def conv_softmax(x, kernal, scope=None):
with tf.name_scope(scope):
W = weight_xavier_init(shape=kernal,
n_inputs=kernal[0] * kernal[1] * kernal[2] *
kernal[3],
n_outputs=kernal[-1],
activefunction='sigomd',
variable_name=scope + 'W')
B = bias_variable([kernal[-1]], variable_name=scope + 'B')
conv = conv3d(x, W) + B
conv = tf.nn.softmax(conv)
return conv
def deconv_relu(x, kernal, samefeture=False, scope=None):
with tf.name_scope(scope):
W = weight_xavier_init(shape=kernal,
n_inputs=kernal[0] * kernal[1] * kernal[2] *
kernal[-1],
n_outputs=kernal[-2],
activefunction='relu',
variable_name=scope + 'W')
B = bias_variable([kernal[-2]], variable_name=scope + 'B')
conv = deconv3d(x, W, samefeture, True) + B
conv = tf.nn.relu(conv)
return conv
def conv_bn_relu_drop(x, kernal, phase, drop, image_z=None, height=None, width=None, scope=None):
"""
:param x:
:param kernal:
:param phase:
:param drop:
:param image_z:
:param height:
:param width:
:param scope:
:return:
"""
with tf.name_scope(scope):
W = weight_xavier_init(shape=kernal, n_inputs=kernal[0] * kernal[1] * kernal[2] * kernal[3],
n_outputs=kernal[-1], activefunction='relu', variable_name=scope + 'conv_W')
B = bias_variable([kernal[-1]], variable_name=scope + 'conv_B')
conv = conv3d(x, W) + B
conv = normalizationlayer(conv, is_train=phase, height=height, width=width, image_z=image_z, norm_type='group',
scope=scope)
conv = tf.nn.dropout(tf.nn.relu(conv), drop)
return conv
def gatingsignal3d(x, kernal, phase, image_z=None, height=None, width=None, scope=None):
"""this is simply 1x1x1 convolution, bn, activation,Gating Signal(Query)
:param x:
:param kernal:(1,1,1,inputfilters,outputfilters)
:param phase:
:param drop:
:param image_z:
:param height:
:param width:
:param scope:
:return:
"""
with tf.name_scope(scope):
W = weight_xavier_init(shape=kernal, n_inputs=kernal[0] * kernal[1] * kernal[2] * kernal[3],
n_outputs=kernal[-1], activefunction='relu', variable_name=scope + 'conv_W')
B = bias_variable([kernal[-1]], variable_name=scope + 'conv_B')
conv = conv3d(x, W) + B
conv = normalizationlayer(conv, is_train=phase, height=height, width=width, image_z=image_z, norm_type='group',
scope=scope)
conv = tf.nn.relu(conv)
return conv
def positionAttentionblock(x,
inputfilters,
outfilters,
kernal_size=1,
scope=None):
"""
Position attention module
:param x:
:param inputfilters:inputfilter number
:param outfilters:outputfilter number
:param scope:
:return:
"""
with tf.name_scope(scope):
m_batchsize, Z, H, W, C = x.get_shape().as_list()
kernalquery = (kernal_size, kernal_size, kernal_size, inputfilters,
outfilters)
Wquery = weight_xavier_init(shape=kernalquery,
n_inputs=kernalquery[0] * kernalquery[1] *
kernalquery[2] * kernalquery[3],
n_outputs=kernalquery[-1],
activefunction='relu',
variable_name=scope + 'conv_Wquery')
Bquery = bias_variable([kernalquery[-1]],
variable_name=scope + 'conv_Bquery')
query_conv = conv3d(x, Wquery) + Bquery
query_conv_new = tf.reshape(query_conv, [-1, Z * H * W])
kernalkey = (kernal_size, kernal_size, kernal_size, inputfilters,
outfilters)
Wkey = weight_xavier_init(shape=kernalkey,
n_inputs=kernalkey[0] * kernalkey[1] *
kernalkey[2] * kernalkey[3],
n_outputs=kernalkey[-1],
activefunction='relu',
variable_name=scope + 'conv_Wkey')
Bkey = bias_variable([kernalkey[-1]],
variable_name=scope + 'conv_Bkey')
key_conv = conv3d(x, Wkey) + Bkey
key_conv_new = tf.reshape(key_conv, [-1, Z * H * W])
# OOM,such as 512x512x32 then matric is 8388608x8388608
# key_conv_new = tf.transpose(key_conv_new, [0, 2, 1])
# (2,2,2,3)*(2,2,3,4)=(2,2,2,4),(2,2,3)*(2,3,4)=(2,2,4)
# energy = tf.matmul(query_conv_new, key_conv_new) # (m_batchsize,Z*H*W,Z*H*W)
energy = tf.multiply(query_conv_new, key_conv_new)
attention = tf.nn.sigmoid(energy)
kernalproj = (kernal_size, kernal_size, kernal_size, inputfilters,
outfilters)
Wproj = weight_xavier_init(shape=kernalproj,
n_inputs=kernalproj[0] * kernalproj[1] *
kernalproj[2] * kernalproj[3],
n_outputs=kernalproj[-1],
activefunction='relu',
variable_name=scope + 'conv_Wproj')
Bproj = bias_variable([kernalproj[-1]],
variable_name=scope + 'conv_Bproj')
proj_value = conv3d(x, Wproj) + Bproj
proj_value_new = tf.reshape(proj_value, [-1, Z * H * W])
out = tf.multiply(attention, proj_value_new)
out_new = tf.reshape(out, [-1, Z, H, W, C])
out_new = resnet_Add(out_new, x)
return out_new
