def build_deeplabv3(inputs, num_classes, preset_model='DeepLabV3', frontend="Res101", weight_decay=1e-5, is_training=True):
"""
Builds the DeepLabV3 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:
DeepLabV3 model
"""
logits, end_points, frontend_scope, restore_variables = frontend_builder.build_frontend(
inputs, frontend, is_training=is_training)
label_size = tf.shape(inputs)[1:3]
net = AtrousSpatialPyramidPoolingModule(end_points['pool4'])
net = Upsampling(net, label_size)
net = slim.conv2d(net, num_classes, [
1, 1], activation_fn=None, scope='logits')
return net, restore_variables
def build_custom(inputs,
num_classes,
frontend="ResNet101",
weight_decay=1e-5,
is_training=True,
pretrained_dir="models"):
logits, end_points, frontend_scope, init_fn = frontend_builder.build_frontend(
inputs, frontend, is_training=is_training)
up_1 = conv_transpose_block(end_points["pool2"], strides=4, n_filters=64)
up_2 = conv_transpose_block(end_points["pool3"], strides=8, n_filters=64)
up_3 = conv_transpose_block(end_points["pool4"], strides=16, n_filters=64)
up_4 = conv_transpose_block(end_points["pool5"], strides=32, n_filters=64)
features = tf.concat([up_1, up_2, up_3, up_4], axis=-1)
features = conv_block(inputs=features, n_filters=256, filter_size=[1, 1])
features = conv_block(inputs=features, n_filters=64, filter_size=[3, 3])
features = conv_block(inputs=features, n_filters=64, filter_size=[3, 3])
features = conv_block(inputs=features, n_filters=64, filter_size=[3, 3])
net = slim.conv2d(features, num_classes, [1, 1], scope='logits')
return net
def build_deeplabv3_plus(inputs,
num_classes,
preset_model='DeepLabV3+',
frontend="ResNet101",
weight_decay=1e-5,
is_training=True,
pretrained_dir="models"):
"""
Builds the DeepLabV3 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:
DeepLabV3 model
"""
logits, end_points, frontend_scope, init_fn = frontend_builder.build_frontend(
inputs, frontend, is_training=is_training)
label_size = tf.shape(inputs)[1:3]
encoder_features = end_points['pool2']
net = AtrousSpatialPyramidPoolingModule(end_points['pool4'])
net = slim.conv2d(net,
256, [1, 1],
scope="conv_1x1_output",
activation_fn=None)
decoder_features = Upsampling(net, label_size / 4)
encoder_features = slim.conv2d(encoder_features,
48, [1, 1],
activation_fn=tf.nn.relu,
normalizer_fn=None)
net = tf.concat((encoder_features, decoder_features), axis=3)
net = slim.conv2d(net,
256, [3, 3],
activation_fn=tf.nn.relu,
normalizer_fn=None)
net = slim.conv2d(net,
256, [3, 3],
activation_fn=tf.nn.relu,
normalizer_fn=None)
net = Upsampling(net, label_size)
net = slim.conv2d(net,
num_classes, [1, 1],
activation_fn=None,
scope='logits')
return net, init_fn
def build_refinenet(inputs, num_classes, preset_model='RefineNet', frontend="ResNet101", weight_decay=1e-5, upscaling_method="bilinear", is_training=True):
"""
Builds the RefineNet 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:
RefineNet model
"""
logits, end_points, frontend_scope, restore_variables = frontend_builder.build_frontend(
inputs, frontend, is_training=is_training)
high = [end_points['pool5'], end_points['pool4'],
end_points['pool3'], end_points['pool2']]
low = [None, None, None, None]
# Get the feature maps to the proper size with bottleneck
high[0] = slim.conv2d(high[0], 512, 1)
high[1] = slim.conv2d(high[1], 256, 1)
high[2] = slim.conv2d(high[2], 256, 1)
high[3] = slim.conv2d(high[3], 256, 1)
# RefineNet
# Only input ResNet 1/32
low[0] = RefineBlock(high_inputs=high[0], low_inputs=None)
# High input = ResNet 1/16, Low input = Previous 1/16
low[1] = RefineBlock(high[1], low[0])
# High input = ResNet 1/8, Low input = Previous 1/8
low[2] = RefineBlock(high[2], low[1])
# High input = ResNet 1/4, Low input = Previous 1/4
low[3] = RefineBlock(high[3], low[2])
# g[3]=Upsampling(g[3],scale=4)
net = low[3]
net = ResidualConvUnit(net)
net = ResidualConvUnit(net)
if upscaling_method.lower() == "conv":
net = ConvUpscaleBlock(net, 128, kernel_size=[3, 3], scale=2)
net = ConvBlock(net, 128)
net = ConvUpscaleBlock(net, 64, kernel_size=[3, 3], scale=2)
net = ConvBlock(net, 64)
elif upscaling_method.lower() == "bilinear":
net = Upsampling(net, scale=4)
net = slim.conv2d(net, num_classes, [
1, 1], activation_fn=None, scope='logits')
return net, restore_variables
def build_pspnet(inputs,
label_size,
num_classes,
preset_model='PSPNet',
frontend="ResNet101",
pooling_type="MAX",
weight_decay=1e-5,
upscaling_method="conv",
is_training=True,
pretrained_dir="models"):
"""
Builds the PSPNet model.
