Question about ema_model

[DapengFeng]

pillar-od/train.py

Lines 115 to 130 in 3eac6a9

	model = builder.PillarModel(
	class_id=FLAGS.class_id,
	norm_type=FLAGS.norm_type,
	act_type=FLAGS.act_type,
	nms_iou_threshold=FLAGS.nms_iou_threshold,
	nms_score_threshold=FLAGS.nms_score_threshold,
	max_nms_boxes=FLAGS.max_nms_boxes,
	use_oriented_per_class_nms=FLAGS.use_oriented_per_class_nms)
	ema_model = builder.PillarModel(
	class_id=FLAGS.class_id,
	norm_type=FLAGS.norm_type,
	act_type=FLAGS.act_type,
	nms_iou_threshold=FLAGS.nms_iou_threshold,
	nms_score_threshold=FLAGS.nms_score_threshold,
	max_nms_boxes=FLAGS.max_nms_boxes,
	use_oriented_per_class_nms=FLAGS.use_oriented_per_class_nms)

pillar-od/train.py

Lines 208 to 226 in 3eac6a9

	with tf.GradientTape() as tape:
	preds = model(inputs, training=True)
	if optimizer.iterations < 1:
	ema_model(inputs, training=False)
	cls_loss, loc_loss = model.compute_loss(inputs, preds)
	mean_cls_loss = tf.reduce_mean(cls_loss)
	mean_loc_loss = tf.reduce_mean(loc_loss)
	weight_loss = tf.reduce_sum(model.losses)
	loss = (mean_cls_loss + mean_loc_loss + weight_loss)
	training_vars = model.trainable_variables
	grads = tape.gradient(loss, training_vars)
	optimizer.apply_gradients(zip(grads, training_vars))
	train_loss_metric.update_state(loss)
	train_loc_loss_metric.update_state(mean_loc_loss)
	train_cls_loss_metric.update_state(mean_cls_loss)
	weight_loss_metric.update_state(weight_loss)
	_momentum_update(model, ema_model)

Thanks for your great work. But I still confuse about the effect of ema_model, since the pointpillars does not apply the same setting. Could you explain the reason why you apply ema_model here, and add an ablation study on ema_model?