Backbone add mixin tests (#22542)

* Add out_indices to backbones, deprecate out_features

* Update - can specify both out_features and out_indices but not both

* Add backbone mixin tests

* Test tidy up

* Add test_backbone for convnext

* Remove redefinition of method

* Update for Dinat and Nat backbones

* Update tests

* Smarter indexing

* Add checks on config creation for backbone

* PR comments

tests/models/swin/test_modeling_swin.py

@@ -22,6 +22,7 @@ from transformers import SwinConfig
 from transformers.testing_utils import require_torch, require_vision, slow, torch_device
 from transformers.utils import cached_property, is_torch_available, is_vision_available
 
+from ...test_backbone_common import BackboneTesterMixin
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
 from ...test_pipeline_mixin import PipelineTesterMixin
@@ -69,6 +70,7 @@ class SwinModelTester:
         type_sequence_label_size=10,
         encoder_stride=8,
         out_features=["stage1", "stage2"],
+        out_indices=[1, 2],
     ):
         self.parent = parent
         self.batch_size = batch_size
@@ -95,6 +97,7 @@ class SwinModelTester:
         self.type_sequence_label_size = type_sequence_label_size
         self.encoder_stride = encoder_stride
         self.out_features = out_features
+        self.out_indices = out_indices
 
     def prepare_config_and_inputs(self):
         pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
@@ -128,6 +131,7 @@ class SwinModelTester:
             initializer_range=self.initializer_range,
             encoder_stride=self.encoder_stride,
             out_features=self.out_features,
+            out_indices=self.out_indices,
         )
 
     def create_and_check_model(self, config, pixel_values, labels):
@@ -502,3 +506,12 @@ class SwinModelIntegrationTest(unittest.TestCase):
         self.assertEqual(outputs.logits.shape, expected_shape)
         expected_slice = torch.tensor([-0.0948, -0.6454, -0.0921]).to(torch_device)
         self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))
+
+
+@require_torch
+class SwinBackboneTest(unittest.TestCase, BackboneTesterMixin):
+    all_model_classes = (SwinBackbone,) if is_torch_available() else ()
+    config_class = SwinConfig
+
+    def setUp(self):
+        self.model_tester = SwinModelTester(self)