a1172cf54f3a14fb57e6b6405ce58f29ef543334,niftynet/network/dense_vnet.py,DenseVNet,layer_op,#DenseVNet#Any#Any#Any#,70

Before Change

            skip,down = sd(down,
                           is_training=is_training,
                           keep_prob=hp["p_channels_selected"])
            tf.summary.scalar("skip{}".format(idx),tf.reduce_mean(tf.square(skip)),[tf.GraphKeys.SUMMARIES])
            if not down is None:
                tf.summary.scalar("down{}".format(idx),tf.reduce_mean(tf.square(down)),[tf.GraphKeys.SUMMARIES])
            all_segmentation_features.append(image_resize(skip,output_shape))
        segmentation = ConvolutionalLayer(

After Change

            segmentation = segmentation + \
                           SpatialPriorBlock([12] * spatial_rank, output_shape)
        segmentation = image_resize(segmentation, input_size[1:-1])
        image3_axial("seg", tf.nn.softmax(segmentation)[:, :, :, :, 1:] * 255.,
                     3, [tf.GraphKeys.SUMMARIES])
        image3_axial("img",
                     tf.minimum(255., tf.maximum(0.,

Instances