wehub-resource-sync
118 lines
4.1 KiB
C++
118 lines
4.1 KiB
C++
#include <stdio.h>
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#include "onnxOpConverter.hpp"
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#include "logkit.h"
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DECLARE_OP_CONVERTER(ResizeOnnx);
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MNN::OpType ResizeOnnx::opType() {
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return MNN::OpType_Interp;
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}
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MNN::OpParameter ResizeOnnx::type() {
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return MNN::OpParameter_Interp;
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}
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void ResizeOnnx::run(MNN::OpT* dstOp, const onnx::NodeProto* onnxNode, OnnxScope* scope) {
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std::unique_ptr<MNN::InterpT> resizeParam(new MNN::InterpT);
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std::string resizeMode = "";
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std::string coordMode = "half_pixel";
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std::string nearestMode = "round_prefer_floor";
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float cubicFactor = -0.75f;
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for (int i = 0; i < onnxNode->attribute_size(); ++i) {
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const auto& attr = onnxNode->attribute(i);
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const auto& key = attr.name();
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if (key == "mode") {
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resizeMode = attr.s();
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} else if (key == "coordinate_transformation_mode") {
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coordMode = attr.s();
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} else if (key == "nearest_mode") {
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nearestMode = attr.s();
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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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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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LOG(ERROR) << "Don't support " << nearestMode << " nearest mode, use round_prefer_floor instead";
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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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LOG(ERROR) << "Unsupported Resize mode " << resizeMode << ", use bilinear instead";
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resizeParam->resizeType = 2;
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}
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resizeParam->alignCorners = (coordMode == "align_corners");
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resizeParam->halfPixelCenters = (coordMode == "half_pixel");
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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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// Treat an ONNX optional input as "absent" if its name is empty OR it points to a
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// named-but-empty initializer (shape produces zero elements) — both conventions are
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// valid ONNX. Otherwise fall back to whatever the producer emits (constant or dynamic).
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auto isOptionalInputProvided = [&](int idx) -> bool {
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if (idx >= onnxNode->input_size()) {
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return false;
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}
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const auto& name = onnxNode->input(idx);
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if (name.empty()) {
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return false;
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}
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auto iter = scope->mInitializers.find(name);
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if (iter == scope->mInitializers.end()) {
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return true;
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}
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int64_t numel = 1;
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for (int i = 0; i < iter->second->dims_size(); ++i) {
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numel *= iter->second->dims(i);
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}
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return numel > 0;
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};
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// ONNX opset 10 Resize has only (X, scales). Opset 11+ uses (X, roi, scales, sizes).
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// sizes (when given) takes precedence over scales.
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int shapeInputIdx = -1;
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if (isOptionalInputProvided(3)) {
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shapeInputIdx = 3;
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} else if (isOptionalInputProvided(2)) {
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shapeInputIdx = 2;
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} else if (onnxNode->input_size() == 2 && isOptionalInputProvided(1)) {
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shapeInputIdx = 1; // opset 10
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}
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std::vector<int> inputIndexes;
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auto dataIndex = scope->lookupTensor(onnxNode->input(0));
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if (dataIndex >= 0) {
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inputIndexes.emplace_back(dataIndex);
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}
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if (shapeInputIdx >= 0) {
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auto shapeIndex = scope->lookupTensor(onnxNode->input(shapeInputIdx));
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if (shapeIndex >= 0) {
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inputIndexes.emplace_back(shapeIndex);
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}
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}
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dstOp->inputIndexes = std::move(inputIndexes);
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dstOp->main.value = resizeParam.release();
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dstOp->defaultDimentionFormat = MNN::MNN_DATA_FORMAT_NCHW;
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}
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REGISTER_CONVERTER(ResizeOnnx, Resize);
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