ultralytics 8.3.64 new torchvision.ops access in model YAMLs (#18680)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

docs/en/models/yolov8.md

@@ -8,7 +8,7 @@ keywords: YOLOv8, real-time object detection, YOLO series, Ultralytics, computer
 
 ## Overview
 
-YOLOv8 is the latest iteration in the YOLO series of real-time object detectors, offering cutting-edge performance in terms of accuracy and speed. Building upon the advancements of previous YOLO versions, YOLOv8 introduces new features and optimizations that make it an ideal choice for various [object detection](https://www.ultralytics.com/glossary/object-detection) tasks in a wide range of applications.
+YOLOv8 was released by Ultralytic on January 10th, 2023, offering cutting-edge performance in terms of accuracy and speed. Building upon the advancements of previous YOLO versions, YOLOv8 introduced new features and optimizations that make it an ideal choice for various [object detection](https://www.ultralytics.com/glossary/object-detection) tasks in a wide range of applications.
 
 ![Ultralytics YOLOv8](https://github.com/ultralytics/docs/releases/download/0/yolov8-comparison-plots.avif)
 

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
 
-__version__ = "8.3.63"
+__version__ = "8.3.64"
 
 import os
 

ultralytics/nn/modules/block.py

@@ -1139,10 +1139,10 @@ class TorchVision(nn.Module):
         else:
             self.m = torchvision.models.__dict__[model](pretrained=bool(weights))
         if unwrap:
-            layers = list(self.m.children())[:-truncate]
+            layers = list(self.m.children())
             if isinstance(layers[0], nn.Sequential):  # Second-level for some models like EfficientNet, Swin
                 layers = [*list(layers[0].children()), *layers[1:]]
-            self.m = nn.Sequential(*layers)
+            self.m = nn.Sequential(*(layers[:-truncate] if truncate else layers))
             self.split = split
         else:
             self.split = False

ultralytics/nn/tasks.py

@@ -955,7 +955,13 @@ def parse_model(d, ch, verbose=True):  # model_dict, input_channels(3)
     ch = [ch]
     layers, save, c2 = [], [], ch[-1]  # layers, savelist, ch out
     for i, (f, n, m, args) in enumerate(d["backbone"] + d["head"]):  # from, number, module, args
-        m = getattr(torch.nn, m[3:]) if "nn." in m else globals()[m]  # get module
+        m = (
+            getattr(torch.nn, m[3:])
+            if "nn." in m
+            else getattr(__import__("torchvision").ops, m[16:])
+            if "torchvision.ops." in m
+            else globals()[m]
+        )  # get module
         for j, a in enumerate(args):
             if isinstance(a, str):
                 with contextlib.suppress(ValueError):