fix batch size

core/general-server/op/general_infer_helper.h

@@ -37,6 +37,8 @@ struct GeneralBlob {
   double infer_time;
   std::vector<std::string> fetch_name_vector;
 
+  int _batch_size;
+
   void Clear() {
     size_t tensor_count = tensor_vector.size();
     for (size_t ti = 0; ti < tensor_count; ++ti) {
@@ -45,6 +47,10 @@ struct GeneralBlob {
     tensor_vector.clear();
   }
 
+  int SetBatchSize(int batch_size) { _batch_size = batch_size; }
+
+  int GetBatchSize() const { return _batch_size; }
+  /*
   int GetBatchSize() const {
     if (tensor_vector.size() > 0) {
       if (tensor_vector[0].lod.size() == 1) {
@@ -56,7 +62,7 @@ struct GeneralBlob {
       return -1;
     }
   }
-
+  */
   std::string ShortDebugString() const { return "Not implemented!"; }
 };
 

core/general-server/op/general_infer_op.cpp

@@ -12,11 +12,11 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
+#include "core/general-server/op/general_infer_op.h"
 #include <algorithm>
 #include <iostream>
 #include <memory>
 #include <sstream>
-#include "core/general-server/op/general_infer_op.h"
 #include "core/predictor/framework/infer.h"
 #include "core/predictor/framework/memory.h"
 #include "core/predictor/framework/resource.h"
@@ -36,33 +36,31 @@ using baidu::paddle_serving::predictor::InferManager;
 using baidu::paddle_serving::predictor::PaddleGeneralModelConfig;
 
 int GeneralInferOp::inference() {
-  const GeneralBlob * input_blob =
-      get_depend_argument<GeneralBlob>(pre_name());
+  const GeneralBlob *input_blob = get_depend_argument<GeneralBlob>(pre_name());
 
-  GeneralBlob * output_blob = mutable_data<GeneralBlob>();
+  GeneralBlob *output_blob = mutable_data<GeneralBlob>();
 
   if (!input_blob) {
-    LOG(ERROR) << "Failed mutable depended argument, op:"
-               << pre_name();
+    LOG(ERROR) << "Failed mutable depended argument, op:" << pre_name();
     return -1;
   }
 
   const TensorVector *in = &input_blob->tensor_vector;
   TensorVector *out = &output_blob->tensor_vector;
   int batch_size = input_blob->GetBatchSize();
+  output_blob->SetBatchSize(batch_size);
 
   VLOG(2) << "infer batch size: " << batch_size;
   // infer
-  Timer timeline;
-  double infer_time = 0.0;
-  timeline.Start();
+  // Timer timeline;
+  // double infer_time = 0.0;
+  // timeline.Start();
   if (InferManager::instance().infer(GENERAL_MODEL_NAME, in, out, batch_size)) {
     LOG(ERROR) << "Failed do infer in fluid model: " << GENERAL_MODEL_NAME;
     return -1;
   }
-  timeline.Pause();
-  infer_time = timeline.ElapsedUS();
-
+  // timeline.Pause();
+  // infer_time = timeline.ElapsedUS();
   return 0;
 }
 DEFINE_OP(GeneralInferOp);

core/general-server/op/general_reader_op.cpp

@@ -12,12 +12,12 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
+#include "core/general-server/op/general_reader_op.h"
 #include <algorithm>
 #include <iostream>
 #include <memory>
 #include <sstream>
 #include "core/general-server/op/general_infer_helper.h"
-#include "core/general-server/op/general_reader_op.h"
 #include "core/predictor/framework/infer.h"
 #include "core/predictor/framework/memory.h"
 
@@ -73,7 +73,6 @@ int GeneralReaderOp::inference() {
 
   int batch_size = req->insts_size();
   int input_var_num = 0;
-
   std::vector<int64_t> elem_type;
   std::vector<int64_t> elem_size;
   std::vector<int64_t> capacity;
@@ -81,6 +80,8 @@ int GeneralReaderOp::inference() {
   GeneralBlob *res = mutable_data<GeneralBlob>();
   TensorVector *out = &res->tensor_vector;
 
+  res->SetBatchSize(batch_size);
+
   if (!res) {
     LOG(ERROR) << "Failed get op tls reader object output";
   }

core/general-server/op/general_response_op.cpp

@@ -12,12 +12,12 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
+#include "core/general-server/op/general_response_op.h"
 #include <algorithm>
 #include <iostream>
 #include <memory>
 #include <sstream>
 #include "core/general-server/op/general_infer_helper.h"
-#include "core/general-server/op/general_response_op.h"
 #include "core/predictor/framework/infer.h"
 #include "core/predictor/framework/memory.h"
 #include "core/predictor/framework/resource.h"
@@ -37,12 +37,10 @@ using baidu::paddle_serving::predictor::InferManager;
 using baidu::paddle_serving::predictor::PaddleGeneralModelConfig;
 
 int GeneralResponseOp::inference() {
-  const GeneralBlob *input_blob =
-      get_depend_argument<GeneralBlob>(pre_name());
+  const GeneralBlob *input_blob = get_depend_argument<GeneralBlob>(pre_name());
 
   if (!input_blob) {
-    LOG(ERROR) << "Failed mutable depended argument, op: "
-               << pre_name();
+    LOG(ERROR) << "Failed mutable depended argument, op: " << pre_name();
     return -1;
   }
 
@@ -75,7 +73,7 @@ int GeneralResponseOp::inference() {
 
   for (int i = 0; i < batch_size; ++i) {
     FetchInst *fetch_inst = res->add_insts();
-    for (auto & idx : fetch_index) {
+    for (auto &idx : fetch_index) {
       Tensor *tensor = fetch_inst->add_tensor_array();
       // currently only response float tensor or lod_tensor
       tensor->set_elem_type(1);
@@ -85,8 +83,7 @@ int GeneralResponseOp::inference() {
       } else {
         VLOG(2) << "out[" << idx << "] is tensor";
         for (int k = 1; k < in->at(idx).shape.size(); ++k) {
-          VLOG(2) << "shape[" << k - 1 << "]: "
-                  << in->at(idx).shape[k];
+          VLOG(2) << "shape[" << k - 1 << "]: " << in->at(idx).shape[k];
           tensor->add_shape(in->at(idx).shape[k]);
         }
       }
@@ -94,7 +91,7 @@ int GeneralResponseOp::inference() {
   }
 
   int var_idx = 0;
-  for (auto & idx : fetch_index) {
+  for (auto &idx : fetch_index) {
     float *data_ptr = static_cast<float *>(in->at(idx).data.data());
     int cap = 1;
     for (int j = 1; j < in->at(idx).shape.size(); ++j) {
@@ -102,8 +99,8 @@ int GeneralResponseOp::inference() {
     }
     if (model_config->_is_lod_fetch[idx]) {
       for (int j = 0; j < batch_size; ++j) {
-        for (int k = in->at(idx).lod[0][j];
-             k < in->at(idx).lod[0][j + 1]; k++) {
+        for (int k = in->at(idx).lod[0][j]; k < in->at(idx).lod[0][j + 1];
+             k++) {
           res->mutable_insts(j)->mutable_tensor_array(var_idx)->add_data(
               reinterpret_cast<char *>(&(data_ptr[k])), sizeof(float));
         }