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tests/models/sam2/test_processor_sam2.py

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+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import unittest
+
+import numpy as np
+
+from transformers.testing_utils import (
+    require_torch,
+    require_torchvision,
+    require_vision,
+)
+from transformers.utils import is_torch_available, is_vision_available
+
+from ...test_processing_common import ProcessorTesterMixin
+
+
+if is_vision_available():
+    from transformers import Sam2Processor
+
+if is_torch_available():
+    import torch
+
+
+@require_vision
+@require_torchvision
+class Sam2ProcessorTest(ProcessorTesterMixin, unittest.TestCase):
+    processor_class = Sam2Processor
+
+    def prepare_image_inputs(self):
+        """This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True,
+        or a list of PyTorch tensors if one specifies torchify=True.
+        """
+        image_inputs = torch.randint(0, 256, size=(1, 3, 30, 400), dtype=torch.uint8)
+        # image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs]
+        return image_inputs
+
+    def prepare_mask_inputs(self):
+        """This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True,
+        or a list of PyTorch tensors if one specifies torchify=True.
+        """
+        mask_inputs = torch.randint(0, 256, size=(1, 30, 400), dtype=torch.uint8)
+        # mask_inputs = [Image.fromarray(x) for x in mask_inputs]
+        return mask_inputs
+
+    def test_image_processor_no_masks(self):
+        image_processor = self.get_component("image_processor")
+        processor = self.get_processor()
+
+        image_input = self.prepare_image_inputs()
+
+        input_feat_extract = image_processor(image_input)
+        input_processor = processor(images=image_input)
+
+        for key in input_feat_extract.keys():
+            if key == "pixel_values":
+                for input_feat_extract_item, input_processor_item in zip(
+                    input_feat_extract[key], input_processor[key]
+                ):
+                    np.testing.assert_array_equal(input_feat_extract_item, input_processor_item)
+            else:
+                self.assertEqual(input_feat_extract[key], input_processor[key])
+
+        for image in input_feat_extract.pixel_values:
+            self.assertEqual(image.shape, (3, 1024, 1024))
+
+        for original_size in input_feat_extract.original_sizes:
+            np.testing.assert_array_equal(original_size, np.array([30, 400]))
+
+    def test_image_processor_with_masks(self):
+        image_processor = self.get_component("image_processor")
+
+        processor = self.get_processor()
+
+        image_input = self.prepare_image_inputs()
+        mask_input = self.prepare_mask_inputs()
+
+        input_feat_extract = image_processor(images=image_input, segmentation_maps=mask_input, return_tensors="pt")
+        input_processor = processor(images=image_input, segmentation_maps=mask_input, return_tensors="pt")
+
+        for key in input_feat_extract.keys():
+            self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2)
+
+        for label in input_feat_extract.labels:
+            self.assertEqual(label.shape, (256, 256))
+
+    @require_torch
+    def test_post_process_masks(self):
+        processor = self.get_processor()
+        dummy_masks = [torch.ones((1, 3, 5, 5))]
+
+        original_sizes = [[1764, 2646]]
+
+        masks = processor.post_process_masks(dummy_masks, original_sizes)
+        self.assertEqual(masks[0].shape, (1, 3, 1764, 2646))
+
+        masks = processor.post_process_masks(dummy_masks, torch.tensor(original_sizes))
+        self.assertEqual(masks[0].shape, (1, 3, 1764, 2646))
+
+        # should also work with np
+        dummy_masks = [np.ones((1, 3, 5, 5))]
+        masks = processor.post_process_masks(dummy_masks, np.array(original_sizes))
+
+        self.assertEqual(masks[0].shape, (1, 3, 1764, 2646))
+
+        dummy_masks = [[1, 0], [0, 1]]
+        with self.assertRaises(TypeError):
+            masks = processor.post_process_masks(dummy_masks, np.array(original_sizes))