opus1 0.2.1

//! The RDOVAE encoder of deep redundancy (DRED): its layers and the constants
//! of its C header.
//!
//! Generated by `tools/dnn-gen` from libopus 1.6.1 `dnn/dred_rdovae_enc_data.h`
//! and `dnn/dred_rdovae_enc_data.c` (`init_rdovaeenc`). Do not edit: change the
//! generator and run it again (`tools/dnn-gen/README.md`).

use crate::Error;
use super::{Layer, LayerSpec, LinearSpec};
use crate::dnn::nnet::LinearLayer;
use crate::dnn::weights::WeightList;

/// The file of this model's weights in `dnn-weights/`.
pub const BLOB_FILE: &str = "dred_rdovae_enc.bin";

// The constants of `dnn/dred_rdovae_enc_data.h`, in the same order.

/// `ENC_DENSE1_OUT_SIZE`.
pub const ENC_DENSE1_OUT_SIZE: usize = 64;

/// `ENC_ZDENSE_OUT_SIZE`.
pub const ENC_ZDENSE_OUT_SIZE: usize = 32;

/// `GDENSE2_OUT_SIZE`.
pub const GDENSE2_OUT_SIZE: usize = 56;

/// `GDENSE1_OUT_SIZE`.
pub const GDENSE1_OUT_SIZE: usize = 128;

/// `ENC_CONV_DENSE1_OUT_SIZE`.
pub const ENC_CONV_DENSE1_OUT_SIZE: usize = 64;

/// `ENC_CONV_DENSE2_OUT_SIZE`.
pub const ENC_CONV_DENSE2_OUT_SIZE: usize = 64;

/// `ENC_CONV_DENSE3_OUT_SIZE`.
pub const ENC_CONV_DENSE3_OUT_SIZE: usize = 64;

/// `ENC_CONV_DENSE4_OUT_SIZE`.
pub const ENC_CONV_DENSE4_OUT_SIZE: usize = 64;

/// `ENC_CONV_DENSE5_OUT_SIZE`.
pub const ENC_CONV_DENSE5_OUT_SIZE: usize = 64;

/// `ENC_GRU1_OUT_SIZE`.
pub const ENC_GRU1_OUT_SIZE: usize = 32;

/// `ENC_GRU1_STATE_SIZE`.
pub const ENC_GRU1_STATE_SIZE: usize = 32;

/// `ENC_GRU2_OUT_SIZE`.
pub const ENC_GRU2_OUT_SIZE: usize = 32;

/// `ENC_GRU2_STATE_SIZE`.
pub const ENC_GRU2_STATE_SIZE: usize = 32;

/// `ENC_GRU3_OUT_SIZE`.
pub const ENC_GRU3_OUT_SIZE: usize = 32;

/// `ENC_GRU3_STATE_SIZE`.
pub const ENC_GRU3_STATE_SIZE: usize = 32;

/// `ENC_GRU4_OUT_SIZE`.
pub const ENC_GRU4_OUT_SIZE: usize = 32;

/// `ENC_GRU4_STATE_SIZE`.
pub const ENC_GRU4_STATE_SIZE: usize = 32;

/// `ENC_GRU5_OUT_SIZE`.
pub const ENC_GRU5_OUT_SIZE: usize = 32;

/// `ENC_GRU5_STATE_SIZE`.
pub const ENC_GRU5_STATE_SIZE: usize = 32;

/// `ENC_CONV1_OUT_SIZE`.
pub const ENC_CONV1_OUT_SIZE: usize = 64;

/// `ENC_CONV1_IN_SIZE`.
pub const ENC_CONV1_IN_SIZE: usize = 64;

/// `ENC_CONV1_STATE_SIZE`, `(64 * (1))` in C.
pub const ENC_CONV1_STATE_SIZE: usize = 64;

/// `ENC_CONV2_OUT_SIZE`.
pub const ENC_CONV2_OUT_SIZE: usize = 64;

/// `ENC_CONV2_IN_SIZE`.
pub const ENC_CONV2_IN_SIZE: usize = 64;

/// `ENC_CONV2_STATE_SIZE`, `(64 * (1))` in C.
pub const ENC_CONV2_STATE_SIZE: usize = 64;

