model.py

# Converted to TensorFlow .caffemodel
# with the DeepLab-ResNet configuration.
# The batch normalisation layer is provided by
# the slim library (https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/slim).

from network import DeepLabNetwork, PSPNetwork
import tensorflow as tf

class DeepLabResNetModel(DeepLabNetwork):
    def setup(self, is_training, num_classes):
        '''Network definition.

        Args:
          is_training: whether to update the running mean and variance of the batch normalisation layer.
                       If the batch size is small, it is better to keep the running mean and variance of
                       the-pretrained model frozen.
          num_classes: number of classes to predict (including background).
        '''
        (self.feed('data')
             .conv(7, 7, 64, 2, 2, biased=False, relu=False, name='conv1')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn_conv1')
             .max_pool(3, 3, 2, 2, name='pool1')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res2a_branch1')
             .batch_normalization(is_training=is_training, activation_fn=None, name='bn2a_branch1'))

        (self.feed('pool1')
             .conv(1, 1, 64, 1, 1, biased=False, relu=False, name='res2a_branch2a')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn2a_branch2a')
             .conv(3, 3, 64, 1, 1, biased=False, relu=False, name='res2a_branch2b')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn2a_branch2b')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res2a_branch2c')
             .batch_normalization(is_training=is_training, activation_fn=None, name='bn2a_branch2c'))

        (self.feed('bn2a_branch1',
                   'bn2a_branch2c')
             .add(name='res2a')
             .relu(name='res2a_relu')
             .conv(1, 1, 64, 1, 1, biased=False, relu=False, name='res2b_branch2a')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn2b_branch2a')
             .conv(3, 3, 64, 1, 1, biased=False, relu=False, name='res2b_branch2b')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn2b_branch2b')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res2b_branch2c')
             .batch_normalization(is_training=is_training, activation_fn=None, name='bn2b_branch2c'))

        (self.feed('res2a_relu',
                   'bn2b_branch2c')
             .add(name='res2b')
             .relu(name='res2b_relu')
             .conv(1, 1, 64, 1, 1, biased=False, relu=False, name='res2c_branch2a')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn2c_branch2a')
             .conv(3, 3, 64, 1, 1, biased=False, relu=False, name='res2c_branch2b')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn2c_branch2b')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res2c_branch2c')
             .batch_normalization(is_training=is_training, activation_fn=None, name='bn2c_branch2c'))

        (self.feed('res2b_relu',
                   'bn2c_branch2c')
             .add(name='res2c')
             .relu(name='res2c_relu')
             .conv(1, 1, 512, 2, 2, biased=False, relu=False, name='res3a_branch1')
             .batch_normalization(is_training=is_training, activation_fn=None, name='bn3a_branch1'))

        (self.feed('res2c_relu')
             .conv(1, 1, 128, 2, 2, biased=False, relu=False, name='res3a_branch2a')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn3a_branch2a')
             .conv(3, 3, 128, 1, 1, biased=False, relu=False, name='res3a_branch2b')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn3a_branch2b')
             .conv(1, 1, 512, 1, 1, biased=False, relu=False, name='res3a_branch2c')
             .batch_normalization(is_training=is_training, activation_fn=None, name='bn3a_branch2c'))

        (self.feed('bn3a_branch1',
                   'bn3a_branch2c')
             .add(name='res3a')
             .relu(name='res3a_relu')
             .conv(1, 1, 128, 1, 1, biased=False, relu=False, name='res3b1_branch2a')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn3b1_branch2a')
             .conv(3, 3, 128, 1, 1, biased=False, relu=False, name='res3b1_branch2b')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn3b1_branch2b')
             .conv(1, 1, 512, 1, 1, biased=False, relu=False, name='res3b1_branch2c')
             .batch_normalization(is_training=is_training, activation_fn=None, name='bn3b1_branch2c'))

        (self.feed('res3a_relu',
                   'bn3b1_branch2c')
             .add(name='res3b1')
             .relu(name='res3b1_relu')
             .conv(1, 1, 128, 1, 1, biased=False, relu=False, name='res3b2_branch2a')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn3b2_branch2a')
             .conv(3, 3, 128, 1, 1, biased=False, relu=False, name='res3b2_branch2b')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn3b2_branch2b')
             .conv(1, 1, 512, 1, 1, biased=False, relu=False, name='res3b2_branch2c')
             .batch_normalization(is_training=is_training, activation_fn=None, name='bn3b2_branch2c'))

        (self.feed('res3b1_relu',
                   'bn3b2_branch2c')
             .add(name='res3b2')
             .relu(name='res3b2_relu')
             .conv(1, 1, 128, 1, 1, biased=False, relu=False, name='res3b3_branch2a')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn3b3_branch2a')
             .conv(3, 3, 128, 1, 1, biased=False, relu=False, name='res3b3_branch2b')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn3b3_branch2b')
             .conv(1, 1, 512, 1, 1, biased=False, relu=False, name='res3b3_branch2c')
             .batch_normalization(is_training=is_training, activation_fn=None, name='bn3b3_branch2c'))

        (self.feed('res3b2_relu',
                   'bn3b3_branch2c')
             .add(name='res3b3')
             .relu(name='res3b3_relu')
             .conv(1, 1, 1024, 2, 2, biased=False, relu=False, name='res4a_branch1')
             .batch_normalization(is_training=is_training, activation_fn=None, name='bn4a_branch1'))

        ### block 4
        (self.feed('res3b3_relu')
             .conv(1, 1, 256, 2, 2, biased=False, relu=False, name='res4a_branch2a')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4a_branch2a')
             .atrous_conv(3, 3, 256, 1, padding='SAME', biased=False, relu=False, name='res4a_branch2b')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4a_branch2b')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4a_branch2c')
             .batch_normalization(is_training=is_training, activation_fn=None, name='bn4a_branch2c'))

        (self.feed('bn4a_branch1',
                   'bn4a_branch2c')
             .add(name='res4a')
             .relu(name='res4a_relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b1_branch2a')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b1_branch2a')
             .atrous_conv(3, 3, 256, 4, padding='SAME', biased=False, relu=False, name='res4b1_branch2b')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b1_branch2b')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b1_branch2c')
             .batch_normalization(is_training=is_training, activation_fn=None, name='bn4b1_branch2c'))

        (self.feed('res4a_relu',
                   'bn4b1_branch2c')
             .add(name='res4b1')
             .relu(name='res4b1_relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b2_branch2a')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b2_branch2a')
             .atrous_conv(3, 3, 256, 2, padding='SAME', biased=False, relu=False, name='res4b2_branch2b')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b2_branch2b')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b2_branch2c')
             .batch_normalization(is_training=is_training, activation_fn=None, name='bn4b2_branch2c'))

        ### block 4

        ### block 5
        (self.feed('res4b1_relu',
                   'bn4b2_branch2c')
             .add(name='res4b2')
             .relu(name='res4b2_relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b3_branch2a')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b3_branch2a')
             .atrous_conv(3, 3, 256, 2, padding='SAME', biased=False, relu=False, name='res4b3_branch2b')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b3_branch2b')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b3_branch2c')
             .batch_normalization(is_training=is_training, activation_fn=None, name='bn4b3_branch2c'))

        (self.feed('res4b2_relu',
                   'bn4b3_branch2c')
             .add(name='res4b3')
             .relu(name='res4b3_relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b4_branch2a')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b4_branch2a')
             .atrous_conv(3, 3, 256, 4, padding='SAME', biased=False, relu=False, name='res4b4_branch2b')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b4_branch2b')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b4_branch2c')
             .batch_normalization(is_training=is_training, activation_fn=None, name='bn4b4_branch2c'))

