add yolov8

config/__init__.py

@@ -89,6 +89,7 @@ from .model_config.yolov3_config import yolov3_cfg
 from .model_config.yolov4_config import yolov4_cfg
 from .model_config.yolov5_config import yolov5_cfg
 from .model_config.yolov7_config import yolov7_cfg
+from .model_config.yolov8_config import yolov8_cfg
 from .model_config.yolox_config import yolox_cfg
 ## My RTCDet series
 from .model_config.rtcdet_config import rtcdet_cfg
@@ -116,6 +117,9 @@ def build_model_config(args):
     # YOLOv7
     elif args.model in ['yolov7_tiny', 'yolov7', 'yolov7_x']:
         cfg = yolov7_cfg[args.model]
+    # YOLOv8
+    elif args.model in ['yolov8_n', 'yolov8_s', 'yolov8_m', 'yolov8_l', 'yolov8_x']:
+        cfg = yolov8_cfg[args.model]
     # YOLOX
     elif args.model in ['yolox_n', 'yolox_s', 'yolox_m', 'yolox_l', 'yolox_x']:
         cfg = yolox_cfg[args.model]

config/model_config/yolov8_config.py

@@ -0,0 +1,109 @@
+# yolov8-v2 Config
+
+
+yolov8_cfg = {
+    'yolov8_n':{
+        # ---------------- Model config ----------------
+        ## Backbone
+        'backbone': 'yolov8',
+        'pretrained': False,
+        'bk_act': 'silu',
+        'bk_norm': 'BN',
+        'bk_depthwise': False,
+        'width': 0.25,
+        'depth': 0.34,
+        'ratio': 2.0,
+        'stride': [8, 16, 32],  # P3, P4, P5
+        'max_stride': 32,
+        'reg_max': 16,
+        ## Neck: SPP
+        'neck': 'sppf',
+        'neck_expand_ratio': 0.5,
+        'pooling_size': 5,
+        'neck_act': 'silu',
+        'neck_norm': 'BN',
+        'neck_depthwise': False,
+        ## Neck: PaFPN
+        'fpn': 'yolov8_pafpn',
+        'fpn_act': 'silu',
+        'fpn_norm': 'BN',
+        'fpn_depthwise': False,
+        ## Head
+        'head': 'decoupled_head',
+        'head_act': 'silu',
+        'head_norm': 'BN',
+        'num_cls_head': 2,
+        'num_reg_head': 2,
+        'head_depthwise': False,
+        # ---------------- Train config ----------------
+        ## Input
+        'multi_scale': [0.5, 1.5], # 320 -> 960
+        'trans_type': 'yolov5_nano',
+        # ---------------- Assignment config ----------------
+        ## Matcher
+        'matcher': "tal",
+        'matcher_hpy': {'topk_candidates': 10,
+                        'alpha': 0.5,
+                        'beta':  6.0},
+        # ---------------- Loss config ----------------
+        'loss_cls_weight': 0.5,
+        'loss_box_weight': 7.5,
+        'loss_dfl_weight': 1.5,
+        'loss_box_aux': False,
+        # ---------------- Train config ----------------
+        'trainer_type': 'yolov8',
+    },
+
+    'yolov8_l':{
+        # ---------------- Model config ----------------
+        ## Backbone
+        'backbone': 'yolov8',
+        'pretrained': False,
+        'bk_act': 'silu',
+        'bk_norm': 'BN',
+        'bk_depthwise': False,
+        'width': 1.0,
+        'depth': 1.0,
+        'ratio': 1.0,
+        'stride': [8, 16, 32],  # P3, P4, P5
+        'max_stride': 32,
+        'reg_max': 16,
+        ## Neck: SPP
+        'neck': 'sppf',
+        'neck_expand_ratio': 0.5,
+        'pooling_size': 5,
+        'neck_act': 'silu',
+        'neck_norm': 'BN',
+        'neck_depthwise': False,
+        ## Neck: PaFPN
+        'fpn': 'yolov8_pafpn',
+        'fpn_act': 'silu',
+        'fpn_norm': 'BN',
+        'fpn_depthwise': False,
+        ## Head
+        'head': 'decoupled_head',
+        'head_act': 'silu',
+        'head_norm': 'BN',
+        'num_cls_head': 2,
+        'num_reg_head': 2,
+        'head_depthwise': False,
+        # ---------------- Train config ----------------
+        ## Input
+        'multi_scale': [0.5, 1.5], # 320 -> 960
+        'trans_type': 'yolov5_large',
+        # ---------------- Assignment config ----------------
+        ## Matcher
+        'matcher': "tal",
+        'matcher_hpy': {'topk_candidates': 10,
+                        'alpha': 0.5,
+                        'beta':  6.0},
+        # ---------------- Loss config ----------------
+        'loss_cls_weight': 0.5,
+        'loss_box_weight': 7.5,
+        'loss_dfl_weight': 1.5,
+        'loss_box_aux': False,
+        # ---------------- Train config ----------------
+        'trainer_type': 'yolov8',
+    },
+
+}

models/detectors/__init__.py

@@ -9,6 +9,7 @@ from .yolov3.build import build_yolov3
 from .yolov4.build import build_yolov4
 from .yolov5.build import build_yolov5
 from .yolov7.build import build_yolov7
+from .yolov8.build import build_yolov8
 from .yolox.build import build_yolox
 # My RTCDet
 from .rtcdet.build import build_rtcdet
@@ -47,6 +48,10 @@ def build_model(args,
     elif args.model in ['yolov7_tiny', 'yolov7', 'yolov7_x']:
         model, criterion = build_yolov7(
             args, model_cfg, device, num_classes, trainable, deploy)
+    # YOLOv8
+    elif args.model in ['yolov8_n', 'yolov8_s', 'yolov8_m', 'yolov8_l', 'yolov8_x']:
+        model, criterion = build_yolov8(
+            args, model_cfg, device, num_classes, trainable, deploy)
     # YOLOX   
     elif args.model in ['yolox_n', 'yolox_s', 'yolox_m', 'yolox_l', 'yolox_x']:
         model, criterion = build_yolox(

models/detectors/yolov8/README.md

@@ -0,0 +1,53 @@
+# YOLOv4:
+
+|    Model    |     Backbone    | Batch | Scale | AP<sup>val<br>0.5:0.95 | AP<sup>val<br>0.5 | FLOPs<br><sup>(G) | Params<br><sup>(M) | Weight |
+|-------------|-----------------|-------|-------|------------------------|-------------------|-------------------|--------------------|--------|
+| YOLOv4-Tiny | CSPDarkNet-Tiny | 1xb16 |  640  |        31.0            |       49.1        |   8.1             |   2.9              | [ckpt](https://github.com/yjh0410/RT-ODLab/releases/download/yolo_tutorial_ckpt/yolov4_t_coco.pth) |
+| YOLOv4      | CSPDarkNet-53   | 1xb16 |  640  |        46.6            |       65.8        |   162.7           |   61.5             | [ckpt](https://github.com/yjh0410/RT-ODLab/releases/download/yolo_tutorial_ckpt/yolov4_coco.pth) |
+
+- For training, we train YOLOv4 and YOLOv4-Tiny with 250 epochs on COCO.
+- For data augmentation, we use the large scale jitter (LSJ), Mosaic augmentation and Mixup augmentation, following the setting of [YOLOv5](https://github.com/ultralytics/yolov5).
+- For optimizer, we use SGD with momentum 0.937, weight decay 0.0005 and base lr 0.01.
+- For learning rate scheduler, we use linear decay scheduler.
+- For YOLOv4's structure, we use decoupled head, following the setting of [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX).
+
+## Train YOLOv4
+### Single GPU
+Taking training YOLOv4 on COCO as the example,
+```Shell
+python train.py --cuda -d coco --root path/to/coco -m yolov4 -bs 16 -size 640 --wp_epoch 3 --max_epoch 300 --eval_epoch 10 --no_aug_epoch 20 --ema --fp16 --multi_scale 
+```
+
+### Multi GPU
+Taking training YOLOv4 on COCO as the example,
+```Shell
+python -m torch.distributed.run --nproc_per_node=8 train.py --cuda -dist -d coco --root /data/datasets/ -m yolov4 -bs 128 -size 640 --wp_epoch 3 --max_epoch 300  --eval_epoch 10 --no_aug_epoch 20 --ema --fp16 --sybn --multi_scale --save_folder weights/ 
+```
+
+## Test YOLOv4
+Taking testing YOLOv4 on COCO-val as the example,
+```Shell
+python test.py --cuda -d coco --root path/to/coco -m yolov4 --weight path/to/yolov4.pth -size 640 -vt 0.4 --show 
+```
+
+## Evaluate YOLOv4
+Taking evaluating YOLOv4 on COCO-val as the example,
+```Shell
+python eval.py --cuda -d coco-val --root path/to/coco -m yolov4 --weight path/to/yolov4.pth 
+```
+
+## Demo
+### Detect with Image
+```Shell
+python demo.py --mode image --path_to_img path/to/image_dirs/ --cuda -m yolov4 --weight path/to/weight -size 640 -vt 0.4 --show
+```
+
+### Detect with Video
+```Shell
+python demo.py --mode video --path_to_vid path/to/video --cuda -m yolov4 --weight path/to/weight -size 640 -vt 0.4 --show --gif
+```
+
+### Detect with Camera
+```Shell
+python demo.py --mode camera --cuda -m yolov4 --weight path/to/weight -size 640 -vt 0.4 --show --gif
+```