Arguments:
inputs: The input tensor
label_size: Size of the final label tensor. We need to know this for proper upscaling
preset_model: Which model you want to use. Select which ResNet model to use for feature extraction
num_classes: Number of classes
pooling_type: Max or Average pooling
Returns:
PSPNet model
"""
logits, end_points, frontend_scope, init_fn = frontend_builder.build_frontend(
inputs, frontend, is_training=is_training)
feature_map_shape = [int(x / 8.0) for x in label_size]
print(feature_map_shape)
psp = PyramidPoolingModule(end_points['pool3'],
feature_map_shape=feature_map_shape,
pooling_type=pooling_type)
net = slim.conv2d(psp, 512, [3, 3], activation_fn=None)
net = slim.batch_norm(net, fused=True)
net = tf.nn.relu(net)
if upscaling_method.lower() == "conv":
net = ConvUpscaleBlock(net, 256, kernel_size=[3, 3], scale=2)
net = ConvBlock(net, 256)
net = ConvUpscaleBlock(net, 128, kernel_size=[3, 3], scale=2)
net = ConvBlock(net, 128)
net = ConvUpscaleBlock(net, 64, kernel_size=[3, 3], scale=2)
net = ConvBlock(net, 64)
elif upscaling_method.lower() == "bilinear":
net = Upsampling(net, label_size)
net = slim.conv2d(net,
num_classes, [1, 1],
activation_fn=None,
scope='logits')
return net, init_fn
def build_dense_aspp(inputs,
num_classes,
preset_model='DenseASPP',
frontend="ResNet101",
weight_decay=1e-5,
is_training=True):
logits, end_points, frontend_scope, restore_variables = frontend_builder.build_frontend(
inputs, frontend, is_training=is_training)
init_features = end_points['pool3']
### First block, rate = 3
d_3_features = DilatedConvBlock(init_features,
n_filters=256,
kernel_size=[1, 1])
d_3 = DilatedConvBlock(d_3_features,
n_filters=64,
rate=3,
kernel_size=[3, 3])
### Second block, rate = 6
d_4 = tf.concat([init_features, d_3], axis=-1)
d_4 = DilatedConvBlock(d_4, n_filters=256, kernel_size=[1, 1])
d_4 = DilatedConvBlock(d_4, n_filters=64, rate=6, kernel_size=[3, 3])
### Third block, rate = 12
d_5 = tf.concat([init_features, d_3, d_4], axis=-1)
d_5 = DilatedConvBlock(d_5, n_filters=256, kernel_size=[1, 1])
d_5 = DilatedConvBlock(d_5, n_filters=64, rate=12, kernel_size=[3, 3])
### Fourth block, rate = 18
d_6 = tf.concat([init_features, d_3, d_4, d_5], axis=-1)
d_6 = DilatedConvBlock(d_6, n_filters=256, kernel_size=[1, 1])
d_6 = DilatedConvBlock(d_6, n_filters=64, rate=18, kernel_size=[3, 3])
### Fifth block, rate = 24
d_7 = tf.concat([init_features, d_3, d_4, d_5, d_6], axis=-1)
d_7 = DilatedConvBlock(d_7, n_filters=256, kernel_size=[1, 1])
d_7 = DilatedConvBlock(d_7, n_filters=64, rate=24, kernel_size=[3, 3])
full_block = tf.concat([init_features, d_3, d_4, d_5, d_6, d_7], axis=-1)
net = slim.conv2d(full_block,
num_classes, [1, 1],
activation_fn=None,
scope='logits')
net = Upsampling(net, scale=8)
return net, restore_variables
def build_gcn(inputs,
num_classes,
preset_model='GCN',
frontend="ResNet101",
weight_decay=1e-5,
is_training=True,
upscaling_method="bilinear",
pretrained_dir="models"):
"""
Builds the GCN 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:
GCN model
"""
logits, end_points, frontend_scope, init_fn = frontend_builder.build_frontend(
inputs, frontend, is_training=is_training)
res = [
end_points['pool5'], end_points['pool4'], end_points['pool3'],
end_points['pool2']
]
down_5 = GlobalConvBlock(res[0], n_filters=21, size=3)
down_5 = BoundaryRefinementBlock(down_5, n_filters=21, kernel_size=[3, 3])
down_5 = ConvUpscaleBlock(down_5,
n_filters=21,
kernel_size=[3, 3],
scale=2)
down_4 = GlobalConvBlock(res[1], n_filters=21, size=3)
down_4 = BoundaryRefinementBlock(down_4, n_filters=21, kernel_size=[3, 3])
down_4 = tf.add(down_4, down_5)
down_4 = BoundaryRefinementBlock(down_4, n_filters=21, kernel_size=[3, 3])
down_4 = ConvUpscaleBlock(down_4,
n_filters=21,
kernel_size=[3, 3],
scale=2)
down_3 = GlobalConvBlock(res[2], n_filters=21, size=3)
down_3 = BoundaryRefinementBlock(down_3, n_filters=21, kernel_size=[3, 3])
down_3 = tf.add(down_3, down_4)
down_3 = BoundaryRefinementBlock(down_3, n_filters=21, kernel_size=[3, 3])
down_3 = ConvUpscaleBlock(down_3,
n_filters=21,
kernel_size=[3, 3],
scale=2)
down_2 = GlobalConvBlock(res[3], n_filters=21, size=3)
down_2 = BoundaryRefinementBlock(down_2, n_filters=21, kernel_size=[3, 3])
down_2 = tf.add(down_2, down_3)
down_2 = BoundaryRefinementBlock(down_2, n_filters=21, kernel_size=[3, 3])
down_2 = ConvUpscaleBlock(down_2,
n_filters=21,
kernel_size=[3, 3],
scale=2)
net = BoundaryRefinementBlock(down_2, n_filters=21, kernel_size=[3, 3])
net = ConvUpscaleBlock(net, n_filters=21, kernel_size=[3, 3], scale=2)
net = BoundaryRefinementBlock(net, n_filters=21, kernel_size=[3, 3])
net = slim.conv2d(net,
num_classes, [1, 1],
activation_fn=None,
scope='logits')
return net, init_fn
