def SemanticFeatureGenerationBlock(inputs, D_features, D_prime_features, O_features, bottleneck_factor=2, cardinality=32):
d_1 = ConvBlock(inputs, D_features, kernel_size=[3, 3])
pool_1 = slim.pool(d_1, [5, 5], stride=[1, 1], pooling_type='MAX')
d_prime_1 = ConvBlock(pool_1, D_prime_features, kernel_size=[3, 3])
d_2 = ConvBlock(pool_1, D_features, kernel_size=[3, 3])
pool_2 = slim.pool(d_2, [5, 5], stride=[1, 1], pooling_type='MAX')
d_prime_2 = ConvBlock(pool_2, D_prime_features, kernel_size=[3, 3])
d_3 = ConvBlock(pool_2, D_features, kernel_size=[3, 3])
pool_3 = slim.pool(d_3, [5, 5], stride=[1, 1], pooling_type='MAX')
d_prime_3 = ConvBlock(pool_3, D_prime_features, kernel_size=[3, 3])
d_4 = ConvBlock(pool_3, D_features, kernel_size=[3, 3])
pool_4 = slim.pool(d_4, [5, 5], stride=[1, 1], pooling_type='MAX')
d_prime_4 = ConvBlock(pool_4, D_prime_features, kernel_size=[3, 3])
net = tf.concat([d_prime_1, d_prime_2, d_prime_3, d_prime_4], axis=-1)
net = ConvBlock(net, n_filters=D_features, kernel_size=[3, 3])
net = ResNeXtBlock(net, n_filters_out=D_features, bottleneck_factor=bottleneck_factor)
net = ResNeXtBlock(net, n_filters_out=D_features, bottleneck_factor=bottleneck_factor)
net = ResNeXtBlock(net, n_filters_out=D_features, bottleneck_factor=bottleneck_factor)
net = ResNeXtBlock(net, n_filters_out=D_features, bottleneck_factor=bottleneck_factor)
net = ConvBlock(net, O_features, kernel_size=[3, 3])
return net
def FullResolutionResidualUnit(pool_stream, res_stream, n_filters_3, n_filters_1, pool_scale):
"""
A full resolution residual unit
Arguments:
pool_stream: The inputs from the pooling stream
res_stream: The inputs from the residual stream
n_filters_3: Number of output feature maps for each 3x3 conv
n_filters_1: Number of output feature maps for each 1x1 conv
pool_scale: scale of the pooling layer i.e window size and stride
Returns:
Output of full resolution residual block
"""
G = tf.concat([pool_stream, slim.pool(res_stream, [pool_scale, pool_scale], stride=[pool_scale, pool_scale], pooling_type='MAX')], axis=-1)
net = slim.conv2d(G, n_filters_3, kernel_size=3, activation_fn=None)
net = slim.batch_norm(net, fused=True)
net = tf.nn.relu(net)
net = slim.conv2d(net, n_filters_3, kernel_size=3, activation_fn=None)
net = slim.batch_norm(net, fused=True)
pool_stream_out = tf.nn.relu(net)
net = slim.conv2d(pool_stream_out, n_filters_1, kernel_size=1, activation_fn=None)
net = Upsampling(net, scale=pool_scale)
res_stream_out = tf.add(res_stream, net)
return pool_stream_out, res_stream_out
def TransitionDown(inputs, n_filters, dropout_p=0.2, scope=None):
"""
Transition Down (TD) for FC-DenseNet
Apply 1x1 BN + ReLU + conv then 2x2 max pooling
"""
with tf.name_scope(scope) as sc:
l = preact_conv(inputs,
n_filters,
kernel_size=[1, 1],
dropout_p=dropout_p)
l = slim.pool(l, [2, 2], stride=[2, 2], pooling_type='MAX')
return l
def InterpBlock(net, level, feature_map_shape, pooling_type):
