SuperPointModel -> SuperPointForKeypointDetection (#29757)

docs/source/en/model_doc/auto.md

@@ -250,6 +250,10 @@ The following auto classes are available for the following computer vision tasks
 
 [[autodoc]] AutoModelForVideoClassification
 
+### AutoModelForKeypointDetection
+
+[[autodoc]] AutoModelForKeypointDetection
+
 ### AutoModelForMaskedImageModeling
 
 [[autodoc]] AutoModelForMaskedImageModeling

docs/source/en/model_doc/superpoint.md

@@ -113,10 +113,8 @@ The original code can be found [here](https://github.com/magicleap/SuperPointPre
 
 - preprocess
 
-## SuperPointModel
+## SuperPointForKeypointDetection
 
-[[autodoc]] SuperPointModel
+[[autodoc]] SuperPointForKeypointDetection
 
 - forward
-
-

src/transformers/__init__.py

@@ -1487,6 +1487,7 @@ else:
             "MODEL_FOR_IMAGE_SEGMENTATION_MAPPING",
             "MODEL_FOR_IMAGE_TO_IMAGE_MAPPING",
             "MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING",
+            "MODEL_FOR_KEYPOINT_DETECTION_MAPPING",
             "MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING",
             "MODEL_FOR_MASKED_LM_MAPPING",
             "MODEL_FOR_MASK_GENERATION_MAPPING",
@@ -1527,6 +1528,7 @@ else:
             "AutoModelForImageSegmentation",
             "AutoModelForImageToImage",
             "AutoModelForInstanceSegmentation",
+            "AutoModelForKeypointDetection",
             "AutoModelForMaskedImageModeling",
             "AutoModelForMaskedLM",
             "AutoModelForMaskGeneration",
@@ -3341,7 +3343,7 @@ else:
     _import_structure["models.superpoint"].extend(
         [
             "SUPERPOINT_PRETRAINED_MODEL_ARCHIVE_LIST",
-            "SuperPointModel",
+            "SuperPointForKeypointDetection",
             "SuperPointPreTrainedModel",
         ]
     )
@@ -6319,6 +6321,7 @@ if TYPE_CHECKING:
             MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
             MODEL_FOR_IMAGE_TO_IMAGE_MAPPING,
             MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
+            MODEL_FOR_KEYPOINT_DETECTION_MAPPING,
             MODEL_FOR_MASK_GENERATION_MAPPING,
             MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
             MODEL_FOR_MASKED_LM_MAPPING,
@@ -6359,6 +6362,7 @@ if TYPE_CHECKING:
             AutoModelForImageSegmentation,
             AutoModelForImageToImage,
             AutoModelForInstanceSegmentation,
+            AutoModelForKeypointDetection,
             AutoModelForMaskedImageModeling,
             AutoModelForMaskedLM,
             AutoModelForMaskGeneration,
@@ -7852,7 +7856,7 @@ if TYPE_CHECKING:
         )
         from .models.superpoint import (
             SUPERPOINT_PRETRAINED_MODEL_ARCHIVE_LIST,
-            SuperPointModel,
+            SuperPointForKeypointDetection,
             SuperPointPreTrainedModel,
         )
         from .models.swiftformer import (

src/transformers/models/auto/__init__.py

@@ -52,6 +52,7 @@ else:
         "MODEL_FOR_IMAGE_MAPPING",
         "MODEL_FOR_IMAGE_SEGMENTATION_MAPPING",
         "MODEL_FOR_IMAGE_TO_IMAGE_MAPPING",
+        "MODEL_FOR_KEYPOINT_DETECTION_MAPPING",
         "MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING",
         "MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING",
         "MODEL_FOR_MASKED_LM_MAPPING",
@@ -92,6 +93,7 @@ else:
         "AutoModelForImageSegmentation",
         "AutoModelForImageToImage",
         "AutoModelForInstanceSegmentation",
+        "AutoModelForKeypointDetection",
         "AutoModelForMaskGeneration",
         "AutoModelForTextEncoding",
         "AutoModelForMaskedImageModeling",
@@ -117,7 +119,6 @@ else:
         "AutoModelWithLMHead",
         "AutoModelForZeroShotImageClassification",
         "AutoModelForZeroShotObjectDetection",
-        "AutoModelForKeypointDetection",
     ]
 
 try:
@@ -239,6 +240,7 @@ if TYPE_CHECKING:
             MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
             MODEL_FOR_IMAGE_TO_IMAGE_MAPPING,
             MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
+            MODEL_FOR_KEYPOINT_DETECTION_MAPPING,
             MODEL_FOR_MASK_GENERATION_MAPPING,
             MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
             MODEL_FOR_MASKED_LM_MAPPING,

src/transformers/models/auto/modeling_auto.py

@@ -207,7 +207,6 @@ MODEL_MAPPING_NAMES = OrderedDict(
         ("squeezebert", "SqueezeBertModel"),
         ("stablelm", "StableLmModel"),
         ("starcoder2", "Starcoder2Model"),
-        ("superpoint", "SuperPointModel"),
         ("swiftformer", "SwiftFormerModel"),
         ("swin", "SwinModel"),
         ("swin2sr", "Swin2SRModel"),
@@ -1225,6 +1224,14 @@ MODEL_FOR_MASK_GENERATION_MAPPING_NAMES = OrderedDict(
     ]
 )
 
