mirror of https://github.com/alibaba/MNN.git
61 lines
1.8 KiB
C++
61 lines
1.8 KiB
C++
//
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// ShapeBatchMatMul.cpp
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// MNN
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//
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// Created by MNN on 2019/03/25.
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// Copyright © 2018, Alibaba Group Holding Limited
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//
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#include "shape/SizeComputer.hpp"
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#include "core/Macro.h"
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#include "core/TensorUtils.hpp"
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namespace MNN {
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class BatchMatMulComputer : 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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MNN_ASSERT(2 == inputs.size());
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MNN_ASSERT(1 == outputs.size());
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auto param = op->main_as_BatchMatMulParam();
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auto input0 = inputs[0];
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auto input1 = inputs[1];
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MNN_ASSERT(input0->dimensions() == input1->dimensions());
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const int dimensions = input0->dimensions();
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MNN_ASSERT(dimensions >= 2);
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for (int i = 0; i < dimensions - 2; ++i) {
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MNN_ASSERT(input0->length(i) == input1->length(i));
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}
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auto output = outputs[0];
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output->buffer().type = input0->buffer().type;
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TensorUtils::copyShape(input0, output, true);
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auto k0 = input0->length(dimensions - 1);
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auto k1 = input1->length(dimensions - 2);
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if (param->adjX()) {
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k0 = input0->length(dimensions - 2);
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output->setLength(dimensions - 2, input0->length(dimensions - 1));
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} else {
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output->setLength(dimensions - 2, input0->length(dimensions - 2));
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}
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if (param->adjY()) {
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k1 = input1->length(dimensions - 1);
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output->setLength(dimensions - 1, input1->length(dimensions - 2));
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} else {
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output->setLength(dimensions - 1, input1->length(dimensions - 1));
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}
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if (k0 != k1) {
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return false;
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}
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return true;
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}
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};
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REGISTER_SHAPE(BatchMatMulComputer, OpType_BatchMatMul);
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} // namespace MNN
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