Method build

src/framework/modules/streaming_conv_modules.cpp:125–163

        config_.dilation,
        config_.use_bias,
    }).build(ctx, input_4d, {weight_4d, weights.bias});
    return core::reshape_tensor(
        ctx,
        output_4d,
        core::TensorShape::from_dims({
            input.shape.dims[0],
            config_.channels,
            depthwise_conv1d_output_frames(config_, input.shape.dims[2]),
        }));
}

PointwiseConv1dModule::PointwiseConv1dModule(PointwiseConv1dConfig config) : config_(config) {}

core::TensorValue PointwiseConv1dModule::build(
    core::ModuleBuildContext & ctx,
    const core::TensorValue & input,
    const PointwiseConv1dWeights & weights) const {
    if (config_.quant) {
        core::validate_rank_between(input, 3, 3, "input");
        core::validate_shape(
            input,
            core::TensorShape::from_dims({input.shape.dims[0], config_.in_channels, input.shape.dims[2]}),
            "input");
        core::TensorValue weight = weights.weight;
        if (weight.shape.rank == 3) {
            core::validate_shape(
                weight,
                core::TensorShape::from_dims({config_.out_channels, config_.in_channels, 1}),
                "weight");
            weight = core::reshape_tensor(
                ctx,
                weight,
                core::TensorShape::from_dims({config_.out_channels, config_.in_channels}));
        } else {
            core::validate_shape(
                weight,
                core::TensorShape::from_dims({config_.out_channels, config_.in_channels}),
                "weight");
        }
        auto x = TransposeModule({{0, 2, 1, 3}, 3}).build(ctx, input);
        x = LinearModule({config_.in_channels, config_.out_channels, config_.use_bias}).build(ctx, x, {weight, weights.bias});
        return TransposeModule({{0, 2, 1, 3}, 3}).build(ctx, x);