ResNet
一、简介
ResNet（Residual Neural Network）由微软研究院的Kaiming He等四名华人提出，ResNet是一种残差网络。
二、亮点
1、超深的网络结构
2、提出了residual模块
3、使用Batch Normalization加速训练
三、residual结构
右图中1X1的卷积核是用来降维和升维的
3X3X256X256+3X3X256X256=1179648
1x1x256x64+3x3x64x64+1x1x64x256=69632
当你使用的传达结构越多，你所节省的参数也就越多
四、ResNet搭建
import tensorflow as tf
import tensorflow.contrib.slim as slim
import collection
from datetime import datetime
import time
import math
from tensorflow.contrib.layers.python.layers import utils
#定义Block类
class Block(collections.namedtuple('Block',['scope','unit_fn','args'])):
#定义相关函数
#定义降采样方法
def subsample(inputs,factor,scope=None):
if factor==1:
return inputs
else:
return slim.max_pool2d(inputs,kernel_size=[1,1],stride=factor,scope=scope)
#定义卷积操作方法
def conv2d_same(inputs,num_outputs,kernel_size,stride,scope=None):
if stride==1:
return slim.conv2d(inputs,num_outputs,kernel_size,stride=1,padding='SAME',scope=scope)
else:
#修整Inputs,对inputs进行补零操作
padding_total=kernel_size-1
padding_beg=padding_total//2
padding_end=padding_total-padding_beg
inputs=tf.pad(inputs,[[0,0],[padding_beg,padding_end],[padding_beg,padding_end],[0,0]])
return slim.conv2d(inputs,num_outputs,kernel_size,stride=stride,padding='VALID',scope=scope)
#定义堆叠block方法
@slim.add_arg_scope
def stack_block_dense(net,blocks,outputs_collections=None):
for block in blocks:
#双层循环，遍历blocks，遍历res unit堆叠
with tf.variable_scope(block.scope,'block',[net]) as sc:
#用两个tf.variable_scope将残差学习单元命名为block1/unit_1的形式
for i ,unit in enumerate(block.args):
with tf.variable_scope('unit_%d'%(i+1),values=[net]):
#利用第二层循环拿到block中的args,将其展开为depth,depth_bottleneck,strdie
unit_depth,unit_depth_bottleneck,unit_stride=unit
#使用残差学习单元的生成函数unit_fn，顺序的创建并连接所有的残差学习单元
net=block.unit_fn(net,
depth=unit_depth,
depth_bottleneck=unit_depth_bottleneck,
stride=unit_stride)
#使用utils.collect_named_outputs将输出net添加到collection中
net=utils.collect_named_outputs(outputs_collections,sc.name,net)
return net
#定义resnet参数
def resnet_arg_scope(is_training=True,
weight_decay=0.0001,
batch_norm_decay=0.97,
batch_norm_epsilon=1e-5,
batch_norm_scale=True):
#创建resnet通用的arg_scope
batch_norm_params={
'is_training':is_training,
'decay':batch_norm_decay,
'epsilon':batch_norm_epsilon,
'scale':batch_norm_scale,
'updates_collections':tf.GraphKeys.UPDATE_OPS }
with slim.arg_scope(
[slim.conv2d],
weights_regularizer=slim.l2_regularizer(weight_decay),
weights_initializer=slim.variance_scaling_initializer(),
activation_fn=tf.nn.relu,
normalizer_fn=slim.batch_norm,
normalizer_params=batch_norm_params ):
with slim.arg_scope([slim.batch_norm],**batch_norm_params):
with slim.arg_scope([slim.max_pool2d],padding='SAME') as arg_sc:
return arg_sc
#定义瓶颈函数（核心方法）
@slim.add_arg_scope
def bottleneck(inputs,depth,depth_bottleneck,stride,outputs_collections=None,scope=None):
with tf.variable_scope(scope,'bottleneck_v2',[inputs]) as sc:
#获取输入的最后一个维度，输出通道数
depth_in=utils.last_dimension(inputs.get_shape(),min_rank=4)
#对输入进行batch_borm，接着用relu进行预激活
preact=slim.batch_norm(inputs,activation_fn=tf.nn.relu,scope='preact')
if depth==depth_in:
#如果残差单元输入通道和输出通道数一样，就对inputs进行降采样
shortcut=subsample(inputs,stride,'shortcut')
else:
#如果残差单元输入通道与输出通道数不一样，就使用stride步长的1*1卷积改变其通道数，是的输入通道和输出通道数一样
shortcut=slim.conv2d(preact,depth,[1,1],stride=stride,normalizer_fn=None,activation_fn=None,scope='shortcut')
#定义残差
#第一步：1*1，stride=1，输出通道数为depth_bottleneck的卷积
#第二步：3*3，stride=stride，输出通道数为depth_bottleneck的卷积
#第三步：1*1，stride=1，输出通道数为depth的卷积
residual=slim.conv2d(preact,depth_bottleneck,[1,1],stride=1,scope='conv1')
residual=slim.conv2d(residual,depth_bottleneck,[3,3],stride=stride,scope='conv2')
residual=slim.conv2d(residual,depth,[1,1],stride=1,scope='conv3')
output=shortcut+residual
#将结果添加到outputs_collections
return utils.collect_named_outputs(outputs_collections,sc.name,output)
