def _expanding_block(self, block_num, _input, layer, option='concat'):
output = _conv2d(
_input,
kernel_size=1,
stride=1,
output_feature=self.output_channels[str(block_num)][0],
use_bias=True,
name='conv_1')
#print('ex1',output)
output = deconv_bn_prelu(
output,
output_channels=self.output_channels[str(block_num)][1],
is_training=self.is_training,
name='deconv')
#print('de1',output)
if option == 'sum':
output = tf.add(output, layer, name='elemwise_sum')
else:
output = tf.concat(values=(output, layer), axis=-1, name='concat')
# 26*50*40*64 / 52*100*80*32 / 104*200*160*16
output = conv_bn_prelu(output,
kernel_size=3,
stride=1,
output_channels=int(output.get_shape()[-1]),
name='conv_2',
is_training=self.is_training)
#print('out1',output)
output = conv_bn_prelu(output,
kernel_size=3,
stride=1,
output_channels=int(output.get_shape()[-1]),
name='conv_3',
is_training=self.is_training)
#print('out2',output)
return output
def _expanding_block(self, block_num, _input, layer, option='concat'):
# 13*25*20*32 / 26*50*40*32 / 52*100*80*16
#output = conv_bn_prelu(_input, kernel_size=1, stride=1, output_channels=self.output_channels[str(block_num)][0],
# name='conv_1', is_training=self.is_training)
output = _conv3d(
_input,
kernel_size=1,
stride=1,
output_feature=self.output_channels[str(block_num)][0],
use_bias=True,
name='conv_1')
output = deconv_bn_prelu(
output,
output_channels=self.output_channels[str(block_num)][1],
is_training=self.is_training,
name='deconv')
# 26*50*40*32 / 52*100*80*16 / 104*200*160*8
# 26*50*40*64 / 52*100*80*32 / 104*200*160*16
output = tf.concat(values=(output, layer), axis=4, name='concat')
# 26*50*40*64 / 52*100*80*32 / 104*200*160*16
output = conv_bn_prelu(output,
kernel_size=3,
stride=1,
output_channels=int(output.get_shape()[-1]),
name='conv_2',
is_training=self.is_training)
output = conv_bn_prelu(output,
kernel_size=3,
stride=1,
output_channels=int(output.get_shape()[-1]),
name='conv_3',
is_training=self.is_training)
print(output)
return output