mirror of https://github.com/alibaba/MNN.git
74 lines
2.5 KiB
C++
74 lines
2.5 KiB
C++
//
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// ShapeConcat.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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#include "core/Macro.h"
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#include "core/SizeComputer.hpp"
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namespace MNN {
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class ConcatSizeComputer : public SizeComputer {
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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(1 == outputs.size());
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MNN_ASSERT(inputs.size() >= 2);
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auto& ob = outputs[0]->buffer();
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int basicAxis = 0;
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if (op->type() == OpType_Concat) {
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basicAxis = op->main_as_Axis()->axis();
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} else if (op->type() == OpType_QuantizedConcat) {
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basicAxis = op->main_as_QuantizedConcat()->axis();
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}
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bool valid = false;
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int axis = basicAxis;
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// Concat-inputs may have scalar which should be delete
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for (const auto& input : inputs) {
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if (0 == input->buffer().dimensions) {
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continue;
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} else {
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auto inputDimensions = input->buffer().dimensions;
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::memcpy(ob.dim, input->buffer().dim, sizeof(halide_dimension_t) * inputDimensions);
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ob.dimensions = inputDimensions;
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ob.type = input->buffer().type;
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if (axis < 0) {
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axis = inputDimensions + axis;
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}
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valid = true;
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break;
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}
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}
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if (!valid) {
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return false;
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}
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int sum = 0;
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for (auto t : inputs) {
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if (0 == t->buffer().dimensions) {
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continue;
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}
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sum += t->buffer().dim[axis].extent;
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ob.type = t->buffer().type;
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for (int i = 0; i < t->dimensions(); ++i) {
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if (axis == i) {
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continue;
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}
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if (t->length(i) != outputs[0]->length(i)) {
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auto name = op->name() ? op->name()->c_str() : "";
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MNN_PRINT("Error for concat size of op %s, %d input not match output\n", name, i);
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return false;
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}
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}
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}
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ob.dim[axis].extent = sum;
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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(ConcatSizeComputer, OpType_Concat);
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REGISTER_SHAPE(ConcatSizeComputer, OpType_QuantizedConcat);
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} // namespace MNN
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