Commit
•
13f7275
1
Parent(s):
0000334
Update/inplace ops (#5233)
Browse files* Clip Objects365 autodownload labels (#5214)
Fixes out of bounds labels that seem to affect ~10% of images in dataset.
* Inplace ops
- data/Objects365.yaml +4 -3
- detect.py +1 -1
- utils/loggers/wandb/wandb_utils.py +1 -1
data/Objects365.yaml
CHANGED
@@ -63,7 +63,7 @@ download: |
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from pycocotools.coco import COCO
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from tqdm import tqdm
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-
from utils.general import download,
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# Make Directories
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dir = Path(yaml['path']) # dataset root dir
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@@ -105,7 +105,8 @@ download: |
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annIds = coco.getAnnIds(imgIds=im["id"], catIds=catIds, iscrowd=None)
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for a in coco.loadAnns(annIds):
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x, y, w, h = a['bbox'] # bounding box in xywh (xy top-left corner)
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x, y
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-
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except Exception as e:
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print(e)
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from pycocotools.coco import COCO
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from tqdm import tqdm
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+
from utils.general import Path, download, np, xyxy2xywhn
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# Make Directories
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dir = Path(yaml['path']) # dataset root dir
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annIds = coco.getAnnIds(imgIds=im["id"], catIds=catIds, iscrowd=None)
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for a in coco.loadAnns(annIds):
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x, y, w, h = a['bbox'] # bounding box in xywh (xy top-left corner)
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xyxy = np.array([x, y, x + w, y + h])[None] # pixels(1,4)
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x, y, w, h = xyxy2xywhn(xyxy, w=width, h=height, clip=True)[0] # normalized and clipped
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file.write(f"{cid} {x:.5f} {y:.5f} {w:.5f} {h:.5f}\n")
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except Exception as e:
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print(e)
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detect.py
CHANGED
@@ -139,7 +139,7 @@ def run(weights=ROOT / 'yolov5s.pt', # model.pt path(s)
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else:
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img = torch.from_numpy(img).to(device)
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img = img.half() if half else img.float() # uint8 to fp16/32
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-
img
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if len(img.shape) == 3:
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img = img[None] # expand for batch dim
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t2 = time_sync()
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else:
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img = torch.from_numpy(img).to(device)
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img = img.half() if half else img.float() # uint8 to fp16/32
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img /= 255.0 # 0 - 255 to 0.0 - 1.0
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if len(img.shape) == 3:
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img = img[None] # expand for batch dim
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t2 = time_sync()
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utils/loggers/wandb/wandb_utils.py
CHANGED
@@ -433,7 +433,7 @@ class WandbLogger():
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"box_caption": "%s %.3f" % (names[cls], conf),
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"scores": {"class_score": conf},
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"domain": "pixel"})
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-
total_conf
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boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space
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id = self.val_table_path_map[Path(path).name]
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self.result_table.add_data(self.current_epoch,
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"box_caption": "%s %.3f" % (names[cls], conf),
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"scores": {"class_score": conf},
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"domain": "pixel"})
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+
total_conf += conf
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boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space
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id = self.val_table_path_map[Path(path).name]
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self.result_table.add_data(self.current_epoch,
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