add yolov8 core，waiting update later

Author: positive666

README.md

@@ -18,9 +18,10 @@
 ##  <div align="left">🚀 yolov5_reserach PLUS— High-level</div>
 
 
-更新升级中.....
+更新升级中.....add V8 core 
 
 ### <div align="left">⭐新闻板块【实时更新/计划】</div>
+  - 2023/3/1  add v8 core:春节期间看了下V8，最近V8又更新了，由于近半年项目比较多也是耽误了好久(原版本是将V8的所有功能全部融合到了V5的代码中，但是训练的时候发生了问题，排查发现问题发生在V5的数据读取处理，所以暂时使用V8的训练结构代码，也便于区分)，然后抓紧时间不停更新；
 	- 2022/11/23 修复已知BUG，V7.0版本更新兼容，年底比较忙后续忙完业务会大更新~
 	- 2022/10/20 修复适配V7结构和额外任务引起的一些代码问题，实时更新V5的代码优化部分，添加了工具grad_cam在tools目录。
 	- 2022/9/19 修复已知BUG，更新了实时的V5BUG修复和代码优化融合验证，核心检测、分类、分割的部分CI验证，关键点检测实测训练正常，基本功能整理完毕。

classify/train.py

@@ -44,7 +44,7 @@
                            download, increment_path, init_seeds, print_args, yaml_save)
 from utils.loggers import GenericLogger
 from utils.plots import imshow_cls
-from utils.torch_utils import (ModelEMA, model_info, reshape_classifier_output, select_device, smart_DDP,
+from utils.torch_utils import (ModelEMA, de_parallel,model_info, reshape_classifier_output, select_device, smart_DDP,
                                smart_optimizer, smartCrossEntropyLoss, torch_distributed_zero_first)
 
 LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1))  # https://pytorch.org/docs/stable/elastic/run.html
@@ -259,7 +259,7 @@ def train(opt, device):
         # Plot examples
         images, labels = (x[:25] for x in next(iter(testloader)))  # first 25 images and labels
         pred = torch.max(ema.ema(images.to(device)), 1)[1]
-        file = imshow_cls(images, labels, pred, model.names, verbose=False, f=save_dir / 'test_images.jpg')
+        file = imshow_cls(images, labels, pred, de_parallel(model).names, verbose=False, f=save_dir / 'test_images.jpg')
 
         # Log results
         meta = {"epochs": epochs, "top1_acc": best_fitness, "date": datetime.now().isoformat()}

detect.py

@@ -42,7 +42,7 @@
 from models.common import DetectMultiBackend
 from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadStreams, LoadScreenshots
 from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,
-                           increment_path, non_max_suppression, print_args, scale_boxes, strip_optimizer, xyxy2xywh)
+                           increment_path, non_max_suppression,yolov8_non_max_suppression, print_args, scale_boxes, strip_optimizer, xyxy2xywh)
 from utils.plots import Annotator, colors, save_one_box
 from utils.torch_utils import select_device,  smart_inference_mode, TracedModel
 
@@ -76,6 +76,7 @@ def run(
         half=False,  # use FP16 half-precision inference
         dnn=False,  # use OpenCV DNN for ONNX inference
         trace=False,
+        v8_det=False,
         vid_stride=1,  # video frame-rate stride
 ):
     source = str(source)
@@ -131,8 +132,8 @@ def run(
 
         # NMS
         with dt[2]:
-            pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
-
+            pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det) if not v8_det else yolov8_non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
+            
         # Second-stage classifier (optional)
         # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)
 
@@ -247,6 +248,7 @@ def parse_opt():
     parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
     parser.add_argument('--vid-stride', type=int, default=1, help='video frame-rate stride')
     parser.add_argument('--trace', action='store_true', help='trace model')
+    parser.add_argument('--v8_det', action='store_true', help='trace model')
     opt = parser.parse_args()
     opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1  # expand
     print_args(vars(opt))