first attempt of C implementation of fec encoder (not tested yet due to NEON/DOT_PROD not being separable)

dnn/training_tf2/dump_nfec_model.py

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+import argparse
+import os
+
+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)
+
+args = parser.parse_args()
+
+# now import the heavy stuff
+from keraslayerdump import dump_conv1d_layer, dump_dense_layer, dump_gru_layer
+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}
+
+#include "nnet.h"
+
+"""
+    )
+
+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} */
+
+#include "{header_name}"
+
+"""
+    )
+
+def finish_source(source_fid):
+    pass
+
+
+if __name__ == "__main__":
+
+    model, encoder, decoder, qembedding = new_rdovae_model(20, args.latent_dim, cond_size=args.cond_size)
+    model.load_weights(args.weights)
+
+
+    # for the time being only dump 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("nfec_enc_data.c", 'w')
+    header_fid = open("nfec_enc_data.h", 'w')
+
+    start_header(header_fid, "nfec_enc_data.h")
+    start_source(source_fid, "nfec_enc_data.h", os.path.basename(args.weights))
+
+    # dump GRUs
+    max_rnn_neurons = max(
+        [
+            dump_gru_layer(encoder.get_layer(name), source_fid, header_fid)
+            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 NFEC_NUM_FEATURES 20
+
+#define NFEC_LATENT_DIM {args.latent_dim}
+
+#define NFEC_ENC_MAX_RNN_NEURONS {max_rnn_neurons}
+
+#define NFEC_ENC_MAX_CONV_INPUTS {max_conv_inputs}
+
+"""
+    )
+
+    finish_header(header_fid)
+    finish_source(source_fid)
+
+    header_fid.close()
+    source_fid.close()
+