ref: ea4d8f54c3f0bd269ebd2ee97214d84d62628d81
parent: 50966eecc5024b3b648889319153d534a147cc71
author: Jan Buethe <jbuethe@amazon.de>
date: Wed Oct 19 13:18:25 EDT 2022
added statistical model to dump_nfec_model
--- a/dnn/training_tf2/dump_nfec_model.py
+++ b/dnn/training_tf2/dump_nfec_model.py
@@ -1,6 +1,7 @@
import argparse
import os
+
parser = argparse.ArgumentParser()
parser.add_argument('weights', metavar="<weight file>", type=str, help='model weight file in hdf5 format')@@ -10,7 +11,8 @@
args = parser.parse_args()
# now import the heavy stuff
-from keraslayerdump import dump_conv1d_layer, dump_dense_layer, dump_gru_layer
+import tensorflow as tf
+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):
@@ -47,6 +49,31 @@
pass
+def dump_statistical_model(qembedding, f, fh):
+ w = qembedding.weights[0].numpy()
+ levels, dim = w.shape
+ N = dim // 6
+
+ quant_scales = tf.math.softplus(w[:, : N]).numpy()
+ dead_zone_theta = 0.5 + 0.05 * tf.math.softplus(w[:, N : 2 * N]).numpy()
+ r = 0.5 + 0.5 * tf.math.sigmoid(w[:, 4 * N : 5 * N]).numpy()
+ theta = tf.math.sigmoid(w[:, 5 * N : 6 * N]).numpy()
+
+ printVector(f, quant_scales[:], 'nfec_stats_quant_scales')
+ printVector(f, dead_zone_theta[:], 'nfec_stats_dead_zone_theta')
+ printVector(f, r, 'nfec_stats_r')
+ printVector(f, theta, 'nfec_stats_theta')
+
+ fh.write(
+f"""
+extern float nfec_stats_quant_scales;
+extern float nfec_stats_dead_zone_theta;
+extern float nfec_stats_r;
+extern float nfec_stats_theta;
+
+"""
+ )
+
if __name__ == "__main__":
model, encoder, decoder, qembedding = new_rdovae_model(20, args.latent_dim, cond_size=args.cond_size)
@@ -53,7 +80,7 @@
model.load_weights(args.weights)
- # for the time being only dump encoder
+ # encoder
encoder_dense_names = [
'enc_dense1',
'enc_dense3',
@@ -114,6 +141,29 @@
"""
)
+
+ finish_header(header_fid)
+ finish_source(source_fid)
+
+ header_fid.close()
+ source_fid.close()
+
+ # statistical model
+ source_fid = open("nfec_stats_data.c", 'w')+ header_fid = open("nfec_stats_data.h", 'w')+
+ start_header(header_fid, "nfec_stats_data.h")
+ start_source(source_fid, "nfec_stats_data.h", os.path.basename(args.weights))
+
+ num_levels = qembedding.weights[0].shape[0]
+ header_fid.write(
+f"""
+#define NFEC_STATS_NUM_LEVELS {num_levels}+
+"""
+ )
+
+ dump_statistical_model(qembedding, source_fid, header_fid)
finish_header(header_fid)
finish_source(source_fid)
--- a/dnn/training_tf2/keraslayerdump.py
+++ b/dnn/training_tf2/keraslayerdump.py
@@ -157,4 +157,4 @@
hf.write('#define {}_STATE_SIZE ({}*{})\n'.format(name.upper(), weights[0].shape[1], (weights[0].shape[0]-1))) hf.write('#define {}_DELAY {}\n'.format(name.upper(), (weights[0].shape[0]-1)//2)) hf.write('extern const Conv1DLayer {};\n\n'.format(name));- return max_conv_inputs
\ No newline at end of file
+ return max_conv_inputs
--
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