conv_kernel.cpp

/* Copyright (c) 2016 Baidu, Inc. All Rights Reserved.
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#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 {
    LOG(kLOG_DEBUG) << param;

    const Tensor *input = param.Input();

    // 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();

    DLOG << " compute end get Attrs " << strides[0];

    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);

    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