/// `ENC_CONV3_OUT_SIZE`.
pub const ENC_CONV3_OUT_SIZE: usize = 64;

/// `ENC_CONV3_IN_SIZE`.
pub const ENC_CONV3_IN_SIZE: usize = 64;

/// `ENC_CONV3_STATE_SIZE`, `(64 * (1))` in C.
pub const ENC_CONV3_STATE_SIZE: usize = 64;

/// `ENC_CONV4_OUT_SIZE`.
pub const ENC_CONV4_OUT_SIZE: usize = 64;

/// `ENC_CONV4_IN_SIZE`.
pub const ENC_CONV4_IN_SIZE: usize = 64;

/// `ENC_CONV4_STATE_SIZE`, `(64 * (1))` in C.
pub const ENC_CONV4_STATE_SIZE: usize = 64;

/// `ENC_CONV5_OUT_SIZE`.
pub const ENC_CONV5_OUT_SIZE: usize = 64;

/// `ENC_CONV5_IN_SIZE`.
pub const ENC_CONV5_IN_SIZE: usize = 64;

/// `ENC_CONV5_STATE_SIZE`, `(64 * (1))` in C.
pub const ENC_CONV5_STATE_SIZE: usize = 64;

/// The layers in the order of `init_rdovaeenc`: the arrays that each one reads,
/// and its shape. The names are the array names of the blob.
pub const LAYERS: [LayerSpec; 24] = [
    LayerSpec::Linear(LinearSpec {
        name: "enc_dense1",
        bias: Some("enc_dense1_bias"),
        subias: None,
        weights: None,
        float_weights: Some("enc_dense1_weights_float"),
        weights_idx: None,
        diag: None,
        scale: None,
        nb_inputs: 40,
        nb_outputs: 64,
    }),
    LayerSpec::Linear(LinearSpec {
        name: "enc_zdense",
        bias: Some("enc_zdense_bias"),
        subias: Some("enc_zdense_subias"),
        weights: Some("enc_zdense_weights_int8"),
        float_weights: Some("enc_zdense_weights_float"),
        weights_idx: None,
        diag: None,
        scale: Some("enc_zdense_scale"),
        nb_inputs: 544,
        nb_outputs: 32,
    }),
    LayerSpec::Linear(LinearSpec {
        name: "gdense2",
        bias: Some("gdense2_bias"),
        subias: Some("gdense2_subias"),
        weights: Some("gdense2_weights_int8"),
        float_weights: Some("gdense2_weights_float"),
        weights_idx: None,
        diag: None,
        scale: Some("gdense2_scale"),
        nb_inputs: 128,
        nb_outputs: 56,
    }),
    LayerSpec::Linear(LinearSpec {
        name: "gdense1",
        bias: Some("gdense1_bias"),
        subias: Some("gdense1_subias"),
        weights: Some("gdense1_weights_int8"),
        float_weights: Some("gdense1_weights_float"),
        weights_idx: Some("gdense1_weights_idx"),
        diag: None,
        scale: Some("gdense1_scale"),
        nb_inputs: 544,
        nb_outputs: 128,
    }),
    LayerSpec::Linear(LinearSpec {
        name: "enc_conv_dense1",
        bias: Some("enc_conv_dense1_bias"),
        subias: Some("enc_conv_dense1_subias"),
        weights: Some("enc_conv_dense1_weights_int8"),
        float_weights: Some("enc_conv_dense1_weights_float"),
        weights_idx: Some("enc_conv_dense1_weights_idx"),
        diag: None,
        scale: Some("enc_conv_dense1_scale"),
        nb_inputs: 96,
        nb_outputs: 64,
    }),
    LayerSpec::Linear(LinearSpec {
        name: "enc_conv_dense2",
        bias: Some("enc_conv_dense2_bias"),
        subias: Some("enc_conv_dense2_subias"),
        weights: Some("enc_conv_dense2_weights_int8"),
        float_weights: Some("enc_conv_dense2_weights_float"),
        weights_idx: Some("enc_conv_dense2_weights_idx"),
        diag: None,
        scale: Some("enc_conv_dense2_scale"),
        nb_inputs: 192,
        nb_outputs: 64,
    }),
    LayerSpec::Linear(LinearSpec {
        name: "enc_conv_dense3",
        bias: Some("enc_conv_dense3_bias"),
        subias: Some("enc_conv_dense3_subias"),
        weights: Some("enc_conv_dense3_weights_int8"),
        float_weights: Some("enc_conv_dense3_weights_float"),
        weights_idx: Some("enc_conv_dense3_weights_idx"),
        diag: None,
        scale: Some("enc_conv_dense3_scale"),
        nb_inputs: 288,
        nb_outputs: 64,
    }),
    LayerSpec::Linear(LinearSpec {
        name: "enc_conv_dense4",
        bias: Some("enc_conv_dense4_bias"),
        subias: Some("enc_conv_dense4_subias"),
        weights: Some("enc_conv_dense4_weights_int8"),
        float_weights: Some("enc_conv_dense4_weights_float"),
        weights_idx: Some("enc_conv_dense4_weights_idx"),
        diag: None,
        scale: Some("enc_conv_dense4_scale"),
        nb_inputs: 384,
        nb_outputs: 64,
    }),
    LayerSpec::Linear(LinearSpec {
        name: "enc_conv_dense5",
        bias: Some("enc_conv_dense5_bias"),
        subias: Some("enc_conv_dense5_subias"),
        weights: Some("enc_conv_dense5_weights_int8"),
        float_weights: Some("enc_conv_dense5_weights_float"),
        weights_idx: Some("enc_conv_dense5_weights_idx"),
        diag: None,
        scale: Some("enc_conv_dense5_scale"),
        nb_inputs: 480,
        nb_outputs: 64,
    }),
    LayerSpec::Linear(LinearSpec {
        name: "enc_gru1_input",
        bias: Some("enc_gru1_input_bias"),
        subias: Some("enc_gru1_input_subias"),
        weights: Some("enc_gru1_input_weights_int8"),
        float_weights: Some("enc_gru1_input_weights_float"),
        weights_idx: Some("enc_gru1_input_weights_idx"),
        diag: None,
        scale: Some("enc_gru1_input_scale"),
        nb_inputs: 64,
        nb_outputs: 96,
    }),
    LayerSpec::Linear(LinearSpec {
        name: "enc_gru1_recurrent",
        bias: Some("enc_gru1_recurrent_bias"),
        subias: Some("enc_gru1_recurrent_subias"),
        weights: Some("enc_gru1_recurrent_weights_int8"),
        float_weights: Some("enc_gru1_recurrent_weights_float"),
        weights_idx: None,
        diag: None,
        scale: Some("enc_gru1_recurrent_scale"),
        nb_inputs: 32,
        nb_outputs: 96,
