New model support RTDETR (#29077)

* fill out docs string in configuration
https://github.com/huggingface/transformers/pull/29077/files/75dcd3a0e82cca36f12178b65bbd071ab7b25088#r1506391856



* reduce the input image size for the tests

* remove the unappropriate tests

* only 5 failes exists

* make style

* fill up missed architecture for object detection in docs

* fix auto modeling

* simple fix in missing import

* major change including backbone refactor and objectdetectionoutput refactor

* minor fix only 4 fails left

* intermediate fix

* revert __init__.py

* revert __init__.py

* make style

* fixes in pr_docs

* intermediate fix

* make style

* two fixes

* pass doctest

* only one fix left

* intermediate commit

* all fixed

* Update src/transformers/models/rt_detr/image_processing_rt_detr.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/rt_detr/convert_rt_detr_original_pytorch_checkpoint_to_pytorch.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/rt_detr/configuration_rt_detr.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update tests/models/rt_detr/test_modeling_rt_detr.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* function class above the model definition in dice_loss

* Update src/transformers/models/rt_detr/modeling_rt_detr.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* simple fix

* layernorm add config.layer_norm_eps

* fix inputs_docstring

* make style

* simple fix

* add custom coco loading test in image_processor

* fix error in BaseModelOutput
https://github.com/huggingface/transformers/pull/29077#discussion_r1516657790



* simple typo

* Update src/transformers/models/rt_detr/modeling_rt_detr.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* intermediate fix

* fix with load_backbone format

* remove unused configuration

* 3 fix test left

* make style

* Update src/transformers/models/rt_detr/image_processing_rt_detr.py
Co-authored-by: Sounak Dey <dey.sounak@gmail.com>

* change last_hidden_state to first index

* all pass fix
TO DO: minor update in comments

* make fix-copies

* remove deepcopy

* pr_document fix

* revert deepcopy due to the issue of unexpceted behavior in decoderlayer

* add atol in final

* add no_split_module

* _no_split_modules = None

* device transfer for model parallelism

* minor fix

* make fix-copies

* fix typo

* add test_image_processor with post_processing

* Update src/transformers/models/rt_detr/configuration_rt_detr.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* add config in RTDETRPredictionHead

* Update src/transformers/models/rt_detr/modeling_rt_detr.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* set lru_cache with max_size 32

* Update src/transformers/models/rt_detr/configuration_rt_detr.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* add lru_cache import and configuration change

* change the order of definition

* make fix-copies

* add docs and change config error

* revert strange make-fix

* Update src/transformers/models/rt_detr/modeling_rt_detr.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* test pass

* fix get_clones related and remove deepcopy

* Update src/transformers/models/rt_detr/configuration_rt_detr.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* Update src/transformers/models/rt_detr/configuration_rt_detr.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* Update src/transformers/models/rt_detr/image_processing_rt_detr.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* Update src/transformers/models/rt_detr/image_processing_rt_detr.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* Update src/transformers/models/rt_detr/modeling_rt_detr.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* Update src/transformers/models/rt_detr/modeling_rt_detr.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* Update src/transformers/models/rt_detr/image_processing_rt_detr.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* Update src/transformers/models/rt_detr/modeling_rt_detr.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* Update src/transformers/models/rt_detr/image_processing_rt_detr.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* nit for paper section

* Update src/transformers/models/rt_detr/configuration_rt_detr.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* rename denoising related parameters

* Update src/transformers/models/rt_detr/image_processing_rt_detr.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* check the image transformation logic

* make style

* make style

* Update src/transformers/models/rt_detr/configuration_rt_detr.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* Update src/transformers/models/rt_detr/modeling_rt_detr.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* Update src/transformers/models/rt_detr/modeling_rt_detr.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* Update src/transformers/models/rt_detr/modeling_rt_detr.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* Update src/transformers/models/rt_detr/modeling_rt_detr.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* Update src/transformers/models/rt_detr/modeling_rt_detr.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* pe_encoding -> positional_encoding_temperature

* remove TODO

* Update src/transformers/models/rt_detr/image_processing_rt_detr.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* remove eval_idx since transformer DETR is giving all decoder output

* Update src/transformers/models/rt_detr/configuration_rt_detr.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* Update src/transformers/models/rt_detr/configuration_rt_detr.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* change variable name

* make style and docs import update

* Revert "Update src/transformers/models/rt_detr/image_processing_rt_detr.py"

This reverts commit 74aa3e1de0ca0cd3d354161d38ef28b4389c0eee.

* fix typo

* add postprocessing in docs

* move import scipy to top

* change varaible name

* make fix-copies

* remove eval_idx in test

* move to after first sentence

* update image_processor since box loss requires normalized one

* change appropriate name to auxiliary_outputs

* Update src/transformers/models/rt_detr/__init__.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* Update src/transformers/models/rt_detr/__init__.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* Update docs/source/en/model_doc/rt_detr.md
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* Update docs/source/en/model_doc/rt_detr.md
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* make style

* remove panoptic related comments

* make style

* revert valid_processor_keys

* fix aux related test

* make style

* change origination from config to backbone API

* enable the dn_loss

* fix test and conversion

* renewal weight initialization

* change initializer_range

* make fix-up

* fix the loss issue in the auxiliary output and denoising part

* change weight loss to original RTDETR

* fix in initialization

* sync shape format of dn and aux

* make style

* stable fine-tuning and compatible conversion for resnet101

* make style

* skip input_embed

* change encoder related variable

* enable converting rtdetr_r101

* add r101 related conversion code

* Update src/transformers/models/rt_detr/modeling_rt_detr.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/rt_detr/modeling_rt_detr.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update docs/source/en/model_doc/rt_detr.md
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/rt_detr/configuration_rt_detr.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/__init__.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/__init__.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/rt_detr/image_processing_rt_detr.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/rt_detr/image_processing_rt_detr.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/rt_detr/modeling_rt_detr.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* change name _shape to _reshape

* Update src/transformers/__init__.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/__init__.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* maket style

* make fix-copies

* remove deprecated import

* more fix

* remove last_hidden_state for task-specific model

* Revert "remove last_hidden_state for task-specific model"

This reverts commit ccb7a34051d69b9fc7aa17ed8644664d3fdbdaca.