models/detectors/yolov8/build.py

@@ -0,0 +1,43 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+
+import torch
+import torch.nn as nn
+
+from .loss import build_criterion
+from .yolov8 import YOLOv8
+
+
+# build object detector
+def build_yolov8(args, cfg, device, num_classes=80, trainable=False, deploy=False):
+    print('==============================')
+    print('Build {} ...'.format(args.model.upper()))
+    
+    print('==============================')
+    print('Model Configuration: \n', cfg)
+    
+    # -------------- Build YOLO --------------
+    model = YOLOv8(
+        cfg=cfg,
+        device=device, 
+        num_classes=num_classes,
+        trainable=trainable,
+        conf_thresh=args.conf_thresh,
+        nms_thresh=args.nms_thresh,
+        topk=args.topk,
+        deploy=deploy,
+        nms_class_agnostic=args.nms_class_agnostic
+        )
+
+    # -------------- Initialize YOLO --------------
+    for m in model.modules():
+        if isinstance(m, nn.BatchNorm2d):
+            m.eps = 1e-3
+            m.momentum = 0.03    
+            
+    # -------------- Build criterion --------------
+    criterion = None
+    if trainable:
+        # build criterion for training
+        criterion = build_criterion(cfg, device, num_classes)
+    return model, criterion

models/detectors/yolov8/loss.py

@@ -0,0 +1,307 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from utils.box_ops import bbox2dist, bbox_iou
+from utils.distributed_utils import get_world_size, is_dist_avail_and_initialized
+
+from .matcher import TaskAlignedAssigner
+
+
+class Criterion(object):
+    def __init__(self, cfg, device, num_classes=80):
+        # --------------- Basic parameters ---------------
+        self.cfg = cfg
+        self.device = device
+        self.num_classes = num_classes
+        self.reg_max = cfg['reg_max']
+        self.use_dfl = cfg['reg_max'] > 1
+        # --------------- Loss config ---------------
+        ## loss func
+        self.cls_lossf = ClassificationLoss(cfg, reduction='none')
+        self.reg_lossf = RegressionLoss(num_classes, cfg['reg_max'] - 1, self.use_dfl)
+        ## loss weight
+        self.loss_cls_weight = cfg['loss_cls_weight']
+        self.loss_box_weight = cfg['loss_box_weight']
+        self.loss_dfl_weight = cfg['loss_dfl_weight']
+        # matcher
+        self.matcher_hpy = cfg['matcher_hpy']
+        self.matcher = TaskAlignedAssigner(num_classes     = num_classes,
+                                           topk_candidates = self.matcher_hpy['topk_candidates'],
+                                           alpha           = self.matcher_hpy['alpha'],
+                                           beta            = self.matcher_hpy['beta']
+                                           )
+
+    def loss_classes(self, pred_cls, gt_score):
+        # compute bce loss
+        loss_cls = F.binary_cross_entropy_with_logits(pred_cls, gt_score, reduction='none')
+
+        return loss_cls
+    
+    def loss_bboxes(self, pred_box, gt_box, bbox_weight):
+        # regression loss
+        ious = bbox_iou(pred_box, gt_box, xywh=False, CIoU=True)
+        loss_box = (1.0 - ious.squeeze(-1)) * bbox_weight
+
+        return loss_box
+    
+    def loss_dfl(self, pred_reg, gt_box, anchor, stride, bbox_weight=None):
+        # rescale coords by stride
+        gt_box_s = gt_box / stride
+        anchor_s = anchor / stride
+
+        # compute deltas
+        gt_ltrb_s = bbox2dist(anchor_s, gt_box_s, self.cfg['reg_max'] - 1)
+
+        gt_left = gt_ltrb_s.to(torch.long)
+        gt_right = gt_left + 1
+
+        weight_left = gt_right.to(torch.float) - gt_ltrb_s
+        weight_right = 1 - weight_left
+
+        # loss left
+        loss_left = F.cross_entropy(
+            pred_reg.view(-1, self.cfg['reg_max']),
+            gt_left.view(-1),
+            reduction='none').view(gt_left.shape) * weight_left
+        # loss right
+        loss_right = F.cross_entropy(
+            pred_reg.view(-1, self.cfg['reg_max']),
+            gt_right.view(-1),
+            reduction='none').view(gt_left.shape) * weight_right
+
+        loss_dfl = (loss_left + loss_right).mean(-1)
+        
+        if bbox_weight is not None:
+            loss_dfl *= bbox_weight
+
+        return loss_dfl
+
+    def __call__(self, outputs, targets, epoch=0):        
+        """
+            outputs['pred_cls']: List(Tensor) [B, M, C]
+            outputs['pred_regs']: List(Tensor) [B, M, 4*(reg_max+1)]
+            outputs['pred_boxs']: List(Tensor) [B, M, 4]
+            outputs['anchors']: List(Tensor) [M, 2]
+            outputs['strides']: List(Int) [8, 16, 32] output stride
+            outputs['stride_tensor']: List(Tensor) [M, 1]
+            targets: (List) [dict{'boxes': [...], 
+                                 'labels': [...], 
+                                 'orig_size': ...}, ...]
+        """
+        bs = outputs['pred_cls'][0].shape[0]
+        device = outputs['pred_cls'][0].device
+        strides = outputs['stride_tensor']
+        anchors = outputs['anchors']
+        anchors = torch.cat(anchors, dim=0)
+        num_anchors = anchors.shape[0]
+
+        # preds: [B, M, C]
+        cls_preds = torch.cat(outputs['pred_cls'], dim=1)
+        reg_preds = torch.cat(outputs['pred_reg'], dim=1)
+        box_preds = torch.cat(outputs['pred_box'], dim=1)
+        
+        # --------------- label assignment ---------------
+        gt_score_targets = []
+        gt_bbox_targets = []
+        fg_masks = []
+        for batch_idx in range(bs):
+            tgt_labels = targets[batch_idx]["labels"].to(device)     # [Mp,]
+            tgt_boxs = targets[batch_idx]["boxes"].to(device)        # [Mp, 4]
+
+            # check target
+            if len(tgt_labels) == 0 or tgt_boxs.max().item() == 0.:
+                # There is no valid gt
+                fg_mask = cls_preds.new_zeros(1, num_anchors).bool()               #[1, M,]
+                gt_score = cls_preds.new_zeros((1, num_anchors, self.num_classes)) #[1, M, C]
+                gt_box = cls_preds.new_zeros((1, num_anchors, 4))                  #[1, M, 4]
+            else:
+                tgt_labels = tgt_labels[None, :, None]      # [1, Mp, 1]
+                tgt_boxs = tgt_boxs[None]                   # [1, Mp, 4]
+                (
+                    _,
+                    gt_box,     # [1, M, 4]
+                    gt_score,   # [1, M, C]
+                    fg_mask,    # [1, M,]
+                    _
+                ) = self.matcher(
+                    pd_scores = cls_preds[batch_idx:batch_idx+1].detach().sigmoid(), 
+                    pd_bboxes = box_preds[batch_idx:batch_idx+1].detach(),
+                    anc_points = anchors,
+                    gt_labels = tgt_labels,
+                    gt_bboxes = tgt_boxs
+                    )
+            gt_score_targets.append(gt_score)
+            gt_bbox_targets.append(gt_box)
+            fg_masks.append(fg_mask)
+
+        # List[B, 1, M, C] -> Tensor[B, M, C] -> Tensor[BM, C]
+        fg_masks = torch.cat(fg_masks, 0).view(-1)                                    # [BM,]
+        gt_score_targets = torch.cat(gt_score_targets, 0).view(-1, self.num_classes)  # [BM, C]
+        gt_bbox_targets = torch.cat(gt_bbox_targets, 0).view(-1, 4)                   # [BM, 4]
+        bbox_weight = gt_score_targets[fg_masks].sum(-1)                              # [BM,]
+        num_fgs = max(gt_score_targets.sum(), 1)
+        
+        # average loss normalizer across all the GPUs
+        if is_dist_avail_and_initialized():
+            torch.distributed.all_reduce(num_fgs)
+        num_fgs = (num_fgs / get_world_size()).clamp(1.0)
+
+        # ------------------ Classification loss ------------------
+        cls_preds = cls_preds.view(-1, self.num_classes)
+        loss_cls = self.loss_classes(cls_preds, gt_score_targets)
+        loss_cls = loss_cls.sum() / num_fgs
+
+        # ------------------ Regression loss ------------------
+        box_preds_pos = box_preds.view(-1, 4)[fg_masks]
+        box_targets_pos = gt_bbox_targets.view(-1, 4)[fg_masks]
+        loss_box = self.loss_bboxes(box_preds_pos, box_targets_pos, bbox_weight)
+        loss_box = loss_box.sum() / num_fgs
+
+        # ------------------ Distribution focal loss  ------------------
+        ## process anchors
+        anchors = torch.cat(outputs['anchors'], dim=0)
+        anchors = anchors[None].repeat(bs, 1, 1).view(-1, 2)
+        ## process stride tensors
+        strides = torch.cat(outputs['stride_tensor'], dim=0)
+        strides = strides.unsqueeze(0).repeat(bs, 1, 1).view(-1, 1)
+        ## fg preds
+        reg_preds_pos = reg_preds.view(-1, 4*self.cfg['reg_max'])[fg_masks]
+        anchors_pos = anchors[fg_masks]
+        strides_pos = strides[fg_masks]
+        ## compute dfl
+        loss_dfl = self.loss_dfl(reg_preds_pos, box_targets_pos, anchors_pos, strides_pos, bbox_weight)
+        loss_dfl = loss_dfl.sum() / num_fgs
+
+        # total loss
+        if not self.use_dfl:
+            losses = loss_cls * self.loss_cls_weight + loss_box * self.loss_box_weight
+            loss_dict = dict(
+                    loss_cls = loss_cls,
+                    loss_box = loss_box,
+                    losses = losses
+            )
+        else:
+            losses = loss_cls * self.loss_cls_weight + loss_box * self.loss_box_weight + loss_dfl * self.loss_dfl_weight
+            loss_dict = dict(
+                    loss_cls = loss_cls,
+                    loss_box = loss_box,
+                    loss_dfl = loss_dfl,
+                    losses = losses
+            )
+
+        return loss_dict
+    
+
+class ClassificationLoss(nn.Module):
+    def __init__(self, cfg, reduction='none'):
+        super(ClassificationLoss, self).__init__()
+        self.cfg = cfg
+        self.reduction = reduction
+        # For VFL
+        self.alpha = 0.75
+        self.gamma = 2.0
+
+
+    def binary_cross_entropy(self, pred_logits, gt_score):
+        loss = F.binary_cross_entropy_with_logits(
+            pred_logits.float(), gt_score.float(), reduction='none')
+
+        if self.reduction == 'sum':
+            loss = loss.sum()
+        elif self.reduction == 'mean':
+            loss = loss.mean()
+
+        return loss
+
+
+    def forward(self, pred_logits, gt_score):
+        if self.cfg['cls_loss'] == 'bce':
+            return self.binary_cross_entropy(pred_logits, gt_score)
+
+
+class RegressionLoss(nn.Module):
+    def __init__(self, num_classes, reg_max, use_dfl):
+        super(RegressionLoss, self).__init__()
+        self.num_classes = num_classes
+        self.reg_max = reg_max
+        self.use_dfl = use_dfl
+
+
+    def df_loss(self, pred_regs, target):
+        gt_left = target.to(torch.long)
+        gt_right = gt_left + 1
+        weight_left = gt_right.to(torch.float) - target
+        weight_right = 1 - weight_left
+        # loss left
+        loss_left = F.cross_entropy(
+            pred_regs.view(-1, self.reg_max + 1),
+            gt_left.view(-1),
+            reduction='none').view(gt_left.shape) * weight_left
+        # loss right
+        loss_right = F.cross_entropy(
+            pred_regs.view(-1, self.reg_max + 1),
+            gt_right.view(-1),
+            reduction='none').view(gt_left.shape) * weight_right
+
+        loss = (loss_left + loss_right).mean(-1, keepdim=True)
+        
+        return loss
+
+
+    def forward(self, pred_regs, pred_boxs, anchors, gt_boxs, bbox_weight, fg_masks, strides):
+        """
+        Input:
+            pred_regs: (Tensor) [BM, 4*(reg_max + 1)]
+            pred_boxs: (Tensor) [BM, 4]
+            anchors: (Tensor) [BM, 2]
+            gt_boxs: (Tensor) [BM, 4]
+            bbox_weight: (Tensor) [BM, 1]
+            fg_masks: (Tensor) [BM,]
+            strides: (Tensor) [BM, 1]
+        """
+        # select positive samples mask
+        num_pos = fg_masks.sum()
+
+        if num_pos > 0:
+            pred_boxs_pos = pred_boxs[fg_masks]
+            gt_boxs_pos = gt_boxs[fg_masks]
+
+            # iou loss
+            ious = bbox_iou(pred_boxs_pos,
+                            gt_boxs_pos,
+                            xywh=False,
+                            CIoU=True)
+            loss_iou = (1.0 - ious) * bbox_weight
+               
+            # dfl loss
+            if self.use_dfl:
+                pred_regs_pos = pred_regs[fg_masks]
+                gt_boxs_s = gt_boxs / strides
+                anchors_s = anchors / strides
+                gt_ltrb_s = bbox2dist(anchors_s, gt_boxs_s, self.reg_max)
+                gt_ltrb_s_pos = gt_ltrb_s[fg_masks]
+                loss_dfl = self.df_loss(pred_regs_pos, gt_ltrb_s_pos)
+                loss_dfl *= bbox_weight
+            else:
+                loss_dfl = pred_regs.sum() * 0.
+
+        else:
+            loss_iou = pred_regs.sum() * 0.
+            loss_dfl = pred_regs.sum() * 0.
+
+        return loss_iou, loss_dfl
+
+
+def build_criterion(cfg, device, num_classes):
+    criterion = Criterion(
+        cfg=cfg,
+        device=device,
+        num_classes=num_classes
+        )
+
+    return criterion
+
+
+if __name__ == "__main__":
+    pass

