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model.py
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model.py
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from network import Network
import tensorflow as tf
class ICNet(Network):
def setup(self, is_training, num_classes, evalutaion):
(self.feed('data')
.interp(s_factor=0.5, name='data_sub2')
.conv(3, 3, 32, 2, 2, biased=True, padding='SAME', relu=True, name='conv1_1_3x3_s2')
.conv(3, 3, 32, 1, 1, biased=True, padding='SAME', relu=True, name='conv1_2_3x3')
.conv(3, 3, 64, 1, 1, biased=True, padding='SAME', relu=True, name='conv1_3_3x3')
.zero_padding(paddings=1, name='padding0')
.max_pool(3, 3, 2, 2, name='pool1_3x3_s2')
.conv(1, 1, 128, 1, 1, biased=True, relu=False, name='conv2_1_1x1_proj'))
(self.feed('pool1_3x3_s2')
.conv(1, 1, 32, 1, 1, biased=True, relu=True, name='conv2_1_1x1_reduce')
.zero_padding(paddings=1, name='padding1')
.conv(3, 3, 32, 1, 1, biased=True, relu=True, name='conv2_1_3x3')
.conv(1, 1, 128, 1, 1, biased=True, relu=False, name='conv2_1_1x1_increase'))
(self.feed('conv2_1_1x1_proj',
'conv2_1_1x1_increase')
.add(name='conv2_1')
.relu(name='conv2_1/relu')
.conv(1, 1, 32, 1, 1, biased=True, relu=True, name='conv2_2_1x1_reduce')
.zero_padding(paddings=1, name='padding2')
.conv(3, 3, 32, 1, 1, biased=True, relu=True, name='conv2_2_3x3')
.conv(1, 1, 128, 1, 1, biased=True, relu=False, name='conv2_2_1x1_increase'))
(self.feed('conv2_1/relu',
'conv2_2_1x1_increase')
.add(name='conv2_2')
.relu(name='conv2_2/relu')
.conv(1, 1, 32, 1, 1, biased=True, relu=True, name='conv2_3_1x1_reduce')
.zero_padding(paddings=1, name='padding3')
.conv(3, 3, 32, 1, 1, biased=True, relu=True, name='conv2_3_3x3')
.conv(1, 1, 128, 1, 1, biased=True, relu=False, name='conv2_3_1x1_increase'))
(self.feed('conv2_2/relu',
'conv2_3_1x1_increase')
.add(name='conv2_3')
.relu(name='conv2_3/relu')
.conv(1, 1, 256, 2, 2, biased=True, relu=False, name='conv3_1_1x1_proj'))
(self.feed('conv2_3/relu')
.conv(1, 1, 64, 2, 2, biased=True, relu=True, name='conv3_1_1x1_reduce')
.zero_padding(paddings=1, name='padding4')
.conv(3, 3, 64, 1, 1, biased=True, relu=True, name='conv3_1_3x3')
.conv(1, 1, 256, 1, 1, biased=True, relu=False, name='conv3_1_1x1_increase'))
(self.feed('conv3_1_1x1_proj',
'conv3_1_1x1_increase')
.add(name='conv3_1')
.relu(name='conv3_1/relu')
.interp(s_factor=0.5, name='conv3_1_sub4')
.conv(1, 1, 64, 1, 1, biased=True, relu=True, name='conv3_2_1x1_reduce')
.zero_padding(paddings=1, name='padding5')
.conv(3, 3, 64, 1, 1, biased=True, relu=True, name='conv3_2_3x3')
.conv(1, 1, 256, 1, 1, biased=True, relu=False, name='conv3_2_1x1_increase'))
(self.feed('conv3_1_sub4',
'conv3_2_1x1_increase')
.add(name='conv3_2')
.relu(name='conv3_2/relu')
.conv(1, 1, 64, 1, 1, biased=True, relu=True, name='conv3_3_1x1_reduce')
.zero_padding(paddings=1, name='padding6')
.conv(3, 3, 64, 1, 1, biased=True, relu=True, name='conv3_3_3x3')
.conv(1, 1, 256, 1, 1, biased=True, relu=False, name='conv3_3_1x1_increase'))
(self.feed('conv3_2/relu',
'conv3_3_1x1_increase')
.add(name='conv3_3')
.relu(name='conv3_3/relu')
.conv(1, 1, 64, 1, 1, biased=True, relu=True, name='conv3_4_1x1_reduce')
.zero_padding(paddings=1, name='padding7')
.conv(3, 3, 64, 1, 1, biased=True, relu=True, name='conv3_4_3x3')
.conv(1, 1, 256, 1, 1, biased=True, relu=False, name='conv3_4_1x1_increase'))
