From 13f72755558d7a9adbd7b1a4879509fb39a932d2 Mon Sep 17 00:00:00 2001
From: Glenn Jocher <glenn.jocher@ultralytics.com>
Date: Mon, 18 Oct 2021 14:24:48 +0200
Subject: [PATCH] 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               | 7 ++++---
 detect.py                          | 2 +-
 utils/loggers/wandb/wandb_utils.py | 2 +-
 3 files changed, 6 insertions(+), 5 deletions(-)

diff --git a/data/Objects365.yaml b/data/Objects365.yaml
index 5c0a732253e3..97a424fd03a0 100644
--- a/data/Objects365.yaml
+++ b/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)
diff --git a/detect.py b/detect.py
index d9961f5f457d..ff8e32acbaed 100644
--- a/detect.py
+++ b/detect.py
@@ -139,7 +139,7 @@ def wrap_frozen_graph(gd, inputs, outputs):
         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()
diff --git a/utils/loggers/wandb/wandb_utils.py b/utils/loggers/wandb/wandb_utils.py
index 5c92ed947c7b..a4940afd2308 100644
--- a/utils/loggers/wandb/wandb_utils.py
+++ b/utils/loggers/wandb/wandb_utils.py
@@ -433,7 +433,7 @@ def log_training_progress(self, predn, path, names):
                      "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,