Function load_activation

src/framework/modules/bigvgan_vocoder.cpp:197–229

}

ActivationWeights load_activation(
    core::BackendWeightStore & store,
    const assets::TensorSource & source,
    const std::string & prefix,
    int64_t channels,
    assets::TensorStorageType storage_type) {
    (void) storage_type;
    const auto alpha_raw = source.require_f32(prefix + ".act.alpha", {channels});
    const auto beta_raw = source.require_f32(prefix + ".act.beta", {channels});
    std::vector<float> alpha(static_cast<size_t>(channels), 0.0F);
    std::vector<float> inv_beta(static_cast<size_t>(channels), 0.0F);
    for (int64_t index = 0; index < channels; ++index) {
        alpha[static_cast<size_t>(index)] = std::exp(alpha_raw[static_cast<size_t>(index)]);
        inv_beta[static_cast<size_t>(index)] =
            1.0F / (std::exp(beta_raw[static_cast<size_t>(index)]) + 1.0e-9F);
    }
    ActivationWeights out;
    out.alpha = store.make_from_f32(core::TensorShape::from_dims({channels}), assets::TensorStorageType::F32, std::move(alpha));
    out.inv_beta = store.make_from_f32(core::TensorShape::from_dims({channels}), assets::TensorStorageType::F32, std::move(inv_beta));
    const auto up_filter = reversed_filter(
        source.require_f32(prefix + ".upsample.filter", {1, 1, kBigVganActivationKernel}));
    const auto down_filter =
        source.require_f32(prefix + ".downsample.lowpass.filter", {1, 1, kBigVganActivationKernel});
    out.up_filter = store.make_from_f32(
        core::TensorShape::from_dims({channels, 1, 1, kBigVganActivationKernel}),
        assets::TensorStorageType::F32,
        expand_activation_filter_2d(up_filter, channels));
    out.down_filter = store.make_from_f32(
        core::TensorShape::from_dims({channels, 1, 1, kBigVganActivationKernel}),
        assets::TensorStorageType::F32,
        expand_activation_filter_2d(down_filter, channels));
    return out;
}

ResBlockWeights load_resblock(
    core::BackendWeightStore & store,