models/detectors/yolov8/matcher.py

@@ -0,0 +1,176 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from utils.box_ops import bbox_iou
+
+
+# -------------------------- Task Aligned Assigner --------------------------
+class TaskAlignedAssigner(nn.Module):
+    def __init__(self,
+                 num_classes     = 80,
+                 topk_candidates = 10,
+                 alpha           = 0.5,
+                 beta            = 6.0, 
+                 eps             = 1e-9):
+        super(TaskAlignedAssigner, self).__init__()
+        self.topk_candidates = topk_candidates
+        self.num_classes = num_classes
+        self.bg_idx = num_classes
+        self.alpha = alpha
+        self.beta = beta
+        self.eps = eps
+
+    @torch.no_grad()
+    def forward(self,
+                pd_scores,
+                pd_bboxes,
+                anc_points,
+                gt_labels,
+                gt_bboxes):
+        self.bs = pd_scores.size(0)
+        self.n_max_boxes = gt_bboxes.size(1)
+
+        mask_pos, align_metric, overlaps = self.get_pos_mask(
+            pd_scores, pd_bboxes, gt_labels, gt_bboxes, anc_points)
+
+        target_gt_idx, fg_mask, mask_pos = select_highest_overlaps(
+            mask_pos, overlaps, self.n_max_boxes)
+
+        # assigned target
+        target_labels, target_bboxes, target_scores = self.get_targets(
+            gt_labels, gt_bboxes, target_gt_idx, fg_mask)
+
+        # normalize
+        align_metric *= mask_pos
+        pos_align_metrics = align_metric.amax(axis=-1, keepdim=True)  # b, max_num_obj
+        pos_overlaps = (overlaps * mask_pos).amax(axis=-1, keepdim=True)  # b, max_num_obj
+        norm_align_metric = (align_metric * pos_overlaps / (pos_align_metrics + self.eps)).amax(-2).unsqueeze(-1)
+        target_scores = target_scores * norm_align_metric
+
+        return target_labels, target_bboxes, target_scores, fg_mask.bool(), target_gt_idx
+
+    def get_pos_mask(self, pd_scores, pd_bboxes, gt_labels, gt_bboxes, anc_points):
+        # get anchor_align metric, (b, max_num_obj, h*w)
+        align_metric, overlaps = self.get_box_metrics(pd_scores, pd_bboxes, gt_labels, gt_bboxes)
+        # get in_gts mask, (b, max_num_obj, h*w)
+        mask_in_gts = select_candidates_in_gts(anc_points, gt_bboxes)
+        # get topk_metric mask, (b, max_num_obj, h*w)
+        mask_topk = self.select_topk_candidates(align_metric * mask_in_gts)
+        # merge all mask to a final mask, (b, max_num_obj, h*w)
+        mask_pos = mask_topk * mask_in_gts
+
+        return mask_pos, align_metric, overlaps
+
+    def get_box_metrics(self, pd_scores, pd_bboxes, gt_labels, gt_bboxes):
+        ind = torch.zeros([2, self.bs, self.n_max_boxes], dtype=torch.long)  # 2, b, max_num_obj
+        ind[0] = torch.arange(end=self.bs).view(-1, 1).repeat(1, self.n_max_boxes)  # b, max_num_obj
+        ind[1] = gt_labels.long().squeeze(-1)  # b, max_num_obj
+        # get the scores of each grid for each gt cls
+        bbox_scores = pd_scores[ind[0], :, ind[1]]  # b, max_num_obj, h*w
+
+        overlaps = bbox_iou(gt_bboxes.unsqueeze(2), pd_bboxes.unsqueeze(1), xywh=False,
+                            CIoU=True).squeeze(3).clamp(0)
+        align_metric = bbox_scores.pow(self.alpha) * overlaps.pow(self.beta)
+
+        return align_metric, overlaps
+
+    def select_topk_candidates(self, metrics, largest=True):
+        """
+        Args:
+            metrics: (b, max_num_obj, h*w).
+            topk_mask: (b, max_num_obj, topk) or None
+        """
+        num_anchors = metrics.shape[-1]  # h*w
+        # (b, max_num_obj, topk)
+        topk_metrics, topk_idxs = torch.topk(metrics, self.topk_candidates, dim=-1, largest=largest)
+        topk_mask = (topk_metrics.max(-1, keepdim=True)[0] > self.eps).tile([1, 1, self.topk_candidates])
+        # (b, max_num_obj, topk)
+        topk_idxs[~topk_mask] = 0
+        # (b, max_num_obj, topk, h*w) -> (b, max_num_obj, h*w)
+        is_in_topk = F.one_hot(topk_idxs, num_anchors).sum(-2)
+        # filter invalid bboxes
+        is_in_topk = torch.where(is_in_topk > 1, 0, is_in_topk)
+        
+        return is_in_topk.to(metrics.dtype)
+
+    def get_targets(self, gt_labels, gt_bboxes, target_gt_idx, fg_mask):
+        # assigned target labels, (b, 1)
+        batch_ind = torch.arange(end=self.bs, dtype=torch.int64, device=gt_labels.device)[..., None]
+        target_gt_idx = target_gt_idx + batch_ind * self.n_max_boxes  # (b, h*w)
+        target_labels = gt_labels.long().flatten()[target_gt_idx]  # (b, h*w)
+
+        # assigned target boxes, (b, max_num_obj, 4) -> (b, h*w)
+        target_bboxes = gt_bboxes.view(-1, 4)[target_gt_idx]
+
+        # assigned target scores
+        target_labels.clamp(0)
+        target_scores = F.one_hot(target_labels, self.num_classes)  # (b, h*w, 80)
+        fg_scores_mask = fg_mask[:, :, None].repeat(1, 1, self.num_classes)  # (b, h*w, 80)
+        target_scores = torch.where(fg_scores_mask > 0, target_scores, 0)
+
+        return target_labels, target_bboxes, target_scores
+    
+
+# -------------------------- Basic Functions --------------------------
+def select_candidates_in_gts(xy_centers, gt_bboxes, eps=1e-9):
+    """select the positive anchors's center in gt
+    Args:
+        xy_centers (Tensor): shape(bs*n_max_boxes, num_total_anchors, 4)
+        gt_bboxes (Tensor): shape(bs, n_max_boxes, 4)
+    Return:
+        (Tensor): shape(bs, n_max_boxes, num_total_anchors)
+    """
+    n_anchors = xy_centers.size(0)
+    bs, n_max_boxes, _ = gt_bboxes.size()
+    _gt_bboxes = gt_bboxes.reshape([-1, 4])
+    xy_centers = xy_centers.unsqueeze(0).repeat(bs * n_max_boxes, 1, 1)
+    gt_bboxes_lt = _gt_bboxes[:, 0:2].unsqueeze(1).repeat(1, n_anchors, 1)
+    gt_bboxes_rb = _gt_bboxes[:, 2:4].unsqueeze(1).repeat(1, n_anchors, 1)
+    b_lt = xy_centers - gt_bboxes_lt
+    b_rb = gt_bboxes_rb - xy_centers
+    bbox_deltas = torch.cat([b_lt, b_rb], dim=-1)
+    bbox_deltas = bbox_deltas.reshape([bs, n_max_boxes, n_anchors, -1])
+    return (bbox_deltas.min(axis=-1)[0] > eps).to(gt_bboxes.dtype)
+
+def select_highest_overlaps(mask_pos, overlaps, n_max_boxes):
+    """if an anchor box is assigned to multiple gts,
+        the one with the highest iou will be selected.
+    Args:
+        mask_pos (Tensor): shape(bs, n_max_boxes, num_total_anchors)
+        overlaps (Tensor): shape(bs, n_max_boxes, num_total_anchors)
+    Return:
+        target_gt_idx (Tensor): shape(bs, num_total_anchors)
+        fg_mask (Tensor): shape(bs, num_total_anchors)
+        mask_pos (Tensor): shape(bs, n_max_boxes, num_total_anchors)
+    """
+    fg_mask = mask_pos.sum(axis=-2)
+    if fg_mask.max() > 1:
+        mask_multi_gts = (fg_mask.unsqueeze(1) > 1).repeat([1, n_max_boxes, 1])
+        max_overlaps_idx = overlaps.argmax(axis=1)
+        is_max_overlaps = F.one_hot(max_overlaps_idx, n_max_boxes)
+        is_max_overlaps = is_max_overlaps.permute(0, 2, 1).to(overlaps.dtype)
+        mask_pos = torch.where(mask_multi_gts, is_max_overlaps, mask_pos)
+        fg_mask = mask_pos.sum(axis=-2)
+    target_gt_idx = mask_pos.argmax(axis=-2)
+    return target_gt_idx, fg_mask , mask_pos
