def build_refinenet(inputs,
num_classes,
preset_model='RefineNet-Res101',
weight_decay=1e-5,
is_training=True,
upscaling_method="bilinear",
pretrained_dir="models"):
"""
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
"""
if preset_model == 'RefineNet-Res50':
with slim.arg_scope(
resnet_v2.resnet_arg_scope(weight_decay=weight_decay)):
logits, end_points = resnet_v2.resnet_v2_50(
inputs, is_training=is_training, scope='resnet_v2_50')
# RefineNet requires pre-trained ResNet weights
init_fn = slim.assign_from_checkpoint_fn(
os.path.join(pretrained_dir, 'resnet_v2_50.ckpt'),
slim.get_model_variables('resnet_v2_50'))
elif preset_model == 'RefineNet-Res101':
with slim.arg_scope(
resnet_v2.resnet_arg_scope(weight_decay=weight_decay)):
logits, end_points = resnet_v2.resnet_v2_101(
inputs, is_training=is_training, scope='resnet_v2_101')
# RefineNet requires pre-trained ResNet weights
init_fn = slim.assign_from_checkpoint_fn(
os.path.join(pretrained_dir, 'resnet_v2_101.ckpt'),
slim.get_model_variables('resnet_v2_101'))
elif preset_model == 'RefineNet-Res152':
with slim.arg_scope(
resnet_v2.resnet_arg_scope(weight_decay=weight_decay)):
logits, end_points = resnet_v2.resnet_v2_152(
inputs, is_training=is_training, scope='resnet_v2_152')
# RefineNet requires pre-trained ResNet weights
init_fn = slim.assign_from_checkpoint_fn(
os.path.join(pretrained_dir, 'resnet_v2_152.ckpt'),
slim.get_model_variables('resnet_v2_152'))
else:
raise ValueError(
"Unsupported ResNet model '%s'. This function only supports ResNet 101 and ResNet 152"
% (preset_model))
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
low[0] = RefineBlock(high_inputs=high[0],
low_inputs=None) # Only input ResNet 1/32
low[1] = RefineBlock(
high[1], low[0]) # High input = ResNet 1/16, Low input = Previous 1/16
low[2] = RefineBlock(
high[2], low[1]) # High input = ResNet 1/8, Low input = Previous 1/8
low[3] = RefineBlock(
high[3], low[2]) # High input = ResNet 1/4, Low input = Previous 1/4
# g[3]=Upsampling(g[3],scale=4)
net = low[3]
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, init_fn
def build_deeplabv3_plus(inputs,
num_classes,
preset_model='DeepLabV3+-Res50',
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
"""
if preset_model == 'DeepLabV3_plus-Res50':
with slim.arg_scope(
resnet_v2.resnet_arg_scope(weight_decay=weight_decay)):
logits, end_points = resnet_v2.resnet_v2_50(
inputs, is_training=is_training, scope='resnet_v2_50')
resnet_scope = 'resnet_v2_50'
# DeepLabV3 requires pre-trained ResNet weights
init_fn = slim.assign_from_checkpoint_fn(
os.path.join(pretrained_dir, 'resnet_v2_50.ckpt'),
slim.get_model_variables('resnet_v2_50'))
elif preset_model == 'DeepLabV3_plus-Res101':
with slim.arg_scope(
resnet_v2.resnet_arg_scope(weight_decay=weight_decay)):
logits, end_points = resnet_v2.resnet_v2_101(
inputs, is_training=is_training, scope='resnet_v2_101')
resnet_scope = 'resnet_v2_101'
# DeepLabV3 requires pre-trained ResNet weights
init_fn = slim.assign_from_checkpoint_fn(
os.path.join(pretrained_dir, 'resnet_v2_101.ckpt'),
slim.get_model_variables('resnet_v2_101'))
elif preset_model == 'DeepLabV3_plus-Res152':
with slim.arg_scope(
resnet_v2.resnet_arg_scope(weight_decay=weight_decay)):
logits, end_points = resnet_v2.resnet_v2_152(
inputs, is_training=is_training, scope='resnet_v2_152')
resnet_scope = 'resnet_v2_152'
# DeepLabV3 requires pre-trained ResNet weights
init_fn = slim.assign_from_checkpoint_fn(
os.path.join(pretrained_dir, 'resnet_v2_152.ckpt'),
slim.get_model_variables('resnet_v2_152'))
else:
raise ValueError(
"Unsupported ResNet model '%s'. This function only supports ResNet 50, ResNet 101, and ResNet 152"
% (preset_model))
label_size = tf.shape(inputs)[1:3]
encoder_features = end_points['pool2']
net = AtrousSpatialPyramidPoolingModule(end_points['pool4'])
