This is a Tensorflow 2 based implementation of a generic 3D-Unet.
This repo allows the creation of a 3D-Unet of any structure, letting you to choose the depth of the network, the number of consecutive convolutions, the number of filters on each level, the kernel size on each level, the pooling method (AvgPooling or MaxPooling), the inclusion of dropout after pooling and concatenation, inclusion of batch normalization after convolutions or not, the padding method, the upsampling method (UpSampling or ConvTranspose) and the activation function after convolutions.
To create this network:
This is the example code:
from utils.layers import *
from utils.layers_func import *
unet_model_func = create_unet3d(input_shape=[128, 128, 128, 1],
n_convs=2,
n_filters=[16, 32, 64, 128],
ksize=[3, 3, 3],
padding='same',
pooling='max',
norm='batch_norm',
dropout=[0.25, 0.5, 0.5],
upsampling=True,
activation='relu',
depth=4)
# OR
unet_model = create_unet3d_class(input_shape=[128, 128, 128, 1],
n_convs=2,
n_filters=[16, 32, 64, 128],
ksize=[3, 3, 3],
padding='same',
pooling='max',
norm='batch_norm',
dropout=[0.25, 0.5, 0.5],
upsampling=True,
activation='relu',
depth=4)The difference between these two methods is that create_unet3d will return a model with all the Tensorflow layers that compose the U-Net, while the create_unet3d_class will return a model with only 1 layer, which contains the U-Net structure. For more examples, including training, visit examples.
This framework also includes a Trainer class to apply Knowledge Distilation on segmentation problems that use 3D-Unet. This class allows to train the Teacher and Student models from scratch and then distil the knowledge from Teacher to Student by following this approach:
