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resnet.py
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# -*- coding: utf-8 -*-
from __future__ import absolute_import, print_function, division
import tensorflow as tf
import tensorflow.contrib.slim as slim
from tensorflow.contrib.slim.python.slim.nets.resnet_v1 import resnet_v1_block
import config as cfgs
from tensorflow.contrib.slim.nets import resnet_v1
from tensorflow.contrib.slim.nets import resnet_utils
from tensorflow.contrib.slim.python.slim.nets.resnet_v1 import resnet_v1_block
def resnet_arg_scope(
is_training, weight_decay=cfgs.WEIGHT_DECAY, batch_norm_decay=0.997,
batch_norm_epsilon=1e-5, batch_norm_scale=True):
'''
In Default, we do not use BN to train resnet, since batch_size is too small.
So is_training is False and trainable is False in the batch_norm params.
'''
batch_norm_params = {
'is_training': is_training, 'decay': 0.95,
'epsilon': batch_norm_epsilon, 'scale': batch_norm_scale,
'trainable': is_training,
'updates_collections': tf.GraphKeys.UPDATE_OPS
} #在训练中不使用这层
with slim.arg_scope(
[slim.conv2d],
weights_regularizer=slim.l2_regularizer(weight_decay), #L2正则化
#weights_initializer=slim.variance_scaling_initializer(),
weights_initializer=tf.random_normal_initializer(mean=0.0, stddev=0.01),
trainable=is_training,
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) as arg_sc:
return arg_sc
def resnet_base(img_batch, scope_name, is_training):
'''
this code is derived from light-head rcnn.
https://github.com/zengarden/light_head_rcnn
It is convenient to freeze blocks. So we adapt this mode.
'''
if scope_name == 'resnet_v1_50':
middle_num_units = 6
elif scope_name == 'resnet_v1_101':
middle_num_units = 23 #101第3个block是23
elif scope_name == 'resnet_v1_152':
middle_num_units = 36
else:
raise NotImplementedError('We only support resnet_v1_50 、resnet_v1_101 、resnet152. Check your network name....yjr')
blocks = [resnet_v1_block('block1', base_depth=64, num_units=3, stride=1),
resnet_v1_block('block2', base_depth=128, num_units=4, stride=2),
resnet_v1_block('block3', base_depth=256, num_units=middle_num_units, stride=2),
resnet_v1_block('block4', base_depth=512, num_units=3, stride=2)]
with slim.arg_scope(resnet_arg_scope(is_training=is_training)): #resnet_arg_scope配置参数
with tf.variable_scope(scope_name, scope_name):
# Do the first few layers manually, because 'SAME' padding can behave inconsistently
# for images of different sizes: sometimes 0, sometimes 1
net = resnet_utils.conv2d_same(
img_batch, 64, 7, stride=2, scope='conv1') #RESNET第一个卷积层， 7*7*64， stride=2
net = tf.pad(net, [[0, 0], [1, 1], [1, 1], [0, 0]]) #padding 0 ?? 类似与后面的samepadding?
net = slim.max_pool2d(
net, [3, 3], stride=2, padding='VALID', scope='pool1') #3*3最大池化
#not_freezed = [False] * cfgs.FIXED_BLOCKS + (4-cfgs.FIXED_BLOCKS)*[True] #不冻结的Blocks层
#net = tf.Print(net, [tf.shape(net)], summarize=10, message='net')
with slim.arg_scope(resnet_arg_scope(is_training=is_training)):
C2, end_points_C2 = resnet_v1.resnet_v1(net,
blocks[0:1], #传入的是一个resnet_utils.Block类 一整个Resnet block
global_pool= False,
include_root_block=False,
scope=scope_name) #返回当前构建resnet block层：C2 end_points_C2： collection中已有的特征图 越到后面越多
with slim.arg_scope(resnet_arg_scope(is_training=is_training)):
C3, end_points_C3 = resnet_v1.resnet_v1(C2,
blocks[1:2],
global_pool = False,
include_root_block=False,
scope=scope_name) #构建第二个block模块
with slim.arg_scope(resnet_arg_scope(is_training=is_training)):
C4, end_points_C4 = resnet_v1.resnet_v1(C3,
blocks[2:3],
global_pool = False,
include_root_block=False,
scope=scope_name)
with slim.arg_scope(resnet_arg_scope(is_training=is_training)):
C5, end_points_C5 = resnet_v1.resnet_v1(C4,
blocks[3:4],
num_classes = cfgs.num_classes,
global_pool = True,
include_root_block=False,
scope=scope_name)
C5 = tf.reshape(C5, [-1, cfgs.num_classes])
return C5
# with tf.variable_scope('cls_layer'):
# fc_plat = slim.flatten(C5, scope='flatten')
# fc6 = slim.fully_connected(fc_plat, 256,
# weights_initializer=tf.random_normal_initializer(mean=0.0, stddev=0.01),
# trainable=is_training,
# weights_regularizer=slim.l2_regularizer(0.0005),
# activation_fn=None, scope='fc6') #得到class score
# cls_score = slim.fully_connected(fc6, cfgs.num_classes,
# weights_initializer=tf.random_normal_initializer(mean=0.0, stddev=0.01),
# trainable=is_training,
# weights_regularizer=slim.l2_regularizer(0.0005),
# activation_fn=None, scope='cls_score') #得到class score
# return cls_score