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-rw-r--r--runtimes/contrib/TFLiteSharp/TFLiteNative/src/tflite_nativewrapper.cpp142
1 files changed, 142 insertions, 0 deletions
diff --git a/runtimes/contrib/TFLiteSharp/TFLiteNative/src/tflite_nativewrapper.cpp b/runtimes/contrib/TFLiteSharp/TFLiteNative/src/tflite_nativewrapper.cpp
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+++ b/runtimes/contrib/TFLiteSharp/TFLiteNative/src/tflite_nativewrapper.cpp
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+/*
+ * 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 <iostream>
+#include "tflite_nativewrapper.h"
+#include "tflite_log.h"
+#include <string.h>
+#include <unistd.h>
+#include <errno.h>
+#include <assert.h>
+
+int getNumBytes(TFLiteNativeType dataType)
+{
+ switch (dataType)
+ {
+ case INT32:
+ return 4;
+ case FLOAT32:
+ return 4;
+ case UINT8:
+ return 1;
+ case INT64:
+ return 8;
+ default:
+ return 1;
+ }
+}
+
+/// <summary>
+/// Set the number of threads available to the interpreter.
+/// </summary>
+/// <param name="interpreterHandle">Handle of the interpreter instance.</param>
+/// <param name="numThreads">Number of threads.</param>
+void tflite_interpreter_setNumThreads(long *interpreterHandle, int numThreads)
+{
+ assert(interpreterHandle != nullptr);
+ tflite::Interpreter *interpreter = reinterpret_cast<tflite::Interpreter *>(*interpreterHandle);
+
+ interpreter->SetNumThreads(numThreads);
+
+ TFLITE_NATIVE_LOG(DEBUG, "Number of threads: %d", numThreads);
+ return;
+}
+
+/// <summary>
+/// Creates a Flat Buffer Model from the given .tflite model.
+/// </summary>
+/// <param name="modelPath">Path of the model.</param>
+long long tflite_flatbuffermodel_BuildFromFile(char *modelPath)
+{
+ if (modelPath == nullptr)
+ {
+ TFLITE_NATIVE_LOG(ERROR, "Invalid parameter");
+ return 0;
+ }
+ TFLITE_NATIVE_LOG(ERROR, "Model Path: %s", modelPath);
+
+ if (access(modelPath, F_OK) == -1)
+ {
+ TFLITE_NATIVE_LOG(ERROR, "Failed to access model [%s]", strerror(errno));
+ return 0;
+ }
+
+ auto model = tflite::FlatBufferModel::BuildFromFile(modelPath);
+
+ TFLITE_NATIVE_LOG(DEBUG, "Successfully loaded model");
+ return reinterpret_cast<long>(model.release());
+}
+
+/// <summary>
+/// Creates an interpreter instance taking the flatbuffer model as input.
+/// </summary>
+/// <param name="modelHandle">Address of the flatbuffer model.</param>
+long long tflite_builder_interpreterBuilder(long *modelHandle)
+{
+ assert(modelHandle != nullptr);
+ tflite::FlatBufferModel *model = reinterpret_cast<tflite::FlatBufferModel *>(*modelHandle);
+
+ tflite::ops::builtin::BuiltinOpResolver resolver;
+ std::unique_ptr<tflite::Interpreter> interpreter;
+
+ TfLiteStatus status = tflite::InterpreterBuilder(*model, resolver)(&interpreter);
+
+ if (status != kTfLiteOk)
+ {
+ TFLITE_NATIVE_LOG(DEBUG, "Cannot create interpreter");
+ return 0;
+ }
+ TFLITE_NATIVE_LOG(DEBUG, "CheckPoint interpreter");
+ return reinterpret_cast<long>(interpreter.release());
+}
+
+/// <summary>
+/// Runs the inference given the inputs.
+/// </summary>
+/// <param name="interpreterHandle">Address of the interpreter instance.</param>
+/// <param name="values">Input values for the model.</param>
+/// <param name="inpLength">Length of the input.</param>
+/// <param name="dataType">Data type key of the input.</param>
+void *tflite_interpreter_run(long *interpreterHandle, void *values, int inputLength, int dataType)
+{
+ assert(interpreterHandle != nullptr);
+ tflite::Interpreter *interpreter = reinterpret_cast<tflite::Interpreter *>(*interpreterHandle);
+
+ int inputTensorIndex = interpreter->inputs()[0];
+
+ // TODO:: input tensor size will be passed as a parameter. It is hardcoded for now.
+ interpreter->ResizeInputTensor(inputTensorIndex, {1, 224, 224, 3});
+
+ if (interpreter->AllocateTensors() != kTfLiteOk)
+ {
+ TFLITE_NATIVE_LOG(ERROR, "Failed to allocate tensors!");
+ return nullptr;
+ }
+
+ float *inputTensorPointer = interpreter->typed_tensor<float>(inputTensorIndex);
+
+ int numBytes = getNumBytes((TFLiteNativeType)dataType);
+
+ memcpy(inputTensorPointer, values, inputLength * numBytes);
+
+ if (interpreter->Invoke() != kTfLiteOk)
+ {
+ TFLITE_NATIVE_LOG(ERROR, "Failed to invoke");
+ }
+
+ float *output = interpreter->typed_output_tensor<float>(0);
+ return output;
+}