def __init__(self,
in_channels,
out_channels=3,
output_heads=1,
conv_layer_order='crg',
init_channel_number=32,
**kwargs):
super(TagsUNet3D, self).__init__()
# number of groups for the GroupNorm
num_groups = min(init_channel_number // 2, 32)
# encoder path consist of 4 subsequent Encoder modules
# the number of features maps is the same as in the paper
self.encoders = nn.ModuleList([
Encoder(in_channels,
init_channel_number,
apply_pooling=False,
conv_layer_order=conv_layer_order,
num_groups=num_groups),
Encoder(init_channel_number,
2 * init_channel_number,
conv_layer_order=conv_layer_order,
num_groups=num_groups),
Encoder(2 * init_channel_number,
4 * init_channel_number,
conv_layer_order=conv_layer_order,
num_groups=num_groups),
Encoder(4 * init_channel_number,
8 * init_channel_number,
conv_layer_order=conv_layer_order,
num_groups=num_groups)
])
self.decoders = nn.ModuleList([
Decoder(4 * init_channel_number + 8 * init_channel_number,
4 * init_channel_number,
conv_layer_order=conv_layer_order,
num_groups=num_groups),
Decoder(2 * init_channel_number + 4 * init_channel_number,
2 * init_channel_number,
conv_layer_order=conv_layer_order,
num_groups=num_groups),
Decoder(init_channel_number + 2 * init_channel_number,
init_channel_number,
conv_layer_order=conv_layer_order,
num_groups=num_groups)
])
self.final_heads = nn.ModuleList([
FinalConv(init_channel_number, out_channels, num_groups=num_groups)
for _ in range(output_heads)
])
def __init__(self,
in_channels,
out_channels,
final_sigmoid,
f_maps=64,
layer_order='cr',
num_groups=8,
**kwargs):
super(UNet3D, self).__init__()
if isinstance(f_maps, int):
# use 4 levels in the encoder path as suggested in the paper
f_maps = create_feature_maps(f_maps, number_of_fmaps=4)
# create encoder path consisting of Encoder modules. The length of the encoder is equal to `len(f_maps)`
# uses DoubleConv as a basic_module for the Encoder
encoders = []
for i, out_feature_num in enumerate(f_maps):
if i == 0:
encoder = Encoder(in_channels,
out_feature_num,
apply_pooling=False,
basic_module=DoubleConv,
conv_layer_order=layer_order,
num_groups=num_groups)
else:
encoder = Encoder(f_maps[i - 1],
out_feature_num,
basic_module=DoubleConv,
conv_layer_order=layer_order,
num_groups=num_groups)
encoders.append(encoder)
self.encoders = nn.ModuleList(encoders)
# create decoder path consisting of the Decoder modules. The length of the decoder is equal to `len(f_maps) - 1`
# uses DoubleConv as a basic_module for the Decoder
decoders = []
reversed_f_maps = list(reversed(f_maps))
for i in range(len(reversed_f_maps) - 1):
in_feature_num = reversed_f_maps[i] + reversed_f_maps[i + 1]
out_feature_num = reversed_f_maps[i + 1]
decoder = Decoder(in_feature_num,
out_feature_num,
basic_module=DoubleConv,
conv_layer_order=layer_order,
num_groups=num_groups)
decoders.append(decoder)
self.decoders = nn.ModuleList(decoders)
# in the last layer a 1×1 convolution reduces the number of output
# channels to the number of labels
self.final_conv = nn.Conv3d(f_maps[0], out_channels, 1)
if final_sigmoid:
self.final_activation = nn.Sigmoid()
else:
self.final_activation = nn.Softmax(dim=1)
def __init__(self,
in_channels,
out_channels,
f_maps=16,
num_groups=8,
**kwargs):
super(Noise2NoiseUNet3D, self).__init__()
# Use LeakyReLU activation everywhere except the last layer
conv_layer_order = 'clg'
if isinstance(f_maps, int):
# use 5 levels in the encoder path as suggested in the paper
f_maps = create_feature_maps(f_maps, number_of_fmaps=5)
# create encoder path consisting of Encoder modules. The length of the encoder is equal to `len(f_maps)`
# uses DoubleConv as a basic_module for the Encoder
encoders = []
for i, out_feature_num in enumerate(f_maps):
if i == 0:
encoder = Encoder(in_channels,
out_feature_num,
apply_pooling=False,
basic_module=DoubleConv,
conv_layer_order=conv_layer_order,
num_groups=num_groups)
else:
encoder = Encoder(f_maps[i - 1],
out_feature_num,
basic_module=DoubleConv,
conv_layer_order=conv_layer_order,
num_groups=num_groups)
encoders.append(encoder)
self.encoders = nn.ModuleList(encoders)
# create decoder path consisting of the Decoder modules. The length of the decoder is equal to `len(f_maps) - 1`
# uses DoubleConv as a basic_module for the Decoder
decoders = []
reversed_f_maps = list(reversed(f_maps))
for i in range(len(reversed_f_maps) - 1):
in_feature_num = reversed_f_maps[i] + reversed_f_maps[i + 1]
out_feature_num = reversed_f_maps[i + 1]
decoder = Decoder(in_feature_num,
out_feature_num,
basic_module=DoubleConv,
conv_layer_order=conv_layer_order,
num_groups=num_groups)
decoders.append(decoder)
self.decoders = nn.ModuleList(decoders)
# 1x1x1 conv + simple ReLU in the final convolution
self.final_conv = SingleConv(f_maps[0],
out_channels,
kernel_size=1,
order='cr',
padding=0)
def __init__(self,
in_channels,
out_channels,
final_sigmoid,
init_channel_number=32,
**kwargs):
super(DistanceTransformUNet3D, self).__init__()
# number of groups for the GroupNorm
num_groups = min(init_channel_number // 2, 32)
# encoder path consist of 4 subsequent Encoder modules
# the number of features maps is the same as in the paper
self.encoders = nn.ModuleList([
Encoder(in_channels,
init_channel_number,
apply_pooling=False,
conv_layer_order='crg',
num_groups=num_groups),
Encoder(init_channel_number,
2 * init_channel_number,
pool_type='avg',
conv_layer_order='crg',
num_groups=num_groups)
])
self.decoders = nn.ModuleList([
Decoder(3 * init_channel_number,
init_channel_number,
conv_layer_order='crg',
num_groups=num_groups)
])
# in the last layer a 1×1 convolution reduces the number of output
# channels to the number of labels
self.final_conv = nn.Conv3d(init_channel_number, out_channels, 1)
if final_sigmoid:
self.final_activation = nn.Sigmoid()
else:
self.final_activation = nn.Softmax(dim=1)