推理时 cnn bn 折叠；基于KWS项目
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2023-12-21 14:23:22
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原创: aaa@qq.com
时间: 2020/06/18
- 项目: ML-KWS-for-MCU
- 参考源代码：DSCNN bn folded
- pytorch 实现
- bn折叠好处：推理时速度提升
魔改的cnn的推理时，将bn折叠，即在训练的变量上乘以一个系数从而将bn层在推理时舍去，
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import sys
import os
import numpy as np
import tensorflow as tf
path = os.path.dirname(__file__)
sys.path.append(os.path.join(path, '../'))
import models
import input_data
FLAGS = None
def fold_batch_norm(wanted_words, sample_rate, clip_duration_ms,
window_size_ms, window_stride_ms,
dct_coefficient_count, model_architecture, model_size_info):
"""Creates an audio model with the nodes needed for inference.
Uses the supplied arguments to create a model, and inserts the input and
output nodes that are needed to use the graph for inference.
Args:
wanted_words: Comma-separated list of the words we're trying to recognize.
sample_rate: How many samples per second are in the input audio files.
clip_duration_ms: How many samples to analyze for the audio pattern.
window_size_ms: Time slice duration to estimate frequencies from.
window_stride_ms: How far apart time slices should be.
dct_coefficient_count: Number of frequency bands to analyze.
model_architecture: Name of the kind of model to generate.
"""
tf.logging.set_verbosity(tf.logging.INFO)
sess = tf.InteractiveSession()
words_list = input_data.prepare_words_list(wanted_words.split(','))
model_settings = models.prepare_model_settings(
len(words_list), sample_rate, clip_duration_ms, window_size_ms,
window_stride_ms, dct_coefficient_count)
fingerprint_input = tf.placeholder(
tf.float32, [None, model_settings['fingerprint_size']], name='fingerprint_input')
logits = models.create_model(
fingerprint_input,
model_settings,
FLAGS.model_architecture,
FLAGS.model_size_info,
is_training=False)
ground_truth_input = tf.placeholder(
tf.float32, [None, model_settings['label_count']], name='groundtruth_input')
predicted_indices = tf.argmax(logits, 1)
expected_indices = tf.argmax(ground_truth_input, 1)
correct_prediction = tf.equal(predicted_indices, expected_indices)
confusion_matrix = tf.confusion_matrix(expected_indices, predicted_indices)
evaluation_step = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
models.load_variables_from_checkpoint(sess, FLAGS.checkpoint)
saver = tf.train.Saver(tf.global_variables())
tf.logging.info('Folding batch normalization layer parameters to preceding layer weights/biases')
#epsilon added to variance to avoid division by zero
epsilon = 1e-3 #default epsilon for tf.slim.batch_norm
all_variables = [v.name for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)]
weight_list = ['Variable:0' if i == 0 else 'Variable_'+str(i*2)+':0' for i in range(3)]
biase_list = ['Variable_'+str(2*i+1)+':0' for i in range(3)]
#get batch_norm mean
mean_variables = [v for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
if 'moving_mean' in v.name]
for i, mean_var in enumerate(mean_variables):
mean_name = mean_var.name
mean_values = sess.run(mean_var)
variance_name = mean_name.replace('moving_mean','moving_variance')
variance_var = [v for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES) if v.name == variance_name][0]
variance_values = sess.run(variance_var)
beta_name = mean_name.replace('moving_mean','beta')
beta_var = [v for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES) if v.name == beta_name][0]
beta_values = sess.run(beta_var)
bias_name = biase_list[i]
bias_var = [v for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES) if v.name == bias_name][0]
bias_values = sess.run(bias_var)
wt_name = weight_list[i]
wt_var = [v for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES) if v.name == wt_name][0]
wt_values = sess.run(wt_var)
#Update weights
tf.logging.info('Updating '+wt_name)
# 获取带 BN 的每一个维度
wt_dim = wt_values.shape[-1]
# 在每一个维度上进行计算
if i != 2:
for l in range(wt_values.shape[3]):
for k in range(wt_values.shape[2]):
for j in range(wt_values.shape[1]):
for x in range(wt_values.shape[0]):
# gamma (scale factor) is 1.0
wt_values[x][j][k][l] *= 1.0/np.sqrt(variance_values[l]+epsilon)
else:
for l in range(wt_values.shape[1]):
for k in range(wt_values.shape[0]):
wt_values[k][l] *= 1.0/np.sqrt(variance_values[l]+epsilon)
wt_values = sess.run(tf.assign(wt_var,wt_values))
# Update biases
tf.logging.info('Updating '+bias_name)
biase_dim = wt_values.shape[-1]
for l in range(biase_dim):
bias_values[l] = (1.0*(bias_values[l]-mean_values[l])/np.sqrt(variance_values[l]+epsilon)) \
+ beta_values[l]
bias_values = sess.run(tf.assign(bias_var,bias_values))
#Write fused weights to ckpt file
tf.logging.info('Saving new checkpoint at '+FLAGS.checkpoint+'_bnfused')
saver.save(sess, FLAGS.checkpoint+'_bnfused')
def main(_):