        (self.feed('res4b3_relu',
                   'bn4b4_branch2c')
             .add(name='res4b4')
             .relu(name='res4b4_relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b5_branch2a')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b5_branch2a')
             .atrous_conv(3, 3, 256, 2, padding='SAME', biased=False, relu=False, name='res4b5_branch2b')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b5_branch2b')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b5_branch2c')
             .batch_normalization(is_training=is_training, activation_fn=None, name='bn4b5_branch2c'))

        ### block 5

        ### block 6
        (self.feed('res4b4_relu',
                   'bn4b5_branch2c')
             .add(name='res4b5')
             .relu(name='res4b5_relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b6_branch2a')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b6_branch2a')
             .atrous_conv(3, 3, 256, 2, padding='SAME', biased=False, relu=False, name='res4b6_branch2b')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b6_branch2b')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b6_branch2c')
             .batch_normalization(is_training=is_training, activation_fn=None, name='bn4b6_branch2c'))

        (self.feed('res4b5_relu',
                   'bn4b6_branch2c')
             .add(name='res4b6')
             .relu(name='res4b6_relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b7_branch2a')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b7_branch2a')
             .atrous_conv(3, 3, 256, 4, padding='SAME', biased=False, relu=False, name='res4b7_branch2b')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b7_branch2b')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b7_branch2c')
             .batch_normalization(is_training=is_training, activation_fn=None, name='bn4b7_branch2c'))

        (self.feed('res4b6_relu',
                   'bn4b7_branch2c')
             .add(name='res4b7')
             .relu(name='res4b7_relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b8_branch2a')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b8_branch2a')
             .atrous_conv(3, 3, 256, 2, padding='SAME', biased=False, relu=False, name='res4b8_branch2b')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b8_branch2b')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b8_branch2c')
             .batch_normalization(is_training=is_training, activation_fn=None, name='bn4b8_branch2c'))

        ### block 6

        ### block 7
        (self.feed('res4b7_relu',
                   'bn4b8_branch2c')
             .add(name='res4b8')
             .relu(name='res4b8_relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b9_branch2a')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b9_branch2a')
             .atrous_conv(3, 3, 256, 2, padding='SAME', biased=False, relu=False, name='res4b9_branch2b')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b9_branch2b')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b9_branch2c')
             .batch_normalization(is_training=is_training, activation_fn=None, name='bn4b9_branch2c'))

        (self.feed('res4b8_relu',
                   'bn4b9_branch2c')
             .add(name='res4b9')
             .relu(name='res4b9_relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b10_branch2a')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b10_branch2a')
             .atrous_conv(3, 3, 256, 4, padding='SAME', biased=False, relu=False, name='res4b10_branch2b')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b10_branch2b')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b10_branch2c')
             .batch_normalization(is_training=is_training, activation_fn=None, name='bn4b10_branch2c'))

        (self.feed('res4b9_relu',
                   'bn4b10_branch2c')
             .add(name='res4b10')
             .relu(name='res4b10_relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b11_branch2a')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b11_branch2a')
             .atrous_conv(3, 3, 256, 2, padding='SAME', biased=False, relu=False, name='res4b11_branch2b')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b11_branch2b')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b11_branch2c')
             .batch_normalization(is_training=is_training, activation_fn=None, name='bn4b11_branch2c'))

        ### block 7
        (self.feed('res4b10_relu',
                   'bn4b11_branch2c')
             .add(name='res4b11')
             .relu(name='res4b11_relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b12_branch2a')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b12_branch2a')
             .atrous_conv(3, 3, 256, 2, padding='SAME', biased=False, relu=False, name='res4b12_branch2b')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b12_branch2b')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b12_branch2c')
             .batch_normalization(is_training=is_training, activation_fn=None, name='bn4b12_branch2c'))

        (self.feed('res4b11_relu',
                   'bn4b12_branch2c')
             .add(name='res4b12')
             .relu(name='res4b12_relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b13_branch2a')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b13_branch2a')
             .atrous_conv(3, 3, 256, 4, padding='SAME', biased=False, relu=False, name='res4b13_branch2b')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b13_branch2b')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b13_branch2c')
             .batch_normalization(is_training=is_training, activation_fn=None, name='bn4b13_branch2c'))

        (self.feed('res4b12_relu',
                   'bn4b13_branch2c')
             .add(name='res4b13')
             .relu(name='res4b13_relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b14_branch2a')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b14_branch2a')
             .atrous_conv(3, 3, 256, 2, padding='SAME', biased=False, relu=False, name='res4b14_branch2b')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b14_branch2b')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b14_branch2c')
             .batch_normalization(is_training=is_training, activation_fn=None, name='bn4b14_branch2c'))

        (self.feed('res4b13_relu',
                   'bn4b14_branch2c')
             .add(name='res4b14')
             .relu(name='res4b14_relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b15_branch2a')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b15_branch2a')
             .atrous_conv(3, 3, 256, 2, padding='SAME', biased=False, relu=False, name='res4b15_branch2b')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b15_branch2b')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b15_branch2c')
             .batch_normalization(is_training=is_training, activation_fn=None, name='bn4b15_branch2c'))

        (self.feed('res4b14_relu',
                   'bn4b15_branch2c')
             .add(name='res4b15')
             .relu(name='res4b15_relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b16_branch2a')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b16_branch2a')
             .atrous_conv(3, 3, 256, 4, padding='SAME', biased=False, relu=False, name='res4b16_branch2b')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b16_branch2b')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b16_branch2c')
             .batch_normalization(is_training=is_training, activation_fn=None, name='bn4b16_branch2c'))

        (self.feed('res4b15_relu',
                   'bn4b16_branch2c')
             .add(name='res4b16')
             .relu(name='res4b16_relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b17_branch2a')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b17_branch2a')
             .atrous_conv(3, 3, 256, 2, padding='SAME', biased=False, relu=False, name='res4b17_branch2b')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b17_branch2b')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b17_branch2c')
             .batch_normalization(is_training=is_training, activation_fn=None, name='bn4b17_branch2c'))

        (self.feed('res4b16_relu',
                   'bn4b17_branch2c')
             .add(name='res4b17')
             .relu(name='res4b17_relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b18_branch2a')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b18_branch2a')
             .atrous_conv(3, 3, 256, 2, padding='SAME', biased=False, relu=False, name='res4b18_branch2b')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b18_branch2b')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b18_branch2c')
             .batch_normalization(is_training=is_training, activation_fn=None, name='bn4b18_branch2c'))

        (self.feed('res4b17_relu',
                   'bn4b18_branch2c')
             .add(name='res4b18')
             .relu(name='res4b18_relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b19_branch2a')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b19_branch2a')
             .atrous_conv(3, 3, 256, 4, padding='SAME', biased=False, relu=False, name='res4b19_branch2b')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b19_branch2b')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b19_branch2c')
             .batch_normalization(is_training=is_training, activation_fn=None, name='bn4b19_branch2c'))

        (self.feed('res4b18_relu',
                   'bn4b19_branch2c')
             .add(name='res4b19')
             .relu(name='res4b19_relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b20_branch2a')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b20_branch2a')
             .atrous_conv(3, 3, 256, 2, padding='SAME', biased=False, relu=False, name='res4b20_branch2b')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b20_branch2b')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b20_branch2c')
             .batch_normalization(is_training=is_training, activation_fn=None, name='bn4b20_branch2c'))