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(encoder_features,
256, [3, 3],
activation_fn=tf.nn.relu,
normalizer_fn=None)
net = slim.conv2d(encoder_features,
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_deeplabv3(inputs,
num_classes,
preset_model='DeepLabV3-Res50',
upscaling_method="bilinear",
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
"""
inputs = mean_image_subtraction(inputs)
if preset_model == 'DeepLabV3-Res50':
with slim.arg_scope(
resnet_v2.resnet_arg_scope(weight_decay=weight_decay)):
logits, end_points = resnet_v2.resnet_v2_50(
inputs, is_training=is_training, scope='resnet_v2_50')
resnet_scope = 'resnet_v2_50'
# DeepLabV3 requires pre-trained ResNet weights
init_fn = slim.assign_from_checkpoint_fn(
os.path.join(pretrained_dir, 'resnet_v2_50.ckpt'),
slim.get_model_variables('resnet_v2_50'))
elif preset_model == 'DeepLabV3-Res101':
with slim.arg_scope(
resnet_v2.resnet_arg_scope(weight_decay=weight_decay)):
logits, end_points = resnet_v2.resnet_v2_101(
inputs, is_training=is_training, scope='resnet_v2_101')
resnet_scope = 'resnet_v2_101'
# DeepLabV3 requires pre-trained ResNet weights
init_fn = slim.assign_from_checkpoint_fn(
os.path.join(pretrained_dir, 'resnet_v2_101.ckpt'),
slim.get_model_variables('resnet_v2_101'))
elif preset_model == 'DeepLabV3-Res152':
with slim.arg_scope(
resnet_v2.resnet_arg_scope(weight_decay=weight_decay)):
logits, end_points = resnet_v2.resnet_v2_152(
inputs, is_training=is_training, scope='resnet_v2_152')
resnet_scope = 'resnet_v2_152'
# DeepLabV3 requires pre-trained ResNet weights
init_fn = slim.assign_from_checkpoint_fn(
os.path.join(pretrained_dir, 'resnet_v2_152.ckpt'),
slim.get_model_variables('resnet_v2_152'))
else:
raise ValueError(
"Unsupported ResNet model '%s'. This function only supports ResNet 50, ResNet 101, and ResNet 152"
% (preset_model))
label_size = tf.shape(inputs)[1:3]
net = AtrousSpatialPyramidPoolingModule(end_points['pool5'])
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_pspnet(inputs,
label_size,
num_classes,
preset_model='PSPNet-Res50',
pooling_type="MAX",
weight_decay=1e-5,
upscaling_method="bilinear",
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
"""
inputs = mean_image_subtraction(inputs)
if preset_model == 'PSPNet-Res50':
with slim.arg_scope(
resnet_v2.resnet_arg_scope(weight_decay=weight_decay)):
logits, end_points = resnet_v2.resnet_v2_50(
inputs, is_training=is_training, scope='resnet_v2_50')
resnet_scope = 'resnet_v2_50'
# PSPNet requires pre-trained ResNet weights
init_fn = slim.assign_from_checkpoint_fn(
os.path.join(pretrained_dir, 'resnet_v2_50.ckpt'),
slim.get_model_variables('resnet_v2_50'))
elif preset_model == 'PSPNet-Res101':
with slim.arg_scope(
resnet_v2.resnet_arg_scope(weight_decay=weight_decay)):
logits, end_points = resnet_v2.resnet_v2_101(
inputs, is_training=is_training, scope='resnet_v2_101')
resnet_scope = 'resnet_v2_101'
# PSPNet requires pre-trained ResNet weights
init_fn = slim.assign_from_checkpoint_fn(
os.path.join(pretrained_dir, 'resnet_v2_101.ckpt'),
slim.get_model_variables('resnet_v2_101'))
elif preset_model == 'PSPNet-Res152':
with slim.arg_scope(
resnet_v2.resnet_arg_scope(weight_decay=weight_decay)):
logits, end_points = resnet_v2.resnet_v2_152(
inputs, is_training=is_training, scope='resnet_v2_152')
resnet_scope = 'resnet_v2_152'
# PSPNet requires pre-trained ResNet weights
init_fn = slim.assign_from_checkpoint_fn(
os.path.join(pretrained_dir, 'resnet_v2_152.ckpt'),
slim.get_model_variables('resnet_v2_152'))
else:
raise ValueError(
"Unsupported ResNet model '%s'. This function only supports ResNet 50, ResNet 101, and ResNet 152"
% (preset_model))
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.dropout(net, keep_prob=(0.9))
net = slim.conv2d(net,