    }),
    LayerSpec::Linear(LinearSpec {
        name: "enc_gru2_input",
        bias: Some("enc_gru2_input_bias"),
        subias: Some("enc_gru2_input_subias"),
        weights: Some("enc_gru2_input_weights_int8"),
        float_weights: Some("enc_gru2_input_weights_float"),
        weights_idx: Some("enc_gru2_input_weights_idx"),
        diag: None,
        scale: Some("enc_gru2_input_scale"),
        nb_inputs: 160,
        nb_outputs: 96,
    }),
    LayerSpec::Linear(LinearSpec {
        name: "enc_gru2_recurrent",
        bias: Some("enc_gru2_recurrent_bias"),
        subias: Some("enc_gru2_recurrent_subias"),
        weights: Some("enc_gru2_recurrent_weights_int8"),
        float_weights: Some("enc_gru2_recurrent_weights_float"),
        weights_idx: None,
        diag: None,
        scale: Some("enc_gru2_recurrent_scale"),
        nb_inputs: 32,
        nb_outputs: 96,
    }),
    LayerSpec::Linear(LinearSpec {
        name: "enc_gru3_input",
        bias: Some("enc_gru3_input_bias"),
        subias: Some("enc_gru3_input_subias"),
        weights: Some("enc_gru3_input_weights_int8"),
        float_weights: Some("enc_gru3_input_weights_float"),
        weights_idx: Some("enc_gru3_input_weights_idx"),
        diag: None,
        scale: Some("enc_gru3_input_scale"),
        nb_inputs: 256,
        nb_outputs: 96,
    }),
    LayerSpec::Linear(LinearSpec {
        name: "enc_gru3_recurrent",
        bias: Some("enc_gru3_recurrent_bias"),
        subias: Some("enc_gru3_recurrent_subias"),
        weights: Some("enc_gru3_recurrent_weights_int8"),
        float_weights: Some("enc_gru3_recurrent_weights_float"),
        weights_idx: None,
        diag: None,
        scale: Some("enc_gru3_recurrent_scale"),
        nb_inputs: 32,
        nb_outputs: 96,
    }),
    LayerSpec::Linear(LinearSpec {
        name: "enc_gru4_input",
        bias: Some("enc_gru4_input_bias"),
        subias: Some("enc_gru4_input_subias"),
        weights: Some("enc_gru4_input_weights_int8"),
        float_weights: Some("enc_gru4_input_weights_float"),
        weights_idx: Some("enc_gru4_input_weights_idx"),
        diag: None,
        scale: Some("enc_gru4_input_scale"),
        nb_inputs: 352,
        nb_outputs: 96,
    }),
    LayerSpec::Linear(LinearSpec {
        name: "enc_gru4_recurrent",
        bias: Some("enc_gru4_recurrent_bias"),
        subias: Some("enc_gru4_recurrent_subias"),
        weights: Some("enc_gru4_recurrent_weights_int8"),
        float_weights: Some("enc_gru4_recurrent_weights_float"),
        weights_idx: None,
        diag: None,
        scale: Some("enc_gru4_recurrent_scale"),
        nb_inputs: 32,
        nb_outputs: 96,
    }),
    LayerSpec::Linear(LinearSpec {
        name: "enc_gru5_input",
        bias: Some("enc_gru5_input_bias"),
        subias: Some("enc_gru5_input_subias"),
        weights: Some("enc_gru5_input_weights_int8"),
        float_weights: Some("enc_gru5_input_weights_float"),
        weights_idx: Some("enc_gru5_input_weights_idx"),
        diag: None,
        scale: Some("enc_gru5_input_scale"),
        nb_inputs: 448,
        nb_outputs: 96,
    }),
    LayerSpec::Linear(LinearSpec {
        name: "enc_gru5_recurrent",
        bias: Some("enc_gru5_recurrent_bias"),
        subias: Some("enc_gru5_recurrent_subias"),
        weights: Some("enc_gru5_recurrent_weights_int8"),
        float_weights: Some("enc_gru5_recurrent_weights_float"),
        weights_idx: None,
        diag: None,
        scale: Some("enc_gru5_recurrent_scale"),
        nb_inputs: 32,
        nb_outputs: 96,
    }),
    LayerSpec::Linear(LinearSpec {
        name: "enc_conv1",
        bias: Some("enc_conv1_bias"),
        subias: Some("enc_conv1_subias"),
        weights: Some("enc_conv1_weights_int8"),
        float_weights: Some("enc_conv1_weights_float"),
        weights_idx: None,
        diag: None,
        scale: Some("enc_conv1_scale"),
        nb_inputs: 128,
        nb_outputs: 64,
    }),
    LayerSpec::Linear(LinearSpec {
        name: "enc_conv2",
        bias: Some("enc_conv2_bias"),
        subias: Some("enc_conv2_subias"),
        weights: Some("enc_conv2_weights_int8"),
        float_weights: Some("enc_conv2_weights_float"),
        weights_idx: None,
        diag: None,
        scale: Some("enc_conv2_scale"),
        nb_inputs: 128,
        nb_outputs: 64,
    }),
    LayerSpec::Linear(LinearSpec {
        name: "enc_conv3",
        bias: Some("enc_conv3_bias"),
        subias: Some("enc_conv3_subias"),
        weights: Some("enc_conv3_weights_int8"),
        float_weights: Some("enc_conv3_weights_float"),
        weights_idx: None,
        diag: None,
        scale: Some("enc_conv3_scale"),
        nb_inputs: 128,
        nb_outputs: 64,
    }),
    LayerSpec::Linear(LinearSpec {
        name: "enc_conv4",
        bias: Some("enc_conv4_bias"),
        subias: Some("enc_conv4_subias"),
        weights: Some("enc_conv4_weights_int8"),
        float_weights: Some("enc_conv4_weights_float"),
        weights_idx: None,
        diag: None,
        scale: Some("enc_conv4_scale"),
        nb_inputs: 128,
        nb_outputs: 64,
    }),
    LayerSpec::Linear(LinearSpec {
        name: "enc_conv5",
        bias: Some("enc_conv5_bias"),
        subias: Some("enc_conv5_subias"),
        weights: Some("enc_conv5_weights_int8"),
        float_weights: Some("enc_conv5_weights_float"),
        weights_idx: None,
        diag: None,
        scale: Some("enc_conv5_scale"),
        nb_inputs: 128,
        nb_outputs: 64,
    }),
];

