about the 2-warp loss

[horizon0408]

Hi,

Sorry to bother you again.

According to your paper, the 2-warp consistency loss should compare reconstructed $\ddot{I}$ via 2 warp and $\tilde{I}$ that reconstructed directly.

However, in your train.py code, take the module(a) for example,

if args.type_of_2warp == 1:
    mask_4 = [fw_mask[i][[2,3]] for i in range(4)]
    warp2_est_4 = [Resample2d()(left_est[i][[0,1]], disp_est_scale[i][[2,3]]) for i in range(4)]
    loss += 0.1 * sum([warp_2(warp2_est_4[i], left_pyramid[i][[6,7]], mask_4[i], args) for i in range(4)])
    mask_5 = [bw_mask[i][[2,3]] for i in range(4)]
    warp2_est_5 = [Resample2d()(left_est[i][[6,7]], disp_est_scale_2[i][[2,3]]) for i in range(4)]
    loss += 0.1 * sum([warp_2(warp2_est_5[i], left_pyramid[i][[0,1]], mask_5[i], args) for i in range(4)])

from my understanding, the warp2_est_4 corresponds to $\ddot{L_1}$ and warp2_est_5 corresponds to $\ddot{L_2}$, and you use left_pyramid[i][[6,7]] and left_pyramid[i][[0,1]] to compute warp_2 loss, which means you use the original L1 and L2, rather than the directly reconstructed results $\tilde{L_1}$ and $\tilde{L_2}$ (I think should be left_est[i][[6,7]] and left_est[i][[0,1]]?)here.

Is there any typo here, or do I have misunderstanding?

Looking forward to your reply. Appreciate your help!

[lelimite4444]

Yes, they're typos. It should be left_est, not left_pyramid. Sorry for that, i'll update it as soon as possible.