def pan_feature_extraction(self, name, pan):
with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
with tf.variable_scope('low_level_extraction'):
feature = pan
feature = conv('conv1', feature, 32)
feature = conv('conv2', feature, 32)
with tf.variable_scope('down_sample1'):
feature = self.downsample(feature, 32)
feature = dense_block('dense_block1',
feature,
12,
conv_num=6,
input_include=True)
feature = bottle_neck('bottle_neck1', feature, 64)
with tf.variable_scope('down_sample2'):
feature = self.downsample(feature, 64)
feature = dense_block('dense_block2',
feature,
12,
conv_num=6,
input_include=True)
feature = bottle_neck('bottle_neck2', feature, 64)
feature = dense_block('dense_block3',
feature,
12,
conv_num=8,
input_include=True)
feature = bottle_neck('bottle_neck3', feature, 96)
with tf.variable_scope('upsample1', reuse=tf.AUTO_REUSE):
pan_feature_1 = self.upsample(feature, out_channel=96)
# pan_feature_1 = conv('conv', tf.concat([feature2, feature3], -1), filter_num=64)
with tf.variable_scope('upsample2', reuse=tf.AUTO_REUSE):
pan_feature_2 = self.upsample(pan_feature_1, out_channel=96)
# pan_feature_2 = conv('conv', tf.concat([up_pan_feature_1, feature1], -1), filter_num=64)
return pan_feature_1, pan_feature_2
def ms_feature_extraction(self, name, ms):
with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
with tf.variable_scope('low_level_extraction',
reuse=tf.AUTO_REUSE):
feature1 = ms
feature1 = conv('conv1', feature1, 64)
feature1 = conv('conv2', feature1, 64)
feature2 = dense_block('dense_block1',
feature1,
12,
conv_num=6,
input_include=True)
feature2 = bottle_neck('bottle_neck1', feature2, 64)
feature3 = dense_block('dense_block2',
feature2,
12,
conv_num=6,
input_include=True)
feature3 = bottle_neck('bottle_neck2', feature3, 64)
with tf.variable_scope('combanation'):
feature = tf.concat([feature1, feature2, feature3], -1)
feature = bottle_neck('bottle_neck', feature, 64)
with tf.variable_scope('upsample1', reuse=tf.AUTO_REUSE):
ms_feature_1 = self.upsample(feature, out_channel=64)
with tf.variable_scope('upsample2', reuse=tf.AUTO_REUSE):
ms_feature_2 = self.upsample(ms_feature_1, out_channel=64)
return ms_feature_1, ms_feature_2
def ms_feature_extraction(self, name, ms):
with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
with tf.variable_scope('low_level_extraction',
reuse=tf.AUTO_REUSE):
feature = ms
feature = conv('conv1', feature, 32)
feature = conv('conv2', feature, 32)
feature = dense_block('dense_block1',
feature,
12,
conv_num=4,
input_include=True)
feature = bottle_neck('bottle_neck1', feature, 64)
feature = dense_block('dense_block2',
feature,
12,
conv_num=6,
input_include=True)
feature = bottle_neck('bottle_neck2', feature, 96)
with tf.variable_scope('upsample1', reuse=tf.AUTO_REUSE):
ms_feature_1 = self.upsample(feature, out_channel=96)
with tf.variable_scope('upsample2', reuse=tf.AUTO_REUSE):
ms_feature_2 = self.upsample(ms_feature_1, out_channel=96)
return ms_feature_1, ms_feature_2