+
+MODEL_FOR_KEYPOINT_DETECTION_MAPPING_NAMES = OrderedDict(
+    [
+        ("superpoint", "SuperPointForKeypointDetection"),
+    ]
+)
+
+
 MODEL_FOR_TEXT_ENCODING_MAPPING_NAMES = OrderedDict(
     [
         ("albert", "AlbertModel"),
@@ -1360,6 +1367,10 @@ MODEL_FOR_BACKBONE_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_BA
 
 MODEL_FOR_MASK_GENERATION_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_MASK_GENERATION_MAPPING_NAMES)
 
+MODEL_FOR_KEYPOINT_DETECTION_MAPPING = _LazyAutoMapping(
+    CONFIG_MAPPING_NAMES, MODEL_FOR_KEYPOINT_DETECTION_MAPPING_NAMES
+)
+
 MODEL_FOR_TEXT_ENCODING_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_TEXT_ENCODING_MAPPING_NAMES)
 
 MODEL_FOR_TIME_SERIES_CLASSIFICATION_MAPPING = _LazyAutoMapping(
@@ -1377,6 +1388,10 @@ class AutoModelForMaskGeneration(_BaseAutoModelClass):
     _model_mapping = MODEL_FOR_MASK_GENERATION_MAPPING
 
 
+class AutoModelForKeypointDetection(_BaseAutoModelClass):
+    _model_mapping = MODEL_FOR_KEYPOINT_DETECTION_MAPPING
+
+
 class AutoModelForTextEncoding(_BaseAutoModelClass):
     _model_mapping = MODEL_FOR_TEXT_ENCODING_MAPPING
 

src/transformers/models/superpoint/__init__.py

@@ -40,7 +40,7 @@ except OptionalDependencyNotAvailable:
 else:
     _import_structure["modeling_superpoint"] = [
         "SUPERPOINT_PRETRAINED_MODEL_ARCHIVE_LIST",
-        "SuperPointModel",
+        "SuperPointForKeypointDetection",
         "SuperPointPreTrainedModel",
     ]
 
@@ -67,7 +67,7 @@ if TYPE_CHECKING:
     else:
         from .modeling_superpoint import (
             SUPERPOINT_PRETRAINED_MODEL_ARCHIVE_LIST,
-            SuperPointModel,
+            SuperPointForKeypointDetection,
             SuperPointPreTrainedModel,
         )
 

src/transformers/models/superpoint/configuration_superpoint.py

@@ -26,7 +26,7 @@ SUPERPOINT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
 
 class SuperPointConfig(PretrainedConfig):
     r"""
-    This is the configuration class to store the configuration of a [`SuperPointModel`]. It is used to instantiate a
+    This is the configuration class to store the configuration of a [`SuperPointForKeypointDetection`]. It is used to instantiate a
     SuperPoint model according to the specified arguments, defining the model architecture. Instantiating a
     configuration with the defaults will yield a similar configuration to that of the SuperPoint
     [magic-leap-community/superpoint](https://huggingface.co/magic-leap-community/superpoint) architecture.
@@ -53,12 +53,12 @@ class SuperPointConfig(PretrainedConfig):
 
     Example:
     ```python
-    >>> from transformers import SuperPointConfig, SuperPointModel
+    >>> from transformers import SuperPointConfig, SuperPointForKeypointDetection
 
     >>> # Initializing a SuperPoint superpoint style configuration
     >>> configuration = SuperPointConfig()
     >>> # Initializing a model from the superpoint style configuration
-    >>> model = SuperPointModel(configuration)
+    >>> model = SuperPointForKeypointDetection(configuration)
     >>> # Accessing the model configuration
     >>> configuration = model.config
     ```"""

src/transformers/models/superpoint/convert_superpoint_to_pytorch.py

@@ -18,7 +18,7 @@ import requests
 import torch
 from PIL import Image
 
-from transformers import SuperPointConfig, SuperPointImageProcessor, SuperPointModel
+from transformers import SuperPointConfig, SuperPointForKeypointDetection, SuperPointImageProcessor
 
 
 def get_superpoint_config():
@@ -106,7 +106,7 @@ def convert_superpoint_checkpoint(checkpoint_url, pytorch_dump_folder_path, save
         rename_key(new_state_dict, src, dest)
 
     # Load HuggingFace model
-    model = SuperPointModel(config)
+    model = SuperPointForKeypointDetection(config)
     model.load_state_dict(new_state_dict)
     model.eval()
     print("Successfully loaded weights in the model")

src/transformers/models/superpoint/modeling_superpoint.py

@@ -390,7 +390,7 @@ Args:
     "SuperPoint model outputting keypoints and descriptors.",
     SUPERPOINT_START_DOCSTRING,
 )
-class SuperPointModel(SuperPointPreTrainedModel):
+class SuperPointForKeypointDetection(SuperPointPreTrainedModel):
     """
     SuperPoint model. It consists of a SuperPointEncoder, a SuperPointInterestPointDecoder and a
     SuperPointDescriptorDecoder. SuperPoint was proposed in `SuperPoint: Self-Supervised Interest Point Detection and

src/transformers/utils/dummy_pt_objects.py

@@ -606,6 +606,9 @@ MODEL_FOR_IMAGE_TO_IMAGE_MAPPING = None
 MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING = None
 