# Compute the kernel and stride sizes according to how large the final feature map will be
# When the kernel size and strides are equal, then we can compute the final feature map size
# by simply dividing the current size by the kernel or stride size
# The final feature map sizes are 1x1, 2x2, 3x3, and 6x6. We round to the closest integer
kernel_size = [int(np.round(float(feature_map_shape[0]) / float(level))), int(np.round(float(feature_map_shape[1]) / float(level)))]
stride_size = kernel_size
net = slim.pool(net, kernel_size, stride=stride_size, pooling_type='MAX')
net = slim.conv2d(net, 512, [1, 1], activation_fn=None)
net = slim.batch_norm(net, fused=True)
net = tf.nn.relu(net)
net = Upsampling_by_shape(net, feature_map_shape)
return net
def build_encoder_decoder(inputs,
num_classes,
preset_model="Encoder-Decoder",
dropout_p=0.5,
scope=None):
"""
Builds the Encoder-Decoder model. Inspired by SegNet with some modifications
Optionally includes skip connections
Arguments:
inputs: the input tensor
n_classes: number of classes
dropout_p: dropout rate applied after each convolution (0. for not using)
Returns:
Encoder-Decoder model
"""
if preset_model == "Encoder-Decoder":
has_skip = False
elif preset_model == "Encoder-Decoder-Skip":
has_skip = True
else:
raise ValueError(
"Unsupported Encoder-Decoder model '%s'. This function only supports Encoder-Decoder and Encoder-Decoder-Skip"
% (preset_model))
#####################
# Downsampling path #
#####################
net = conv_block(inputs, 64)
net = conv_block(net, 64)
net = slim.pool(net, [2, 2], stride=[2, 2], pooling_type='MAX')
skip_1 = net
net = conv_block(net, 128)
net = conv_block(net, 128)
net = slim.pool(net, [2, 2], stride=[2, 2], pooling_type='MAX')
skip_2 = net
net = conv_block(net, 256)
net = conv_block(net, 256)
net = conv_block(net, 256)
net = slim.pool(net, [2, 2], stride=[2, 2], pooling_type='MAX')
skip_3 = net
net = conv_block(net, 512)
net = conv_block(net, 512)
net = conv_block(net, 512)
net = slim.pool(net, [2, 2], stride=[2, 2], pooling_type='MAX')
skip_4 = net
net = conv_block(net, 512)
net = conv_block(net, 512)
net = conv_block(net, 512)
net = slim.pool(net, [2, 2], stride=[2, 2], pooling_type='MAX')
#####################
# Upsampling path #
#####################
net = conv_transpose_block(net, 512)
net = conv_block(net, 512)
net = conv_block(net, 512)
net = conv_block(net, 512)
if has_skip:
net = tf.add(net, skip_4)
net = conv_transpose_block(net, 512)
net = conv_block(net, 512)
net = conv_block(net, 512)
net = conv_block(net, 256)
if has_skip:
net = tf.add(net, skip_3)
net = conv_transpose_block(net, 256)
net = conv_block(net, 256)
net = conv_block(net, 256)
net = conv_block(net, 128)
if has_skip:
net = tf.add(net, skip_2)
net = conv_transpose_block(net, 128)
net = conv_block(net, 128)
net = conv_block(net, 64)
if has_skip:
net = tf.add(net, skip_1)
net = conv_transpose_block(net, 64)
net = conv_block(net, 64)
net = conv_block(net, 64)
#####################
# Softmax #
#####################
net = slim.conv2d(net,
num_classes, [1, 1],
activation_fn=None,
scope='logits')
return net
def build_frrn(inputs, num_classes, preset_model='FRRN-A'):
"""
Builds the Full Resolution Residual Network model.
