init

transformers/tests/models/smolvlm/test_video_processing_smolvlm.py

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+# coding=utf-8
+# Copyright 2025 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_STANDARD_MEAN, IMAGENET_STANDARD_STD
+from transformers.testing_utils import require_torch, require_vision
+from transformers.utils import is_torchvision_available, is_vision_available
+
+from ...test_video_processing_common import VideoProcessingTestMixin, prepare_video_inputs
+
+
+if is_vision_available():
+    if is_torchvision_available():
+        from transformers import SmolVLMVideoProcessor
+
+
+class SmolVLMVideoProcessingTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=5,
+        num_frames=8,
+        num_channels=3,
+        min_resolution=30,
+        max_resolution=80,
+        do_resize=True,
+        size=None,
+        do_normalize=True,
+        image_mean=IMAGENET_STANDARD_MEAN,
+        image_std=IMAGENET_STANDARD_STD,
+        do_convert_rgb=True,
+    ):
+        size = size if size is not None else {"longest_edge": 20}
+        self.parent = parent
+        self.batch_size = batch_size
+        self.num_frames = num_frames
+        self.num_channels = num_channels
+        self.min_resolution = min_resolution
+        self.max_resolution = max_resolution
+        self.do_resize = do_resize
+        self.size = size
+        self.max_image_size = size
+        self.do_normalize = do_normalize
+        self.image_mean = image_mean
+        self.image_std = image_std
+        self.do_convert_rgb = do_convert_rgb
+
+    def prepare_video_processor_dict(self):
+        return {
+            "do_resize": self.do_resize,
+            "size": self.size,
+            "do_normalize": self.do_normalize,
+            "image_mean": self.image_mean,
+            "image_std": self.image_std,
+            "do_convert_rgb": self.do_convert_rgb,
+            "max_image_size": self.max_image_size,
+        }
+
+    def expected_output_video_shape(self, videos):
+        return [
+            self.num_frames,
+            self.num_channels,
+            self.max_image_size["longest_edge"],
+            self.max_image_size["longest_edge"],
+        ]
+
+    def prepare_video_inputs(self, equal_resolution=False, return_tensors="pil"):
+        videos = 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,
+        )
+        return videos
+
+
+@require_torch
+@require_vision
+class SmolVLMVideoProcessingTest(VideoProcessingTestMixin, unittest.TestCase):
+    fast_video_processing_class = SmolVLMVideoProcessor if is_torchvision_available() else None
+    input_name = "pixel_values"
+
+    def setUp(self):
+        super().setUp()
+        self.video_processor_tester = SmolVLMVideoProcessingTester(self)
+
+    @property
+    def video_processor_dict(self):
+        return self.video_processor_tester.prepare_video_processor_dict()
+
+    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, {"longest_edge": 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})
+
+    # overwrite, SmolVLM requires to have metadata no matter how we sample
+    def test_call_sample_frames(self):
+        for video_processing_class in self.video_processor_list:
+            video_processing = video_processing_class(**self.video_processor_dict)
+
+            prev_num_frames = self.video_processor_tester.num_frames
+            self.video_processor_tester.num_frames = 8
+            video_inputs = self.video_processor_tester.prepare_video_inputs(
+                equal_resolution=False,
+                return_tensors="torch",
+            )
+
+            # Force set sampling to False. No sampling is expected even when `num_frames` exists
+            video_processing.do_sample_frames = False
+
+            encoded_videos = video_processing(video_inputs[0], return_tensors="pt", num_frames=3)[self.input_name]
+            encoded_videos_batched = video_processing(video_inputs, return_tensors="pt", num_frames=3)[self.input_name]
+            self.assertEqual(encoded_videos.shape[1], 8)
+            self.assertEqual(encoded_videos_batched.shape[1], 8)
+
+            # Set sampling to True. Video frames should be sampled with `num_frames` in the output
+            video_processing.do_sample_frames = True
+            metadata = [[{"duration": 2.0, "total_num_frames": 8, "fps": 4}]]
+            batched_metadata = metadata * len(video_inputs)
+
+            encoded_videos = video_processing(
+                video_inputs[0], return_tensors="pt", num_frames=6, fps=3, video_metadata=metadata
+            )[self.input_name]
+            encoded_videos_batched = video_processing(
+                video_inputs, return_tensors="pt", num_frames=6, fps=3, video_metadata=batched_metadata
+            )[self.input_name]
+            self.assertEqual(encoded_videos.shape[1], 6)
+            self.assertEqual(encoded_videos_batched.shape[1], 6)
+
+            # Assign back the actual num frames in tester
+            self.video_processor_tester.num_frames = prev_num_frames