4421754f9886233e90563eb8088348bb36024095,niftynet/layer/loss_segmentation.py,LossFunction,layer_op,#LossFunction#Any#Any#Any#,46

Before Change

        with tf.device("/cpu:0"):

            batch_size = ground_truth.get_shape()[0].value
            ground_truth = tf.reshape(ground_truth, [batch_size, -1])
            if weight_map is not None:
                weight_map = tf.reshape(weight_map, [batch_size, -1])
            // assumes same gt and weight across scales

After Change

                    // size: (n_voxels, num_classes)
                    // if the ground_truth has only one channel, the shape
                    // becomes: (n_voxels,)
                    spatial_shape = pred_b.get_shape().as_list()[:-1]
                    ref_shape = spatial_shape + [-1]
                    ground_truth_b = tf.reshape(ground_truth[b_ind], ref_shape)
                    if ground_truth_b.get_shape().as_list()[-1] == 1:
                        ground_truth_b = tf.squeeze(ground_truth_b, axis=-1)
                    if weight_map is not None:
                        weight_b = tf.reshape(weight_map[b_ind], ref_shape)
                        if weight_b.get_shape().as_list()[-1] == 1:
                            weight_b = tf.squeeze(weight_b, axis=-1)
                    else:
                        weight_b = None

Instances