!23247 [MSLITE] Fix bug of tf model‘s output names.

Merge pull request !23247 from wangshaocong/convert_r1.4

mindspore/lite/src/lite_session.cc

@@ -209,7 +209,10 @@ int LiteSession::ConvertTensors(const lite::Model *model) {
         MS_LOG(ERROR) << "Graph output shouldn't have data";
         return RET_ERROR;
       }
-      dst_tensor->set_category(Tensor::GRAPH_OUTPUT);
+      // a tensor is as both input and output, would be treated as an input.
+      if (!dst_tensor->IsGraphInput()) {
+        dst_tensor->set_category(Tensor::GRAPH_OUTPUT);
+      }
     }
     if (src_tensor->name() != nullptr) {
       dst_tensor->set_tensor_name(src_tensor->name()->str());
@@ -364,16 +367,13 @@ void LiteSession::InitGraphInOutTensorsMap(const lite::Model *model) {
   InitGraphInputMap(model);
   InitGraphOutputNodeMap(model);
   InitGraphOutputTensorMap(model);
-  for (auto *tensor : this->inputs_) {
-    tensor->set_category(Tensor::Category::GRAPH_INPUT);
-  }
-  for (auto *tensor : this->outputs_) {
-    tensor->set_category(Tensor::Category::GRAPH_OUTPUT);
-  }
 }
 
 void LiteSession::IsolateOutputTensor() {
   for (Tensor *src_tensor : outputs_) {
+    if (src_tensor->IsGraphInput()) {
+      continue;
+    }
     Tensor *new_tensor =
       new Tensor(src_tensor->data_type(), src_tensor->shape(), src_tensor->format(), Tensor::GRAPH_OUTPUT);
     new_tensor->set_allocator(src_tensor->allocator()); /* GPU use opencl allocator */

mindspore/lite/src/mindrt_executor.cc

@@ -24,16 +24,24 @@
 namespace mindspore::lite {
 void MindrtExecutor::PrepareInputData(const std::vector<kernel::LiteKernel *> &kernels,
                                       const std::vector<Tensor *> &inputs) {
-  for (size_t i = 0; i < inputs.size(); ++i) {
-    for (size_t j = 0; j < kernels.size(); ++j) {
-      auto in_tensor_size = kernels[j]->in_tensors().size();
-      for (size_t k = 0; k < in_tensor_size; ++k) {
-        if (inputs[i] != kernels[j]->in_tensors()[k]) {
-          continue;
-        }
-        auto data = std::make_shared<OpData<Tensor>>(op_actors_[j]->GetAID(), inputs[i], static_cast<int>(k));
-        input_data_.emplace_back(data);
+  for (size_t j = 0; j < kernels.size(); ++j) {
+    auto in_tensor_size = kernels[j]->in_tensors().size();
+    for (size_t k = 0; k < in_tensor_size; ++k) {
+      auto tensor = kernels[j]->in_tensors()[k];
+      if (!tensor->IsGraphInput()) {
+        continue;
       }
+      size_t idx = std::find(inputs.begin(), inputs.end(), tensor) - inputs.begin();
+      if (idx == inputs.size()) {
+        MS_LOG(ERROR) << "The input is not found.";
+        return;
+      }
+      auto data = std::make_shared<OpData<Tensor>>(op_actors_[j]->GetAID(), inputs.at(idx), static_cast<int>(k));
+      if (data == nullptr) {
+        MS_LOG(ERROR) << "new opdata failed.";
+        return;
+      }
+      input_data_.emplace_back(data);
     }
   }
 }
@@ -42,6 +50,9 @@ void MindrtExecutor::PrepareOutputData(const std::vector<kernel::LiteKernel *> &
                                        const std::vector<Tensor *> &outputs) {
   for (size_t i = 0; i < outputs.size(); ++i) {
     Tensor *graph_output_tensor = outputs[i];
+    if (graph_output_tensor->IsGraphInput()) {
+      continue;
+    }
     auto current_output_map =
       std::find_if(output_tensor_map_->begin(), output_tensor_map_->end(), [&](const auto output_map_tensor) {
         if (graph_output_tensor == output_map_tensor.second) {

mindspore/lite/tools/converter/parser/tf/tf_model_parser.cc

@@ -1059,7 +1059,10 @@ STATUS TFModelParser::ConvertOps(const tensorflow::NodeDef &node_def,
   MS_ASSERT(func_graph_ptr != nullptr);
   STATUS status = RET_OK;
   const auto &op_type = node_def.op();
-  if (op_type == "Placeholder" || op_type == "Const" || op_type == "Identity" || op_type == "StopGradient") {
+  if (op_type == "Identity" || op_type == "StopGradient") {
+    return RET_OK;
+  } else if (op_type == "Placeholder" || op_type == "Const") {
+    node_output_num_[node_def.name()] = 1;
     return RET_OK;
   }