235 lines
9.7 KiB
Python
235 lines
9.7 KiB
Python
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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
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"""
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Loss functions
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"""
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import torch
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import torch.nn as nn
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from utils.metrics import bbox_iou
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from utils.torch_utils import de_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().__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().__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().__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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class ComputeLoss:
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sort_obj_iou = False
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# Compute losses
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def __init__(self, model, autobalance=False):
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device = next(model.parameters()).device # get model device
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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))
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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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self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0)) # positive, negative BCE targets
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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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m = de_parallel(model).model[-1] # Detect() module
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self.balance = {3: [4.0, 1.0, 0.4]}.get(m.nl, [4.0, 1.0, 0.25, 0.06, 0.02]) # P3-P7
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self.ssi = list(m.stride).index(16) if autobalance else 0 # stride 16 index
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self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, 1.0, h, autobalance
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self.na = m.na # number of anchors
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self.nc = m.nc # number of classes
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self.nl = m.nl # number of layers
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self.anchors = m.anchors
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self.device = device
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def __call__(self, p, targets): # predictions, targets
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lcls = torch.zeros(1, device=self.device) # class loss
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lbox = torch.zeros(1, device=self.device) # box loss
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lobj = torch.zeros(1, device=self.device) # object loss
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tcls, tbox, indices, anchors = self.build_targets(p, targets) # targets
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# Losses
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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(pi.shape[:4], dtype=pi.dtype, device=self.device) # target obj
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n = b.shape[0] # number of targets
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if n:
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# pxy, pwh, _, pcls = pi[b, a, gj, gi].tensor_split((2, 4, 5), dim=1) # faster, requires torch 1.8.0
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pxy, pwh, _, pcls = pi[b, a, gj, gi].split((2, 2, 1, self.nc), 1) # target-subset of predictions
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# Regression
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pxy = pxy.sigmoid() * 2 - 0.5
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pwh = (pwh.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, tbox[i], CIoU=True).squeeze() # iou(prediction, target)
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lbox += (1.0 - iou).mean() # iou loss
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# Objectness
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iou = iou.detach().clamp(0).type(tobj.dtype)
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if self.sort_obj_iou:
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j = iou.argsort()
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b, a, gj, gi, iou = b[j], a[j], gj[j], gi[j], iou[j]
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if self.gr < 1:
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iou = (1.0 - self.gr) + self.gr * iou
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tobj[b, a, gj, gi] = iou # iou ratio
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# Classification
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if self.nc > 1: # cls loss (only if multiple classes)
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t = torch.full_like(pcls, self.cn, device=self.device) # targets
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t[range(n), tcls[i]] = self.cp
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lcls += self.BCEcls(pcls, 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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obji = self.BCEobj(pi[..., 4], tobj)
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lobj += obji * self.balance[i] # obj loss
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if self.autobalance:
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self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item()
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if self.autobalance:
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self.balance = [x / self.balance[self.ssi] for x in self.balance]
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lbox *= self.hyp['box']
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lobj *= self.hyp['obj']
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lcls *= self.hyp['cls']
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bs = tobj.shape[0] # batch size
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return (lbox + lobj + lcls) * bs, torch.cat((lbox, lobj, lcls)).detach()
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def build_targets(self, p, targets):
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# Build targets for compute_loss(), input targets(image,class,x,y,w,h)
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na, nt = self.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=self.device) # normalized to gridspace gain
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ai = torch.arange(na, device=self.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(
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[
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[0, 0],
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[1, 0],
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[0, 1],
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[-1, 0],
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[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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],
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device=self.device).float() * g # offsets
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for i in range(self.nl):
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anchors, shape = self.anchors[i], p[i].shape
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gain[2:6] = torch.tensor(shape)[[3, 2, 3, 2]] # xyxy gain
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# Match targets to anchors
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t = targets * gain # shape(3,n,7)
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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] < self.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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bc, gxy, gwh, a = t.chunk(4, 1) # (image, class), grid xy, grid wh, anchors
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a, (b, c) = a.long().view(-1), bc.long().T # anchors, image, class
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gij = (gxy - offsets).long()
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gi, gj = gij.T # grid indices
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# Append
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indices.append((b, a, gj.clamp_(0, shape[2] - 1), gi.clamp_(0, shape[3] - 1))) # image, anchor, grid
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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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