Arguments:
inputs: The input tensor
preset_model: Which model you want to use. Select FRRN-A or FRRN-B
num_classes: Number of classes
Returns:
FRRN model
"""
if preset_model == 'FRRN-A':
#####################
# Initial Stage
#####################
net = slim.conv2d(inputs, 48, kernel_size=5, activation_fn=None)
net = slim.batch_norm(net, fused=True)
net = tf.nn.relu(net)
net = ResidualUnit(net, n_filters=48, filter_size=3)
net = ResidualUnit(net, n_filters=48, filter_size=3)
net = ResidualUnit(net, n_filters=48, filter_size=3)
#####################
# Downsampling Path
#####################
pool_stream = slim.pool(net, [2, 2], stride=[2, 2], pooling_type='MAX')
res_stream = slim.conv2d(net, 32, kernel_size=1, activation_fn=None)
pool_stream, res_stream = FullResolutionResidualUnit(pool_stream=pool_stream, res_stream=res_stream, n_filters_3=96, n_filters_1=32, pool_scale=2)
pool_stream, res_stream = FullResolutionResidualUnit(pool_stream=pool_stream, res_stream=res_stream, n_filters_3=96, n_filters_1=32, pool_scale=2)
pool_stream, res_stream = FullResolutionResidualUnit(pool_stream=pool_stream, res_stream=res_stream, n_filters_3=96, n_filters_1=32, pool_scale=2)
pool_stream = slim.pool(pool_stream, [2, 2], stride=[2, 2], pooling_type='MAX')
pool_stream, res_stream = FullResolutionResidualUnit(pool_stream=pool_stream, res_stream=res_stream, n_filters_3=192, n_filters_1=32, pool_scale=4)
pool_stream, res_stream = FullResolutionResidualUnit(pool_stream=pool_stream, res_stream=res_stream, n_filters_3=192, n_filters_1=32, pool_scale=4)
pool_stream, res_stream = FullResolutionResidualUnit(pool_stream=pool_stream, res_stream=res_stream, n_filters_3=192, n_filters_1=32, pool_scale=4)
pool_stream, res_stream = FullResolutionResidualUnit(pool_stream=pool_stream, res_stream=res_stream, n_filters_3=192, n_filters_1=32, pool_scale=4)
pool_stream = slim.pool(pool_stream, [2, 2], stride=[2, 2], pooling_type='MAX')
pool_stream, res_stream = FullResolutionResidualUnit(pool_stream=pool_stream, res_stream=res_stream, n_filters_3=384, n_filters_1=32, pool_scale=8)
pool_stream, res_stream = FullResolutionResidualUnit(pool_stream=pool_stream, res_stream=res_stream, n_filters_3=384, n_filters_1=32, pool_scale=8)
pool_stream = slim.pool(pool_stream, [2, 2], stride=[2, 2], pooling_type='MAX')
pool_stream, res_stream = FullResolutionResidualUnit(pool_stream=pool_stream, res_stream=res_stream, n_filters_3=384, n_filters_1=32, pool_scale=16)
pool_stream, res_stream = FullResolutionResidualUnit(pool_stream=pool_stream, res_stream=res_stream, n_filters_3=384, n_filters_1=32, pool_scale=16)
#####################
# Upsampling Path
#####################
pool_stream = Unpooling(pool_stream, 2)
pool_stream, res_stream = FullResolutionResidualUnit(pool_stream=pool_stream, res_stream=res_stream, n_filters_3=192, n_filters_1=32, pool_scale=8)
pool_stream, res_stream = FullResolutionResidualUnit(pool_stream=pool_stream, res_stream=res_stream, n_filters_3=192, n_filters_1=32, pool_scale=8)
pool_stream = Unpooling(pool_stream, 2)
pool_stream, res_stream = FullResolutionResidualUnit(pool_stream=pool_stream, res_stream=res_stream, n_filters_3=192, n_filters_1=32, pool_scale=4)
pool_stream, res_stream = FullResolutionResidualUnit(pool_stream=pool_stream, res_stream=res_stream, n_filters_3=192, n_filters_1=32, pool_scale=4)
pool_stream = Unpooling(pool_stream, 2)
pool_stream, res_stream = FullResolutionResidualUnit(pool_stream=pool_stream, res_stream=res_stream, n_filters_3=96, n_filters_1=32, pool_scale=2)
pool_stream, res_stream = FullResolutionResidualUnit(pool_stream=pool_stream, res_stream=res_stream, n_filters_3=96, n_filters_1=32, pool_scale=2)
pool_stream = Unpooling(pool_stream, 2)
#####################
# Final Stage
#####################
