path: root/dnn/training_tf2/dump_rdovae.py

"""
/* Copyright (c) 2022 Amazon
   Written by Jan Buethe */
/*
   Redistribution and use in source and binary forms, with or without
   modification, are permitted provided that the following conditions
   are met:

   - Redistributions of source code must retain the above copyright
   notice, this list of conditions and the following disclaimer.

   - Redistributions in binary form must reproduce the above copyright
   notice, this list of conditions and the following disclaimer in the
   documentation and/or other materials provided with the distribution.

   THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
   ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
   LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
   A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER
   OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
   EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
   PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
   PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
   LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
   NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
   SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
"""


import argparse
from ftplib import parse150
import os

os.environ['CUDA_VISIBLE_DEVICES'] = ""

parser = argparse.ArgumentParser()

parser.add_argument('weights', metavar="<weight file>", type=str, help='model weight file in hdf5 format')
parser.add_argument('--cond-size', type=int, help="conditioning size (default: 256)", default=256)
parser.add_argument('--latent-dim', type=int, help="dimension of latent space (default: 80)", default=80)
parser.add_argument('--quant-levels', type=int, help="number of quantization steps (default: 16)", default=16)

args = parser.parse_args()

# now import the heavy stuff
import tensorflow as tf
import numpy as np
from keraslayerdump import dump_conv1d_layer, dump_dense_layer, dump_gru_layer, printVector
from rdovae import new_rdovae_model

def start_header(header_fid, header_name):
    header_guard = os.path.basename(header_name)[:-2].upper() + "_H"
    header_fid.write(
f"""
#ifndef {header_guard}
#define {header_guard}

"""
    )

def finish_header(header_fid):
    header_fid.write(
"""
#endif

"""
    )

def start_source(source_fid, header_name, weight_file):
    source_fid.write(
f"""
/* this source file was automatically generated from weight file {weight_file} */

#ifdef HAVE_CONFIG_H
#include "config.h"
#endif

#include "{header_name}"

"""
    )

def finish_source(source_fid):
    pass


def dump_statistical_model(qembedding, f, fh):
    w = qembedding.weights[0].numpy()
    levels, dim = w.shape
    N = dim // 6

    print("dumping statistical model")
    quant_scales    = tf.math.softplus(w[:, : N]).numpy()
    dead_zone       = 0.05 * tf.math.softplus(w[:, N : 2 * N]).numpy()
    r               = tf.math.sigmoid(w[:, 5 * N : 6 * N]).numpy()
    p0              = tf.math.sigmoid(w[:, 4 * N : 5 * N]).numpy()
    p0              = 1 - r ** (0.5 + 0.5 * p0)

    quant_scales_q8 = np.round(quant_scales * 2**8).astype(np.uint16)
    dead_zone_q10   = np.round(dead_zone * 2**10).astype(np.uint16)
    r_q15           = np.round(r * 2**15).astype(np.uint16)
    p0_q15          = np.round(p0 * 2**15).astype(np.uint16)

    printVector(f, quant_scales_q8, 'dred_quant_scales_q8', dtype='opus_uint16', static=False)
    printVector(f, dead_zone_q10, 'dred_dead_zone_q10', dtype='opus_uint16', static=False)
    printVector(f, r_q15, 'dred_r_q15', dtype='opus_uint16', static=False)
    printVector(f, p0_q15, 'dred_p0_q15', dtype='opus_uint16', static=False)

    fh.write(
f"""
extern const opus_uint16 dred_quant_scales_q8[{levels * N}];
extern const opus_uint16 dred_dead_zone_q10[{levels * N}];
extern const opus_uint16 dred_r_q15[{levels * N}];
extern const opus_uint16 dred_p0_q15[{levels * N}];

"""
    )

if __name__ == "__main__":

    model, encoder, decoder, qembedding = new_rdovae_model(20, args.latent_dim, cond_size=args.cond_size, nb_quant=args.quant_levels)
    model.load_weights(args.weights)




