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tests/models/videomt/test_video_processing_videomt.py

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+# Copyright 2026 HuggingFace Inc.
+#
+# 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
+
+from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
+from transformers.testing_utils import require_torch, require_torchvision, require_vision
+from transformers.utils import is_torch_available, is_torchvision_available, is_vision_available
+
+from ...test_video_processing_common import VideoProcessingTestMixin, prepare_video_inputs
+
+
+if is_torch_available():
+    import torch
+
+if is_vision_available():
+    if is_torchvision_available():
+        from transformers import VideomtVideoProcessor
+
+    if is_torch_available():
+        from transformers.models.videomt.modeling_videomt import VideomtForUniversalSegmentationOutput
+
+
+class VideomtVideoProcessingTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=5,
+        num_frames=8,
+        num_channels=3,
+        image_size=18,
+        min_resolution=30,
+        max_resolution=80,
+        do_resize=True,
+        size=None,
+        do_center_crop=False,
+        do_rescale=True,
+        rescale_factor=1 / 255,
+        do_normalize=True,
+        image_mean=IMAGENET_DEFAULT_MEAN,
+        image_std=IMAGENET_DEFAULT_STD,
+        do_convert_rgb=True,
+        num_queries=3,
+        num_classes=2,
+    ):
+        super().__init__()
+        size = size if size is not None else {"height": 20, "width": 20}
+        self.parent = parent
+        self.batch_size = batch_size
+        self.num_frames = num_frames
+        self.num_channels = num_channels
+        self.image_size = image_size
+        self.min_resolution = min_resolution
+        self.max_resolution = max_resolution
+        self.do_resize = do_resize
+        self.size = size
+        self.do_center_crop = do_center_crop
+        self.do_rescale = do_rescale
+        self.rescale_factor = rescale_factor
+        self.do_normalize = do_normalize
+        self.image_mean = image_mean
+        self.image_std = image_std
+        self.do_convert_rgb = do_convert_rgb
+        self.num_queries = num_queries
+        self.num_classes = num_classes
+
+    def prepare_video_processor_dict(self):
+        return {
+            "do_resize": self.do_resize,
+            "size": self.size,
+            "do_center_crop": self.do_center_crop,
+            "do_rescale": self.do_rescale,
+            "rescale_factor": self.rescale_factor,
+            "do_normalize": self.do_normalize,
+            "image_mean": self.image_mean,
+            "image_std": self.image_std,
+            "do_convert_rgb": self.do_convert_rgb,
+        }
+
+    def expected_output_video_shape(self, videos):
+        return self.num_frames, self.num_channels, self.size["height"], self.size["width"]
+
+    def prepare_video_inputs(self, equal_resolution=False, return_tensors="pil"):
+        return prepare_video_inputs(
+            batch_size=self.batch_size,
+            num_frames=self.num_frames,
+            num_channels=self.num_channels,
+            min_resolution=self.min_resolution,
+            max_resolution=self.max_resolution,
+            equal_resolution=equal_resolution,
+            return_tensors=return_tensors,
+        )
+
+    def prepare_fake_videomt_outputs(self, num_frames):
+        height, width = self.size["height"], self.size["width"]
+        return VideomtForUniversalSegmentationOutput(
+            masks_queries_logits=torch.randn((num_frames, self.num_queries, height, width)),
+            class_queries_logits=torch.randn((num_frames, self.num_queries, self.num_classes + 1)),
+        )
+
+
+@require_torch
+@require_vision
+@require_torchvision
+class VideomtVideoProcessingTest(VideoProcessingTestMixin, unittest.TestCase):
+    fast_video_processing_class = VideomtVideoProcessor if is_torchvision_available() else None
+    input_name = "pixel_values_videos"
+
+    def setUp(self):
+        super().setUp()
+        self.video_processor_tester = VideomtVideoProcessingTester(self)
+
+    @property
+    def video_processor_dict(self):
+        return self.video_processor_tester.prepare_video_processor_dict()
+
+    def test_video_processor_properties(self):
+        video_processing = self.fast_video_processing_class(**self.video_processor_dict)
+        self.assertTrue(hasattr(video_processing, "do_resize"))
+        self.assertTrue(hasattr(video_processing, "size"))
+        self.assertTrue(hasattr(video_processing, "do_center_crop"))
+        self.assertTrue(hasattr(video_processing, "do_normalize"))
+        self.assertTrue(hasattr(video_processing, "image_mean"))
+        self.assertTrue(hasattr(video_processing, "image_std"))
+        self.assertTrue(hasattr(video_processing, "do_convert_rgb"))
+        self.assertTrue(hasattr(video_processing, "model_input_names"))
+        self.assertIn("pixel_values_videos", video_processing.model_input_names)
+
+    def test_video_processor_from_dict_with_kwargs(self):
+        video_processor = self.fast_video_processing_class.from_dict(self.video_processor_dict)
+        self.assertEqual(video_processor.size, {"height": 20, "width": 20})
+
+        video_processor = self.fast_video_processing_class.from_dict(self.video_processor_dict, size=42)
+        self.assertEqual(video_processor.size, {"height": 42, "width": 42})
+
+    def test_post_process_semantic_segmentation(self):
+        video_processor = self.fast_video_processing_class(**self.video_processor_dict)
+
+        num_frames = 4
+        target_sizes = [(32, 32)] * num_frames
+        outputs = self.video_processor_tester.prepare_fake_videomt_outputs(num_frames)
+
+        segmentation = video_processor.post_process_semantic_segmentation(outputs, target_sizes)
+
+        self.assertEqual(len(segmentation), num_frames)
+        for seg_map in segmentation:
+            self.assertIsInstance(seg_map, torch.Tensor)
+            self.assertEqual(seg_map.shape, (32, 32))
+
+    def test_post_process_instance_segmentation(self):
+        video_processor = self.fast_video_processing_class(**self.video_processor_dict)
+
+        num_frames = 4
+        target_sizes = [(32, 32)] * num_frames
+        outputs = self.video_processor_tester.prepare_fake_videomt_outputs(num_frames)
+
+        results = video_processor.post_process_instance_segmentation(outputs, target_sizes)
+
+        self.assertEqual(len(results), num_frames)
+        for el in results:
+            self.assertIn("segmentation", el)
+            self.assertIn("segments_info", el)
+            self.assertIsInstance(el["segments_info"], list)
+            self.assertEqual(el["segmentation"].shape, (32, 32))
+
+    def test_post_process_panoptic_segmentation(self):
+        video_processor = self.fast_video_processing_class(**self.video_processor_dict)
+
+        num_frames = 4
+        target_sizes = [(32, 32)] * num_frames
+        outputs = self.video_processor_tester.prepare_fake_videomt_outputs(num_frames)
+
+        results = video_processor.post_process_panoptic_segmentation(outputs, target_sizes)
+
+        self.assertEqual(len(results), num_frames)
+        for el in results:
+            self.assertIn("segmentation", el)
+            self.assertIn("segments_info", el)
+            self.assertIsInstance(el["segments_info"], list)
+            self.assertEqual(el["segmentation"].shape, (32, 32))