net = tf.concat([pool_stream, res_stream], axis=-1)
net = ResidualUnit(net, n_filters=48, filter_size=3)
net = ResidualUnit(net, n_filters=48, filter_size=3)
net = ResidualUnit(net, n_filters=48, filter_size=3)
net = slim.conv2d(net, num_classes, [1, 1], activation_fn=None, scope='logits')
return net
elif preset_model == 'FRRN-B':
#####################
# Initial Stage
#####################
net = slim.conv2d(inputs, 48, kernel_size=5, activation_fn=None)
net = slim.batch_norm(net, fused=True)
net = tf.nn.relu(net)
net = ResidualUnit(net, n_filters=48, filter_size=3)
net = ResidualUnit(net, n_filters=48, filter_size=3)
net = ResidualUnit(net, n_filters=48, filter_size=3)
#####################
# Downsampling Path
#####################
pool_stream = slim.pool(net, [2, 2], stride=[2, 2], pooling_type='MAX')
res_stream = slim.conv2d(net, 32, kernel_size=1, activation_fn=None)
pool_stream, res_stream = FullResolutionResidualUnit(pool_stream=pool_stream, res_stream=res_stream, n_filters_3=96, n_filters_1=32, pool_scale=2)
pool_stream, res_stream = FullResolutionResidualUnit(pool_stream=pool_stream, res_stream=res_stream, n_filters_3=96, n_filters_1=32, pool_scale=2)
pool_stream, res_stream = FullResolutionResidualUnit(pool_stream=pool_stream, res_stream=res_stream, n_filters_3=96, n_filters_1=32, pool_scale=2)
pool_stream = slim.pool(pool_stream, [2, 2], stride=[2, 2], pooling_type='MAX')
pool_stream, res_stream = FullResolutionResidualUnit(pool_stream=pool_stream, res_stream=res_stream, n_filters_3=192, n_filters_1=32, pool_scale=4)
pool_stream, res_stream = FullResolutionResidualUnit(pool_stream=pool_stream, res_stream=res_stream, n_filters_3=192, n_filters_1=32, pool_scale=4)
pool_stream, res_stream = FullResolutionResidualUnit(pool_stream=pool_stream, res_stream=res_stream, n_filters_3=192, n_filters_1=32, pool_scale=4)
pool_stream, res_stream = FullResolutionResidualUnit(pool_stream=pool_stream, res_stream=res_stream, n_filters_3=192, n_filters_1=32, pool_scale=4)
pool_stream = slim.pool(pool_stream, [2, 2], stride=[2, 2], pooling_type='MAX')
pool_stream, res_stream = FullResolutionResidualUnit(pool_stream=pool_stream, res_stream=res_stream, n_filters_3=384, n_filters_1=32, pool_scale=8)
pool_stream, res_stream = FullResolutionResidualUnit(pool_stream=pool_stream, res_stream=res_stream, n_filters_3=384, n_filters_1=32, pool_scale=8)
pool_stream = slim.pool(pool_stream, [2, 2], stride=[2, 2], pooling_type='MAX')
pool_stream, res_stream = FullResolutionResidualUnit(pool_stream=pool_stream, res_stream=res_stream, n_filters_3=384, n_filters_1=32, pool_scale=16)
pool_stream, res_stream = FullResolutionResidualUnit(pool_stream=pool_stream, res_stream=res_stream, n_filters_3=384, n_filters_1=32, pool_scale=16)
pool_stream = slim.pool(pool_stream, [2, 2], stride=[2, 2], pooling_type='MAX')
pool_stream, res_stream = FullResolutionResidualUnit(pool_stream=pool_stream, res_stream=res_stream, n_filters_3=384, n_filters_1=32, pool_scale=32)
pool_stream, res_stream = FullResolutionResidualUnit(pool_stream=pool_stream, res_stream=res_stream, n_filters_3=384, n_filters_1=32, pool_scale=32)
#####################
# Upsampling Path
#####################
pool_stream = Unpooling(pool_stream, 2)
pool_stream, res_stream = FullResolutionResidualUnit(pool_stream=pool_stream, res_stream=res_stream, n_filters_3=192, n_filters_1=32, pool_scale=17)
pool_stream, res_stream = FullResolutionResidualUnit(pool_stream=pool_stream, res_stream=res_stream, n_filters_3=192, n_filters_1=32, pool_scale=16)
pool_stream = Unpooling(pool_stream, 2)
pool_stream, res_stream = FullResolutionResidualUnit(pool_stream=pool_stream, res_stream=res_stream, n_filters_3=192, n_filters_1=32, pool_scale=8)
pool_stream, res_stream = FullResolutionResidualUnit(pool_stream=pool_stream, res_stream=res_stream, n_filters_3=192, n_filters_1=32, pool_scale=8)