(self.feed('conv3_3/relu',
'conv3_4_1x1_increase')
.add(name='conv3_4')
.relu(name='conv3_4/relu')
.conv(1, 1, 512, 1, 1, biased=True, relu=False, name='conv4_1_1x1_proj'))
(self.feed('conv3_4/relu')
.conv(1, 1, 128, 1, 1, biased=True, relu=True, name='conv4_1_1x1_reduce')
.zero_padding(paddings=2, name='padding8')
.atrous_conv(3, 3, 128, 2, biased=True, relu=True, name='conv4_1_3x3')
.conv(1, 1, 512, 1, 1, biased=True, relu=False, name='conv4_1_1x1_increase'))
(self.feed('conv4_1_1x1_proj',
'conv4_1_1x1_increase')
.add(name='conv4_1')
.relu(name='conv4_1/relu')
.conv(1, 1, 128, 1, 1, biased=True, relu=True, name='conv4_2_1x1_reduce')
.zero_padding(paddings=2, name='padding9')
.atrous_conv(3, 3, 128, 2, biased=True, relu=True, name='conv4_2_3x3')
.conv(1, 1, 512, 1, 1, biased=True, relu=False, name='conv4_2_1x1_increase'))
(self.feed('conv4_1/relu',
'conv4_2_1x1_increase')
.add(name='conv4_2')
.relu(name='conv4_2/relu')
.conv(1, 1, 128, 1, 1, biased=True, relu=True, name='conv4_3_1x1_reduce')
.zero_padding(paddings=2, name='padding10')
.atrous_conv(3, 3, 128, 2, biased=True, relu=True, name='conv4_3_3x3')
.conv(1, 1, 512, 1, 1, biased=True, relu=False, name='conv4_3_1x1_increase'))
(self.feed('conv4_2/relu',
'conv4_3_1x1_increase')
.add(name='conv4_3')
.relu(name='conv4_3/relu')
.conv(1, 1, 128, 1, 1, biased=True, relu=True, name='conv4_4_1x1_reduce')
.zero_padding(paddings=2, name='padding11')
.atrous_conv(3, 3, 128, 2, biased=True, relu=True, name='conv4_4_3x3')
.conv(1, 1, 512, 1, 1, biased=True, relu=False, name='conv4_4_1x1_increase'))
(self.feed('conv4_3/relu',
'conv4_4_1x1_increase')
.add(name='conv4_4')
.relu(name='conv4_4/relu')
.conv(1, 1, 128, 1, 1, biased=True, relu=True, name='conv4_5_1x1_reduce')
.zero_padding(paddings=2, name='padding12')
.atrous_conv(3, 3, 128, 2, biased=True, relu=True, name='conv4_5_3x3')
.conv(1, 1, 512, 1, 1, biased=True, relu=False, name='conv4_5_1x1_increase'))
(self.feed('conv4_4/relu',
'conv4_5_1x1_increase')
.add(name='conv4_5')
.relu(name='conv4_5/relu')
.conv(1, 1, 128, 1, 1, biased=True, relu=True, name='conv4_6_1x1_reduce')
.zero_padding(paddings=2, name='padding13')
.atrous_conv(3, 3, 128, 2, biased=True, relu=True, name='conv4_6_3x3')
.conv(1, 1, 512, 1, 1, biased=True, relu=False, name='conv4_6_1x1_increase'))
(self.feed('conv4_5/relu',
'conv4_6_1x1_increase')
.add(name='conv4_6')
.relu(name='conv4_6/relu')
.conv(1, 1, 1024, 1, 1, biased=True, relu=False, name='conv5_1_1x1_proj'))
(self.feed('conv4_6/relu')
.conv(1, 1, 256, 1, 1, biased=True, relu=True, name='conv5_1_1x1_reduce')
.zero_padding(paddings=4, name='padding14')
.atrous_conv(3, 3, 256, 4, biased=True, relu=True, name='conv5_1_3x3')
.conv(1, 1, 1024, 1, 1, biased=True, relu=False, name='conv5_1_1x1_increase'))
(self.feed('conv5_1_1x1_proj',
'conv5_1_1x1_increase')
.add(name='conv5_1')
.relu(name='conv5_1/relu')
.conv(1, 1, 256, 1, 1, biased=True, relu=True, name='conv5_2_1x1_reduce')
.zero_padding(paddings=4, name='padding15')
.atrous_conv(3, 3, 256, 4, biased=True, relu=True, name='conv5_2_3x3')
.conv(1, 1, 1024, 1, 1, biased=True, relu=False, name='conv5_2_1x1_increase'))
(self.feed('conv5_1/relu',
'conv5_2_1x1_increase')
.add(name='conv5_2')
.relu(name='conv5_2/relu')
.conv(1, 1, 256, 1, 1, biased=True, relu=True, name='conv5_3_1x1_reduce')
.zero_padding(paddings=4, name='padding16')