num_classes, [1, 1],
activation_fn=None,
scope='logits')
return net, init_fn
def build_gcn(inputs,
num_classes,
preset_model='GCN-Res101',
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
"""
if preset_model == 'GCN-Res50':
with slim.arg_scope(
resnet_v2.resnet_arg_scope(weight_decay=weight_decay)):
logits, end_points = resnet_v2.resnet_v2_50(
inputs, is_training=is_training, scope='resnet_v2_50')
resnet_scope = 'resnet_v2_50'
# GCN requires pre-trained ResNet weights
init_fn = slim.assign_from_checkpoint_fn(
os.path.join(pretrained_dir, 'resnet_v2_50.ckpt'),
slim.get_model_variables('resnet_v2_50'))
elif preset_model == 'GCN-Res101':
with slim.arg_scope(
resnet_v2.resnet_arg_scope(weight_decay=weight_decay)):
logits, end_points = resnet_v2.resnet_v2_101(
inputs, is_training=is_training, scope='resnet_v2_101')
resnet_scope = 'resnet_v2_101'
# GCN requires pre-trained ResNet weights
init_fn = slim.assign_from_checkpoint_fn(
os.path.join(pretrained_dir, 'resnet_v2_101.ckpt'),
slim.get_model_variables('resnet_v2_101'))
elif preset_model == 'GCN-Res152':
with slim.arg_scope(
resnet_v2.resnet_arg_scope(weight_decay=weight_decay)):
logits, end_points = resnet_v2.resnet_v2_152(
inputs, is_training=is_training, scope='resnet_v2_152')
resnet_scope = 'resnet_v2_152'
# GCN requires pre-trained ResNet weights
init_fn = slim.assign_from_checkpoint_fn(
os.path.join(pretrained_dir, 'resnet_v2_152.ckpt'),
slim.get_model_variables('resnet_v2_152'))
else:
raise ValueError(
"Unsupported ResNet model '%s'. This function only supports ResNet 101 and ResNet 152"
% (preset_model))
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
def build_refinenet(inputs,
num_classes,
preset_model='RefineNet-Res101',
weight_decay=1e-5,
is_training=True,
upscaling_method="bilinear",
pretrained_dir="models"):
"""
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
"""
inputs = mean_image_subtraction(inputs)
if preset_model == 'RefineNet-Res50':
with slim.arg_scope(
resnet_v2.resnet_arg_scope(weight_decay=weight_decay)):
logits, end_points = resnet_v2.resnet_v2_50(
inputs, is_training=is_training, scope='resnet_v2_50')
# RefineNet requires pre-trained ResNet weights
init_fn = slim.assign_from_checkpoint_fn(
os.path.join(pretrained_dir, 'resnet_v2_50.ckpt'),
slim.get_model_variables('resnet_v2_50'))
elif preset_model == 'RefineNet-Res101':
with slim.arg_scope(
resnet_v2.resnet_arg_scope(weight_decay=weight_decay)):
logits, end_points = resnet_v2.resnet_v2_101(
inputs, is_training=is_training, scope='resnet_v2_101')
# RefineNet requires pre-trained ResNet weights
init_fn = slim.assign_from_checkpoint_fn(
os.path.join(pretrained_dir, 'resnet_v2_101.ckpt'),
slim.get_model_variables('resnet_v2_101'))
elif preset_model == 'RefineNet-Res152':
with slim.arg_scope(
resnet_v2.resnet_arg_scope(weight_decay=weight_decay)):
logits, end_points = resnet_v2.resnet_v2_152(
inputs, is_training=is_training, scope='resnet_v2_152')
# RefineNet requires pre-trained ResNet weights
init_fn = slim.assign_from_checkpoint_fn(
os.path.join(pretrained_dir, 'resnet_v2_152.ckpt'),
slim.get_model_variables('resnet_v2_152'))
else:
raise ValueError(
"Unsupported ResNet model '%s'. This function only supports ResNet 101 and ResNet 152"
% (preset_model))
f = [
end_points['pool5'], end_points['pool4'], end_points['pool3'],
end_points['pool2']
]
g = [None, None, None, None]
h = [None, None, None, None]
for i in range(4):
h[i] = slim.conv2d(f[i], 256, 1)
g[0] = RefineBlock(high_inputs=None, low_inputs=h[0])
g[1] = RefineBlock(g[0], h[1])
g[2] = RefineBlock(g[1], h[2])
g[3] = RefineBlock(g[2], h[3])
# g[3]=Upsampling(g[3],scale=4)
net = g[3]
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, scale=4)
net = slim.conv2d(net,
num_classes, [1, 1],
activation_fn=None,
scope='logits')
return net, init_fn