+
+def iou_calculator(box1, box2, eps=1e-9):
+    """Calculate iou for batch
+    Args:
+        box1 (Tensor): shape(bs, n_max_boxes, 1, 4)
+        box2 (Tensor): shape(bs, 1, num_total_anchors, 4)
+    Return:
+        (Tensor): shape(bs, n_max_boxes, num_total_anchors)
+    """
+    box1 = box1.unsqueeze(2)  # [N, M1, 4] -> [N, M1, 1, 4]
+    box2 = box2.unsqueeze(1)  # [N, M2, 4] -> [N, 1, M2, 4]
+    px1y1, px2y2 = box1[:, :, :, 0:2], box1[:, :, :, 2:4]
+    gx1y1, gx2y2 = box2[:, :, :, 0:2], box2[:, :, :, 2:4]
+    x1y1 = torch.maximum(px1y1, gx1y1)
+    x2y2 = torch.minimum(px2y2, gx2y2)
+    overlap = (x2y2 - x1y1).clip(0).prod(-1)
+    area1 = (px2y2 - px1y1).clip(0).prod(-1)
+    area2 = (gx2y2 - gx1y1).clip(0).prod(-1)
+    union = area1 + area2 - overlap + eps
+
+    return overlap / union

models/detectors/yolov8/yolov8.py

@@ -0,0 +1,174 @@
+# --------------- Torch components ---------------
+import torch
+import torch.nn as nn
+
+# --------------- Model components ---------------
+from .yolov8_backbone import build_backbone
+from .yolov8_neck import build_neck
+from .yolov8_pafpn import build_fpn
+from .yolov8_head import build_det_head
+from .yolov8_pred import build_pred_layer
+
+# --------------- External components ---------------
+from utils.misc import multiclass_nms
+
+
+# YOLOv8
+class YOLOv8(nn.Module):
+    def __init__(self,
+                 cfg,
+                 device,
+                 num_classes = 20,
+                 conf_thresh = 0.01,
+                 nms_thresh  = 0.5,
+                 topk        = 100,
+                 trainable   = False,
+                 deploy      = False,
+                 nms_class_agnostic = False):
+        super(YOLOv8, self).__init__()
+        # ---------------------- Basic Parameters ----------------------
+        self.cfg = cfg
+        self.device = device
+        self.stride = cfg['stride']
+        self.reg_max = cfg['reg_max']
+        self.num_classes = num_classes
+        self.trainable = trainable
+        self.conf_thresh = conf_thresh
+        self.nms_thresh = nms_thresh
+        self.topk = topk
+        self.deploy = deploy
+        self.nms_class_agnostic = nms_class_agnostic
+        self.head_dim = round(256*cfg['width'])
+        
+        # ---------------------- Network Parameters ----------------------
+        ## ----------- Backbone -----------
+        self.backbone, feat_dims = build_backbone(cfg)
+
+        ## ----------- Neck: SPP -----------
+        self.neck = build_neck(cfg, feat_dims[-1], feat_dims[-1])
+        feat_dims[-1] = self.neck.out_dim
+        
+        ## ----------- Neck: FPN -----------
+        self.fpn = build_fpn(cfg, feat_dims)
+        self.fpn_dims = self.fpn.out_dim
+
+        ## ----------- Heads -----------
+        self.det_heads = build_det_head(
+            cfg, self.fpn_dims, self.head_dim, 4 * self.reg_max, num_levels=len(self.stride))
+
+        ## ----------- Preds -----------
+        self.pred_layers = build_pred_layer(
+            self.det_heads.cls_head_dim, self.det_heads.reg_head_dim, self.stride,
+            num_classes=num_classes, num_coords=4, num_levels=len(self.stride), reg_max=self.reg_max)
+
+    ## post-process
+    def post_process(self, cls_preds, box_preds):
+        """
+        Input:
+            cls_preds: List(Tensor) [[H x W, C], ...]
+            box_preds: List(Tensor) [[H x W, 4], ...]
+        """
+        all_scores = []
+        all_labels = []
+        all_bboxes = []
+        
+        for cls_pred_i, box_pred_i in zip(cls_preds, box_preds):
+            cls_pred_i = cls_pred_i[0]
+            box_pred_i = box_pred_i[0]
+            
+            # (H x W x C,)
+            scores_i = cls_pred_i.sigmoid().flatten()
+
+            # Keep top k top scoring indices only.
+            num_topk = min(self.topk, box_pred_i.size(0))
+
+            # torch.sort is actually faster than .topk (at least on GPUs)
+            predicted_prob, topk_idxs = scores_i.sort(descending=True)
+            topk_scores = predicted_prob[:num_topk]
+            topk_idxs = topk_idxs[:num_topk]
+
+            # filter out the proposals with low confidence score
+            keep_idxs = topk_scores > self.conf_thresh
+            scores = topk_scores[keep_idxs]
+            topk_idxs = topk_idxs[keep_idxs]
+
+            anchor_idxs = torch.div(topk_idxs, self.num_classes, rounding_mode='floor')
+            labels = topk_idxs % self.num_classes
+
+            bboxes = box_pred_i[anchor_idxs]
+
+            all_scores.append(scores)
+            all_labels.append(labels)
+            all_bboxes.append(bboxes)
+
+        scores = torch.cat(all_scores)
+        labels = torch.cat(all_labels)
+        bboxes = torch.cat(all_bboxes)
+
+        # to cpu & numpy
+        scores = scores.cpu().numpy()
+        labels = labels.cpu().numpy()
+        bboxes = bboxes.cpu().numpy()
+
+        # nms
+        scores, labels, bboxes = multiclass_nms(
+            scores, labels, bboxes, self.nms_thresh, self.num_classes, self.nms_class_agnostic)
+
+        return bboxes, scores, labels
+
+    # ---------------------- Main Process for Inference ----------------------
+    @torch.no_grad()
+    def inference_single_image(self, x):
+        # ---------------- Backbone ----------------
+        pyramid_feats = self.backbone(x)
+
+        # ---------------- Neck: SPP ----------------
+        pyramid_feats[-1] = self.neck(pyramid_feats[-1])
+
+        # ---------------- Neck: PaFPN ----------------
+        pyramid_feats = self.fpn(pyramid_feats)
+
+        # ---------------- Heads ----------------
+        cls_feats, reg_feats = self.det_heads(pyramid_feats)
+
+        # ---------------- Preds ----------------
+        outputs = self.pred_layers(cls_feats, reg_feats)
+
+        all_cls_preds = outputs['pred_cls']
+        all_box_preds = outputs['pred_box']
+
+        if self.deploy:
+            cls_preds = torch.cat(all_cls_preds, dim=1)[0]
+            box_preds = torch.cat(all_box_preds, dim=1)[0]
+            scores = cls_preds.sigmoid()
+            bboxes = box_preds
+            # [n_anchors_all, 4 + C]
+            outputs = torch.cat([bboxes, scores], dim=-1)
+
+            return outputs
+        else:
+            # post process
+            bboxes, scores, labels = self.post_process(all_cls_preds, all_box_preds)
+        
+            return bboxes, scores, labels
+
+    def forward(self, x):
+        if not self.trainable:
+            return self.inference_single_image(x)
+        else:
+            # ---------------- Backbone ----------------
+            pyramid_feats = self.backbone(x)
+
+            # ---------------- Neck: SPP ----------------
+            pyramid_feats[-1] = self.neck(pyramid_feats[-1])
+
+            # ---------------- Neck: PaFPN ----------------
+            pyramid_feats = self.fpn(pyramid_feats)
+
+            # ---------------- Heads ----------------
+            cls_feats, reg_feats = self.det_heads(pyramid_feats)
+
+            # ---------------- Preds ----------------
+            outputs = self.pred_layers(cls_feats, reg_feats)
+            
+            return outputs 

models/detectors/yolov8/yolov8_backbone.py

@@ -0,0 +1,118 @@
+import torch
+import torch.nn as nn
+
+try:
+    from .yolov8_basic import Conv, Yolov8StageBlock
+except:
+    from yolov8_basic import Conv, Yolov8StageBlock
+
+
+# ---------------------------- Basic functions ----------------------------
+## ELAN-CSPNet
+class Yolov8Backbone(nn.Module):
+    def __init__(self, width=1.0, depth=1.0, ratio=1.0, act_type='silu', norm_type='BN', depthwise=False):
+        super(Yolov8Backbone, self).__init__()
+        self.feat_dims = [round(64 * width), round(128 * width), round(256 * width), round(512 * width), round(512 * width * ratio)]
+        
+        # stride = 2
+        self.layer_1 = Conv(3, self.feat_dims[0], k=3, p=1, s=2, act_type=act_type, norm_type=norm_type)
+        
+        # stride = 4
+        self.layer_2 = nn.Sequential(
+            Conv(self.feat_dims[0], self.feat_dims[1], k=3, p=1, s=2, act_type=act_type, norm_type=norm_type),
+            Yolov8StageBlock(in_dim     = self.feat_dims[1],
+                             out_dim    = self.feat_dims[1],
+                             num_blocks = round(3*depth),
+                             shortcut   = True,
+                             act_type   = act_type,
+                             norm_type  = norm_type,
+                             depthwise  = depthwise)
+        )
+        # stride = 8
+        self.layer_3 = nn.Sequential(
+            Conv(self.feat_dims[1], self.feat_dims[2], k=3, p=1, s=2, act_type=act_type, norm_type=norm_type),
+            Yolov8StageBlock(in_dim     = self.feat_dims[2],
+                             out_dim    = self.feat_dims[2],
+                             num_blocks = round(6*depth),
+                             shortcut   = True,
+                             act_type   = act_type,
+                             norm_type  = norm_type,
+                             depthwise  = depthwise)
+        )
+        # stride = 16
+        self.layer_4 = nn.Sequential(
+            Conv(self.feat_dims[2], self.feat_dims[3], k=3, p=1, s=2, act_type=act_type, norm_type=norm_type),
+            Yolov8StageBlock(in_dim     = self.feat_dims[3],
+                             out_dim    = self.feat_dims[3],
+                             num_blocks = round(6*depth),
+                             shortcut   = True,
+                             act_type   = act_type,
+                             norm_type  = norm_type,
+                             depthwise  = depthwise)
+        )
+        # stride = 32
+        self.layer_5 = nn.Sequential(
+            Conv(self.feat_dims[3], self.feat_dims[4], k=3, p=1, s=2, act_type=act_type, norm_type=norm_type),
+            Yolov8StageBlock(in_dim     = self.feat_dims[4],
+                             out_dim    = self.feat_dims[4],
+                             num_blocks = round(3*depth),
+                             shortcut   = True,
+                             act_type   = act_type,
+                             norm_type  = norm_type,
+                             depthwise  = depthwise)
+        )
+
+    def forward(self, x):
+        c1 = self.layer_1(x)
+        c2 = self.layer_2(c1)
+        c3 = self.layer_3(c2)
+        c4 = self.layer_4(c3)
+        c5 = self.layer_5(c4)
+
+        outputs = [c3, c4, c5]
+
+        return outputs
+
+
+# ---------------------------- Functions ----------------------------
+## build Yolov8's Backbone
+def build_backbone(cfg): 
+    # model
+    backbone = Yolov8Backbone(width=cfg['width'],
+                              depth=cfg['depth'],
+                              ratio=cfg['ratio'],
+                              act_type=cfg['bk_act'],
+                              norm_type=cfg['bk_norm'],
+                              depthwise=cfg['bk_depthwise']
+                              )
+    feat_dims = backbone.feat_dims[-3:]
+        
+    return backbone, feat_dims
+
+
+if __name__ == '__main__':
+    import time
+    from thop import profile
+    cfg = {
+        'bk_act': 'silu',
+        'bk_norm': 'BN',
+        'bk_depthwise': False,
+        'width': 1.0,
+        'depth': 1.0,
+        'ratio': 1.0,
+    }
+    model, feats = build_backbone(cfg)
+    x = torch.randn(1, 3, 640, 640)
+    t0 = time.time()
+    outputs = model(x)
+    t1 = time.time()
+    print('Time: ', t1 - t0)
+    for out in outputs:
+        print(out.shape)
+
+    x = torch.randn(1, 3, 640, 640)
+    print('==============================')
+    flops, params = profile(model, inputs=(x, ), verbose=False)
+    print('==============================')
+    print('GFLOPs : {:.2f}'.format(flops / 1e9 * 2))
+    print('Params : {:.2f} M'.format(params / 1e6))