pool_stream = Unpooling(pool_stream, 2)
pool_stream, res_stream = FullResolutionResidualUnit(pool_stream=pool_stream, res_stream=res_stream, n_filters_3=192, n_filters_1=32, pool_scale=4)
pool_stream, res_stream = FullResolutionResidualUnit(pool_stream=pool_stream, res_stream=res_stream, n_filters_3=192, n_filters_1=32, pool_scale=4)
pool_stream = Unpooling(pool_stream, 2)
pool_stream, res_stream = FullResolutionResidualUnit(pool_stream=pool_stream, res_stream=res_stream, n_filters_3=96, n_filters_1=32, pool_scale=2)
pool_stream, res_stream = FullResolutionResidualUnit(pool_stream=pool_stream, res_stream=res_stream, n_filters_3=96, n_filters_1=32, pool_scale=2)
pool_stream = Unpooling(pool_stream, 2)
#####################
# Final Stage
#####################
net = tf.concat([pool_stream, res_stream], axis=-1)
net = ResidualUnit(net, n_filters=48, filter_size=3)
net = ResidualUnit(net, n_filters=48, filter_size=3)
net = ResidualUnit(net, n_filters=48, filter_size=3)
net = slim.conv2d(net, num_classes, [1, 1], activation_fn=None, scope='logits')
return net
else:
raise ValueError("Unsupported FRRN model '%s'. This function only supports FRRN-A and FRRN-B" % (preset_model))
def build_encoder_decoder_skip(inputs, num_classes, dropout_p=0.5, scope=None):
"""
Builds the Encoder-Decoder-Skip model. Inspired by SegNet with some modifications
Includes skip connections
Arguments:
inputs: the input tensor
n_classes: number of classes
dropout_p: dropout rate applied after each convolution (0. for not using)
Returns:
Encoder-Decoder model
"""
#####################
# Downsampling path #
#####################
net = conv_block(inputs, 64)
net = conv_block(net, 64)
net = slim.pool(net, [2, 2], stride=[2, 2], pooling_type='MAX')
skip_1 = net
net = conv_block(net, 128)
net = conv_block(net, 128)
net = slim.pool(net, [2, 2], stride=[2, 2], pooling_type='MAX')
skip_2 = net
net = conv_block(net, 256)
net = conv_block(net, 256)
net = conv_block(net, 256)
net = slim.pool(net, [2, 2], stride=[2, 2], pooling_type='MAX')
skip_3 = net
net = conv_block(net, 512)
net = conv_block(net, 512)
net = conv_block(net, 512)
net = slim.pool(net, [2, 2], stride=[2, 2], pooling_type='MAX')
skip_4 = net
net = conv_block(net, 512)
net = conv_block(net, 512)
net = conv_block(net, 512)
net = slim.pool(net, [2, 2], stride=[2, 2], pooling_type='MAX')
#####################
# Upsampling path #
#####################
net = conv_transpose_block(net, 512)
net = conv_block(net, 512)
net = conv_block(net, 512)
net = conv_block(net, 512)
net = tf.add(net, skip_4)
net = conv_transpose_block(net, 512)
net = conv_block(net, 512)
net = conv_block(net, 512)
net = conv_block(net, 256)
net = tf.add(net, skip_3)
net = conv_transpose_block(net, 256)
net = conv_block(net, 256)
net = conv_block(net, 256)
net = conv_block(net, 128)
net = tf.add(net, skip_2)
net = conv_transpose_block(net, 128)
net = conv_block(net, 128)
net = conv_block(net, 64)
net = tf.add(net, skip_1)
net = conv_transpose_block(net, 64)
net = conv_block(net, 64)
net = conv_block(net, 64)
#####################
# Softmax #
#####################
net = slim.conv2d(net, num_classes, [1, 1], scope='logits')
return net
def build_mobile_unet(inputs, preset_model, num_classes):
has_skip = False
if preset_model == "MobileUNet":
has_skip = False
elif preset_model == "MobileUNet-Skip":
has_skip = True
else:
raise ValueError("Unsupported MobileUNet model '%s'. This function only supports MobileUNet and MobileUNet-Skip" % (preset_model))
#####################
# Downsampling path #
#####################
net = ConvBlock(inputs, 64)
net = DepthwiseSeparableConvBlock(net, 64)
net = slim.pool(net, [2, 2], stride=[2, 2], pooling_type='MAX')
skip_1 = net
