fdscs-pytorch

The original model is from FDSCS.

We has reproduced fdscs in PyTorch. The main work is to implement Cencus, Hamming and Abosulte Difference by the operators of PyTorch.

You can directly put fdscs into the project PSM, then strat training.

Loss function:

maxdisp = ...
imgL, imgR = ...
disp_true = ...


model = fdscs(maxdisp)
mask = disp_true < maxdisp
output = model(imgL, imgR)
output = torch.squeeze(output, 1)

loss = F.smooth_l1_loss(output[mask], disp_true[mask], size_average=True)
L1 = torch.abs(output - disp_true)
pc = (L1 < 1.0).float()
pc = torch.mean(pc[mask])
pc3 = (L1 < 3.0).float()
pc3 = torch.mean(pc3[mask])

print("loss={:.4f}, pc={:.4f}, pc3={:.4f}".format(loss, pc, pc3))