models/detectors/yolov8/yolov8_basic.py

@@ -0,0 +1,137 @@
+import torch
+import torch.nn as nn
+
+
+# --------------------- Basic modules ---------------------
+class SiLU(nn.Module):
+    """export-friendly version of nn.SiLU()"""
+
+    @staticmethod
+    def forward(x):
+        return x * torch.sigmoid(x)
+
+def get_conv2d(c1, c2, k, p, s, d, g, bias=False):
+    conv = nn.Conv2d(c1, c2, k, stride=s, padding=p, dilation=d, groups=g, bias=bias)
+
+    return conv
+
+def get_activation(act_type=None):
+    if act_type == 'relu':
+        return nn.ReLU(inplace=True)
+    elif act_type == 'lrelu':
+        return nn.LeakyReLU(0.1, inplace=True)
+    elif act_type == 'mish':
+        return nn.Mish(inplace=True)
+    elif act_type == 'silu':
+        return nn.SiLU(inplace=True)
+    elif act_type is None:
+        return nn.Identity()
+    else:
+        raise NotImplementedError
+        
+def get_norm(norm_type, dim):
+    if norm_type == 'BN':
+        return nn.BatchNorm2d(dim)
+    elif norm_type == 'GN':
+        return nn.GroupNorm(num_groups=32, num_channels=dim)
+    elif norm_type is None:
+        return nn.Identity()
+    else:
+        raise NotImplementedError
+
+class Conv(nn.Module):
+    def __init__(self, 
+                 c1,                   # in channels
+                 c2,                   # out channels 
+                 k=1,                  # kernel size 
+                 p=0,                  # padding
+                 s=1,                  # padding
+                 d=1,                  # dilation
+                 act_type='lrelu',     # activation
+                 norm_type='BN',       # normalization
+                 depthwise=False):
+        super(Conv, self).__init__()
+        convs = []
+        add_bias = False if norm_type else True
+        if depthwise:
+            convs.append(get_conv2d(c1, c1, k=k, p=p, s=s, d=d, g=c1, bias=add_bias))
+            # depthwise conv
+            if norm_type:
+                convs.append(get_norm(norm_type, c1))
+            if act_type:
+                convs.append(get_activation(act_type))
+            # pointwise conv
+            convs.append(get_conv2d(c1, c2, k=1, p=0, s=1, d=d, g=1, bias=add_bias))
+            if norm_type:
+                convs.append(get_norm(norm_type, c2))
+            if act_type:
+                convs.append(get_activation(act_type))
+
+        else:
+            convs.append(get_conv2d(c1, c2, k=k, p=p, s=s, d=d, g=1, bias=add_bias))
+            if norm_type:
+                convs.append(get_norm(norm_type, c2))
+            if act_type:
+                convs.append(get_activation(act_type))
+            
+        self.convs = nn.Sequential(*convs)
+
+
+    def forward(self, x):
+        return self.convs(x)
+
+
+# --------------------- Yolov8 modules ---------------------
+## Yolov8 BottleNeck
+class Yolov8Bottleneck(nn.Module):
+    def __init__(self,
+                 in_dim,
+                 out_dim,
+                 expand_ratio = 0.5,
+                 kernel_sizes = [3, 3],
+                 shortcut     = True,
+                 act_type     = 'silu',
+                 norm_type    = 'BN',
+                 depthwise    = False,):
+        super(Yolov8Bottleneck, self).__init__()
+        inter_dim = int(out_dim * expand_ratio)  # hidden channels            
+        self.cv1 = Conv(in_dim, inter_dim, k=kernel_sizes[0], p=kernel_sizes[0]//2, norm_type=norm_type, act_type=act_type, depthwise=depthwise)
+        self.cv2 = Conv(inter_dim, out_dim, k=kernel_sizes[1], p=kernel_sizes[1]//2, norm_type=norm_type, act_type=act_type, depthwise=depthwise)
+        self.shortcut = shortcut and in_dim == out_dim
+
+    def forward(self, x):
+        h = self.cv2(self.cv1(x))
+
+        return x + h if self.shortcut else h
+
+# Yolov8 StageBlock
+class Yolov8StageBlock(nn.Module):
+    def __init__(self,
+                 in_dim,
+                 out_dim,
+                 expand_ratio = 0.5,
+                 num_blocks   = 1,
+                 shortcut     = False,
+                 act_type     = 'silu',
+                 norm_type    = 'BN',
+                 depthwise    = False,):
+        super(Yolov8StageBlock, self).__init__()
+        inter_dim = int(out_dim * expand_ratio)
+        self.cv1 = Conv(in_dim, inter_dim, k=1, norm_type=norm_type, act_type=act_type)
+        self.cv2 = Conv(in_dim, inter_dim, k=1, norm_type=norm_type, act_type=act_type)
+        self.m = nn.Sequential(*(
+            Yolov8Bottleneck(inter_dim, inter_dim, 1.0, [3, 3], shortcut, act_type, norm_type, depthwise)
+            for _ in range(num_blocks)))
+        self.cv3 = Conv((2 + num_blocks) * inter_dim, out_dim, k=1, act_type=act_type, norm_type=norm_type)
+
+    def forward(self, x):
+        x1 = self.cv1(x)
+        x2 = self.cv2(x)
+        out = list([x1, x2])
+
+        out.extend(m(out[-1]) for m in self.m)
+
+        out = self.cv3(torch.cat(out, dim=1))
+
+        return out
+    

models/detectors/yolov8/yolov8_head.py

@@ -0,0 +1,151 @@
+import torch
+import torch.nn as nn
+
+try:
+    from .yolov8_basic import Conv
+except:
+    from yolov8_basic import Conv
+
+
+# Single-level Head
+class SingleLevelHead(nn.Module):
+    def __init__(self, in_dim, cls_head_dim, reg_head_dim, num_cls_head, num_reg_head, act_type, norm_type, depthwise):
+        super().__init__()
+        # --------- Basic Parameters ----------
+        self.in_dim = in_dim
+        self.num_cls_head = num_cls_head
+        self.num_reg_head = num_reg_head
+        self.act_type = act_type
+        self.norm_type = norm_type
+        self.depthwise = depthwise
+        
+        # --------- Network Parameters ----------
+        ## cls head
+        cls_feats = []
+        self.cls_head_dim = cls_head_dim
+        for i in range(num_cls_head):
+            if i == 0:
+                cls_feats.append(
+                    Conv(in_dim, self.cls_head_dim, k=3, p=1, s=1, 
+                         act_type=act_type,
+                         norm_type=norm_type,
+                         depthwise=depthwise)
+                        )
+            else:
+                cls_feats.append(
+                    Conv(self.cls_head_dim, self.cls_head_dim, k=3, p=1, s=1, 
+                        act_type=act_type,
+                        norm_type=norm_type,
+                        depthwise=depthwise)
+                        )      
+        ## reg head
+        reg_feats = []
+        self.reg_head_dim = reg_head_dim
+        for i in range(num_reg_head):
+            if i == 0:
+                reg_feats.append(
+                    Conv(in_dim, self.reg_head_dim, k=3, p=1, s=1, 
+                         act_type=act_type,
+                         norm_type=norm_type,
+                         depthwise=depthwise)
+                        )
+            else:
+                reg_feats.append(
+                    Conv(self.reg_head_dim, self.reg_head_dim, k=3, p=1, s=1, 
+                         act_type=act_type,
+                         norm_type=norm_type,
+                         depthwise=depthwise)
+                        )
+        self.cls_feats = nn.Sequential(*cls_feats)
+        self.reg_feats = nn.Sequential(*reg_feats)
+
+
+    def forward(self, x):
+        """
+            in_feats: (Tensor) [B, C, H, W]
+        """
+        cls_feats = self.cls_feats(x)
+        reg_feats = self.reg_feats(x)
+
+        return cls_feats, reg_feats
+    
+
+# Multi-level Head
+class MultiLevelHead(nn.Module):
+    def __init__(self, cfg, in_dims, cls_out_dim, reg_out_dim, num_levels=3):
+        super().__init__()
+        ## ----------- Network Parameters -----------
+        self.multi_level_heads = nn.ModuleList(
+            [SingleLevelHead(
+                in_dims[level],
+                cls_out_dim,            # cls head dim
+                reg_out_dim,            # reg head dim
+                cfg['num_cls_head'],
+                cfg['num_reg_head'],
+                cfg['head_act'],
+                cfg['head_norm'],
+                cfg['head_depthwise'])
+                for level in range(num_levels)
+            ])
+        # --------- Basic Parameters ----------
+        self.in_dims = in_dims
+
+        self.cls_head_dim = self.multi_level_heads[0].cls_head_dim
+        self.reg_head_dim = self.multi_level_heads[0].reg_head_dim
+
+
+    def forward(self, feats):
+        """
+            feats: List[(Tensor)] [[B, C, H, W], ...]
+        """
+        cls_feats = []
+        reg_feats = []
+        for feat, head in zip(feats, self.multi_level_heads):
+            # ---------------- Pred ----------------
+            cls_feat, reg_feat = head(feat)
+
+            cls_feats.append(cls_feat)
+            reg_feats.append(reg_feat)
+
+        return cls_feats, reg_feats
+    
+
+# build detection head
+def build_det_head(cfg, in_dims, cls_out_dim, reg_out_dim, num_levels=3):
+    if cfg['head'] == 'decoupled_head':
+        head = MultiLevelHead(cfg, in_dims, cls_out_dim, reg_out_dim, num_levels) 
+
+    return head
+
+
+if __name__ == '__main__':
+    import time
+    from thop import profile
+    cfg = {
+        'head': 'decoupled_head',
+        'num_cls_head': 2,
+        'num_reg_head': 2,
+        'head_act': 'silu',
+        'head_norm': 'BN',
+        'head_depthwise': False,
+        'reg_max': 16,
+    }
+    fpn_dims = [256, 512, 512]
+    cls_out_dim = 256
+    reg_out_dim = 64
+    # Head-1
+    model = build_det_head(cfg, fpn_dims, cls_out_dim, reg_out_dim, num_levels=3)
+    print(model)
+    fpn_feats = [torch.randn(1, fpn_dims[0], 80, 80), torch.randn(1, fpn_dims[1], 40, 40), torch.randn(1, fpn_dims[2], 20, 20)]
+    t0 = time.time()
+    outputs = model(fpn_feats)
+    t1 = time.time()
+    print('Time: ', t1 - t0)
+    # for out in outputs:
+    #     print(out.shape)
+
+    print('==============================')
+    flops, params = profile(model, inputs=(fpn_feats, ), verbose=False)
+    print('==============================')
+    print('Head-1: GFLOPs : {:.2f}'.format(flops / 1e9 * 2))
+    print('Head-1: Params : {:.2f} M'.format(params / 1e6))

