f6b5e2412d4912caa43384d97b90287b2c4e218f,niftynet/layer/loss.py,,wasserstein_generalised_dice_loss,#Any#Any#Any#,183

Before Change

    pred_proba = tf.nn.softmax(tf.cast(prediction,dtype=tf.float64))
    n_classes = prediction.get_shape()[1].value
    n_voxels = prediction.get_shape()[0].value
    print("prediction shape", n_classes,n_voxels)
    ids = tf.constant(np.arange(n_voxels), dtype=tf.int64)
    ids = tf.stack([ids, ground_truth], axis=1)

    one_hot = tf.SparseTensor(indices=ids,

After Change

                                      ground_truth,
                                      weight_map=None):
    // apply softmax to pred scores
    ground_truth = tf.cast(ground_truth, dtype=tf.int64)
    pred_proba = tf.nn.softmax(tf.cast(prediction,dtype=tf.float64))
    n_classes = prediction.get_shape()[1].value
    n_voxels = prediction.get_shape()[0].value
    ids = tf.constant(np.arange(n_voxels), dtype=tf.int64)

Instances