To compute RecLoss, why do you compute outer_interocular_dist? Why do not you use bbox_size in KptNME2D directly?

[lhyfst]

To compute RecLoss, why do you compute outer_interocular_dist?

SADRNet/src/model/loss.py

Lines 353 to 359 in a5e6fac

	outer_interocular_dist = y_true[uv_kpt_ind[36, 0], uv_kpt_ind[36, 1]] - y_true[
	uv_kpt_ind[45, 0], uv_kpt_ind[45, 1]]
	bbox_size = np.linalg.norm(outer_interocular_dist[0:3])
	dist = torch.from_numpy(dist)
	# loss = np.mean(dist / bbox_size)
	loss = torch.mean(dist / bbox_size)

Why do not you use bbox_size in KptNME2D directly?

SADRNet/src/model/loss.py

Lines 255 to 260 in a5e6fac

	left = torch.min(gt[:, 0, :], dim=1)[0]
	right = torch.max(gt[:, 0, :], dim=1)[0]
	top = torch.min(gt[:, 1, :], dim=1)[0]
	bottom = torch.max(gt[:, 1, :], dim=1)[0]
	bbox_size = torch.sqrt((right - left) * (bottom - top))
	dist = dist / bbox_size

[reshow]

"RecLoss" is not used for training but for evaluation. We follow the evaluation metrics of PRNet and use outer_interocular_dist to evaluate face reconstruction.