mirror of https://github.com/alibaba/MNN.git
60 lines
1.7 KiB
C++
60 lines
1.7 KiB
C++
//
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// ShapeQuantizedReshape.cpp
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// MNN
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//
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// Created by MNN on 2019/01/10.
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// Copyright © 2018, Alibaba Group Holding Limited
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//
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#ifdef MNN_SUPPORT_TFLITE_QUAN
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#include "core/Macro.h"
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#include "core/SizeComputer.hpp"
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namespace MNN {
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class QuantizedReshapeComputer : public SizeComputer {
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public:
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virtual bool onComputeSize(const MNN::Op* op, const std::vector<Tensor*>& inputs,
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const std::vector<Tensor*>& outputs) const override {
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auto layer_param = op->main_as_QuantizedReshape();
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auto input = inputs[0];
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auto output = outputs[0];
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const int32_t* dim_data = nullptr;
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int32_t dimSize = 0;
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dimSize = layer_param->dims()->size();
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dim_data = layer_param->dims()->data();
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int num_element = 1;
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for (int i = 0; i < input->buffer().dimensions; i++) {
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num_element *= input->buffer().dim[i].extent;
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}
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output->buffer().dimensions = dimSize;
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int count_non_minus1 = 1;
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for (int i = 0; i < dimSize; i++) {
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if (dim_data[i] != -1) {
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count_non_minus1 *= dim_data[i];
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}
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}
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MNN_ASSERT((num_element % count_non_minus1) == 0)
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for (int i = 0; i < dimSize; i++) {
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int shape_dim = dim_data[i];
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if (shape_dim == -1) {
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shape_dim = num_element / count_non_minus1;
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}
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output->buffer().dim[i].extent = shape_dim;
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}
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output->setType(DataType_DT_UINT8);
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TensorUtils::getDescribe(outputs[0])->dimensionFormat = TensorUtils::getDescribe(inputs[0])->dimensionFormat;
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return true;
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}
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};
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REGISTER_SHAPE(QuantizedReshapeComputer, OpType_QuantizedReshape);
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} // namespace MNN
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#endif
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