wehub-resource-sync
66 lines
2.1 KiB
C++
66 lines
2.1 KiB
C++
#include "QNNPermute.hpp"
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namespace MNN {
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namespace QNN {
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#ifdef ENABLE_QNN_ONLINE_FINALIZE
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ErrorCode QNNPermute::onEncode(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
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Tensor * input = inputs[0];
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int dim = input->dimensions();
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Tensor::DimensionType inputDimType = inputs[0]->getDimensionType();
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Tensor::DimensionType outputDimType = outputs[0]->getDimensionType();
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MNN_ASSERT(inputDimType == outputDimType);
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#ifdef QNN_VERBOSE
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MNN_PRINT("QNN Permute: %s input0:", mNodeName.c_str());
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auto shape0 = inputs[0]->shape();
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for(int i = 0; i < shape0.size(); i++) {
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MNN_PRINT("%d x ", shape0[i]);
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}
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MNN_PRINT("\noutput:");
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auto outShape = outputs[0]->shape();
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for(int i = 0; i < outShape.size(); i++) {
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MNN_PRINT("%d x ", outShape[i]);
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}
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MNN_PRINT("\n");
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#endif
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mNodeType = "Transpose";
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std::vector<uint32_t> mapRaw(dim, 0);
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if (mOp->type() == OpType_Permute) {
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auto param = mOp->main_as_Permute();
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auto axis = param->dims();
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int size = (int) param->dims()->size();
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MNN_ASSERT(size == dim);
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for (int i = 0; i < dim; i++) {
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int index = axis->Get(i);
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mapRaw[i] = (uint32_t)index;
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}
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} else {
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auto permutation = inputs[1]->host<int32_t>();
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for (int i = 0; i < dim; i++) {
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mapRaw[i] = (uint32_t)permutation[i];
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}
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}
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this->createParamTensor("perm", QNN_DATATYPE_UINT_32, {(uint32_t) dim}, mapRaw.data());
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this->addNodeCommon(inputs, outputs, 1, 1);
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return NO_ERROR;
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}
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class QNNPermuteCreator : public QnnBackend::Creator {
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public:
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virtual QNNCommonExecution * onCreate(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs, const MNN::Op* op,
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Backend* backend) const override {
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return new QNNPermute(backend, op);
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}
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};
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REGISTER_QNN_OP_CREATOR(QNNPermuteCreator, OpType_Permute)
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REGISTER_QNN_OP_CREATOR(QNNPermuteCreator, OpType_Transpose)
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#endif
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} // end namespace QNN
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} // end namespace MNN
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