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Save PyTorch Hub models to /root/hub/cache/dir #3904

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merged 2 commits into from
Jul 7, 2021
Merged

Save PyTorch Hub models to /root/hub/cache/dir #3904

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e486a2e
Create hubconf.py
johnohagan Jul 6, 2021
3ed7852
Add save_dir variable
glenn-jocher Jul 7, 2021
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15 changes: 8 additions & 7 deletions hubconf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,9 +4,12 @@
import torch
model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
"""
from pathlib import Path

import torch

FILE = Path(__file__).absolute()


def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
"""Creates a specified YOLOv5 model
Expand All @@ -23,28 +26,26 @@ def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbo
Returns:
YOLOv5 pytorch model
"""
from pathlib import Path

from models.yolo import Model, attempt_load
from utils.general import check_requirements, set_logging
from utils.google_utils import attempt_download
from utils.torch_utils import select_device

check_requirements(requirements=Path(__file__).parent / 'requirements.txt',
exclude=('tensorboard', 'thop', 'opencv-python'))
check_requirements(requirements=FILE.parent / 'requirements.txt', exclude=('tensorboard', 'thop', 'opencv-python'))
set_logging(verbose=verbose)

fname = Path(name).with_suffix('.pt') # checkpoint filename
save_dir = Path('') if str(name).endswith('.pt') else FILE.parent
path = (save_dir / name).with_suffix('.pt') # checkpoint path
try:
device = select_device(('0' if torch.cuda.is_available() else 'cpu') if device is None else device)

if pretrained and channels == 3 and classes == 80:
model = attempt_load(fname, map_location=device) # download/load FP32 model
model = attempt_load(path, map_location=device) # download/load FP32 model
else:
cfg = list((Path(__file__).parent / 'models').rglob(f'{name}.yaml'))[0] # model.yaml path
model = Model(cfg, channels, classes) # create model
if pretrained:
ckpt = torch.load(attempt_download(fname), map_location=device) # load
ckpt = torch.load(attempt_download(path), map_location=device) # load
msd = model.state_dict() # model state_dict
csd = ckpt['model'].float().state_dict() # checkpoint state_dict as FP32
csd = {k: v for k, v in csd.items() if msd[k].shape == v.shape} # filter
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