* minore change in convert

* remove print

* make style and fix-copies

* add custom rtdetr backbone for r18, r34

* remove print

* change copied

* add pad_size

* make style

* change layertype to optional to pass the CI

* make style

* add test in modeling_resnet_rt_detr

* make fix-copies

* skip tmp file test

* fix comment

* add docs

* change to modeling_resnet file format

* enabling resnet50 above

* Update src/transformers/models/rt_detr/modeling_rt_detr.py
Co-authored-by: Jason Wu <jasonkit@users.noreply.github.com>

* enable all the rtdetr model :)

* finish except CI

* add RTDetrResNetBackbone

* make fix-copies

* fix
TO DO: CI enable

* make style

* rename test

* add docs

* add special fix

* revert resnet

* Update src/transformers/models/rt_detr/modeling_rt_detr_resnet.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* add more comment

* remove swin comment

* Update src/transformers/models/rt_detr/configuration_rt_detr.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* rename convert and add verify backbone

* Update docs/source/en/_toctree.yml
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* Update docs/source/en/model_doc/rt_detr.md
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* Update docs/source/en/model_doc/rt_detr.md
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>

* make style

* requests for docs

* more general test docs

* general script docs

* make fix-copies

* final commit

* Revert "Update src/transformers/models/rt_detr/configuration_rt_detr.py"

This reverts commit d136225cd3f64f510d303ce1d227698174f43fff.

* skip test_model_get_set_embeddings

* remove target

* add changes

* make fix-copies

* remove decoder_attention_mask

* add load_backbone function for auto_backbone

* remove comment

* fix repo name

* Update src/transformers/models/rt_detr/configuration_rt_detr.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* final commit

* remove unused downsample_in_bottleneck

* new test for autobackbone

* change to appropriate indices

* test fix

* fix dict in test_image_processor

* fix test

* [run-slow] rt_detr, rt_detr_resnet

* change the slow test

* [run-slow] rt_detr

* [run-slow] rt_detr, rt_detr_resnet

* make in to same cuda in CSPRepLayer

* [run-slow] rt_detr, rt_detr_resnet

---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: Sounak Dey <dey.sounak@gmail.com>
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
Co-authored-by: Jason Wu <jasonkit@users.noreply.github.com>
Co-authored-by: ChoiSangBum <choisangbum@ChoiSangBumui-MacBookPro.local>

