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Add PyTorch Hub classification CI checks #9027

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Aug 18, 2022
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Add PyTorch Hub classification CI checks #9027

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8fd337b
Add PyTorch Hub classification CI checks
glenn-jocher Aug 18, 2022
56f8ef7
Update hubconf.py
glenn-jocher Aug 18, 2022
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5 changes: 5 additions & 0 deletions .github/workflows/ci-testing.yml
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Expand Up @@ -133,3 +133,8 @@ jobs:
python classify/predict.py --imgsz 32 --weights $b --source ../datasets/mnist2560/test/7/60.png # predict
python classify/predict.py --imgsz 32 --weights $m --source data/images/bus.jpg # predict
python export.py --weights $b --img 64 --imgsz 224 --include torchscript # export
python - <<EOF
import torch
for path in '$m', '$b':
model = torch.hub.load('.', 'custom', path=path, source='local')
EOF
6 changes: 3 additions & 3 deletions hubconf.py
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Expand Up @@ -30,7 +30,7 @@ def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbo

from models.common import AutoShape, DetectMultiBackend
from models.experimental import attempt_load
from models.yolo import Model
from models.yolo import DetectionModel
from utils.downloads import attempt_download
from utils.general import LOGGER, check_requirements, intersect_dicts, logging
from utils.torch_utils import select_device
Expand All @@ -45,13 +45,13 @@ def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbo
if pretrained and channels == 3 and classes == 80:
try:
model = DetectMultiBackend(path, device=device, fuse=autoshape) # detection model
if autoshape:
if autoshape and isinstance(model.model, DetectionModel):
model = AutoShape(model) # for file/URI/PIL/cv2/np inputs and NMS
except Exception:
model = attempt_load(path, device=device, fuse=False) # arbitrary model
else:
cfg = list((Path(__file__).parent / 'models').rglob(f'{path.stem}.yaml'))[0] # model.yaml path
model = Model(cfg, channels, classes) # create model
model = DetectionModel(cfg, channels, classes) # create model
if pretrained:
ckpt = torch.load(attempt_download(path), map_location=device) # load
csd = ckpt['model'].float().state_dict() # checkpoint state_dict as FP32
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