wehub-resource-sync
239 lines
9.7 KiB
C++
239 lines
9.7 KiB
C++
//
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// OnnxUpsample.cpp
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// MNNConverter
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//
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// Created by MNN on 2019/10/24.
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// Copyright © 2018, Alibaba Group Holding Limited
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//
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#include "MNN_generated.h"
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#include "OnnxExtraManager.hpp"
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namespace MNN {
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namespace Express {
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class OnnxUpSampleTransform : public OnnxExtraManager::Transform {
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public:
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virtual EXPRP onExecute(EXPRP expr) const override {
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auto inputs = expr->inputs();
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std::vector<float> scales;
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int scalesSize = 1;
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auto op = expr->get();
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auto extraParam = op->main_as_Extra();
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const int attrSize = extraParam->attr()->size();
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std::string interpMode;
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std::string coordMode = ""; // detect align_corner attribute
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for (int i = 0; i < attrSize; ++i) {
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auto attr = extraParam->attr()->GetAs<Attribute>(i);
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const auto& key = attr->key()->str();
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if (key == "mode") {
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interpMode = attr->s()->str();
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} else if ((inputs.size() == 1) && key == "scales") {
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scalesSize = attr->list()->f()->size();
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scales.resize(scalesSize);
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memcpy(scales.data(), attr->list()->f()->data(), sizeof(float) * scalesSize);
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} else if (key == "coordinate_transformation_mode") {
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coordMode = attr->s()->str();
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}
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}
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std::unique_ptr<OpT> mergeredUpsample(new OpT);
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mergeredUpsample->name = expr->name();
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mergeredUpsample->type = OpType_Interp;
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mergeredUpsample->main.type = OpParameter_Interp;
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std::unique_ptr<InterpT> interpParam(new InterpT);
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const float* scaleDataPtr = scales.data();
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if (inputs.size() == 2) {
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auto scale = inputs[1];
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scaleDataPtr = scale->readMap<float>();
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auto scaleInfo = scale->getInfo();
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if (!scaleDataPtr) {
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mergeredUpsample->main.value = interpParam.release();
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auto output = Variable::create(Expr::create(mergeredUpsample.get(), {inputs[0], inputs[1]}));
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return output->expr().first;
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}
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// scale is constant node
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scalesSize = scaleInfo->size;
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}
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interpParam->widthScale = 1.0f;
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interpParam->heightScale = 1.0f;
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if (scalesSize >= 2 && scalesSize <= 4) {
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MNN_THROW_CHECK(scaleDataPtr[1] == 1.0f, "MNN NOT SUPPORT Upsamle along with channle");
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if (scalesSize >= 3) {
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interpParam->heightScale = scaleDataPtr[2];
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}
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if (scalesSize == 4) {
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interpParam->widthScale = scaleDataPtr[3];
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}
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} else {
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MNN_ERROR("MNN Not support Upsample when scale size = %d\n", scalesSize);
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}
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interpParam->alignCorners = (coordMode == "align_corners");
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// 1:near 2: bilinear 3: cubic
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if (interpMode == "nearest") {
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interpParam->resizeType = 1;
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} else if (interpMode == "bilinear" || interpMode == "linear") {
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interpParam->resizeType = 2;
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} else if (interpMode == "cubic") {
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interpParam->resizeType = 3;
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} else {
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MNN_ERROR("Unsupported Upsample mode! ==> %s\n", interpMode.c_str());
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}
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mergeredUpsample->main.value = interpParam.release();
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auto newInput = inputs[0];
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auto tempOutput = Variable::create(Expr::create(mergeredUpsample.get(), {newInput}));
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tempOutput->setName(expr->name());
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auto output = tempOutput;
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return output->expr().first;
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}
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};
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class OnnxReiszeTransform : public OnnxExtraManager::Transform {
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public:
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virtual EXPRP onExecute(EXPRP expr) const override {
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auto inputs = expr->inputs();
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// input, roi, scales, sizes
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// for more information, please reference from https://github.com/onnx/onnx/blob/master/docs/Operators.md#Resize
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MNN_THROW_CHECK((inputs.size() >= 2), "Onnx Resize should have 2/3/4 inputs!");
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std::string resizeMode = "";
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std::string coordMode = "half_pixel"; // detect align_corner attribute
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std::string nearestMode = "round_prefer_floor";
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auto op = expr->get();
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auto extraParam = op->main_as_Extra();
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const int attrSize = extraParam->attr()->size();
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float cubicFactor = -0.75f;
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for (int i = 0; i < attrSize; ++i) {
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auto attr = extraParam->attr()->GetAs<Attribute>(i);
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const auto& key = attr->key()->str();
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if (key == "mode") {
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resizeMode = attr->s()->str();
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} else if (key == "coordinate_transformation_mode") {
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coordMode = attr->s()->str();
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} else if (key == "nearest_mode") {
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nearestMode = attr->s()->str();