tests/models/rt_detr/test_image_processing_rt_detr.py

+# 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 json
+import unittest
+
+import requests
+
+from transformers.testing_utils import require_torch, require_vision, slow
+from transformers.utils import is_torch_available, is_vision_available
+
+from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
+
+
+if is_vision_available():
+    from PIL import Image
+
+    from transformers import RTDetrImageProcessor
+
+if is_torch_available():
+    import torch
+
+
+class RTDetrImageProcessingTester(unittest.TestCase):
+    def __init__(
+        self,
+        parent,
+        batch_size=4,
+        num_channels=3,
+        do_resize=True,
+        size=None,
+        do_rescale=True,
+        rescale_factor=1 / 255,
+        do_normalize=False,
+        do_pad=False,
+        return_tensors="pt",
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.num_channels = num_channels
+        self.do_resize = do_resize
+        self.size = size if size is not None else {"height": 640, "width": 640}
+        self.do_rescale = do_rescale
+        self.rescale_factor = rescale_factor
+        self.do_normalize = do_normalize
+        self.do_pad = do_pad
+        self.return_tensors = return_tensors
+
+    def prepare_image_processor_dict(self):
+        return {
+            "do_resize": self.do_resize,
+            "size": self.size,
+            "do_rescale": self.do_rescale,
+            "rescale_factor": self.rescale_factor,
+            "do_normalize": self.do_normalize,
+            "do_pad": self.do_pad,
+            "return_tensors": self.return_tensors,
+        }
+
+    def get_expected_values(self):
+        return self.size["height"], self.size["width"]
+
+    def expected_output_image_shape(self, images):
+        height, width = self.get_expected_values()
+        return self.num_channels, height, 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=30,
+            max_resolution=400,
+            equal_resolution=equal_resolution,
+            numpify=numpify,
+            torchify=torchify,
+        )
+
+
+@require_torch
+@require_vision
+class RtDetrImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
+    image_processing_class = RTDetrImageProcessor if is_vision_available() else None
+
+    def setUp(self):
+        super().setUp()
+        self.image_processor_tester = RTDetrImageProcessingTester(self)
+
+    @property
+    def image_processor_dict(self):
+        return self.image_processor_tester.prepare_image_processor_dict()
+
+    def test_image_processor_properties(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, "resample"))
+        self.assertTrue(hasattr(image_processing, "do_rescale"))
+        self.assertTrue(hasattr(image_processing, "rescale_factor"))
+        self.assertTrue(hasattr(image_processing, "return_tensors"))
+
+    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": 640, "width": 640})
+
+    def test_valid_coco_detection_annotations(self):
+        # prepare image and target
+        image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
+        with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt", "r") as f:
+            target = json.loads(f.read())
+
+        params = {"image_id": 39769, "annotations": target}
+
+        # encode them
+        image_processing = RTDetrImageProcessor.from_pretrained("PekingU/rtdetr_r50vd")
+
+        # legal encodings (single image)
+        _ = image_processing(images=image, annotations=params, return_tensors="pt")
+        _ = image_processing(images=image, annotations=[params], return_tensors="pt")
+
+        # legal encodings (batch of one image)
+        _ = image_processing(images=[image], annotations=params, return_tensors="pt")
+        _ = image_processing(images=[image], annotations=[params], return_tensors="pt")
+
+        # legal encoding (batch of more than one image)
+        n = 5
+        _ = image_processing(images=[image] * n, annotations=[params] * n, return_tensors="pt")
+
+        # example of an illegal encoding (missing the 'image_id' key)
+        with self.assertRaises(ValueError) as e:
+            image_processing(images=image, annotations={"annotations": target}, return_tensors="pt")
+
+        self.assertTrue(str(e.exception).startswith("Invalid COCO detection annotations"))
+
+        # example of an illegal encoding (unequal lengths of images and annotations)
+        with self.assertRaises(ValueError) as e:
+            image_processing(images=[image] * n, annotations=[params] * (n - 1), return_tensors="pt")
+
+        self.assertTrue(str(e.exception) == "The number of images (5) and annotations (4) do not match.")
+
+    @slow
+    def test_call_pytorch_with_coco_detection_annotations(self):
+        # prepare image and target
+        image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
+        with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt", "r") as f:
+            target = json.loads(f.read())
+
+        target = {"image_id": 39769, "annotations": target}
+
+        # encode them
+        image_processing = RTDetrImageProcessor.from_pretrained("PekingU/rtdetr_r50vd")
+        encoding = image_processing(images=image, annotations=target, return_tensors="pt")
+
+        # verify pixel values
+        expected_shape = torch.Size([1, 3, 640, 640])
+        self.assertEqual(encoding["pixel_values"].shape, expected_shape)
+
+        expected_slice = torch.tensor([0.5490, 0.5647, 0.5725])
+        self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3], expected_slice, atol=1e-4))
+
+        # verify area
+        expected_area = torch.tensor([2827.9883, 5403.4761, 235036.7344, 402070.2188, 71068.8281, 79601.2812])
+        self.assertTrue(torch.allclose(encoding["labels"][0]["area"], expected_area))
+        # verify boxes
+        expected_boxes_shape = torch.Size([6, 4])
+        self.assertEqual(encoding["labels"][0]["boxes"].shape, expected_boxes_shape)
+        expected_boxes_slice = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215])
+        self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0], expected_boxes_slice, atol=1e-3))
+        # verify image_id
+        expected_image_id = torch.tensor([39769])
+        self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"], expected_image_id))
+        # verify is_crowd
+        expected_is_crowd = torch.tensor([0, 0, 0, 0, 0, 0])
+        self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"], expected_is_crowd))
+        # verify class_labels
+        expected_class_labels = torch.tensor([75, 75, 63, 65, 17, 17])
+        self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"], expected_class_labels))
+        # verify orig_size
+        expected_orig_size = torch.tensor([480, 640])
+        self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"], expected_orig_size))
+        # verify size
+        expected_size = torch.tensor([640, 640])
+        self.assertTrue(torch.allclose(encoding["labels"][0]["size"], expected_size))
+
+    @slow
+    def test_image_processor_outputs(self):
+        image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
+
+        image_processing = self.image_processing_class(**self.image_processor_dict)
+        encoding = image_processing(images=image, return_tensors="pt")
+
+        # verify pixel values: shape
+        expected_shape = torch.Size([1, 3, 640, 640])
+        self.assertEqual(encoding["pixel_values"].shape, expected_shape)
+
+        # verify pixel values: output values