        (self.feed('res4b19_relu',
                   'bn4b20_branch2c')
             .add(name='res4b20')
             .relu(name='res4b20_relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b21_branch2a')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b21_branch2a')
             .atrous_conv(3, 3, 256, 2, padding='SAME', biased=False, relu=False, name='res4b21_branch2b')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b21_branch2b')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b21_branch2c')
             .batch_normalization(is_training=is_training, activation_fn=None, name='bn4b21_branch2c'))

        (self.feed('res4b20_relu',
                   'bn4b21_branch2c')
             .add(name='res4b21')
             .relu(name='res4b21_relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b22_branch2a')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b22_branch2a')
             .atrous_conv(3, 3, 256, 4, padding='SAME', biased=False, relu=False, name='res4b22_branch2b')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b22_branch2b')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b22_branch2c')
             .batch_normalization(is_training=is_training, activation_fn=None, name='bn4b22_branch2c'))

        (self.feed('res4b21_relu',
                   'bn4b22_branch2c')
             .add(name='res4b22')
             .relu(name='res4b22_relu')
             .conv(1, 1, 2048, 1, 1, biased=False, relu=False, name='res5a_branch1')
             .batch_normalization(is_training=is_training, activation_fn=None, name='bn5a_branch1'))

        (self.feed('res4b22_relu')
             .conv(1, 1, 512, 1, 1, biased=False, relu=False, name='res5a_branch2a')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn5a_branch2a')
             .atrous_conv(3, 3, 512, 4, padding='SAME', biased=False, relu=False, name='res5a_branch2b')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn5a_branch2b')
             .conv(1, 1, 2048, 1, 1, biased=False, relu=False, name='res5a_branch2c')
             .batch_normalization(is_training=is_training, activation_fn=None, name='bn5a_branch2c'))

        (self.feed('bn5a_branch1',
                   'bn5a_branch2c')
             .add(name='res5a')
             .relu(name='res5a_relu')
             .conv(1, 1, 512, 1, 1, biased=False, relu=False, name='res5b_branch2a')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn5b_branch2a')
             .atrous_conv(3, 3, 512, 8, padding='SAME', biased=False, relu=False, name='res5b_branch2b')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn5b_branch2b')
             .conv(1, 1, 2048, 1, 1, biased=False, relu=False, name='res5b_branch2c')
             .batch_normalization(is_training=is_training, activation_fn=None, name='bn5b_branch2c'))

        (self.feed('res5a_relu',
                   'bn5b_branch2c')
             .add(name='res5b')
             .relu(name='res5b_relu')
             .conv(1, 1, 512, 1, 1, biased=False, relu=False, name='res5c_branch2a')
             .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn5c_branch2a')
             .atrous_conv(3, 3, 512, 16, padding='SAME', biased=False, relu=False, name='res5c_branch2b')
             .batch_normalization(activation_fn=tf.nn.relu, name='bn5c_branch2b', is_training=is_training)
             .conv(1, 1, 2048, 1, 1, biased=False, relu=False, name='res5c_branch2c')
             .batch_normalization(is_training=is_training, activation_fn=None, name='bn5c_branch2c'))

        (self.feed('res5b_relu',
                   'bn5c_branch2c')
             .add(name='res5c')
             .relu(name='res5c_relu')
             .atrous_conv(3, 3, 256, 6, padding='SAME', relu=False, name='fc1_voc12_c0')
             .batch_normalization(is_training=is_training, activation_fn=None, name='fc1_voc12_c0_bn'))

        (self.feed('res5c_relu')
             .atrous_conv(3, 3, 256, 12, padding='SAME', relu=False, name='fc1_voc12_c1')
             .batch_normalization(is_training=is_training, activation_fn=None, name='fc1_voc12_c1_bn'))

        (self.feed('res5c_relu')
             .atrous_conv(3, 3, 256, 18, padding='SAME', relu=False, name='fc1_voc12_c2')
             .batch_normalization(is_training=is_training, activation_fn=None, name='fc1_voc12_c2_bn'))

        (self.feed('res5c_relu')
             .atrous_conv(1, 1, 256, 1, padding='SAME', relu=False, name='fc1_voc12_c3')
             .batch_normalization(is_training=is_training, activation_fn=None, name='fc1_voc12_c3_bn'))

        (self.feed('fc1_voc12_c0_bn',
                   'fc1_voc12_c1_bn',
                   'fc1_voc12_c2_bn',
                   'fc1_voc12_c3_bn')
             .add(name='fc1_voc12')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='fc_oooo')
             .batch_normalization(is_training=is_training, activation_fn=None, name='fc_oooo_bn')
             .conv(1, 1, num_classes, 1, 1, biased=False, relu=False, name='fc_out'))

class PSPNet101(PSPNetwork):
    def setup(self, is_training, num_classes):
        '''Network definition.
        Args:
          is_training: whether to update the running mean and variance of the batch normalisation layer.
                       If the batch size is small, it is better to keep the running mean and variance of
                       the-pretrained model frozen.
          num_classes: number of classes to predict (including background).
        '''
        (self.feed('data')
             .conv(3, 3, 64, 2, 2, biased=False, relu=False, padding='SAME', name='conv1_1_3x3_s2')
             .batch_normalization(relu=False, name='conv1_1_3x3_s2_bn')
             .relu(name='conv1_1_3x3_s2_bn_relu')
             .conv(3, 3, 64, 1, 1, biased=False, relu=False, padding='SAME', name='conv1_2_3x3')
             .batch_normalization(relu=True, name='conv1_2_3x3_bn')
             .conv(3, 3, 128, 1, 1, biased=False, relu=False, padding='SAME', name='conv1_3_3x3')
             .batch_normalization(relu=True, name='conv1_3_3x3_bn')
             .max_pool(3, 3, 2, 2, padding='SAME', name='pool1_3x3_s2')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv2_1_1x1_proj')
             .batch_normalization(relu=False, name='conv2_1_1x1_proj_bn'))

        (self.feed('pool1_3x3_s2')
             .conv(1, 1, 64, 1, 1, biased=False, relu=False, name='conv2_1_1x1_reduce')
             .batch_normalization(relu=True, name='conv2_1_1x1_reduce_bn')
             .zero_padding(paddings=1, name='padding1')
             .conv(3, 3, 64, 1, 1, biased=False, relu=False, name='conv2_1_3x3')
             .batch_normalization(relu=True, name='conv2_1_3x3_bn')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv2_1_1x1_increase')
             .batch_normalization(relu=False, name='conv2_1_1x1_increase_bn'))

        (self.feed('conv2_1_1x1_proj_bn',
                   'conv2_1_1x1_increase_bn')
             .add(name='conv2_1')
             .relu(name='conv2_1/relu')
             .conv(1, 1, 64, 1, 1, biased=False, relu=False, name='conv2_2_1x1_reduce')
             .batch_normalization(relu=True, name='conv2_2_1x1_reduce_bn')
             .zero_padding(paddings=1, name='padding2')
             .conv(3, 3, 64, 1, 1, biased=False, relu=False, name='conv2_2_3x3')
             .batch_normalization(relu=True, name='conv2_2_3x3_bn')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv2_2_1x1_increase')
             .batch_normalization(relu=False, name='conv2_2_1x1_increase_bn'))

