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109 lines
4.1 KiB
Python
109 lines
4.1 KiB
Python
# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import torch
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from transformers.image_utils import PILImageResampling
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from transformers.testing_utils import require_torch, require_vision
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from transformers.utils import is_vision_available
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from transformers.utils.import_utils import is_torchvision_greater_or_equal
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from ...test_processing_common import ProcessorTesterMixin, url_to_local_path
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if is_vision_available():
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from transformers import LlavaOnevisionVideoProcessor, VideoPrismProcessor, VideoPrismTokenizer
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TENNIS_VIDEO_URL = "https://huggingface.co/datasets/hf-internal-testing/test-videos/resolve/main/tennis_320x240.mp4"
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NUM_FRAMES = 16
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FRAME_SIZE = 288
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# torchvision >= 0.27 supports native Lanczos; older versions fall back to BICUBIC in TorchvisionBackend.resize.
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# Golden values computed from tennis_320x240.mp4 (320x240, 16 frames) resized to 288x288.
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EXPECTED_TENNIS_PIXEL_SLICE_LANCZOS = torch.tensor(
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[
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[0.08627451211214066, 0.0941176563501358, 0.2352941334247589],
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[0.062745101749897, 0.09019608050584793, 0.24313727021217346],
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[0.0784313753247261, 0.1098039299249649, 0.2666666805744171],
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]
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)
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# BICUBIC values are approximate; only LANCZOS path is tested on torchvision >= 0.27.
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EXPECTED_TENNIS_PIXEL_SLICE_BICUBIC = torch.tensor(
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[
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[0.08627451211214066, 0.0941176563501358, 0.2352941334247589],
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[0.062745101749897, 0.09019608050584793, 0.24313727021217346],
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[0.0784313753247261, 0.1098039299249649, 0.2666666805744171],
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]
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)
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def expected_tennis_pixel_slice():
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if is_torchvision_greater_or_equal("0.27"):
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return EXPECTED_TENNIS_PIXEL_SLICE_LANCZOS
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return EXPECTED_TENNIS_PIXEL_SLICE_BICUBIC
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@require_vision
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@require_torch
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class VideoPrismProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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processor_class = VideoPrismProcessor
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video_text_kwargs_max_length = 64
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@classmethod
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def setUpClass(cls):
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cls.tennis_video = url_to_local_path(TENNIS_VIDEO_URL)
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super().setUpClass()
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@classmethod
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def _setup_tokenizer(cls):
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return VideoPrismTokenizer.from_pretrained("google/videoprism-lvt-base-f16r288", revision="refs/pr/2")
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@classmethod
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def _setup_video_processor(cls):
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return LlavaOnevisionVideoProcessor(
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resample=PILImageResampling.LANCZOS,
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size={"height": FRAME_SIZE, "width": FRAME_SIZE},
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do_normalize=False,
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)
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def test_processor_video_tennis_video(self):
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"""VideoPrismProcessor on tennis.mp4 matches video_processor and a golden pixel slice."""
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video_processor = self._setup_video_processor()
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processor = self.processor_class(
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video_processor=video_processor,
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tokenizer=self._setup_tokenizer(),
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)
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video_kwargs = {"do_sample_frames": True, "num_frames": NUM_FRAMES}
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video_only = video_processor(videos=self.tennis_video, return_tensors="pt", **video_kwargs)
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processor_out = processor(videos=self.tennis_video, return_tensors="pt", **video_kwargs)
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pixel_values_videos = processor_out["pixel_values_videos"]
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self.assertEqual(pixel_values_videos.shape[1], NUM_FRAMES)
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self.assertEqual(pixel_values_videos.shape[-2:], (FRAME_SIZE, FRAME_SIZE))
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torch.testing.assert_close(
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video_only["pixel_values_videos"],
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processor_out["pixel_values_videos"],
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rtol=1e-4,
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atol=1e-4,
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)
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torch.testing.assert_close(
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pixel_values_videos[0, 0, 0, 144:147, 144:147],
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expected_tennis_pixel_slice(),
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rtol=1e-4,
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atol=1e-4,
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)
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