net = DepthwiseSeparableConvBlock(net, 128)
net = DepthwiseSeparableConvBlock(net, 128)
net = slim.pool(net, [2, 2], stride=[2, 2], pooling_type='MAX')
skip_2 = net
net = DepthwiseSeparableConvBlock(net, 256)
net = DepthwiseSeparableConvBlock(net, 256)
net = DepthwiseSeparableConvBlock(net, 256)
net = slim.pool(net, [2, 2], stride=[2, 2], pooling_type='MAX')
skip_3 = net
net = DepthwiseSeparableConvBlock(net, 512)
net = DepthwiseSeparableConvBlock(net, 512)
net = DepthwiseSeparableConvBlock(net, 512)
net = slim.pool(net, [2, 2], stride=[2, 2], pooling_type='MAX')
skip_4 = net
net = DepthwiseSeparableConvBlock(net, 512)
net = DepthwiseSeparableConvBlock(net, 512)
net = DepthwiseSeparableConvBlock(net, 512)
net = slim.pool(net, [2, 2], stride=[2, 2], pooling_type='MAX')
#####################
# Upsampling path #
#####################
net = conv_transpose_block(net, 512)
net = DepthwiseSeparableConvBlock(net, 512)
net = DepthwiseSeparableConvBlock(net, 512)
net = DepthwiseSeparableConvBlock(net, 512)
if has_skip:
net = tf.add(net, skip_4)
net = conv_transpose_block(net, 512)
net = DepthwiseSeparableConvBlock(net, 512)
net = DepthwiseSeparableConvBlock(net, 512)
net = DepthwiseSeparableConvBlock(net, 256)
if has_skip:
net = tf.add(net, skip_3)
net = conv_transpose_block(net, 256)
net = DepthwiseSeparableConvBlock(net, 256)
net = DepthwiseSeparableConvBlock(net, 256)
net = DepthwiseSeparableConvBlock(net, 128)
if has_skip:
net = tf.add(net, skip_2)
net = conv_transpose_block(net, 128)
net = DepthwiseSeparableConvBlock(net, 128)
net = DepthwiseSeparableConvBlock(net, 64)
if has_skip:
net = tf.add(net, skip_1)
net = conv_transpose_block(net, 64)
net = DepthwiseSeparableConvBlock(net, 64)
net = DepthwiseSeparableConvBlock(net, 64)
#####################
# Softmax #
#####################
net = slim.conv2d(net, num_classes, [1, 1], activation_fn=None, scope='logits')
return net
def build_adaptnet(inputs, num_classes):
"""
Builds the AdaptNet model.
Arguments:
inputs: The input tensor=
preset_model: Which model you want to use. Select which ResNet model to use for feature extraction
num_classes: Number of classes
Returns:
AdaptNet model
"""
net = ConvBlock(inputs, n_filters=64, kernel_size=[3, 3])
net = ConvBlock(net, n_filters=64, kernel_size=[7, 7], stride=2)
net = slim.pool(net, [2, 2], stride=[2, 2], pooling_type='MAX')
net = ResNetBlock_2(net, filters_1=64, filters_2=256, s=1)
net = ResNetBlock_1(net, filters_1=64, filters_2=256)
net = ResNetBlock_1(net, filters_1=64, filters_2=256)
net = ResNetBlock_2(net, filters_1=128, filters_2=512, s=2)
net = ResNetBlock_1(net, filters_1=128, filters_2=512)
net = ResNetBlock_1(net, filters_1=128, filters_2=512)
skip_connection = ConvBlock(net, n_filters=12, kernel_size=[1, 1])
net = MultiscaleBlock_1(net,
filters_1=128,
filters_2=512,
filters_3=64,
p=1,
d=2)
net = ResNetBlock_2(net, filters_1=256, filters_2=1024, s=2)
net = ResNetBlock_1(net, filters_1=256, filters_2=1024)
net = MultiscaleBlock_1(net,
filters_1=256,
filters_2=1024,
filters_3=64,
p=1,
d=2)
net = MultiscaleBlock_1(net,
filters_1=256,
filters_2=1024,
filters_3=64,
p=1,
d=4)
net = MultiscaleBlock_1(net,
filters_1=256,
filters_2=1024,
filters_3=64,
p=1,
d=8)
net = MultiscaleBlock_1(net,
filters_1=256,
filters_2=1024,
filters_3=64,
p=1,
d=16)
net = MultiscaleBlock_2(net,
filters_1=512,
filters_2=2048,
filters_3=512,
p=2,
d=4)
net = MultiscaleBlock_1(net,
filters_1=512,
filters_2=2048,
filters_3=512,
p=2,
d=8)
net = MultiscaleBlock_1(net,
filters_1=512,
filters_2=2048,
filters_3=512,
p=2,
d=16)
net = ConvBlock(net, n_filters=12, kernel_size=[1, 1])
net = Upsampling(net, scale=2)
net = tf.add(skip_connection, net)
net = Upsampling(net, scale=8)
net = slim.conv2d(net,
num_classes, [1, 1],
activation_fn=None,
scope='logits')
return net
