Enable HF pretrained backbones (#31145)

* Enable load HF or tim backbone checkpoints

* Fix up

* Fix test - pass in proper out_indices

* Update docs

* Fix tvp tests

* Fix doc examples

* Fix doc examples

* Try to resolve DPT backbone param init

* Don't conditionally set to None

* Add condition based on whether backbone is defined

* Address review comments

tests/models/upernet/test_modeling_upernet.py

@@ -19,7 +19,14 @@ import unittest
 from huggingface_hub import hf_hub_download
 
 from transformers import ConvNextConfig, UperNetConfig
-from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
+from transformers.testing_utils import (
+    require_timm,
+    require_torch,
+    require_torch_multi_gpu,
+    require_vision,
+    slow,
+    torch_device,
+)
 from transformers.utils import is_torch_available, is_vision_available
 
 from ...test_configuration_common import ConfigTester
@@ -240,6 +247,33 @@ class UperNetModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase)
                         msg=f"Parameter {name} of model {model_class} seems not properly initialized",
                     )
 
+    @require_timm
+    def test_backbone_selection(self):
+        config, inputs = self.model_tester.prepare_config_and_inputs_for_common()
+
+        config.backbone_config = None
+        config.backbone_kwargs = {"out_indices": [1, 2, 3]}
+        config.use_pretrained_backbone = True
+
+        # Load a timm backbone
+        # We can't load transformer checkpoint with timm backbone, as we can't specify features_only and out_indices
+        config.backbone = "resnet18"
+        config.use_timm_backbone = True
+
+        for model_class in self.all_model_classes:
+            model = model_class(config).to(torch_device).eval()
+            if model.__class__.__name__ == "UperNetForUniversalSegmentation":
+                self.assertEqual(model.backbone.out_indices, [1, 2, 3])
+
+        # Load a HF backbone
+        config.backbone = "microsoft/resnet-18"
+        config.use_timm_backbone = False
+
+        for model_class in self.all_model_classes:
+            model = model_class(config).to(torch_device).eval()
+            if model.__class__.__name__ == "UperNetForUniversalSegmentation":
+                self.assertEqual(model.backbone.out_indices, [1, 2, 3])
+
     @unittest.skip(reason="UperNet does not have tied weights")
     def test_tied_model_weights_key_ignore(self):
         pass