fixing master bug on hardcoded osnet architecture

timm/models/vision_transformer.py

@@ -241,7 +241,7 @@ class HybridEmbed(nn.Module):
                 training = backbone.training
                 if training:
                     backbone.eval()
-                o = self.backbone(torch.zeros(1, in_chans, img_size[0], img_size[1]), return_featuremaps=True) # it works with osnet
+                o = self.backbone(torch.zeros(1, in_chans, img_size[0], img_size[1]))
                 if isinstance(o, (list, tuple)):
                     o = o[-1]  # last feature if backbone outputs list/tuple of features
                 feature_size = o.shape[-2:]
@@ -257,7 +257,7 @@ class HybridEmbed(nn.Module):
         self.proj = nn.Conv2d(feature_dim, embed_dim, 1)
 
     def forward(self, x):
-        x = self.backbone(x, return_featuremaps=True) # it works with osnet
+        x = self.backbone(x)
         if isinstance(x, (list, tuple)):
             x = x[-1]  # last feature if backbone outputs list/tuple of features
         x = self.proj(x).flatten(2).transpose(1, 2)
@@ -784,4 +784,4 @@ def vit_deit_base_distilled_patch16_384(pretrained=False, **kwargs):
     model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
     model = _create_vision_transformer(
         'vit_deit_base_distilled_patch16_384', pretrained=pretrained, distilled=True, **model_kwargs)
-    return model
+    return model