mirror of
https://github.com/xiph/opus.git
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306 lines
No EOL
7.9 KiB
Python
306 lines
No EOL
7.9 KiB
Python
"""
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/* Copyright (c) 2022 Amazon
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Written by Jan Buethe */
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/*
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Redistribution and use in source and binary forms, with or without
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modification, are permitted provided that the following conditions
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are met:
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- Redistributions of source code must retain the above copyright
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notice, this list of conditions and the following disclaimer.
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- Redistributions in binary form must reproduce the above copyright
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notice, this list of conditions and the following disclaimer in the
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documentation and/or other materials provided with the distribution.
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
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A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER
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OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
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PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
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LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
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NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*/
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"""
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import argparse
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from ftplib import parse150
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import os
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os.environ['CUDA_VISIBLE_DEVICES'] = ""
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parser = argparse.ArgumentParser()
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parser.add_argument('weights', metavar="<weight file>", type=str, help='model weight file in hdf5 format')
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parser.add_argument('--cond-size', type=int, help="conditioning size (default: 256)", default=256)
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parser.add_argument('--latent-dim', type=int, help="dimension of latent space (default: 80)", default=80)
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parser.add_argument('--quant-levels', type=int, help="number of quantization steps (default: 16)", default=16)
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args = parser.parse_args()
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# now import the heavy stuff
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import tensorflow as tf
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import numpy as np
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from keraslayerdump import dump_conv1d_layer, dump_dense_layer, dump_gru_layer, printVector
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from rdovae import new_rdovae_model
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def start_header(header_fid, header_name):
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header_guard = os.path.basename(header_name)[:-2].upper() + "_H"
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header_fid.write(
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f"""
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#ifndef {header_guard}
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#define {header_guard}
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"""
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)
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def finish_header(header_fid):
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header_fid.write(
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"""
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#endif
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"""
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)
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def start_source(source_fid, header_name, weight_file):
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source_fid.write(
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f"""
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/* this source file was automatically generated from weight file {weight_file} */
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#ifdef HAVE_CONFIG_H
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#include "config.h"
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#endif
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#include "{header_name}"
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"""
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)
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def finish_source(source_fid):
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pass
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def dump_statistical_model(qembedding, f, fh):
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w = qembedding.weights[0].numpy()
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levels, dim = w.shape
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N = dim // 6
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print("dumping statistical model")
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quant_scales = tf.math.softplus(w[:, : N]).numpy()
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dead_zone = 0.05 * tf.math.softplus(w[:, N : 2 * N]).numpy()
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r = tf.math.sigmoid(w[:, 5 * N : 6 * N]).numpy()
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p0 = tf.math.sigmoid(w[:, 4 * N : 5 * N]).numpy()
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p0 = 1 - r ** (0.5 + 0.5 * p0)
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quant_scales_q8 = np.round(quant_scales * 2**8).astype(np.uint16)
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dead_zone_q10 = np.round(dead_zone * 2**10).astype(np.uint16)
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r_q15 = np.round(r * 2**15).astype(np.uint16)
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p0_q15 = np.round(p0 * 2**15).astype(np.uint16)
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printVector(f, quant_scales_q8, 'dred_quant_scales_q8', dtype='opus_uint16', static=False)
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printVector(f, dead_zone_q10, 'dred_dead_zone_q10', dtype='opus_uint16', static=False)
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printVector(f, r_q15, 'dred_r_q15', dtype='opus_uint16', static=False)
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printVector(f, p0_q15, 'dred_p0_q15', dtype='opus_uint16', static=False)
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fh.write(
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f"""
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extern const opus_uint16 dred_quant_scales_q8[{levels * N}];
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extern const opus_uint16 dred_dead_zone_q10[{levels * N}];
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extern const opus_uint16 dred_r_q15[{levels * N}];
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extern const opus_uint16 dred_p0_q15[{levels * N}];
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"""
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)
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if __name__ == "__main__":
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model, encoder, decoder, qembedding = new_rdovae_model(20, args.latent_dim, cond_size=args.cond_size, nb_quant=args.quant_levels)
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model.load_weights(args.weights)
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# encoder
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encoder_dense_names = [
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'enc_dense1',
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'enc_dense3',
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'enc_dense5',
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'enc_dense7',
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'enc_dense8',
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'gdense1',
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'gdense2'
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]
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encoder_gru_names = [
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'enc_dense2',
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'enc_dense4',
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'enc_dense6'
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]
