clean up

Author: panyx0718

paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.cc

@@ -33,7 +33,7 @@ FastThreadedSSAGraphExecutor::FastThreadedSSAGraphExecutor(
       pool_(strategy.num_threads_ +
             1),  // add one more thread for generate op_deps
       fetch_ctxs_(places) {
-  for (auto &op : ir::GetFilteredNodes<OpHandleBase>(*graph_)) {
+  for (auto &op : ir::FilterByNodeWrapper<OpHandleBase>(*graph_)) {
     int dep = static_cast<int>(op->NotReadyInputSize());
     op_deps_.emplace(op, dep);
     if (dep == 0) {

paddle/fluid/framework/details/multi_devices_graph_check_pass.cc

@@ -46,7 +46,7 @@ bool SSAGraghBuilderWithChecker::IsValidGraph(const ir::Graph *graph) const {
     insert_pending_var(var);
   }
 
-  for (OpHandleBase *op : ir::GetFilteredNodes<OpHandleBase>(*graph)) {
+  for (OpHandleBase *op : ir::FilterByNodeWrapper<OpHandleBase>(*graph)) {
     if (op->Inputs().empty()) {
       ready_ops.insert(op);
     } else {

paddle/fluid/framework/details/multi_devices_graph_pass.cc

@@ -36,6 +36,7 @@ namespace framework {
 namespace details {
 
 namespace {
+// TODO(panyx0718): Clean this up as well.
 // all operators. NOTE that even we use a vector here, the operators is
 // unordered.
 typedef std::vector<OpHandleBase *> GraphOps;

paddle/fluid/framework/details/multi_devices_graph_print_pass.cc

@@ -63,7 +63,7 @@ void GraphvizSSAGraphPrinter::Print(const ir::Graph &graph,
   });
 
   size_t op_id = 0;
-  for (auto &op : ir::GetFilteredNodes<OpHandleBase>(graph)) {
+  for (auto &op : ir::FilterByNodeWrapper<OpHandleBase>(graph)) {
     std::string op_name = "op_" + std::to_string(op_id++);
     sout << op_name << " [label=\"" << op->Name() << "\", shape=rect]"
          << std::endl;

paddle/fluid/framework/details/reference_count_pass.cc

@@ -157,7 +157,7 @@ std::unique_ptr<ir::Graph> ReferenceCountPass::ApplyImpl(
     }
   };
 
-  auto all_ops = ir::GetFilteredNodes<OpHandleBase>(*graph);
+  auto all_ops = ir::FilterByNodeWrapper<OpHandleBase>(*graph);
   for (auto &op : all_ops) {
     auto in_var_names = get_ref_cnts_from_compute_op(op, op->Inputs());
     auto out_var_names = get_ref_cnts_from_compute_op(op, op->Outputs());

paddle/fluid/framework/details/threaded_ssa_graph_executor.cc

@@ -60,7 +60,7 @@ FeedFetchList ThreadedSSAGraphExecutor::Run(
     InsertPendingVar(&pending_vars, ready_vars.get(), var);
   }
 
-  for (auto &op : ir::GetFilteredNodes<OpHandleBase>(*graph_)) {
+  for (auto &op : ir::FilterByNodeWrapper<OpHandleBase>(*graph_)) {
     if (op->Inputs().empty()) {  // Special case, Op has no input.
       ready_ops.insert(op);
     } else {

paddle/fluid/framework/ir/graph_helper.h

@@ -38,7 +38,7 @@ std::map<ir::Node *, std::unordered_set<ir::Node *>> BuildOperationAdjList(
     const Graph &graph);
 
 template <typename T>
-std::vector<T *> GetFilteredNodes(const Graph &graph) {
+std::vector<T *> FilterByNodeWrapper(const Graph &graph) {
   std::vector<T *> ret;
   for (ir::Node *n : graph.Nodes()) {
     if (n->IsWrappedBy<T>()) ret.push_back(&n->Wrapper<T>());

python/paddle/fluid/tests/unittests/test_reader_reset.py

@@ -14,7 +14,6 @@
 
 from __future__ import print_function
 import os
-import sys
 import paddle.fluid as fluid
 import paddle
 import numpy as np
@@ -91,13 +90,11 @@ def main(self, with_double_buffer):
             try:
                 data_val, label_val = parallel_exe.run(fetch_list,
                                                        return_numpy=True)
-                sys.stderr.write('fetched %s\n' % label_val)
                 ins_num = data_val.shape[0]
                 broadcasted_label = np.ones((ins_num, ) + tuple(
                     self.ins_shape)) * label_val.reshape((ins_num, 1))
                 self.assertEqual(data_val.all(), broadcasted_label.all())
                 for l in label_val:
-                    sys.stderr.write('label_val: %s\n' % l[0])
                     self.assertFalse(data_appeared[l[0]])
                     data_appeared[l[0]] = True
 
@@ -107,7 +104,6 @@ def main(self, with_double_buffer):
                     data_appeared = data_appeared[:-parallel_exe.device_count *
                                                   self.batch_size]
                 for i in data_appeared:
-                    sys.stderr.write('appeared %s\n' % i)
                     self.assertTrue(i)
                 if pass_count < self.test_pass_num:
                     data_appeared = [False] * self.total_ins_num