   __global__ void %s(%s)
@@ -326,8 +326,8 @@ class PycudaElemwiseSourceModuleMakeThunkOp(Op):
                                        tuple(n + "[i]" for n in out_name), {})
         c_code_param = ", ".join([_replace_npy_types(var.type.dtype_specs()[1]) + " *" + name
                                   for var, name in
-                                  zip(node.inputs, in_name) +
-                                  zip(node.outputs, out_name)] + ["int size"])
+                                  list(zip(node.inputs, in_name)) +
+                                  list(zip(node.outputs, out_name))] + ["int size"])
         mod = SourceModule("""
   __global__ void %s(%s)
   {

theano/sandbox/cuda/opt.py

@@ -2284,8 +2284,8 @@ def gpuScanOptimization(node):
             scan_outs = [safe_to_gpu(x) for x in thescan.outputs]
             scan_outs = scan_utils.clone(
                 scan_outs,
-                replace=zip(thescan.inputs,
-                            [safe_to_cpu(x) for x in scan_ins]))
+                replace=list(zip(thescan.inputs,
+                                 (safe_to_cpu(x) for x in scan_ins))))
             # We need to construct the hash here, because scan
             # __init__ does not know about cuda ndarray and can not
             # handle graphs with inputs being Cuda Ndarrays
@@ -2330,8 +2330,8 @@ def gpuScanOptimization(node):
             scan_outs = [safe_to_gpu(x) for x in thescan.outputs]
             scan_outs = scan_utils.clone(
                 scan_outs,
-                replace=zip(thescan.inputs,
-                            [safe_to_cpu(x) for x in scan_ins]))
+                replace=list(zip(thescan.inputs,
+                                 (safe_to_cpu(x) for x in scan_ins))))
 
             # We need to construct the hash here, because scan
             # __init__ does not know about cuda ndarray and can not

theano/sandbox/gpuarray/opt.py

@@ -787,8 +787,8 @@ def local_scan_to_gpua(node):
         scan_outs = [safe_to_gpu(x) for x in node.op.outputs]
     scan_outs = scan_utils.clone(
         scan_outs,
-        replace=zip(node.op.inputs,
-                    [safe_to_cpu(x) for x in scan_ins]))
+        replace=list(zip(node.op.inputs,
+                         (safe_to_cpu(x) for x in scan_ins))))
 
     # We need to construct the hash here, because scan
     # __init__ does not know about the gpu and can not

theano/scalar/basic.py

@@ -3293,11 +3293,9 @@ class Composite(ScalarOp):
 
     def init_c_code(self):
         """Return the C code for this Composite Op.  """
-        subd = dict(
-                zip(self.fgraph.inputs,
-                    ["%%(i%i)s" % i for i in xrange(len(self.fgraph.inputs))])
-                + zip(self.fgraph.outputs,
-                    ["%%(o%i)s" % i for i in xrange(len(self.fgraph.outputs))]))
+        subd = dict(chain(
+            ((e, "%%(i%i)s" % i) for i, e in enumerate(self.fgraph.inputs)),
+            ((e, "%%(o%i)s" % i) for i, e in enumerate(self.fgraph.outputs))))
 
         for var in self.fgraph.variables:
             if var.owner is None:

theano/scan_module/scan_opt.py

@@ -346,7 +346,7 @@ class PushOutNonSeqScan(gof.Optimizer):
                              **dict(return_list=True))[0].owner
 
             fgraph.replace_all_validate_remove(
-                zip(node.outputs, nw_node.outputs),
+                list(zip(node.outputs, nw_node.outputs)),
                 remove=[node],
                 reason='scanOp_pushout_nonseqs_ops')
             return True
@@ -558,7 +558,7 @@ class PushOutSeqScan(gof.Optimizer):
                              **dict(return_list=True))[0].owner
 
             fgraph.replace_all_validate_remove(
-                zip(node.outputs, nw_node.outputs),
+                list(zip(node.outputs, nw_node.outputs)),
                 remove=[node],
                 reason='scanOp_pushout_seqs_ops')
             return True
@@ -921,7 +921,7 @@ class PushOutScanOutput(gof.Optimizer):
                 new_scan_node.outputs[new_node_new_outputs_idx+nb_new_outs:])
 
         fgraph.replace_all_validate_remove(
-            zip(old_scan_node.outputs, new_node_old_outputs),
+            list(zip(old_scan_node.outputs, new_node_old_outputs)),
             remove=[old_scan_node],
             reason='scanOp_pushout_output')
 
