Beit postprocessing (#19099)

* add post_process_semantic_segmentation method to BeiTFeatureExtractor

docs/source/en/model_doc/beit.mdx

@@ -82,6 +82,7 @@ contributed by [kamalkraj](https://huggingface.co/kamalkraj). The original code
 
 [[autodoc]] BeitFeatureExtractor
     - __call__
+    - post_process_semantic_segmentation
 
 ## BeitModel
 

src/transformers/models/beit/feature_extraction_beit.py

@@ -14,7 +14,7 @@
 # limitations under the License.
 """Feature extractor class for BEiT."""
 
-from typing import Optional, Union
+from typing import List, Optional, Tuple, Union
 
 import numpy as np
 from PIL import Image
@@ -27,9 +27,12 @@ from ...image_utils import (
     ImageInput,
     is_torch_tensor,
 )
-from ...utils import TensorType, logging
+from ...utils import TensorType, is_torch_available, logging
 
 
+if is_torch_available():
+    import torch
+
 logger = logging.get_logger(__name__)
 
 
@@ -222,3 +225,44 @@ class BeitFeatureExtractor(FeatureExtractionMixin, ImageFeatureExtractionMixin):
         encoded_inputs = BatchFeature(data=data, tensor_type=return_tensors)
 
         return encoded_inputs
+
+    def post_process_semantic_segmentation(self, outputs, target_sizes: Union[TensorType, List[Tuple]] = None):
+        """
+        Converts the output of [`BeitForSemanticSegmentation`] into semantic segmentation maps. Only supports PyTorch.
+
+        Args:
+            outputs ([`BeitForSemanticSegmentation`]):
+                Raw outputs of the model.
+            target_sizes (`torch.Tensor` of shape `(batch_size, 2)` or `List[Tuple]` of length `batch_size`, *optional*):
+                Torch Tensor (or list) corresponding to the requested final size (h, w) of each prediction. If left to
+                None, predictions will not be resized.
+        Returns:
+            semantic_segmentation: `torch.Tensor` of shape `(batch_size, 2)` or `List[torch.Tensor]` of length
+            `batch_size`, where each item is a semantic segmentation map of of the corresponding target_sizes entry (if
+            `target_sizes` is specified). Each entry of each `torch.Tensor` correspond to a semantic class id.
+        """
+        logits = outputs.logits
+
+        if len(logits) != len(target_sizes):
+            raise ValueError("Make sure that you pass in as many target sizes as the batch dimension of the logits")
+
+        if target_sizes is not None and target_sizes.shape[1] != 2:
+            raise ValueError("Each element of target_sizes must contain the size (h, w) of each image of the batch")
+
+        semantic_segmentation = logits.argmax(dim=1)
+
+        # Resize semantic segmentation maps
+        if target_sizes is not None:
+            if is_torch_tensor(target_sizes):
+                target_sizes = target_sizes.numpy()
+
+            resized_maps = []
+            semantic_segmentation = semantic_segmentation.numpy()
+
+            for idx in range(len(semantic_segmentation)):
+                resized = self.resize(image=semantic_segmentation[idx], size=target_sizes[idx])
+                resized_maps.append(resized)
+
+            semantic_segmentation = [torch.Tensor(np.array(image)) for image in resized_maps]
+
+        return semantic_segmentation