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| # Copyright 2019-present NAVER Corp. | |
| # CC BY-NC-SA 3.0 | |
| # Available only for non-commercial use | |
| import pdb | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from nets.ap_loss import APLoss | |
| class PixelAPLoss(nn.Module): | |
| """Computes the pixel-wise AP loss: | |
| Given two images and ground-truth optical flow, computes the AP per pixel. | |
| feat1: (B, C, H, W) pixel-wise features extracted from img1 | |
| feat2: (B, C, H, W) pixel-wise features extracted from img2 | |
| aflow: (B, 2, H, W) absolute flow: aflow[...,y1,x1] = x2,y2 | |
| """ | |
| def __init__(self, sampler, nq=20): | |
| nn.Module.__init__(self) | |
| self.aploss = APLoss(nq, min=0, max=1, euc=False) | |
| self.name = "pixAP" | |
| self.sampler = sampler | |
| def loss_from_ap(self, ap, rel): | |
| return 1 - ap | |
| def forward(self, descriptors, aflow, **kw): | |
| # subsample things | |
| scores, gt, msk, qconf = self.sampler(descriptors, kw.get("reliability"), aflow) | |
| # compute pixel-wise AP | |
| n = qconf.numel() | |
| if n == 0: | |
| return 0 | |
| scores, gt = scores.view(n, -1), gt.view(n, -1) | |
| ap = self.aploss(scores, gt).view(msk.shape) | |
| pixel_loss = self.loss_from_ap(ap, qconf) | |
| loss = pixel_loss[msk].mean() | |
| return loss | |
| class ReliabilityLoss(PixelAPLoss): | |
| """same than PixelAPLoss, but also train a pixel-wise confidence | |
| that this pixel is going to have a good AP. | |
| """ | |
| def __init__(self, sampler, base=0.5, **kw): | |
| PixelAPLoss.__init__(self, sampler, **kw) | |
| assert 0 <= base < 1 | |
| self.base = base | |
| self.name = "reliability" | |
| def loss_from_ap(self, ap, rel): | |
| return 1 - ap * rel - (1 - rel) * self.base | |