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unit.py
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import numpy as np
import tensorflow as tf
def max_min(x):
return (x-np.min(x))/(np.max(x)-np.min(x))
def af(x):
return tf.nn.swish(x)
def classifer_share3d_3(feature,training,reuse=True,cube_size=3):
print(feature)
feature = tf.expand_dims(feature, 4)
f_num = 64
with tf.variable_scope('classifer', reuse=reuse):
with tf.variable_scope('conv0'):
conv0 = tf.layers.conv3d(feature, f_num, (cube_size,1,8), strides=(1,1,3), padding='valid')
conv0 = tf.layers.batch_normalization(conv0,training=training)
conv0 = af(conv0)
print(conv0)
with tf.variable_scope('conv1'):
conv1 = tf.layers.conv3d(conv0, f_num * 2, (1,cube_size,3), strides=(1,1,2), padding='valid')
conv1 = tf.layers.batch_normalization(conv1,training=training)
conv1 = af(conv1)
print(conv1)
with tf.variable_scope('conv2'):
conv2 = tf.layers.conv3d(conv1, f_num * 4, (1,1,3), strides=(1,1,2), padding='valid')
conv2 = tf.layers.batch_normalization(conv2,training=training)
conv2 = af(conv2)
print(conv2)
with tf.variable_scope('global_info'):
f_shape = int(conv2.get_shape().as_list()[3])
feature = tf.layers.conv3d(conv2, f_num * 8, (1,1,f_shape), (1,1,1))
feature = tf.layers.flatten(feature)
print(feature)
return feature
def classifer_share3d_6(feature, training, reuse=True, cube_size=3):
print(feature)
feature = tf.expand_dims(feature, 4)
f_num = 64
with tf.variable_scope('classifer', reuse=reuse):
with tf.variable_scope('conv00'):
conv0 = tf.layers.conv3d(feature, f_num, (cube_size, 1, 8), strides=(1, 1, 3), padding='valid')
conv0 = tf.layers.batch_normalization(conv0, training=training)
conv0 = af(conv0)
print(conv0)
with tf.variable_scope('conv01'):
conv0 = tf.layers.conv3d(conv0, f_num, (1, 1, 3), strides=(1, 1, 1), padding='valid')
conv0 = tf.layers.batch_normalization(conv0, training=training)
conv0 = af(conv0)
print(conv0)
with tf.variable_scope('conv10'):
conv1 = tf.layers.conv3d(conv0, f_num * 2, (1, cube_size, 3), strides=(1, 1, 2), padding='valid')
conv1 = tf.layers.batch_normalization(conv1, training=training)
conv1 = af(conv1)
print(conv1)
with tf.variable_scope('conv11'):
conv1 = tf.layers.conv3d(conv1, f_num * 2, (1, 1, 3), strides=(1, 1, 1), padding='valid')
conv1 = tf.layers.batch_normalization(conv1, training=training)
conv1 = af(conv1)
print(conv1)
with tf.variable_scope('conv20'):
conv2 = tf.layers.conv3d(conv1, f_num * 4, (1, 1, 3), strides=(1, 1, 2), padding='valid')
conv2 = tf.layers.batch_normalization(conv2, training=training)
conv2 = af(conv2)
print(conv2)
with tf.variable_scope('conv21'):
conv2 = tf.layers.conv3d(conv2, f_num * 4, (1, 1, 3), strides=(1, 1, 2), padding='valid')
conv2 = tf.layers.batch_normalization(conv2, training=training)
conv2 = af(conv2)
print(conv2)
with tf.variable_scope('global_info'):
f_shape = int(conv2.get_shape().as_list()[3])
feature = tf.layers.conv3d(conv2, f_num * 8, (1, 1, f_shape), (1, 1, 1))
feature = tf.layers.flatten(feature)
print(feature)
return feature
def classifer_share3d_9(feature, training, reuse=True, cube_size=3):
print(feature)
feature = tf.expand_dims(feature, 4)
f_num = 64
with tf.variable_scope('classifer', reuse=reuse):
with tf.variable_scope('conv00'):
conv0 = tf.layers.conv3d(feature, f_num, (cube_size, 1, 8), strides=(1, 1, 3), padding='valid')
conv0 = tf.layers.batch_normalization(conv0, training=training)
