//
//  ShapeConvTranspose3D.cpp
//  MNN
//
//  Created by MNN on 2019/01/10.
//  Copyright © 2018, Alibaba Group Holding Limited
//

#include <math.h>
#include "shape/SizeComputer.hpp"
#include "core/Macro.h"
#include "core/TensorUtils.hpp"
namespace MNN {
class ConvTranspose3DSizeComputer : public SizeComputer {
public:
    virtual bool onComputeSize(const MNN::Op* op, const std::vector<Tensor*>& inputs,
                               const std::vector<Tensor*>& outputs) const override {
//            MNN_ASSERT(1 == inputs.size());
        MNN_ASSERT(1 == outputs.size());

        auto layer        = op->main_as_Convolution3D()->common();
        auto input = inputs[0];
        int dimensions = input->dimensions();
        int convolutinDim = dimensions - 2;

        auto& outputBuffer         = outputs[0]->buffer();
        outputBuffer.dimensions    = input->buffer().dimensions;
        outputBuffer.dim[0].extent = input->buffer().dim[0].extent;
        outputBuffer.dim[1].extent = layer->outputCount();

        for (int i = 0; i < convolutinDim; ++i) {
            const int inputLength = input->length(i + 2), stride = (*layer->strides())[i];
            if (inputLength <= 0) {
                return false;
            }
            int outputLength;
            if (layer->padMode() == PadMode_SAME) {
                outputLength =inputLength * stride;
            } else {
                int padL = 0;
                int padR = 0;
                int kernel = layer->kernels()->data()[i];
                int dialate = 1;
                if (nullptr != layer->pads()) {
                    padL = layer->pads()->data()[i];
                    if (layer->pads()->size() == 6) {
                        padR = layer->pads()->data()[i + 3];
                    } else {
                        padR = padL;
                    }
                }
                if (nullptr != layer->dilates()) {
                    dialate = layer->dilates()->data()[i];
                }
                const int dialatedKernel = (kernel - 1) * dialate + 1;
//                    outputLength = (inputLength + 2 * pad - dialatedKernel) / stride + 1;
                outputLength = (inputLength - 1) * stride + dialatedKernel - padR - padL;
                if (layer->outPads() != nullptr) {
                    outputLength = outputLength + layer->outPads()->data()[i];
                }
            }
            outputBuffer.dim[i + 2].extent = outputLength;
        }

        outputBuffer.type = input->getType();

        TensorUtils::getDescribe(outputs[0])->dimensionFormat = TensorUtils::getDescribe(inputs[0])->dimensionFormat;
        return true;
    }
};

REGISTER_SHAPE(ConvTranspose3DSizeComputer, OpType_ConvTranspose3D);
} // namespace MNN