From 9405684bbe1ac9bc04fe6e7610553fe7c32aabc4 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Wed, 12 May 2021 20:25:09 +0200 Subject: [PATCH 1/2] Scope all hubconf.py imports for torch.hub.list() --- hubconf.py | 12 +++++------- 1 file changed, 5 insertions(+), 7 deletions(-) diff --git a/hubconf.py b/hubconf.py index 1876441d8a89..b1380caa4f3a 100644 --- a/hubconf.py +++ b/hubconf.py @@ -5,15 +5,8 @@ model = torch.hub.load('ultralytics/yolov5', 'yolov5s') """ -from pathlib import Path - import torch -from utils.general import check_requirements, set_logging - -dependencies = ['torch', 'yaml'] -check_requirements(Path(__file__).parent / 'requirements.txt', exclude=('tensorboard', 'pycocotools', 'thop')) - def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True): """Creates a specified YOLOv5 model @@ -29,10 +22,15 @@ 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(Path(__file__).parent / 'requirements.txt', exclude=('tensorboard', 'pycocotools', 'thop')) + set_logging(verbose=verbose) fname = Path(name).with_suffix('.pt') # checkpoint filename try: From 285e5ce5078af16ca2baf57cefd97c5f654014b8 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Wed, 12 May 2021 20:26:19 +0200 Subject: [PATCH 2/2] Update hubconf.py --- hubconf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/hubconf.py b/hubconf.py index b1380caa4f3a..3b3dfe0e9e23 100644 --- a/hubconf.py +++ b/hubconf.py @@ -30,8 +30,8 @@ def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbo from utils.torch_utils import select_device check_requirements(Path(__file__).parent / 'requirements.txt', exclude=('tensorboard', 'pycocotools', 'thop')) - set_logging(verbose=verbose) + fname = Path(name).with_suffix('.pt') # checkpoint filename try: if pretrained and channels == 3 and classes == 80: