377989c11903f69f042fee717ce6be66bd6eb43a,niftynet/layer/loss_segmentation.py,,generalised_dice_loss,#Any#Any#Any#Any#,120

Before Change

        Simple (inverse of volume) and Uniform (no weighting))
    :return: the loss
    
    ground_truth = tf.to_int64(ground_truth)
    n_voxels = ground_truth.shape[0].value
    n_classes = prediction.shape[1].value
    ids = tf.constant(np.arange(n_voxels), dtype=tf.int64)
    ids = tf.stack([ids, ground_truth], axis=1)
    one_hot = tf.SparseTensor(indices=ids,
                              values=tf.ones([n_voxels], dtype=tf.float32),
                              dense_shape=[n_voxels, n_classes])

    if weight_map is not None:
        weight_map_nclasses = tf.reshape(
            tf.tile(weight_map, [n_classes]), prediction.get_shape())

After Change

        Simple (inverse of volume) and Uniform (no weighting))
    :return: the loss
    
    prediction = tf.cast(prediction, tf.float32)
    one_hot = labels_to_one_hot(ground_truth, tf.shape(prediction))
    n_classes = prediction.shape[1].value

    if weight_map is not None:

Instances