deeplabv3_s4

[irfanICMLL]

coding:utf-8

import tensorflow as tf

from models.network import NetWork

class DeepLabV2(NetWork):
def setup(self, is_training, num_classes):
inputs = self.inputs.popitems()[0]
assert type(inputs) == str
(self.feed(inputs)
.atrous_conv([3, 3], num_classes, 6, padding='SAME', relu=False, name='fc1_voc12_c0'))

    (self.feed(inputs)
     .atrous_conv([3, 3], num_classes, 12, padding='SAME', relu=False, name='fc1_voc12_c1'))

    (self.feed(inputs)
     .atrous_conv([3, 3], num_classes, 18, padding='SAME', relu=False, name='fc1_voc12_c2'))

    (self.feed(inputs)
     .atrous_conv([3, 3], num_classes, 24, padding='SAME', relu=False, name='fc1_voc12_c3'))

    (self.feed('fc1_voc12_c0',
               'fc1_voc12_c1',
               'fc1_voc12_c2',
               'fc1_voc12_c3')
     .add(name='fc1_voc12'))

def topredict(self, raw_output, origin_shape):
    raw_output = tf.image.resize_bilinear(raw_output, origin_shape)
    raw_output = tf.argmax(raw_output, dimension=3)
    prediction = tf.expand_dims(raw_output, dim=3)
    return prediction

class DeepLabV3(NetWork):
def setup(self, is_training, num_classes):
inputs = self.inputs.popitem()[0]
assert type(inputs) == str

    (self.feed(inputs)
     .conv([1, 1], 512, [1, 1], biased=False, relu=False, name='fc_res6a_branch2a')
     .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='fc_bn6a_branch2a')
     .atrous_conv([3, 3], 512, 16, padding='SAME', biased=False, relu=False, name='fc_res6a_branch2b')
     .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='fc_bn6a_branch2b')
     .conv([1, 1], 2048, [1, 1], biased=False, relu=False, name='fc_res6a_branch2c')
     .batch_normalization(is_training=is_training, activation_fn=None, name='fc_bn6a_branch2c'))

    (self.feed(inputs,
               'fc_bn6a_branch2c')
     .add(name='fc_res6a')
     .relu(name='fc_res6a_relu')
     .conv([1, 1], 512, [1, 1], biased=False, relu=False, name='fc_res6b_branch2a')
     .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='fc_bn6b_branch2a')
     .atrous_conv([3, 3], 512, 32, padding='SAME', biased=False, relu=False, name='fc_res6b_branch2b')
     .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='fc_bn6b_branch2b')
     .conv([1, 1], 2048, [1, 1], biased=False, relu=False, name='fc_res6b_branch2c')
     .batch_normalization(is_training=is_training, activation_fn=None, name='fc_bn6b_branch2c'))

    (self.feed('fc_res6a_relu',
               'fc_bn6b_branch2c')
     .add(name='fc_res6b')
     .relu(name='fc_res6b_relu')
     .conv([1, 1], 512, [1, 1], biased=False, relu=False, name='fc_res6c_branch2a')
     .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='fc_bn6c_branch2a')
     .atrous_conv([3, 3], 512, 64, padding='SAME', biased=False, relu=False, name='fc_res6c_branch2b')
     .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='fc_bn6c_branch2b')
     .conv([1, 1], 2048, [1, 1], biased=False, relu=False, name='fc_res6c_branch2c')
     .batch_normalization(is_training=is_training, activation_fn=None, name='fc_bn6c_branch2c'))

    (self.feed('fc_res6b_relu',
               'fc_bn6c_branch2c')
     .add(name='fc_res6c')
     .relu(name='fc_res6c_relu')
     .conv([1, 1], 512, [1, 1], biased=False, relu=False, name='fc_res7a_branch2a')
     .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='fc_bn7a_branch2a')
     .atrous_conv([3, 3], 512, 32, padding='SAME', biased=False, relu=False, name='fc_res7a_branch2b')
     .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='fc_bn7a_branch2b')
     .conv([1, 1], 2048, [1, 1], biased=False, relu=False, name='fc_res7a_branch2c')
     .batch_normalization(is_training=is_training, activation_fn=None, name='fc_bn7a_branch2c'))

    (self.feed('fc_res6c_relu',
               'fc_bn7a_branch2c')
     .add(name='fc_res7a')
     .relu(name='fc_res7a_relu')
     .conv([1, 1], 512, [1, 1], biased=False, relu=False, name='fc_res7b_branch2a')
     .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='fc_bn7b_branch2a')
     .atrous_conv([3, 3], 512, 64, padding='SAME', biased=False, relu=False, name='fc_res7b_branch2b')
     .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='fc_bn7b_branch2b')
     .conv([1, 1], 2048, [1, 1], biased=False, relu=False, name='fc_res7b_branch2c')
     .batch_normalization(is_training=is_training, activation_fn=None, name='fc_bn7b_branch2c'))

