From ec69700a9cfbd3bf07c3898ea481a5657f4eeb5e Mon Sep 17 00:00:00 2001
From: "Kin-Yiu, Wong" <102582011@cc.ncu.edu.tw>
Date: Wed, 13 Jul 2022 10:31:45 +0800
Subject: [PATCH] main code

update aux training
---
 utils/loss.py | 499 ++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 499 insertions(+)

diff --git a/utils/loss.py b/utils/loss.py
index a414ba24..6448cacf 100644
--- a/utils/loss.py
+++ b/utils/loss.py
@@ -1163,3 +1163,502 @@ class ComputeLossBinOTA:
             anch.append(anchors[a])  # anchors
 
         return indices, anch
+
+
+class ComputeLossAuxOTA:
+    # Compute losses
+    def __init__(self, model, autobalance=False):
+        super(ComputeLossAuxOTA, self).__init__()
+        device = next(model.parameters()).device  # get model device
+        h = model.hyp  # hyperparameters
+
+        # Define criteria
+        BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device))
+        BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device))
+
+        # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
+        self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0))  # positive, negative BCE targets
+
+        # Focal loss
+        g = h['fl_gamma']  # focal loss gamma
+        if g > 0:
+            BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)
+
+        det = model.module.model[-1] if is_parallel(model) else model.model[-1]  # Detect() module
+        self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.06, .02])  # P3-P7
+        self.ssi = list(det.stride).index(16) if autobalance else 0  # stride 16 index
+        self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, model.gr, h, autobalance
+        for k in 'na', 'nc', 'nl', 'anchors', 'stride':
+            setattr(self, k, getattr(det, k))
+
+    def __call__(self, p, targets, imgs):  # predictions, targets, model   
+        device = targets.device
+        lcls, lbox, lobj = torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device)
+        bs_aux, as_aux_, gjs_aux, gis_aux, targets_aux, anchors_aux = self.build_targets2(p[:self.nl], targets, imgs)
+        bs, as_, gjs, gis, targets, anchors = self.build_targets(p[:self.nl], targets, imgs)
+        pre_gen_gains_aux = [torch.tensor(pp.shape, device=device)[[3, 2, 3, 2]] for pp in p[:self.nl]] 
+        pre_gen_gains = [torch.tensor(pp.shape, device=device)[[3, 2, 3, 2]] for pp in p[:self.nl]] 
+    
+
+        # Losses
+        for i in range(self.nl):  # layer index, layer predictions
+            pi = p[i]
+            pi_aux = p[i+self.nl]
+            b, a, gj, gi = bs[i], as_[i], gjs[i], gis[i]  # image, anchor, gridy, gridx
+            b_aux, a_aux, gj_aux, gi_aux = bs_aux[i], as_aux_[i], gjs_aux[i], gis_aux[i]  # image, anchor, gridy, gridx
+            tobj = torch.zeros_like(pi[..., 0], device=device)  # target obj
+            tobj_aux = torch.zeros_like(pi_aux[..., 0], device=device)  # target obj
+
+            n = b.shape[0]  # number of targets
+            n_aux = b_aux.shape[0]  # number of targets
+            if n:
+                ps = pi[b, a, gj, gi]  # prediction subset corresponding to targets
+                ps_aux = pi_aux[b_aux, a_aux, gj_aux, gi_aux]  # prediction subset corresponding to targets
+
+                # Regression
+                grid = torch.stack([gi, gj], dim=1)
+                grid_aux = torch.stack([gi_aux, gj_aux], dim=1)
+                pxy = ps[:, :2].sigmoid() * 2. - 0.5
+                pxy_aux = ps_aux[:, :2].sigmoid() * 2. - 0.5
+                #pxy = ps[:, :2].sigmoid() * 3. - 1.