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} else if (key == "cubic_coeff_a") {
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cubicFactor = attr->f();
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}
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}
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std::unique_ptr<OpT> mergeredResize(new OpT);
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mergeredResize->type = OpType_Interp;
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mergeredResize->main.type = OpParameter_Interp;
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std::unique_ptr<InterpT> resizeParam(new InterpT);
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// 1:near 2: bilinear 3: cubic
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if (resizeMode == "nearest") {
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if (nearestMode == "round_prefer_floor") {
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resizeParam->resizeType = 4;
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} else if (nearestMode == "floor") {
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resizeParam->resizeType = 1;
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} else {
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MNN_ERROR("Don't support %s neareset mode, use round_prefer_floor instead\n", nearestMode.c_str());
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resizeParam->resizeType = 4;
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}
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} else if (resizeMode == "bilinear" || resizeMode == "linear") {
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resizeParam->resizeType = 2;
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} else if (resizeMode == "cubic") {
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resizeParam->resizeType = 3;
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resizeParam->cubicCoeffA = cubicFactor;
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} else {
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MNN_ERROR("Unsupported Upsample mode! ==> %s, use bilinear instead\n", resizeMode.c_str());
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resizeParam->resizeType = 2;
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}
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// For compability of old mnn
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resizeParam->alignCorners = (coordMode == "align_corners");
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resizeParam->halfPixelCenters = (coordMode == "half_pixel");
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/*
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coordinate_transformation_mode: string
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This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original
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tensor.
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The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example.
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Denote x_resized as the coordinate of axis x in the resized tensor, x_original as the coordinate of axis x in
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the original tensor, length_original as the length of the original tensor in axis x, length_resized as the
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length of the resized tensor in axis x, roi_x = (start_x, end_x) of the axis x in input "roi", scale =
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length_resized / length_original,
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if coordinate_transformation_mode is "half_pixel",
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x_original = (x_resized + 0.5) / scale - 0.5,
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if coordinate_transformation_mode is "pytorch_half_pixel",
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x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0,
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if coordinate_transformation_mode is "align_corners",
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x_original = x_resized * (length_original - 1) / (length_resized - 1),
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if coordinate_transformation_mode is "asymmetric",
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x_original = x_resized / scale,
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if coordinate_transformation_mode is "tf_half_pixel_for_nn",
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x_original = (x_resized + 0.5) / scale,
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if coordinate_transformation_mode is "tf_crop_and_resize",
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x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) *
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(length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1).
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*/
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#define SET_MODE(str, c) \
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if (coordMode == str) \
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resizeParam->ctm = MNN::CoordinateTransformationMode_##c
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SET_MODE("align_corners", AlignCorners);
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SET_MODE("half_pixel", HalfPixels);
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SET_MODE("pytorch_half_pixel", PytorchHalfPixels);
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SET_MODE("tf_half_pixel_for_nn", TensorflowHalfPixels);
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SET_MODE("tf_crop_and_resize", TensorflowCropAndResize);
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SET_MODE("asymmetric", Asymmetric);
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#undef SET_MODE
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VARP output;
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if (inputs.size() == 2) {
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mergeredResize->main.value = resizeParam.release();
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auto output = Variable::create(Expr::create(mergeredResize.get(), {inputs[0], inputs[1]}));
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output->setName(expr->name());
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return output->expr().first;
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}
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if (inputs.size() == 3) {
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auto scaleT = inputs[2];
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// Compute shape dynamic
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mergeredResize->main.value = resizeParam.release();
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auto resizeExpr = Expr::create(mergeredResize.get(), {inputs[0], {inputs[2]}});
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resizeExpr->setName(expr->name());
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output = Variable::create(resizeExpr);
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return output->expr().first;
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}
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if (inputs.size() == 4) {
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auto sizes = inputs[3];
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auto name = sizes->name();
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mergeredResize->main.value = resizeParam.release();
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auto resizeExpr = Expr::create(mergeredResize.get(), {inputs[0], inputs[3]});
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resizeExpr->setName(expr->name());
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output = Variable::create(resizeExpr);
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return output->expr().first;
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}
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return output->expr().first;
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}
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};
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static auto gRigister = []() {
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OnnxExtraManager::get()->insert("Upsample",
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std::shared_ptr<OnnxExtraManager::Transform>(new OnnxUpSampleTransform));
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// Resize has a dedicated ONNX converter. Keep this legacy extra transform registered only for old models
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// that may still carry Resize as an Extra op.
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OnnxExtraManager::get()->insert("Resize", std::shared_ptr<OnnxExtraManager::Transform>(new OnnxReiszeTransform));
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return true;
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}();
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} // namespace Express
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} // namespace MNN
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