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update build_targets() (#589)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Glenn Jocher 5 years ago committed by GitHub
parent 8bf3cff875
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1 changed files with 48 additions and 55 deletions
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utils/utils.py
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@ -308,7 +308,7 @@ def compute_ap(recall, precision):
def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False):
# Returns the IoU of box1 to box2. box1 is 4, box2 is nx4
box2 = box2.t()
box2 = box2.T
# Get the coordinates of bounding boxes
if x1y1x2y2: # x1, y1, x2, y2 = box1
@ -347,7 +347,7 @@ def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False):
v = (4 / math.pi ** 2) * torch.pow(torch.atan(w2 / h2) - torch.atan(w1 / h1), 2)
with torch.no_grad():
alpha = v / (1 - iou + v + 1e-16)
return iou - (rho2 / c2 + v * alpha ) # CIoU
return iou - (rho2 / c2 + v * alpha) # CIoU
return iou
@ -369,8 +369,8 @@ def box_iou(box1, box2):
# box = 4xn
return (box[2] - box[0]) * (box[3] - box[1])
area1 = box_area(box1.t())
area2 = box_area(box2.t())
area1 = box_area(box1.T)
area2 = box_area(box2.T)
# inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)
inter = (torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2)
@ -439,70 +439,62 @@ class BCEBlurWithLogitsLoss(nn.Module):
def compute_loss(p, targets, model): # predictions, targets, model
device = targets.device
ft = torch.cuda.FloatTensor if p[0].is_cuda else torch.Tensor
lcls, lbox, lobj = ft([0]).to(device), ft([0]).to(device), ft([0]).to(device)
lcls, lbox, lobj = torch.zeros(3, 1, device=device)
tcls, tbox, indices, anchors = build_targets(p, targets, model) # targets
h = model.hyp # hyperparameters
red = 'mean' # Loss reduction (sum or mean)
# Define criteria
BCEcls = nn.BCEWithLogitsLoss(pos_weight=ft([h['cls_pw']]), reduction=red).to(device)
BCEobj = nn.BCEWithLogitsLoss(pos_weight=ft([h['obj_pw']]), reduction=red).to(device)
BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.Tensor([h['cls_pw']])).to(device)
BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.Tensor([h['obj_pw']])).to(device)
# class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
# Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
cp, cn = smooth_BCE(eps=0.0)
# focal loss
# Focal loss
g = h['fl_gamma'] # focal loss gamma
if g > 0:
BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)
# per output
# Losses
nt = 0 # number of targets
np = len(p) # number of outputs
balance = [4.0, 1.0, 0.4] if np == 3 else [4.0, 1.0, 0.4, 0.1] # P3-5 or P3-6
for i, pi in enumerate(p): # layer index, layer predictions
b, a, gj, gi = indices[i] # image, anchor, gridy, gridx
tobj = torch.zeros_like(pi[..., 0]).to(device) # target obj
tobj = torch.zeros_like(pi[..., 0], device=device) # target obj
nb = b.shape[0] # number of targets
if nb:
nt += nb # cumulative targets
n = b.shape[0] # number of targets
if n:
nt += n # cumulative targets
ps = pi[b, a, gj, gi] # prediction subset corresponding to targets
# GIoU
# Regression
pxy = ps[:, :2].sigmoid() * 2. - 0.5
pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i]
pbox = torch.cat((pxy, pwh), 1).to(device) # predicted box
giou = bbox_iou(pbox.t(), tbox[i], x1y1x2y2=False, GIoU=True) # giou(prediction, target)
lbox += (1.0 - giou).sum() if red == 'sum' else (1.0 - giou).mean() # giou loss
giou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True) # giou(prediction, target)
lbox += (1.0 - giou).mean() # giou loss
# Obj
# Objectness
tobj[b, a, gj, gi] = (1.0 - model.gr) + model.gr * giou.detach().clamp(0).type(tobj.dtype) # giou ratio
# Class
# Classification
if model.nc > 1: # cls loss (only if multiple classes)
t = torch.full_like(ps[:, 5:], cn).to(device) # targets
t[range(nb), tcls[i]] = cp
lcls += BCEcls(ps[:, 5:], t) # BCE
t = torch.full_like(ps[:, 5:], cn, device=device) # targets
t[range(n), tcls[i]] = cp
lcls = lcls + BCEcls(ps[:, 5:], t) # BCE
# Append targets to text 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)]
lobj += BCEobj(pi[..., 4], tobj) * balance[i] # obj loss
lobj = lobj + BCEobj(pi[..., 4], tobj) * balance[i] # obj loss
s = 3 / np # output count scaling
lbox *= h['giou'] * s
lobj *= h['obj'] * s * (1.4 if np == 4 else 1.)
