/* Copyright (c) 2016 Baidu, Inc. All Rights Reserved.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
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furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
==============================================================================*/

#include "operators/kernel/conv_kernel.h"

namespace paddle_mobile {
    namespace operators {

        bool IsExpand(const std::vector<int64_t> &filter_dim,
                      const std::vector<int> &strides,
                      const std::vector<int> &paddings,
                      const std::vector<int> &dilations) {
            bool filter_1 = true, strides_1 = true, padding_0 = true,
                 dilation_1 = true;
            for (size_t j = 0; j < strides.size(); ++j) {
                filter_1 =
                    filter_1 && (static_cast<int>(filter_dim[j + 2]) == 1);
                strides_1 = strides_1 && (strides[j] == 1);
                padding_0 = padding_0 && (paddings[j] == 0);
                dilation_1 = dilation_1 && (dilations[j] == 1);
            }
            return !(filter_1 && strides_1 && padding_0 && dilation_1);
        }

        template <>
        void ConvKernel<CPU, float, ConvParam>::Compute(
            const ConvParam &param) const {
            const Tensor *input = param.Input();

            std::cout << " conv param " << param << std::endl;

            // The filter will be reshaped in the calculations,
            // so here use an assignment operation,
            // that avoids modifying the variable in the Scope.
            Tensor filter = *param.Filter();

            Tensor *output = param.Output();
            //            output->mutable_data<T>(context.GetPlace());

            int groups = param.Groups();
            std::vector<int> strides = param.Strides();
            std::vector<int> paddings = param.Paddings();
            std::vector<int> dilations = param.Dilations();

            std::cout << " compute end get Attrs " << strides[0] << std::endl;

            const int batch_size = static_cast<int>(input->dims()[0]);

            // filter_shape_vec: {k_o, k_i, k_h, k_w} or {k_o, k_i, k_d, k_h,
            // k_w}
            std::vector<int64_t> filter_shape_vec(
                framework::vectorize(filter.dims()));
            // output_shape_vec: {o_n, o_c, o_h, o_w} or {o_n, o_c, o_d, o_h,
            // o_w}
            std::vector<int64_t> output_shape_vec(
                framework::vectorize(output->dims()));

            // use col_shape in the im2col calculation
            // col_shape_vec: {i_c/g, k_h, k_w, o_h, o_w} or {i_c/g, k_d, k_h,
            // k_w, o_d,
            // o_h, o_w}
            size_t data_dim = filter_shape_vec.size() - 2;
            std::vector<int64_t> col_shape_vec(1 + 2 * data_dim);
            col_shape_vec[0] = input->dims()[1] / groups;
            for (size_t j = 0; j < data_dim; ++j) {
                col_shape_vec[j + 1] = filter_shape_vec[j + 2];
                col_shape_vec[j + 1 + data_dim] = output_shape_vec[j + 2];
            }
            framework::DDim col_shape(framework::make_ddim(col_shape_vec));

            // use col_matrix_shape in the gemm calculation
            // size: (i_c/g * k_h * k_w, o_h * o_w) or (i_c/g * k_d * k_h * k_w,
            // o_d *
            // o_h * o_w)
            framework::DDim col_matrix_shape =
                framework::flatten_to_2d(col_shape, data_dim + 1);

            bool is_expand =
                IsExpand(filter_shape_vec, strides, paddings, dilations);
            Tensor col;
            // col_matrix shares the same piece of data with col,
            // but will be reshaped into a two-dimensional matrix shape
            // to call the matrix multiplication interface.
            Tensor col_matrix;
            if (is_expand) {
                col.mutable_data<float>(col_shape);
                col_matrix.ShareDataWith(col);
                col_matrix.Resize(col_matrix_shape);
            }

            framework::DDim input_shape = framework::slice_ddim(
                input->dims(), 1, static_cast<int>(input->dims().size()));

            framework::DDim filter_matrix_shape = {
                filter.dims()[0], filter.numel() / filter.dims()[0]};
            filter.Resize(filter_matrix_shape);

            std::cout << " input dim " << input->dims() << std::endl;

            std::cout << " output dim " << output->dims() << std::endl;

            framework::DDim output_matrix_shape = {
                output->dims()[1],
                output->numel() / (output->dims()[0] * output->dims()[1])};

            // convolution operator: im2col(or vol2col) + gemm
            int in_step = static_cast<int>(input->dims()[1]) / groups;
            int out_step = static_cast<int>(output->dims()[1]) / groups;

            math::Vol2ColFunctor<CPU, float> vol2col;
            math::Im2ColFunctor<math::ColFormat::kCFO, CPU, float> im2col;

            //            auto& dev_ctx = context.template
            //            device_context<DeviceContext>();
            for (int i = 0; i < batch_size; i++) {
                Tensor in_batch = input->Slice(i, i + 1).Resize(input_shape);
                Tensor out_batch =
                    output->Slice(i, i + 1).Resize(output_matrix_shape);

                for (int g = 0; g < groups; g++) {
                    Tensor in_slice =
                        in_batch.Slice(g * in_step, (g + 1) * in_step);

                    if (!is_expand) {
                        col.ShareDataWith(in_slice);
                        col_matrix.ShareDataWith(col);
                        col_matrix.Resize(col_matrix_shape);
                    } else if (data_dim == 2U) {
                        // im2col
                        im2col(in_slice, dilations, strides,
                               std::vector<int>{paddings[0], paddings[1],
                                                paddings[0], paddings[1]},
                               &col);
                    } else if (data_dim == 3U) {
                        // vol2col
                        vol2col(in_slice, dilations, strides, paddings, &col);
                    }

                    // gemm
                    Tensor out_slice =
                        out_batch.Slice(g * out_step, (g + 1) * out_step);
                    Tensor filter_slice =
                        filter.Slice(g * out_step, (g + 1) * out_step);
                    math::matmul<float>(filter_slice, false, col_matrix, false,
                                        float(1.0), &out_slice, float(0.0));
                }
            }
        }

        template class ConvKernel<CPU, float, ConvParam>;

    } // namespace operators
} // namespace paddle_mobile