Update/inplace ops (#5233)

* Clip Objects365 autodownload labels (#5214)

Fixes out of bounds labels that seem to affect ~10% of images in dataset.

* Inplace ops

data/Objects365.yaml

@@ -63,7 +63,7 @@ download: |
   from pycocotools.coco import COCO
   from tqdm import tqdm
   
-  from utils.general import download, Path
+  from utils.general import Path, download, np, xyxy2xywhn
   
   # Make Directories
   dir = Path(yaml['path'])  # dataset root dir
@@ -105,7 +105,8 @@ download: |
                       annIds = coco.getAnnIds(imgIds=im["id"], catIds=catIds, iscrowd=None)
                       for a in coco.loadAnns(annIds):
                           x, y, w, h = a['bbox']  # bounding box in xywh (xy top-left corner)
-                          x, y = x + w / 2, y + h / 2  # xy to center
-                          file.write(f"{cid} {x / width:.5f} {y / height:.5f} {w / width:.5f} {h / height:.5f}\n")
+                          xyxy = np.array([x, y, x + w, y + h])[None]  # pixels(1,4)
+                          x, y, w, h = xyxy2xywhn(xyxy, w=width, h=height, clip=True)[0]  # normalized and clipped
+                          file.write(f"{cid} {x:.5f} {y:.5f} {w:.5f} {h:.5f}\n")
               except Exception as e:
                   print(e)

detect.py

@@ -139,7 +139,7 @@ def run(weights=ROOT / 'yolov5s.pt',  # model.pt path(s)
         else:
             img = torch.from_numpy(img).to(device)
             img = img.half() if half else img.float()  # uint8 to fp16/32
-        img = img / 255.0  # 0 - 255 to 0.0 - 1.0
+        img /= 255.0  # 0 - 255 to 0.0 - 1.0
         if len(img.shape) == 3:
             img = img[None]  # expand for batch dim
         t2 = time_sync()

utils/loggers/wandb/wandb_utils.py

@@ -433,7 +433,7 @@ class WandbLogger():
                      "box_caption": "%s %.3f" % (names[cls], conf),
                      "scores": {"class_score": conf},
                      "domain": "pixel"})
-                total_conf = total_conf + conf
+                total_conf += conf
         boxes = {"predictions": {"box_data": box_data, "class_labels": names}}  # inference-space
         id = self.val_table_path_map[Path(path).name]
         self.result_table.add_data(self.current_epoch,