mirror of https://github.com/alibaba/MNN.git
118 lines
3.9 KiB
C++
118 lines
3.9 KiB
C++
//
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// ShapeBinaryOp.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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#include <vector>
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namespace MNN {
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class BinaryOpComputer : public SizeComputer {
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public:
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static bool outputBool(int operation) {
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if (operation == BinaryOpOperation_GREATER_EQUAL) {
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return true;
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}
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if (operation == BinaryOpOperation_GREATER) {
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return true;
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}
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if (operation == BinaryOpOperation_LESS) {
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return true;
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}
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if (operation == BinaryOpOperation_LESS_EQUAL) {
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return true;
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}
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if (operation == BinaryOpOperation_EQUAL) {
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return true;
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}
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return false;
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}
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virtual bool onComputeSize(const 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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// set output type & format
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auto input0 = inputs[0], input1 = inputs[1], output = outputs[0];
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auto &buffer = output->buffer();
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const auto opType = op->main_as_BinaryOp()->opType();
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if (outputBool(opType)) {
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buffer.type = halide_type_of<int32_t>();
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} else {
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buffer.type = input0->getType();
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}
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if (input0->getType().code != input1->getType().code) {
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MNN_PRINT("Error for binary op: input0's type != input1's type\n");
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return false;
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}
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if (input0->dimensions() < input1->dimensions()) {
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auto temp = input0;
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input0 = input1;
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input1 = temp;
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}
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TensorUtils::getDescribe(output)->dimensionFormat = TensorUtils::getDescribe(input0)->dimensionFormat;
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// if one scalar input -> just copy the other
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if (input1->dimensions() == 0) {
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TensorUtils::copyShape(input0, output);
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return true;
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}
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// else if inputs shape equals -> just copy any one
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bool sameShape = true;
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if (input0->dimensions() == input1->dimensions()) {
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for (int i = 0; i < input0->buffer().dimensions; i++) {
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if (input0->buffer().dim[i].extent != input1->buffer().dim[i].extent) {
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sameShape = false;
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break;
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}
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}
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}
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else {
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sameShape = false;
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}
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if (sameShape) {
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TensorUtils::copyShape(input0, output);
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return true;
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}
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// else if broadcast NOT supported -> failed
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const int maxDimensions = input0->dimensions();
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const int diffDimension = input0->dimensions() - input1->dimensions();
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std::vector<int> outputDims(maxDimensions);
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for (int i = 0; i < maxDimensions; i++) {
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outputDims[i] = input0->buffer().dim[i].extent;
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}
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for (int i = diffDimension; i < maxDimensions; i++) {
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const int input1Index = i - diffDimension;
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int dim1 = input1->buffer().dim[input1Index].extent;
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if (dim1 != outputDims[i] && (dim1 != 1 && outputDims[i] != 1)) {
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MNN_PRINT("Don't support broadcast for binaryOp, i0=%d, i1=%d\n", outputDims[i], dim1);
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return false;
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}
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if (dim1 == outputDims[i]) {
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continue;
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}
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if (dim1 != outputDims[i] && (dim1 == 1 || outputDims[i] == 1)) {
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outputDims[i] = outputDims[i] * dim1;
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} else {
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MNN_PRINT("Error, the logic flow should never get here");
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return false;
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}
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}
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buffer.dimensions = maxDimensions;
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for (int i = 0; i < maxDimensions; i++) {
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buffer.dim[i].extent = outputDims[i];
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}
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return true;
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}
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};
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REGISTER_SHAPE(BinaryOpComputer, OpType_BinaryOp);
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} // namespace MNN
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