// // ShapeTopKV2.cpp // MNN // // Created by MNN on 2019/01/10. // Copyright © 2018, Alibaba Group Holding Limited // #include "shape/SizeComputer.hpp" #include "core/Macro.h" namespace MNN { class TopKV2SizeComputer : public SizeComputer { virtual bool onComputeSize(const MNN::Op* op, const std::vector& inputs, const std::vector& outputs) const override { MNN_ASSERT(2 == inputs.size() || 3 == inputs.size()); MNN_ASSERT(2 == outputs.size()); auto input = inputs[0]; auto kTensor = inputs[1]; MNN_ASSERT(kTensor->elementSize() == 1); // Scalar MNN_ASSERT(kTensor->getType().code == halide_type_int); const int k = kTensor->host()[0]; const int inputDimension = input->buffer().dimensions; int axis = (inputs.size() == 3 ? inputs[2]->host()[0] : inputDimension - 1); if (axis < 0) axis += input->dimensions(); // outputs: 0 --> data, 1 --> index auto outputData = outputs[0]; outputData->buffer().dimensions = inputDimension; memcpy(outputData->buffer().dim, input->buffer().dim, inputDimension * sizeof(halide_dimension_t)); outputData->buffer().dim[axis].extent = k; outputData->buffer().type = input->buffer().type; auto outputIndices = outputs[1]; outputIndices->buffer().dimensions = inputDimension; memcpy(outputIndices->buffer().dim, input->buffer().dim, inputDimension * sizeof(halide_dimension_t)); outputIndices->buffer().dim[axis].extent = k; outputIndices->setType(MNN::DataType_DT_INT32); TensorUtils::getDescribe(outputs[0])->dimensionFormat = TensorUtils::getDescribe(inputs[0])->dimensionFormat; TensorUtils::getDescribe(outputs[1])->dimensionFormat = TensorUtils::getDescribe(inputs[1])->dimensionFormat; return true; } }; REGISTER_SHAPE_INPUTS(TopKV2SizeComputer, OpType_TopKV2, (std::vector{1,2})); } // namespace MNN