models/detectors/yolov8/yolov8_neck.py

@@ -0,0 +1,104 @@
+import torch
+import torch.nn as nn
+
+try:
+    from .yolov8_basic import Conv
+except:
+    from yolov8_basic import Conv
+
+
+# Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher
+class SPPF(nn.Module):
+    """
+        This code referenced to https://github.com/ultralytics/yolov5
+    """
+    def __init__(self, cfg, in_dim, out_dim, expand_ratio=0.5):
+        super().__init__()
+        inter_dim = int(in_dim * expand_ratio)
+        self.out_dim = out_dim
+        self.cv1 = Conv(in_dim, inter_dim, k=1, act_type=cfg['neck_act'], norm_type=cfg['neck_norm'])
+        self.cv2 = Conv(inter_dim * 4, out_dim, k=1, act_type=cfg['neck_act'], norm_type=cfg['neck_norm'])
+        self.m = nn.MaxPool2d(kernel_size=cfg['pooling_size'], stride=1, padding=cfg['pooling_size'] // 2)
+
+    def forward(self, x):
+        x = self.cv1(x)
+        y1 = self.m(x)
+        y2 = self.m(y1)
+
+        return self.cv2(torch.cat((x, y1, y2, self.m(y2)), 1))
+
+
+# SPPF block with CSP module
+class SPPFBlockCSP(nn.Module):
+    """
+        CSP Spatial Pyramid Pooling Block
+    """
+    def __init__(self, cfg, in_dim, out_dim, expand_ratio):
+        super(SPPFBlockCSP, self).__init__()
+        inter_dim = int(in_dim * expand_ratio)
+        self.out_dim = out_dim
+        self.cv1 = Conv(in_dim, inter_dim, k=1, act_type=cfg['neck_act'], norm_type=cfg['neck_norm'])
+        self.cv2 = Conv(in_dim, inter_dim, k=1, act_type=cfg['neck_act'], norm_type=cfg['neck_norm'])
+        self.m = nn.Sequential(
+            Conv(inter_dim, inter_dim, k=3, p=1, 
+                 act_type=cfg['neck_act'], norm_type=cfg['neck_norm'], 
+                 depthwise=cfg['neck_depthwise']),
+            SPPF(cfg, inter_dim, inter_dim, expand_ratio=1.0),
+            Conv(inter_dim, inter_dim, k=3, p=1, 
+                 act_type=cfg['neck_act'], norm_type=cfg['neck_norm'], 
+                 depthwise=cfg['neck_depthwise'])
+        )
+        self.cv3 = Conv(inter_dim * 2, self.out_dim, k=1, act_type=cfg['neck_act'], norm_type=cfg['neck_norm'])
+
+        
+    def forward(self, x):
+        x1 = self.cv1(x)
+        x2 = self.cv2(x)
+        x3 = self.m(x2)
+        y = self.cv3(torch.cat([x1, x3], dim=1))
+
+        return y
+
+
+def build_neck(cfg, in_dim, out_dim):
+    model = cfg['neck']
+    print('==============================')
+    print('Neck: {}'.format(model))
+    # build neck
+    if model == 'sppf':
+        neck = SPPF(cfg, in_dim, out_dim, cfg['neck_expand_ratio'])
+    elif model == 'csp_sppf':
+        neck = SPPFBlockCSP(cfg, in_dim, out_dim, cfg['neck_expand_ratio'])
+
+    return neck
+
+
+if __name__ == '__main__':
+    import time
+    from thop import profile
+    cfg = {
+        ## Neck: SPP
+        'neck': 'sppf',
+        'neck_expand_ratio': 0.5,
+        'pooling_size': 5,
+        'neck_act': 'silu',
+        'neck_norm': 'BN',
+        'neck_depthwise': False,
+    }
+    in_dim = 512
+    out_dim = 512
+    # Head-1
+    model = build_neck(cfg, in_dim, out_dim)
+    feat = torch.randn(1, in_dim, 20, 20)
+    t0 = time.time()
+    outputs = model(feat)
+    t1 = time.time()
+    print('Time: ', t1 - t0)
+    # for out in outputs:
+    #     print(out.shape)
+
+    print('==============================')
+    flops, params = profile(model, inputs=(feat, ), verbose=False)
+    print('==============================')
+    print('FPN: GFLOPs : {:.2f}'.format(flops / 1e9 * 2))
+    print('FPN: Params : {:.2f} M'.format(params / 1e6))

models/detectors/yolov8/yolov8_pafpn.py

@@ -0,0 +1,143 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+try:
+    from .yolov8_basic import Conv, Yolov8StageBlock
+except:
+    from yolov8_basic import Conv, Yolov8StageBlock
+
+
+# PaFPN-ELAN
+class Yolov8PaFPN(nn.Module):
+    def __init__(self, 
+                 in_dims   = [256, 512, 512],
+                 width     = 1.0,
+                 depth     = 1.0,
+                 ratio     = 1.0,
+                 act_type  = 'silu',
+                 norm_type = 'BN',
+                 depthwise = False):
+        super(Yolov8PaFPN, self).__init__()
+        print('==============================')
+        print('FPN: {}'.format("Yolov8 PaFPN"))
+        # ---------------- Basic parameters ----------------
+        self.in_dims = in_dims
+        self.width = width
+        self.depth = depth
+        self.out_dim = [round(256 * width), round(512 * width), round(512 * width * ratio)]
+        c3, c4, c5 = in_dims
+
+        # ---------------- Top dwon ----------------
+        ## P5 -> P4
+        self.top_down_layer_1 = Yolov8StageBlock(in_dim       = c5 + c4,
+                                                 out_dim      = round(512*width),
+                                                 expand_ratio = 0.5,
+                                                 num_blocks   = round(3*depth),
+                                                 shortcut     = False,
+                                                 act_type     = act_type,
+                                                 norm_type    = norm_type,
+                                                 depthwise    = depthwise,
+                                                 )
+        ## P4 -> P3
+        self.top_down_layer_2 = Yolov8StageBlock(in_dim       = round(512*width) + c3,
+                                                 out_dim      = round(256*width),
+                                                 expand_ratio = 0.5,
+                                                 num_blocks   = round(3*depth),
+                                                 shortcut     = False,
+                                                 act_type     = act_type,
+                                                 norm_type    = norm_type,
+                                                 depthwise    = depthwise,
+                                                 )
+        # ---------------- Bottom up ----------------
+        ## P3 -> P4
+        self.dowmsample_layer_1 = Conv(round(256*width), round(256*width), k=3, p=1, s=2, act_type=act_type, norm_type=norm_type, depthwise=depthwise)
+        self.bottom_up_layer_1 = Yolov8StageBlock(in_dim       = round(256*width) + round(512*width),
+                                                  out_dim      = round(512*width),
+                                                  expand_ratio = 0.5,
+                                                  num_blocks   = round(3*depth),
+                                                  shortcut     = False,
+                                                  act_type     = act_type,
+                                                  norm_type    = norm_type,
+                                                  depthwise    = depthwise,
+                                                  )
+        ## P4 -> P5
+        self.dowmsample_layer_2 = Conv(round(512*width), round(512*width), k=3, p=1, s=2, act_type=act_type, norm_type=norm_type, depthwise=depthwise)
+        self.bottom_up_layer_2 = Yolov8StageBlock(in_dim = round(512 * width) + c5,
+                                                  out_dim=round(512 * width * ratio),
+                                                  expand_ratio = 0.5,
+                                                  num_blocks   = round(3*depth),
+                                                  shortcut     = False,
+                                                  act_type     = act_type,
+                                                  norm_type    = norm_type,
+                                                  depthwise    = depthwise,
+                                                  )
+
+    def forward(self, features):
+        c3, c4, c5 = features
+
+        # Top down
+        ## P5 -> P4
+        c6 = F.interpolate(c5, scale_factor=2.0)
+        c7 = torch.cat([c6, c4], dim=1)
+        c8 = self.top_down_layer_1(c7)
+        ## P4 -> P3
+        c9 = F.interpolate(c8, scale_factor=2.0)
+        c10 = torch.cat([c9, c3], dim=1)
+        c11 = self.top_down_layer_2(c10)
+
+        # Bottom up
+        # p3 -> P4
+        c12 = self.dowmsample_layer_1(c11)
+        c13 = torch.cat([c12, c8], dim=1)
+        c14 = self.bottom_up_layer_1(c13)
+        # P4 -> P5
+        c15 = self.dowmsample_layer_2(c14)
+        c16 = torch.cat([c15, c5], dim=1)
+        c17 = self.bottom_up_layer_2(c16)
+
+        out_feats = [c11, c14, c17] # [P3, P4, P5]
+        
+        return out_feats
+
+
+def build_fpn(cfg, in_dims):
+    model = cfg['fpn']
+    # build neck
+    if model == 'yolov8_pafpn':
+        fpn_net = Yolov8PaFPN(in_dims=in_dims,
+                             width=cfg['width'],
+                             depth=cfg['depth'],
+                             ratio=cfg['ratio'],
+                             act_type=cfg['fpn_act'],
+                             norm_type=cfg['fpn_norm'],
+                             depthwise=cfg['fpn_depthwise']
+                             )
+    return fpn_net
+
+
+if __name__ == '__main__':
+    import time
+    from thop import profile
+    cfg = {
+        'fpn': 'yolov8_pafpn',
+        'fpn_act': 'silu',
+        'fpn_norm': 'BN',
+        'fpn_depthwise': False,
+        'width': 1.0,
+        'depth': 1.0,
+        'ratio': 1.0,
+    }
+    model = build_fpn(cfg, in_dims=[256, 512, 512])
+    pyramid_feats = [torch.randn(1, 256, 80, 80), torch.randn(1, 512, 40, 40), torch.randn(1, 512, 20, 20)]
+    t0 = time.time()
+    outputs = model(pyramid_feats)
+    t1 = time.time()
+    print('Time: ', t1 - t0)
+    for out in outputs:
+        print(out.shape)
+
+    print('==============================')
+    flops, params = profile(model, inputs=(pyramid_feats, ), verbose=False)
+    print('==============================')
+    print('GFLOPs : {:.2f}'.format(flops / 1e9 * 2))
+    print('Params : {:.2f} M'.format(params / 1e6))

