ultralytics 8.0.54 TFLite export improvements and fixes (#1447)

Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

ultralytics/yolo/engine/exporter.py

@@ -188,7 +188,7 @@ class Exporter:
                 m.dynamic = self.args.dynamic
                 m.export = True
                 m.format = self.args.format
-            elif isinstance(m, C2f) and not edgetpu:
+            elif isinstance(m, C2f) and not any((saved_model, pb, tflite, edgetpu, tfjs)):
                 # EdgeTPU does not support FlexSplitV while split provides cleaner ONNX graph
                 m.forward = m.forward_split
 

ultralytics/yolo/engine/model.py

@@ -8,8 +8,8 @@ from ultralytics.nn.tasks import (ClassificationModel, DetectionModel, Segmentat
                                   guess_model_task, nn)
 from ultralytics.yolo.cfg import get_cfg
 from ultralytics.yolo.engine.exporter import Exporter
-from ultralytics.yolo.utils import (DEFAULT_CFG, DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, ONLINE, RANK, ROOT,
-                                    callbacks, is_git_dir, is_pip_package, yaml_load)
+from ultralytics.yolo.utils import (DEFAULT_CFG, DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, RANK, ROOT, callbacks,
+                                    is_git_dir, yaml_load)
 from ultralytics.yolo.utils.checks import check_file, check_imgsz, check_pip_update_available, check_yaml
 from ultralytics.yolo.utils.downloads import GITHUB_ASSET_STEMS
 from ultralytics.yolo.utils.torch_utils import smart_inference_mode
@@ -153,16 +153,10 @@ class YOLO:
                             f"'yolo export model=yolov8n.pt', but exported formats like ONNX, TensorRT etc. only "
                             f"support 'predict' and 'val' modes, i.e. 'yolo predict model=yolov8n.onnx'.")
 
-    def _check_pip_update(self):
+    @smart_inference_mode()
+    def reset_weights(self):
         """
-        Inform user of ultralytics package update availability
-        """
-        if ONLINE and is_pip_package():
-            check_pip_update_available()
-
-    def reset(self):
-        """
-        Resets the model modules.
+        Resets the model modules parameters to randomly initialized values, losing all training information.
         """
         self._check_is_pytorch_model()
         for m in self.model.modules():
@@ -170,6 +164,18 @@ class YOLO:
                 m.reset_parameters()
         for p in self.model.parameters():
             p.requires_grad = True
+        return self
+
+    @smart_inference_mode()
+    def load(self, weights='yolov8n.pt'):
+        """
+        Transfers parameters with matching names and shapes from 'weights' to model.
+        """
+        self._check_is_pytorch_model()
+        if isinstance(weights, (str, Path)):
+            weights, self.ckpt = attempt_load_one_weight(weights)
+        self.model.load(weights)
+        return self
 
     def info(self, verbose=False):
         """
@@ -299,7 +305,7 @@ class YOLO:
             **kwargs (Any): Any number of arguments representing the training configuration.
         """
         self._check_is_pytorch_model()
-        self._check_pip_update()
+        check_pip_update_available()
         overrides = self.overrides.copy()
         overrides.update(kwargs)
         if kwargs.get('cfg'):

ultralytics/yolo/engine/results.py

@@ -48,7 +48,7 @@ class Results:
         self.probs = probs if probs is not None else None
         self.names = names
         self.path = path
-        self._keys = [k for k in ('boxes', 'masks', 'probs') if getattr(self, k) is not None]
+        self._keys = ('boxes', 'masks', 'probs')
 
     def pandas(self):
         pass
@@ -56,7 +56,7 @@ class Results:
 
     def __getitem__(self, idx):
         r = Results(orig_img=self.orig_img, path=self.path, names=self.names)
-        for k in self._keys:
+        for k in self.keys:
             setattr(r, k, getattr(self, k)[idx])
         return r
 
@@ -70,30 +70,30 @@ class Results:
 
     def cpu(self):
         r = Results(orig_img=self.orig_img, path=self.path, names=self.names)
-        for k in self._keys:
+        for k in self.keys:
             setattr(r, k, getattr(self, k).cpu())
         return r
 
     def numpy(self):
         r = Results(orig_img=self.orig_img, path=self.path, names=self.names)
-        for k in self._keys:
+        for k in self.keys:
             setattr(r, k, getattr(self, k).numpy())
         return r
 
     def cuda(self):
         r = Results(orig_img=self.orig_img, path=self.path, names=self.names)
-        for k in self._keys:
+        for k in self.keys:
             setattr(r, k, getattr(self, k).cuda())
         return r
 
     def to(self, *args, **kwargs):
         r = Results(orig_img=self.orig_img, path=self.path, names=self.names)
-        for k in self._keys:
+        for k in self.keys:
             setattr(r, k, getattr(self, k).to(*args, **kwargs))
         return r
 
     def __len__(self):
-        for k in self._keys:
+        for k in self.keys:
             return len(getattr(self, k))
 
     def __str__(self):
@@ -107,6 +107,10 @@ class Results:
         name = self.__class__.__name__
         raise AttributeError(f"'{name}' object has no attribute '{attr}'. See valid attributes below.\n{self.__doc__}")
 
+    @property
+    def keys(self):
+        return [k for k in self._keys if getattr(self, k) is not None]
+
     def plot(self, show_conf=True, line_width=None, font_size=None, font='Arial.ttf', pil=False, example='abc'):
         """
         Plots the detection results on an input RGB image. Accepts a numpy array (cv2) or a PIL Image.