        (self.feed('conv2_1/relu',
                   'conv2_2_1x1_increase_bn')
             .add(name='conv2_2')
             .relu(name='conv2_2/relu')
             .conv(1, 1, 64, 1, 1, biased=False, relu=False, name='conv2_3_1x1_reduce')
             .batch_normalization(relu=True, name='conv2_3_1x1_reduce_bn')
             .zero_padding(paddings=1, name='padding3')
             .conv(3, 3, 64, 1, 1, biased=False, relu=False, name='conv2_3_3x3')
             .batch_normalization(relu=True, name='conv2_3_3x3_bn')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv2_3_1x1_increase')
             .batch_normalization(relu=False, name='conv2_3_1x1_increase_bn'))

        (self.feed('conv2_2/relu',
                   'conv2_3_1x1_increase_bn')
             .add(name='conv2_3')
             .relu(name='conv2_3/relu')
             .conv(1, 1, 512, 2, 2, biased=False, relu=False, name='conv3_1_1x1_proj')
             .batch_normalization(relu=False, name='conv3_1_1x1_proj_bn'))

        (self.feed('conv2_3/relu')
             .conv(1, 1, 128, 2, 2, biased=False, relu=False, name='conv3_1_1x1_reduce')
             .batch_normalization(relu=True, name='conv3_1_1x1_reduce_bn')
             .zero_padding(paddings=1, name='padding4')
             .conv(3, 3, 128, 1, 1, biased=False, relu=False, name='conv3_1_3x3')
             .batch_normalization(relu=True, name='conv3_1_3x3_bn')
             .conv(1, 1, 512, 1, 1, biased=False, relu=False, name='conv3_1_1x1_increase')
             .batch_normalization(relu=False, name='conv3_1_1x1_increase_bn'))

        (self.feed('conv3_1_1x1_proj_bn',
                   'conv3_1_1x1_increase_bn')
             .add(name='conv3_1')
             .relu(name='conv3_1/relu')
             .conv(1, 1, 128, 1, 1, biased=False, relu=False, name='conv3_2_1x1_reduce')
             .batch_normalization(relu=True, name='conv3_2_1x1_reduce_bn')
             .zero_padding(paddings=1, name='padding5')
             .conv(3, 3, 128, 1, 1, biased=False, relu=False, name='conv3_2_3x3')
             .batch_normalization(relu=True, name='conv3_2_3x3_bn')
             .conv(1, 1, 512, 1, 1, biased=False, relu=False, name='conv3_2_1x1_increase')
             .batch_normalization(relu=False, name='conv3_2_1x1_increase_bn'))

        (self.feed('conv3_1/relu',
                   'conv3_2_1x1_increase_bn')
             .add(name='conv3_2')
             .relu(name='conv3_2/relu')
             .conv(1, 1, 128, 1, 1, biased=False, relu=False, name='conv3_3_1x1_reduce')
             .batch_normalization(relu=True, name='conv3_3_1x1_reduce_bn')
             .zero_padding(paddings=1, name='padding6')
             .conv(3, 3, 128, 1, 1, biased=False, relu=False, name='conv3_3_3x3')
             .batch_normalization(relu=True, name='conv3_3_3x3_bn')
             .conv(1, 1, 512, 1, 1, biased=False, relu=False, name='conv3_3_1x1_increase')
             .batch_normalization(relu=False, name='conv3_3_1x1_increase_bn'))

        (self.feed('conv3_2/relu',
                   'conv3_3_1x1_increase_bn')
             .add(name='conv3_3')
             .relu(name='conv3_3/relu')
             .conv(1, 1, 128, 1, 1, biased=False, relu=False, name='conv3_4_1x1_reduce')
             .batch_normalization(relu=True, name='conv3_4_1x1_reduce_bn')
             .zero_padding(paddings=1, name='padding7')
             .conv(3, 3, 128, 1, 1, biased=False, relu=False, name='conv3_4_3x3')
             .batch_normalization(relu=True, name='conv3_4_3x3_bn')
             .conv(1, 1, 512, 1, 1, biased=False, relu=False, name='conv3_4_1x1_increase')
             .batch_normalization(relu=False, name='conv3_4_1x1_increase_bn'))

        (self.feed('conv3_3/relu',
                   'conv3_4_1x1_increase_bn')
             .add(name='conv3_4')
             .relu(name='conv3_4/relu')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_1_1x1_proj')
             .batch_normalization(relu=False, name='conv4_1_1x1_proj_bn'))

        (self.feed('conv3_4/relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_1_1x1_reduce')
             .batch_normalization(relu=True, name='conv4_1_1x1_reduce_bn')
             .zero_padding(paddings=2, name='padding8')
             .atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_1_3x3')
             .batch_normalization(relu=True, name='conv4_1_3x3_bn')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_1_1x1_increase')
             .batch_normalization(relu=False, name='conv4_1_1x1_increase_bn'))

        (self.feed('conv4_1_1x1_proj_bn',
                   'conv4_1_1x1_increase_bn')
             .add(name='conv4_1')
             .relu(name='conv4_1/relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_2_1x1_reduce')
             .batch_normalization(relu=True, name='conv4_2_1x1_reduce_bn')
             .zero_padding(paddings=2, name='padding9')
             .atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_2_3x3')
             .batch_normalization(relu=True, name='conv4_2_3x3_bn')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_2_1x1_increase')
             .batch_normalization(relu=False, name='conv4_2_1x1_increase_bn'))

        (self.feed('conv4_1/relu',
                   'conv4_2_1x1_increase_bn')
             .add(name='conv4_2')
             .relu(name='conv4_2/relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_3_1x1_reduce')
             .batch_normalization(relu=True, name='conv4_3_1x1_reduce_bn')
             .zero_padding(paddings=2, name='padding10')
             .atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_3_3x3')
             .batch_normalization(relu=True, name='conv4_3_3x3_bn')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_3_1x1_increase')
             .batch_normalization(relu=False, name='conv4_3_1x1_increase_bn'))

        (self.feed('conv4_2/relu',
                   'conv4_3_1x1_increase_bn')
             .add(name='conv4_3')
             .relu(name='conv4_3/relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_4_1x1_reduce')
             .batch_normalization(relu=True, name='conv4_4_1x1_reduce_bn')
             .zero_padding(paddings=2, name='padding11')
             .atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_4_3x3')
             .batch_normalization(relu=True, name='conv4_4_3x3_bn')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_4_1x1_increase')
             .batch_normalization(relu=False, name='conv4_4_1x1_increase_bn'))

        (self.feed('conv4_3/relu',
                   'conv4_4_1x1_increase_bn')
             .add(name='conv4_4')
             .relu(name='conv4_4/relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_5_1x1_reduce')
             .batch_normalization(relu=True, name='conv4_5_1x1_reduce_bn')
             .zero_padding(paddings=2, name='padding12')
             .atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_5_3x3')
             .batch_normalization(relu=True, name='conv4_5_3x3_bn')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_5_1x1_increase')
             .batch_normalization(relu=False, name='conv4_5_1x1_increase_bn'))

        (self.feed('conv4_4/relu',
                   'conv4_5_1x1_increase_bn')
             .add(name='conv4_5')
             .relu(name='conv4_5/relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_6_1x1_reduce')
             .batch_normalization(relu=True, name='conv4_6_1x1_reduce_bn')
             .zero_padding(paddings=2, name='padding13')
             .atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_6_3x3')
             .batch_normalization(relu=True, name='conv4_6_3x3_bn')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_6_1x1_increase')
             .batch_normalization(relu=False, name='conv4_6_1x1_increase_bn'))

