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import torch
import torch.nn.functional as F
from modules.dataset.megadepth import megadepth_warper
from modules.training import utils
from third_party.alike_wrapper import extract_alike_kpts
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
def smooth_l1_loss(input, target, beta=2.0, size_average=True):
diff = torch.abs(input - target)
loss = torch.where(diff < beta, 0.5 * diff ** 2 / beta, diff - 0.5 * beta)
return loss.mean() if size_average else loss.sum()
def fine_loss(f1, f2, pts1, pts2, fine_module, ws=7):
'''
Compute Fine features and spatial loss
'''
C, H, W = f1.shape
N = len(pts1)
#Sort random offsets
with torch.no_grad():
a = -(ws//2)
b = (ws//2)
offset_gt = (a - b) * torch.rand(N, 2, device = f1.device) + b
pts2_random = pts2 + offset_gt
#pdb.set_trace()
patches1 = utils.crop_patches(f1.unsqueeze(0), (pts1+0.5).long(), size=ws).view(C, N, ws * ws).permute(1, 2, 0) #[N, ws*ws, C]
patches2 = utils.crop_patches(f2.unsqueeze(0), (pts2_random+0.5).long(), size=ws).view(C, N, ws * ws).permute(1, 2, 0) #[N, ws*ws, C]
#Apply transformer
patches1, patches2 = fine_module(patches1, patches2)
features = patches1.view(N, ws, ws, C)[:, ws//2, ws//2, :].view(N, 1, 1, C) # [N, 1, 1, C]
patches2 = patches2.view(N, ws, ws, C) # [N, w, w, C]
#Dot Product
heatmap_match = (features * patches2).sum(-1)
offset_coords = utils.subpix_softmax2d(heatmap_match)
#Invert offset because center crop inverts it
offset_gt = -offset_gt
#MSE
error = ((offset_coords - offset_gt)**2).sum(-1).mean()
#error = smooth_l1_loss(offset_coords, offset_gt)
return error
def alike_distill_loss(kpts, img):
C, H, W = kpts.shape
kpts = kpts.permute(1,2,0)
img = img.permute(1,2,0).expand(-1,-1,3).cpu().numpy() * 255
with torch.no_grad():
alike_kpts = torch.tensor( extract_alike_kpts(img), device=kpts.device )
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)
loss = F.nll_loss(kpts, labels, reduction = 'mean')
return loss, acc
def keypoint_position_loss(kpts1, kpts2, pts1, pts2, softmax_temp = 1.0):
'''
Computes coordinate classification loss, by re-interpreting the 64 bins to 8x8 grid and optimizing
for correct offsets
'''
C, H, W = kpts1.shape
kpts1 = kpts1.permute(1,2,0) * softmax_temp
kpts2 = kpts2.permute(1,2,0) * softmax_temp
with torch.no_grad():
#Generate meshgrid
x, y = torch.meshgrid(torch.arange(W, device=kpts1.device), torch.arange(H, device=kpts1.device), indexing ='xy')
xy = torch.cat([x.unsqueeze(-1), y.unsqueeze(-1)], dim=-1)
xy*=8
#Generate collision map
hashmap = torch.ones((H*8, W*8, 2), dtype = torch.long, device = kpts1.device) * -1
hashmap[(pts1[:,1]).long(), (pts1[:,0]).long(), :] = (pts2).long()
#Estimate offset of src kpts
_, kpts1_offsets = kpts1.max(dim=-1)
kpts1_offsets_x = kpts1_offsets % 8
kpts1_offsets_y = kpts1_offsets // 8
kpts1_offsets_xy = torch.cat([kpts1_offsets_x.unsqueeze(-1),
kpts1_offsets_y.unsqueeze(-1)], dim=-1)
#pdb.set_trace()
kpts1_coords = xy + kpts1_offsets_xy
#find src -> tgt pts
kpts1_coords = kpts1_coords.view(-1,2)
gt_12 = hashmap[kpts1_coords[:,1], kpts1_coords[:,0]]
mask_valid = torch.all(gt_12 >= 0, dim=-1)
gt_12 = gt_12[mask_valid]
#find offset labels
labels2 = (gt_12/8) - (gt_12/8).long()
labels2 = (labels2 * 8).long()
labels2 = labels2[:, 0] + 8*labels2[:, 1] #linear index
kpts2_selected = kpts2[(gt_12[:, 1]/8).long(), (gt_12[:, 0]/8).long()]
kpts1_selected = F.log_softmax(kpts1.view(-1,C)[mask_valid], dim=-1)
kpts2_selected = F.log_softmax(kpts2_selected, dim=-1)
#Here we enforce softmax to keep current max on src kps
with torch.no_grad():
_, labels1 = kpts1_selected.max(dim=-1)
predicted2 = kpts2_selected.max(dim=-1)[1]
acc = (labels2 == predicted2)
acc = acc.sum() / len(acc)
loss = F.nll_loss(kpts1_selected, labels1, reduction = 'mean') + \
F.nll_loss(kpts2_selected, labels2, reduction = 'mean')
#pdb.set_trace()
return loss, acc
def coordinate_classification_loss(coords1, pts1, pts2, conf):
'''
Computes the fine coordinate classification loss, by re-interpreting the 64 bins to 8x8 grid and optimizing
for correct offsets after warp
'''
#Do not backprop coordinate warps
with torch.no_grad():
coords1_detached = pts1 * 8
#find offset
offsets1_detached = (coords1_detached/8) - (coords1_detached/8).long()
offsets1_detached = (offsets1_detached * 8).long()
labels1 = offsets1_detached[:, 0] + 8*offsets1_detached[:, 1]
#pdb.set_trace()
coords1_log = F.log_softmax(coords1, dim=-1)
predicted = coords1.max(dim=-1)[1]
acc = (labels1 == predicted)
acc = acc[conf > 0.1]
acc = acc.sum() / len(acc)
loss = F.nll_loss(coords1_log, labels1, 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 keypoint_loss(heatmap, target):
# Compute L1 loss
L1_loss = F.l1_loss(heatmap, target)
return L1_loss * 3.0
def hard_triplet_loss(X,Y, margin = 0.5):
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 = torch.cdist(X, Y, p=2.0)
dist_pos = torch.diag(dist_mat)
dist_neg = dist_mat + 100.*torch.eye(*dist_mat.size(), dtype = dist_mat.dtype,
device = dist_mat.get_device() if dist_mat.is_cuda else torch.device("cpu"))
#filter repeated patches on negative distances to avoid weird stuff on gradients
dist_neg = dist_neg + dist_neg.le(0.01).float()*100.
#Margin Ranking Loss
hard_neg = torch.min(dist_neg, 1)[0]
loss = torch.clamp(margin + dist_pos - hard_neg, min=0.)
return loss.mean()
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