1""" 2/* Copyright (c) 2022 Amazon 3 Written by Jan Buethe */ 4/* 5 Redistribution and use in source and binary forms, with or without 6 modification, are permitted provided that the following conditions 7 are met: 8 9 - Redistributions of source code must retain the above copyright 10 notice, this list of conditions and the following disclaimer. 11 12 - Redistributions in binary form must reproduce the above copyright 13 notice, this list of conditions and the following disclaimer in the 14 documentation and/or other materials provided with the distribution. 15 16 THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS 17 ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT 18 LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR 19 A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER 20 OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, 21 EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, 22 PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR 23 PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF 24 LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING 25 NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS 26 SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 27*/ 28""" 29 30 31import argparse 32import os 33import sys 34 35os.environ['CUDA_VISIBLE_DEVICES'] = "" 36 37parser = argparse.ArgumentParser() 38 39parser.add_argument('weights', metavar="<weight file>", type=str, help='model weight file in hdf5 format') 40parser.add_argument('output', metavar="<output folder>", type=str, help='output exchange folder') 41parser.add_argument('--cond-size', type=int, help="conditioning size (default: 256)", default=256) 42parser.add_argument('--latent-dim', type=int, help="dimension of latent space (default: 80)", default=80) 43parser.add_argument('--quant-levels', type=int, help="number of quantization steps (default: 16)", default=16) 44 45args = parser.parse_args() 46 47# now import the heavy stuff 48from rdovae import new_rdovae_model 49from wexchange.tf import dump_tf_weights, load_tf_weights 50 51 52exchange_name = { 53 'enc_dense1' : 'encoder_stack_layer1_dense', 54 'enc_dense3' : 'encoder_stack_layer3_dense', 55 'enc_dense5' : 'encoder_stack_layer5_dense', 56 'enc_dense7' : 'encoder_stack_layer7_dense', 57 'enc_dense8' : 'encoder_stack_layer8_dense', 58 'gdense1' : 'encoder_state_layer1_dense', 59 'gdense2' : 'encoder_state_layer2_dense', 60 'enc_dense2' : 'encoder_stack_layer2_gru', 61 'enc_dense4' : 'encoder_stack_layer4_gru', 62 'enc_dense6' : 'encoder_stack_layer6_gru', 63 'bits_dense' : 'encoder_stack_layer9_conv', 64 'qembedding' : 'statistical_model_embedding', 65 'state1' : 'decoder_state1_dense', 66 'state2' : 'decoder_state2_dense', 67 'state3' : 'decoder_state3_dense', 68 'dec_dense1' : 'decoder_stack_layer1_dense', 69 'dec_dense3' : 'decoder_stack_layer3_dense', 70 'dec_dense5' : 'decoder_stack_layer5_dense', 71 'dec_dense7' : 'decoder_stack_layer7_dense', 72 'dec_dense8' : 'decoder_stack_layer8_dense', 73 'dec_final' : 'decoder_stack_layer9_dense', 74 'dec_dense2' : 'decoder_stack_layer2_gru', 75 'dec_dense4' : 'decoder_stack_layer4_gru', 76 'dec_dense6' : 'decoder_stack_layer6_gru' 77} 78 79 80if __name__ == "__main__": 81 82 model, encoder, decoder, qembedding = new_rdovae_model(20, args.latent_dim, cond_size=args.cond_size, nb_quant=args.quant_levels) 83 model.load_weights(args.weights) 84 85 os.makedirs(args.output, exist_ok=True) 86 87 # encoder 88 encoder_dense_names = [ 89 'enc_dense1', 90 'enc_dense3', 91 'enc_dense5', 92 'enc_dense7', 93 'enc_dense8', 94 'gdense1', 95 'gdense2' 96 ] 97 98 encoder_gru_names = [ 99 'enc_dense2', 100 'enc_dense4', 101 'enc_dense6' 102 ] 103 104 encoder_conv1d_names = [ 105 'bits_dense' 106 ] 107 108 109 for name in encoder_dense_names + encoder_gru_names + encoder_conv1d_names: 110 print(f"writing layer {exchange_name[name]}...") 111 dump_tf_weights(os.path.join(args.output, exchange_name[name]), encoder.get_layer(name)) 112 113 # qembedding 114 print(f"writing layer {exchange_name['qembedding']}...") 115 dump_tf_weights(os.path.join(args.output, exchange_name['qembedding']), qembedding) 116 117 # decoder 118 decoder_dense_names = [ 119 'state1', 120 'state2', 121 'state3', 122 'dec_dense1', 123 'dec_dense3', 124 'dec_dense5', 125 'dec_dense7', 126 'dec_dense8', 127 'dec_final' 128 ] 129 130 decoder_gru_names = [ 131 'dec_dense2', 132 'dec_dense4', 133 'dec_dense6' 134 ] 135 136 for name in decoder_dense_names + decoder_gru_names: 137 print(f"writing layer {exchange_name[name]}...") 138 dump_tf_weights(os.path.join(args.output, exchange_name[name]), decoder.get_layer(name)) 139