Function get_channel_wise_args

dnn/test/arm_common/conv_bias_multi_thread.cpp:139–215

}

std::vector<conv_bias::TestArg> get_channel_wise_args(
        std::vector<size_t> kernel, size_t stride, bool no_bias, bool no_nonlinemode,
        bool no_full_bias, bool support_relu) {
    using namespace conv_bias;
    using Param = param::ConvBias;
    using NLMode = param::ConvBias::NonlineMode;
    std::vector<TestArg> args;

    auto pack = [&](size_t n, size_t group, size_t w, size_t h, size_t kernel,
                    size_t stride, NLMode nlmode, bool pad) {
        Param param;
        param.stride_h = stride;
        param.stride_w = stride;
        if (pad) {
            param.pad_h = kernel / 2;
            param.pad_w = kernel / 2;
        } else {
            param.pad_h = 0;
            param.pad_w = 0;
        }
        param.nonlineMode = nlmode;
        param.format = param::ConvBias::Format::NCHW;
        param.sparse = param::ConvBias::Sparse::GROUP;

        args.emplace_back(
                param, TensorShape{n, group, h, w},
                TensorShape{group, 1, 1, kernel, kernel}, TensorShape{});
        if (!no_bias) {
            args.emplace_back(
                    param, TensorShape{n, group, h, w},
                    TensorShape{group, 1, 1, kernel, kernel},
                    TensorShape{1, group, 1, 1});
        }
        if (!no_full_bias) {
            args.emplace_back(
                    param, TensorShape{n, group, h, w},
                    TensorShape{group, 1, 1, kernel, kernel},
                    TensorShape{
                            n, group, (h + 2 * param.pad_w - kernel) / stride + 1,
                            (w + 2 * param.pad_w - kernel) / stride + 1});
        }
    };

    std::vector<NLMode> nonlinemode = {NLMode::IDENTITY};
    if (!no_nonlinemode) {
        nonlinemode.emplace_back(NLMode::RELU);
        nonlinemode.emplace_back(NLMode::H_SWISH);
    } else if (support_relu) {
        nonlinemode.emplace_back(NLMode::RELU);
    }

    for (size_t n : {1, 2}) {
        for (auto nlmode : nonlinemode) {
            for (bool pad : {true}) {
                for (size_t group : {1, 3, 7}) {
                    for (size_t size : {4, 6, 7, 9, 16, 20, 32, 55}) {
                        for (size_t kern : kernel) {
                            pack(n, group, size, size, kern, stride, nlmode, pad);