Model coverage cleanup (#4585)

ultralytics/models/sam/modules/tiny_encoder.py

@@ -30,40 +30,6 @@ class Conv2d_BN(torch.nn.Sequential):
         torch.nn.init.constant_(bn.bias, 0)
         self.add_module('bn', bn)
 
-    @torch.no_grad()
-    def fuse(self):
-        c, bn = self._modules.values()
-        w = bn.weight / (bn.running_var + bn.eps) ** 0.5
-        w = c.weight * w[:, None, None, None]
-        b = bn.bias - bn.running_mean * bn.weight / (bn.running_var + bn.eps) ** 0.5
-        m = torch.nn.Conv2d(w.size(1) * self.c.groups,
-                            w.size(0),
-                            w.shape[2:],
-                            stride=self.c.stride,
-                            padding=self.c.padding,
-                            dilation=self.c.dilation,
-                            groups=self.c.groups)
-        m.weight.data.copy_(w)
-        m.bias.data.copy_(b)
-        return m
-
-
-# NOTE: This module and timm package is needed only for training.
-# from ultralytics.utils.checks import check_requirements
-# check_requirements('timm')
-# from timm.models.layers import DropPath as TimmDropPath
-# from timm.models.layers import trunc_normal_
-# class DropPath(TimmDropPath):
-#
-#     def __init__(self, drop_prob=None):
-#         super().__init__(drop_prob=drop_prob)
-#         self.drop_prob = drop_prob
-#
-#     def __repr__(self):
-#         msg = super().__repr__()
-#         msg += f'(drop_prob={self.drop_prob})'
-#         return msg
-
 
 class PatchEmbed(nn.Module):