.atrous_conv(3, 3, 256, 4, biased=True, relu=True, name='conv5_3_3x3')
.conv(1, 1, 1024, 1, 1, biased=True, relu=False, name='conv5_3_1x1_increase'))
(self.feed('conv5_2/relu',
'conv5_3_1x1_increase')
.add(name='conv5_3')
.relu(name='conv5_3/relu'))
shape = self.layers['conv5_3/relu'].get_shape().as_list()[1:3]
h, w = shape
if self.evaluation: # Change to same configuration as original prototxt
(self.feed('conv5_3/relu')
.avg_pool(33, 65, 33, 65, name='conv5_3_pool1')
.resize_bilinear(shape, name='conv5_3_pool1_interp'))
(self.feed('conv5_3/relu')
.avg_pool(17, 33, 16, 32, name='conv5_3_pool2')
.resize_bilinear(shape, name='conv5_3_pool2_interp'))
(self.feed('conv5_3/relu')
.avg_pool(13, 25, 10, 20, name='conv5_3_pool3')
.resize_bilinear(shape, name='conv5_3_pool3_interp'))
(self.feed('conv5_3/relu')
.avg_pool(8, 15, 5, 10, name='conv5_3_pool6')
.resize_bilinear(shape, name='conv5_3_pool6_interp'))
else: # In inference phase, we support different size of images as input.
(self.feed('conv5_3/relu')
.avg_pool(h, w, h, w, name='conv5_3_pool1')
.resize_bilinear(shape, name='conv5_3_pool1_interp'))
(self.feed('conv5_3/relu')
.avg_pool(h/2, w/2, h/2, w/2, name='conv5_3_pool2')
.resize_bilinear(shape, name='conv5_3_pool2_interp'))
(self.feed('conv5_3/relu')
.avg_pool(h/3, w/3, h/3, w/3, name='conv5_3_pool3')
.resize_bilinear(shape, name='conv5_3_pool3_interp'))
(self.feed('conv5_3/relu')
.avg_pool(h/6, w/6, h/6, w/6, name='conv5_3_pool6')
.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')
.add(name='conv5_3_sum')
.conv(1, 1, 256, 1, 1, biased=True, relu=True, name='conv5_4_k1')
.interp(z_factor=2.0, name='conv5_4_interp')
.zero_padding(paddings=2, name='padding17')
.atrous_conv(3, 3, 128, 2, biased=True, relu=False, name='conv_sub4'))
(self.feed('conv3_1/relu')
.conv(1, 1, 128, 1, 1, biased=True, relu=False, name='conv3_1_sub2_proj'))
(self.feed('conv_sub4',
'conv3_1_sub2_proj')
.add(name='sub24_sum')
.relu(name='sub24_sum/relu')
.interp(z_factor=2.0, name='sub24_sum_interp')
.zero_padding(paddings=2, name='padding18')
.atrous_conv(3, 3, 128, 2, biased=True, relu=False, name='conv_sub2'))
(self.feed('data')
.conv(3, 3, 32, 2, 2, biased=True, padding='SAME', relu=True, name='conv1_sub1')
.conv(3, 3, 32, 2, 2, biased=True, padding='SAME', relu=True, name='conv2_sub1')
.conv(3, 3, 64, 2, 2, biased=True, padding='SAME', relu=True, name='conv3_sub1')
.conv(1, 1, 128, 1, 1, biased=True, relu=False, name='conv3_sub1_proj'))
(self.feed('conv_sub2',
'conv3_sub1_proj')
.add(name='sub12_sum')
.relu(name='sub12_sum/relu')
.interp(z_factor=2.0, name='sub12_sum_interp')
.conv(1, 1, num_classes, 1, 1, biased=True, relu=False, name='conv6_cls'))
class ICNet_BN(Network):
def setup(self, is_training, num_classes, evaluation):
(self.feed('data')
.interp(s_factor=0.5, name='data_sub2')
.conv(3, 3, 32, 2, 2, biased=False, padding='SAME', relu=False, name='conv1_1_3x3_s2')
.batch_normalization(relu=True, name='conv1_1_3x3_s2_bn')
.conv(3, 3, 32, 1, 1, biased=False, padding='SAME', relu=False, name='conv1_2_3x3')
.batch_normalization(relu=True, name='conv1_2_3x3_bn')
.conv(3, 3, 64, 1, 1, biased=False, padding='SAME', relu=False, name='conv1_3_3x3')
.batch_normalization(relu=True, name='conv1_3_3x3_bn')
.zero_padding(paddings=1, name='padding0')
.max_pool(3, 3, 2, 2, name='pool1_3x3_s2')
