wehub-resource-sync
467 lines
20 KiB
C++
467 lines
20 KiB
C++
//
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// ConvolutionTflite.cpp
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// MNNConverter
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//
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// Created by MNN on 2019/01/31.
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// Copyright © 2018, Alibaba Group Holding Limited
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//
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#include <stdio.h>
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#include "TfliteUtils.hpp"
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#include "liteOpConverter.hpp"
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#include "core/OpCommonUtils.hpp"
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#include "core/IDSTEncoder.hpp"
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DECLARE_OP_COVERTER(Conv2DTflite);
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MNN::OpType Conv2DTflite::opType(int quantizedModel) {
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if (quantizedModel == 1)
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return MNN::OpType_TfQuantizedConv2D;
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return MNN::OpType_Convolution;
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}
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MNN::OpParameter Conv2DTflite::type(int quantizedModel) {
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if (quantizedModel == 1)
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return MNN::OpParameter_TfQuantizedConv2D;
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return MNN::OpParameter_Convolution2D;
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}
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void Conv2DTflite::run(MNN::OpT* dstOp, const std::unique_ptr<tflite::OperatorT>& tfliteOp,
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const std::vector<std::unique_ptr<tflite::TensorT>>& tfliteTensors,
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const std::vector<std::unique_ptr<tflite::BufferT>>& tfliteModelBuffer,
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const std::vector<std::unique_ptr<tflite::OperatorCodeT>>& tfliteOpSet, int quantizedModel) {
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// 3|2 inputs: input tensor, weight, (bias)
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const int inputSize = tfliteOp->inputs.size();
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DCHECK(inputSize == 2 || inputSize == 3) << "tflite Conv2D input ERROR! ";
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const auto& tfliteConvOption = tfliteOp->builtin_options.AsConv2DOptions();
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const int inputIndex = tfliteOp->inputs[0];
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const int weightIndex = tfliteOp->inputs[1];
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const int outputIndex = tfliteOp->outputs[0];
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const auto& inputTensor = tfliteTensors[inputIndex];
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const auto& weightTensor = tfliteTensors[weightIndex];
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const auto& outputTensor = tfliteTensors[outputIndex];
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if (weightTensor->type == tflite::TensorType_INT8 || weightTensor->type == tflite::TensorType_INT4) {
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quantizedModel = 2;
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dstOp->type = MNN::OpType_Convolution;
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dstOp->main.type = MNN::OpParameter_Convolution2D;
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} else if (weightTensor->type == tflite::TensorType_UINT8) {
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quantizedModel = 1;
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dstOp->type = MNN::OpType_TfQuantizedConv2D;
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dstOp->main.type = MNN::OpParameter_TfQuantizedConv2D;
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} else {
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MNN_ASSERT(weightTensor->type == tflite::TensorType_FLOAT32);
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quantizedModel = 0;
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dstOp->type = MNN::OpType_Convolution;
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dstOp->main.type = MNN::OpParameter_Convolution2D;
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}
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auto inputShape = inputTensor->shape;
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int group = 1;
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// co kh kw ci
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const auto& weightShape = weightTensor->shape;
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DCHECK(weightShape.size() == 4) << "Conv2D weight ERROR!";
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const int co = weightShape[0];
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const int kh = weightShape[1];
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const int kw = weightShape[2];
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const int ci = weightShape[3];
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const int weightSize = co * kh * kw * ci;
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if (inputShape.size() == 4 && inputShape[3] > ci) {
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group = inputShape[3] / ci;
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}
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if (quantizedModel == 1) { // UINT8_QUANT
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auto conv2dParamQuan = new MNN::TfQuantizedConv2DT;
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conv2dParamQuan->modelFormat = MNN::ModeFormat_TFLITE;
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conv2dParamQuan->common = std::unique_ptr<MNN::Convolution2DCommonT>(new MNN::Convolution2DCommonT);
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// filterOffset
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conv2dParamQuan->filterQuantizedParam = std::unique_ptr<MNN::QuantizedParamT>(new MNN::QuantizedParamT);
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if (weightTensor->quantization->zero_point.size() > 0) {
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conv2dParamQuan->filterQuantizedParam->zeroPoint = weightTensor->quantization->zero_point[0];
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} else {
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conv2dParamQuan->filterQuantizedParam->zeroPoint = 0;
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}
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if (weightTensor->quantization->scale.size() > 0) {
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conv2dParamQuan->filterQuantizedParam->scale = weightTensor->quantization->scale[0];
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} else {
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conv2dParamQuan->filterQuantizedParam->scale = 0.0;
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}
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// input
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conv2dParamQuan->inputQuantizedParam = std::unique_ptr<MNN::QuantizedParamT>(new MNN::QuantizedParamT);
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if (inputTensor->quantization->zero_point.size() > 0) {
