206 lines
8.2 KiB
Python
206 lines
8.2 KiB
Python
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# Loss functions
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import torch
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import torch.nn as nn
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from utils.general import bbox_iou
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from utils.torch_utils import is_parallel
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def smooth_BCE(eps=0.1): # https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441
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# return positive, negative label smoothing BCE targets
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return 1.0 - 0.5 * eps, 0.5 * eps
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class BCEBlurWithLogitsLoss(nn.Module):
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# BCEwithLogitLoss() with reduced missing label effects.
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def __init__(self, alpha=0.05):
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super(BCEBlurWithLogitsLoss, self).__init__()
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self.loss_fcn = nn.BCEWithLogitsLoss(reduction='none') # must be nn.BCEWithLogitsLoss()
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self.alpha = alpha
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def forward(self, pred, true):
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loss = self.loss_fcn(pred, true)
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pred = torch.sigmoid(pred) # prob from logits
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dx = pred - true # reduce only missing label effects
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# dx = (pred - true).abs() # reduce missing label and false label effects
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alpha_factor = 1 - torch.exp((dx - 1) / (self.alpha + 1e-4))
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loss *= alpha_factor
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return loss.mean()
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class FocalLoss(nn.Module):
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# Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)
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def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
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super(FocalLoss, self).__init__()
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self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss()
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self.gamma = gamma
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self.alpha = alpha
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self.reduction = loss_fcn.reduction
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self.loss_fcn.reduction = 'none' # required to apply FL to each element
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def forward(self, pred, true):
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loss = self.loss_fcn(pred, true)
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# p_t = torch.exp(-loss)
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# loss *= self.alpha * (1.000001 - p_t) ** self.gamma # non-zero power for gradient stability
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# TF implementation https://github.com/tensorflow/addons/blob/v0.7.1/tensorflow_addons/losses/focal_loss.py
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pred_prob = torch.sigmoid(pred) # prob from logits
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p_t = true * pred_prob + (1 - true) * (1 - pred_prob)
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alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha)
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modulating_factor = (1.0 - p_t) ** self.gamma
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loss *= alpha_factor * modulating_factor
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if self.reduction == 'mean':
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return loss.mean()
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elif self.reduction == 'sum':
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return loss.sum()
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else: # 'none'
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return loss
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class QFocalLoss(nn.Module):
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# Wraps Quality focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)
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def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
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super(QFocalLoss, self).__init__()
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self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss()
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self.gamma = gamma
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self.alpha = alpha
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self.reduction = loss_fcn.reduction
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self.loss_fcn.reduction = 'none' # required to apply FL to each element
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def forward(self, pred, true):
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loss = self.loss_fcn(pred, true)
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pred_prob = torch.sigmoid(pred) # prob from logits
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alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha)
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modulating_factor = torch.abs(true - pred_prob) ** self.gamma
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loss *= alpha_factor * modulating_factor
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if self.reduction == 'mean':
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return loss.mean()
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elif self.reduction == 'sum':
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return loss.sum()
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else: # 'none'
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return loss
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def compute_loss(p, targets, model): # predictions, targets, model
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device = targets.device
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lcls, lbox, lobj = torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device)
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tcls, tbox, indices, anchors = build_targets(p, targets, model) # targets
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h = model.hyp # hyperparameters
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# Define criteria
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BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device)) # weight=model.class_weights)
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BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device))
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# Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
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cp, cn = smooth_BCE(eps=0.0)
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# Focal loss
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g = h['fl_gamma'] # focal loss gamma
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if g > 0:
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BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)
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# Losses
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nt = 0 # number of targets
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no = len(p) # number of outputs
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balance = [4.0, 1.0, 0.4] if no == 3 else [4.0, 1.0, 0.4, 0.1] # P3-5 or P3-6
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for i, pi in enumerate(p): # layer index, layer predictions
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b, a, gj, gi = indices[i] # image, anchor, gridy, gridx
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tobj = torch.zeros_like(pi[..., 0], device=device) # target obj
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n = b.shape[0] # number of targets
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if n:
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nt += n # cumulative targets
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ps = pi[b, a, gj, gi] # prediction subset corresponding to targets
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# Regression
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pxy = ps[:, :2].sigmoid() * 2. - 0.5
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pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i]
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pbox = torch.cat((pxy, pwh), 1) # predicted box
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iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True) # iou(prediction, target)
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lbox += (1.0 - iou).mean() # iou loss
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# Objectness
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tobj[b, a, gj, gi] = (1.0 - model.gr) + model.gr * iou.detach().clamp(0).type(tobj.dtype) # iou ratio
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# Classification
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if model.nc > 1: # cls loss (only if multiple classes)
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t = torch.full_like(ps[:, 5:], cn, device=device) # targets
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t[range(n), tcls[i]] = cp
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lcls += BCEcls(ps[:, 5:], t) # BCE
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# Append targets to text file
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# with open('targets.txt', 'a') as file:
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# [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)]
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lobj += BCEobj(pi[..., 4], tobj) * balance[i] # obj loss
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s = 3 / no # output count scaling
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lbox *= h['box'] * s
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lobj *= h['obj'] * s * (1.4 if no == 4 else 1.)
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lcls *= h['cls'] * s
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bs = tobj.shape[0] # batch size
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loss = lbox + lobj + lcls
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return loss * bs, torch.cat((lbox, lobj, lcls, loss)).detach()
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def build_targets(p, targets, model):
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# Build targets for compute_loss(), input targets(image,class,x,y,w,h)
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det = model.module.model[-1] if is_parallel(model) else model.model[-1] # Detect() module
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na, nt = det.na, targets.shape[0] # number of anchors, targets
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tcls, tbox, indices, anch = [], [], [], []
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gain = torch.ones(7, device=targets.device) # normalized to gridspace gain
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ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt)
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targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2) # append anchor indices
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g = 0.5 # bias
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off = torch.tensor([[0, 0],
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[1, 0], [0, 1], [-1, 0], [0, -1], # j,k,l,m
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# [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm
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], device=targets.device).float() * g # offsets
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for i in range(det.nl):
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anchors = det.anchors[i]
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gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain
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# Match targets to anchors
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t = targets * gain
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if nt:
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# Matches
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r = t[:, :, 4:6] / anchors[:, None] # wh ratio
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j = torch.max(r, 1. / r).max(2)[0] < model.hyp['anchor_t'] # compare
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# j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))
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t = t[j] # filter
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# Offsets
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gxy = t[:, 2:4] # grid xy
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gxi = gain[[2, 3]] - gxy # inverse
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j, k = ((gxy % 1. < g) & (gxy > 1.)).T
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l, m = ((gxi % 1. < g) & (gxi > 1.)).T
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j = torch.stack((torch.ones_like(j), j, k, l, m))
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t = t.repeat((5, 1, 1))[j]
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offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]
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else:
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t = targets[0]
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offsets = 0
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# Define
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b, c = t[:, :2].long().T # image, class
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gxy = t[:, 2:4] # grid xy
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gwh = t[:, 4:6] # grid wh
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gij = (gxy - offsets).long()
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gi, gj = gij.T # grid xy indices
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# Append
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a = t[:, 6].long() # anchor indices
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indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1))) # image, anchor, grid indices
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tbox.append(torch.cat((gxy - gij, gwh), 1)) # box
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anch.append(anchors[a]) # anchors
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tcls.append(c) # class
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return tcls, tbox, indices, anch
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