lcls *= h['cls'] * s
bs = tobj.shape[0] # batch size
if red == 'sum':
g = 3.0 # loss gain
lobj *= g / bs
if nt:
lcls *= g / nt / model.nc
lbox *= g / nt
loss = lbox + lobj + lcls
return loss * bs, torch.cat((lbox, lobj, lcls, loss)).detach()
@ -510,40 +502,40 @@ def compute_loss(p, targets, model): # predictions, targets, model
def build_targets(p, targets, model):
# Build targets for compute_loss(), input targets(image,class,x,y,w,h)
det = model.module.model[-1] if type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel) \
else model.model[-1] # Detect() module
det = model.module.model[-1] if torch_utils.is_parallel(model) else model.model[-1] # Detect() module
na, nt = det.na, targets.shape[0] # number of anchors, targets
tcls, tbox, indices, anch = [], [], [], []
gain = torch.ones(6, device=targets.device) # normalized to gridspace gain
off = torch.tensor([[1, 0], [0, 1], [-1, 0], [0, -1]], device=targets.device).float() # overlap offsets
at = torch.arange(na).view(na, 1).repeat(1, nt) # anchor tensor, same as .repeat_interleave(nt)
gain = torch.ones(7, device=targets.device) # normalized to gridspace gain
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
g = 0.5 # bias
off = torch.tensor([[0, 0],
[1, 0], [0, 1], [-1, 0], [0, -1], # j,k,l,m
# [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm
], device=targets.device).float() * g # offsets
g = 0.5 # offset
style = 'rect4'
for i in range(det.nl):
anchors = det.anchors[i]
gain[2:] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain
gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain
# Match targets to anchors
a, t, offsets = [], targets * gain, 0
t, offsets = targets * gain, 0
if nt:
r = t[None, :, 4:6] / anchors[:, None] # wh ratio
# Matches
r = t[:, :, 4:6] / anchors[:, None] # wh ratio
j = torch.max(r, 1. / r).max(2)[0] < model.hyp['anchor_t'] # compare
# j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n) = wh_iou(anchors(3,2), gwh(n,2))
a, t = at[j], t.repeat(na, 1, 1)[j] # filter
# j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))
t = t[j] # filter
# overlaps
# Offsets
gxy = t[:, 2:4] # grid xy
z = torch.zeros_like(gxy)
if style == 'rect2':
j, k = ((gxy % 1. < g) & (gxy > 1.)).T
a, t = torch.cat((a, a[j], a[k]), 0), torch.cat((t, t[j], t[k]), 0)
offsets = torch.cat((z, z[j] + off[0], z[k] + off[1]), 0) * g
elif style == 'rect4':
j, k = ((gxy % 1. < g) & (gxy > 1.)).T
l, m = ((gxy % 1. > (1 - g)) & (gxy < (gain[[2, 3]] - 1.))).T
a, t = torch.cat((a, a[j], a[k], a[l], a[m]), 0), torch.cat((t, t[j], t[k], t[l], t[m]), 0)
offsets = torch.cat((z, z[j] + off[0], z[k] + off[1], z[l] + off[2], z[m] + off[3]), 0) * g
gxi = gain[[2, 3]] - gxy # inverse
j, k = ((gxy % 1. < g) & (gxy > 1.)).T
l, m = ((gxi % 1. < g) & (gxi > 1.)).T
j = torch.stack((torch.ones_like(j), j, k, l, m))
t = t.repeat((5, 1, 1))[j]
offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]
# Define
b, c = t[:, :2].long().T # image, class
@ -553,6 +545,7 @@ def build_targets(p, targets, model):
gi, gj = gij.T # grid xy indices
# Append
a = t[:, 6].long() # anchor indices
indices.append((b, a, gj, gi)) # image, anchor, grid indices
tbox.append(torch.cat((gxy - gij, gwh), 1)) # box
anch.append(anchors[a]) # anchors
@ -599,7 +592,7 @@ def non_max_suppression(prediction, conf_thres=0.1, iou_thres=0.6, merge=False,
# Detections matrix nx6 (xyxy, conf, cls)
if multi_label:
i, j = (x[:, 5:] > conf_thres).nonzero(as_tuple=False).t()
i, j = (x[:, 5:] > conf_thres).nonzero(as_tuple=False).T
x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1)
else: # best class only
conf, j = x[:, 5:].max(1, keepdim=True)

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