1 /* Copyright 2018 The TensorFlow Authors. All Rights Reserved. 2 3 Licensed under the Apache License, Version 2.0 (the "License"); 4 you may not use this file except in compliance with the License. 5 You may obtain a copy of the License at 6 7 http://www.apache.org/licenses/LICENSE-2.0 8 9 Unless required by applicable law or agreed to in writing, software 10 distributed under the License is distributed on an "AS IS" BASIS, 11 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 See the License for the specific language governing permissions and 13 limitations under the License. 14 ==============================================================================*/ 15 16 #ifndef TENSORFLOW_LITE_TOOLS_BENCHMARK_BENCHMARK_TFLITE_MODEL_H_ 17 #define TENSORFLOW_LITE_TOOLS_BENCHMARK_BENCHMARK_TFLITE_MODEL_H_ 18 19 #include <algorithm> 20 #include <map> 21 #include <memory> 22 #include <random> 23 #include <string> 24 #include <utility> 25 #include <vector> 26 27 #include "tensorflow/lite/model.h" 28 #include "tensorflow/lite/profiling/profiler.h" 29 #include "tensorflow/lite/tools/benchmark/benchmark_model.h" 30 31 namespace tflite { 32 namespace benchmark { 33 34 // Benchmarks a TFLite model by running tflite interpreter. 35 class BenchmarkTfLiteModel : public BenchmarkModel { 36 public: 37 struct InputLayerInfo { InputLayerInfoInputLayerInfo38 InputLayerInfo() : has_value_range(false) {} 39 40 std::string name; 41 std::vector<int> shape; 42 43 // The input value is randomly generated when benchmarking the NN model. 44 // However, the NN model might require the value be limited to a certain 45 // range [low, high] for this particular input layer. For simplicity, 46 // support integer value first. 47 bool has_value_range; 48 int low; 49 int high; 50 51 // The input value will be loaded from 'input_file_path' INSTEAD OF being 52 // randomly generated. Note the input file will be opened in binary mode. 53 std::string input_file_path; 54 }; 55 56 explicit BenchmarkTfLiteModel(BenchmarkParams params = DefaultParams()); 57 ~BenchmarkTfLiteModel() override; 58 59 std::vector<Flag> GetFlags() override; 60 void LogParams() override; 61 TfLiteStatus ValidateParams() override; 62 uint64_t ComputeInputBytes() override; 63 TfLiteStatus Init() override; 64 TfLiteStatus RunImpl() override; 65 static BenchmarkParams DefaultParams(); 66 67 protected: 68 TfLiteStatus PrepareInputData() override; 69 TfLiteStatus ResetInputsAndOutputs() override; 70 71 int64_t MayGetModelFileSize() override; 72 73 virtual TfLiteStatus LoadModel(); 74 75 // Allow subclasses to create a customized Op resolver during init. 76 virtual std::unique_ptr<tflite::OpResolver> GetOpResolver() const; 77 78 // Allow subclass to initialize a customized tflite interpereter. 79 virtual TfLiteStatus InitInterpreter(); 80 81 // Create a BenchmarkListener that's specifically for TFLite profiling if 82 // necessary. 83 virtual std::unique_ptr<BenchmarkListener> MayCreateProfilingListener() const; 84 85 void CleanUp(); 86 87 std::unique_ptr<tflite::FlatBufferModel> model_; 88 std::unique_ptr<tflite::Interpreter> interpreter_; 89 std::unique_ptr<tflite::ExternalCpuBackendContext> external_context_; 90 91 private: 92 // Implement type erasure with unique_ptr with custom deleter. 93 using VoidUniquePtr = std::unique_ptr<void, void (*)(void*)>; 94 95 struct InputTensorData { InputTensorDataInputTensorData96 InputTensorData() : data(nullptr, nullptr) {} 97 98 VoidUniquePtr data; 99 size_t bytes; 100 }; 101 102 template <typename T, typename Distribution> CreateInputTensorData(int num_elements,Distribution distribution)103 inline InputTensorData CreateInputTensorData(int num_elements, 104 Distribution distribution) { 105 InputTensorData tmp; 106 tmp.bytes = sizeof(T) * num_elements; 107 T* raw = new T[num_elements]; 108 std::generate_n(raw, num_elements, [&]() { 109 return static_cast<T>(distribution(random_engine_)); 110 }); 111 tmp.data = VoidUniquePtr(static_cast<void*>(raw), 112 [](void* ptr) { delete[] static_cast<T*>(ptr); }); 113 return tmp; 114 } 115 116 InputTensorData CreateRandomTensorData(const TfLiteTensor& t, 117 const InputLayerInfo* layer_info); 118 119 InputTensorData LoadInputTensorData(const TfLiteTensor& t, 120 const std::string& input_file_path); 121 AddOwnedListener(std::unique_ptr<BenchmarkListener> listener)122 void AddOwnedListener(std::unique_ptr<BenchmarkListener> listener) { 123 if (listener == nullptr) return; 124 owned_listeners_.emplace_back(std::move(listener)); 125 AddListener(owned_listeners_.back().get()); 126 } 127 128 std::vector<InputLayerInfo> inputs_; 129 std::vector<InputTensorData> inputs_data_; 130 std::vector<std::unique_ptr<BenchmarkListener>> owned_listeners_; 131 std::mt19937 random_engine_; 132 std::vector<Interpreter::TfLiteDelegatePtr> owned_delegates_; 133 // Always TFLITE_LOG the benchmark result. 134 BenchmarkLoggingListener log_output_; 135 }; 136 137 } // namespace benchmark 138 } // namespace tflite 139 140 #endif // TENSORFLOW_LITE_TOOLS_BENCHMARK_BENCHMARK_TFLITE_MODEL_H_ 141