/// libopus `RDOVAEEnc` (`dnn/dred_rdovae_enc_data.h`): the layers of the model,
/// in the order of the C struct. Each layer borrows its arrays from a weight
/// list.
#[derive(Clone, Copy, Default)]
pub struct RdovaeEnc<'w> {
    /// `enc_dense1`: float weights, 40 inputs, 64 outputs.
    pub enc_dense1: LinearLayer<'w>,
    /// `enc_zdense`: int8 weights, 544 inputs, 32 outputs.
    pub enc_zdense: LinearLayer<'w>,
    /// `gdense2`: int8 weights, 128 inputs, 56 outputs.
    pub gdense2: LinearLayer<'w>,
    /// `gdense1`: int8 sparse weights, 544 inputs, 128 outputs.
    pub gdense1: LinearLayer<'w>,
    /// `enc_conv_dense1`: int8 sparse weights, 96 inputs, 64 outputs.
    pub enc_conv_dense1: LinearLayer<'w>,
    /// `enc_conv_dense2`: int8 sparse weights, 192 inputs, 64 outputs.
    pub enc_conv_dense2: LinearLayer<'w>,
    /// `enc_conv_dense3`: int8 sparse weights, 288 inputs, 64 outputs.
    pub enc_conv_dense3: LinearLayer<'w>,
    /// `enc_conv_dense4`: int8 sparse weights, 384 inputs, 64 outputs.
    pub enc_conv_dense4: LinearLayer<'w>,
    /// `enc_conv_dense5`: int8 sparse weights, 480 inputs, 64 outputs.
    pub enc_conv_dense5: LinearLayer<'w>,
    /// `enc_gru1_input`: int8 sparse weights, 64 inputs, 96 outputs.
    pub enc_gru1_input: LinearLayer<'w>,
    /// `enc_gru1_recurrent`: int8 weights, 32 inputs, 96 outputs.
    pub enc_gru1_recurrent: LinearLayer<'w>,
    /// `enc_gru2_input`: int8 sparse weights, 160 inputs, 96 outputs.
    pub enc_gru2_input: LinearLayer<'w>,
    /// `enc_gru2_recurrent`: int8 weights, 32 inputs, 96 outputs.
    pub enc_gru2_recurrent: LinearLayer<'w>,
    /// `enc_gru3_input`: int8 sparse weights, 256 inputs, 96 outputs.
    pub enc_gru3_input: LinearLayer<'w>,
    /// `enc_gru3_recurrent`: int8 weights, 32 inputs, 96 outputs.
    pub enc_gru3_recurrent: LinearLayer<'w>,
    /// `enc_gru4_input`: int8 sparse weights, 352 inputs, 96 outputs.
    pub enc_gru4_input: LinearLayer<'w>,
    /// `enc_gru4_recurrent`: int8 weights, 32 inputs, 96 outputs.
    pub enc_gru4_recurrent: LinearLayer<'w>,
    /// `enc_gru5_input`: int8 sparse weights, 448 inputs, 96 outputs.
    pub enc_gru5_input: LinearLayer<'w>,
    /// `enc_gru5_recurrent`: int8 weights, 32 inputs, 96 outputs.
    pub enc_gru5_recurrent: LinearLayer<'w>,
    /// `enc_conv1`: int8 weights, 128 inputs, 64 outputs.
    pub enc_conv1: LinearLayer<'w>,
    /// `enc_conv2`: int8 weights, 128 inputs, 64 outputs.
    pub enc_conv2: LinearLayer<'w>,
    /// `enc_conv3`: int8 weights, 128 inputs, 64 outputs.
    pub enc_conv3: LinearLayer<'w>,
    /// `enc_conv4`: int8 weights, 128 inputs, 64 outputs.
    pub enc_conv4: LinearLayer<'w>,
    /// `enc_conv5`: int8 weights, 128 inputs, 64 outputs.
    pub enc_conv5: LinearLayer<'w>,
}

