9f0eca493539f189c20c9bc5ecd3a353e682c8d7,pointcnn.py,,xconv,#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#,10

Before Change

        fts_X = nn_fts_input

    fts_conv = pf.separable_conv2d(fts_X, C, tag + "fts_conv", is_training, (1, K), depth_multiplier=depth_multiplier)
    return tf.squeeze(fts_conv, axis=2, name=tag + "fts_conv_3d")


class PointCNN:
    def __init__(self, points, features, num_class, is_training, setting, task):

After Change

    fts_conv_3d = tf.squeeze(fts_conv, axis=2, name=tag + "fts_conv_3d")

    if with_global:
        fts_global_0 = pf.dense(qrs, C // 4, tag + "fts_global_0", is_training)
        fts_global = pf.dense(fts_global_0, C // 4, tag + "fts_global_", is_training)
        return tf.concat([fts_global, fts_conv_3d], axis=-1, name=tag + "fts_conv_3d_with_global")
    else:
        return fts_conv_3d

Instances