        (self.feed('conv4_5/relu',
                   'conv4_6_1x1_increase_bn')
             .add(name='conv4_6')
             .relu(name='conv4_6/relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_7_1x1_reduce')
             .batch_normalization(relu=True, name='conv4_7_1x1_reduce_bn')
             .zero_padding(paddings=2, name='padding14')
             .atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_7_3x3')
             .batch_normalization(relu=True, name='conv4_7_3x3_bn')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_7_1x1_increase')
             .batch_normalization(relu=False, name='conv4_7_1x1_increase_bn'))

        (self.feed('conv4_6/relu',
                   'conv4_7_1x1_increase_bn')
             .add(name='conv4_7')
             .relu(name='conv4_7/relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_8_1x1_reduce')
             .batch_normalization(relu=True, name='conv4_8_1x1_reduce_bn')
             .zero_padding(paddings=2, name='padding15')
             .atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_8_3x3')
             .batch_normalization(relu=True, name='conv4_8_3x3_bn')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_8_1x1_increase')
             .batch_normalization(relu=False, name='conv4_8_1x1_increase_bn'))

        (self.feed('conv4_7/relu',
                   'conv4_8_1x1_increase_bn')
             .add(name='conv4_8')
             .relu(name='conv4_8/relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_9_1x1_reduce')
             .batch_normalization(relu=True, name='conv4_9_1x1_reduce_bn')
             .zero_padding(paddings=2, name='padding16')
             .atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_9_3x3')
             .batch_normalization(relu=True, name='conv4_9_3x3_bn')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_9_1x1_increase')
             .batch_normalization(relu=False, name='conv4_9_1x1_increase_bn'))

        (self.feed('conv4_8/relu',
                   'conv4_9_1x1_increase_bn')
             .add(name='conv4_9')
             .relu(name='conv4_9/relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_10_1x1_reduce')
             .batch_normalization(relu=True, name='conv4_10_1x1_reduce_bn')
             .zero_padding(paddings=2, name='padding17')
             .atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_10_3x3')
             .batch_normalization(relu=True, name='conv4_10_3x3_bn')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_10_1x1_increase')
             .batch_normalization(relu=False, name='conv4_10_1x1_increase_bn'))

        (self.feed('conv4_9/relu',
                   'conv4_10_1x1_increase_bn')
             .add(name='conv4_10')
             .relu(name='conv4_10/relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_11_1x1_reduce')
             .batch_normalization(relu=True, name='conv4_11_1x1_reduce_bn')
             .zero_padding(paddings=2, name='padding18')
             .atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_11_3x3')
             .batch_normalization(relu=True, name='conv4_11_3x3_bn')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_11_1x1_increase')
             .batch_normalization(relu=False, name='conv4_11_1x1_increase_bn'))

        (self.feed('conv4_10/relu',
                   'conv4_11_1x1_increase_bn')
             .add(name='conv4_11')
             .relu(name='conv4_11/relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_12_1x1_reduce')
             .batch_normalization(relu=True, name='conv4_12_1x1_reduce_bn')
             .zero_padding(paddings=2, name='padding19')
             .atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_12_3x3')
             .batch_normalization(relu=True, name='conv4_12_3x3_bn')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_12_1x1_increase')
             .batch_normalization(relu=False, name='conv4_12_1x1_increase_bn'))

        (self.feed('conv4_11/relu',
                   'conv4_12_1x1_increase_bn')
             .add(name='conv4_12')
             .relu(name='conv4_12/relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_13_1x1_reduce')
             .batch_normalization(relu=True, name='conv4_13_1x1_reduce_bn')
             .zero_padding(paddings=2, name='padding20')
             .atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_13_3x3')
             .batch_normalization(relu=True, name='conv4_13_3x3_bn')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_13_1x1_increase')
             .batch_normalization(relu=False, name='conv4_13_1x1_increase_bn'))

        (self.feed('conv4_12/relu',
                   'conv4_13_1x1_increase_bn')
             .add(name='conv4_13')
             .relu(name='conv4_13/relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_14_1x1_reduce')
             .batch_normalization(relu=True, name='conv4_14_1x1_reduce_bn')
             .zero_padding(paddings=2, name='padding21')
             .atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_14_3x3')
             .batch_normalization(relu=True, name='conv4_14_3x3_bn')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_14_1x1_increase')
             .batch_normalization(relu=False, name='conv4_14_1x1_increase_bn'))

        (self.feed('conv4_13/relu',
                   'conv4_14_1x1_increase_bn')
             .add(name='conv4_14')
             .relu(name='conv4_14/relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_15_1x1_reduce')
             .batch_normalization(relu=True, name='conv4_15_1x1_reduce_bn')
             .zero_padding(paddings=2, name='padding22')
             .atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_15_3x3')
             .batch_normalization(relu=True, name='conv4_15_3x3_bn')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_15_1x1_increase')
             .batch_normalization(relu=False, name='conv4_15_1x1_increase_bn'))

        (self.feed('conv4_14/relu',
                   'conv4_15_1x1_increase_bn')
             .add(name='conv4_15')
             .relu(name='conv4_15/relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_16_1x1_reduce')
             .batch_normalization(relu=True, name='conv4_16_1x1_reduce_bn')
             .zero_padding(paddings=2, name='padding23')
             .atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_16_3x3')
             .batch_normalization(relu=True, name='conv4_16_3x3_bn')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_16_1x1_increase')
             .batch_normalization(relu=False, name='conv4_16_1x1_increase_bn'))

        (self.feed('conv4_15/relu',
                   'conv4_16_1x1_increase_bn')
             .add(name='conv4_16')
             .relu(name='conv4_16/relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_17_1x1_reduce')
             .batch_normalization(relu=True, name='conv4_17_1x1_reduce_bn')
             .zero_padding(paddings=2, name='padding24')
             .atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_17_3x3')
             .batch_normalization(relu=True, name='conv4_17_3x3_bn')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_17_1x1_increase')
             .batch_normalization(relu=False, name='conv4_17_1x1_increase_bn'))

        (self.feed('conv4_16/relu',
                   'conv4_17_1x1_increase_bn')
             .add(name='conv4_17')
             .relu(name='conv4_17/relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_18_1x1_reduce')
             .batch_normalization(relu=True, name='conv4_18_1x1_reduce_bn')
             .zero_padding(paddings=2, name='padding25')
             .atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_18_3x3')
             .batch_normalization(relu=True, name='conv4_18_3x3_bn')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_18_1x1_increase')
             .batch_normalization(relu=False, name='conv4_18_1x1_increase_bn'))

        (self.feed('conv4_17/relu',
                   'conv4_18_1x1_increase_bn')
             .add(name='conv4_18')
             .relu(name='conv4_18/relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_19_1x1_reduce')
             .batch_normalization(relu=True, name='conv4_19_1x1_reduce_bn')
             .zero_padding(paddings=2, name='padding26')
             .atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_19_3x3')
             .batch_normalization(relu=True, name='conv4_19_3x3_bn')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_19_1x1_increase')
             .batch_normalization(relu=False, name='conv4_19_1x1_increase_bn'))

