Merge 8a5d9730ab into a207844b1c

detect.py

@@ -45,7 +45,7 @@ def detect(save_img=False):
     classify = False
     if classify:
         modelc = load_classifier(name='resnet101', n=2)  # initialize
-        modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval()
+        modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model'],weights_only=False).to(device).eval()
 
     # Set Dataloader
     vid_path, vid_writer = None, None

hubconf.py

@@ -37,7 +37,7 @@ def create(name, pretrained, channels, classes, autoshape):
         if pretrained:
             fname = f'{name}.pt'  # checkpoint filename
             attempt_download(fname)  # download if not found locally
-            ckpt = torch.load(fname, map_location=torch.device('cpu'))  # load
+            ckpt = torch.load(fname, map_location=torch.device('cpu'), weights_only=False)  # 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
@@ -65,7 +65,7 @@ def custom(path_or_model='path/to/model.pt', autoshape=True):
     Returns:
         pytorch model
     """
-    model = torch.load(path_or_model, map_location=torch.device('cpu')) if isinstance(path_or_model, str) else path_or_model  # load checkpoint
+    model = torch.load(path_or_model, map_location=torch.device('cpu'), weights_only=False) if isinstance(path_or_model, str) else path_or_model  # load checkpoint
     if isinstance(model, dict):
         model = model['ema' if model.get('ema') else 'model']  # load model
 

models/experimental.py

@@ -249,7 +249,7 @@ def attempt_load(weights, map_location=None):
     model = Ensemble()
     for w in weights if isinstance(weights, list) else [weights]:
         attempt_download(w)
-        ckpt = torch.load(w, map_location=map_location)  # load
+        ckpt = torch.load(w, map_location=map_location, weights_only=False)  # load
         model.append(ckpt['ema' if ckpt.get('ema') else 'model'].float().fuse().eval())  # FP32 model
     
     # Compatibility updates

tools/reparameterization.ipynb

@@ -56,7 +56,7 @@
     "\n",
     "device = select_device('0', batch_size=1)\n",
     "# model trained by cfg/training/*.yaml\n",
-    "ckpt = torch.load('cfg/training/yolov7_training.pt', map_location=device)\n",
+    "ckpt = torch.load('cfg/training/yolov7_training.pt', map_location=device, weights_only=False)\n",
     "# reparameterized model in cfg/deploy/*.yaml\n",
     "model = Model('cfg/deploy/yolov7.yaml', ch=3, nc=80).to(device)\n",
     "\n",
@@ -118,7 +118,7 @@
     "\n",
     "device = select_device('0', batch_size=1)\n",
     "# model trained by cfg/training/*.yaml\n",
-    "ckpt = torch.load('cfg/training/yolov7x_trainig.pt', map_location=device)\n",
+    "ckpt = torch.load('cfg/training/yolov7x_trainig.pt', map_location=device, weights_only=False)\n",
     "# reparameterized model in cfg/deploy/*.yaml\n",
     "model = Model('cfg/deploy/yolov7x.yaml', ch=3, nc=80).to(device)\n",
     "\n",
@@ -180,7 +180,7 @@
     "\n",
     "device = select_device('0', batch_size=1)\n",
     "# model trained by cfg/training/*.yaml\n",
-    "ckpt = torch.load('cfg/training/yolov7-w6_trainig.pt', map_location=device)\n",
+    "ckpt = torch.load('cfg/training/yolov7-w6_trainig.pt', map_location=device, weights_only=False)\n",
     "# reparameterized model in cfg/deploy/*.yaml\n",
     "model = Model('cfg/deploy/yolov7-w6.yaml', ch=3, nc=80).to(device)\n",
     "\n",
@@ -266,7 +266,7 @@
     "\n",
     "device = select_device('0', batch_size=1)\n",
     "# model trained by cfg/training/*.yaml\n",
-    "ckpt = torch.load('cfg/training/yolov7-e6.pt', map_location=device)\n",
+    "ckpt = torch.load('cfg/training/yolov7-e6.pt', map_location=device, weights_only=False)\n",
     "# reparameterized model in cfg/deploy/*.yaml\n",
     "model = Model('cfg/deploy/yolov7-e6.yaml', ch=3, nc=80).to(device)\n",
     "\n",
@@ -352,7 +352,7 @@
     "\n",
     "device = select_device('0', batch_size=1)\n",
     "# model trained by cfg/training/*.yaml\n",
-    "ckpt = torch.load('cfg/training/yolov7-d6_trainig.pt', map_location=device)\n",
+    "ckpt = torch.load('cfg/training/yolov7-d6_trainig.pt', map_location=device, weights_only=False)\n",
     "# reparameterized model in cfg/deploy/*.yaml\n",
     "model = Model('cfg/deploy/yolov7-d6.yaml', ch=3, nc=80).to(device)\n",
     "\n",
@@ -438,7 +438,7 @@
     "\n",
     "device = select_device('0', batch_size=1)\n",
     "# model trained by cfg/training/*.yaml\n",
-    "ckpt = torch.load('cfg/training/yolov7-e6e_trainig.pt', map_location=device)\n",
+    "ckpt = torch.load('cfg/training/yolov7-e6e_trainig.pt', map_location=device, weights_only=False)\n",
     "# reparameterized model in cfg/deploy/*.yaml\n",
     "model = Model('cfg/deploy/yolov7-e6e.yaml', ch=3, nc=80).to(device)\n",
     "\n",

