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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. - 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 |