Remove .autoshape() method (ultralytics#5694)

hubconf.py

@@ -27,6 +27,7 @@ def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbo
     """
     from pathlib import Path
 
+    from models.common import AutoShape
     from models.experimental import attempt_load
     from models.yolo import Model
     from utils.downloads import attempt_download
@@ -55,7 +56,7 @@ def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbo
                 if len(ckpt['model'].names) == classes:
                     model.names = ckpt['model'].names  # set class names attribute
         if autoshape:
-            model = model.autoshape()  # for file/URI/PIL/cv2/np inputs and NMS
+            model = AutoShape(model)  # for file/URI/PIL/cv2/np inputs and NMS
         return model.to(device)
 
     except Exception as e:

models/common.py

@@ -23,7 +23,7 @@
 from utils.general import (LOGGER, check_requirements, check_suffix, colorstr, increment_path, make_divisible,
                            non_max_suppression, scale_coords, xywh2xyxy, xyxy2xywh)
 from utils.plots import Annotator, colors, save_one_box
-from utils.torch_utils import time_sync
+from utils.torch_utils import copy_attr, time_sync
 
 
 def autopad(k, p=None):  # kernel, padding
@@ -405,12 +405,10 @@ class AutoShape(nn.Module):
 
     def __init__(self, model):
         super().__init__()
+        LOGGER.info('Adding AutoShape... ')
+        copy_attr(self, model, include=('yaml', 'nc', 'hyp', 'names', 'stride', 'abc'), exclude=())  # copy attributes
         self.model = model.eval()
 
-    def autoshape(self):
-        LOGGER.info('AutoShape already enabled, skipping... ')  # model already converted to model.autoshape()
-        return self
-
     def _apply(self, fn):
         # Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers
         self = super()._apply(fn)

models/yolo.py

@@ -22,8 +22,7 @@
 from utils.autoanchor import check_anchor_order
 from utils.general import LOGGER, check_version, check_yaml, make_divisible, print_args
 from utils.plots import feature_visualization
-from utils.torch_utils import (copy_attr, fuse_conv_and_bn, initialize_weights, model_info, scale_img, select_device,
-                               time_sync)
+from utils.torch_utils import fuse_conv_and_bn, initialize_weights, model_info, scale_img, select_device, time_sync
 
 try:
     import thop  # for FLOPs computation
@@ -226,12 +225,6 @@ def fuse(self):  # fuse model Conv2d() + BatchNorm2d() layers
         self.info()
         return self
 
-    def autoshape(self):  # add AutoShape module
-        LOGGER.info('Adding AutoShape... ')
-        m = AutoShape(self)  # wrap model
-        copy_attr(m, self, include=('yaml', 'nc', 'hyp', 'names', 'stride'), exclude=())  # copy attributes
-        return m
-
     def info(self, verbose=False, img_size=640):  # print model information
         model_info(self, verbose, img_size)