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import os
import sys
import torch
import torch.nn as nn
import torch.nn.functional as F
import time
def dual_softmax_loss(X, Y, temp = 0.2):
if X.size() != Y.size() or X.dim() != 2 or Y.dim() != 2:
raise RuntimeError('Error: X and Y shapes must match and be 2D matrices')
dist_mat = (X @ Y.t()) * temp
conf_matrix12 = F.log_softmax(dist_mat, dim=1)
conf_matrix21 = F.log_softmax(dist_mat.t(), dim=1)
with torch.no_grad():
conf12 = torch.exp( conf_matrix12 ).max(dim=-1)[0]
conf21 = torch.exp( conf_matrix21 ).max(dim=-1)[0]
conf = conf12 * conf21
target = torch.arange(len(X), device = X.device)
loss = F.nll_loss(conf_matrix12, target) + \
F.nll_loss(conf_matrix21, target)
return loss, conf
class LiftFeatLoss(nn.Module):
def __init__(self,device,lam_descs=1,lam_fb_descs=1,lam_kpts=1,lam_heatmap=1,lam_normals=1,lam_coordinates=1,lam_fb_coordinates=1,depth_spvs=False):
super().__init__()
# loss parameters
self.lam_descs=lam_descs
self.lam_fb_descs=lam_fb_descs
self.lam_kpts=lam_kpts
self.lam_heatmap=lam_heatmap
self.lam_normals=lam_normals
self.lam_coordinates=lam_coordinates
self.lam_fb_coordinates=lam_fb_coordinates
self.depth_spvs=depth_spvs
self.running_descs_loss=0
self.running_kpts_loss=0
self.running_heatmaps_loss=0
self.loss_descs=0
self.loss_fb_descs=0
self.loss_kpts=0
self.loss_heatmaps=0
self.loss_normals=0
self.loss_coordinates=0
self.loss_fb_coordinates=0
self.acc_coarse=0
self.acc_fb_coarse=0
self.acc_kpt=0
self.acc_coordinates=0
self.acc_fb_coordinates=0
# device
self.dev=device
def check_accuracy(self,m1,m2,pts1=None,pts2=None,plot=False):
with torch.no_grad():
#dist_mat = torch.cdist(X,Y)
dist_mat = m1 @ m2.t()
nn = torch.argmax(dist_mat, dim=1)
#nn = torch.argmin(dist_mat, dim=1)
correct = nn == torch.arange(len(m1), device = m1.device)
if pts1 is not None and plot:
import matplotlib.pyplot as plt
canvas = torch.zeros((60, 80),device=m1.device)
pts1 = pts1[~correct]
canvas[pts1[:,1].long(), pts1[:,0].long()] = 1
canvas = canvas.cpu().numpy()
plt.imshow(canvas), plt.show()
acc = correct.sum().item() / len(m1)
return acc
def compute_descriptors_loss(self,descs1,descs2,pts):
loss=[]
acc=0
B,_,H,W=descs1.shape
conf_list=[]
for b in range(B):
pts1,pts2=pts[b][:,:2],pts[b][:,2:]
m1=descs1[b,:,pts1[:,1].long(),pts1[:,0].long()].permute(1,0)
m2=descs2[b,:,pts2[:,1].long(),pts2[:,0].long()].permute(1,0)
loss_per,conf_per=dual_softmax_loss(m1,m2)
loss.append(loss_per.unsqueeze(0))
conf_list.append(conf_per)
acc_coarse_per=self.check_accuracy(m1,m2)
acc += acc_coarse_per
loss=torch.cat(loss,dim=-1).mean()
acc /= B
return loss,acc,conf_list
def alike_distill_loss(self,kpts,alike_kpts):
C, H, W = kpts.shape
kpts = kpts.permute(1,2,0)
# get ALike keypoints
with torch.no_grad():
labels = torch.ones((H, W), dtype = torch.long, device = kpts.device) * 64 # -> Default is non-keypoint (bin 64)
offsets = (((alike_kpts/8) - (alike_kpts/8).long())*8).long()
offsets = offsets[:, 0] + 8*offsets[:, 1] # Linear IDX
labels[(alike_kpts[:,1]/8).long(), (alike_kpts[:,0]/8).long()] = offsets
kpts = kpts.view(-1,C)
labels = labels.view(-1)
mask = labels < 64
idxs_pos = mask.nonzero().flatten()
idxs_neg = (~mask).nonzero().flatten()
perm = torch.randperm(idxs_neg.size(0))[:len(idxs_pos)//32]
idxs_neg = idxs_neg[perm]
idxs = torch.cat([idxs_pos, idxs_neg])
kpts = kpts[idxs]
labels = labels[idxs]
with torch.no_grad():
predicted = kpts.max(dim=-1)[1]
acc = (labels == predicted)
acc = acc.sum() / len(acc)
kpts = F.log_softmax(kpts,dim=-1)
loss = F.nll_loss(kpts, labels, reduction = 'mean')
return loss, acc
def compute_keypoints_loss(self,kpts1,kpts2,alike_kpts1,alike_kpts2):
loss=[]
acc=0
B,_,H,W=kpts1.shape
for b in range(B):
loss_per1,acc_per1=self.alike_distill_loss(kpts1[b],alike_kpts1[b])
loss_per2,acc_per2=self.alike_distill_loss(kpts2[b],alike_kpts2[b])
loss_per=(loss_per1+loss_per2)
acc_per=(acc_per1+acc_per2)/2
loss.append(loss_per.unsqueeze(0))
acc += acc_per
loss=torch.cat(loss,dim=-1).mean()
acc /= B
return loss,acc
def compute_heatmaps_loss(self,heatmaps1,heatmaps2,pts,conf_list):
loss=[]
B,_,H,W=heatmaps1.shape
for b in range(B):
pts1,pts2=pts[b][:,:2],pts[b][:,2:]
h1=heatmaps1[b,0,pts1[:,1].long(),pts1[:,0].long()]
h2=heatmaps2[b,0,pts2[:,1].long(),pts2[:,0].long()]
conf=conf_list[b]
loss_per1=F.l1_loss(h1,conf)
loss_per2=F.l1_loss(h2,conf)
loss_per=(loss_per1+loss_per2)
loss.append(loss_per.unsqueeze(0))
loss=torch.cat(loss,dim=-1).mean()
return loss
def normal_loss(self,normal,target_normal):
# import pdb;pdb.set_trace()
normal = normal.permute(1, 2, 0)
target_normal = target_normal.permute(1,2,0)
# loss = F.l1_loss(d_feat, depth_anything_normal_feat)
dot = torch.cosine_similarity(normal, target_normal, dim=2)
valid_mask = target_normal[:, :, 0].float() \
* (dot.detach() < 0.999).float() \
* (dot.detach() > -0.999).float()
valid_mask = valid_mask > 0.0
al = torch.acos(dot[valid_mask])
loss = torch.mean(al)
return loss
def compute_normals_loss(self,normals1,normals2,DA_normals1,DA_normals2,megadepth_batch_size,coco_batch_size):
loss=[]
# import pdb;pdb.set_trace()
# only MegaDepth image need depth-normal
normals1=normals1[coco_batch_size:,...]