conv0 = af(conv0)
print(conv0)
with tf.variable_scope('conv01'):
conv0 = tf.layers.conv3d(conv0, f_num, (1, 1, 3), strides=(1, 1, 1), padding='valid')
conv0 = tf.layers.batch_normalization(conv0, training=training)
conv0 = af(conv0)
print(conv0)
with tf.variable_scope('conv02'):
conv0 = tf.layers.conv3d(conv0, f_num, (1, 1, 3), strides=(1, 1, 1), padding='same')
conv0 = tf.layers.batch_normalization(conv0, training=training)
conv0 = af(conv0)
print(conv0)
with tf.variable_scope('conv10'):
conv1 = tf.layers.conv3d(conv0, f_num * 2, (1, cube_size, 3), strides=(1, 1, 2), padding='valid')
conv1 = tf.layers.batch_normalization(conv1, training=training)
conv1 = af(conv1)
print(conv1)
with tf.variable_scope('conv11'):
conv1 = tf.layers.conv3d(conv1, f_num * 2, (1, 1, 3), strides=(1, 1, 1), padding='valid')
conv1 = tf.layers.batch_normalization(conv1, training=training)
conv1 = af(conv1)
print(conv1)
with tf.variable_scope('conv12'):
conv1 = tf.layers.conv3d(conv1, f_num * 2, (1, 1, 3), strides=(1, 1, 1), padding='same')
conv1 = tf.layers.batch_normalization(conv1, training=training)
conv1 = af(conv1)
print(conv1)
with tf.variable_scope('conv20'):
conv2 = tf.layers.conv3d(conv1, f_num * 4, (1, 1, 3), strides=(1, 1, 2), padding='valid')
conv2 = tf.layers.batch_normalization(conv2, training=training)
conv2 = af(conv2)
print(conv2)
with tf.variable_scope('conv21'):
conv2 = tf.layers.conv3d(conv2, f_num * 4, (1, 1, 3), strides=(1, 1, 1), padding='valid')
conv2 = tf.layers.batch_normalization(conv2, training=training)
conv2 = af(conv2)
print(conv2)
with tf.variable_scope('conv22'):
conv2 = tf.layers.conv3d(conv2, f_num * 4, (1, 1, 3), strides=(1, 1, 1), padding='same')
conv2 = tf.layers.batch_normalization(conv2, training=training)
conv2 = af(conv2)
print(conv2)
with tf.variable_scope('global_info'):
f_shape = int(conv2.get_shape().as_list()[3])
feature = tf.layers.conv3d(conv2, f_num * 8, (1, 1, f_shape), (1, 1, 1))
feature = tf.layers.flatten(feature)
print(feature)
return feature
def classifer_share3d_12(feature, training, reuse=True, cube_size=3):
print(feature)
feature = tf.expand_dims(feature, 4)
f_num = 64
with tf.variable_scope('classifer', reuse=reuse):
with tf.variable_scope('conv00'):
conv0 = tf.layers.conv3d(feature, f_num, (cube_size, 1, 8), strides=(1, 1, 3), padding='valid')
conv0 = tf.layers.batch_normalization(conv0, training=training)
conv0 = af(conv0)
print(conv0)
with tf.variable_scope('conv01'):
conv0 = tf.layers.conv3d(conv0, f_num, (1, 1, 3), strides=(1, 1, 1), padding='valid')
conv0 = tf.layers.batch_normalization(conv0, training=training)
conv0 = af(conv0)
print(conv0)
with tf.variable_scope('conv02'):
conv0 = tf.layers.conv3d(conv0, f_num, (1, 1, 3), strides=(1, 1, 1), padding='same')
conv0 = tf.layers.batch_normalization(conv0, training=training)
conv0 = af(conv0)
print(conv0)
with tf.variable_scope('conv03'):
conv0 = tf.layers.conv3d(conv0, f_num, (1, 1, 3), strides=(1, 1, 1), padding='same')
conv0 = tf.layers.batch_normalization(conv0, training=training)
conv0 = af(conv0)
print(conv0)
with tf.variable_scope('conv10'):
conv1 = tf.layers.conv3d(conv0, f_num * 2, (1, cube_size, 3), strides=(1, 1, 2), padding='valid')
conv1 = tf.layers.batch_normalization(conv1, training=training)
conv1 = af(conv1)
print(conv1)
with tf.variable_scope('conv11'):
conv1 = tf.layers.conv3d(conv1, f_num * 2, (1, 1, 3), strides=(1, 1, 1), padding='valid')