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conv2dParamQuan->inputQuantizedParam->zeroPoint = inputTensor->quantization->zero_point[0];
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} else {
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conv2dParamQuan->inputQuantizedParam->zeroPoint = 0;
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}
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if (inputTensor->quantization->scale.size() > 0) {
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conv2dParamQuan->inputQuantizedParam->scale = inputTensor->quantization->scale[0];
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} else {
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conv2dParamQuan->inputQuantizedParam->scale = 0.0;
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}
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// output
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conv2dParamQuan->outputQuantizedParam = std::unique_ptr<MNN::QuantizedParamT>(new MNN::QuantizedParamT);
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if (outputTensor->quantization->scale.size() > 0) {
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conv2dParamQuan->outputQuantizedParam->zeroPoint = outputTensor->quantization->zero_point[0];
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} else {
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conv2dParamQuan->outputQuantizedParam->zeroPoint = 0;
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}
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if (outputTensor->quantization->scale.size() > 0) {
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conv2dParamQuan->outputQuantizedParam->scale = outputTensor->quantization->scale[0];
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} else {
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conv2dParamQuan->outputQuantizedParam->scale = 0.0;
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}
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// kernel size
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conv2dParamQuan->common->kernelX = kw;
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conv2dParamQuan->common->kernelY = kh;
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conv2dParamQuan->common->outputCount = co;
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// default
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conv2dParamQuan->common->group = group;
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conv2dParamQuan->common->dilateX = tfliteConvOption->dilation_w_factor;
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conv2dParamQuan->common->dilateY = tfliteConvOption->dilation_h_factor;
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conv2dParamQuan->depthMultiplier = 1;
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// stride
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conv2dParamQuan->common->strideX = tfliteConvOption->stride_w;
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conv2dParamQuan->common->strideY = tfliteConvOption->stride_h;
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const auto tflitePadMode = tfliteConvOption->padding;
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if (tflitePadMode == tflite::Padding_SAME) {
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conv2dParamQuan->common->padMode = MNN::PadMode_SAME;
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} else if (tflitePadMode == tflite::Padding_VALID) {
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conv2dParamQuan->common->padMode = MNN::PadMode_VALID;
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}
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// weight
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DCHECK(weightTensor->type == tflite::TensorType_UINT8) << "Data type ERROR";
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// nhwc->hwcn
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int out_size = kh * kw * ci;
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int in_size = co;
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std::vector<uint8_t> filter_hwcn;
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filter_hwcn.resize(weightSize);
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for (int i = 0; i < out_size; i++) {
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for (int j = 0; j < in_size; j++) {
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filter_hwcn[i * in_size + j] = tfliteModelBuffer[weightTensor->buffer]->data[i + j * out_size];
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}
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}
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conv2dParamQuan->weight = filter_hwcn;
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conv2dParamQuan->biasflag = (inputSize == 3);
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DCHECK(conv2dParamQuan->biasflag == true);
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const auto& biasTensor = tfliteTensors[tfliteOp->inputs[2]];
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if (inputSize == 3) {
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DCHECK(biasTensor->type == tflite::TensorType_INT32) << "Bias Type ERROR";
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const auto& biasData = tfliteModelBuffer[biasTensor->buffer]->data;
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conv2dParamQuan->biasQuantizedParam = std::unique_ptr<MNN::QuantizedParamT>(new MNN::QuantizedParamT);
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conv2dParamQuan->biasQuantizedParam->zeroPoint = biasTensor->quantization->zero_point[0];
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conv2dParamQuan->biasQuantizedParam->scale = biasTensor->quantization->scale[0];
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DCHECK(biasData.size() / 4 == co) << "Bias Data ERROR";
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auto biasDataPtr = biasData.data();
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const int32_t* realBiasDataPtr = (int32_t*)biasDataPtr;
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std::vector<int32_t> biasInt32Vec(realBiasDataPtr, realBiasDataPtr + co);
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conv2dParamQuan->bias = biasInt32Vec;
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}
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conv2dParamQuan->activationType = (MNN::FusedActivation)tfliteConvOption->fused_activation_function;
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dstOp->main.value = conv2dParamQuan;
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} else if (quantizedModel == 2) { // INT8_QUANT
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std::unique_ptr<MNN::Convolution2DT> convolution2DQuant(new MNN::Convolution2DT);
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convolution2DQuant->common = std::unique_ptr<MNN::Convolution2DCommonT>(new MNN::Convolution2DCommonT);
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auto& common = convolution2DQuant->common;
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common->relu = false;
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common->relu6 = false;
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const auto acticationFun = tfliteConvOption->fused_activation_function;
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if (acticationFun == tflite::ActivationFunctionType_RELU) {
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common->relu = true;
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} else if (acticationFun == tflite::ActivationFunctionType_RELU6) {