    (self.feed('fc_res7a_relu',
               'fc_bn7b_branch2c')
     .add(name='fc_res7b')
     .relu(name='fc_res7b_relu')
     .conv([1, 1], 512, [1, 1], biased=False, relu=False, name='fc_res7c_branch2a')
     .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='fc_bn7c_branch2a')
     .atrous_conv([3, 3], 512, 128, padding='SAME', biased=False, relu=False, name='fc_res7c_branch2b')
     .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='fc_bn7c_branch2b')
     .conv([1, 1], 2048, [1, 1], biased=False, relu=False, name='fc_res7c_branch2c')
     .batch_normalization(is_training=is_training, activation_fn=None, name='fc_bn7c_branch2c'))

    (self.feed('fc_res7b_relu',
               'fc_bn7c_branch2c')
     .add(name='fc_res7c')
     .relu(name='fc_res7c_relu')
     .conv([1, 1], 512, [1, 1], biased=False, relu=False, name='fc_res8a_branch2a')
     .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='fc_bn8a_branch2a')
     .atrous_conv([3, 3], 512, 64, padding='SAME', biased=False, relu=False, name='fc_res8a_branch2b')
     .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='fc_bn8a_branch2b')
     .conv([1, 1], 2048, [1, 1], biased=False, relu=False, name='fc_res8a_branch2c')
     .batch_normalization(is_training=is_training, activation_fn=None, name='fc_bn8a_branch2c'))

    (self.feed('fc_res7c_relu',
               'fc_bn8a_branch2c')
     .add(name='fc_res8a')
     .relu(name='fc_res8a_relu')
     .conv([1, 1], 512, [1, 1], biased=False, relu=False, name='fc_res8b_branch2a')
     .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='fc_bn8b_branch2a')
     .atrous_conv([3, 3], 512, 128, padding='SAME', biased=False, relu=False, name='fc_res8b_branch2b')
     .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='fc_bn8b_branch2b')
     .conv([1, 1], 2048, [1, 1], biased=False, relu=False, name='fc_res8b_branch2c')
     .batch_normalization(is_training=is_training, activation_fn=None, name='fc_bn8b_branch2c'))

    (self.feed('fc_res8a_relu',
               'fc_bn8b_branch2c')
     .add(name='fc_res8b')
     .relu(name='fc_res8b_relu')
     .conv([1, 1], 512, [1, 1], biased=False, relu=False, name='fc_res8c_branch2a')
     .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='fc_bn8c_branch2a')
     .atrous_conv([3, 3], 512, 256, padding='SAME', biased=False, relu=False, name='fc_res8c_branch2b')
     .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='fc_bn8c_branch2b')
     .conv([1, 1], 2048, [1, 1], biased=False, relu=False, name='fc_res8c_branch2c')
     .batch_normalization(is_training=is_training, activation_fn=None, name='fc_bn8c_branch2c'))

    # gzy
    (self.feed('fc_res8b_relu',
               'fc_bn8c_branch2c') 
     .add(name='fc_res8c')
     .relu(name='fc_res8c_relu'))
     

    (self.feed('fc_res8b_relu') # cichushaoxiugai origina: fc_res8c_relu
     .atrous_conv([3, 3], 256, 24, padding='SAME', relu=False, name='fc1_voc12_c0')
     .batch_normalization(is_training=is_training, activation_fn=None, name='fc1_voc12_bn0'))

    (self.feed('fc_res8b_relu')
     .atrous_conv([3, 3], 256, 48, padding='SAME', relu=False, name='fc1_voc12_c1')
     .batch_normalization(is_training=is_training, activation_fn=None, name='fc1_voc12_bn1'))

    (self.feed('fc_res8b_relu')
     .atrous_conv([3, 3], 256, 72, padding='SAME', relu=False, name='fc1_voc12_c2')
     .batch_normalization(is_training=is_training, activation_fn=None, name='fc1_voc12_bn2'))

    (self.feed('fc_res8b_relu')
     .conv([1, 1], 256, [1, 1], relu=False, name='fc1_voc12_c3')
     .batch_normalization(is_training=is_training, activation_fn=None, name='fc1_voc12_bn3'))

    layer = self.get_appointed_layer('fc_res8c_relu')
    new_shape = tf.shape(layer)[1:3]
    (self.feed('fc_res8b_relu')
     .global_average_pooling(name='fc1_voc12_mp0')
     .conv([1, 1], 256, [1, 1], relu=False, name='fc1_voc12_c4')
     .batch_normalization(is_training=is_training, activation_fn=None, name='fc1_voc12_bn4')
     .resize(new_shape, name='fc1_voc12_bu0'))

    (self.feed('fc1_voc12_bn0',
               'fc1_voc12_bn1',
               'fc1_voc12_bn2',
               'fc1_voc12_bn3',
               'fc1_voc12_bu0')
     .concat(axis=3, name='fc1_voc12'))

    (self.feed('fc1_voc12')
     .conv([1, 1], 256, [1, 1], relu=False, name='fc2_voc12_c0')
     .batch_normalization(is_training=is_training, activation_fn=None, name='fc2_voc12_bn0')
     .conv([1, 1], num_classes, [1, 1], relu=False, name='fc2_voc12_c1'))

def topredict(self, raw_output, origin_shape):
    raw_output = tf.image.resize_bilinear(raw_output, origin_shape)
    raw_output = tf.argmax(raw_output, dimension=3)
    prediction = tf.expand_dims(raw_output, dim=3)
    return prediction