# Create the model and load its weights.
fold_batch_norm(FLAGS.wanted_words, FLAGS.sample_rate,
FLAGS.clip_duration_ms, FLAGS.window_size_ms,
FLAGS.window_stride_ms, FLAGS.dct_coefficient_count,
FLAGS.model_architecture, FLAGS.model_size_info)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
'--data_url',
type=str,
# pylint: disable=line-too-long
default='',
# default='http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz',
# pylint: enable=line-too-long
help='Location of speech training data archive on the web.')
parser.add_argument(
'--data_dir',
type=str,
# default='/tmp/speech_dataset/',
default='../../data',
help="""
Where to download the speech training data to.
""")
parser.add_argument(
'--silence_percentage',
type=float,
default=10.0,
help="""\
How much of the training data should be silence.
""")
parser.add_argument(
'--unknown_percentage',
type=float,
default=10.0,
help="""\
How much of the training data should be unknown words.
""")
parser.add_argument(
'--testing_percentage',
type=int,
default=10,
help='What percentage of wavs to use as a test set.')
parser.add_argument(
'--validation_percentage',
type=int,
default=10,
help='What percentage of wavs to use as a validation set.')
parser.add_argument(
'--sample_rate',
type=int,
default=16000,
help='Expected sample rate of the wavs',)
parser.add_argument(
'--clip_duration_ms',
type=int,
default=1000,
help='Expected duration in milliseconds of the wavs',)
parser.add_argument(
'--window_size_ms',
type=float,
default=40.0,
help='How long each spectrogram timeslice is',)
parser.add_argument(
'--window_stride_ms',
type=float,
default=40.0,
help='How long each spectrogram timeslice is',)
parser.add_argument(
'--dct_coefficient_count',
type=int,
default=10,
help='How many bins to use for the MFCC fingerprint',)
parser.add_argument(
'--batch_size',
type=int,
default=100,
help='How many items to train with at once',)
parser.add_argument(
'--wanted_words',
type=str,
default='yes,no,up,down,left,right,on,off,stop,go,nihaoxr,xrxr',
help='Words to use (others will be added to an unknown label)',)
parser.add_argument(
'--checkpoint',
type=str,
default='../train_model/526_cnn/best/cnn_8884.ckpt-13200',
help='Checkpoint to load the weights from.')
parser.add_argument(
'--model_architecture',
type=str,
default='cnn2',
help='What model architecture to use')
parser.add_argument(
'--model_size_info',
type=int,
nargs="+",
default=[128,128,128],
help='Model dimensions - different for various models')
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
执行：
python3 ./utils/fold_batchnorm_cnn.py \
--data_dir ../data \
--dct_coefficient_count 10 \
--window_size_ms 32 \
--window_stride_ms 20 \
--checkpoint ./train_model/615_cnn_with_32_frame/best/cnn2_9127.ckpt-14000 \
--model_architecture cnn2 \
--model_size_info 28 10 4 1 1 30 10 4 2 1 16 128