def conv_op(x,name,n_out,training,useBN,kh=3,kw=3,dh=1,dw=1,padding="SAME",activation=tf.nn.relu,
):
'''
x:输入
kh,kw:卷集核的大小
n_out:输出的通道数
dh,dw:strides大小
name:op的名字
'''
n_in = x.get_shape()[-1].value
with tf.name_scope(name) as scope:
w = tf.get_variable(scope+"w",shape=[kh, kw, n_in, n_out],dtype=tf.float32,
initializer=tf.contrib.layers.xavier_initializer_conv2d())
b = tf.get_variable(scope+"b",shape=[n_out],dtype=tf.float32,
initializer=tf.constant_initializer(0.01))
conv = tf.nn.conv2d(x,w,[1,dh,dw,1],padding=padding)
z = tf.nn.bias_add(conv,b)
if useBN:
z = tf.layers.batch_normalization(z,trainable=training)
if activation:
z = activation(z)
return z
def max_pool_op(x, name, kh=2, kw=2, dh=2, dw=2,padding="SAME"):
return tf.nn.max_pool(x,
ksize=[1, kh, kw, 1],
strides=[1, dh, dw, 1],
padding=padding,
name=name)
def avg_pool_op(x, name, kh=2, kw=2, dh=2, dw=2,padding="SAME"):
return tf.nn.avg_pool(x,
ksize=[1, kh, kw, 1],
strides=[1, dh, dw, 1],
padding=padding,
name=name)
def fc_op(x,name,n_out,activation=tf.nn.relu):
n_in = x.get_shape()[-1].value
with tf.name_scope(name) as scope:
w = tf.get_variable(scope+"w",shape=[n_in,n_out],
dtype=tf.float32,
initializer=tf.contrib.layers.xavier_initializer())
b = tf.get_variable(scope+"b",shape=[n_out],dtype=tf.float32,
initializer=tf.constant_initializer(0.01))
fc = tf.matmul(x,w) + b
out = activation(fc)
return fc,out
def res_block_layers(x,name,n_out_list,change_dimension=False,block_stride = 1):
if change_dimension:
short_cut_conv = conv_op(x,name+"_ShortcutConv",n_out_list[1],training=True,useBN=True,kh=1,kw=1,dh=block_stride,dw=block_stride,
padding="SAME",activation=None)
else:
short_cut_conv = x
block_conv_1 = conv_op(x,name+"_lovalConv1",n_out_list[0],training=True,useBN=True,kh=1,kw=1,dh=block_stride,dw=block_stride,
padding="SAME", activation=tf.nn.relu)
block_conv_2 = conv_op(block_conv_1,name+"_lovalConv2",n_out_list[0],training=True,useBN=True,kh=3,kw=3,dh=1,dw=1,
padding="SAME", activation=tf.nn.relu)
block_conv_3 = conv_op(block_conv_2,name+"_lovalConv3",n_out_list[1],training=True,useBN=True,kh=1,kw=1,dh=1,dw=1,
padding="SAME", activation=None)
block_res = tf.add(short_cut_conv, block_conv_3)
res = tf.nn.relu(block_res)
return res
def bulid_resNet(x,num_class,training=True,usBN=True):
conv1 = conv_op(x, "conv1",64,training,usBN, 3,3,1,1)
pool1 = max_pool_op(x, "pool1", kh=3, kw=3)
block1_1 = res_block_layers(pool1,"block1_1",[64, 256],True,1)
block1_2 = res_block_layers(block1_1,"block1_2", [64, 256],False,1)
block1_3 = res_block_layers(block1_2,"block1_3", [64, 256],False,1 )
block2_1 = res_block_layers(block1_3,"block2_1",[128, 512],True, 2)
block2_2 = res_block_layers(block2_1, "block2_2", [128, 512],False,1)
block2_3 = res_block_layers(block2_2, "block2_3", [128, 512],False,1)
block2_4 = res_block_layers(block2_3, "block2_4", [128, 512],False,1)
block3_1 = res_block_layers(block2_4, "block3_1", [256, 1024], True, 2)
block3_2 = res_block_layers(block3_1 , "block3_2", [256, 1024],False,1)
block3_3 = (block3_2, "block3_3", [256, 1024],False,1)
block3_4 = res_block_layers(block3_3, "block3_4", [256, 1024],False,1)
block3_5 = res_block_layers(block3_4, "block3_5", [256, 1024],False,1)
block3_6 = res_block_layers(block3_5, "block3_6", [256, 1024],False,1)
block4_1 = res_block_layers(block3_6, "block4_1", [512, 2048], True, 2)
block4_2 = res_block_layers(block4_1, "block4_2", [512, 2048],False,1)
block4_3 = res_block_layers(block4_2, "block4_3", [512, 2048],False,1)
pool2 = avg_pool_op(block4_3, "pool2", kh=7, kw=7, dh=1, dw=1,padding="SAME")
shape = pool2.get_shape()
fc_in = tf.reshape(pool2,[-1,shape[1].value*shape[2].value*shape[3].value])
logits,prob = fc_op(fc_in,"fc1",num_class,activation=tf.nn.softmax)
return logits,prob