train.py

@@ -68,7 +68,7 @@ def train(hyp, opt, device, tb_writer=None):
     loggers = {'wandb': None}  # loggers dict
     if rank in [-1, 0]:
         opt.hyp = hyp  # add hyperparameters
-        run_id = torch.load(weights, map_location=device).get('wandb_id') if weights.endswith('.pt') and os.path.isfile(weights) else None
+        run_id = torch.load(weights, map_location=device, weights_only=False).get('wandb_id') if weights.endswith('.pt') and os.path.isfile(weights) else None
         wandb_logger = WandbLogger(opt, Path(opt.save_dir).stem, run_id, data_dict)
         loggers['wandb'] = wandb_logger.wandb
         data_dict = wandb_logger.data_dict
@@ -84,7 +84,7 @@ def train(hyp, opt, device, tb_writer=None):
     if pretrained:
         with torch_distributed_zero_first(rank):
             attempt_download(weights)  # download if not found locally
-        ckpt = torch.load(weights, map_location=device)  # load checkpoint
+        ckpt = torch.load(weights, map_location=device, weights_only=False)  # load checkpoint
         model = Model(opt.cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device)  # create
         exclude = ['anchor'] if (opt.cfg or hyp.get('anchors')) and not opt.resume else []  # exclude keys
         state_dict = ckpt['model'].float().state_dict()  # to FP32

train_aux.py

@@ -68,7 +68,7 @@ def train(hyp, opt, device, tb_writer=None):
     loggers = {'wandb': None}  # loggers dict
     if rank in [-1, 0]:
         opt.hyp = hyp  # add hyperparameters
-        run_id = torch.load(weights).get('wandb_id') if weights.endswith('.pt') and os.path.isfile(weights) else None
+        run_id = torch.load(weights, weights_only=False).get('wandb_id') if weights.endswith('.pt') and os.path.isfile(weights) else None
         wandb_logger = WandbLogger(opt, Path(opt.save_dir).stem, run_id, data_dict)
         loggers['wandb'] = wandb_logger.wandb
         data_dict = wandb_logger.data_dict
@@ -84,7 +84,7 @@ def train(hyp, opt, device, tb_writer=None):
     if pretrained:
         with torch_distributed_zero_first(rank):
             attempt_download(weights)  # download if not found locally
-        ckpt = torch.load(weights, map_location=device)  # load checkpoint
+        ckpt = torch.load(weights, map_location=device, weights_only=False)  # load checkpoint
         model = Model(opt.cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device)  # create
         exclude = ['anchor'] if (opt.cfg or hyp.get('anchors')) and not opt.resume else []  # exclude keys
         state_dict = ckpt['model'].float().state_dict()  # to FP32

utils/aws/resume.py

@@ -13,7 +13,7 @@ sys.path.append('./')  # to run '$ python *.py' files in subdirectories
 port = 0  # --master_port
 path = Path('').resolve()
 for last in path.rglob('*/**/last.pt'):
-    ckpt = torch.load(last)
+    ckpt = torch.load(last, weights_only=False)
     if ckpt['optimizer'] is None:
         continue
 

utils/datasets.py

@@ -389,7 +389,7 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
         self.label_files = img2label_paths(self.img_files)  # labels
         cache_path = (p if p.is_file() else Path(self.label_files[0]).parent).with_suffix('.cache')  # cached labels
         if cache_path.is_file():
-            cache, exists = torch.load(cache_path), True  # load
+            cache, exists = torch.load(cache_path, weights_only=False), True  # load
             #if cache['hash'] != get_hash(self.label_files + self.img_files) or 'version' not in cache:  # changed
             #    cache, exists = self.cache_labels(cache_path, prefix), False  # re-cache
         else:

utils/general.py

@@ -799,7 +799,7 @@ def non_max_suppression_kpt(prediction, conf_thres=0.25, iou_thres=0.45, classes
 
 def strip_optimizer(f='best.pt', s=''):  # from utils.general import *; strip_optimizer()
     # Strip optimizer from 'f' to finalize training, optionally save as 's'
-    x = torch.load(f, map_location=torch.device('cpu'))
+    x = torch.load(f, map_location=torch.device('cpu'), weights_only=False)
     if x.get('ema'):
         x['model'] = x['ema']  # replace model with ema
     for k in 'optimizer', 'training_results', 'wandb_id', 'ema', 'updates':  # keys