    # encoder
    encoder_dense_names = [
        'enc_dense1',
        'enc_dense3',
        'enc_dense5',
        'enc_dense7',
        'enc_dense8',
        'gdense1',
        'gdense2'
    ]

    encoder_gru_names = [
        'enc_dense2',
        'enc_dense4',
        'enc_dense6'
    ]

    encoder_conv1d_names = [
        'bits_dense'
    ]

    source_fid = open("dred_rdovae_enc_data.c", 'w')
    header_fid = open("dred_rdovae_enc_data.h", 'w')

    start_header(header_fid, "dred_rdovae_enc_data.h")
    start_source(source_fid, "dred_rdovae_enc_data.h", os.path.basename(args.weights))

    header_fid.write(
f"""
#include "dred_rdovae_constants.h"

#include "nnet.h"
"""
    )

    # dump GRUs
    max_rnn_neurons_enc = max(
        [
            dump_gru_layer(encoder.get_layer(name), source_fid, header_fid, dotp=True, sparse=True)
            for name in encoder_gru_names
        ]
    )

    # dump conv layers
    max_conv_inputs = max(
        [
            dump_conv1d_layer(encoder.get_layer(name), source_fid, header_fid)
            for name in encoder_conv1d_names
        ]
    )

    # dump Dense layers
    for name in encoder_dense_names:
        layer = encoder.get_layer(name)
        dump_dense_layer(layer, source_fid, header_fid)

    # some global constants
    header_fid.write(
f"""

#define DRED_ENC_MAX_RNN_NEURONS {max_rnn_neurons_enc}

#define DRED_ENC_MAX_CONV_INPUTS {max_conv_inputs}

"""
    )

    finish_header(header_fid)
    finish_source(source_fid)

    header_fid.close()
    source_fid.close()

    # statistical model
    source_fid = open("dred_rdovae_stats_data.c", 'w')
    header_fid = open("dred_rdovae_stats_data.h", 'w')

    start_header(header_fid, "dred_rdovae_stats_data.h")
    start_source(source_fid, "dred_rdovae_stats_data.h", os.path.basename(args.weights))

    header_fid.write(
"""

#include "opus_types.h"

"""
    )

    dump_statistical_model(qembedding, source_fid, header_fid)

    finish_header(header_fid)
    finish_source(source_fid)

    header_fid.close()
    source_fid.close()

    # decoder
    decoder_dense_names = [
        'state1',
        'state2',
        'state3',
        'dec_dense1',
        'dec_dense3',
        'dec_dense5',
        'dec_dense7',
        'dec_dense8',
        'dec_final'
    ]

    decoder_gru_names = [
        'dec_dense2',
        'dec_dense4',
        'dec_dense6'
    ]

    source_fid = open("dred_rdovae_dec_data.c", 'w')
    header_fid = open("dred_rdovae_dec_data.h", 'w')

    start_header(header_fid, "dred_rdovae_dec_data.h")
    start_source(source_fid, "dred_rdovae_dec_data.h", os.path.basename(args.weights))

    header_fid.write(
f"""
#include "dred_rdovae_constants.h"

#include "nnet.h"
"""
    )


    # dump GRUs
    max_rnn_neurons_dec = max(
        [
            dump_gru_layer(decoder.get_layer(name), source_fid, header_fid, dotp=True, sparse=True)
            for name in decoder_gru_names
        ]
    )

    # dump Dense layers
    for name in decoder_dense_names:
        layer = decoder.get_layer(name)
        dump_dense_layer(layer, source_fid, header_fid)

    # some global constants
    header_fid.write(
f"""

#define DRED_DEC_MAX_RNN_NEURONS {max_rnn_neurons_dec}

"""
    )

    finish_header(header_fid)
    finish_source(source_fid)

    header_fid.close()
    source_fid.close()

    # common constants
    header_fid = open("dred_rdovae_constants.h", 'w')
    start_header(header_fid, "dred_rdovae_constants.h")

    header_fid.write(
f"""
#define DRED_NUM_FEATURES 20

#define DRED_LATENT_DIM {args.latent_dim}

#define DRED_STATE_DIM {24}

#define DRED_NUM_QUANTIZATION_LEVELS {qembedding.weights[0].shape[0]}

#define DRED_MAX_RNN_NEURONS {max(max_rnn_neurons_enc, max_rnn_neurons_dec)}

#define DRED_MAX_CONV_INPUTS {max_conv_inputs}
"""
    )

    finish_header(header_fid)