Implementation of SuperPoint and AutoModelForKeypointDetection (#28966)

* Added SuperPoint docs

* Added tests

* Removed commented part

* Commit to create and fix add_superpoint branch with a new branch

* Fixed dummy_pt_objects

* Committed missing files

* Fixed README.md

* Apply suggestions from code review

Fixed small changes

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Moved ImagePointDescriptionOutput from modeling_outputs.py to modeling_superpoint.py

* Removed AutoModelForKeypointDetection and related stuff

* Fixed inconsistencies in image_processing_superpoint.py

* Moved infer_on_model logic simply in test_inference

* Fixed bugs, added labels to forward method with checks whether it is properly a None value, also added tests about this logic in test_modeling_superpoint.py

* Added tests to SuperPointImageProcessor to ensure that images are properly converted to grayscale

* Removed remaining mentions of MODEL_FOR_KEYPOINT_DETECTION_MAPPING

* Apply suggestions from code review

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Fixed from (w, h) to (h, w) as input for tests

* Removed unnecessary condition

* Moved last_hidden_state to be the first returned

* Moved last_hidden_state to be the first returned (bis)

* Moved last_hidden_state to be the first returned (ter)

* Switched image_width and image_height in tests to match recent changes

* Added config as first SuperPointConvBlock init argument

* Reordered README's after merge

* Added missing first config argument to SuperPointConvBlock instantiations

* Removed formatting error

* Added SuperPoint to README's de, pt-br, ru, te and vi

* Checked out README_fr.md

* Fixed README_fr.md

* Test fix README_fr.md

* Test fix README_fr.md

* Last make fix-copies !

* Updated checkpoint path

* Removed unused SuperPoint doc

* Added missing image

* Update src/transformers/models/superpoint/modeling_superpoint.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Removed unnecessary import

* Update src/transformers/models/superpoint/modeling_superpoint.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Added SuperPoint to _toctree.yml

---------

Co-authored-by: steven <steven.bucaillle@gmail.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: Steven Bucaille <steven.bucaille@buawei.com>

tests/models/superpoint/test_image_processing_superpoint.py

@@ -0,0 +1,111 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import unittest
+
+import numpy as np
+
+from transformers.testing_utils import require_torch, require_vision
+from transformers.utils import is_vision_available
+
+from ...test_image_processing_common import (
+    ImageProcessingTestMixin,
+    prepare_image_inputs,
+)
+
+
+if is_vision_available():
+    from transformers import SuperPointImageProcessor
+
+
+class SuperPointImageProcessingTester(unittest.TestCase):
+    def __init__(
+        self,
+        parent,
+        batch_size=7,
+        num_channels=3,
+        image_size=18,
+        min_resolution=30,
+        max_resolution=400,
+        do_resize=True,
+        size=None,
+    ):
+        size = size if size is not None else {"height": 480, "width": 640}
+        self.parent = parent
+        self.batch_size = batch_size
+        self.num_channels = num_channels
+        self.image_size = image_size
+        self.min_resolution = min_resolution
+        self.max_resolution = max_resolution
+        self.do_resize = do_resize
+        self.size = size
+
+    def prepare_image_processor_dict(self):
+        return {
+            "do_resize": self.do_resize,
+            "size": self.size,
+        }
+
+    def expected_output_image_shape(self, images):
+        return self.num_channels, self.size["height"], self.size["width"]
+
+    def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
+        return prepare_image_inputs(
+            batch_size=self.batch_size,
+            num_channels=self.num_channels,
+            min_resolution=self.min_resolution,
+            max_resolution=self.max_resolution,
+            equal_resolution=equal_resolution,
+            numpify=numpify,
+            torchify=torchify,
+        )
+
+
+@require_torch
+@require_vision
+class SuperPointImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
+    image_processing_class = SuperPointImageProcessor if is_vision_available() else None
+
+    def setUp(self) -> None:
+        self.image_processor_tester = SuperPointImageProcessingTester(self)
+
+    @property
+    def image_processor_dict(self):
+        return self.image_processor_tester.prepare_image_processor_dict()
+
+    def test_image_processing(self):
+        image_processing = self.image_processing_class(**self.image_processor_dict)
+        self.assertTrue(hasattr(image_processing, "do_resize"))
+        self.assertTrue(hasattr(image_processing, "size"))
+        self.assertTrue(hasattr(image_processing, "do_rescale"))
+        self.assertTrue(hasattr(image_processing, "rescale_factor"))
+
+    def test_image_processor_from_dict_with_kwargs(self):
+        image_processor = self.image_processing_class.from_dict(self.image_processor_dict)
+        self.assertEqual(image_processor.size, {"height": 480, "width": 640})
+
+        image_processor = self.image_processing_class.from_dict(
+            self.image_processor_dict, size={"height": 42, "width": 42}
+        )
+        self.assertEqual(image_processor.size, {"height": 42, "width": 42})
+
+    @unittest.skip(reason="SuperPointImageProcessor is always supposed to return a grayscaled image")
+    def test_call_numpy_4_channels(self):
+        pass
+
+    def test_input_image_properly_converted_to_grayscale(self):
+        image_processor = self.image_processing_class.from_dict(self.image_processor_dict)
+        image_inputs = self.image_processor_tester.prepare_image_inputs()
+        pre_processed_images = image_processor.preprocess(image_inputs)
+        for image in pre_processed_images["pixel_values"]:
+            self.assertTrue(np.all(image[0, ...] == image[1, ...]) and np.all(image[1, ...] == image[2, ...]))