Fix load and resume and update autodownload endpoint (#136)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

ultralytics/yolo/engine/model.py

@@ -82,7 +82,7 @@ class YOLO:
         self.ckpt_path = weights
         self.task = self.model.args["task"]
         self.overrides = self.model.args
-        self.overrides["device"] = ''  # reset device
+        self._reset_ckpt_args(self.overrides)
         self.ModelClass, self.TrainerClass, self.ValidatorClass, self.PredictorClass = \
             self._guess_ops_from_task(self.task)
 
@@ -199,27 +199,6 @@ class YOLO:
 
         self.trainer.train()
 
-    def resume(self, task=None, model=None):
-        """
-        Resume a training task. Requires either `task` or `model`. `model` takes the higher precedence.
-        Args:
-            task (str): The task type you want to resume. Automatically finds the last run to resume if `model` is not specified.
-            model (str): The model checkpoint to resume from. If not found, the last run of the given task type is resumed.
-                         If `model` is specified
-        """
-        if task:
-            if task.lower() not in MODEL_MAP:
-                raise SyntaxError(f"unrecognised task - {task}. Supported tasks are {MODEL_MAP.keys()}")
-        else:
-            ckpt = torch.load(model, map_location="cpu")
-            task = ckpt["train_args"]["task"]
-            del ckpt
-        self.ModelClass, self.TrainerClass, self.ValidatorClass, self.PredictorClass = self._guess_ops_from_task(
-            task=task.lower())
-        self.trainer = self.TrainerClass(overrides={"task": task.lower(), "resume": model or True})
-
-        self.trainer.train()
-
     def to(self, device):
         self.model.to(device)
 
@@ -240,3 +219,10 @@ class YOLO:
 
     def forward(self, imgs):
         return self.__call__(imgs)
+
+    @staticmethod
+    def _reset_ckpt_args(args):
+        args.pop("device", None)
+        args.pop("project", None)
+        args.pop("name", None)
+        args.pop("batch_size", None)

ultralytics/yolo/engine/trainer.py

@@ -367,7 +367,7 @@ class BaseTrainer:
         if not pretrained:
             model = check_file(model)
         ckpt = self.load_ckpt(model) if pretrained else None
-        self.model = self.load_model(model_cfg=None if pretrained else model, weights=ckpt)  # model
+        self.model = self.load_model(model_cfg=None if pretrained else model, weights=ckpt["model"])  # model
         return ckpt
 
     def load_ckpt(self, ckpt):

ultralytics/yolo/utils/downloads.py

@@ -45,11 +45,12 @@ def is_url(url, check=True):
         return False
 
 
-def attempt_download(file, repo='ultralytics/yolov5', release='v6.2'):
+def attempt_download(file, repo='ultralytics/assets', release='v0.0.0'):
     # Attempt file download from GitHub release assets if not found locally. release = 'latest', 'v6.2', etc.
 
     def github_assets(repository, version='latest'):
         # Return GitHub repo tag and assets (i.e. ['yolov8n.pt', 'yolov5m.pt', ...])
+        # Return GitHub repo tag and assets (i.e. ['yolov8n.pt', 'yolov8s.pt', ...])
         if version != 'latest':
             version = f'tags/{version}'  # i.e. tags/v6.2
         response = requests.get(f'https://api.github.com/repos/{repository}/releases/{version}').json()  # github api
@@ -70,6 +71,7 @@ def attempt_download(file, repo='ultralytics/yolov5', release='v6.2'):
 
         # GitHub assets
         assets = [f'yolov5{size}{suffix}.pt' for size in 'nsmlx' for suffix in ('', '6', '-cls', '-seg')]  # default
+        assets = [f'yolov8{size}{suffix}.pt' for size in 'nsmlx' for suffix in ('', '6', '-cls', '-seg')]  # default
         try:
             tag, assets = github_assets(repo, release)
         except Exception:

ultralytics/yolo/v8/detect/train.py

@@ -54,12 +54,9 @@ class DetectionTrainer(BaseTrainer):
         self.model.names = self.data["names"]
 
     def load_model(self, model_cfg=None, weights=None, verbose=True):
-        model = DetectionModel(model_cfg or getattr(weights, 'yaml', None) or weights['model'].yaml,
+        model = DetectionModel(model_cfg or weights.yaml, ch=3, nc=self.data["nc"], verbose=verbose)
-                               ch=3,
-                               nc=self.data["nc"],
-                               verbose=verbose)
         if weights:
-            model.load(weights['model'] if isinstance(weights, dict) else weights, verbose)
+            model.load(weights, verbose)
         return model
 
     def get_validator(self):

ultralytics/yolo/v8/segment/train.py

@@ -17,12 +17,9 @@ from ultralytics.yolo.utils.torch_utils import de_parallel
 class SegmentationTrainer(v8.detect.DetectionTrainer):
 
     def load_model(self, model_cfg=None, weights=None, verbose=True):
-        model = SegmentationModel(model_cfg or getattr(weights, 'yaml', None) or weights['model'].yaml,
+        model = SegmentationModel(model_cfg or weights.yaml, ch=3, nc=self.data["nc"], verbose=verbose)
-                                  ch=3,
-                                  nc=self.data["nc"],
-                                  verbose=verbose)
         if weights:
-            model.load(weights['model'] if isinstance(weights, dict) else weights, verbose)
+            model.load(weights, verbose)
         return model
 
     def get_validator(self):