@@ -981,7 +981,7 @@ class ScanInplaceOptimizer(Optimizer):
                 new_outs = new_op(*inputs, **dict(return_list=True))
                 try:
                     fgraph.replace_all_validate_remove(
-                        zip(node.outputs, new_outs),
+                        list(zip(node.outputs, new_outs)),
                         remove=[node],
                         reason='scanOp_make_inplace')
                     op = new_op
@@ -1702,7 +1702,7 @@ class ScanMerge(gof.Optimizer):
         if not isinstance(new_outs, (list, tuple)):
             new_outs = [new_outs]
 
-        return zip(outer_outs, new_outs)
+        return list(zip(outer_outs, new_outs))
 
     def belongs_to_set(self, node, set_nodes):
         """
@@ -2126,10 +2126,10 @@ class PushOutDot1(gof.Optimizer):
                             new_out = tensor.dot(val, out_seq)
 
                         pos = node.outputs.index(outer_out)
-                        old_new = zip(node.outputs[:pos], new_outs[:pos])
+                        old_new = list(zip(node.outputs[:pos], new_outs[:pos]))
                         old = node.outputs[pos].clients[0][0].outputs[0]
                         old_new.append((old, new_out))
-                        old_new += zip(node.outputs[pos+1:], new_outs[pos:])
+                        old_new += list(zip(node.outputs[pos+1:], new_outs[pos:]))
                         fgraph.replace_all_validate_remove(
                             old_new, remove=[node], reason='scan_pushout_dot1')
 

theano/scan_module/tests/test_scan.py

@@ -4479,7 +4479,7 @@ class ScanGpuTests:
         # Compute the cost and take the gradient wrt params
         cost = tensor.sum((l2_out - yout) ** 2)
         grads = tensor.grad(cost, nparams)
-        updates = zip(nparams, [n - g for n, g in zip(nparams, grads)])
+        updates = list(zip(nparams, (n - g for n, g in zip(nparams, grads))))
 
         # Compile the theano function
         feval_backprop = theano.function([xin, yout], cost, updates=updates,

theano/tensor/blas.py

@@ -1578,7 +1578,7 @@ class GemmOptimizer(Optimizer):
                     assert len(new_outputs) == len(node.outputs)
                     try:
                         fgraph.replace_all_validate_remove(
-                            zip(node.outputs, new_outputs),
+                            list(zip(node.outputs, new_outputs)),
                             [old_dot22],
                             reason='GemmOptimizer',
                             # For now we disable the warning as we know case

theano/tensor/elemwise.py

@@ -787,7 +787,7 @@ class Elemwise(OpenMPOp):
             super(Elemwise, self).perform(node, inputs, output_storage)
 
         maxsize = max(len(input.shape) for input in inputs)
-        for dims in izip(*[zip(input.shape, sinput.type.broadcastable)
+        for dims in izip(*[list(zip(input.shape, sinput.type.broadcastable))
                           for input, sinput in zip(inputs, node.inputs)]):
             if max(d for d, b in dims) != 1 and (1, False) in dims:
                 # yes there may be more compact ways to write this code,
@@ -930,7 +930,7 @@ class Elemwise(OpenMPOp):
         # assert that inames and inputs order stay consistent.
         # This is to protect again futur change of uniq.
         assert len(inames) == len(inputs)
-        ii, iii = zip(*gof.utils.uniq(zip(_inames, node.inputs)))
+        ii, iii = list(zip(*gof.utils.uniq(list(zip(_inames, node.inputs)))))
         assert all([x == y for x, y in zip(ii, inames)])
         assert all([x == y for x, y in zip(iii, inputs)])
 
