1 files changed, 114 insertions, 0 deletions
diff --git a/runtimes/contrib/tflite_classify/src/InferenceInterface.cc b/runtimes/contrib/tflite_classify/src/InferenceInterface.cc
new file mode 100644
index 000000000..160943477
--- /dev/null
+++ b/runtimes/contrib/tflite_classify/src/InferenceInterface.cc
@@ -0,0 +1,114 @@
+/*
+ * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "InferenceInterface.h"
+
+using namespace tflite;
+using namespace tflite::ops::builtin;
+
+InferenceInterface::InferenceInterface(const std::string &model_file, const bool use_nnapi)
+    : _interpreter(nullptr), _model(nullptr), _sess(nullptr)
+{
+  // Load model
+  StderrReporter error_reporter;
+  _model = FlatBufferModel::BuildFromFile(model_file.c_str(), &error_reporter);
+  BuiltinOpResolver resolver;
+  InterpreterBuilder builder(*_model, resolver);
+  builder(&_interpreter);
+
+  if (use_nnapi)
+  {
+    _sess = std::make_shared<nnfw::tflite::NNAPISession>(_interpreter.get());
+  }
+  else
+  {
+    _sess = std::make_shared<nnfw::tflite::InterpreterSession>(_interpreter.get());
+  }
+
+  _sess->prepare();
+}
+
+InferenceInterface::~InferenceInterface() { _sess->teardown(); }
+
+void InferenceInterface::feed(const std::string &input_name, const std::vector<float> &data,
+                              const int batch, const int height, const int width, const int channel)
+{
+  // Set input tensor
+  for (const auto &id : _interpreter->inputs())
+  {
+    if (_interpreter->tensor(id)->name == input_name)
+    {
+      assert(_interpreter->tensor(id)->type == kTfLiteFloat32);
+      float *p = _interpreter->tensor(id)->data.f;
+
+      // TODO consider batch
+      for (int y = 0; y < height; ++y)
+      {
+        for (int x = 0; x < width; ++x)
+        {
+          for (int c = 0; c < channel; ++c)
+          {
+            *p++ = data[y * width * channel + x * channel + c];
+          }
+        }
+      }
+    }
+  }
+}
+
+void InferenceInterface::run(const std::string &output_name)
+{
+  // Run model
+  _sess->run();
+}
+
+void InferenceInterface::fetch(const std::string &output_name, std::vector<float> &outputs)
+{
+  // Get output tensor
+  for (const auto &id : _interpreter->outputs())
+  {
+    if (_interpreter->tensor(id)->name == output_name)
+    {
+      assert(_interpreter->tensor(id)->type == kTfLiteFloat32);
+      assert(getTensorSize(output_name) == outputs.capacity());
+      float *p = _interpreter->tensor(id)->data.f;
+
+      outputs.clear();
+      for (int i = 0; i < outputs.capacity(); ++i)
+      {
+        outputs.push_back(p[i]);
+      }
+    }
+  }
+}
+
+int InferenceInterface::getTensorSize(const std::string &name)
+{
+  for (const auto &id : _interpreter->outputs())
+  {
+    if (_interpreter->tensor(id)->name == name)
+    {
+      TfLiteTensor *t = _interpreter->tensor(id);
+      int v = 1;
+      for (int i = 0; i < t->dims->size; ++i)
+      {
+        v *= t->dims->data[i];
+      }
+      return v;
+    }
+  }
+  return -1;
+}