+                pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i]
+                pwh_aux = (ps_aux[:, 2:4].sigmoid() * 2) ** 2 * anchors_aux[i]
+                pbox = torch.cat((pxy, pwh), 1)  # predicted box
+                pbox_aux = torch.cat((pxy_aux, pwh_aux), 1)  # predicted box
+                selected_tbox = targets[i][:, 2:6] * pre_gen_gains[i]
+                selected_tbox_aux = targets_aux[i][:, 2:6] * pre_gen_gains_aux[i]
+                selected_tbox[:, :2] -= grid
+                selected_tbox_aux[:, :2] -= grid_aux
+                iou = bbox_iou(pbox.T, selected_tbox, x1y1x2y2=False, CIoU=True)  # iou(prediction, target)
+                iou_aux = bbox_iou(pbox_aux.T, selected_tbox_aux, x1y1x2y2=False, CIoU=True)  # iou(prediction, target)
+                lbox += (1.0 - iou).mean() + 0.25 * (1.0 - iou_aux).mean()  # iou loss
+
+                # Objectness
+                tobj[b, a, gj, gi] = (1.0 - self.gr) + self.gr * iou.detach().clamp(0).type(tobj.dtype)  # iou ratio
+                tobj_aux[b_aux, a_aux, gj_aux, gi_aux] = (1.0 - self.gr) + self.gr * iou_aux.detach().clamp(0).type(tobj_aux.dtype)  # iou ratio
+
+                # Classification
+                selected_tcls = targets[i][:, 1].long()
+                selected_tcls_aux = targets_aux[i][:, 1].long()
+                if self.nc > 1:  # cls loss (only if multiple classes)
+                    t = torch.full_like(ps[:, 5:], self.cn, device=device)  # targets
+                    t_aux = torch.full_like(ps_aux[:, 5:], self.cn, device=device)  # targets
+                    t[range(n), selected_tcls] = self.cp
+                    t_aux[range(n_aux), selected_tcls_aux] = self.cp
+                    lcls += self.BCEcls(ps[:, 5:], t) + 0.25 * self.BCEcls(ps_aux[:, 5:], t_aux)  # 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)]
+
+            obji = self.BCEobj(pi[..., 4], tobj)
+            obji_aux = self.BCEobj(pi_aux[..., 4], tobj_aux)
+            lobj += obji * self.balance[i] + 0.25 * obji_aux * self.balance[i] # obj loss
+            if self.autobalance:
+                self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item()
+
+        if self.autobalance:
+            self.balance = [x / self.balance[self.ssi] for x in self.balance]
+        lbox *= self.hyp['box']
+        lobj *= self.hyp['obj']
+        lcls *= self.hyp['cls']
+        bs = tobj.shape[0]  # batch size
+
+        loss = lbox + lobj + lcls
+        return loss * bs, torch.cat((lbox, lobj, lcls, loss)).detach()
+
+    def build_targets(self, p, targets, imgs):
+        
+        indices, anch = self.find_3_positive(p, targets)
+
+        matching_bs = [[] for pp in p]
+        matching_as = [[] for pp in p]
+        matching_gjs = [[] for pp in p]
+        matching_gis = [[] for pp in p]
+        matching_targets = [[] for pp in p]
+        matching_anchs = [[] for pp in p]
+        
+        nl = len(p)    
+    
+        for batch_idx in range(p[0].shape[0]):
+        
+            b_idx = targets[:, 0]==batch_idx
+            this_target = targets[b_idx]
+            if this_target.shape[0] == 0:
+                continue
+                
+            txywh = this_target[:, 2:6] * imgs[batch_idx].shape[1]
+            txyxy = xywh2xyxy(txywh)
+
+            pxyxys = []
+            p_cls = []
+            p_obj = []
+            from_which_layer = []
+            all_b = []
+            all_a = []
+            all_gj = []
+            all_gi = []
+            all_anch = []
+            
+            for i, pi in enumerate(p):
+                
+                b, a, gj, gi = indices[i]
+                idx = (b == batch_idx)
+                b, a, gj, gi = b[idx], a[idx], gj[idx], gi[idx]                
+                all_b.append(b)
+                all_a.append(a)
+                all_gj.append(gj)
+                all_gi.append(gi)
+                all_anch.append(anch[i][idx])
+                from_which_layer.append(torch.ones(size=(len(b),)) * i)
+                
+                fg_pred = pi[b, a, gj, gi]                
+                p_obj.append(fg_pred[:, 4:5])
+                p_cls.append(fg_pred[:, 5:])
+                
+                grid = torch.stack([gi, gj], dim=1)
+                pxy = (fg_pred[:, :2].sigmoid() * 2. - 0.5 + grid) * self.stride[i] #/ 8.
+                #pxy = (fg_pred[:, :2].sigmoid() * 3. - 1. + grid) * self.stride[i]
+                pwh = (fg_pred[:, 2:4].sigmoid() * 2) ** 2 * anch[i][idx] * self.stride[i] #/ 8.
