a6d07af248a7594b8dfedbf8368ddac3f901f3ec,niftynet/layer/loss_segmentation.py,,dice_plus_xent_loss,#Any#Any#Any#,252

Before Change

    :return: the loss (cross_entropy + Dice)

    
    if weight_map is not None:
        raise NotImplementedError

    prediction = tf.cast(prediction, tf.float32)
    loss_xent = cross_entropy(prediction, ground_truth)

    softmax_of_logits = tf.nn.softmax(prediction, axis=-1)

After Change

    one_hot = labels_to_one_hot(ground_truth, num_classes=num_classes)
    softmax_of_logits = tf.nn.softmax(prediction)

    if weight_map is not None:
        weight_map_nclasses = tf.tile(tf.expand_dims(tf.reshape(weight_map, [-1]), 1), [1, num_classes])
        dice_numerator = 2.0 * tf.sparse_reduce_sum(weight_map_nclasses * one_hot * softmax_of_logits,
                                                    reduction_axes=[0])
        dice_denominator = tf.reduce_sum(weight_map_nclasses * softmax_of_logits,
                                         reduction_indices=[0]) + \
                           tf.sparse_reduce_sum(one_hot * weight_map_nclasses, reduction_axes=[0])

    else:
        dice_numerator = 2.0 * tf.sparse_reduce_sum(one_hot * softmax_of_logits,reduction_axes=[0])
        dice_denominator = tf.reduce_sum(softmax_of_logits, reduction_indices=[0]) + \
                           tf.sparse_reduce_sum(one_hot, reduction_axes=[0])

        // dice_numerator = -2.0 * tf.sparse_reduce_sum(one_hot * softmax_of_logits, reduction_axes=[0])
        // dice_denominator = tf.reduce_sum(softmax_of_logits, reduction_indices=[0]) + \
        //                    tf.sparse_reduce_sum(one_hot, reduction_axes=[0])

    epsilon = 0.00001
    loss_dice = -(dice_numerator + epsilon) / (dice_denominator + epsilon)
    dice_numerator = tf.Print(dice_denominator, [dice_numerator, dice_denominator, loss_dice])

Instances