impl<'w> RdovaeEnc<'w> {
    /// libopus `init_rdovaeenc`: builds every layer from the arrays of `list`,
    /// in the order of `LAYERS`. Fails at the first layer whose arrays are
    /// missing, have the wrong size or are malformed; then nothing is built.
    pub fn init(list: &WeightList<'w>) -> Result<Self, Error> {
        Ok(RdovaeEnc {
            enc_dense1: LAYERS[0].linear(list)?,
            enc_zdense: LAYERS[1].linear(list)?,
            gdense2: LAYERS[2].linear(list)?,
            gdense1: LAYERS[3].linear(list)?,
            enc_conv_dense1: LAYERS[4].linear(list)?,
            enc_conv_dense2: LAYERS[5].linear(list)?,
            enc_conv_dense3: LAYERS[6].linear(list)?,
            enc_conv_dense4: LAYERS[7].linear(list)?,
            enc_conv_dense5: LAYERS[8].linear(list)?,
            enc_gru1_input: LAYERS[9].linear(list)?,
            enc_gru1_recurrent: LAYERS[10].linear(list)?,
            enc_gru2_input: LAYERS[11].linear(list)?,
            enc_gru2_recurrent: LAYERS[12].linear(list)?,
            enc_gru3_input: LAYERS[13].linear(list)?,
            enc_gru3_recurrent: LAYERS[14].linear(list)?,
            enc_gru4_input: LAYERS[15].linear(list)?,
            enc_gru4_recurrent: LAYERS[16].linear(list)?,
            enc_gru5_input: LAYERS[17].linear(list)?,
            enc_gru5_recurrent: LAYERS[18].linear(list)?,
            enc_conv1: LAYERS[19].linear(list)?,
            enc_conv2: LAYERS[20].linear(list)?,
            enc_conv3: LAYERS[21].linear(list)?,
            enc_conv4: LAYERS[22].linear(list)?,
            enc_conv5: LAYERS[23].linear(list)?,
        })
    }