+        expected_slice = torch.tensor([0.5490196347236633, 0.5647059082984924, 0.572549045085907])
+        self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3], expected_slice, atol=1e-5))
+
+    def test_multiple_images_processor_outputs(self):
+        images_urls = [
+            "http://images.cocodataset.org/val2017/000000000139.jpg",
+            "http://images.cocodataset.org/val2017/000000000285.jpg",
+            "http://images.cocodataset.org/val2017/000000000632.jpg",
+            "http://images.cocodataset.org/val2017/000000000724.jpg",
+            "http://images.cocodataset.org/val2017/000000000776.jpg",
+            "http://images.cocodataset.org/val2017/000000000785.jpg",
+            "http://images.cocodataset.org/val2017/000000000802.jpg",
+            "http://images.cocodataset.org/val2017/000000000872.jpg",
+        ]
+
+        images = []
+        for url in images_urls:
+            image = Image.open(requests.get(url, stream=True).raw)
+            images.append(image)
+
+        # apply image processing
+        image_processing = self.image_processing_class(**self.image_processor_dict)
+        encoding = image_processing(images=images, return_tensors="pt")
+
+        # verify if pixel_values is part of the encoding
+        self.assertIn("pixel_values", encoding)
+
+        # verify pixel values: shape
+        expected_shape = torch.Size([8, 3, 640, 640])
+        self.assertEqual(encoding["pixel_values"].shape, expected_shape)
+
+        # verify pixel values: output values
+        expected_slices = torch.tensor(
+            [
+                [0.5333333611488342, 0.5568627715110779, 0.5647059082984924],
+                [0.5372549295425415, 0.4705882668495178, 0.4274510145187378],
+                [0.3960784673690796, 0.35686275362968445, 0.3686274588108063],
+                [0.20784315466880798, 0.1882353127002716, 0.15294118225574493],
+                [0.364705890417099, 0.364705890417099, 0.3686274588108063],
+                [0.8078432083129883, 0.8078432083129883, 0.8078432083129883],
+                [0.4431372880935669, 0.4431372880935669, 0.4431372880935669],
+                [0.19607844948768616, 0.21176472306251526, 0.3607843220233917],
+            ]
+        )
+        self.assertTrue(torch.allclose(encoding["pixel_values"][:, 1, 0, :3], expected_slices, atol=1e-5))
+
+    @slow
+    def test_batched_coco_detection_annotations(self):
+        image_0 = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
+        image_1 = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png").resize((800, 800))
+
+        with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt", "r") as f:
+            target = json.loads(f.read())
+
+        annotations_0 = {"image_id": 39769, "annotations": target}
+        annotations_1 = {"image_id": 39769, "annotations": target}
+
+        # Adjust the bounding boxes for the resized image
+        w_0, h_0 = image_0.size
+        w_1, h_1 = image_1.size
+        for i in range(len(annotations_1["annotations"])):
+            coords = annotations_1["annotations"][i]["bbox"]
+            new_bbox = [
+                coords[0] * w_1 / w_0,
+                coords[1] * h_1 / h_0,
+                coords[2] * w_1 / w_0,
+                coords[3] * h_1 / h_0,
+            ]
+            annotations_1["annotations"][i]["bbox"] = new_bbox
+
+        images = [image_0, image_1]
+        annotations = [annotations_0, annotations_1]
+
+        image_processing = RTDetrImageProcessor()
+        encoding = image_processing(
+            images=images,
+            annotations=annotations,
+            return_segmentation_masks=True,
+            return_tensors="pt",  # do_convert_annotations=True
+        )
+
+        # Check the pixel values have been padded
+        postprocessed_height, postprocessed_width = 640, 640
+        expected_shape = torch.Size([2, 3, postprocessed_height, postprocessed_width])
+        self.assertEqual(encoding["pixel_values"].shape, expected_shape)
+
+        # Check the bounding boxes have been adjusted for padded images
+        self.assertEqual(encoding["labels"][0]["boxes"].shape, torch.Size([6, 4]))
+        self.assertEqual(encoding["labels"][1]["boxes"].shape, torch.Size([6, 4]))
+        expected_boxes_0 = torch.tensor(
+            [
+                [0.6879, 0.4609, 0.0755, 0.3691],
+                [0.2118, 0.3359, 0.2601, 0.1566],
+                [0.5011, 0.5000, 0.9979, 1.0000],
+                [0.5010, 0.5020, 0.9979, 0.9959],
+                [0.3284, 0.5944, 0.5884, 0.8112],
+                [0.8394, 0.5445, 0.3213, 0.9110],
+            ]
+        )
+        expected_boxes_1 = torch.tensor(
+            [
+                [0.5503, 0.2765, 0.0604, 0.2215],
+                [0.1695, 0.2016, 0.2080, 0.0940],
+                [0.5006, 0.4933, 0.9977, 0.9865],
+                [0.5008, 0.5002, 0.9983, 0.9955],
+                [0.2627, 0.5456, 0.4707, 0.8646],
+                [0.7715, 0.4115, 0.4570, 0.7161],
+            ]
+        )
+        self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1e-3))
+        self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1e-3))
+
+        # Check if do_convert_annotations=False, then the annotations are not converted to centre_x, centre_y, width, height
+        # format and not in the range [0, 1]
+        encoding = image_processing(
+            images=images,
+            annotations=annotations,
+            return_segmentation_masks=True,
+            do_convert_annotations=False,
+            return_tensors="pt",
+        )
+        self.assertEqual(encoding["labels"][0]["boxes"].shape, torch.Size([6, 4]))
+        self.assertEqual(encoding["labels"][1]["boxes"].shape, torch.Size([6, 4]))
+        # Convert to absolute coordinates
+        unnormalized_boxes_0 = torch.vstack(
+            [
+                expected_boxes_0[:, 0] * postprocessed_width,
+                expected_boxes_0[:, 1] * postprocessed_height,
+                expected_boxes_0[:, 2] * postprocessed_width,
+                expected_boxes_0[:, 3] * postprocessed_height,
+            ]
+        ).T
+        unnormalized_boxes_1 = torch.vstack(
+            [
+                expected_boxes_1[:, 0] * postprocessed_width,
+                expected_boxes_1[:, 1] * postprocessed_height,
+                expected_boxes_1[:, 2] * postprocessed_width,
+                expected_boxes_1[:, 3] * postprocessed_height,
+            ]
+        ).T
+        # Convert from centre_x, centre_y, width, height to x_min, y_min, x_max, y_max
+        expected_boxes_0 = torch.vstack(
+            [
+                unnormalized_boxes_0[:, 0] - unnormalized_boxes_0[:, 2] / 2,
+                unnormalized_boxes_0[:, 1] - unnormalized_boxes_0[:, 3] / 2,
+                unnormalized_boxes_0[:, 0] + unnormalized_boxes_0[:, 2] / 2,
+                unnormalized_boxes_0[:, 1] + unnormalized_boxes_0[:, 3] / 2,
+            ]
+        ).T
+        expected_boxes_1 = torch.vstack(
+            [
+                unnormalized_boxes_1[:, 0] - unnormalized_boxes_1[:, 2] / 2,
+                unnormalized_boxes_1[:, 1] - unnormalized_boxes_1[:, 3] / 2,
+                unnormalized_boxes_1[:, 0] + unnormalized_boxes_1[:, 2] / 2,
+                unnormalized_boxes_1[:, 1] + unnormalized_boxes_1[:, 3] / 2,
+            ]
+        ).T
+        self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1))
+        self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1))