+                pxywh = torch.cat([pxy, pwh], dim=-1)
+                pxyxy = xywh2xyxy(pxywh)
+                pxyxys.append(pxyxy)
+            
+            pxyxys = torch.cat(pxyxys, dim=0)
+            if pxyxys.shape[0] == 0:
+                continue
+            p_obj = torch.cat(p_obj, dim=0)
+            p_cls = torch.cat(p_cls, dim=0)
+            from_which_layer = torch.cat(from_which_layer, dim=0)
+            all_b = torch.cat(all_b, dim=0)
+            all_a = torch.cat(all_a, dim=0)
+            all_gj = torch.cat(all_gj, dim=0)
+            all_gi = torch.cat(all_gi, dim=0)
+            all_anch = torch.cat(all_anch, dim=0)
+        
+            pair_wise_iou = box_iou(txyxy, pxyxys)
+
+            pair_wise_iou_loss = -torch.log(pair_wise_iou + 1e-8)
+
+            top_k, _ = torch.topk(pair_wise_iou, min(20, pair_wise_iou.shape[1]), dim=1)
+            dynamic_ks = torch.clamp(top_k.sum(1).int(), min=1)
+
+            gt_cls_per_image = (
+                F.one_hot(this_target[:, 1].to(torch.int64), self.nc)
+                .float()
+                .unsqueeze(1)
+                .repeat(1, pxyxys.shape[0], 1)
+            )
+
+            num_gt = this_target.shape[0]
+            cls_preds_ = (
+                p_cls.float().unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_()
+                * p_obj.unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_()
+            )
+
+            y = cls_preds_.sqrt_()
+            pair_wise_cls_loss = F.binary_cross_entropy_with_logits(
+               torch.log(y/(1-y)) , gt_cls_per_image, reduction="none"
+            ).sum(-1)
+            del cls_preds_
+        
+            cost = (
+                pair_wise_cls_loss
+                + 3.0 * pair_wise_iou_loss
+            )
+
+            matching_matrix = torch.zeros_like(cost)
+
+            for gt_idx in range(num_gt):
+                _, pos_idx = torch.topk(
+                    cost[gt_idx], k=dynamic_ks[gt_idx].item(), largest=False
+                )
+                matching_matrix[gt_idx][pos_idx] = 1.0
+
+            del top_k, dynamic_ks
+            anchor_matching_gt = matching_matrix.sum(0)
+            if (anchor_matching_gt > 1).sum() > 0:
+                _, cost_argmin = torch.min(cost[:, anchor_matching_gt > 1], dim=0)
+                matching_matrix[:, anchor_matching_gt > 1] *= 0.0
+                matching_matrix[cost_argmin, anchor_matching_gt > 1] = 1.0
+            fg_mask_inboxes = matching_matrix.sum(0) > 0.0
+            matched_gt_inds = matching_matrix[:, fg_mask_inboxes].argmax(0)
+        
+            from_which_layer = from_which_layer[fg_mask_inboxes]
+            all_b = all_b[fg_mask_inboxes]
+            all_a = all_a[fg_mask_inboxes]
+            all_gj = all_gj[fg_mask_inboxes]
+            all_gi = all_gi[fg_mask_inboxes]
+            all_anch = all_anch[fg_mask_inboxes]
+        
+            this_target = this_target[matched_gt_inds]
+        
+            for i in range(nl):
+                layer_idx = from_which_layer == i
+                matching_bs[i].append(all_b[layer_idx])
+                matching_as[i].append(all_a[layer_idx])
+                matching_gjs[i].append(all_gj[layer_idx])
+                matching_gis[i].append(all_gi[layer_idx])
+                matching_targets[i].append(this_target[layer_idx])
+                matching_anchs[i].append(all_anch[layer_idx])
+
+        for i in range(nl):
+            matching_bs[i] = torch.cat(matching_bs[i], dim=0)
+            matching_as[i] = torch.cat(matching_as[i], dim=0)
+            matching_gjs[i] = torch.cat(matching_gjs[i], dim=0)
+            matching_gis[i] = torch.cat(matching_gis[i], dim=0)
+            matching_targets[i] = torch.cat(matching_targets[i], dim=0)
+            matching_anchs[i] = torch.cat(matching_anchs[i], dim=0)
+
+        return matching_bs, matching_as, matching_gjs, matching_gis, matching_targets, matching_anchs
+
+    def build_targets2(self, p, targets, imgs):
+        
+        indices, anch = self.find_5_positive(p, targets)
+
+        matching_bs = [[] for pp in p]
+        matching_as = [[] for pp in p]
+        matching_gjs = [[] for pp in p]
+        matching_gis = [[] for pp in p]
+        matching_targets = [[] for pp in p]
+        matching_anchs = [[] for pp in p]
+        