models/detectors/yolov8/yolov8_pred.py

@@ -0,0 +1,154 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+# Single-level pred layer
+class SingleLevelPredLayer(nn.Module):
+    def __init__(self, cls_dim, reg_dim, num_classes, num_coords=4):
+        super().__init__()
+        # --------- Basic Parameters ----------
+        self.cls_dim = cls_dim
+        self.reg_dim = reg_dim
+        self.num_classes = num_classes
+        self.num_coords = num_coords
+
+        # --------- Network Parameters ----------
+        self.cls_pred = nn.Conv2d(cls_dim, num_classes, kernel_size=1)
+        self.reg_pred = nn.Conv2d(reg_dim, num_coords, kernel_size=1)                
+
+        self.init_bias()
+        
+
+    def init_bias(self):
+        # Init bias
+        init_prob = 0.01
+        bias_value = -torch.log(torch.tensor((1. - init_prob) / init_prob))
+        # cls pred
+        b = self.cls_pred.bias.view(1, -1)
+        b.data.fill_(bias_value.item())
+        self.cls_pred.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
+        # reg pred
+        b = self.reg_pred.bias.view(-1, )
+        b.data.fill_(1.0)
+        self.reg_pred.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
+        w = self.reg_pred.weight
+        w.data.fill_(0.)
+        self.reg_pred.weight = torch.nn.Parameter(w, requires_grad=True)
+
+
+    def forward(self, cls_feat, reg_feat):
+        """
+            in_feats: (Tensor) [B, C, H, W]
+        """
+        cls_pred = self.cls_pred(cls_feat)
+        reg_pred = self.reg_pred(reg_feat)
+
+        return cls_pred, reg_pred
+    
+
+# Multi-level pred layer
+class MultiLevelPredLayer(nn.Module):
+    def __init__(self, cls_dim, reg_dim, strides, num_classes, num_coords=4, num_levels=3, reg_max=16):
+        super().__init__()
+        # --------- Basic Parameters ----------
+        self.cls_dim = cls_dim
+        self.reg_dim = reg_dim
+        self.strides = strides
+        self.num_classes = num_classes
+        self.num_coords = num_coords
+        self.num_levels = num_levels
+        self.reg_max = reg_max
+
+        # ----------- Network Parameters -----------
+        ## pred layers
+        self.multi_level_preds = nn.ModuleList(
+            [SingleLevelPredLayer(
+                cls_dim,
+                reg_dim,
+                num_classes,
+                num_coords * self.reg_max)
+                for _ in range(num_levels)
+            ])
+        ## proj conv
+        self.proj = nn.Parameter(torch.linspace(0, reg_max, reg_max), requires_grad=False)
+        self.proj_conv = nn.Conv2d(self.reg_max, 1, kernel_size=1, bias=False)
+        self.proj_conv.weight = nn.Parameter(self.proj.view([1, reg_max, 1, 1]).clone().detach(), requires_grad=False)
+
+    def generate_anchors(self, level, fmp_size):
+        """
+            fmp_size: (List) [H, W]
+        """
+        # generate grid cells
+        fmp_h, fmp_w = fmp_size
+        anchor_y, anchor_x = torch.meshgrid([torch.arange(fmp_h), torch.arange(fmp_w)])
+        # [H, W, 2] -> [HW, 2]
+        anchors = torch.stack([anchor_x, anchor_y], dim=-1).float().view(-1, 2)
+        anchors += 0.5  # add center offset
+        anchors *= self.strides[level]
+
+        return anchors
+        
+    def forward(self, cls_feats, reg_feats):
+        all_anchors = []
+        all_strides = []
+        all_cls_preds = []
+        all_reg_preds = []
+        all_box_preds = []
+        all_delta_preds = []
+        for level in range(self.num_levels):
+            # pred
+            cls_pred, reg_pred = self.multi_level_preds[level](cls_feats[level], reg_feats[level])
+
+            # generate anchor boxes: [M, 4]
+            B, _, H, W = cls_pred.size()
+            fmp_size = [H, W]
+            anchors = self.generate_anchors(level, fmp_size)
+            anchors = anchors.to(cls_pred.device)
+            # stride tensor: [M, 1]
+            stride_tensor = torch.ones_like(anchors[..., :1]) * self.strides[level]
+            
+            # [B, C, H, W] -> [B, H, W, C] -> [B, M, C]
+            cls_pred = cls_pred.permute(0, 2, 3, 1).contiguous().view(B, -1, self.num_classes)
+            reg_pred = reg_pred.permute(0, 2, 3, 1).contiguous().view(B, -1, 4*self.reg_max)
+
+            # ----------------------- Decode bbox -----------------------
+            B, M = reg_pred.shape[:2]
+            # [B, M, 4*(reg_max)] -> [B, M, 4, reg_max] -> [B, 4, M, reg_max]
+            delta_pred = reg_pred.reshape([B, M, 4, self.reg_max])
+            # [B, M, 4, reg_max] -> [B, reg_max, 4, M]
+            delta_pred = delta_pred.permute(0, 3, 2, 1).contiguous()
+            # [B, reg_max, 4, M] -> [B, 1, 4, M]
+            delta_pred = self.proj_conv(F.softmax(delta_pred, dim=1))
+            # [B, 1, 4, M] -> [B, 4, M] -> [B, M, 4]
+            delta_pred = delta_pred.view(B, 4, M).permute(0, 2, 1).contiguous()
+            ## tlbr -> xyxy
+            x1y1_pred = anchors[None] - delta_pred[..., :2] * self.strides[level]
+            x2y2_pred = anchors[None] + delta_pred[..., 2:] * self.strides[level]
+            box_pred = torch.cat([x1y1_pred, x2y2_pred], dim=-1)
+
+            all_cls_preds.append(cls_pred)
+            all_reg_preds.append(reg_pred)
+            all_box_preds.append(box_pred)
+            all_delta_preds.append(delta_pred)
+            all_anchors.append(anchors)
+            all_strides.append(stride_tensor)
+        
+        # output dict
+        outputs = {"pred_cls": all_cls_preds,        # List(Tensor) [B, M, C]
+                   "pred_reg": all_reg_preds,        # List(Tensor) [B, M, 4*(reg_max)]
+                   "pred_box": all_box_preds,        # List(Tensor) [B, M, 4]
+                   "pred_delta": all_delta_preds,    # List(Tensor) [B, M, 4]
+                   "anchors": all_anchors,           # List(Tensor) [M, 2]
+                   "strides": self.strides,          # List(Int) = [8, 16, 32]
+                   "stride_tensor": all_strides      # List(Tensor) [M, 1]
+                   }
+
+        return outputs
+    
+
+# build detection head
+def build_pred_layer(cls_dim, reg_dim, strides, num_classes, num_coords=4, num_levels=3, reg_max=16):
+    pred_layers = MultiLevelPredLayer(cls_dim, reg_dim, strides, num_classes, num_coords, num_levels, reg_max) 
+
+    return pred_layers

train_single_gpu.sh

@@ -1,59 +1,59 @@
 # -------------------------- Train RTCDet series --------------------------
-python train.py \
-        --cuda \
-        -d coco \
-        --root /data/datasets/ \
-        -m rtrdet_l \
-        -bs 16 \
-        -size 640 \
-        --wp_epoch 1 \
-        --max_epoch 300 \
-        --eval_epoch 10 \
-        --no_aug_epoch 20 \
-        --grad_accumulate 1 \
-        --ema \
-        --fp16 \
-        --multi_scale \
-        --eval_first \
-        # --load_cache \
-        # --resume weights/coco/yolox_m/yolox_m_best.pth \
-        # --eval_first
-
-# -------------------------- Train YOLOX & YOLOv7 series --------------------------
 # python train.py \
 #         --cuda \
 #         -d coco \
 #         --root /data/datasets/ \
-#         -m yolox_s \
-#         -bs 8 \
+#         -m rtrdet_l \
+#         -bs 16 \
 #         -size 640 \
-#         --wp_epoch 3 \
+#         --wp_epoch 1 \
 #         --max_epoch 300 \
 #         --eval_epoch 10 \
-#         --no_aug_epoch 15 \
-#         --grad_accumulate 8 \
+#         --no_aug_epoch 20 \
+#         --grad_accumulate 1 \
 #         --ema \
 #         --fp16 \
 #         --multi_scale \
+#         --eval_first \
 #         # --load_cache \
 #         # --resume weights/coco/yolox_m/yolox_m_best.pth \
 #         # --eval_first
 
-# -------------------------- Train YOLOv1~v5 series --------------------------
+# -------------------------- Train YOLOX & YOLOv7 series --------------------------
 # python train.py \
 #         --cuda \
 #         -d coco \
-#         --root /mnt/share/ssd2/dataset/ \
-#         -m yolov5_s \
-#         -bs 16 \
+#         --root /data/datasets/ \
+#         -m yolox_s \
+#         -bs 8 \
 #         -size 640 \
 #         --wp_epoch 3 \
 #         --max_epoch 300 \
 #         --eval_epoch 10 \
-#         --no_aug_epoch 10 \
+#         --no_aug_epoch 15 \
+#         --grad_accumulate 8 \
 #         --ema \
 #         --fp16 \
 #         --multi_scale \
 #         # --load_cache \
-#         # --resume weights/coco/yolov5_l/yolov5_l_best.pth \
+#         # --resume weights/coco/yolox_m/yolox_m_best.pth \
 #         # --eval_first
+
+# -------------------------- Train YOLOv1~v5 series --------------------------
+python train.py \
+        --cuda \
+        -d coco \
+        --root /mnt/share/ssd2/dataset/ \
+        -m yolov8_n \
+        -bs 16 \
+        -size 640 \
+        --wp_epoch 3 \
+        --max_epoch 500 \
+        --eval_epoch 10 \
+        --no_aug_epoch 10 \
+        --ema \
+        --fp16 \
+        --multi_scale \
+        # --load_cache \
+        # --resume weights/coco/yolov5_l/yolov5_l_best.pth \
+        # --eval_first