lasagne_voxresnet.py

import lasagne
from lasagne.layers import InputLayer
from lasagne.layers.dnn import Conv3DDNNLayer
from lasagne.layers.dnn import Pool3DDNNLayer
from lasagne.layers import BatchNormLayer
from lasagne.layers import NonlinearityLayer
from lasagne.layers import ElemwiseSumLayer
from lasagne.layers import DenseLayer
from lasagne.nonlinearities import identity, softmax


def build_net():
    """Method for VoxResNet Building.

    Returns
    -------
    dictionary
        Network dictionary.
    """
    net = {}
    net['input'] = InputLayer((None, 1, 110, 110, 110))
    net['conv1a'] = Conv3DDNNLayer(net['input'], 32, 3, pad='same',
                                   nonlinearity=identity)
    net['bn1a'] = BatchNormLayer(net['conv1a'])
    net['relu1a'] = NonlinearityLayer(net['bn1a'])
    net['conv1b'] = Conv3DDNNLayer(net['relu1a'], 32, 3, pad='same',
                                   nonlinearity=identity)
    net['bn1b'] = BatchNormLayer(net['conv1b'])
    net['relu1b'] = NonlinearityLayer(net['bn1b'])
    net['conv1c'] = Conv3DDNNLayer(net['relu1b'], 64, 3, stride=(2, 2, 2),
                                   pad='same', nonlinearity=identity)
    # VoxRes block 2
    net['voxres2_bn1'] = BatchNormLayer(net['conv1c'])
    net['voxres2_relu1'] = NonlinearityLayer(net['voxres2_bn1'])
    net['voxres2_conv1'] = Conv3DDNNLayer(net['voxres2_relu1'], 64, 3,
                                          pad='same', nonlinearity=identity)
    net['voxres2_bn2'] = BatchNormLayer(net['voxres2_conv1'])
    net['voxres2_relu2'] = NonlinearityLayer(net['voxres2_bn2'])
    net['voxres2_conv2'] = Conv3DDNNLayer(net['voxres2_relu2'], 64, 3,
                                          pad='same', nonlinearity=identity)
    net['voxres2_out'] = ElemwiseSumLayer([net['conv1c'],
                                           net['voxres2_conv2']])
    # VoxRes block 3
    net['voxres3_bn1'] = BatchNormLayer(net['voxres2_out'])
    net['voxres3_relu1'] = NonlinearityLayer(net['voxres3_bn1'])
    net['voxres3_conv1'] = Conv3DDNNLayer(net['voxres3_relu1'], 64, 3,
                                          pad='same', nonlinearity=identity)
    net['voxres3_bn2'] = BatchNormLayer(net['voxres3_conv1'])
    net['voxres3_relu2'] = NonlinearityLayer(net['voxres3_bn2'])
    net['voxres3_conv2'] = Conv3DDNNLayer(net['voxres3_relu2'], 64, 3,
                                          pad='same', nonlinearity=identity)
    net['voxres3_out'] = ElemwiseSumLayer([net['voxres2_out'],
                                           net['voxres3_conv2']])

    net['bn4'] = BatchNormLayer(net['voxres3_out'])
    net['relu4'] = NonlinearityLayer(net['bn4'])
    net['conv4'] = Conv3DDNNLayer(net['relu4'], 64, 3, stride=(2, 2, 2),
                                  pad='same', nonlinearity=identity)
    # VoxRes block 5
    net['voxres5_bn1'] = BatchNormLayer(net['conv4'])
    net['voxres5_relu1'] = NonlinearityLayer(net['voxres5_bn1'])
    net['voxres5_conv1'] = Conv3DDNNLayer(net['voxres5_relu1'], 64, 3,
                                          pad='same', nonlinearity=identity)
    net['voxres5_bn2'] = BatchNormLayer(net['voxres5_conv1'])
    net['voxres5_relu2'] = NonlinearityLayer(net['voxres5_bn2'])
    net['voxres5_conv2'] = Conv3DDNNLayer(net['voxres5_relu2'], 64, 3,
                                          pad='same', nonlinearity=identity)
    net['voxres5_out'] = ElemwiseSumLayer([net['conv4'], net['voxres5_conv2']])
    # VoxRes block 6
    net['voxres6_bn1'] = BatchNormLayer(net['voxres5_out'])
    net['voxres6_relu1'] = NonlinearityLayer(net['voxres6_bn1'])
    net['voxres6_conv1'] = Conv3DDNNLayer(net['voxres6_relu1'], 64, 3,
                                          pad='same', nonlinearity=identity)
    net['voxres6_bn2'] = BatchNormLayer(net['voxres6_conv1'])
    net['voxres6_relu2'] = NonlinearityLayer(net['voxres6_bn2'])
    net['voxres6_conv2'] = Conv3DDNNLayer(net['voxres6_relu2'], 64, 3,
                                          pad='same', nonlinearity=identity)
    net['voxres6_out'] = ElemwiseSumLayer([net['voxres5_out'],
                                           net['voxres6_conv2']])

    net['bn7'] = BatchNormLayer(net['voxres6_out'])
    net['relu7'] = NonlinearityLayer(net['bn7'])
    net['conv7'] = Conv3DDNNLayer(net['relu7'], 128, 3, stride=(2, 2, 2),
                                  pad='same', nonlinearity=identity)

    # VoxRes block 8
    net['voxres8_bn1'] = BatchNormLayer(net['conv7'])
    net['voxres8_relu1'] = NonlinearityLayer(net['voxres8_bn1'])
    net['voxres8_conv1'] = Conv3DDNNLayer(net['voxres8_relu1'], 128, 3,
                                          pad='same', nonlinearity=identity)
    net['voxres8_bn2'] = BatchNormLayer(net['voxres8_conv1'])
    net['voxres8_relu2'] = NonlinearityLayer(net['voxres8_bn2'])
    net['voxres8_conv2'] = Conv3DDNNLayer(net['voxres8_relu2'], 128, 3,
                                          pad='same', nonlinearity=identity)
    net['voxres8_out'] = ElemwiseSumLayer([net['conv7'], net['voxres8_conv2']])
    # VoxRes block 9
    net['voxres9_bn1'] = BatchNormLayer(net['voxres8_out'])
    net['voxres9_relu1'] = NonlinearityLayer(net['voxres9_bn1'])
    net['voxres9_conv1'] = Conv3DDNNLayer(net['voxres9_relu1'], 128, 3,
                                          pad='same', nonlinearity=identity)
    net['voxres9_bn2'] = BatchNormLayer(net['voxres9_conv1'])
    net['voxres9_relu2'] = NonlinearityLayer(net['voxres9_bn2'])
    net['voxres9_conv2'] = Conv3DDNNLayer(net['voxres9_relu2'], 128, 3,
                                          pad='same', nonlinearity=identity)
    net['voxres9_out'] = ElemwiseSumLayer([net['voxres8_out'],
                                           net['voxres9_conv2']])

    net['pool10'] = Pool3DDNNLayer(net['voxres9_out'], 7)
    net['fc11'] = DenseLayer(net['pool10'], 128)
    net['prob'] = DenseLayer(net['fc11'], 2, nonlinearity=softmax)

    return net