normals2=normals2[coco_batch_size:,...]
for b in range(len(DA_normals1)):
normal1,normal2=normals1[b],normals2[b]
loss_per1=self.normal_loss(normal1,DA_normals1[b].permute(2,0,1))
loss_per2=self.normal_loss(normal2,DA_normals2[b].permute(2,0,1))
loss_per=(loss_per1+loss_per2)
loss.append(loss_per.unsqueeze(0))
loss=torch.cat(loss,dim=-1).mean()
return loss
def coordinate_loss(self,coordinate,conf,pts1):
with torch.no_grad():
coordinate_detached = pts1 * 8
offset_detached = (coordinate_detached/8) - (coordinate_detached/8).long()
offset_detached = (offset_detached * 8).long()
label = offset_detached[:, 0] + 8*offset_detached[:, 1]
#pdb.set_trace()
coordinate_log = F.log_softmax(coordinate, dim=-1)
predicted = coordinate.max(dim=-1)[1]
acc = (label == predicted)
acc = acc[conf > 0.1]
acc = acc.sum() / len(acc)
loss = F.nll_loss(coordinate_log, label, reduction = 'none')
#Weight loss by confidence, giving more emphasis on reliable matches
conf = conf / conf.sum()
loss = (loss * conf).sum()
return loss*2., acc
def compute_coordinates_loss(self,coordinates,pts,conf_list):
loss=[]
acc=0
B,_,H,W=coordinates.shape
for b in range(B):
pts1,pts2=pts[b][:,:2],pts[b][:,2:]
coordinate=coordinates[b,:,pts1[:,1].long(),pts1[:,0].long()].permute(1,0)
conf=conf_list[b]
loss_per,acc_per=self.coordinate_loss(coordinate,conf,pts1)
loss.append(loss_per.unsqueeze(0))
acc += acc_per
loss=torch.cat(loss,dim=-1).mean()
acc /= B
return loss,acc
def forward(self,
descs1,fb_descs1,kpts1,normals1,
descs2,fb_descs2,kpts2,normals2,
pts,coordinates,fb_coordinates,
alike_kpts1,alike_kpts2,
DA_normals1,DA_normals2,
megadepth_batch_size,coco_batch_size
):
# import pdb;pdb.set_trace()
self.loss_descs,self.acc_coarse,conf_list=self.compute_descriptors_loss(descs1,descs2,pts)
self.loss_fb_descs,self.acc_fb_coarse,fb_conf_list=self.compute_descriptors_loss(fb_descs1,fb_descs2,pts)
# start=time.perf_counter()
self.loss_kpts,self.acc_kpt=self.compute_keypoints_loss(kpts1,kpts2,alike_kpts1,alike_kpts2)
# end=time.perf_counter()
# print(f"kpts loss cost {end-start} seconds")
# start=time.perf_counter()
self.loss_normals=self.compute_normals_loss(normals1,normals2,DA_normals1,DA_normals2,megadepth_batch_size,coco_batch_size)
# end=time.perf_counter()
# print(f"normal loss cost {end-start} seconds")
self.loss_coordinates,self.acc_coordinates=self.compute_coordinates_loss(coordinates,pts,conf_list)
self.loss_fb_coordinates,self.acc_fb_coordinates=self.compute_coordinates_loss(fb_coordinates,pts,fb_conf_list)
return {
'loss_descs':self.lam_descs*self.loss_descs,
'acc_coarse':self.acc_coarse,
'loss_coordinates':self.lam_coordinates*self.loss_coordinates,
'acc_coordinates':self.acc_coordinates,
'loss_fb_descs':self.lam_fb_descs*self.loss_fb_descs,
'acc_fb_coarse':self.acc_fb_coarse,
'loss_fb_coordinates':self.lam_fb_coordinates*self.loss_fb_coordinates,
'acc_fb_coordinates':self.acc_fb_coordinates,
'loss_kpts':self.lam_kpts*self.loss_kpts,
'acc_kpt':self.acc_kpt,
'loss_normals':self.lam_normals*self.loss_normals,
}
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