+        nl = len(p)    
+    
+        for batch_idx in range(p[0].shape[0]):
+        
+            b_idx = targets[:, 0]==batch_idx
+            this_target = targets[b_idx]
+            if this_target.shape[0] == 0:
+                continue
+                
+            txywh = this_target[:, 2:6] * imgs[batch_idx].shape[1]
+            txyxy = xywh2xyxy(txywh)
+
+            pxyxys = []
+            p_cls = []
+            p_obj = []
+            from_which_layer = []
+            all_b = []
+            all_a = []
+            all_gj = []
+            all_gi = []
+            all_anch = []
+            
+            for i, pi in enumerate(p):
+                
+                b, a, gj, gi = indices[i]
+                idx = (b == batch_idx)
+                b, a, gj, gi = b[idx], a[idx], gj[idx], gi[idx]                
+                all_b.append(b)
+                all_a.append(a)
+                all_gj.append(gj)
+                all_gi.append(gi)
+                all_anch.append(anch[i][idx])
+                from_which_layer.append(torch.ones(size=(len(b),)) * i)
+                
+                fg_pred = pi[b, a, gj, gi]                
+                p_obj.append(fg_pred[:, 4:5])
+                p_cls.append(fg_pred[:, 5:])
+                
+                grid = torch.stack([gi, gj], dim=1)
+                pxy = (fg_pred[:, :2].sigmoid() * 2. - 0.5 + grid) * self.stride[i] #/ 8.
+                #pxy = (fg_pred[:, :2].sigmoid() * 3. - 1. + grid) * self.stride[i]
+                pwh = (fg_pred[:, 2:4].sigmoid() * 2) ** 2 * anch[i][idx] * self.stride[i] #/ 8.
+                pxywh = torch.cat([pxy, pwh], dim=-1)
+                pxyxy = xywh2xyxy(pxywh)
+                pxyxys.append(pxyxy)
+            
+            pxyxys = torch.cat(pxyxys, dim=0)
+            if pxyxys.shape[0] == 0:
+                continue
+            p_obj = torch.cat(p_obj, dim=0)
+            p_cls = torch.cat(p_cls, dim=0)
+            from_which_layer = torch.cat(from_which_layer, dim=0)
+            all_b = torch.cat(all_b, dim=0)
+            all_a = torch.cat(all_a, dim=0)
+            all_gj = torch.cat(all_gj, dim=0)
+            all_gi = torch.cat(all_gi, dim=0)
+            all_anch = torch.cat(all_anch, dim=0)
+        
+            pair_wise_iou = box_iou(txyxy, pxyxys)
+
+            pair_wise_iou_loss = -torch.log(pair_wise_iou + 1e-8)
+
+            top_k, _ = torch.topk(pair_wise_iou, min(20, pair_wise_iou.shape[1]), dim=1)
+            dynamic_ks = torch.clamp(top_k.sum(1).int(), min=1)
+
+            gt_cls_per_image = (
+                F.one_hot(this_target[:, 1].to(torch.int64), self.nc)
+                .float()
+                .unsqueeze(1)
+                .repeat(1, pxyxys.shape[0], 1)
+            )
+
+            num_gt = this_target.shape[0]
+            cls_preds_ = (
+                p_cls.float().unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_()
+                * p_obj.unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_()
+            )
+
+            y = cls_preds_.sqrt_()
+            pair_wise_cls_loss = F.binary_cross_entropy_with_logits(
+               torch.log(y/(1-y)) , gt_cls_per_image, reduction="none"
+            ).sum(-1)
+            del cls_preds_
+        
+            cost = (
+                pair_wise_cls_loss
+                + 3.0 * pair_wise_iou_loss
+            )
+
+            matching_matrix = torch.zeros_like(cost)
+
+            for gt_idx in range(num_gt):
+                _, pos_idx = torch.topk(
+                    cost[gt_idx], k=dynamic_ks[gt_idx].item(), largest=False
+                )
+                matching_matrix[gt_idx][pos_idx] = 1.0
+
+            del top_k, dynamic_ks
+            anchor_matching_gt = matching_matrix.sum(0)
+            if (anchor_matching_gt > 1).sum() > 0:
+                _, cost_argmin = torch.min(cost[:, anchor_matching_gt > 1], dim=0)
+                matching_matrix[:, anchor_matching_gt > 1] *= 0.0
+                matching_matrix[cost_argmin, anchor_matching_gt > 1] = 1.0
+            fg_mask_inboxes = matching_matrix.sum(0) > 0.0
+            matched_gt_inds = matching_matrix[:, fg_mask_inboxes].argmax(0)
+        
+            from_which_layer = from_which_layer[fg_mask_inboxes]
+            all_b = all_b[fg_mask_inboxes]
+            all_a = all_a[fg_mask_inboxes]
