Function graph_transpose

src/framework/audio/deepfilternet2.cpp:228–255

}

core::TensorValue graph_transpose(
    core::ModuleBuildContext & ctx,
    const core::TensorValue & input,
    const std::array<int, core::kMaxTensorRank> & axes,
    size_t rank) {
    if (rank != input.shape.rank) {
        throw std::runtime_error("DeepFilterNet2 transpose rank mismatch");
    }
    core::TensorShape output_shape = {};
    output_shape.rank = rank;
    std::array<bool, core::kMaxTensorRank> seen = {false, false, false, false};
    std::array<int, core::kMaxTensorRank> ggml_axes = {0, 1, 2, 3};
    for (size_t out_axis = 0; out_axis < rank; ++out_axis) {
        const int in_axis = axes[out_axis];
        if (in_axis < 0 || in_axis >= static_cast<int>(rank) || seen[static_cast<size_t>(in_axis)]) {
            throw std::runtime_error("DeepFilterNet2 transpose axes must be a permutation");
        }
        seen[static_cast<size_t>(in_axis)] = true;
        output_shape.dims[out_axis] = input.shape.dims[static_cast<size_t>(in_axis)];
        const int out_ggml_axis = core::logical_axis_to_ggml_axis(rank, static_cast<int>(out_axis));
        const int in_ggml_axis = core::logical_axis_to_ggml_axis(rank, in_axis);
        ggml_axes[static_cast<size_t>(in_ggml_axis)] = out_ggml_axis;
    }
    return core::wrap_tensor(
        ggml_permute(ctx.ggml, graph_contiguous(ctx, input).tensor, ggml_axes[0], ggml_axes[1], ggml_axes[2], ggml_axes[3]),
        output_shape,
        input.type);
}

core::TensorValue graph_add(core::ModuleBuildContext & ctx, const core::TensorValue & lhs, const core::TensorValue & rhs) {
    return modules::AddModule().build(ctx, lhs, rhs);