#定义生成resnet_v2的主函数
def resnet_v2(inputs,
blocks,
num_classes=None,
global_pool=True,
include_root_block=True,
reuse=None,
scope=None):
with tf.variable_scope(scope,'resnet_v2',[inputs],reuse=reuse) as sc:
end_point_collections=sc.original_name_scope+'_end_points'
#用slim.arg_scope将slim.conv2d bottleneck stack_blocks_dense 3个函数的参数outputs_collections设置为end_point_collections
with slim.arg_scope([slim.conv2d,bottleneck,stack_block_dense],outputs_collections=end_point_collections):
net=inputs
if include_root_block:
#根据include_root_block标记，创建resnet最前面一层的卷积神经网络
with slim.arg_scope([slim.conv2d],activation_fn=None,normalizer_fn=None):
net=conv2d_same(net,64,7,stride=2,scope='conv1')
net=slim.max_pool2d(net,[3,3],stride=2,scope='pool1')
#利用stack_blocks_dense将残差学习模块完成
net=stack_block_dense(net,blocks)
net=slim.batch_norm(net,activation_fn=tf.nn.relu,scope='postnorm')
if global_pool:
#根据标记添加平均池化层
net=tf.reduce_mean(net,[1,2],name='pool5',keep_dims=True)
if num_classes is not None:
#根据是否有分类数，添加一个输出通道为num_classes的1*1卷积
net=slim.conv2d(net,num_classes,[1,1],activation_fn=None,normalizer_fn=None,scope='logits')
#utils.convert_collection_to_dict将collection转化为dict
end_points=utils.convert_collection_to_dict(end_point_collections)
if num_classes is not None:
#添加一个softmax输出层
end_points['prediction']=slim.softmax(net,scope='prediction')
return net,end_points
#定义resnet_v2_50的生成方法
def resnet_v2_50(inputs,
num_classes=None,
global_pool=True,
reuse=None,
scope='resnet_v2_50'
):
#设计50层的resnet
#四个blocks的units数量为3、4、6、3，总层数为（3+4+6+3）*3+2=50
#钱3个blocks包含步长为2的层，总尺寸244/(4*2*2*2)=7 输出通道变为2048
blocks=[
Block('block1',bottleneck,[(256,64,1)]*2+[(256,64,5)]),
Block('block2', bottleneck, [(512, 128, 1)] * 3 + [(512, 128, 2)]),
Block('block3', bottleneck, [(1024, 256, 1) ]* 5 + [(1024, 256, 2)]),
Block('block4', bottleneck, [(2048, 512, 1)] * 3)
]
return resnet_v2(inputs,blocks,num_classes,global_pool,include_root_block=True,reuse=reuse,scope=scope)
#定义resnet_v2_101的生成方法
def resnet_v2_101(inputs,
num_classes=None,
global_pool=True,
reuse=None,
scope='resnet_v2_50'
):
#设计50层的resnet
#四个blocks的units数量为3、4、23、3，总层数为（3+4+23+3）*3+2=101
#钱3个blocks包含步长为2的层，总尺寸244/(4*2*2*2)=7 输出通道变为2048
blocks = [
Block('block1', bottleneck, [(256, 64, 1)] * 2 + [(256, 64, 5)]),
Block('block2', bottleneck, [(512, 128, 1)] * 3 + [(512, 128, 2)]),
Block('block3', bottleneck, [(1024, 256, 1)] * 22 + [(1024, 256, 2)]),
Block('block4', bottleneck, [(2048, 512, 1)] * 3)
]
return resnet_v2(inputs,blocks,num_classes,global_pool,include_root_block=True,reuse=reuse,scope=scope)
#定义测试运行时间函数
def time_tensorflow_run(session, target, info_string):
num_steps_burn_in = 10
total_duration = 0.0
total_duration_squared = 0.0
for i in range(num_batches+num_steps_burn_in):
start_time = time.time()
_ = session.run(target)
duration = time.time()-start_time
if i >= num_steps_burn_in:
if not i % 10:
print('%s: step %d, duration = %.3f' %(datetime.now(), i-num_steps_burn_in, duration))
total_duration += duration
total_duration_squared += duration*duration
mn = total_duration/num_batches
vr = total_duration_squared/num_batches-mn*mn
sd = math.sqrt(vr)
print('%s: %s across %d steps, %.3f +/- %3.3f sec/batch' %(datetime.now(), info_string, num_batches, mn, sd))
if __name__=='__main__':
batch_size = 32
height, width = 224, 224
inputs = tf.random_uniform((batch_size, height, width, 3))
with slim.arg_scope(resnet_arg_scope(is_training=False)):
net, end_points = resnet_v2_50(inputs, 1000)
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
num_batches = 100
time_tensorflow_run(sess, net, 'Forward')借鉴：https://blog.csdn.net/sinat_37554070/article/details/80353081
本文地址：https://blog.csdn.net/shmilyam/article/details/107879180
