Handle empty change indices in SAM's mask to rle conversion (huggingf…

…ace#35665)

* Handle empty change indices in RLE conversion for masks

* [test] Add unit tests for RLE encoding of masks in SamProcessor

* [test] Update RLE conversion tests to use TensorFlow implementation

* [test] Fix formatting in SamProcessorTest according to check_code_quality action

* [test] Fix formatting in SamProcessorTest according to check_code_quality

* [test] Refactored rle test cases into one test and used tf tensors in tf test cases

* [test] Fix: removed self parameter from refactored methods

* [test] Removed nested methods in run-length encoding tests for PyTorch and TensorFlow

* [test] Added description to individual to run-length encoding tests for PyTorch and TensorFlow.

src/transformers/models/sam/image_processing_sam.py

@@ -1373,6 +1373,14 @@ def _mask_to_rle_pytorch(input_mask: "torch.Tensor"):
     out = []
     for i in range(batch_size):
         cur_idxs = change_indices[change_indices[:, 0] == i, 1] + 1
+        if len(cur_idxs) == 0:
+            # No changes => either all 0 or all 1
+            # If the entire mask is 0, RLE is [height*width] or if the entire mask is 1, RLE is [0, height*width].
+            if input_mask[i, 0] == 0:
+                out.append({"size": [height, width], "counts": [height * width]})
+            else:
+                out.append({"size": [height, width], "counts": [0, height * width]})
+            continue
         btw_idxs = cur_idxs[1:] - cur_idxs[:-1]
         counts = [] if input_mask[i, 0] == 0 else [0]
         counts += [cur_idxs[0].item()] + btw_idxs.tolist() + [height * width - cur_idxs[-1]]
@@ -1396,6 +1404,14 @@ def _mask_to_rle_tf(input_mask: "tf.Tensor"):
     out = []
     for i in range(batch_size):
         cur_idxs = change_indices[change_indices[:, 0] == i, 1] + 1
+        if len(cur_idxs) == 0:
+            # No changes => either all 0 or all 1
+            # If the entire mask is 0, RLE is [height*width] or if the entire mask is 1, RLE is [0, height*width].
+            if input_mask[i, 0] == 0:
+                out.append({"size": [height, width], "counts": [height * width]})
+            else:
+                out.append({"size": [height, width], "counts": [0, height * width]})
+            continue
         btw_idxs = cur_idxs[1:] - cur_idxs[:-1]
         counts = [] if input_mask[i, 0] == 0 else [0]
         counts += [cur_idxs[0].item()] + btw_idxs.tolist() + [height * width - cur_idxs[-1]]

tests/models/sam/test_processor_sam.py

@@ -37,9 +37,13 @@
 if is_torch_available():
     import torch
 
+    from transformers.models.sam.image_processing_sam import _mask_to_rle_pytorch
+
 if is_tf_available():
     import tensorflow as tf
 
+    from transformers.models.sam.image_processing_sam import _mask_to_rle_tf
+
 
 @require_vision
 @require_torchvision
@@ -161,6 +165,42 @@ def test_post_process_masks(self):
         with self.assertRaises(ValueError):
             masks = processor.post_process_masks(dummy_masks, np.array(original_sizes), np.array(reshaped_input_size))
 
+    def test_rle_encoding(self):
+        """
+        Test the run-length encoding function.
+        """
+        # Test that a mask of all zeros returns a single run [height * width].
+        input_mask = torch.zeros((1, 2, 2), dtype=torch.long)  # shape: 1 x 2 x 2
+        rle = _mask_to_rle_pytorch(input_mask)
+
+        self.assertEqual(len(rle), 1)
+        self.assertEqual(rle[0]["size"], [2, 2])
+        # For a 2x2 all-zero mask, we expect a single run of length 4:
+        self.assertEqual(rle[0]["counts"], [4])
+
+        # Test that a mask of all ones returns [0, height * width].
+        input_mask = torch.ones((1, 2, 2), dtype=torch.long)  # shape: 1 x 2 x 2
+        rle = _mask_to_rle_pytorch(input_mask)
+
+        self.assertEqual(len(rle), 1)
+        self.assertEqual(rle[0]["size"], [2, 2])
+        # For a 2x2 all-one mask, we expect two runs: [0, 4].
+        self.assertEqual(rle[0]["counts"], [0, 4])
+
+        # Test a mask with mixed 0s and 1s to ensure the run-length encoding is correct.
+        # Example mask:
+        # Row 0: [0, 1]
+        # Row 1: [1, 1]
+        # This is shape (1, 2, 2).
+        # Flattened in Fortran order -> [0, 1, 1, 1].
+        # The RLE for [0,1,1,1] is [1, 3].
+        input_mask = torch.tensor([[[0, 1], [1, 1]]], dtype=torch.long)
+        rle = _mask_to_rle_pytorch(input_mask)
+
+        self.assertEqual(len(rle), 1)
+        self.assertEqual(rle[0]["size"], [2, 2])
+        self.assertEqual(rle[0]["counts"], [1, 3])  # 1 zero, followed by 3 ones
+
 
 @require_vision
 @require_tf
@@ -244,6 +284,42 @@ def test_post_process_masks(self):
                 dummy_masks, np.array(original_sizes), np.array(reshaped_input_size), return_tensors="tf"
             )
 
+    def test_rle_encoding(self):
+        """
+        Test the run-length encoding function.
+        """
+        # Test that a mask of all zeros returns a single run [height * width].
+        input_mask = tf.zeros((1, 2, 2), dtype=tf.int64)  # shape: 1 x 2 x 2
+        rle = _mask_to_rle_tf(input_mask)
+
+        self.assertEqual(len(rle), 1)
+        self.assertEqual(rle[0]["size"], [2, 2])
+        # For a 2x2 all-zero mask, we expect a single run of length 4:
+        self.assertEqual(rle[0]["counts"], [4])
+
+        # Test that a mask of all ones returns [0, height * width].
+        input_mask = tf.ones((1, 2, 2), dtype=tf.int64)  # shape: 1 x 2 x 2
+        rle = _mask_to_rle_tf(input_mask)
+
+        self.assertEqual(len(rle), 1)
+        self.assertEqual(rle[0]["size"], [2, 2])
+        # For a 2x2 all-one mask, we expect two runs: [0, 4].
+        self.assertEqual(rle[0]["counts"], [0, 4])
+
+        # Test a mask with mixed 0s and 1s to ensure the run-length encoding is correct.
+        # Example mask:
+        # Row 0: [0, 1]
+        # Row 1: [1, 1]
+        # This is shape (1, 2, 2).
+        # Flattened in Fortran order -> [0, 1, 1, 1].
+        # The RLE for [0,1,1,1] is [1, 3].
+        input_mask = tf.tensor([[[0, 1], [1, 1]]], dtype=tf.int64)
+        rle = _mask_to_rle_tf(input_mask)
+
+        self.assertEqual(len(rle), 1)
+        self.assertEqual(rle[0]["size"], [2, 2])
+        self.assertEqual(rle[0]["counts"], [1, 3])  # 1 zero, followed by 3 ones
+
 
 @require_vision
 @require_torchvision