    /// Every layer, in the order of `LAYERS`.
    pub fn layers(&self) -> [Layer<'_, 'w>; 24] {
        [
            Layer::Linear(&self.enc_dense1),
            Layer::Linear(&self.enc_zdense),
            Layer::Linear(&self.gdense2),
            Layer::Linear(&self.gdense1),
            Layer::Linear(&self.enc_conv_dense1),
            Layer::Linear(&self.enc_conv_dense2),
            Layer::Linear(&self.enc_conv_dense3),
            Layer::Linear(&self.enc_conv_dense4),
            Layer::Linear(&self.enc_conv_dense5),
            Layer::Linear(&self.enc_gru1_input),
            Layer::Linear(&self.enc_gru1_recurrent),
            Layer::Linear(&self.enc_gru2_input),
            Layer::Linear(&self.enc_gru2_recurrent),
            Layer::Linear(&self.enc_gru3_input),
            Layer::Linear(&self.enc_gru3_recurrent),
            Layer::Linear(&self.enc_gru4_input),
            Layer::Linear(&self.enc_gru4_recurrent),
            Layer::Linear(&self.enc_gru5_input),
            Layer::Linear(&self.enc_gru5_recurrent),
            Layer::Linear(&self.enc_conv1),
            Layer::Linear(&self.enc_conv2),
            Layer::Linear(&self.enc_conv3),
            Layer::Linear(&self.enc_conv4),
            Layer::Linear(&self.enc_conv5),
        ]
    }
}