        (self.feed('conv4_18/relu',
                   'conv4_19_1x1_increase_bn')
             .add(name='conv4_19')
             .relu(name='conv4_19/relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_20_1x1_reduce')
             .batch_normalization(relu=True, name='conv4_20_1x1_reduce_bn')
             .zero_padding(paddings=2, name='padding27')
             .atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_20_3x3')
             .batch_normalization(relu=True, name='conv4_20_3x3_bn')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_20_1x1_increase')
             .batch_normalization(relu=False, name='conv4_20_1x1_increase_bn'))

        (self.feed('conv4_19/relu',
                   'conv4_20_1x1_increase_bn')
             .add(name='conv4_20')
             .relu(name='conv4_20/relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_21_1x1_reduce')
             .batch_normalization(relu=True, name='conv4_21_1x1_reduce_bn')
             .zero_padding(paddings=2, name='padding28')
             .atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_21_3x3')
             .batch_normalization(relu=True, name='conv4_21_3x3_bn')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_21_1x1_increase')
             .batch_normalization(relu=False, name='conv4_21_1x1_increase_bn'))

        (self.feed('conv4_20/relu',
                   'conv4_21_1x1_increase_bn')
             .add(name='conv4_21')
             .relu(name='conv4_21/relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_22_1x1_reduce')
             .batch_normalization(relu=True, name='conv4_22_1x1_reduce_bn')
             .zero_padding(paddings=2, name='padding29')
             .atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_22_3x3')
             .batch_normalization(relu=True, name='conv4_22_3x3_bn')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_22_1x1_increase')
             .batch_normalization(relu=False, name='conv4_22_1x1_increase_bn'))

        (self.feed('conv4_21/relu',
                   'conv4_22_1x1_increase_bn')
             .add(name='conv4_22')
             .relu(name='conv4_22/relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_23_1x1_reduce')
             .batch_normalization(relu=True, name='conv4_23_1x1_reduce_bn')
             .zero_padding(paddings=2, name='padding30')
             .atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_23_3x3')
             .batch_normalization(relu=True, name='conv4_23_3x3_bn')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_23_1x1_increase')
             .batch_normalization(relu=False, name='conv4_23_1x1_increase_bn'))

        (self.feed('conv4_22/relu',
                   'conv4_23_1x1_increase_bn')
             .add(name='conv4_23')
             .relu(name='conv4_23/relu')
             .conv(1, 1, 2048, 1, 1, biased=False, relu=False, name='conv5_1_1x1_proj')
             .batch_normalization(relu=False, name='conv5_1_1x1_proj_bn'))

        (self.feed('conv4_23/relu')
             .conv(1, 1, 512, 1, 1, biased=False, relu=False, name='conv5_1_1x1_reduce')
             .batch_normalization(relu=True, name='conv5_1_1x1_reduce_bn')
             .zero_padding(paddings=4, name='padding31')
             .atrous_conv(3, 3, 512, 4, biased=False, relu=False, name='conv5_1_3x3')
             .batch_normalization(relu=True, name='conv5_1_3x3_bn')
             .conv(1, 1, 2048, 1, 1, biased=False, relu=False, name='conv5_1_1x1_increase')
             .batch_normalization(relu=False, name='conv5_1_1x1_increase_bn'))

        (self.feed('conv5_1_1x1_proj_bn',
                   'conv5_1_1x1_increase_bn')
             .add(name='conv5_1')
             .relu(name='conv5_1/relu')
             .conv(1, 1, 512, 1, 1, biased=False, relu=False, name='conv5_2_1x1_reduce')
             .batch_normalization(relu=True, name='conv5_2_1x1_reduce_bn')
             .zero_padding(paddings=4, name='padding32')
             .atrous_conv(3, 3, 512, 4, biased=False, relu=False, name='conv5_2_3x3')
             .batch_normalization(relu=True, name='conv5_2_3x3_bn')
             .conv(1, 1, 2048, 1, 1, biased=False, relu=False, name='conv5_2_1x1_increase')
             .batch_normalization(relu=False, name='conv5_2_1x1_increase_bn'))

        (self.feed('conv5_1/relu',
                   'conv5_2_1x1_increase_bn')
             .add(name='conv5_2')
             .relu(name='conv5_2/relu')
             .conv(1, 1, 512, 1, 1, biased=False, relu=False, name='conv5_3_1x1_reduce')
             .batch_normalization(relu=True, name='conv5_3_1x1_reduce_bn')
             .zero_padding(paddings=4, name='padding33')
             .atrous_conv(3, 3, 512, 4, biased=False, relu=False, name='conv5_3_3x3')
             .batch_normalization(relu=True, name='conv5_3_3x3_bn')
             .conv(1, 1, 2048, 1, 1, biased=False, relu=False, name='conv5_3_1x1_increase')
             .batch_normalization(relu=False, name='conv5_3_1x1_increase_bn'))

        (self.feed('conv5_2/relu',
                   'conv5_3_1x1_increase_bn')
             .add(name='conv5_3')
             .relu(name='conv5_3/relu'))

        conv5_3 = self.layers['conv5_3/relu']
        shape = tf.shape(conv5_3)[1:3]

        (self.feed('conv5_3/relu')
             .avg_pool(90, 90, 90, 90, name='conv5_3_pool1')
             .conv(1, 1, 512, 1, 1, biased=False, relu=False, name='conv5_3_pool1_conv')
             .batch_normalization(relu=True, name='conv5_3_pool1_conv_bn')
             .resize_bilinear(shape, name='conv5_3_pool1_interp'))

        (self.feed('conv5_3/relu')
             .avg_pool(45, 45, 45, 45, name='conv5_3_pool2')
             .conv(1, 1, 512, 1, 1, biased=False, relu=False, name='conv5_3_pool2_conv')
             .batch_normalization(relu=True, name='conv5_3_pool2_conv_bn')
             .resize_bilinear(shape, name='conv5_3_pool2_interp'))

        (self.feed('conv5_3/relu')
             .avg_pool(30, 30, 30, 30, name='conv5_3_pool3')
             .conv(1, 1, 512, 1, 1, biased=False, relu=False, name='conv5_3_pool3_conv')
             .batch_normalization(relu=True, name='conv5_3_pool3_conv_bn')
             .resize_bilinear(shape, name='conv5_3_pool3_interp'))

        (self.feed('conv5_3/relu')
             .avg_pool(15, 15, 15, 15, name='conv5_3_pool6')
             .conv(1, 1, 512, 1, 1, biased=False, relu=False, name='conv5_3_pool6_conv')
             .batch_normalization(relu=True, name='conv5_3_pool6_conv_bn')
             .resize_bilinear(shape, name='conv5_3_pool6_interp'))

        (self.feed('conv5_3/relu',
                   'conv5_3_pool6_interp',
                   'conv5_3_pool3_interp',
                   'conv5_3_pool2_interp',
                   'conv5_3_pool1_interp')
             .concat(axis=-1, name='conv5_3_concat')
             .conv(3, 3, 512, 1, 1, biased=False, relu=False, padding='SAME', name='conv5_4')
             .batch_normalization(relu=True, name='conv5_4_bn')
             .conv(1, 1, num_classes, 1, 1, biased=True, relu=False, name='conv6'))

class PSPNet50(PSPNetwork):
    def setup(self, is_training, num_classes):
        '''Network definition.
        Args:
          is_training: whether to update the running mean and variance of the batch normalisation layer.
                       If the batch size is small, it is better to keep the running mean and variance of
                       the-pretrained model frozen.
          num_classes: number of classes to predict (including background).
        '''
        (self.feed('data')
             .conv(3, 3, 64, 2, 2, biased=False, relu=False, padding='SAME', name='conv1_1_3x3_s2')
             .batch_normalization(relu=False, name='conv1_1_3x3_s2_bn')
             .relu(name='conv1_1_3x3_s2_bn_relu')
             .conv(3, 3, 64, 1, 1, biased=False, relu=False, padding='SAME', name='conv1_2_3x3')
             .batch_normalization(relu=True, name='conv1_2_3x3_bn')
             .conv(3, 3, 128, 1, 1, biased=False, relu=False, padding='SAME', name='conv1_3_3x3')
             .batch_normalization(relu=True, name='conv1_3_3x3_bn')
             .max_pool(3, 3, 2, 2, padding='SAME', name='pool1_3x3_s2')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv2_1_1x1_proj')
             .batch_normalization(relu=False, name='conv2_1_1x1_proj_bn'))

