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| import torch | |
| import torch.nn as nn | |
| from .reliability_loss import APLoss | |
| class MultiPixelAPLoss(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.sampler = sampler | |
| self.base = 0.25 | |
| self.dec_base = 0.20 | |
| def loss_from_ap(self, ap, rel, noise_ap, noise_rel): | |
| dec_ap = torch.clamp(ap - noise_ap, min=0, max=1) | |
| return (1 - ap * noise_rel - (1 - noise_rel) * self.base), ( | |
| 1.0 - dec_ap * (1 - noise_rel) - noise_rel * self.dec_base | |
| ) | |
| def forward( | |
| self, | |
| feat0, | |
| feat1, | |
| noise_feat0, | |
| noise_feat1, | |
| conf0, | |
| conf1, | |
| noise_conf0, | |
| noise_conf1, | |
| pos0, | |
| pos1, | |
| B, | |
| H, | |
| W, | |
| N=1500, | |
| ): | |
| # subsample things | |
| scores, noise_scores, gt, msk, qconf, noise_qconf = self.sampler( | |
| feat0, | |
| feat1, | |
| noise_feat0, | |
| noise_feat1, | |
| conf0, | |
| conf1, | |
| noise_conf0, | |
| noise_conf1, | |
| pos0, | |
| pos1, | |
| B, | |
| H, | |
| W, | |
| N=1500, | |
| ) | |
| # compute pixel-wise AP | |
| n = qconf.numel() | |
| if n == 0: | |
| return 0, 0 | |
| scores, noise_scores, gt = scores.view(n, -1), noise_scores, gt.view(n, -1) | |
| ap = self.aploss(scores, gt).view(msk.shape) | |
| noise_ap = self.aploss(noise_scores, gt).view(msk.shape) | |
| pixel_loss = self.loss_from_ap(ap, qconf, noise_ap, noise_qconf) | |
| loss = pixel_loss[0][msk].mean(), pixel_loss[1][msk].mean() | |
| return loss | |