Add segmentation + object detection image processors (#20160)

* Add transforms for object detection

* DETR models + Yolos

* Scrappy additions

* Maskformer image processor

* Fix up; MaskFormer tests

* Update owlvit processor

* Add to docs

* OwlViT tests

* Update pad logic

* Remove changes to transforms

* Import fn directly

* Update to include pad transformation

* Remove uninstended changes

* Add new owlvit post processing function

* Tidy up

* Fix copies

* Fix some copies

* Include device fix

* Fix scipy imports

* Update _pad_image

* Update padding functionality

* Fix bug

* Properly handle ignore index

* Fix up

* Remove defaults to None in docstrings

* Fix docstrings & docs

* Fix sizes bug

* Resolve conflicts in init

* Cast to float after resizing

* Tidy & add size if missing

* Allow kwards when processing for owlvit

* Update test values

docs/source/en/model_doc/conditional_detr.mdx

@@ -21,7 +21,7 @@ The abstract from the paper is the following:
 *The recently-developed DETR approach applies the transformer encoder and decoder architecture to object detection and achieves promising performance. In this paper, we handle the critical issue, slow training convergence, and present a conditional cross-attention mechanism for fast DETR training. Our approach is motivated by that the cross-attention in DETR relies highly on the content embeddings for localizing the four extremities and predicting the box, which increases the need for high-quality content embeddings and thus the training difficulty. Our approach, named conditional DETR, learns a conditional spatial query from the decoder embedding for decoder multi-head cross-attention. The benefit is that through the conditional spatial query, each cross-attention head is able to attend to a band containing a distinct region, e.g., one object extremity or a region inside the object box. This narrows down the spatial range for localizing the distinct regions for object classification and box regression, thus relaxing the dependence on the content embeddings and easing the training. Empirical results show that conditional DETR converges 6.7× faster for the backbones R50 and R101 and 10× faster for stronger backbones DC5-R50 and DC5-R101. Code is available at https://github.com/Atten4Vis/ConditionalDETR.*
 
 <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/conditional_detr_curve.jpg"
-alt="drawing" width="600"/> 
+alt="drawing" width="600"/>
 
 <small> Conditional DETR shows much faster convergence compared to the original DETR. Taken from the <a href="https://arxiv.org/abs/2108.06152">original paper</a>.</small>
 
@@ -32,6 +32,16 @@ This model was contributed by [DepuMeng](https://huggingface.co/DepuMeng). The o
 
 [[autodoc]] ConditionalDetrConfig
 
+## ConditionalDetrImageProcessor
+
+[[autodoc]] ConditionalDetrImageProcessor
+    - preprocess
+    - pad_and_create_pixel_mask
+    - post_process_object_detection
+    - post_process_instance_segmentation
+    - post_process_semantic_segmentation
+    - post_process_panoptic_segmentation
+
 ## ConditionalDetrFeatureExtractor
 
 [[autodoc]] ConditionalDetrFeatureExtractor
@@ -55,4 +65,4 @@ This model was contributed by [DepuMeng](https://huggingface.co/DepuMeng). The o
 ## ConditionalDetrForSegmentation
 
 [[autodoc]] ConditionalDetrForSegmentation
-    - forward
+    - forward

docs/source/en/model_doc/deformable_detr.mdx

@@ -27,12 +27,19 @@ Tips:
 - Training Deformable DETR is equivalent to training the original [DETR](detr) model. Demo notebooks can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/DETR).
 
 <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/deformable_detr_architecture.png"
-alt="drawing" width="600"/> 
+alt="drawing" width="600"/>
 
 <small> Deformable DETR architecture. Taken from the <a href="https://arxiv.org/abs/2010.04159">original paper</a>.</small>
 
 This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be found [here](https://github.com/fundamentalvision/Deformable-DETR).
 
+## DeformableDetrImageProcessor
+
+[[autodoc]] DeformableDetrImageProcessor
+    - preprocess
+    - pad_and_create_pixel_mask
+    - post_process_object_detection
+
 ## DeformableDetrFeatureExtractor
 
 [[autodoc]] DeformableDetrFeatureExtractor
@@ -55,4 +62,4 @@ This model was contributed by [nielsr](https://huggingface.co/nielsr). The origi
 ## DeformableDetrForObjectDetection
 
 [[autodoc]] DeformableDetrForObjectDetection
-    - forward
+    - forward

docs/source/en/model_doc/detr.mdx

@@ -114,7 +114,7 @@ Tips:
   It is advised to use a batch size of 2 per GPU. See [this Github thread](https://github.com/facebookresearch/detr/issues/150) for more info.
 
