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 #include "tensorflow/lite/tools/utils.h" 31 32 namespace tflite { 33 namespace benchmark { 34 35 // Splits the input_layer_name and input_layer_value_files and stores them in 36 // the name_file_pair. In the case of failures, return an error status, and the 37 // the state of name_file_pair is unchanged. 38 // 39 // BenchmarkTfLiteModel takes --input_layer_value_files flag, which is a comma- 40 // separated list of input_layer_name:input_value_file_path pairs, 41 // e.g. input1:/tmp/path. 42 // 43 // As TensorFlow allows ':' in the tensor names (e.g. input:0 to denote the 44 // output index), having ':' as the delimiter can break the benchmark code 45 // unexpectedly. To avoid this issue, we allow escaping ':' char with '\:' for 46 // this particular flag only. This function handles splitting the name and file 47 // path that contains escaped colon. 48 // 49 // For example, "input\:0:/tmp/path" will be divided into input:0 and /tmp/path. 50 TfLiteStatus SplitInputLayerNameAndValueFile( 51 const std::string& name_and_value_file, 52 std::pair<std::string, std::string>& name_file_pair); 53 54 // Benchmarks a TFLite model by running tflite interpreter. 55 class BenchmarkTfLiteModel : public BenchmarkModel { 56 public: 57 struct InputLayerInfo { InputLayerInfoInputLayerInfo58 InputLayerInfo() : has_value_range(false), low(0), high(0) {} 59 60 std::string name; 61 std::vector<int> shape; 62 63 // The input value is randomly generated when benchmarking the NN model. 64 // However, the NN model might require the value be limited to a certain 65 // range [low, high] for this particular input layer. For simplicity, 66 // support integer value first. 67 bool has_value_range; 68 int low; 69 int high; 70 71 // The input value will be loaded from 'input_file_path' INSTEAD OF being 72 // randomly generated. Note the input file will be opened in binary mode. 73 std::string input_file_path; 74 }; 75 76 explicit BenchmarkTfLiteModel(BenchmarkParams params = DefaultParams()); 77 ~BenchmarkTfLiteModel() override; 78 79 std::vector<Flag> GetFlags() override; 80 void LogParams() override; 81 TfLiteStatus ValidateParams() override; 82 uint64_t ComputeInputBytes() override; 83 TfLiteStatus Init() override; 84 TfLiteStatus RunImpl() override; 85 static BenchmarkParams DefaultParams(); 86 87 protected: 88 TfLiteStatus PrepareInputData() override; 89 TfLiteStatus ResetInputsAndOutputs() override; 90 91 int64_t MayGetModelFileSize() override; 92 93 virtual TfLiteStatus LoadModel(); 94 95 // Allow subclasses to create a customized Op resolver during init. 96 virtual std::unique_ptr<tflite::OpResolver> GetOpResolver() const; 97 98 // Allow subclass to initialize a customized tflite interpereter. 99 virtual TfLiteStatus InitInterpreter(); 100 101 // Create a BenchmarkListener that's specifically for TFLite profiling if 102 // necessary. 103 virtual std::unique_ptr<BenchmarkListener> MayCreateProfilingListener() const; 104 105 void CleanUp(); 106 107 utils::InputTensorData LoadInputTensorData( 108 const TfLiteTensor& t, const std::string& input_file_path); 109 110 std::vector<InputLayerInfo> inputs_; 111 std::vector<utils::InputTensorData> inputs_data_; 112 std::unique_ptr<tflite::FlatBufferModel> model_; 113 std::unique_ptr<tflite::Interpreter> interpreter_; 114 std::unique_ptr<tflite::ExternalCpuBackendContext> external_context_; 115 116 private: 117 utils::InputTensorData CreateRandomTensorData( 118 const TfLiteTensor& t, const InputLayerInfo* layer_info); 119 AddOwnedListener(std::unique_ptr<BenchmarkListener> listener)120 void AddOwnedListener(std::unique_ptr<BenchmarkListener> listener) { 121 if (listener == nullptr) return; 122 owned_listeners_.emplace_back(std::move(listener)); 123 AddListener(owned_listeners_.back().get()); 124 } 125 126 std::vector<std::unique_ptr<BenchmarkListener>> owned_listeners_; 127 std::mt19937 random_engine_; 128 std::vector<Interpreter::TfLiteDelegatePtr> owned_delegates_; 129 // Always TFLITE_LOG the benchmark result. 130 BenchmarkLoggingListener log_output_; 131 }; 132 133 } // namespace benchmark 134 } // namespace tflite 135 136 #endif // TENSORFLOW_LITE_TOOLS_BENCHMARK_BENCHMARK_TFLITE_MODEL_H_ 137