conv1 = tf.layers.batch_normalization(conv1, training=training)
conv1 = af(conv1)
print(conv1)
with tf.variable_scope('conv12'):
conv1 = tf.layers.conv3d(conv1, f_num * 2, (1, 1, 3), strides=(1, 1, 1), padding='same')
conv1 = tf.layers.batch_normalization(conv1, training=training)
conv1 = af(conv1)
print(conv1)
with tf.variable_scope('conv13'):
conv1 = tf.layers.conv3d(conv1, f_num * 2, (1, 1, 3), strides=(1, 1, 1), padding='same')
conv1 = tf.layers.batch_normalization(conv1, training=training)
conv1 = af(conv1)
print(conv1)
with tf.variable_scope('conv20'):
conv2 = tf.layers.conv3d(conv1, f_num * 4, (1, 1, 3), strides=(1, 1, 2), padding='valid')
conv2 = tf.layers.batch_normalization(conv2, training=training)
conv2 = af(conv2)
print(conv2)
with tf.variable_scope('conv21'):
conv2 = tf.layers.conv3d(conv2, f_num * 4, (1, 1, 3), strides=(1, 1, 1), padding='valid')
conv2 = tf.layers.batch_normalization(conv2, training=training)
conv2 = af(conv2)
print(conv2)
with tf.variable_scope('conv22'):
conv2 = tf.layers.conv3d(conv2, f_num * 4, (1, 1, 3), strides=(1, 1, 1), padding='same')
conv2 = tf.layers.batch_normalization(conv2, training=training)
conv2 = af(conv2)
print(conv2)
with tf.variable_scope('conv23'):
conv2 = tf.layers.conv3d(conv2, f_num * 4, (1, 1, 3), strides=(1, 1, 1), padding='same')
conv2 = tf.layers.batch_normalization(conv2, training=training)
conv2 = af(conv2)
print(conv2)
with tf.variable_scope('global_info'):
f_shape = int(conv2.get_shape().as_list()[3])
feature = tf.layers.conv3d(conv2, f_num * 8, (1, 1, f_shape), (1, 1, 1))
feature = tf.layers.flatten(feature)
print(feature)
return feature
def classifer_cluster(feature,training,cluster_num,reuse=False):
with tf.variable_scope('classifer_cluster', reuse=reuse):
feature = af(feature)
with tf.variable_scope('fc'):
fc = tf.layers.dense(feature,256)
fc = tf.layers.batch_normalization(fc,training=training)
fc = af(fc)
with tf.variable_scope('cluster_pre_label'):
cluster_pre_label = tf.layers.dense(fc,cluster_num)
return fc,cluster_pre_label
def classifer_classification(feature,training,class_num,reuse = False):
with tf.variable_scope('classifer_classification', reuse=reuse):
feature = af(feature)
with tf.variable_scope('fc'):
fc = tf.layers.dense(feature,256)
fc = tf.layers.batch_normalization(fc,training=training)
fc = af(fc)
with tf.variable_scope('classification_pre_label'):
classification_pre_label = tf.layers.dense(fc,class_num)
return fc,classification_pre_label
def classifer_combine(fc1,fc2,concate_way,class_num,training,reuse=False):
with tf.variable_scope('combine', reuse=reuse):
if concate_way == 0:
fusion = fc1
if concate_way == 1:
fusion = tf.concat([fc1,fc2],axis=1)
if concate_way == 2:
fusion = tf.multiply(fc1,fc2)
if concate_way == 3:
fc1 = tf.expand_dims(fc1,2)
fc2 = tf.expand_dims(fc2,2)
fusion = tf.concat([fc1, fc2], axis=2)
fusion = tf.expand_dims(fusion,3)
fusion = tf.layers.conv2d(fusion,1,(1,2),(1,1))
fusion = tf.layers.flatten(fusion)
if concate_way == 4:
fc1 = tf.layers.dense(fc1, 16, activation=af)
fc1 = tf.layers.batch_normalization(fc1,training=training)
fc2 = tf.layers.dense(fc2, 32, activation=af)
fc2 = tf.layers.batch_normalization(fc2,training=training)
fc1 = tf.expand_dims(fc1,2)
fc2 = tf.expand_dims(fc2,1)
fusion = tf.matmul(fc1,fc2)
fusion = tf.layers.flatten(fusion)
with tf.variable_scope('logits'):
logits = tf.layers.dense(fusion,class_num)
return logits