(batch_size, num_point, num_point_gt)
| 5 | |
| 6 | |
| 7 | def placeholder_inputs(batch_size, num_point, num_point_gt): |
| 8 | pointclouds_pl = tf.placeholder(tf.float32, shape=(batch_size, num_point, 3)) |
| 9 | pointclouds_Y = tf.placeholder(tf.float32, shape=(batch_size, num_point_gt, 3)) |
| 10 | pointclouds_gt = tf.placeholder(tf.float32, shape=(batch_size, num_point_gt, 3)) |
| 11 | is_training = tf.placeholder(tf.bool,shape=[]) |
| 12 | return pointclouds_pl, pointclouds_Y, pointclouds_gt, is_training |
| 13 | |
| 14 | |
| 15 | def get_model(X_inputs, Y_inputs, is_training, bn_decay=None, weight_decay=None): |
nothing calls this directly
no outgoing calls
no test coverage detected