wehub-resource-sync
112 lines
3.9 KiB
C++
112 lines
3.9 KiB
C++
//
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// ShapeShape.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 <algorithm>
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#include <utility>
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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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static std::pair<int, int> _resolveShapeRange(const Op* op, int rank) {
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int start = 0;
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int end = rank;
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if (auto param = op->main_as_ShapeParam()) {
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if (param->hasStart()) {
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start = param->start();
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if (start < 0) {
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start += rank;
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}
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}
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if (param->hasEnd()) {
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end = param->end();
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if (end < 0) {
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end += rank;
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}
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}
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}
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start = std::max(0, std::min(start, rank));
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end = std::max(start, std::min(end, rank));
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return std::make_pair(start, end);
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}
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class ShapeSizeComputer : 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 <= inputs.size());
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MNN_ASSERT(1 == outputs.size());
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auto& ib = inputs[0]->buffer();
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auto& ob = outputs[0]->buffer();
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ob.dimensions = 1;
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outputs[0]->setType(DataType_DT_INT32);
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TensorUtils::getDescribe(outputs[0])->dimensionFormat = op->defaultDimentionFormat();
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auto inputFormat = TensorUtils::getDescribe(inputs[0])->dimensionFormat;
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int rank = ib.dimensions;
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if (inputFormat == MNN_DATA_FORMAT_NC4HW4 && op->defaultDimentionFormat() == MNN_DATA_FORMAT_NHWC) {
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// For compability
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rank = 4;
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}
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auto range = _resolveShapeRange(op, rank);
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ob.dim[0].extent = range.second - range.first;
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return true;
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}
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};
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REGISTER_SHAPE(ShapeSizeComputer, OpType_Shape);
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class ShapeRasterComputer : 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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auto extra = op->main_as_Extra();
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if (!extra) {
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// copy dims
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MNN_ASSERT(1 <= inputs.size());
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outputs[0]->buffer().type = inputs[0]->buffer().type;
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TensorUtils::copyShape(inputs[0], outputs[0], true);
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} else {
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if (inputs.size() > 0) {
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outputs[0]->buffer().type = inputs[0]->buffer().type;
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TensorUtils::getDescribe(outputs[0])->dimensionFormat = TensorUtils::getDescribe(inputs[0])->dimensionFormat;
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}
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for (int i = 0; i < extra->attr()->size(); i++) {
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auto attr = extra->attr()->Get(i);
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if (attr->key()->str() == "shape") {
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outputs[0]->buffer().dimensions = 0;
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if (attr->list()->i() != nullptr) {
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int len = attr->list()->i()->size();
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outputs[0]->buffer().dimensions = len;
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for (int j = 0; j < len; j++) {
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outputs[0]->setLength(j, attr->list()->i()->Get(j));
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}
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}
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continue;
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}
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if (attr->key()->str() == "code") {
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outputs[0]->buffer().type.code = (halide_type_code_t)attr->i();
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continue;
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}
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if (attr->key()->str() == "bits") {
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outputs[0]->buffer().type.bits = attr->i();
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continue;
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}
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if (attr->key()->str() == "format") {
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TensorUtils::getDescribe(outputs[0])->dimensionFormat = (MNN_DATA_FORMAT)attr->i();
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continue;
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}
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}
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}
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return true;
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}
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};
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REGISTER_SHAPE(ShapeRasterComputer, OpType_Raster);
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} // namespace MNN
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