神经网络推理加速：合并卷积和BN层运算原理及实验

最新推荐文章于 2025-09-21 09:29:54 发布

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CC 4.0 BY-SA版权

原文链接：https://blog.youkuaiyun.com/u014114990/article/details/90665141

本文介绍如何将Batch Normalization（BN）层与卷积层合并，以提升深度学习模型的推理速度，通过实验证明，此操作能显著提高ResNet50模型在CPU和GPU上的前向传播效率。

转自：https://blog.youkuaiyun.com/u014114990/article/details/90665141

1. 为什么要合并BN层

在训练深度网络模型时，BN（Batch Normalization）层能够加速网络收敛，并且能够控制过拟合，一般放在卷积层之后。BN 层将数据归一化后，能够有效解决梯度消失与梯度爆炸问题。虽然 BN 层在训练时起到了积极作用，然而，在网络前向推断时多了一些层的运算，影响了模型的性能，且占用了更多的内存或者显存空间。目前，很多先进的网络模型（ResNet，MobileNet，Xception，ShuffleNet 等）都使用了BN技术，因此，我们有必要将 BN 层的参数合并到卷积层，来提升模型前向推断的速度。

２. BN层与卷积层合并的数学原理

卷积层中

3. 实验结果

机器：显卡 GTX 1080Ti，i7 CPU

本实验对比了Resnet50 模型合并BN层前后的性能，分类精度保持不变，速度显著提升。

模型	CPU前向时间	GPU前向时间
Resnet50（合并前）	176.17ms	11.03ms
Resnet50（合并后）	161.69ms	7.3ms
提升	10%	51%

