Because anakin do NOT use glog, so we revert anakin related change

test=develop

Author: velconia

paddle/fluid/inference/api/api_anakin_engine.cc

@@ -50,7 +50,7 @@ template <typename Target>
 bool PaddleInferenceAnakinPredictor<Target>::Init(
     const contrib::AnakinConfig &config) {
   if (!(graph_.load(config.model_file))) {
-    VLOG(30) << "fail to load graph from " << config.model_file;
+    VLOG(3) << "fail to load graph from " << config.model_file;
     return false;
   }
   auto inputs = graph_.get_ins();
@@ -76,15 +76,15 @@ bool PaddleInferenceAnakinPredictor<Target>::Run(
     std::vector<PaddleTensor> *output_data, int batch_size) {
   for (const auto &input : inputs) {
     if (input.dtype != PaddleDType::FLOAT32) {
-      VLOG(30) << "Only support float type inputs. " << input.name
-               << "'s type is not float";
+      VLOG(3) << "Only support float type inputs. " << input.name
+              << "'s type is not float";
       return false;
     }
     auto d_tensor_in_p = executor_p_->get_in(input.name);
     auto net_shape = d_tensor_in_p->shape();
     if (net_shape.size() != input.shape.size()) {
-      VLOG(30) << " input  " << input.name
-               << "'s shape size should be equal to that of net";
+      VLOG(3) << " input  " << input.name
+              << "'s shape size should be equal to that of net";
       return false;
     }
     int sum = 1;
@@ -105,15 +105,15 @@ bool PaddleInferenceAnakinPredictor<Target>::Run(
 
     if (input.lod.size() > 0) {
       if (input.lod.size() > 1) {
-        VLOG(30) << " input lod first dim should <=1, but you set "
-                 << input.lod.size();
+        VLOG(3) << " input lod first dim should <=1, but you set "
+                << input.lod.size();
         return false;
       }
       std::vector<int> offset(input.lod[0].begin(), input.lod[0].end());
       d_tensor_in_p->set_seq_offset(offset);
-      VLOG(30) << "offset.size(): " << offset.size();
+      VLOG(3) << "offset.size(): " << offset.size();
       for (int i = 0; i < offset.size(); i++) {
-        VLOG(30) << offset[i];
+        VLOG(3) << offset[i];
       }
     }
 
@@ -124,7 +124,7 @@ bool PaddleInferenceAnakinPredictor<Target>::Run(
       if (cudaMemcpy(d_data_p, static_cast<float *>(input.data.data()),
                      d_tensor_in_p->valid_size() * sizeof(float),
                      cudaMemcpyHostToDevice) != 0) {
-        VLOG(30) << "copy data from CPU to GPU error";
+        VLOG(3) << "copy data from CPU to GPU error";
         return false;
       }
     }
@@ -141,7 +141,7 @@ bool PaddleInferenceAnakinPredictor<Target>::Run(
 #endif
 
   if (output_data->empty()) {
-    VLOG(30) << "At least one output should be set with tensors' names.";
+    VLOG(3) << "At least one output should be set with tensors' names.";
     return false;
   }
   for (auto &output : *output_data) {
@@ -157,7 +157,7 @@ bool PaddleInferenceAnakinPredictor<Target>::Run(
       if (cudaMemcpy(output.data.data(), tensor->mutable_data(),
                      tensor->valid_size() * sizeof(float),
                      cudaMemcpyDeviceToHost) != 0) {
-        VLOG(30) << "copy data from GPU to CPU error";
+        VLOG(3) << "copy data from GPU to CPU error";
         return false;
       }
     }
@@ -181,14 +181,14 @@ anakin::Net<Target, anakin::saber::AK_FLOAT, anakin::Precision::FP32>
 template <typename Target>
 std::unique_ptr<PaddlePredictor>
 PaddleInferenceAnakinPredictor<Target>::Clone() {
-  VLOG(30) << "Anakin Predictor::clone";
+  VLOG(3) << "Anakin Predictor::clone";
   std::unique_ptr<PaddlePredictor> cls(
       new PaddleInferenceAnakinPredictor<Target>());
   // construct executer from other graph
   auto anakin_predictor_p =
       dynamic_cast<PaddleInferenceAnakinPredictor<Target> *>(cls.get());
   if (!anakin_predictor_p) {
-    VLOG(30) << "fail to call Init";
+    VLOG(3) << "fail to call Init";
     return nullptr;
   }
   anakin_predictor_p->get_executer().init(graph_);
@@ -206,10 +206,10 @@ template <>
 std::unique_ptr<PaddlePredictor>
 CreatePaddlePredictor<contrib::AnakinConfig, PaddleEngineKind::kAnakin>(
     const contrib::AnakinConfig &config) {
-  VLOG(30) << "Anakin Predictor create.";
+  VLOG(3) << "Anakin Predictor create.";
   if (config.target_type == contrib::AnakinConfig::NVGPU) {
 #ifdef PADDLE_WITH_CUDA
-    VLOG(30) << "Anakin Predictor create on [ NVIDIA GPU ].";
+    VLOG(3) << "Anakin Predictor create on [ NVIDIA GPU ].";
     std::unique_ptr<PaddlePredictor> x(
         new PaddleInferenceAnakinPredictor<anakin::NV>(config));
     return x;
@@ -218,12 +218,12 @@ CreatePaddlePredictor<contrib::AnakinConfig, PaddleEngineKind::kAnakin>(
     return nullptr;
 #endif
   } else if (config.target_type == contrib::AnakinConfig::X86) {
-    VLOG(30) << "Anakin Predictor create on [ Intel X86 ].";
+    VLOG(3) << "Anakin Predictor create on [ Intel X86 ].";
     std::unique_ptr<PaddlePredictor> x(
         new PaddleInferenceAnakinPredictor<anakin::X86>(config));
     return x;
   } else {
-    VLOG(30) << "Anakin Predictor create on unknown platform.";
+    VLOG(3) << "Anakin Predictor create on unknown platform.";
     return nullptr;
   }
 }

paddle/fluid/inference/tests/api/anakin_rnn1_tester.cc

@@ -217,9 +217,9 @@ void single_test() {
     LOG(INFO) << "sequence_length = " << seq_offset[seq_offset.size() - 1];
 
     float* data_o = static_cast<float*>(outputs[0].data.data());
-    VLOG(30) << "outputs[0].data.length() = " << outputs[0].data.length();
+    VLOG(3) << "outputs[0].data.length() = " << outputs[0].data.length();
     for (size_t j = 0; j < outputs[0].data.length(); ++j) {
-      VLOG(30) << "output[" << j << "]: " << data_o[j];
+      VLOG(3) << "output[" << j << "]: " << data_o[j];
     }
   }
 }