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encoder_conv1d_names = [
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'bits_dense'
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]
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source_fid = open("dred_rdovae_enc_data.c", 'w')
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header_fid = open("dred_rdovae_enc_data.h", 'w')
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start_header(header_fid, "dred_rdovae_enc_data.h")
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start_source(source_fid, "dred_rdovae_enc_data.h", os.path.basename(args.weights))
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header_fid.write(
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f"""
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#include "dred_rdovae_constants.h"
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#include "nnet.h"
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"""
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)
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# dump GRUs
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max_rnn_neurons_enc = max(
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[
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dump_gru_layer(encoder.get_layer(name), source_fid, header_fid, dotp=True, sparse=True)
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for name in encoder_gru_names
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]
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)
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# dump conv layers
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max_conv_inputs = max(
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[
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dump_conv1d_layer(encoder.get_layer(name), source_fid, header_fid)
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for name in encoder_conv1d_names
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]
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)
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# dump Dense layers
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for name in encoder_dense_names:
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layer = encoder.get_layer(name)
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dump_dense_layer(layer, source_fid, header_fid)
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# some global constants
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header_fid.write(
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f"""
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#define DRED_ENC_MAX_RNN_NEURONS {max_rnn_neurons_enc}
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#define DRED_ENC_MAX_CONV_INPUTS {max_conv_inputs}
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"""
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)
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finish_header(header_fid)
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finish_source(source_fid)
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header_fid.close()
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source_fid.close()
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# statistical model
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source_fid = open("dred_rdovae_stats_data.c", 'w')
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header_fid = open("dred_rdovae_stats_data.h", 'w')
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start_header(header_fid, "dred_rdovae_stats_data.h")
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start_source(source_fid, "dred_rdovae_stats_data.h", os.path.basename(args.weights))
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header_fid.write(
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"""
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#include "opus_types.h"
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"""
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)
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dump_statistical_model(qembedding, source_fid, header_fid)
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finish_header(header_fid)
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finish_source(source_fid)
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header_fid.close()
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source_fid.close()
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# decoder
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decoder_dense_names = [
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'state1',
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'state2',
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'state3',
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'dec_dense1',
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'dec_dense3',
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'dec_dense5',
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'dec_dense7',
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'dec_dense8',
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'dec_final'
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]
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decoder_gru_names = [
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'dec_dense2',
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'dec_dense4',
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'dec_dense6'
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]
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source_fid = open("dred_rdovae_dec_data.c", 'w')
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header_fid = open("dred_rdovae_dec_data.h", 'w')
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start_header(header_fid, "dred_rdovae_dec_data.h")
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start_source(source_fid, "dred_rdovae_dec_data.h", os.path.basename(args.weights))
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header_fid.write(
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f"""
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#include "dred_rdovae_constants.h"
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#include "nnet.h"
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"""
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)
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# dump GRUs
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max_rnn_neurons_dec = max(
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[
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dump_gru_layer(decoder.get_layer(name), source_fid, header_fid, dotp=True, sparse=True)
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for name in decoder_gru_names
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]
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)
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# dump Dense layers
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for name in decoder_dense_names:
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layer = decoder.get_layer(name)
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dump_dense_layer(layer, source_fid, header_fid)
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# some global constants
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header_fid.write(
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f"""
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#define DRED_DEC_MAX_RNN_NEURONS {max_rnn_neurons_dec}
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"""
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)
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finish_header(header_fid)
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finish_source(source_fid)
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header_fid.close()
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source_fid.close()
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# common constants
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header_fid = open("dred_rdovae_constants.h", 'w')
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start_header(header_fid, "dred_rdovae_constants.h")
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header_fid.write(
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f"""
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#define DRED_NUM_FEATURES 20
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#define DRED_LATENT_DIM {args.latent_dim}
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#define DRED_STATE_DIM {24}
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#define DRED_NUM_QUANTIZATION_LEVELS {qembedding.weights[0].shape[0]}
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#define DRED_MAX_RNN_NEURONS {max(max_rnn_neurons_enc, max_rnn_neurons_dec)}
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#define DRED_MAX_CONV_INPUTS {max_conv_inputs}
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"""
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)
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finish_header(header_fid) |