mirrors
/
yolov7
mirror of https://github.com/WongKinYiu/yolov7.git
1
0
Fork 0
Code Issues Projects Releases Wiki Activity

main code 

@albertfaromatics https://github.com/WongKinYiu/yolov7/issues/35#issuecomment-1178800685
pull/90/head
Kin-Yiu, Wong 2022-07-10 10:19:41 +08:00 committed by GitHub
parent c587e8f467
commit 54627aa3ac
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 3 additions and 3 deletions
utils

6
utils/loss.py
View File

@ -501,7 +501,7 @@ class ComputeLoss:
# Build targets for compute_loss(), input targets(image,class,x,y,w,h) # Build targets for compute_loss(), input targets(image,class,x,y,w,h)
na, nt = self.na, targets.shape[0] # number of anchors, targets na, nt = self.na, targets.shape[0] # number of anchors, targets
tcls, tbox, indices, anch = [], [], [], [] tcls, tbox, indices, anch = [], [], [], []
gain = torch.ones(7, device=targets.device) # normalized to gridspace gain gain = torch.ones(7, device=targets.device).long() # normalized to gridspace gain
ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt) ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt)
targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2) # append anchor indices targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2) # append anchor indices
@ -775,7 +775,7 @@ class ComputeLossOTA:
matching_anchs[i].append(all_anch[layer_idx]) matching_anchs[i].append(all_anch[layer_idx])
for i in range(nl): for i in range(nl):
if matching_gis[i] != []: if matching_targets[i] != []:
matching_bs[i] = torch.cat(matching_bs[i], dim=0) matching_bs[i] = torch.cat(matching_bs[i], dim=0)
matching_as[i] = torch.cat(matching_as[i], dim=0) matching_as[i] = torch.cat(matching_as[i], dim=0)
matching_gjs[i] = torch.cat(matching_gjs[i], dim=0) matching_gjs[i] = torch.cat(matching_gjs[i], dim=0)
@ -796,7 +796,7 @@ class ComputeLossOTA:
# Build targets for compute_loss(), input targets(image,class,x,y,w,h) # Build targets for compute_loss(), input targets(image,class,x,y,w,h)
na, nt = self.na, targets.shape[0] # number of anchors, targets na, nt = self.na, targets.shape[0] # number of anchors, targets
indices, anch = [], [] indices, anch = [], []
gain = torch.ones(7, device=targets.device) # normalized to gridspace gain gain = torch.ones(7, device=targets.device).long() # normalized to gridspace gain
ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt) ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt)
targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2) # append anchor indices targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2) # append anchor indices