Add DeiT (PyTorch) (#11056)

* First draft of deit

* More improvements

* Remove DeiTTokenizerFast from init

* Conversion script works

* Add DeiT to ViT conversion script

* Add tests, add head model, add support for deit in vit conversion script

* Update model checkpoint names

* Update image_mean and image_std, set resample to bicubic

* Improve docs

* Docs improvements

* Add DeiTForImageClassificationWithTeacher to init

* Address comments by @sgugger

* Improve feature extractors

* Make fix-copies

* Minor fixes

* Address comments by @patil-suraj

* All models uploaded

* Fix tests

* Remove labels argument from DeiTForImageClassificationWithTeacher

* Fix-copies, style and quality

* Fix tests

* Fix typo

* Multiple docs improvements

* More docs fixes

tests/test_modeling_vit.py

@@ -155,20 +155,10 @@ class ViTModelTest(ModelTesterMixin, unittest.TestCase):
 
     def setUp(self):
         self.model_tester = ViTModelTester(self)
-        self.config_tester = ConfigTester(self, config_class=ViTConfig, hidden_size=37)
+        self.config_tester = ConfigTester(self, config_class=ViTConfig, has_text_modality=False, hidden_size=37)
 
     def test_config(self):
-        config = self.config_tester.config_class(**self.config_tester.inputs_dict)
-        # we omit vocab_size since ViT does not use this
-        self.config_tester.parent.assertTrue(hasattr(config, "hidden_size"))
-        self.config_tester.parent.assertTrue(hasattr(config, "num_attention_heads"))
-        self.config_tester.parent.assertTrue(hasattr(config, "num_hidden_layers"))
-
-        self.config_tester.create_and_test_config_to_json_string()
-        self.config_tester.create_and_test_config_to_json_file()
-        self.config_tester.create_and_test_config_from_and_save_pretrained()
-        self.config_tester.create_and_test_config_with_num_labels()
-        self.config_tester.check_config_can_be_init_without_params()
+        self.config_tester.run_common_tests()
 
     def test_inputs_embeds(self):
         # ViT does not use inputs_embeds
@@ -351,10 +341,7 @@ class ViTModelIntegrationTest(unittest.TestCase):
         inputs = feature_extractor(images=image, return_tensors="pt").to(torch_device)
 
         # forward pass
-        # currently failing
-        # see https://discuss.pytorch.org/t/runtimeerror-expected-object-of-scalar-type-double-but-got-scalar-type-float-for-argument-2-weight/38961/2
-        outputs = model(inputs["pixel_values"])
-        # outputs = model(**inputs)
+        outputs = model(**inputs)
 
         # verify the logits
         expected_shape = torch.Size((1, 1000))