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common->relu6 = true;
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} else if (acticationFun > tflite::ActivationFunctionType_NONE) {
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DLOG(ERROR) << "MNN Convolution do not Support fused_activation_function: " << acticationFun;
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dstOp->type = MNN::OpType_MAX;
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return;
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}
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common->group = group;
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common->outputCount = co;
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common->inputCount = ci * group;
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common->kernelX = kw;
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common->kernelY = kh;
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common->dilateX = tfliteConvOption->dilation_w_factor;
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common->dilateY = tfliteConvOption->dilation_h_factor;
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common->strideX = tfliteConvOption->stride_w;
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common->strideY = tfliteConvOption->stride_h;
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common->padMode = MNN::PadMode_SAME;
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if (tfliteConvOption->padding == tflite::Padding_VALID) {
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common->padMode = MNN::PadMode_VALID;
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}
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// weight
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if (tfliteModelBuffer[weightTensor->buffer]->data.data() == nullptr) {
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//MNN_ERROR("Has not const weight data for tflite convolution\n");
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dstOp->main.value = convolution2DQuant.release();
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return;
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}
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std::vector<int8_t> weightTmp;
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auto weight = reinterpret_cast<const int8_t*>(tfliteModelBuffer[weightTensor->buffer]->data.data());
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if (weightTensor->type == tflite::TensorType_INT4) {
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// Add one to assume has enough memory
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weightTmp.resize(weightSize + 1);
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auto originSize = tfliteModelBuffer[weightTensor->buffer]->data.size();
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// Int4 -> Int8
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int halfSize = (weightSize + 1) / 2;
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auto srcInt4 = reinterpret_cast<const uint8_t*>(tfliteModelBuffer[weightTensor->buffer]->data.data());
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for (int v=0; v<halfSize; ++v) {
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int srcValue = srcInt4[v];
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weightTmp[2 * v] = srcValue & 0x0F;
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weightTmp[2 * v + 1] = (srcValue >> 4) & 0x0F;
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}
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for (int v=0; v<weightSize; ++v) {
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if (weightTmp[v] >= 8) {
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weightTmp[v] = weightTmp[v] - 16;
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}
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}
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weight = weightTmp.data();
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} else {
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MNN_ASSERT(weightTensor->type == tflite::TensorType_INT8);
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MNN_ASSERT(tfliteModelBuffer[weightTensor->buffer]->data.size() == weightSize);
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}
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MNN_ASSERT(weightTensor->quantization->scale.size() == co);
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convolution2DQuant->symmetricQuan.reset(new MNN::QuantizedFloatParamT);
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std::vector<int8_t> transposeWeight(weightSize, 0);
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auto alpha = weightTensor->quantization->scale.data();
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// TODO: Support zero
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float scaleIn = inputTensor->quantization->scale[0];
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float scaleOut = outputTensor->quantization->scale[0];
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// [co, kh, kw, ci] -> [co, ci, kh, kw]
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const int area = kh * kw;
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for (int i = 0; i < co; i ++) {
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for (int j = 0; j < ci; j++) {
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for (int k = 0; k < area; k++) {
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transposeWeight[i * ci * area + j * area + k] = weight[i * area * ci + k * ci + j];
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}
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}
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}
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convolution2DQuant->quanParameter = IDSTEncoder::encode(nullptr, weightTensor->quantization->scale, kh * kw * ci, co, false, transposeWeight.data(), -128);
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// bias
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convolution2DQuant->bias.resize(co);
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if (inputSize == 3) {
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const auto& biasTensor = tfliteTensors[tfliteOp->inputs[2]];
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auto bias = reinterpret_cast<const int*>(tfliteModelBuffer[biasTensor->buffer]->data.data());
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// int to float
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for (int i = 0; i < co; i++) {
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convolution2DQuant->bias[i] = bias[i] * (scaleIn * alpha[i]);
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}
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}
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dstOp->main.value = convolution2DQuant.release();
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} else {
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std::unique_ptr<MNN::Convolution2DT> convolution2DFloat(new MNN::Convolution2DT);
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convolution2DFloat->common = std::unique_ptr<MNN::Convolution2DCommonT>(new MNN::Convolution2DCommonT);
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auto& common = convolution2DFloat->common;
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common->relu = false;
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common->relu6 = false;
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const auto acticationFun = tfliteConvOption->fused_activation_function;
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if (acticationFun == tflite::ActivationFunctionType_RELU) {
