get complementary loss and adaptive sample weight. Compares least likely prediction (from strong augment) with argmin of weak augment.

Get a yacs CfgNode object with default values.

Get a yacs CfgNode object with default values.

TF code def interleave(x, batch): s = x.get_shape().as_list() return tf.reshape(tf.transpose(tf.reshape(x, [-1, batch] + s[1:

normalize the itensity of an nd volume based on the mean and std of nonzeor region inputs: volume: the input nd volume outputs:

loads previous checkpoint Args: path (str): path to checkpoint model (model): model to restore checkpoint to optimizer (o

Change first convolution layer input channels. In case: in_channels == 1 or in_channels == 2 -> reuse original weights in_channels

Change first convolution layer input channels. In case: in_channels == 1 or in_channels == 2 -> reuse original weights in_channels

Change first convolution chennels

Change first convolution chennels