 
+MODEL_FOR_KEYPOINT_DETECTION_MAPPING = None
+
+
 MODEL_FOR_MASK_GENERATION_MAPPING = None
 
 
@@ -778,6 +781,13 @@ class AutoModelForInstanceSegmentation(metaclass=DummyObject):
         requires_backends(self, ["torch"])
 
 
+class AutoModelForKeypointDetection(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
 class AutoModelForMaskedImageModeling(metaclass=DummyObject):
     _backends = ["torch"]
 
@@ -8029,7 +8039,7 @@ class Starcoder2PreTrainedModel(metaclass=DummyObject):
 SUPERPOINT_PRETRAINED_MODEL_ARCHIVE_LIST = None
 
 
-class SuperPointModel(metaclass=DummyObject):
+class SuperPointForKeypointDetection(metaclass=DummyObject):
     _backends = ["torch"]
 
     def __init__(self, *args, **kwargs):

tests/models/superpoint/test_modeling_superpoint.py

@@ -28,7 +28,7 @@ if is_torch_available():
 
     from transformers import (
         SUPERPOINT_PRETRAINED_MODEL_ARCHIVE_LIST,
-        SuperPointModel,
+        SuperPointForKeypointDetection,
     )
 
 if is_vision_available():
@@ -86,7 +86,7 @@ class SuperPointModelTester:
         )
 
     def create_and_check_model(self, config, pixel_values):
-        model = SuperPointModel(config=config)
+        model = SuperPointForKeypointDetection(config=config)
         model.to(torch_device)
         model.eval()
         result = model(pixel_values)
@@ -109,7 +109,7 @@ class SuperPointModelTester:
 
 @require_torch
 class SuperPointModelTest(ModelTesterMixin, unittest.TestCase):
-    all_model_classes = (SuperPointModel,) if is_torch_available() else ()
+    all_model_classes = (SuperPointForKeypointDetection,) if is_torch_available() else ()
     all_generative_model_classes = () if is_torch_available() else ()
 
     fx_compatible = False
@@ -134,31 +134,31 @@ class SuperPointModelTest(ModelTesterMixin, unittest.TestCase):
     def create_and_test_config_common_properties(self):
         return
 
-    @unittest.skip(reason="SuperPointModel does not use inputs_embeds")
+    @unittest.skip(reason="SuperPointForKeypointDetection does not use inputs_embeds")
     def test_inputs_embeds(self):
         pass
 
-    @unittest.skip(reason="SuperPointModel does not support input and output embeddings")
+    @unittest.skip(reason="SuperPointForKeypointDetection does not support input and output embeddings")
     def test_model_common_attributes(self):
         pass
 
-    @unittest.skip(reason="SuperPointModel does not use feedforward chunking")
+    @unittest.skip(reason="SuperPointForKeypointDetection does not use feedforward chunking")
     def test_feed_forward_chunking(self):
         pass
 
-    @unittest.skip(reason="SuperPointModel is not trainable")
+    @unittest.skip(reason="SuperPointForKeypointDetection is not trainable")
     def test_training(self):
         pass
 
-    @unittest.skip(reason="SuperPointModel is not trainable")
+    @unittest.skip(reason="SuperPointForKeypointDetection is not trainable")
     def test_training_gradient_checkpointing(self):
         pass
 
-    @unittest.skip(reason="SuperPointModel is not trainable")
+    @unittest.skip(reason="SuperPointForKeypointDetection is not trainable")
     def test_training_gradient_checkpointing_use_reentrant(self):
         pass
 
-    @unittest.skip(reason="SuperPointModel is not trainable")
+    @unittest.skip(reason="SuperPointForKeypointDetection is not trainable")
     def test_training_gradient_checkpointing_use_reentrant_false(self):
         pass
 
@@ -219,7 +219,7 @@ class SuperPointModelTest(ModelTesterMixin, unittest.TestCase):
     @slow
     def test_model_from_pretrained(self):
         for model_name in SUPERPOINT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
-            model = SuperPointModel.from_pretrained(model_name)
+            model = SuperPointForKeypointDetection.from_pretrained(model_name)
             self.assertIsNotNone(model)
 
     def test_forward_labels_should_be_none(self):
@@ -254,7 +254,7 @@ class SuperPointModelIntegrationTest(unittest.TestCase):
 
     @slow
     def test_inference(self):
-        model = SuperPointModel.from_pretrained("magic-leap-community/superpoint").to(torch_device)
+        model = SuperPointForKeypointDetection.from_pretrained("magic-leap-community/superpoint").to(torch_device)
         preprocessor = self.default_image_processor
         images = prepare_imgs()
         inputs = preprocessor(images=images, return_tensors="pt").to(torch_device)