Repository: huggingface/transformers
Pull request: Add an alternative scenario to EoMT `post_process_semantic_segmentation` in case `path_offsets` is None

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Fixes #42712

This PR adds a check if `path_offsets` is None to `post_process_semantic_segmentation` function of EoMT image processors.

If `path_offsets` is not None, it executes `merge_image_patches` function, and if it is None (which means that image was not split into patches), we just run the default image post processing scenario, same as in other vision models


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@yonigozlan @molbap

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Changed files:
filename: src/transformers/models/eomt/image_processing_eomt.py
patch: @@ -815,7 +815,19 @@ def post_process_semantic_segmentation(
 
         segmentation_logits = torch.einsum("bqc, bqhw -> bchw", masks_classes, masks_probs)
 
-        output_logits = self.merge_image_patches(segmentation_logits, patch_offsets, target_sizes, size)
+        if patch_offsets:
+            output_logits = self.merge_image_patches(segmentation_logits, patch_offsets, target_sizes, size)
+        else:
+            output_logits = []
+
+            for idx in range(len(segmentation_logits)):
+                resized_logits = torch.nn.functional.interpolate(
+                    segmentation_logits[idx].unsqueeze(dim=0),
+                    size=target_sizes[idx],
+                    mode="bilinear",
+                    align_corners=False,
+                )
+                output_logits.append(resized_logits[0])
 
         preds = [logit.argmax(dim=0) for logit in output_logits]
         return preds

filename: src/transformers/models/eomt/image_processing_eomt_fast.py
patch: @@ -385,7 +385,19 @@ def post_process_semantic_segmentation(
 
         segmentation_logits = torch.einsum("bqc, bqhw -> bchw", masks_classes, masks_probs)
 
-        output_logits = self.merge_image_patches(segmentation_logits, patch_offsets, target_sizes, size)
+        if patch_offsets:
+            output_logits = self.merge_image_patches(segmentation_logits, patch_offsets, target_sizes, size)
+        else:
+            output_logits = []
+
+            for idx in range(len(segmentation_logits)):
+                resized_logits = torch.nn.functional.interpolate(
+                    segmentation_logits[idx].unsqueeze(dim=0),
+                    size=target_sizes[idx],
+                    mode="bilinear",
+                    align_corners=False,
+                )
+                output_logits.append(resized_logits[0])
 
         preds = [logit.argmax(dim=0) for logit in output_logits]
         return preds