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common->relu = true;
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} else if (acticationFun == tflite::ActivationFunctionType_RELU6) {
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common->relu6 = true;
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} else if (acticationFun > tflite::ActivationFunctionType_NONE) {
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DLOG(ERROR) << "MNN Convolution do not Support fused_activation_function: " << acticationFun;
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dstOp->type = MNN::OpType_MAX;
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return;
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}
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common->group = group;
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common->outputCount = co;
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common->inputCount = ci * group;
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common->kernelX = kw;
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common->kernelY = kh;
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common->dilateX = tfliteConvOption->dilation_w_factor;
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common->dilateY = tfliteConvOption->dilation_h_factor;
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common->strideX = tfliteConvOption->stride_w;
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common->strideY = tfliteConvOption->stride_h;
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common->padMode = MNN::PadMode_SAME;
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if (tfliteConvOption->padding == tflite::Padding_VALID) {
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common->padMode = MNN::PadMode_VALID;
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}
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// weight
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if (tfliteModelBuffer[weightTensor->buffer]->data.data() == nullptr) {
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//MNN_ERROR("Has not const weight data for tflite convolution\n");
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dstOp->main.value = convolution2DFloat.release();
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return;
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}
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std::vector<float> weightData;
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weightData.resize(weightSize);
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switch (weightTensor->type) {
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case tflite::TensorType_FLOAT32:
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{
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auto originalWeightPtr = reinterpret_cast<const float*>(tfliteModelBuffer[weightTensor->buffer]->data.data());
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convertDataFormatTflite(originalWeightPtr, weightData.data(), kh, kw, ci, co);
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break;
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}
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case tflite::TensorType_UINT8:
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{
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auto originalWeightPtr = reinterpret_cast<const int8_t*>(tfliteModelBuffer[weightTensor->buffer]->data.data());
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convertDataFormatTfliteDequant<int8_t>(originalWeightPtr, weightData.data(), kh, kw, ci, co, weightTensor->quantization.get());
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break;
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}
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default:
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DLOG(ERROR) << "MNN Convolution do not Support weight type: " << weightTensor->type;
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}
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convolution2DFloat->weight = weightData;
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// bias
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std::vector<float> biasData(co, 0.0f);
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if (inputSize == 3) {
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const auto& biasTensor = tfliteTensors[tfliteOp->inputs[2]];
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auto biasDataPtr = reinterpret_cast<const float*>(tfliteModelBuffer[biasTensor->buffer]->data.data());
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::memcpy(biasData.data(), biasDataPtr, sizeof(float) * co);
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}
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convolution2DFloat->bias = biasData;
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dstOp->main.value = convolution2DFloat.release();
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}
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// set input output index
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dstOp->inputIndexes.resize(1);
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dstOp->outputIndexes.resize(1);
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dstOp->inputIndexes[0] = tfliteOp->inputs[0];
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dstOp->outputIndexes[0] = tfliteOp->outputs[0];
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}
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DECLARE_OP_COVERTER(TransposeConvTflite);
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MNN::OpType TransposeConvTflite::opType(int quantizedModel){
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return MNN::OpType_Deconvolution;
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}
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MNN::OpParameter TransposeConvTflite::type(int quantizedModel){
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return MNN::OpParameter_Convolution2D;
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}
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void TransposeConvTflite::run(MNN::OpT *dstOp, const std::unique_ptr<tflite::OperatorT> &tfliteOp, const std::vector<std::unique_ptr<tflite::TensorT> > &tfliteTensors, const std::vector<std::unique_ptr<tflite::BufferT> > &tfliteModelBuffer, const std::vector<std::unique_ptr<tflite::OperatorCodeT> > &tfliteOpSet, int quantizedModel){
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DCHECK(!quantizedModel) << "TransposeConv not support quantized model";
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// 3|4 inputs: output shape, weight, input tensor, (bias)
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const int inputSize = tfliteOp->inputs.size();
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DCHECK(inputSize == 3 || inputSize == 4) << "tflite Conv2D input ERROR! ";
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/*
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enum Padding : byte { SAME, VALID }
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table TransposeConvOptions {
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padding:Padding;
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stride_w:int;
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stride_h:int;
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}
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*/
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const auto& tfliteConvOption = tfliteOp->builtin_options.AsTransposeConvOptions();
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// weight index