        (self.feed('pool1_3x3_s2')
             .conv(1, 1, 64, 1, 1, biased=False, relu=False, name='conv2_1_1x1_reduce')
             .batch_normalization(relu=True, name='conv2_1_1x1_reduce_bn')
             .zero_padding(paddings=1, name='padding1')
             .conv(3, 3, 64, 1, 1, biased=False, relu=False, name='conv2_1_3x3')
             .batch_normalization(relu=True, name='conv2_1_3x3_bn')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv2_1_1x1_increase')
             .batch_normalization(relu=False, name='conv2_1_1x1_increase_bn'))

        (self.feed('conv2_1_1x1_proj_bn',
                   'conv2_1_1x1_increase_bn')
             .add(name='conv2_1')
             .relu(name='conv2_1/relu')
             .conv(1, 1, 64, 1, 1, biased=False, relu=False, name='conv2_2_1x1_reduce')
             .batch_normalization(relu=True, name='conv2_2_1x1_reduce_bn')
             .zero_padding(paddings=1, name='padding2')
             .conv(3, 3, 64, 1, 1, biased=False, relu=False, name='conv2_2_3x3')
             .batch_normalization(relu=True, name='conv2_2_3x3_bn')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv2_2_1x1_increase')
             .batch_normalization(relu=False, name='conv2_2_1x1_increase_bn'))

        (self.feed('conv2_1/relu',
                   'conv2_2_1x1_increase_bn')
             .add(name='conv2_2')
             .relu(name='conv2_2/relu')
             .conv(1, 1, 64, 1, 1, biased=False, relu=False, name='conv2_3_1x1_reduce')
             .batch_normalization(relu=True, name='conv2_3_1x1_reduce_bn')
             .zero_padding(paddings=1, name='padding3')
             .conv(3, 3, 64, 1, 1, biased=False, relu=False, name='conv2_3_3x3')
             .batch_normalization(relu=True, name='conv2_3_3x3_bn')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv2_3_1x1_increase')
             .batch_normalization(relu=False, name='conv2_3_1x1_increase_bn'))

        (self.feed('conv2_2/relu',
                   'conv2_3_1x1_increase_bn')
             .add(name='conv2_3')
             .relu(name='conv2_3/relu')
             .conv(1, 1, 512, 2, 2, biased=False, relu=False, name='conv3_1_1x1_proj')
             .batch_normalization(relu=False, name='conv3_1_1x1_proj_bn'))

        (self.feed('conv2_3/relu')
             .conv(1, 1, 128, 2, 2, biased=False, relu=False, name='conv3_1_1x1_reduce')
             .batch_normalization(relu=True, name='conv3_1_1x1_reduce_bn')
             .zero_padding(paddings=1, name='padding4')
             .conv(3, 3, 128, 1, 1, biased=False, relu=False, name='conv3_1_3x3')
             .batch_normalization(relu=True, name='conv3_1_3x3_bn')
             .conv(1, 1, 512, 1, 1, biased=False, relu=False, name='conv3_1_1x1_increase')
             .batch_normalization(relu=False, name='conv3_1_1x1_increase_bn'))

        (self.feed('conv3_1_1x1_proj_bn',
                   'conv3_1_1x1_increase_bn')
             .add(name='conv3_1')
             .relu(name='conv3_1/relu')
             .conv(1, 1, 128, 1, 1, biased=False, relu=False, name='conv3_2_1x1_reduce')
             .batch_normalization(relu=True, name='conv3_2_1x1_reduce_bn')
             .zero_padding(paddings=1, name='padding5')
             .conv(3, 3, 128, 1, 1, biased=False, relu=False, name='conv3_2_3x3')
             .batch_normalization(relu=True, name='conv3_2_3x3_bn')
             .conv(1, 1, 512, 1, 1, biased=False, relu=False, name='conv3_2_1x1_increase')
             .batch_normalization(relu=False, name='conv3_2_1x1_increase_bn'))

        (self.feed('conv3_1/relu',
                   'conv3_2_1x1_increase_bn')
             .add(name='conv3_2')
             .relu(name='conv3_2/relu')
             .conv(1, 1, 128, 1, 1, biased=False, relu=False, name='conv3_3_1x1_reduce')
             .batch_normalization(relu=True, name='conv3_3_1x1_reduce_bn')
             .zero_padding(paddings=1, name='padding6')
             .conv(3, 3, 128, 1, 1, biased=False, relu=False, name='conv3_3_3x3')
             .batch_normalization(relu=True, name='conv3_3_3x3_bn')
             .conv(1, 1, 512, 1, 1, biased=False, relu=False, name='conv3_3_1x1_increase')
             .batch_normalization(relu=False, name='conv3_3_1x1_increase_bn'))

        (self.feed('conv3_2/relu',
                   'conv3_3_1x1_increase_bn')
             .add(name='conv3_3')
             .relu(name='conv3_3/relu')
             .conv(1, 1, 128, 1, 1, biased=False, relu=False, name='conv3_4_1x1_reduce')
             .batch_normalization(relu=True, name='conv3_4_1x1_reduce_bn')
             .zero_padding(paddings=1, name='padding7')
             .conv(3, 3, 128, 1, 1, biased=False, relu=False, name='conv3_4_3x3')
             .batch_normalization(relu=True, name='conv3_4_3x3_bn')
             .conv(1, 1, 512, 1, 1, biased=False, relu=False, name='conv3_4_1x1_increase')
             .batch_normalization(relu=False, name='conv3_4_1x1_increase_bn'))

        (self.feed('conv3_3/relu',
                   'conv3_4_1x1_increase_bn')
             .add(name='conv3_4')
             .relu(name='conv3_4/relu')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_1_1x1_proj')
             .batch_normalization(relu=False, name='conv4_1_1x1_proj_bn'))

        (self.feed('conv3_4/relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_1_1x1_reduce')
             .batch_normalization(relu=True, name='conv4_1_1x1_reduce_bn')
             .zero_padding(paddings=2, name='padding8')
             .atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_1_3x3')
             .batch_normalization(relu=True, name='conv4_1_3x3_bn')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_1_1x1_increase')
             .batch_normalization(relu=False, name='conv4_1_1x1_increase_bn'))

        (self.feed('conv4_1_1x1_proj_bn',
                   'conv4_1_1x1_increase_bn')
             .add(name='conv4_1')
             .relu(name='conv4_1/relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_2_1x1_reduce')
             .batch_normalization(relu=True, name='conv4_2_1x1_reduce_bn')
             .zero_padding(paddings=2, name='padding9')
             .atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_2_3x3')
             .batch_normalization(relu=True, name='conv4_2_3x3_bn')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_2_1x1_increase')
             .batch_normalization(relu=False, name='conv4_2_1x1_increase_bn'))

