def build(self):
# 提取特征，属于公共部分
block1, block2, block3, block4 = self._feature(self.input_data)
blocks = [block1, block2, block3, block4]
block4_shape = Tools.get_shape(block4) # 45, 512
block3_shape = Tools.get_shape(block3) # 90, 512
block2_shape = Tools.get_shape(block2) # 180, 256
block1_shape = Tools.get_shape(block1) # 360, 128
segments = []
segments_output = []
######################################################
# 确定初始attention的输入点：建议在进入attention时输入
######################################################
with tf.variable_scope(name_or_scope="attention_4"):
net_output = self._decoder(block4, block4_shape, block3_shape, name="4")
net_segment_output = self._segment(block4, block4_shape, block3_shape, name="segment_side_4")
segments.append(net_segment_output) # segment
pass
segments_output.append(net_output)
block3_add = Net.add([block3, net_output], name='attention_3_add')
with tf.variable_scope(name_or_scope="attention_3"):
net_output = self._decoder(block3_add, block3_shape, block2_shape, name="3")
net_segment_output = self._segment(block3_add, block3_shape, block2_shape, name="segment_side_3")
segments.append(net_segment_output) # segment
pass
segments_output.append(net_output)
block2_add = Net.add([block2, net_output], name="attention_2_net_output_relu")
with tf.variable_scope(name_or_scope="attention_2"):
net_output = self._decoder(block2_add, block2_shape, block1_shape, name="2")
net_segment_output = self._segment(block2_add, block2_shape, block1_shape, name="segment_side_2")
segments.append(net_segment_output) # segment
pass
segments_output.append(net_output)
block1_add = Net.add([block1, net_output], name="attention_1_concat")
with tf.variable_scope(name_or_scope="attention_1"):
net_output = self._decoder(block1_add, block1_shape, block1_shape, name="1")
net_segment_output = self._segment(block1_add, block1_shape, block1_shape, name="segment_side_1")
segments.append(net_segment_output) # segment
pass
segments_output.append(net_output)
net_output = Net.conv(net_output, 3, 3, self.num_classes, 1, 1,
biased=True, relu=False, name='attention_0_21')
segments.append(net_output) # segment
features = {"block": blocks, "segment": segments_output}
return segments, features
def build(self):
# 提取特征，属于公共部分
block1, block2, block3, block4 = self._feature(self.input_data)
block4_shape = Tools.get_shape(block4) # 45, 512
block3_shape = Tools.get_shape(block3) # 90, 512
block2_shape = Tools.get_shape(block2) # 180, 256
block1_shape = Tools.get_shape(block1) # 360, 128
segments = []
######################################################
# 确定初始attention的输入点：建议在进入attention时输入
######################################################
with tf.variable_scope(name_or_scope="attention_4"):
net_output = self._decoder(block4, block4_shape, block3_shape, name="4")
pass
block3_add = Net.add([block3, net_output], name='attention_3_add')
with tf.variable_scope(name_or_scope="attention_3"):
net_output = self._decoder(block3_add, block3_shape, block2_shape, name="3")
pass
block2_add = Net.add([block2, net_output], name="attention_2_net_output_relu")
with tf.variable_scope(name_or_scope="attention_2"):
net_output = self._decoder(block2_add, block2_shape, block1_shape, name="2")
pass
block1_add = Net.add([block1, net_output], name="attention_1_concat")
with tf.variable_scope(name_or_scope="attention_1"):
net_output = self._decoder(block1_add, block1_shape, block1_shape, name="1")
pass
net_output = Net.conv(net_output, 3, 3, 2, 1, 1, biased=True, relu=False, name='attention_0')
segments.append(net_output) # segment
return segments
def build_old(self):
# 提取特征，属于公共部分
block1, block2, block3, block4 = self._feature(self.input_data)
blocks = [block1, block2, block3, block4]
block4_shape = Tools.get_shape(block4) # 45, 512
block3_shape = Tools.get_shape(block3) # 90, 512
block2_shape = Tools.get_shape(block2) # 180, 256
block1_shape = Tools.get_shape(block1) # 360, 128
adds = []
adds_in_block = []
adds_in_2 = []
adds = []
temps = []
segments = []
segments_output = []
######################################################
# 确定初始attention的输入点：建议在进入attention时输入
######################################################
with tf.variable_scope(name_or_scope="attention_4"):
# 0
net_segment_output, temp = self._segment(block4,
block4_shape,
block3_shape,
name="segment_side_4")
temps.append(temp)
segments.append(net_segment_output) # segment
net_output = self._decoder(block4,
block4_shape,
block3_shape,
name="4")
segments_output.append(net_output)
pass
with tf.variable_scope(name_or_scope="attention_3"):
# 1 ==>
# net_segment_block_output, temp = self._segment(block3, block3_shape, block2_shape, name="segment_side_3_block")
# temps.append(temp)
# segments.append(net_segment_block_output) # segment
# 2
block3_add = Net.add([block3, net_output], name='add')
adds_in_block.append(block3)
adds_in_2.append(net_output)
adds.append(block3_add)
net_segment_output, temp = self._segment(block3_add,
block3_shape,
block2_shape,
name="segment_side_3")
temps.append(temp)
segments.append(net_segment_output) # segment
net_output = self._decoder(block3_add,
block3_shape,
block2_shape,
name="3")
segments_output.append(net_output)
pass
with tf.variable_scope(name_or_scope="attention_2"):
# 3 ==>
# net_segment_block_output, temp = self._segment(block2, block2_shape, block1_shape, name="segment_side_2_block")
# temps.append(temp)
# segments.append(net_segment_block_output) # segment
# 4
block2_add = Net.add([block2, net_output], name="add")
adds_in_block.append(block2)
adds_in_2.append(net_output)
adds.append(block2_add)
net_segment_output, temp = self._segment(block2_add,
block2_shape,
block1_shape,
name="segment_side_2")
temps.append(temp)
segments.append(net_segment_output) # segment
net_output = self._decoder(block2_add,
block2_shape,
block1_shape,
name="2")
segments_output.append(net_output)
pass
with tf.variable_scope(name_or_scope="attention_1"):
# 5 ==>
# net_segment_block_output, temp = self._segment(block1, block1_shape, block1_shape, name="segment_side_1_block")
# temps.append(temp)
# segments.append(net_segment_block_output) # segment
# 6
block1_add = Net.add([block1, net_output], name="add")
adds_in_block.append(block1)
adds_in_2.append(net_output)
adds.append(block1_add)
net_segment_output, temp = self._segment(block1_add,
block1_shape,
block1_shape,
name="segment_side_1")
temps.append(temp)
segments.append(net_segment_output) # segment
net_output = self._decoder(block1_add,
block1_shape,
block1_shape,
name="1")
segments_output.append(net_output)
pass
# 7
net_output = Net.conv(net_output,
3,
3,
2,
1,
1,
biased=True,
relu=False,
name='attention_0')
segments.append(net_output) # segment
features = {
"block": blocks,
"segment": segments_output,
"temp": temps,
"add": adds,
"adds_in_block": adds_in_block,
"adds_in_2": adds_in_2
}
return segments, features
