fix the bug that subgraph outputs are ommited in special case

mindspore/lite/src/lite_kernel.cc

@@ -315,23 +315,24 @@ std::vector<lite::Tensor *> LiteKernelUtil::SubgraphOutputTensors(const std::vec
   for (const auto &output_kernel : output_nodes) {
     auto &outer_out_kernels = output_kernel->out_kernels();
     auto &out_kernel_out_tensors = output_kernel->out_tensors();
-    if (outer_out_kernels.empty()) {
-      for (auto out_kernel_out_tensor : out_kernel_out_tensors) {
+    for (auto out_kernel_out_tensor : out_kernel_out_tensors) {
+      if (out_kernel_out_tensor->IsGraphOutput()) {
         output_tensors.insert(out_kernel_out_tensor);
       }
-      continue;
     }
-    for (auto outer_out_kernel : outer_out_kernels) {
-      auto iter = std::find(kernels.begin(), kernels.end(), outer_out_kernel);
-      if (iter != kernels.end()) {
-        continue;
-      }
-      auto &outer_out_kernel_in_tensors = outer_out_kernel->in_tensors();
-      for (auto out_kernel_out_tensor : out_kernel_out_tensors) {
-        auto outer_out_kernel_in_tensors_iter =
-          std::find(outer_out_kernel_in_tensors.begin(), outer_out_kernel_in_tensors.end(), out_kernel_out_tensor);
-        if (outer_out_kernel_in_tensors_iter != outer_out_kernel_in_tensors.end()) {
-          output_tensors.insert(out_kernel_out_tensor);
+    if (!outer_out_kernels.empty()) {
+      for (auto outer_out_kernel : outer_out_kernels) {
+        auto iter = std::find(kernels.begin(), kernels.end(), outer_out_kernel);
+        if (iter != kernels.end()) {
+          continue;
+        }
+        auto &outer_out_kernel_in_tensors = outer_out_kernel->in_tensors();
+        for (auto out_kernel_out_tensor : out_kernel_out_tensors) {
+          auto outer_out_kernel_in_tensors_iter =
+            std::find(outer_out_kernel_in_tensors.begin(), outer_out_kernel_in_tensors.end(), out_kernel_out_tensor);
+          if (outer_out_kernel_in_tensors_iter != outer_out_kernel_in_tensors.end()) {
+            output_tensors.insert(out_kernel_out_tensor);
+          }
         }
       }
     }

mindspore/lite/src/lite_session.cc

@@ -176,6 +176,7 @@ int LiteSession::ConvertTensors(const lite::Model *model) {
   uint32_t tensor_count = model->all_tensors_.size();
   MS_ASSERT(!model->sub_graphs_.empty());
   auto model_input_indices = model->sub_graphs_.front()->input_indices_;
+  auto model_output_indices = model->sub_graphs_.front()->output_indices_;
   for (uint32_t i = 0; i < tensor_count; ++i) {
     auto *src_tensor = model->all_tensors_[i];
     if (src_tensor == nullptr) {
@@ -197,6 +198,9 @@ int LiteSession::ConvertTensors(const lite::Model *model) {
     if (IsContain(model_input_indices, i)) {
       dst_tensor->set_category(Tensor::GRAPH_INPUT);
     }
+    if (IsContain(model_output_indices, i)) {
+      dst_tensor->set_category(Tensor::GRAPH_OUTPUT);
+    }
     if (src_tensor->name() != nullptr) {
       dst_tensor->set_tensor_name(src_tensor->name()->str());
     }

mindspore/lite/src/tensor.h

@@ -50,6 +50,7 @@ class Tensor : public mindspore::tensor::MSTensor {
     CONST_SCALAR,  // weight scalar
     VAR,           // activation tensor
     GRAPH_INPUT,
+    GRAPH_OUTPUT,
   };
   Tensor() = default;
 
@@ -163,6 +164,8 @@ class Tensor : public mindspore::tensor::MSTensor {
 
   bool IsGraphInput() const { return this->category_ == GRAPH_INPUT; }
 
+  bool IsGraphOutput() const { return this->category_ == GRAPH_OUTPUT; }
+
   void Prepare() {
     if (allocator_ != nullptr) {
       data_ = allocator_->Prepare(data_);

mindspore/lite/test/models_for_process_only.cfg

@@ -27,4 +27,3 @@ quant_aware_bank_card_detection_inception.onnx
 quant_aware_bank_card_recognition_fcny.onnx
 quant_aware_identify_card_detect.onnx
 tiny-yolov3-11.onnx;2;1,416,416,3:1,2
-ml_video_edit_person_divison_pic;2

mindspore/lite/test/models_with_multiple_inputs.cfg

@@ -9,3 +9,4 @@ ml_video_edit_video_segment_gauss_adaptis_part2.pb;2
 ml_video_edit_video_segment_gauss_adaptis_part2_pb2tflite.tflite;2
 decoder.onnx;2;1,7,512:1,7
 fasterrcnn_crop.pb;1;420,630,3
+ml_video_edit_person_divison_pic;2

mindspore/lite/test/run_benchmark_nets.sh

@@ -294,20 +294,26 @@ function Run_Converter() {
           tflite)
             model_fmk="TFLITE"
             ;;
-          caffemodel)
-            model_name=${model_name%.*}
-            model_fmk="CAFFE"
-            ;;
           onnx)
             model_fmk="ONNX"
             ;;
           mindir)
             model_fmk="MINDIR"
             ;;
+          *)
+            model_type="caffe"
+            model_fmk="CAFFE"
+            ;;
         esac
-        echo ${model_name} >> "${run_converter_log_file}"
-        echo './converter_lite  --fmk='${model_fmk}' --modelFile='${models_path}'/'${model_name}' --outputFile='${ms_models_path}'/'${model_name} >> "${run_converter_log_file}"
-        ./converter_lite  --fmk=${model_fmk} --modelFile=${models_path}/${model_name} --outputFile=${ms_models_path}/${model_name}
+        if [[ $model_fmk == "CAFFE" ]]; then
+          echo ${model_name} >> "${run_converter_log_file}"
+          echo './converter_lite  --fmk='${model_fmk}' --modelFile='$models_path/${model_name}'.prototxt --weightFile='$models_path'/'${model_name}'.caffemodel --outputFile='${ms_models_path}'/'${model_name} >> "${run_converter_log_file}"
+          ./converter_lite  --fmk=${model_fmk} --modelFile=${models_path}/${model_name}.prototxt --weightFile=${models_path}/${model_name}.caffemodel --outputFile=${ms_models_path}/${model_name}
+        else
+          echo ${model_name} >> "${run_converter_log_file}"
+          echo './converter_lite  --fmk='${model_fmk}' --modelFile='${models_path}'/'${model_name}' --outputFile='${ms_models_path}'/'${model_name} >> "${run_converter_log_file}"
+          ./converter_lite  --fmk=${model_fmk} --modelFile=${models_path}/${model_name} --outputFile=${ms_models_path}/${model_name}
+        fi
         if [ $? = 0 ]; then
             converter_result='converter '${model_type}' '${model_name}' pass';echo ${converter_result} >> ${run_converter_result_file}
         else