Fix MaskFormerImageProcessor.post_process_instance_segmentation

docs/source/en/model_doc/mask2former.mdx

@@ -22,8 +22,8 @@ The abstract from the paper is the following:
 of semantics defines a task. While only the semantics of each task differ, current research focuses on designing specialized architectures for each task. We present Masked-attention Mask Transformer (Mask2Former), a new architecture capable of addressing any image segmentation task (panoptic, instance or semantic). Its key components include masked attention, which extracts localized features by constraining cross-attention within predicted mask regions. In addition to reducing the research effort by at least three times, it outperforms the best specialized architectures by a significant margin on four popular datasets. Most notably, Mask2Former sets a new state-of-the-art for panoptic segmentation (57.8 PQ on COCO), instance segmentation (50.1 AP on COCO) and semantic segmentation (57.7 mIoU on ADE20K).*
 
 Tips:
-- Mask2Former uses the same preprocessing and postprocessing steps as [MaskFormer](maskformer). Use [`MaskFormerImageProcessor`] or [`AutoImageProcessor`] to prepare images and optional targets for the model.
-- To get the final segmentation, depending on the task, you can call [`~MaskFormerImageProcessor.post_process_semantic_segmentation`] or [`~MaskFormerImageProcessor.post_process_instance_segmentation`] or [`~MaskFormerImageProcessor.post_process_panoptic_segmentation`]. All three tasks can be solved using [`Mask2FormerForUniversalSegmentation`] output, panoptic segmentation accepts an optional `label_ids_to_fuse` argument to fuse instances of the target object/s (e.g. sky) together.
+- Mask2Former uses the same preprocessing and postprocessing steps as [MaskFormer](maskformer). Use [`Mask2FormerImageProcessor`] or [`AutoImageProcessor`] to prepare images and optional targets for the model.
+- To get the final segmentation, depending on the task, you can call [`~Mask2FormerImageProcessor.post_process_semantic_segmentation`] or [`~Mask2FormerImageProcessor.post_process_instance_segmentation`] or [`~Mask2FormerImageProcessor.post_process_panoptic_segmentation`]. All three tasks can be solved using [`Mask2FormerForUniversalSegmentation`] output, panoptic segmentation accepts an optional `label_ids_to_fuse` argument to fuse instances of the target object/s (e.g. sky) together.
 
 This model was contributed by [Shivalika Singh](https://huggingface.co/shivi) and [Alara Dirik](https://huggingface.co/adirik). The original code can be found [here](https://github.com/facebookresearch/Mask2Former).
 
@@ -55,3 +55,12 @@ The resource should ideally demonstrate something new instead of duplicating an
 
 [[autodoc]] Mask2FormerForUniversalSegmentation
     - forward
+
+## Mask2FormerImageProcessor
+
+[[autodoc]] Mask2FormerImageProcessor
+    - preprocess
+    - encode_inputs
+    - post_process_semantic_segmentation
+    - post_process_instance_segmentation
+    - post_process_panoptic_segmentation

src/transformers/__init__.py

@@ -799,6 +799,7 @@
     _import_structure["models.layoutlmv2"].extend(["LayoutLMv2FeatureExtractor", "LayoutLMv2ImageProcessor"])
     _import_structure["models.layoutlmv3"].extend(["LayoutLMv3FeatureExtractor", "LayoutLMv3ImageProcessor"])
     _import_structure["models.levit"].extend(["LevitFeatureExtractor", "LevitImageProcessor"])
+    _import_structure["models.mask2former"].append("Mask2FormerImageProcessor")
     _import_structure["models.maskformer"].extend(["MaskFormerFeatureExtractor", "MaskFormerImageProcessor"])
     _import_structure["models.mobilenet_v1"].extend(["MobileNetV1FeatureExtractor", "MobileNetV1ImageProcessor"])
     _import_structure["models.mobilenet_v2"].extend(["MobileNetV2FeatureExtractor", "MobileNetV2ImageProcessor"])
@@ -4152,6 +4153,7 @@
         from .models.layoutlmv2 import LayoutLMv2FeatureExtractor, LayoutLMv2ImageProcessor
         from .models.layoutlmv3 import LayoutLMv3FeatureExtractor, LayoutLMv3ImageProcessor
         from .models.levit import LevitFeatureExtractor, LevitImageProcessor
+        from .models.mask2former import Mask2FormerImageProcessor
         from .models.maskformer import MaskFormerFeatureExtractor, MaskFormerImageProcessor
         from .models.mobilenet_v1 import MobileNetV1FeatureExtractor, MobileNetV1ImageProcessor
         from .models.mobilenet_v2 import MobileNetV2FeatureExtractor, MobileNetV2ImageProcessor

src/transformers/models/auto/image_processing_auto.py

@@ -62,7 +62,7 @@
         ("layoutlmv2", "LayoutLMv2ImageProcessor"),
         ("layoutlmv3", "LayoutLMv3ImageProcessor"),
         ("levit", "LevitImageProcessor"),
-        ("mask2former", "MaskFormerImageProcessor"),
+        ("mask2former", "Mask2FormerImageProcessor"),
         ("maskformer", "MaskFormerImageProcessor"),
         ("mobilenet_v1", "MobileNetV1ImageProcessor"),
         ("mobilenet_v2", "MobileNetV2ImageProcessor"),

src/transformers/models/mask2former/__init__.py

@@ -27,6 +27,13 @@
     ],
 }
 
+try:
+    if not is_vision_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
+    _import_structure["image_processing_mask2former"] = ["Mask2FormerImageProcessor"]
 
 try:
     if not is_torch_available():
@@ -44,6 +51,14 @@
 if TYPE_CHECKING:
     from .configuration_mask2former import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, Mask2FormerConfig
 
+    try:
+        if not is_vision_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
+        from .image_processing_mask2former import Mask2FormerImageProcessor
+
     try:
         if not is_torch_available():
             raise OptionalDependencyNotAvailable()

src/transformers/models/mask2former/convert_mask2former_original_pytorch_checkpoint_to_pytorch.py

@@ -33,8 +33,8 @@
 from transformers import (
     Mask2FormerConfig,
     Mask2FormerForUniversalSegmentation,
+    Mask2FormerImageProcessor,
     Mask2FormerModel,
-    MaskFormerImageProcessor,
     SwinConfig,
 )
 from transformers.models.mask2former.modeling_mask2former import (
@@ -193,11 +193,11 @@ def __call__(self, original_config: object) -> Mask2FormerConfig:
 
 
 class OriginalMask2FormerConfigToFeatureExtractorConverter:
-    def __call__(self, original_config: object) -> MaskFormerImageProcessor:
+    def __call__(self, original_config: object) -> Mask2FormerImageProcessor:
         model = original_config.MODEL
         model_input = original_config.INPUT
 
-        return MaskFormerImageProcessor(
+        return Mask2FormerImageProcessor(
             image_mean=(torch.tensor(model.PIXEL_MEAN) / 255).tolist(),
             image_std=(torch.tensor(model.PIXEL_STD) / 255).tolist(),
             size=model_input.MIN_SIZE_TEST,
@@ -847,7 +847,7 @@ def using_dirs(checkpoints_dir: Path, config_dir: Path) -> Iterator[Tuple[object
 def test(
     original_model,
     our_model: Mask2FormerForUniversalSegmentation,
-    feature_extractor: MaskFormerImageProcessor,
+    feature_extractor: Mask2FormerImageProcessor,
     tolerance: float,
 ):
     with torch.no_grad():