Why normalize twice since the target encoder also normalize the feature at last？

[jianghaojun]

ijepa/src/train.py

Lines 295 to 298 in 52c1ae9

	def forward_target():
	with torch.no_grad():
	h = target_encoder(imgs)
	h = F.layer_norm(h, (h.size(-1),)) # normalize over feature-dim

ijepa/src/models/vision_transformer.py

Lines 422 to 425 in 52c1ae9

	if self.norm is not None:
	x = self.norm(x)
	return x