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const int weightIndex = tfliteOp->inputs[1];
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const auto& weightTensor = tfliteTensors[weightIndex];
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// co kh kw ci
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const auto& weightShape = weightTensor->shape;
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DCHECK(weightShape.size() == 4) << "Conv2D weight ERROR!";
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const int co = weightShape[0];
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const int kh = weightShape[1];
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const int kw = weightShape[2];
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const int ci = weightShape[3];
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const int weightSize = co * kh * kw * ci;
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{
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auto convolution2DFloat = new MNN::Convolution2DT;
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// weight
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std::vector<float> weightData;
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weightData.resize(weightSize);
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auto originalWeightPtr = reinterpret_cast<const float*>(tfliteModelBuffer[weightTensor->buffer]->data.data());
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convertDataFormatTflite(originalWeightPtr, weightData.data(), kh, kw, ci, co, true);
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convolution2DFloat->weight = weightData;
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// bias
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std::vector<float> biasData(co, 0.0f);
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if (inputSize == 4) {
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const auto& biasTensor = tfliteTensors[tfliteOp->inputs[2]];
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auto biasDataPtr = reinterpret_cast<const float*>(tfliteModelBuffer[biasTensor->buffer]->data.data());
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if(biasDataPtr){
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::memcpy(biasData.data(), biasDataPtr, sizeof(float) * co);
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}
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}
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convolution2DFloat->bias = biasData;
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convolution2DFloat->common = std::unique_ptr<MNN::Convolution2DCommonT>(new MNN::Convolution2DCommonT);
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auto& common = convolution2DFloat->common;
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common->relu = false;
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common->relu6 = false;
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common->group = 1;
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common->outputCount = co;
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common->inputCount = ci;
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common->kernelX = kw;
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common->kernelY = kh;
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common->dilateX = 1;
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common->dilateY = 1;
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common->strideX = tfliteConvOption->stride_w;
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common->strideY = tfliteConvOption->stride_h;
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common->padMode = MNN::PadMode_SAME;
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common->hasOutputShape = true;
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dstOp->main.value = convolution2DFloat;
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}
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// set input output index
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dstOp->inputIndexes.resize(2);
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dstOp->outputIndexes.resize(1);
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dstOp->inputIndexes[0] = tfliteOp->inputs[2];
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dstOp->inputIndexes[1] = tfliteOp->inputs[0];
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dstOp->outputIndexes[0] = tfliteOp->outputs[0];
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}
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DECLARE_OP_COVERTER(FullConnectedTflite);
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MNN::OpType FullConnectedTflite::opType(int quantizedModel) {
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return MNN::OpType_Extra;
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}
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MNN::OpParameter FullConnectedTflite::type(int quantizedModel) {
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return MNN::OpParameter_Extra;
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}
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void FullConnectedTflite::run(MNN::OpT* dstOp, const std::unique_ptr<tflite::OperatorT>& tfliteOp,
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const std::vector<std::unique_ptr<tflite::TensorT>>& tfliteTensors,
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const std::vector<std::unique_ptr<tflite::BufferT>>& tfliteModelBuffer,
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const std::vector<std::unique_ptr<tflite::OperatorCodeT>>& tfliteOpSet, int quantizedModel) {
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dstOp->main.value = new MNN::ExtraT;
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auto dstP = dstOp->main.AsExtra();
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dstP->engine = "Tflite";
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dstP->type = "FULL_CONNECT";
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const auto& option = tfliteOp->builtin_options.AsFullyConnectedOptions();
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dstP->attr.resize(3);
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dstP->attr[0].reset(new MNN::AttributeT);
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dstP->attr[0]->key = "keep_num_dims";
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dstP->attr[0]->b = option->keep_num_dims;
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|
|
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dstP->attr[1].reset(new MNN::AttributeT);
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dstP->attr[1]->key = "weights_format";
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dstP->attr[1]->i = option->weights_format;
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|
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dstP->attr[2].reset(new MNN::AttributeT);
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dstP->attr[2]->key = "fused_activation_function";
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dstP->attr[2]->i = option->fused_activation_function;
|
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}
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using namespace tflite;
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REGISTER_CONVERTER(Conv2DTflite, BuiltinOperator_CONV_2D);
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REGISTER_CONVERTER(TransposeConvTflite, BuiltinOperator_TRANSPOSE_CONV);
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REGISTER_CONVERTER(FullConnectedTflite, BuiltinOperator_FULLY_CONNECTED);
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