 There are three ways to instantiate a DETR model (depending on what you prefer):
-  
+
 Option 1: Instantiate DETR with pre-trained weights for entire model
 ```py
 >>> from transformers import DetrForObjectDetection
@@ -166,6 +166,15 @@ mean Average Precision (mAP) and Panoptic Quality (PQ). The latter objects are i
 
 [[autodoc]] DetrConfig
 
+## DetrImageProcessor
+
+[[autodoc]] DetrImageProcessor
+    - preprocess
+    - post_process_object_detection
+    - post_process_semantic_segmentation
+    - post_process_instance_segmentation
+    - post_process_panoptic_segmentation
+
 ## DetrFeatureExtractor
 
 [[autodoc]] DetrFeatureExtractor

docs/source/en/model_doc/maskformer.mdx

@@ -57,6 +57,15 @@ This model was contributed by [francesco](https://huggingface.co/francesco). The
 
 [[autodoc]] MaskFormerConfig
 
+## MaskFormerImageProcessor
+
+[[autodoc]] MaskFormerImageProcessor
+    - preprocess
+    - encode_inputs
+    - post_process_semantic_segmentation
+    - post_process_instance_segmentation
+    - post_process_panoptic_segmentation
+
 ## MaskFormerFeatureExtractor
 
 [[autodoc]] MaskFormerFeatureExtractor

docs/source/en/model_doc/owlvit.mdx

@@ -22,7 +22,7 @@ The abstract from the paper is the following:
 
 ## Usage
 
-OWL-ViT is a zero-shot text-conditioned object detection model. OWL-ViT uses [CLIP](clip) as its multi-modal backbone, with a ViT-like Transformer to get visual features and a causal language model to get the text features. To use CLIP for detection, OWL-ViT removes the final token pooling layer of the vision model and attaches a lightweight classification and box head to each transformer output token. Open-vocabulary classification is enabled by replacing the fixed classification layer weights with the class-name embeddings obtained from the text model. The authors first train CLIP from scratch and fine-tune it end-to-end with the classification and box heads on standard detection datasets using a bipartite matching loss. One or multiple text queries per image can be used to perform zero-shot text-conditioned object detection. 
+OWL-ViT is a zero-shot text-conditioned object detection model. OWL-ViT uses [CLIP](clip) as its multi-modal backbone, with a ViT-like Transformer to get visual features and a causal language model to get the text features. To use CLIP for detection, OWL-ViT removes the final token pooling layer of the vision model and attaches a lightweight classification and box head to each transformer output token. Open-vocabulary classification is enabled by replacing the fixed classification layer weights with the class-name embeddings obtained from the text model. The authors first train CLIP from scratch and fine-tune it end-to-end with the classification and box heads on standard detection datasets using a bipartite matching loss. One or multiple text queries per image can be used to perform zero-shot text-conditioned object detection.
 
 [`OwlViTFeatureExtractor`] can be used to resize (or rescale) and normalize images for the model and [`CLIPTokenizer`] is used to encode the text. [`OwlViTProcessor`] wraps [`OwlViTFeatureExtractor`] and [`CLIPTokenizer`] into a single instance to both encode the text and prepare the images. The following example shows how to perform object detection using [`OwlViTProcessor`] and [`OwlViTForObjectDetection`].
 
@@ -76,6 +76,13 @@ This model was contributed by [adirik](https://huggingface.co/adirik). The origi
 
 [[autodoc]] OwlViTVisionConfig
 
+## OwlViTImageProcessor
+
+[[autodoc]] OwlViTImageProcessor
+    - preprocess
+    - post_process
+    - post_process_image_guided_detection
+
 ## OwlViTFeatureExtractor
 
 [[autodoc]] OwlViTFeatureExtractor

docs/source/en/model_doc/yolos.mdx

@@ -27,7 +27,7 @@ Tips:
 - Demo notebooks (regarding inference and fine-tuning on custom data) can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/YOLOS).
 
 <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/yolos_architecture.png"
-alt="drawing" width="600"/> 
+alt="drawing" width="600"/>
 
 <small> YOLOS architecture. Taken from the <a href="https://arxiv.org/abs/2106.00666">original paper</a>.</small>
 
@@ -37,6 +37,12 @@ This model was contributed by [nielsr](https://huggingface.co/nielsr). The origi
 
 [[autodoc]] YolosConfig
 
+## YolosImageProcessor
+
+[[autodoc]] YolosImageProcessor
+    - preprocess
+    - pad
+    - post_process_object_detection
 
 ## YolosFeatureExtractor
 
@@ -55,4 +61,4 @@ This model was contributed by [nielsr](https://huggingface.co/nielsr). The origi
 ## YolosForObjectDetection
 
 [[autodoc]] YolosForObjectDetection
-    - forward
+    - forward