Merge branch 'fix_Mac_compile_errors' into dev_data_balance

paddle/contrib/inference/CMakeLists.txt

@@ -61,7 +61,7 @@ cc_library(paddle_inference_tensorrt_subgraph_engine
 inference_api_test(test_paddle_inference_api_tensorrt_subgraph_engine ARGS test_word2vec)
 endif()
 
-if (WITH_ANAKIN AND WITH_TESTING) # only needed in CI
+if (WITH_ANAKIN) # only needed in CI
     # Due to Anakin do not have official library releases and the versions of protobuf and cuda do not match Paddle's,
     # so anakin library will not be merged to our official inference library. To use anakin prediction API, one need to
     # compile the libinference_anakin_api.a and compile with anakin.so.
@@ -71,10 +71,12 @@ if (WITH_ANAKIN AND WITH_TESTING) # only needed in CI
     target_compile_options(inference_anakin_api_shared BEFORE PUBLIC ${ANAKIN_COMPILE_EXTRA_FLAGS})
     target_link_libraries(inference_anakin_api anakin anakin_saber_common)
     target_link_libraries(inference_anakin_api_shared anakin anakin_saber_common)
-    cc_test(inference_anakin_test SRCS paddle_inference_api_anakin_engine_tester.cc
+    if (WITH_TESTING)
+        cc_test(inference_anakin_test SRCS paddle_inference_api_anakin_engine_tester.cc
                                   ARGS --model=${ANAKIN_INSTALL_DIR}/mobilenet_v2.anakin.bin
                                   DEPS inference_anakin_api)
-    target_compile_options(inference_anakin_test BEFORE PUBLIC ${ANAKIN_COMPILE_EXTRA_FLAGS})
+        target_compile_options(inference_anakin_test BEFORE PUBLIC ${ANAKIN_COMPILE_EXTRA_FLAGS})
+     endif(WITH_TESTING)
 endif()
 
 if(WITH_TESTING)

paddle/fluid/framework/parallel_executor.cc

@@ -253,6 +253,9 @@ void ParallelExecutor::FeedAndSplitTensorIntoLocalScopes(
       t->set_lod(lod_tensors[j].lod());
     }
   }
+  for (auto &p : member_->places_) {
+    platform::DeviceContextPool::Instance().Get(p)->Wait();
+  }
 }
 
 ParallelExecutor::~ParallelExecutor() {

paddle/fluid/inference/analysis/tensorrt_subgraph_node_mark_pass.h

@@ -16,6 +16,10 @@
  * This file defines TensorRTSubgraphNodeMarkPass which helps to mark the ops
  * that supported by TensorRT engine.
  */
+
+#pragma once
+
+#include <string>
 #include "paddle/fluid/inference/analysis/pass.h"
 #include "paddle/fluid/inference/analysis/subgraph_splitter.h"
 
@@ -30,7 +34,8 @@ class TensorRTSubgraphNodeMarkPass : public DataFlowGraphPass {
  public:
   using teller_t = SubGraphSplitter::NodeInsideSubgraphTeller;
 
-  TensorRTSubgraphNodeMarkPass(const teller_t& teller) : teller_(teller) {}
+  explicit TensorRTSubgraphNodeMarkPass(const teller_t& teller)
+      : teller_(teller) {}
 
   bool Initialize(Argument* argument) override { return true; }
 
@@ -38,8 +43,10 @@ class TensorRTSubgraphNodeMarkPass : public DataFlowGraphPass {
   // sub-graph into TensorRT.
   void Run(DataFlowGraph* graph) override;
 
-  std::string repr() const { return "tensorrt-sub-subgraph-mark"; }
-  std::string description() const { return "tensorrt sub-graph mark pass"; }
+  std::string repr() const override { return "tensorrt-sub-subgraph-mark"; }
+  std::string description() const override {
+    return "tensorrt sub-graph mark pass";
+  }
 
   Pass* CreateGraphvizDebugerPass() const override;
   bool Finalize() override;

paddle/fluid/inference/analysis/tensorrt_subgraph_pass.h

@@ -14,6 +14,7 @@ limitations under the License. */
 
 #pragma once
 
+#include <string>
 #include "paddle/fluid/inference/analysis/node.h"
 #include "paddle/fluid/inference/analysis/pass.h"
 #include "paddle/fluid/inference/analysis/subgraph_splitter.h"
@@ -30,7 +31,7 @@ class TensorRTSubGraphPass : public DataFlowGraphPass {
   // Tell whether to transform a sub-graph into TensorRT.
   using NodeInsideSubgraphTeller = SubGraphFuse::NodeInsideSubgraphTeller;
 