.conv(1, 1, 128, 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, 32, 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, 32, 1, 1, biased=False, relu=False, name='conv2_1_3x3')
.batch_normalization(relu=True, name='conv2_1_3x3_bn')
.conv(1, 1, 128, 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, 32, 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, 32, 1, 1, biased=False, relu=False, name='conv2_2_3x3')
.batch_normalization(relu=True, name='conv2_2_3x3_bn')
.conv(1, 1, 128, 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, 32, 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, 32, 1, 1, biased=False, relu=False, name='conv2_3_3x3')
.batch_normalization(relu=True, name='conv2_3_3x3_bn')
.conv(1, 1, 128, 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, 256, 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, 64, 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, 64, 1, 1, biased=False, relu=False, name='conv3_1_3x3')
.batch_normalization(relu=True, name='conv3_1_3x3_bn')
.conv(1, 1, 256, 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')
.interp(s_factor=0.5, name='conv3_1_sub4')
.conv(1, 1, 64, 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, 64, 1, 1, biased=False, relu=False, name='conv3_2_3x3')
.batch_normalization(relu=True, name='conv3_2_3x3_bn')
.conv(1, 1, 256, 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_sub4',
'conv3_2_1x1_increase_bn')
.add(name='conv3_2')
.relu(name='conv3_2/relu')
.conv(1, 1, 64, 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, 64, 1, 1, biased=False, relu=False, name='conv3_3_3x3')
.batch_normalization(relu=True, name='conv3_3_3x3_bn')
.conv(1, 1, 256, 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, 64, 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, 64, 1, 1, biased=False, relu=False, name='conv3_4_3x3')
.batch_normalization(relu=True, name='conv3_4_3x3_bn')
.conv(1, 1, 256, 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, 512, 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, 128, 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, 128, 2, biased=False, relu=False, name='conv4_1_3x3')
.batch_normalization(relu=True, name='conv4_1_3x3_bn')
.conv(1, 1, 512, 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, 128, 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, 128, 2, biased=False, relu=False, name='conv4_2_3x3')
.batch_normalization(relu=True, name='conv4_2_3x3_bn')
.conv(1, 1, 512, 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, 128, 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, 128, 2, biased=False, relu=False, name='conv4_3_3x3')
.batch_normalization(relu=True, name='conv4_3_3x3_bn')
.conv(1, 1, 512, 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, 128, 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, 128, 2, biased=False, relu=False, name='conv4_4_3x3')
.batch_normalization(relu=True, name='conv4_4_3x3_bn')
.conv(1, 1, 512, 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, 128, 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, 128, 2, biased=False, relu=False, name='conv4_5_3x3')
.batch_normalization(relu=True, name='conv4_5_3x3_bn')
.conv(1, 1, 512, 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, 128, 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, 128, 2, biased=False, relu=False, name='conv4_6_3x3')
.batch_normalization(relu=True, name='conv4_6_3x3_bn')
