//
//  ShapeROIAlign.cpp
//  MNN
//
//  Created by MNN on 2021/11/02.
//  Copyright © 2018, Alibaba Group Holding Limited
//

#include "core/Macro.h"
#include "shape/SizeComputer.hpp"

namespace MNN {

class ROIAlignComputer : public SizeComputer {
    virtual bool onComputeSize(const MNN::Op* op, const std::vector<Tensor*>& inputs,
                               const std::vector<Tensor*>& outputs) const override {
        MNN_ASSERT(2 == inputs.size() || 3 == inputs.size() || 4 == inputs.size());
        MNN_ASSERT(1 == outputs.size());

        if (inputs.size() == 2 || inputs.size() == 3) {
            // copy dims
            auto& input  = inputs[0]->buffer();
            auto& output = outputs[0]->buffer();
            memcpy(output.dim, input.dim, sizeof(halide_dimension_t) * input.dimensions);
            output.type = halide_type_of<float>();

            // width & height
            auto roi                                              = op->main_as_RoiParameters();
            output.dim[3].extent                                  = roi->pooledWidth();
            output.dim[2].extent                                  = roi->pooledHeight();
            output.dim[0].extent                                  = inputs[1]->batch();
            TensorUtils::getDescribe(outputs[0])->dimensionFormat = TensorUtils::getDescribe(inputs[0])->dimensionFormat;
        }

        // backward mode, fourth input is backward diff, output is the grad of inputs[0]
        if (inputs.size() == 4) {
            TensorUtils::copyShape(inputs[0], outputs[0], true);
            outputs[0]->buffer().type = inputs[0]->getType();
        }

        return true;
    }
};

REGISTER_SHAPE(ROIAlignComputer, OpType_ROIAlign);
} // namespace MNN