tests/models/rt_detr/test_modeling_rt_detr.py

+# coding = utf-8
+# Copyright 2024 The HuggingFace Inc. 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.
+"""Testing suite for the PyTorch RT_DETR model."""
+
+import inspect
+import math
+import unittest
+
+from transformers import (
+    RTDetrConfig,
+    RTDetrImageProcessor,
+    RTDetrResNetConfig,
+    is_torch_available,
+    is_vision_available,
+)
+from transformers.testing_utils import require_torch, require_vision, torch_device
+from transformers.utils import cached_property
+
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor
+from ...test_pipeline_mixin import PipelineTesterMixin
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import RTDetrForObjectDetection, RTDetrModel
+
+if is_vision_available():
+    from PIL import Image
+
+
+CHECKPOINT = "PekingU/rtdetr_r50vd"  # TODO: replace
+
+
+class RTDetrModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=3,
+        is_training=True,
+        use_labels=True,
+        n_targets=3,
+        num_labels=10,
+        initializer_range=0.02,
+        layer_norm_eps=1e-5,
+        batch_norm_eps=1e-5,
+        # backbone
+        backbone_config=None,
+        # encoder HybridEncoder
+        encoder_hidden_dim=32,
+        encoder_in_channels=[128, 256, 512],
+        feat_strides=[8, 16, 32],
+        encoder_layers=1,
+        encoder_ffn_dim=64,
+        encoder_attention_heads=2,
+        dropout=0.0,
+        activation_dropout=0.0,
+        encode_proj_layers=[2],
+        positional_encoding_temperature=10000,
+        encoder_activation_function="gelu",
+        activation_function="silu",
+        eval_size=None,
+        normalize_before=False,
+        # decoder RTDetrTransformer
+        d_model=32,
+        num_queries=30,
+        decoder_in_channels=[32, 32, 32],
+        decoder_ffn_dim=64,
+        num_feature_levels=3,
+        decoder_n_points=4,
+        decoder_layers=2,
+        decoder_attention_heads=2,
+        decoder_activation_function="relu",
+        attention_dropout=0.0,
+        num_denoising=0,
+        label_noise_ratio=0.5,
+        box_noise_scale=1.0,
+        learn_initial_query=False,
+        anchor_image_size=[64, 64],
+        image_size=64,
+        disable_custom_kernels=True,
+        with_box_refine=True,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.num_channels = 3
+        self.is_training = is_training
+        self.use_labels = use_labels
+        self.n_targets = n_targets
+        self.num_labels = num_labels
+        self.initializer_range = initializer_range
+        self.layer_norm_eps = layer_norm_eps
+        self.batch_norm_eps = batch_norm_eps
+        self.backbone_config = backbone_config
+        self.encoder_hidden_dim = encoder_hidden_dim
+        self.encoder_in_channels = encoder_in_channels
+        self.feat_strides = feat_strides
+        self.encoder_layers = encoder_layers
+        self.encoder_ffn_dim = encoder_ffn_dim
+        self.encoder_attention_heads = encoder_attention_heads
+        self.dropout = dropout
+        self.activation_dropout = activation_dropout
+        self.encode_proj_layers = encode_proj_layers
+        self.positional_encoding_temperature = positional_encoding_temperature
+        self.encoder_activation_function = encoder_activation_function
+        self.activation_function = activation_function
+        self.eval_size = eval_size
+        self.normalize_before = normalize_before
+        self.d_model = d_model
+        self.num_queries = num_queries
+        self.decoder_in_channels = decoder_in_channels
+        self.decoder_ffn_dim = decoder_ffn_dim
+        self.num_feature_levels = num_feature_levels
+        self.decoder_n_points = decoder_n_points
+        self.decoder_layers = decoder_layers
+        self.decoder_attention_heads = decoder_attention_heads
+        self.decoder_activation_function = decoder_activation_function
+        self.attention_dropout = attention_dropout
+        self.num_denoising = num_denoising
+        self.label_noise_ratio = label_noise_ratio
+        self.box_noise_scale = box_noise_scale
+        self.learn_initial_query = learn_initial_query
+        self.anchor_image_size = anchor_image_size
+        self.image_size = image_size
+        self.disable_custom_kernels = disable_custom_kernels
+        self.with_box_refine = with_box_refine
+
+        self.encoder_seq_length = math.ceil(self.image_size / 32) * math.ceil(self.image_size / 32)
+
+    def prepare_config_and_inputs(self):
+        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
+
+        pixel_mask = torch.ones([self.batch_size, self.image_size, self.image_size], device=torch_device)
+
+        labels = None
+        if self.use_labels:
+            # labels is a list of Dict (each Dict being the labels for a given example in the batch)
+            labels = []
+            for i in range(self.batch_size):
+                target = {}
+                target["class_labels"] = torch.randint(
+                    high=self.num_labels, size=(self.n_targets,), device=torch_device
+                )
+                target["boxes"] = torch.rand(self.n_targets, 4, device=torch_device)
+                labels.append(target)
+
+        config = self.get_config()
+        config.num_labels = self.num_labels
+        return config, pixel_values, pixel_mask, labels
+
+    def get_config(self):
+        hidden_sizes = [10, 20, 30, 40]
+        backbone_config = RTDetrResNetConfig(
+            embeddings_size=10,
+            hidden_sizes=hidden_sizes,
+            depths=[1, 1, 2, 1],
+            out_features=["stage2", "stage3", "stage4"],
+            out_indices=[2, 3, 4],
+        )
+        return RTDetrConfig.from_backbone_configs(
+            backbone_config=backbone_config,
+            encoder_hidden_dim=self.encoder_hidden_dim,
+            encoder_in_channels=hidden_sizes[1:],
+            feat_strides=self.feat_strides,
+            encoder_layers=self.encoder_layers,
+            encoder_ffn_dim=self.encoder_ffn_dim,
+            encoder_attention_heads=self.encoder_attention_heads,
+            dropout=self.dropout,
+            activation_dropout=self.activation_dropout,
+            encode_proj_layers=self.encode_proj_layers,
+            positional_encoding_temperature=self.positional_encoding_temperature,
+            encoder_activation_function=self.encoder_activation_function,
+            activation_function=self.activation_function,
+            eval_size=self.eval_size,
+            normalize_before=self.normalize_before,
+            d_model=self.d_model,
+            num_queries=self.num_queries,
+            decoder_in_channels=self.decoder_in_channels,
+            decoder_ffn_dim=self.decoder_ffn_dim,
+            num_feature_levels=self.num_feature_levels,
+            decoder_n_points=self.decoder_n_points,
+            decoder_layers=self.decoder_layers,
+            decoder_attention_heads=self.decoder_attention_heads,
+            decoder_activation_function=self.decoder_activation_function,
+            attention_dropout=self.attention_dropout,
+            num_denoising=self.num_denoising,
+            label_noise_ratio=self.label_noise_ratio,
+            box_noise_scale=self.box_noise_scale,
+            learn_initial_query=self.learn_initial_query,
+            anchor_image_size=self.anchor_image_size,
+            image_size=self.image_size,
+            disable_custom_kernels=self.disable_custom_kernels,
+            with_box_refine=self.with_box_refine,
+        )
+
+    def prepare_config_and_inputs_for_common(self):
+        config, pixel_values, pixel_mask, labels = self.prepare_config_and_inputs()
+        inputs_dict = {"pixel_values": pixel_values}
+        return config, inputs_dict
+
+    def create_and_check_rt_detr_model(self, config, pixel_values, pixel_mask, labels):
+        model = RTDetrModel(config=config)
+        model.to(torch_device)
+        model.eval()
+
+        result = model(pixel_values=pixel_values, pixel_mask=pixel_mask)
+        result = model(pixel_values)
+
+        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.num_queries, self.d_model))
+
+    def create_and_check_rt_detr_object_detection_head_model(self, config, pixel_values, pixel_mask, labels):
+        model = RTDetrForObjectDetection(config=config)
+        model.to(torch_device)
+        model.eval()
+
+        result = model(pixel_values=pixel_values, pixel_mask=pixel_mask)
+        result = model(pixel_values)