-  TensorRTSubGraphPass(const NodeInsideSubgraphTeller& teller);
+  explicit TensorRTSubGraphPass(const NodeInsideSubgraphTeller& teller);
 
   bool Initialize(Argument* argument) override { return true; }
 
@@ -40,8 +41,8 @@ class TensorRTSubGraphPass : public DataFlowGraphPass {
 
   bool Finalize() override { return true; }
 
-  std::string repr() const { return "tensorrt-sub-graph"; }
-  std::string description() const { return "tensorrt sub graph pass"; }
+  std::string repr() const override { return "tensorrt-sub-graph"; }
+  std::string description() const override { return "tensorrt sub graph pass"; }
 
  private:
   NodeInsideSubgraphTeller node_inside_subgraph_teller_;
@@ -49,4 +50,4 @@ class TensorRTSubGraphPass : public DataFlowGraphPass {
 
 }  // namespace analysis
 }  // namespace inference
-}  // paddle
+}  // namespace paddle

paddle/scripts/paddle_build.sh

@@ -106,6 +106,7 @@ function cmake_gen() {
         -DWITH_FLUID_ONLY=${WITH_FLUID_ONLY:-OFF}
         -DCMAKE_EXPORT_COMPILE_COMMANDS=ON
         -DWITH_CONTRIB=${WITH_CONTRIB:-ON}
+        -DWITH_ANAKIN=${WITH_ANAKIN:-ON}
         -DWITH_INFERENCE_DEMO=${WITH_INFERENCE_DEMO:-ON}
     ========================================
 EOF

python/paddle/fluid/layers/nn.py

@@ -5078,12 +5078,12 @@ def mean_iou(input, label, num_classes):
     out_correct = helper.create_tmp_variable(dtype='int32')
     helper.append_op(
         type="mean_iou",
-        inputs={"predictions": input,
-                "labels": label},
+        inputs={"Predictions": input,
+                "Labels": label},
         outputs={
-            "out_mean_iou": out_mean_iou,
-            "out_wrong": out_wrong,
-            "out_correct": out_correct
+            "OutMeanIou": out_mean_iou,
+            "OutWrong": out_wrong,
+            "OutCorrect": out_correct
         },
         attrs={"num_classes": num_classes})
     return out_mean_iou, out_wrong, out_correct

python/paddle/fluid/optimizer.py

@@ -1113,7 +1113,6 @@ class ModelAverage(Optimizer):
 
     Args:
         average_window_rate: The rate of average window.
-        params_grads: A list of parameter-grad variable pairs.
         min_average_window: The minimum size of average window.
         max_average_window: The maximum size of average window.
 
@@ -1122,8 +1121,8 @@ class ModelAverage(Optimizer):
       .. code-block:: python
 
         optimizer = fluid.optimizer.Momentum()
-        _, params_grads = optimizer.minimize(cost)
-        model_average = fluid.optimizer.ModelAverage(params_grads, 0.15,
+        optimizer.minimize(cost)
+        model_average = fluid.optimizer.ModelAverage(0.15,
                                                 min_average_window=10000,
                                                 max_average_window=20000)
         for pass_id in range(args.pass_num):
@@ -1137,7 +1136,6 @@ class ModelAverage(Optimizer):
 
     def __init__(self,
                  average_window_rate,
-                 params_grads=None,
                  min_average_window=10000,
                  max_average_window=10000,
                  **kwargs):
@@ -1146,21 +1144,16 @@ class ModelAverage(Optimizer):
         self.min_average_window = min_average_window
         self.max_average_window = max_average_window
 
-        self.params_grads = [] if params_grads is None else params_grads
-        params = {}
-        for param, grad in self.params_grads:
-            if param.do_model_average != False:
-                params[param.name] = (param, grad)
+        self.params_grads = []
         for param in framework.default_main_program().global_block(
         ).all_parameters():
-            if param.name not in params and param.do_model_average != False:
+            if param.do_model_average != False:
                 grad = param.block.create_var(
                     name=unique_name.generate(".".join([param.name, 'tmp'])),
                     dtype=param.dtype,
                     persistable=False,
                     stop_gradient=True)
-                params[param.name] = (param, grad)
-        self.params_grads = params.values()
+                self.params_grads.append((param, grad))
 
         for param, grad in self.params_grads:
             self._append_average_accumulate_op(param)

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

@@ -401,7 +401,7 @@ class TestBook(unittest.TestCase):
             self.assertIsNotNone(output)
         print(str(program))
 
-    def test_maxout(self):
+    def test_crop(self):
         program = Program()
         with program_guard(program):
             x = layers.data(name='x', shape=[3, 5], dtype="float32")
@@ -410,6 +410,15 @@ class TestBook(unittest.TestCase):
             self.assertIsNotNone(output)
         print(str(program))
 
+    def test_mean_iou(self):
+        program = Program()
+        with program_guard(program):
+            x = layers.data(name='x', shape=[16], dtype='float32')
+            y = layers.data(name='label', shape=[1], dtype='int64')
+            iou = layers.mean_iou(x, y, 2)
+            self.assertIsNotNone(iou)
+        print(str(program))
+
 
 if __name__ == '__main__':
     unittest.main()