@@ -948,8 +948,9 @@ class Elemwise(OpenMPOp):
 
         # These are the outputs that we will need to allocate
         # (output, name, name of the c type), transposed
-        real = zip(*[(r, s, r.type.dtype_specs()[1])
-                     for r, s in izip(node.outputs, onames) if r not in dmap])
+        real = list(zip(*[(r, s, r.type.dtype_specs()[1])
+                          for r, s in izip(node.outputs, onames)
+                          if r not in dmap]))
         if real:
             real_outputs, real_onames, real_odtypes = real
         else:
@@ -958,8 +959,9 @@ class Elemwise(OpenMPOp):
         # Outputs that are aliased with an input (inplace)
         # (output, name), transposed (c type name not needed since we don't
         # need to allocate.
-        aliased = zip(*[(r, s)
-                        for (r, s) in izip(node.outputs, onames) if r in dmap])
+        aliased = list(zip(*[(r, s)
+                             for (r, s) in izip(node.outputs, onames)
+                             if r in dmap]))
         if aliased:
             aliased_outputs, aliased_onames = aliased
         else:
@@ -985,7 +987,7 @@ class Elemwise(OpenMPOp):
 
         # Check if all inputs (except broadcasted scalar) are fortran.
         # In that case, create an fortran output ndarray.
-        z = zip(inames, inputs)
+        z = list(zip(inames, inputs))
         alloc_fortran = ' && '.join(["PyArray_ISFORTRAN(%s)" % arr
                                      for arr, var in z
                                      if not all(var.broadcastable)])
@@ -1156,7 +1158,7 @@ class Elemwise(OpenMPOp):
                     }
                     """ % locals()
             if contig is not None:
-                z = zip(inames + onames, inputs + node.outputs)
+                z = list(zip(inames + onames, inputs + node.outputs))
                 cond1 = ' && '.join(["PyArray_ISCONTIGUOUS(%s)" % arr
                                     for arr, var in z
                                     if not all(var.broadcastable)])

theano/tensor/elemwise_cgen.py

@@ -236,7 +236,7 @@ def make_loop(loop_orders, dtypes, loop_tasks, sub, openmp=None):
 
     s = ""
 
-    for i, (pre_task, task), indices in reversed(zip(xrange(len(loop_tasks) - 1), loop_tasks, zip(*loop_orders))):
+    for i, (pre_task, task), indices in reversed(list(zip(xrange(len(loop_tasks) - 1), loop_tasks, list(zip(*loop_orders))))):
             s = loop_over(preloops.get(i, "") + pre_task, s + task, indices, i)
 
     s += loop_tasks[-1]
@@ -521,7 +521,7 @@ def make_loop_careduce(loop_orders, dtypes, loop_tasks, sub):
         s = preloops.get(0, "")
     else:
         s = ""
-        for i, (pre_task, task), indices in reversed(zip(xrange(len(loop_tasks) - 1), loop_tasks, zip(*loop_orders))):
+        for i, (pre_task, task), indices in reversed(list(zip(xrange(len(loop_tasks) - 1), loop_tasks, list(zip(*loop_orders))))):
             s = loop_over(preloops.get(i, "") + pre_task, s + task, indices, i)
 
     s += loop_tasks[-1]

theano/tensor/opt.py

@@ -5776,7 +5776,7 @@ class FusionOptimizer(Optimizer):
                         assert len(new_outputs) == len(node.outputs)
                         try:
                             fgraph.replace_all_validate(
-                                zip(node.outputs, new_outputs),
+                                list(zip(node.outputs, new_outputs)),
                                 reason=self.__class__.__name__)
                             did_something = True
                             nb_replacement += 1