Method add_conv

src/gopt/test/network.cpp:6–40

using namespace mgb;

SymbolVar Network::add_conv(
        SymbolVar f, size_t output_channels, KernSize kern_size, DType out_dtype,
        bool has_relu, Stride stride, Padding padding) {
    static int weight_idx = 0;
    static int bias_idx = 0;

    size_t input_channels = f.node()->shape()[1];
    auto weight = add_cvar(
            ssprintf("w%d", weight_idx).c_str(),
            {output_channels, input_channels, kern_size[0], kern_size[1]});
    auto bias = add_cvar(ssprintf("b%d", bias_idx).c_str(), {1, output_channels, 1, 1});
    if (out_dtype.category() == DTypeCategory::QUANTIZED) {
        weight = add_type_cvt(weight, out_dtype);
        bias = add_type_cvt(bias, dtype::QuantizedS32{1.f});
    }
    opr::ConvBias::Param param;
    param.stride_h = stride[0], param.stride_w = stride[1];
    param.pad_h = padding[0], param.pad_w = padding[1];
    if (has_relu) {
        param.nonlineMode = opr::ConvBias::Param::NonlineMode::RELU;
    } else {
        param.nonlineMode = opr::ConvBias::Param::NonlineMode::IDENTITY;
    }

    SymbolVar conv;
    if (out_dtype.category() == DTypeCategory::QUANTIZED) {
        conv = opr::ConvBias::make(
                f, weight, bias, param, {}, OperatorNodeConfig{out_dtype});
    } else {
        conv = opr::ConvBias::make(f, weight, bias, param, {});
    }
    weight_idx++;
    bias_idx++;
    return conv;
}

SymbolVar Network::add_group_conv(
        SymbolVar f, size_t output_channels, size_t groups, KernSize kern_size,