blob: 973fd5d9be87fa0dde86a6623e781e336a270e5d [file] [log] [blame] [edit]
/*
* Copyright (c) 2021, Alliance for Open Media. All rights reserved
*
* This source code is subject to the terms of the BSD 3-Clause Clear License
* and the Alliance for Open Media Patent License 1.0. If the BSD 3-Clause Clear
* License was not distributed with this source code in the LICENSE file, you
* can obtain it at aomedia.org/license/software-license/bsd-3-c-c/. If the
* Alliance for Open Media Patent License 1.0 was not distributed with this
* source code in the PATENTS file, you can obtain it at
* aomedia.org/license/patent-license/.
*/
/*!\file
* \brief This binary demonstrates how to build an executable with TensorFlow
* Lite. A library for aomenc/aomdec is almost identical in structure,
* except that "main" will not be present, and a header file will be added.
*/
#include <cstdio>
#include <memory>
#include "common/tf_lite_includes.h"
namespace {
/**
* Model data. Steps to create:
*
* 1. Build your model like normal.
* 2. Follow the steps at https://www.tensorflow.org/lite/convert/python_api
* to convert into a TF-lite model. Optionally quantize the model to make
* it smaller (see the Quantization section at
* https://www.tensorflow.org/lite/guide/get_started)
* 3. Run `xxd -i model.tflite > model.cc` to make it a CC file.
* 4. Change the declaration to be const.
* 5. Either include directly or create .h/.cc files that expose the array.
*/
const unsigned char tf_lite_model_data[] = {
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};
/**
* These lines were generated by building the binary
* tensorflow/lite/tools/generate_op_registrations and running it with
* the TF-Lite model as input. In order to build that binary, follow
* these instructions:
*
* 1.) Check out a clean copy of tensorflow/ in a different directory. Pin to
* commit 18445b0e39b677a21c86b4cf3d2bcb823f27e3e2. Make sure you are
* in the root of the tensorflow/ directory.
* 2.) Save the diff (between the <pre>...</pre> tags below) into diff.txt.
* This diff fixes an issue with the current binary's flag parsing,
* which fails silently.
* 3.) Run: patch tensorflow/lite/tools/gen_op_registration_main.cc diff.txt
* 4.) Run: bazel build tensorflow/lite/tools/generate_op_registrations
* 5.) Run: ./bazel-bin/tensorflow/lite/tools/generate_op_registrations \
/path/to/model.tflite /path/to/output.cc
* for appropriate paths to the input and output files.
<pre>
153,155c153,155
< std::string input_models;
< std::string output_registration;
< std::string tflite_path;
---
> std::string input_model(argv[1]);
> std::string output_registration(argv[2]);
> std::string tflite_path("tensorflow/lite");
158,159d157
< ParseFlagAndInit(&argc, argv, &input_models, &output_registration,
< &tflite_path, &namespace_flag, &for_micro);
163,171c161
< if (!input_models.empty()) {
< std::vector<std::string> models = absl::StrSplit(input_models, ',');
< for (const std::string& input_model : models) {
< AddOpsFromModel(input_model, &builtin_ops, &custom_ops);
< }
< }
< for (int i = 1; i < argc; i++) {
< AddOpsFromModel(argv[i], &builtin_ops, &custom_ops);
< }
---
> AddOpsFromModel(input_model, &builtin_ops, &custom_ops);
</pre>
*/
void RegisterSpecificOps(tflite::MutableOpResolver *resolver) {
resolver->AddBuiltin(tflite::BuiltinOperator_FULLY_CONNECTED,
tflite::ops::builtin::Register_FULLY_CONNECTED(), 3, 3);
resolver->AddBuiltin(tflite::BuiltinOperator_SOFTMAX,
tflite::ops::builtin::Register_SOFTMAX());
}
TfLiteDelegate *GetTfliteXnnpackDelegate(int num_threads) {
TfLiteXNNPackDelegateOptions xnnpack_options =
TfLiteXNNPackDelegateOptionsDefault();
xnnpack_options.num_threads = num_threads;
return TfLiteXNNPackDelegateCreate(&xnnpack_options);
}
} // namespace
int main() {
// Toy model that takes in four floats and returns two classification
// probabilities.
auto model = tflite::GetModel(tf_lite_model_data);
// Build the interpreter.
tflite::MutableOpResolver resolver;
RegisterSpecificOps(&resolver);
tflite::InterpreterBuilder builder(model, resolver);
std::unique_ptr<tflite::Interpreter> interpreter;
builder(&interpreter);
std::unique_ptr<tflite::ErrorReporter> reporter(
tflite::DefaultErrorReporter());
if (interpreter->AllocateTensors() != kTfLiteOk) {
reporter->Report("Failed at allocating tensors");
return EXIT_FAILURE;
}
// Using single-thread as an example. Can be changed as required.
TfLiteDelegate *xnnpack_delegate = GetTfliteXnnpackDelegate(1);
if (interpreter->ModifyGraphWithDelegate(xnnpack_delegate) != kTfLiteOk) {
reporter->Report("Failed at modifying graph with XNNPack delegate");
return EXIT_FAILURE;
}
// Always uses (0, 1, 2, 3) as inputs.
float *input = interpreter->typed_input_tensor<float>(0);
for (int i = 0; i < 4; ++i) {
input[i] = i;
}
auto status = interpreter->Invoke();
if (status != kTfLiteOk) {
reporter->Report("Failed at invoke");
return EXIT_FAILURE;
}
float y0 = interpreter->typed_output_tensor<float>(0)[0];
float y1 = interpreter->typed_output_tensor<float>(0)[1];
printf("Sample classification probability: %f, %f\n", y0, y1);
return EXIT_SUCCESS;
}