10b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. 20b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle 30b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew SelleLicensed under the Apache License, Version 2.0 (the "License"); 40b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selleyou may not use this file except in compliance with the License. 50b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew SelleYou may obtain a copy of the License at 60b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle 70b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle http://www.apache.org/licenses/LICENSE-2.0 80b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle 90b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew SelleUnless required by applicable law or agreed to in writing, software 100b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selledistributed under the License is distributed on an "AS IS" BASIS, 110b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew SelleWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 120b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew SelleSee the License for the specific language governing permissions and 130b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Sellelimitations under the License. 140b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle==============================================================================*/ 150b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle#include <memory> 160b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle#include <string> 170b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle#include <unordered_map> 180b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle#include <vector> 190b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle 200b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle#include "tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h" 210b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle#include "tensorflow/contrib/lite/toco/model.h" 220b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle#include "tensorflow/contrib/lite/toco/model_flags.pb.h" 230b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle#include "tensorflow/contrib/lite/toco/tooling_util.h" 240b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle#include "tensorflow/core/platform/logging.h" 250b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle 260b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Sellenamespace toco { 270b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle 280b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle// This inserts an operator whose output is a float array (name: 290b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle// flags.input_array()). It has to wait for any existing operators that 300b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle// generate this output to be removed by graph transformations. Note that there 310b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle// may be more than one operator that takes the input_array as their input, and 320b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle// that some of these may be removed by graph transformations. 330b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Sellebool AddDequantizeOperatorToInput(const string& input_name, const Operator* op, 340b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle GraphTransformation* transformation, 350b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle Model* model) { 360b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle // An operator with the required output may be a dequantize operator already 370b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle // created. Alternatively it may be an operator that needs to be removed 380b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle // because it is unused, in which case we wait for RemoveUnusedOp to do its 390b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle // work. 400b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle if (GetOpWithOutput(*model, input_name)) { 410b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle return false; 420b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle } 430b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle 440b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle // We only apply for the first operator if there is more than one. This is 450b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle // not strictly necessary for ordering correctness, since we insert the 460b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle // dequant operator at the beginning of the op sequence, but it makes the 470b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle // insertion more predictable (eg forward vs backwards operator sweep). 480b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle if (CountOpsWithInput(*model, input_name) > 1) { 490b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle if (op != GetFirstOpWithInput(*model, input_name)) { 500b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle return false; 510b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle } 520b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle } 530b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle 540b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle auto& input_array = model->GetArray(input_name); 550b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle if (input_array.data_type != ArrayDataType::kFloat) { 560b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle return false; 570b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle } 580b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle 590b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle if (input_array.final_data_type == input_array.data_type || 600b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle input_array.final_data_type == ArrayDataType::kNone) { 610b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle return false; 620b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle } 630b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle 640b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle const auto& dequantized_input_name = 650b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle AvailableArrayName(*model, input_name + "_dequantized"); 660b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle for (auto& other_op : model->operators) { 670b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle for (string& other_op_input : other_op->inputs) { 680b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle if (other_op_input == input_name) { 690b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle other_op_input = dequantized_input_name; 700b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle } 710b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle } 720b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle } 730b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle 740b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle auto& dequantized_input_array = 750b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle model->GetOrCreateArray(dequantized_input_name); 760b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle auto* image_input_op = new DequantizeOperator; 770b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle image_input_op->inputs = {input_name}; 780b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle image_input_op->outputs = {dequantized_input_name}; 790b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle model->operators.emplace(model->operators.begin(), image_input_op); 800b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle 810b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle CHECK(input_array.final_data_type == ArrayDataType::kUint8); 820b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle input_array.data_type = ArrayDataType::kUint8; 830b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle dequantized_input_array.data_type = ArrayDataType::kFloat; 840b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle const auto& input_minmax = input_array.GetMinMax(); 850b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle auto& dequantized_input_minmax = dequantized_input_array.GetOrCreateMinMax(); 860b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle dequantized_input_minmax = input_minmax; 870b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle auto& input_qparams = input_array.GetOrCreateQuantizationParams(); 880b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle GetQuantizationParamsFromMinMax<ArrayDataType::kUint8>( 890b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle model->flags, input_minmax, &input_qparams); 900b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle 910b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle transformation->AddMessageF( 920b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle "Created %s" 930b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle " to handle quantized input image data, taking over existing" 940b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle " mean_value and std_value flags. Cleared those flags.", 950b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle LogName(*image_input_op)); 960b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle 970b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle return true; 980b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle} 990b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle 1000b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Sellebool MakeInitialDequantizeOperator::Run(Model* model, std::size_t op_index) { 1010b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle // This is effectively a transformation applied to edges. We iterate over the 1020b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle // specified node (op) and proceed for input edges. 1030b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle const auto it = model->operators.begin() + op_index; 1040b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle const auto* op = it->get(); 1050b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle bool change_made = false; 1060b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle for (auto& input : op->inputs) { 1070b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle for (auto& input_array : *model->flags.mutable_input_arrays()) { 1080b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle if (input_array.name() == input) { 1090b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle if (AddDequantizeOperatorToInput(input_array.name(), op, this, model)) { 1100b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle change_made = true; 1110b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle input_array.clear_mean_value(); 1120b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle input_array.clear_std_value(); 1130b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle } 1140b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle } 1150b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle } 1160b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle } 1170b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle return change_made; 1180b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle} 1190b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle 1200b15439f8f0f2d4755587f4096c3ea04cb199d23Andrew Selle} // namespace toco 121