4. 合并的python脚本

该脚本需要caffe的python接口

#!/usr/bin/env python
# -*- coding: UTF-8 -*-
import numpy as np
import sys
import os
import os.path as osp
import google.protobuf as pb
import google.protobuf.text_format
from argparse import ArgumentParser
import caffe
caffe.set_mode_cpu()
def load_and_fill_biases(src_model, src_weights, dst_model, dst_weights):
with open(src_model) as f:
model = caffe.proto.caffe_pb2.NetParameter()
pb.text_format.Merge(f.read(), model)
for i, layer in enumerate(model.layer):
if layer.type == 'Convolution': # or layer.type == 'Scale':
# Add bias layer if needed
if layer.convolution_param.bias_term == False:
layer.convolution_param.bias_term = True
layer.convolution_param.bias_filler.type = 'constant'
layer.convolution_param.bias_filler.value = 0.0
with open(dst_model, 'w') as f:
f.write(pb.text_format.MessageToString(model))
caffe.set_mode_cpu()
net_src = caffe.Net(src_model, src_weights, caffe.TEST)
net_dst = caffe.Net(dst_model, caffe.TEST)
for key in net_src.params.keys():
for i in range(len(net_src.params[key])):
net_dst.params[key][i].data[:] = net_src.params[key][i].data[:]
if dst_weights is not None:
# Store params
pass
return net_dst
def merge_conv_and_bn(net, i_conv, i_bn, i_scale):
# This is based on Kyeheyon's work
assert(i_conv != None)
assert(i_bn != None)
def copy_double(data):
return np.array(data, copy=True, dtype=np.double)
key_conv = net._layer_names[i_conv]
key_bn = net._layer_names[i_bn]
key_scale = net._layer_names[i_scale] if i_scale else None
# Copy
bn_mean = copy_double(net.params[key_bn][0].data)
bn_variance = copy_double(net.params[key_bn][1].data)
num_bn_samples = copy_double(net.params[key_bn][2].data)
# and Invalidate the BN layer
net.params[key_bn][0].data[:] = 0
net.params[key_bn][1].data[:] = 1
net.params[key_bn][2].data[:] = 1
if num_bn_samples[0] == 0:
num_bn_samples[0] = 1
if net.params.has_key(key_scale):
print 'Combine {:s} + {:s} + {:s}'.format(key_conv, key_bn, key_scale)
scale_weight = copy_double(net.params[key_scale][0].data)
scale_bias = copy_double(net.params[key_scale][1].data)
net.params[key_scale][0].data[:] = 1
net.params[key_scale][1].data[:] = 0
else:
print 'Combine {:s} + {:s}'.format(key_conv, key_bn)
scale_weight = 1
scale_bias = 0
weight = copy_double(net.params[key_conv][0].data)
bias = copy_double(net.params[key_conv][1].data)
alpha = scale_weight / np.sqrt(bn_variance / num_bn_samples[0] + 1e-5)
net.params[key_conv][1].data[:] = bias * alpha + (scale_bias - (bn_mean / num_bn_samples[0]) * alpha)
for i in range(len(alpha)):
net.params[key_conv][0].data[i] = weight[i] * alpha[i]
def merge_batchnorms_in_net(net):
# for each BN
for i, layer in enumerate(net.layers):
if layer.type != 'BatchNorm':
continue
l_name = net._layer_names[i]
l_bottom = net.bottom_names[l_name]
assert(len(l_bottom) == 1)
l_bottom = l_bottom[0]
l_top = net.top_names[l_name]
assert(len(l_top) == 1)
l_top = l_top[0]
can_be_absorbed = True
# Search all (bottom) layers
for j in xrange(i - 1, -1, -1):
tops_of_j = net.top_names[net._layer_names[j]]
if l_bottom in tops_of_j:
if net.layers[j].type not in ['Convolution', 'InnerProduct']:
can_be_absorbed = False
else:
# There must be only one layer
conv_ind = j
break
if not can_be_absorbed:
continue
# find the following Scale
scale_ind = None
for j in xrange(i + 1, len(net.layers)):
bottoms_of_j = net.bottom_names[net._layer_names[j]]
if l_top in bottoms_of_j:
if scale_ind:
# Followed by two or more layers
scale_ind = None
break
if net.layers[j].type in ['Scale']:
scale_ind = j
top_of_j = net.top_names[net._layer_names[j]][0]
if top_of_j == bottoms_of_j[0]:
# On-the-fly => Can be merged
break
else:
# Followed by a layer which is not 'Scale'
scale_ind = None
break
merge_conv_and_bn(net, conv_ind, i, scale_ind)
return net
def process_model(net, src_model, dst_model, func_loop, func_finally):
with open(src_model) as f:
model = caffe.proto.caffe_pb2.NetParameter()
pb.text_format.Merge(f.read(), model)
for i, layer in enumerate(model.layer):
map(lambda x: x(layer, net, model, i), func_loop)
map(lambda x: x(net, model), func_finally)
with open(dst_model, 'w') as f:
f.write(pb.text_format.MessageToString(model))
# Functions to remove (redundant) BN and Scale layers
to_delete_empty = []
def pick_empty_layers(layer, net, model, i):
if layer.type not in ['BatchNorm', 'Scale']:
return
bottom = layer.bottom[0]
top = layer.top[0]
if (bottom != top):
# Not supperted yet
return
if layer.type == 'BatchNorm':
zero_mean = np.all(net.params[layer.name][0].data == 0)
one_var = np.all(net.params[layer.name][1].data == 1)
if zero_mean and one_var:
print 'Delete layer: {}'.format(layer.name)
to_delete_empty.append(layer)
if layer.type == 'Scale':
no_scaling = np.all(net.params[layer.name][0].data == 1)
zero_bias = np.all(net.params[layer.name][1].data == 0)
if no_scaling and zero_bias:
print 'Delete layer: {}'.format(layer.name)
to_delete_empty.append(layer)
def remove_empty_layers(net, model):
map(model.layer.remove, to_delete_empty)
# A function to add 'engine: CAFFE' param into 1x1 convolutions
def set_engine_caffe(layer, net, model, i):
if layer.type == 'Convolution':
if layer.convolution_param.kernel_size == 1\
or (layer.convolution_param.kernel_h == layer.convolution_param.kernel_w == 1):
layer.convolution_param.engine = dict(layer.convolution_param.Engine.items())['CAFFE']
def main():
# Set default output file names
if args.output_model is None:
file_name = osp.splitext(args.model)[0]
args.output_model = file_name + '_inference.prototxt'
if args.output_weights is None:
file_name = osp.splitext(args.weights)[0]
args.output_weights = file_name + '_inference.caffemodel'
net = load_and_fill_biases(args.model, args.weights, args.model + '.temp.pt', None)
net = merge_batchnorms_in_net(net)
process_model(net, args.model + '.temp.pt', args.output_model,
[pick_empty_layers, set_engine_caffe],
[remove_empty_layers])
# Store params
net.save(args.output_weights)
if __name__ == '__main__':
parser = ArgumentParser(
description="Generate Batch Normalized model for inference")
parser.add_argument('--model', default="MobileNetSSD_deploy.prototxt", help="The net definition prototxt")
parser.add_argument('--weights', default="MobileNetSSD_deploy.caffemodel", help="The weights caffemodel")
parser.add_argument('--output_model')
parser.add_argument('--output_weights')
args = parser.parse_args()
main()

脚本下载地址：

https://download.youkuaiyun.com/download/kangdi7547/10578152

神经网络推理加速： 合并卷积和BN层运算原理及实验

神经网络推理加速：合并卷积和BN层运算原理及实验