+
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_queries, self.num_labels))
+        self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_queries, 4))
+
+        result = model(pixel_values=pixel_values, pixel_mask=pixel_mask, labels=labels)
+
+        self.parent.assertEqual(result.loss.shape, ())
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_queries, self.num_labels))
+        self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_queries, 4))
+
+
+@require_torch
+class RTDetrModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    all_model_classes = (RTDetrModel, RTDetrForObjectDetection) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {"image-feature-extraction": RTDetrModel, "object-detection": RTDetrForObjectDetection}
+        if is_torch_available()
+        else {}
+    )
+    is_encoder_decoder = True
+    test_torchscript = False
+    test_pruning = False
+    test_head_masking = False
+    test_missing_keys = False
+
+    # special case for head models
+    def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
+        inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
+
+        if return_labels:
+            if model_class.__name__ == "RTDetrForObjectDetection":
+                labels = []
+                for i in range(self.model_tester.batch_size):
+                    target = {}
+                    target["class_labels"] = torch.ones(
+                        size=(self.model_tester.n_targets,), device=torch_device, dtype=torch.long
+                    )
+                    target["boxes"] = torch.ones(
+                        self.model_tester.n_targets, 4, device=torch_device, dtype=torch.float
+                    )
+                    labels.append(target)
+                inputs_dict["labels"] = labels
+
+        return inputs_dict
+
+    def setUp(self):
+        self.model_tester = RTDetrModelTester(self)
+        self.config_tester = ConfigTester(
+            self,
+            config_class=RTDetrConfig,
+            has_text_modality=False,
+            common_properties=["hidden_size", "num_attention_heads"],
+        )
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    def test_rt_detr_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_rt_detr_model(*config_and_inputs)
+
+    def test_rt_detr_object_detection_head_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_rt_detr_object_detection_head_model(*config_and_inputs)
+
+    @unittest.skip(reason="RTDetr does not use inputs_embeds")
+    def test_inputs_embeds(self):
+        pass
+
+    @unittest.skip(reason="RTDetr does not use test_inputs_embeds_matches_input_ids")
+    def test_inputs_embeds_matches_input_ids(self):
+        pass
+
+    @unittest.skip(reason="RTDetr does not support input and output embeddings")
+    def test_model_get_set_embeddings(self):
+        pass
+
+    @unittest.skip(reason="RTDetr does not support input and output embeddings")
+    def test_model_common_attributes(self):
+        pass
+
+    @unittest.skip(reason="RTDetr does not use token embeddings")
+    def test_resize_tokens_embeddings(self):
+        pass
+
+    @unittest.skip(reason="Feed forward chunking is not implemented")
+    def test_feed_forward_chunking(self):
+        pass
+
+    def test_attention_outputs(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.return_dict = True
+
+        for model_class in self.all_model_classes:
+            inputs_dict["output_attentions"] = True
+            inputs_dict["output_hidden_states"] = False
+            config.return_dict = True
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+            attentions = outputs.encoder_attentions
+            self.assertEqual(len(attentions), self.model_tester.encoder_layers)
+
+            # check that output_attentions also work using config
+            del inputs_dict["output_attentions"]
+            config.output_attentions = True
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+            attentions = outputs.encoder_attentions
+            self.assertEqual(len(attentions), self.model_tester.encoder_layers)
+
+            self.assertListEqual(
+                list(attentions[0].shape[-3:]),
+                [
+                    self.model_tester.encoder_attention_heads,
+                    self.model_tester.encoder_seq_length,
+                    self.model_tester.encoder_seq_length,
+                ],
+            )
+            out_len = len(outputs)
+
+            correct_outlen = 13
+
+            # loss is at first position
+            if "labels" in inputs_dict:
+                correct_outlen += 1  # loss is added to beginning
+            # Object Detection model returns pred_logits and pred_boxes
+            if model_class.__name__ == "RTDetrForObjectDetection":
+                correct_outlen += 2
+
+            self.assertEqual(out_len, correct_outlen)
+
+            # decoder attentions
+            decoder_attentions = outputs.decoder_attentions
+            self.assertIsInstance(decoder_attentions, (list, tuple))
+            self.assertEqual(len(decoder_attentions), self.model_tester.decoder_layers)
+            self.assertListEqual(
+                list(decoder_attentions[0].shape[-3:]),
+                [
+                    self.model_tester.decoder_attention_heads,
+                    self.model_tester.num_queries,
+                    self.model_tester.num_queries,
+                ],
+            )
+
+            # cross attentions
+            cross_attentions = outputs.cross_attentions
+            self.assertIsInstance(cross_attentions, (list, tuple))
+            self.assertEqual(len(cross_attentions), self.model_tester.decoder_layers)
+            self.assertListEqual(
+                list(cross_attentions[0].shape[-3:]),
+                [
+                    self.model_tester.decoder_attention_heads,
+                    self.model_tester.num_feature_levels,
+                    self.model_tester.decoder_n_points,
+                ],
+            )
+
+            # Check attention is always last and order is fine
+            inputs_dict["output_attentions"] = True
+            inputs_dict["output_hidden_states"] = True
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+
+            if hasattr(self.model_tester, "num_hidden_states_types"):
+                added_hidden_states = self.model_tester.num_hidden_states_types
+            else:
+                # RTDetr should maintin encoder_hidden_states output
+                added_hidden_states = 2
+            self.assertEqual(out_len + added_hidden_states, len(outputs))
+
+            self_attentions = outputs.encoder_attentions
+
+            self.assertEqual(len(self_attentions), self.model_tester.encoder_layers)
+            self.assertListEqual(
+                list(self_attentions[0].shape[-3:]),
+                [
+                    self.model_tester.encoder_attention_heads,
+                    self.model_tester.encoder_seq_length,
+                    self.model_tester.encoder_seq_length,
+                ],
+            )
+
+    def test_hidden_states_output(self):
+        def check_hidden_states_output(inputs_dict, config, model_class):
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+
+            hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
+
+            expected_num_layers = getattr(
+                self.model_tester, "expected_num_hidden_layers", len(self.model_tester.encoder_in_channels) - 1
+            )
+            self.assertEqual(len(hidden_states), expected_num_layers)
+
+            self.assertListEqual(
+                list(hidden_states[1].shape[-2:]),
+                [
+                    self.model_tester.image_size // self.model_tester.feat_strides[-1],
+                    self.model_tester.image_size // self.model_tester.feat_strides[-1],
+                ],
+            )
+
+            if config.is_encoder_decoder:
+                hidden_states = outputs.decoder_hidden_states
+
+                expected_num_layers = getattr(
+                    self.model_tester, "expected_num_hidden_layers", self.model_tester.decoder_layers + 1
+                )
+
+                self.assertIsInstance(hidden_states, (list, tuple))
+                self.assertEqual(len(hidden_states), expected_num_layers)
+
+                self.assertListEqual(
+                    list(hidden_states[0].shape[-2:]),
+                    [self.model_tester.num_queries, self.model_tester.d_model],
+                )
+
