model.py 文件源码

def __init__(self, in_channels=1, n_classes=4):
        init = chainer.initializers.HeNormal(scale=0.01)
        super(VoxResNet, self).__init__(
            conv1a=L.ConvolutionND(3, in_channels, 32, 3, pad=1, initialW=init),
            bnorm1a=L.BatchNormalization(32),
            conv1b=L.ConvolutionND(3, 32, 32, 3, pad=1, initialW=init),
            bnorm1b=L.BatchNormalization(32),
            conv1c=L.ConvolutionND(3, 32, 64, 3, stride=2, pad=1, initialW=init),
            voxres2=VoxResModule(),
            voxres3=VoxResModule(),
            bnorm3=L.BatchNormalization(64),
            conv4=L.ConvolutionND(3, 64, 64, 3, stride=2, pad=1, initialW=init),
            voxres5=VoxResModule(),
            voxres6=VoxResModule(),
            bnorm6=L.BatchNormalization(64),
            conv7=L.ConvolutionND(3, 64, 64, 3, stride=2, pad=1, initialW=init),
            voxres8=VoxResModule(),
            voxres9=VoxResModule(),
            c1deconv=L.DeconvolutionND(3, 32, 32, 3, pad=1, initialW=init),
            c1conv=L.ConvolutionND(3, 32, n_classes, 3, pad=1, initialW=init),
            c2deconv=L.DeconvolutionND(3, 64, 64, 4, stride=2, pad=1, initialW=init),
            c2conv=L.ConvolutionND(3, 64, n_classes, 3, pad=1, initialW=init),
            c3deconv=L.DeconvolutionND(3, 64, 64, 6, stride=4, pad=1, initialW=init),
            c3conv=L.ConvolutionND(3, 64, n_classes, 3, pad=1, initialW=init),
            c4deconv=L.DeconvolutionND(3, 64, 64, 10, stride=8, pad=1, initialW=init),
            c4conv=L.ConvolutionND(3, 64, n_classes, 3, pad=1, initialW=init)
        )