        (self.feed('conv4_1/relu',
                   'conv4_2_1x1_increase_bn')
             .add(name='conv4_2')
             .relu(name='conv4_2/relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_3_1x1_reduce')
             .batch_normalization(relu=True, name='conv4_3_1x1_reduce_bn')
             .zero_padding(paddings=2, name='padding10')
             .atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_3_3x3')
             .batch_normalization(relu=True, name='conv4_3_3x3_bn')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_3_1x1_increase')
             .batch_normalization(relu=False, name='conv4_3_1x1_increase_bn'))

        (self.feed('conv4_2/relu',
                   'conv4_3_1x1_increase_bn')
             .add(name='conv4_3')
             .relu(name='conv4_3/relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_4_1x1_reduce')
             .batch_normalization(relu=True, name='conv4_4_1x1_reduce_bn')
             .zero_padding(paddings=2, name='padding11')
             .atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_4_3x3')
             .batch_normalization(relu=True, name='conv4_4_3x3_bn')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_4_1x1_increase')
             .batch_normalization(relu=False, name='conv4_4_1x1_increase_bn'))

        (self.feed('conv4_3/relu',
                   'conv4_4_1x1_increase_bn')
             .add(name='conv4_4')
             .relu(name='conv4_4/relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_5_1x1_reduce')
             .batch_normalization(relu=True, name='conv4_5_1x1_reduce_bn')
             .zero_padding(paddings=2, name='padding12')
             .atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_5_3x3')
             .batch_normalization(relu=True, name='conv4_5_3x3_bn')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_5_1x1_increase')
             .batch_normalization(relu=False, name='conv4_5_1x1_increase_bn'))

        (self.feed('conv4_4/relu',
                   'conv4_5_1x1_increase_bn')
             .add(name='conv4_5')
             .relu(name='conv4_5/relu')
             .conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv4_6_1x1_reduce')
             .batch_normalization(relu=True, name='conv4_6_1x1_reduce_bn')
             .zero_padding(paddings=2, name='padding13')
             .atrous_conv(3, 3, 256, 2, biased=False, relu=False, name='conv4_6_3x3')
             .batch_normalization(relu=True, name='conv4_6_3x3_bn')
             .conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='conv4_6_1x1_increase')
             .batch_normalization(relu=False, name='conv4_6_1x1_increase_bn'))

        (self.feed('conv4_5/relu',
                   'conv4_6_1x1_increase_bn')
             .add(name='conv4_6')
             .relu(name='conv4_6/relu')
             .conv(1, 1, 2048, 1, 1, biased=False, relu=False, name='conv5_1_1x1_proj')
             .batch_normalization(relu=False, name='conv5_1_1x1_proj_bn'))

        (self.feed('conv4_6/relu')
             .conv(1, 1, 512, 1, 1, biased=False, relu=False, name='conv5_1_1x1_reduce')
             .batch_normalization(relu=True, name='conv5_1_1x1_reduce_bn')
             .zero_padding(paddings=4, name='padding31')
             .atrous_conv(3, 3, 512, 4, biased=False, relu=False, name='conv5_1_3x3')
             .batch_normalization(relu=True, name='conv5_1_3x3_bn')
             .conv(1, 1, 2048, 1, 1, biased=False, relu=False, name='conv5_1_1x1_increase')
             .batch_normalization(relu=False, name='conv5_1_1x1_increase_bn'))

        (self.feed('conv5_1_1x1_proj_bn',
                   'conv5_1_1x1_increase_bn')
             .add(name='conv5_1')
             .relu(name='conv5_1/relu')
             .conv(1, 1, 512, 1, 1, biased=False, relu=False, name='conv5_2_1x1_reduce')
             .batch_normalization(relu=True, name='conv5_2_1x1_reduce_bn')
             .zero_padding(paddings=4, name='padding32')
             .atrous_conv(3, 3, 512, 4, biased=False, relu=False, name='conv5_2_3x3')
             .batch_normalization(relu=True, name='conv5_2_3x3_bn')
             .conv(1, 1, 2048, 1, 1, biased=False, relu=False, name='conv5_2_1x1_increase')
             .batch_normalization(relu=False, name='conv5_2_1x1_increase_bn'))

        (self.feed('conv5_1/relu',
                   'conv5_2_1x1_increase_bn')
             .add(name='conv5_2')
             .relu(name='conv5_2/relu')
             .conv(1, 1, 512, 1, 1, biased=False, relu=False, name='conv5_3_1x1_reduce')
             .batch_normalization(relu=True, name='conv5_3_1x1_reduce_bn')
             .zero_padding(paddings=4, name='padding33')
             .atrous_conv(3, 3, 512, 4, biased=False, relu=False, name='conv5_3_3x3')
             .batch_normalization(relu=True, name='conv5_3_3x3_bn')
             .conv(1, 1, 2048, 1, 1, biased=False, relu=False, name='conv5_3_1x1_increase')
             .batch_normalization(relu=False, name='conv5_3_1x1_increase_bn'))

        (self.feed('conv5_2/relu',
                   'conv5_3_1x1_increase_bn')
             .add(name='conv5_3')
             .relu(name='conv5_3/relu'))

        conv5_3 = self.layers['conv5_3/relu']
        shape = tf.shape(conv5_3)[1:3]

        (self.feed('conv5_3/relu')
             .avg_pool(60, 60, 60, 60, name='conv5_3_pool1')
             .conv(1, 1, 512, 1, 1, biased=False, relu=False, name='conv5_3_pool1_conv')
             .batch_normalization(relu=True, name='conv5_3_pool1_conv_bn')
             .resize_bilinear(shape, name='conv5_3_pool1_interp'))

        (self.feed('conv5_3/relu')
             .avg_pool(30, 30, 30, 30, name='conv5_3_pool2')
             .conv(1, 1, 512, 1, 1, biased=False, relu=False, name='conv5_3_pool2_conv')
             .batch_normalization(relu=True, name='conv5_3_pool2_conv_bn')
             .resize_bilinear(shape, name='conv5_3_pool2_interp'))

        (self.feed('conv5_3/relu')
             .avg_pool(20, 20, 20, 20, name='conv5_3_pool3')
             .conv(1, 1, 512, 1, 1, biased=False, relu=False, name='conv5_3_pool3_conv')
             .batch_normalization(relu=True, name='conv5_3_pool3_conv_bn')
             .resize_bilinear(shape, name='conv5_3_pool3_interp'))

        (self.feed('conv5_3/relu')
             .avg_pool(10, 10, 10, 10, name='conv5_3_pool6')
             .conv(1, 1, 512, 1, 1, biased=False, relu=False, name='conv5_3_pool6_conv')
             .batch_normalization(relu=True, name='conv5_3_pool6_conv_bn')
             .resize_bilinear(shape, name='conv5_3_pool6_interp'))

        (self.feed('conv5_3/relu',
                   'conv5_3_pool6_interp',
                   'conv5_3_pool3_interp',
                   'conv5_3_pool2_interp',
                   'conv5_3_pool1_interp')
             .concat(axis=-1, name='conv5_3_concat')
             .conv(3, 3, 512, 1, 1, biased=False, relu=False, padding='SAME', name='conv5_4')
             .batch_normalization(relu=True, name='conv5_4_bn')
             .conv(1, 1, num_classes, 1, 1, biased=True, relu=False, name='conv6'))