[DETR and friends] Remove is_timm_available (#21814)

* First draft

* Fix to_dict

* Improve conversion script

* Update config

* Remove timm dependency

* Fix dummies

* Fix typo, add integration test

* Upload 101 model as well

* Remove timm dummies

* Fix style

---------

Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>

tests/models/detr/test_modeling_detr.py

@@ -20,7 +20,7 @@ import math
 import unittest
 
 from transformers import DetrConfig, is_timm_available, is_vision_available
-from transformers.testing_utils import require_timm, require_vision, slow, torch_device
+from transformers.testing_utils import require_timm, require_torch, require_vision, slow, torch_device
 from transformers.utils import cached_property
 
 from ...generation.test_utils import GenerationTesterMixin
@@ -510,7 +510,7 @@ def prepare_img():
 @require_timm
 @require_vision
 @slow
-class DetrModelIntegrationTests(unittest.TestCase):
+class DetrModelIntegrationTestsTimmBackbone(unittest.TestCase):
     @cached_property
     def default_feature_extractor(self):
         return DetrFeatureExtractor.from_pretrained("facebook/detr-resnet-50") if is_vision_available() else None
@@ -626,3 +626,33 @@ class DetrModelIntegrationTests(unittest.TestCase):
         self.assertTrue(torch.allclose(results["segmentation"][:3, :3], expected_slice_segmentation, atol=1e-4))
         self.assertTrue(len(results["segments_info"]), expected_number_of_segments)
         self.assertDictEqual(results["segments_info"][0], expected_first_segment)
+
+
+@require_vision
+@require_torch
+@slow
+class DetrModelIntegrationTests(unittest.TestCase):
+    @cached_property
+    def default_feature_extractor(self):
+        return (
+            DetrFeatureExtractor.from_pretrained("facebook/detr-resnet-50", revision="no_timm")
+            if is_vision_available()
+            else None
+        )
+
+    def test_inference_no_head(self):
+        model = DetrModel.from_pretrained("facebook/detr-resnet-50", revision="no_timm").to(torch_device)
+
+        feature_extractor = self.default_feature_extractor
+        image = prepare_img()
+        encoding = feature_extractor(images=image, return_tensors="pt").to(torch_device)
+
+        with torch.no_grad():
+            outputs = model(**encoding)
+
+        expected_shape = torch.Size((1, 100, 256))
+        assert outputs.last_hidden_state.shape == expected_shape
+        expected_slice = torch.tensor(
+            [[0.0616, -0.5146, -0.4032], [-0.7629, -0.4934, -1.7153], [-0.4768, -0.6403, -0.7826]]
+        ).to(torch_device)
+        self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3], expected_slice, atol=1e-4))