| /* |
| * Copyright (c) 2020, Alliance for Open Media. All rights reserved |
| * |
| * This source code is subject to the terms of the BSD 2 Clause License and |
| * the Alliance for Open Media Patent License 1.0. If the BSD 2 Clause License |
| * was not distributed with this source code in the LICENSE file, you can |
| * obtain it at www.aomedia.org/license/software. 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 www.aomedia.org/license/patent. |
| */ |
| |
| /*!\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[] = { |
| 0x1c, 0x00, 0x00, 0x00, 0x54, 0x46, 0x4c, 0x33, 0x00, 0x00, 0x12, 0x00, 0x1c, |
| 0x00, 0x04, 0x00, 0x08, 0x00, 0x0c, 0x00, 0x10, 0x00, 0x14, 0x00, 0x00, 0x00, |
| 0x18, 0x00, 0x12, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0xf4, 0x04, 0x00, |
| 0x00, 0x4c, 0x01, 0x00, 0x00, 0x34, 0x01, 0x00, 0x00, 0x34, 0x00, 0x00, 0x00, |
| 0x04, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xb0, |
| 0xfb, 0xff, 0xff, 0x08, 0x00, 0x00, 0x00, 0x09, 0x00, 0x00, 0x00, 0x13, 0x00, |
| 0x00, 0x00, 0x6d, 0x69, 0x6e, 0x5f, 0x72, 0x75, 0x6e, 0x74, 0x69, 0x6d, 0x65, |
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| 0x00, 0xba, 0xfe, 0xff, 0xff, 0x04, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, |
| 0x31, 0x2e, 0x35, 0x2e, 0x30, 0x00, 0x00, 0x00, 0xb0, 0xfb, 0xff, 0xff, 0xb4, |
| 0xfb, 0xff, 0xff, 0xb8, 0xfb, 0xff, 0xff, 0xbc, 0xfb, 0xff, 0xff, 0xde, 0xfe, |
| 0xff, 0xff, 0x04, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x83, 0xad, 0x05, |
| 0x3e, 0x0a, 0x72, 0x13, 0x3f, 0x83, 0xfb, 0x88, 0x3f, 0x2a, 0xf1, 0x8b, 0xbe, |
| 0xfa, 0xfe, 0xff, 0xff, 0x04, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, 0x86, |
| 0xc7, 0x46, 0x3f, 0x57, 0xb7, 0x35, 0xbf, 0x78, 0x2c, 0xeb, 0x3e, 0x25, 0xab, |
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| 0x3e, 0x25, 0x67, 0x83, 0xbf, 0x2a, 0xc0, 0x7a, 0xbf, 0xf8, 0x46, 0x9d, 0x3f, |
| 0x0b, 0x18, 0x6b, 0xbf, 0x0d, 0xe7, 0xc1, 0x3e, 0xb4, 0xd9, 0x15, 0xbf, 0x8d, |
| 0x62, 0xa3, 0x3e, 0xb4, 0x5e, 0xab, 0xbb, 0xb8, 0x49, 0x3a, 0x3e, 0x46, 0xff, |
| 0xff, 0xff, 0x04, 0x00, 0x00, 0x00, 0x20, 0x00, 0x00, 0x00, 0x5a, 0xdc, 0x3d, |
| 0x3d, 0xdd, 0x63, 0x17, 0xbe, 0x4a, 0x05, 0x1a, 0xbf, 0x27, 0xde, 0x3d, 0x3e, |
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| 0x30, 0x00, 0xbf, 0x72, 0xff, 0xff, 0xff, 0x04, 0x00, 0x00, 0x00, 0x08, 0x00, |
| 0x00, 0x00, 0x76, 0xf6, 0x0a, 0xbf, 0x76, 0xf6, 0x0a, 0x3f, 0x68, 0xfc, 0xff, |
| 0xff, 0x0f, 0x00, 0x00, 0x00, 0x54, 0x4f, 0x43, 0x4f, 0x20, 0x43, 0x6f, 0x6e, |
| 0x76, 0x65, 0x72, 0x74, 0x65, 0x64, 0x2e, 0x00, 0x01, 0x00, 0x00, 0x00, 0x10, |
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| 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x00, 0x06, 0x00, 0x05, 0x00, 0x06, 0x00, |
| 0x00, 0x00, 0x00, 0x19, 0x0a, 0x00, 0x0c, 0x00, 0x07, 0x00, 0x00, 0x00, 0x08, |
| 0x00, 0x0a, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0x03, 0x00, 0x00, 0x00 |
| }; |
| |
| /** |
| * 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.) Apply the following diff to |
| * tensorflow/lite/tools/gen_op_registration_main.cc: |
| <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> |
| * This diff fixes an issue with the current binary's flag parsing |
| * library, which is failing silently. |
| * |
| * 3. Run "bazel build tensorflow/lite/tools/generate_op_registrations" |
| * and then run: |
| <pre> |
| ./bazel-bin/tensorflow/lite/tools/generate_op_registrations \ |
| /path/to/model.tflite /path/to/output.cc |
| </pre> |
| * For appropriate paths to the input and output files. |
| */ |
| 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()); |
| } |
| |
| } // 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"); |
| 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"); |
| 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; |
| } |