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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2019-08-18 21:24:48 +02:00
parent 4050650669
commit b779e6ef69
2 changed files with 18 additions and 17 deletions
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5
models.py
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@ -155,7 +155,7 @@ class YOLOLayer(nn.Module):
# io[..., 2:4] = ((torch.sigmoid(io[..., 2:4]) * 2) ** 3) * self.anchor_wh # wh power method # io[..., 2:4] = ((torch.sigmoid(io[..., 2:4]) * 2) ** 3) * self.anchor_wh # wh power method
io[..., :4] *= self.stride io[..., :4] *= self.stride
arc = 'normal' # (normal, uCE, uBCE, uBCEs) detection architectures arc = 'uBCEs' # (normal, uCE, uBCE, uBCEs) detection architectures
if arc == 'normal': if arc == 'normal':
torch.sigmoid_(io[..., 4:]) torch.sigmoid_(io[..., 4:])
elif arc == 'uCE': # unified CE (1 background + 80 classes) elif arc == 'uCE': # unified CE (1 background + 80 classes)
@ -165,7 +165,8 @@ class YOLOLayer(nn.Module):
torch.sigmoid_(io[..., 4:]) torch.sigmoid_(io[..., 4:])
io[..., 4] = 1 - io[..., 4] io[..., 4] = 1 - io[..., 4]
elif arc == 'uBCEs': # unified BCE simplified (80 classes) elif arc == 'uBCEs': # unified BCE simplified (80 classes)
torch.sigmoid_(io[..., 4:]) torch.sigmoid_(io[..., 5:])
io[..., 4] = 1
if self.nc == 1: if self.nc == 1:
io[..., 5] = 1 # single-class model https://github.com/ultralytics/yolov3/issues/235 io[..., 5] = 1 # single-class model https://github.com/ultralytics/yolov3/issues/235

30
utils/utils.py
View File

@ -329,51 +329,51 @@ def compute_loss(p, targets, model): # predictions, targets, model
arc = 'normal' # (normal, uCE, uBCE, uBCEs) detection architectures arc = 'normal' # (normal, uCE, uBCE, uBCEs) detection architectures
for i, pi0 in enumerate(p): # layer i predictions, i for i, pi0 in enumerate(p): # layer i predictions, i
b, a, gj, gi = indices[i] # image, anchor, gridy, gridx b, a, gj, gi = indices[i] # image, anchor, gridy, gridx
tobj = torch.zeros_like(pi0[..., 0]) # target obj tobj = torch.zeros_like(pi[..., 0]) # target obj
# Compute losses # Compute losses
nb = len(b) nb = len(b)
if nb: # number of targets if nb: # number of targets
pi = pi0[b, a, gj, gi] # predictions closest to anchors ps = pi[b, a, gj, gi] # prediction subset corresponding to targets
tobj[b, a, gj, gi] = 1.0 # obj tobj[b, a, gj, gi] = 1.0 # obj
# pi[..., 2:4] = torch.sigmoid(pi[..., 2:4]) # wh power loss (uncomment) # ps[:, 2:4] = torch.sigmoid(ps[:, 2:4]) # wh power loss (uncomment)
# GIoU # GIoU
pxy = torch.sigmoid(pi[..., 0:2]) # pxy = pxy * s - (s - 1) / 2, s = 1.5 (scale_xy) pxy = torch.sigmoid(ps[:, 0:2]) # pxy = pxy * s - (s - 1) / 2, s = 1.5 (scale_xy)
pbox = torch.cat((pxy, torch.exp(pi[..., 2:4]) * anchor_vec[i]), 1) # predicted pbox = torch.cat((pxy, torch.exp(ps[:, 2:4]) * anchor_vec[i]), 1) # predicted box
giou = bbox_iou(pbox.t(), tbox[i], x1y1x2y2=False, GIoU=True) # giou computation giou = bbox_iou(pbox.t(), tbox[i], x1y1x2y2=False, GIoU=True) # giou computation
lbox += (1.0 - giou).mean() # giou loss lbox += (1.0 - giou).mean() # giou loss
if arc == 'normal' and model.nc > 1: # cls loss (only if multiple classes) if arc == 'normal' and model.nc > 1: # cls loss (only if multiple classes)
t = torch.zeros_like(pi[..., 5:]) # targets t = torch.zeros_like(ps[:, 5:]) # targets
t[range(nb), tcls[i]] = 1.0 t[range(nb), tcls[i]] = 1.0
lcls += BCEcls(pi[..., 5:], t) # BCE lcls += BCEcls(ps[:, 5:], t) # BCE
# lcls += CE(pi[..., 5:], tcls[i]) # CE # lcls += CE(ps[:, 5:], tcls[i]) # CE
# Append targets to text file # Append targets to text file
# with open('targets.txt', 'a') as file: # with open('targets.txt', 'a') as file:
# [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)] # [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)]
if arc == 'normal': if arc == 'normal':
lobj += BCEobj(pi0[..., 4], tobj) # obj loss lobj += BCEobj(pi[..., 4], tobj) # obj loss
elif arc == 'uCE': # unified CE (1 background + 80 classes), hyps 20 elif arc == 'uCE': # unified CE (1 background + 80 classes), hyps 20
t = torch.zeros_like(pi0[..., 0], dtype=torch.long) # targets t = torch.zeros_like(pi[..., 0], dtype=torch.long) # targets
if nb: if nb:
t[b, a, gj, gi] = tcls[i] + 1 t[b, a, gj, gi] = tcls[i] + 1
lcls += CE(pi0[..., 4:].view(-1, model.nc + 1), t.view(-1)) lcls += CE(pi[..., 4:].view(-1, model.nc + 1), t.view(-1))
elif arc == 'uBCE': # unified BCE (1 background + 80 classes), hyps 200-30 elif arc == 'uBCE': # unified BCE (1 background + 80 classes), hyps 200-30
t = torch.zeros_like(pi0[..., 5:]) # targets t = torch.zeros_like(pi[..., 5:]) # targets
if nb: if nb:
t[b, a, gj, gi, tcls[i]] = 1.0 t[b, a, gj, gi, tcls[i]] = 1.0
lcls += BCEcls(pi0[..., 5:], t) lcls += BCEcls(pi[..., 5:], t)
elif arc == 'uBCEs': # unified BCE simplified (80 classes) elif arc == 'uBCEs': # unified BCE simplified (80 classes)
t = torch.zeros_like(pi0[..., 5:]) # targets t = torch.zeros_like(pi[..., 5:]) # targets
if nb: if nb:
t[b, a, gj, gi, tcls[i]] = 1.0 t[b, a, gj, gi, tcls[i]] = 1.0
lcls += BCEcls(pi0[..., 5:], t) lcls += BCEcls(pi[..., 5:], t)
lbox *= k * h['giou'] lbox *= k * h['giou']
lobj *= k * h['obj'] lobj *= k * h['obj']