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            inputs_dict["output_hidden_states"] = True
+            check_hidden_states_output(inputs_dict, config, model_class)
+
+            # check that output_hidden_states also work using config
+            del inputs_dict["output_hidden_states"]
+            config.output_hidden_states = True
+
+            check_hidden_states_output(inputs_dict, config, model_class)
+
+    def test_retain_grad_hidden_states_attentions(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.output_hidden_states = True
+        config.output_attentions = True
+
+        model_class = self.all_model_classes[0]
+        model = model_class(config)
+        model.to(torch_device)
+
+        inputs = self._prepare_for_class(inputs_dict, model_class)
+
+        outputs = model(**inputs)
+
+        # we take the first output since last_hidden_state is the first item
+        output = outputs[0]
+
+        encoder_hidden_states = outputs.encoder_hidden_states[0]
+        encoder_attentions = outputs.encoder_attentions[0]
+        encoder_hidden_states.retain_grad()
+        encoder_attentions.retain_grad()
+
+        decoder_attentions = outputs.decoder_attentions[0]
+        decoder_attentions.retain_grad()
+
+        cross_attentions = outputs.cross_attentions[0]
+        cross_attentions.retain_grad()
+
+        output.flatten()[0].backward(retain_graph=True)
+
+        self.assertIsNotNone(encoder_hidden_states.grad)
+        self.assertIsNotNone(encoder_attentions.grad)
+        self.assertIsNotNone(decoder_attentions.grad)
+        self.assertIsNotNone(cross_attentions.grad)
+
+    def test_forward_signature(self):
+        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            signature = inspect.signature(model.forward)
+            arg_names = [*signature.parameters.keys()]
+            expected_arg_names = ["pixel_values"]
+            self.assertListEqual(arg_names[:1], expected_arg_names)
+
+    def test_different_timm_backbone(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        # let's pick a random timm backbone
+        config.backbone = "tf_mobilenetv3_small_075"
+        config.backbone_config = None
+        config.use_timm_backbone = True
+        config.backbone_kwargs = {"out_indices": [2, 3, 4]}
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+
+            if model_class.__name__ == "RTDetrForObjectDetection":
+                expected_shape = (
+                    self.model_tester.batch_size,
+                    self.model_tester.num_queries,
+                    self.model_tester.num_labels,
+                )
+                self.assertEqual(outputs.logits.shape, expected_shape)
+                # Confirm out_indices was propogated to backbone
+                self.assertEqual(len(model.model.backbone.intermediate_channel_sizes), 3)
+            else:
+                # Confirm out_indices was propogated to backbone
+                self.assertEqual(len(model.backbone.intermediate_channel_sizes), 3)
+
+            self.assertTrue(outputs)
+
+    def test_hf_backbone(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        # Load a pretrained HF checkpoint as backbone
+        config.backbone = "microsoft/resnet-18"
+        config.backbone_config = None
+        config.use_timm_backbone = False
+        config.use_pretrained_backbone = True
+        config.backbone_kwargs = {"out_indices": [2, 3, 4]}
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+
+            if model_class.__name__ == "RTDetrForObjectDetection":
+                expected_shape = (
+                    self.model_tester.batch_size,
+                    self.model_tester.num_queries,
+                    self.model_tester.num_labels,
+                )
+                self.assertEqual(outputs.logits.shape, expected_shape)
+                # Confirm out_indices was propogated to backbone
+                self.assertEqual(len(model.model.backbone.intermediate_channel_sizes), 3)
+            else:
+                # Confirm out_indices was propogated to backbone
+                self.assertEqual(len(model.backbone.intermediate_channel_sizes), 3)
+
+            self.assertTrue(outputs)
+
+    def test_initialization(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        configs_no_init = _config_zero_init(config)
+        for model_class in self.all_model_classes:
+            model = model_class(config=configs_no_init)
+            # Skip the check for the backbone
+            for name, module in model.named_modules():
+                if module.__class__.__name__ == "RTDetrConvEncoder":
+                    backbone_params = [f"{name}.{key}" for key in module.state_dict().keys()]
+                    break
+
+            for name, param in model.named_parameters():
+                if param.requires_grad:
+                    if (
+                        "level_embed" in name
+                        or "sampling_offsets.bias" in name
+                        or "value_proj" in name
+                        or "output_proj" in name
+                        or "reference_points" in name
+                        or name in backbone_params
+                    ):
+                        continue
+                    self.assertIn(
+                        ((param.data.mean() * 1e9).round() / 1e9).item(),
+                        [0.0, 1.0],
+                        msg=f"Parameter {name} of model {model_class} seems not properly initialized",
+                    )
+
+
+TOLERANCE = 1e-4
+
+
+# We will verify our results on an image of cute cats
+def prepare_img():
+    image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
+    return image
+
+
+@require_torch
+@require_vision
+class RTDetrModelIntegrationTest(unittest.TestCase):
+    @cached_property
+    def default_image_processor(self):
+        return RTDetrImageProcessor.from_pretrained(CHECKPOINT) if is_vision_available() else None
+
+    def test_inference_object_detection_head(self):
+        model = RTDetrForObjectDetection.from_pretrained(CHECKPOINT).to(torch_device)
+
+        image_processor = self.default_image_processor
+        image = prepare_img()
+        inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
+
+        with torch.no_grad():
+            outputs = model(**inputs)
+
+        expected_shape_logits = torch.Size((1, 300, model.config.num_labels))
+        self.assertEqual(outputs.logits.shape, expected_shape_logits)
+
+        expected_logits = torch.tensor(
+            [
+                [-4.64763879776001, -5.001153945922852, -4.978509902954102],
+                [-4.159348487854004, -4.703853607177734, -5.946484565734863],
+                [-4.437461853027344, -4.65836238861084, -6.235235691070557],
+            ]
+        ).to(torch_device)
+        expected_boxes = torch.tensor(
+            [
+                [0.1688060760498047, 0.19992263615131378, 0.21225441992282867],
+                [0.768376350402832, 0.41226309537887573, 0.4636859893798828],
+                [0.25953856110572815, 0.5483334064483643, 0.4777486026287079],
+            ]
+        ).to(torch_device)
+
+        self.assertTrue(torch.allclose(outputs.logits[0, :3, :3], expected_logits, atol=1e-4))
+
+        expected_shape_boxes = torch.Size((1, 300, 4))
+        self.assertEqual(outputs.pred_boxes.shape, expected_shape_boxes)
+        self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3], expected_boxes, atol=1e-4))
+
+        # verify postprocessing
+        results = image_processor.post_process_object_detection(
+            outputs, threshold=0.0, target_sizes=[image.size[::-1]]
+        )[0]
+        expected_scores = torch.tensor(
+            [0.9703017473220825, 0.9599503874778748, 0.9575679302215576, 0.9506784677505493], device=torch_device
+        )
+        expected_labels = [57, 15, 15, 65]
+        expected_slice_boxes = torch.tensor(
+            [
+                [0.13774872, 0.37821293, 640.13074, 476.21088],
+                [343.38132, 24.276838, 640.1404, 371.49573],
+                [13.225126, 54.179348, 318.98422, 472.2207],
+                [40.114475, 73.44104, 175.9573, 118.48469],
+            ],
+            device=torch_device,
+        )
+
+        self.assertTrue(torch.allclose(results["scores"][:4], expected_scores, atol=1e-4))
+        self.assertSequenceEqual(results["labels"][:4].tolist(), expected_labels)
+        self.assertTrue(torch.allclose(results["boxes"][:4], expected_slice_boxes, atol=1e-4))

