pointnet_part_seg.py 文件源码

def get_loss(l_pred, seg_pred, label, seg, weight, end_points):
    per_instance_label_loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=l_pred, labels=label)
    label_loss = tf.reduce_mean(per_instance_label_loss)

    # size of seg_pred is batch_size x point_num x part_cat_num
    # size of seg is batch_size x point_num
    per_instance_seg_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=seg_pred, labels=seg), axis=1)
    seg_loss = tf.reduce_mean(per_instance_seg_loss)

    per_instance_seg_pred_res = tf.argmax(seg_pred, 2)

    # Enforce the transformation as orthogonal matrix
    transform = end_points['transform'] # BxKxK
    K = transform.get_shape()[1].value
    mat_diff = tf.matmul(transform, tf.transpose(transform, perm=[0,2,1])) - tf.constant(np.eye(K), dtype=tf.float32)
    mat_diff_loss = tf.nn.l2_loss(mat_diff) 


    total_loss = weight * seg_loss + (1 - weight) * label_loss + mat_diff_loss * 1e-3

    return total_loss, label_loss, per_instance_label_loss, seg_loss, per_instance_seg_loss, per_instance_seg_pred_res