+            all_gj = all_gj[fg_mask_inboxes]
+            all_gi = all_gi[fg_mask_inboxes]
+            all_anch = all_anch[fg_mask_inboxes]
+        
+            this_target = this_target[matched_gt_inds]
+        
+            for i in range(nl):
+                layer_idx = from_which_layer == i
+                matching_bs[i].append(all_b[layer_idx])
+                matching_as[i].append(all_a[layer_idx])
+                matching_gjs[i].append(all_gj[layer_idx])
+                matching_gis[i].append(all_gi[layer_idx])
+                matching_targets[i].append(this_target[layer_idx])
+                matching_anchs[i].append(all_anch[layer_idx])
+
+        for i in range(nl):
+            matching_bs[i] = torch.cat(matching_bs[i], dim=0)
+            matching_as[i] = torch.cat(matching_as[i], dim=0)
+            matching_gjs[i] = torch.cat(matching_gjs[i], dim=0)
+            matching_gis[i] = torch.cat(matching_gis[i], dim=0)
+            matching_targets[i] = torch.cat(matching_targets[i], dim=0)
+            matching_anchs[i] = torch.cat(matching_anchs[i], dim=0)
+
+        return matching_bs, matching_as, matching_gjs, matching_gis, matching_targets, matching_anchs              
+
+    def find_5_positive(self, p, targets):
+        # Build targets for compute_loss(), input targets(image,class,x,y,w,h)
+        na, nt = self.na, targets.shape[0]  # number of anchors, targets
+        indices, anch = [], []
+        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 = 1.0  # 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
+
+        for i in range(self.nl):
+            anchors = self.anchors[i]
+            gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]]  # xyxy gain
+
+            # Match targets to anchors
+            t = targets * gain
+            if nt:
+                # Matches
+                r = t[:, :, 4:6] / anchors[:, None]  # wh ratio
+                j = torch.max(r, 1. / r).max(2)[0] < self.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))
+                t = t[j]  # filter
+
+                # Offsets
+                gxy = t[:, 2:4]  # grid xy
+                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]
+            else:
+                t = targets[0]
+                offsets = 0
+
+            # Define
+            b, c = t[:, :2].long().T  # image, class
+            gxy = t[:, 2:4]  # grid xy
+            gwh = t[:, 4:6]  # grid wh
+            gij = (gxy - offsets).long()
+            gi, gj = gij.T  # grid xy indices
+
+            # Append
+            a = t[:, 6].long()  # anchor indices
+            indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1)))  # image, anchor, grid indices
+            anch.append(anchors[a])  # anchors
+
+        return indices, anch                 
+
+    def find_3_positive(self, p, targets):
+        # Build targets for compute_loss(), input targets(image,class,x,y,w,h)
+        na, nt = self.na, targets.shape[0]  # number of anchors, targets
+        indices, anch = [], []
+        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
+
+        for i in range(self.nl):
+            anchors = self.anchors[i]
+            gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]]  # xyxy gain
+
+            # Match targets to anchors
+            t = targets * gain
+            if nt:
+                # Matches
+                r = t[:, :, 4:6] / anchors[:, None]  # wh ratio
+                j = torch.max(r, 1. / r).max(2)[0] < self.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))
+                t = t[j]  # filter
+
+                # Offsets
+                gxy = t[:, 2:4]  # grid xy
+                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]
+            else:
+                t = targets[0]
+                offsets = 0
+
+            # Define
+            b, c = t[:, :2].long().T  # image, class
+            gxy = t[:, 2:4]  # grid xy
+            gwh = t[:, 4:6]  # grid wh
+            gij = (gxy - offsets).long()
+            gi, gj = gij.T  # grid xy indices
+
+            # Append
+            a = t[:, 6].long()  # anchor indices
+            indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1)))  # image, anchor, grid indices
+            anch.append(anchors[a])  # anchors
+
+        return indices, anch