tests/models/rt_detr/test_modeling_rt_detr_resnet.py

+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. 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
+
+from transformers import RTDetrResNetConfig
+from transformers.testing_utils import require_torch, torch_device
+from transformers.utils.import_utils import is_torch_available
+
+from ...test_backbone_common import BackboneTesterMixin
+from ...test_modeling_common import floats_tensor, ids_tensor
+
+
+if is_torch_available():
+    from transformers import RTDetrResNetBackbone
+
+
+class RTDetrResNetModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=3,
+        image_size=32,
+        num_channels=3,
+        embeddings_size=10,
+        hidden_sizes=[10, 20, 30, 40],
+        depths=[1, 1, 2, 1],
+        is_training=True,
+        use_labels=True,
+        hidden_act="relu",
+        num_labels=3,
+        scope=None,
+        out_features=["stage2", "stage3", "stage4"],
+        out_indices=[2, 3, 4],
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.image_size = image_size
+        self.num_channels = num_channels
+        self.embeddings_size = embeddings_size
+        self.hidden_sizes = hidden_sizes
+        self.depths = depths
+        self.is_training = is_training
+        self.use_labels = use_labels
+        self.hidden_act = hidden_act
+        self.num_labels = num_labels
+        self.scope = scope
+        self.num_stages = len(hidden_sizes)
+        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])
+
+        labels = None
+        if self.use_labels:
+            labels = ids_tensor([self.batch_size], self.num_labels)
+
+        config = self.get_config()
+
+        return config, pixel_values, labels
+
+    def get_config(self):
+        return RTDetrResNetConfig(
+            num_channels=self.num_channels,
+            embeddings_size=self.embeddings_size,
+            hidden_sizes=self.hidden_sizes,
+            depths=self.depths,
+            hidden_act=self.hidden_act,
+            num_labels=self.num_labels,
+            out_features=self.out_features,
+            out_indices=self.out_indices,
+        )
+
+    def create_and_check_backbone(self, config, pixel_values, labels):
+        model = RTDetrResNetBackbone(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(pixel_values)
+
+        # verify feature maps
+        self.parent.assertEqual(len(result.feature_maps), len(config.out_features))
+        self.parent.assertListEqual(list(result.feature_maps[0].shape), [self.batch_size, self.hidden_sizes[1], 4, 4])
+
+        # verify channels
+        self.parent.assertEqual(len(model.channels), len(config.out_features))
+        self.parent.assertListEqual(model.channels, config.hidden_sizes[1:])
+
+        # verify backbone works with out_features=None
+        config.out_features = None
+        model = RTDetrResNetBackbone(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(pixel_values)
+
+        # verify feature maps
+        self.parent.assertEqual(len(result.feature_maps), 1)
+        self.parent.assertListEqual(list(result.feature_maps[0].shape), [self.batch_size, self.hidden_sizes[-1], 1, 1])
+
+        # verify channels
+        self.parent.assertEqual(len(model.channels), 1)
+        self.parent.assertListEqual(model.channels, [config.hidden_sizes[-1]])
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, pixel_values, labels = config_and_inputs
+        inputs_dict = {"pixel_values": pixel_values}
+        return config, inputs_dict
+
+
+@require_torch
+class RTDetrResNetBackboneTest(BackboneTesterMixin, unittest.TestCase):
+    all_model_classes = (RTDetrResNetBackbone,) if is_torch_available() else ()
+    has_attentions = False
+    config_class = RTDetrResNetConfig
+
+    def setUp(self):
+        self.model_tester = RTDetrResNetModelTester(self)