.conv(1, 1, 512, 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, 1024, 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, 256, 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='padding14')
.atrous_conv(3, 3, 256, 4, biased=False, relu=False, name='conv5_1_3x3')
.batch_normalization(relu=True, name='conv5_1_3x3_bn')
.conv(1, 1, 1024, 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, 256, 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='padding15')
.atrous_conv(3, 3, 256, 4, biased=False, relu=False, name='conv5_2_3x3')
.batch_normalization(relu=True, name='conv5_2_3x3_bn')
.conv(1, 1, 1024, 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, 256, 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='padding16')
.atrous_conv(3, 3, 256, 4, biased=False, relu=False, name='conv5_3_3x3')
.batch_normalization(relu=True, name='conv5_3_3x3_bn')
.conv(1, 1, 1024, 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'))
shape = self.layers['conv5_3/relu'].get_shape().as_list()[1:3]
h, w = shape
(self.feed('conv5_3/relu')
.avg_pool(h, w, h, w, name='conv5_3_pool1')
.resize_bilinear(shape, name='conv5_3_pool1_interp'))
(self.feed('conv5_3/relu')
.avg_pool(h/2, w/2, h/2, w/2, name='conv5_3_pool2')
.resize_bilinear(shape, name='conv5_3_pool2_interp'))
(self.feed('conv5_3/relu')
.avg_pool(h/3, w/3, h/3, w/3, name='conv5_3_pool3')
.resize_bilinear(shape, name='conv5_3_pool3_interp'))
(self.feed('conv5_3/relu')
.avg_pool(h/4, w/4, h/4, w/4, name='conv5_3_pool6')
.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')
.add(name='conv5_3_sum')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='conv5_4_k1')
.batch_normalization(relu=True, name='conv5_4_k1_bn')
.interp(z_factor=2.0, name='conv5_4_interp')
.zero_padding(paddings=2, name='padding17')
.atrous_conv(3, 3, 128, 2, biased=False, relu=False, name='conv_sub4')
.batch_normalization(relu=False, name='conv_sub4_bn'))
(self.feed('conv3_1/relu')
.conv(1, 1, 128, 1, 1, biased=False, relu=False, name='conv3_1_sub2_proj')
.batch_normalization(relu=False, name='conv3_1_sub2_proj_bn'))
(self.feed('conv_sub4_bn',
'conv3_1_sub2_proj_bn')
.add(name='sub24_sum')
.relu(name='sub24_sum/relu')
.interp(z_factor=2.0, name='sub24_sum_interp')
.zero_padding(paddings=2, name='padding18')
.atrous_conv(3, 3, 128, 2, biased=False, relu=False, name='conv_sub2')
.batch_normalization(relu=False, name='conv_sub2_bn'))
(self.feed('data')
.conv(3, 3, 32, 2, 2, biased=False, padding='SAME', relu=False, name='conv1_sub1')
.batch_normalization(relu=True, name='conv1_sub1_bn')
.conv(3, 3, 32, 2, 2, biased=False, padding='SAME', relu=False, name='conv2_sub1')
.batch_normalization(relu=True, name='conv2_sub1_bn')
.conv(3, 3, 64, 2, 2, biased=False, padding='SAME', relu=False, name='conv3_sub1')
.batch_normalization(relu=True, name='conv3_sub1_bn')
.conv(1, 1, 128, 1, 1, biased=False, relu=False, name='conv3_sub1_proj')
.batch_normalization(relu=False, name='conv3_sub1_proj_bn'))
(self.feed('conv_sub2_bn',
'conv3_sub1_proj_bn')
.add(name='sub12_sum')
.relu(name='sub12_sum/relu')
.interp(z_factor=2.0, name='sub12_sum_interp')
.conv(1, 1, num_classes, 1, 1, biased=True, relu=False, name='conv6_cls'))
(self.feed('conv5_4_interp')
.conv(1, 1, num_classes, 1, 1, biased=True, relu=False, name='sub4_out'))
(self.feed('sub24_sum_interp')
.conv(1, 1, num_classes, 1, 1, biased=True, relu=False, name='sub24_out'))