mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-03 21:00:08 +06:00
27459025b8
* apply updates smolVLM (still needs workaround for chat template) * add other models * dump qwen omni for now, come back later * port qwen omni from their impl * wait, all qwens sample videos in same way! * clean up * make smolvlm backwards compatible and fix padding * dix some tests * fox smolvlm tests * more clean up and test fixing * delete unused arg * fix * address comments * style * fix test
117 lines
4.3 KiB
Python
117 lines
4.3 KiB
Python
# coding=utf-8
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# Copyright 2025 HuggingFace Inc.
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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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from transformers.image_utils import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
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from transformers.testing_utils import require_torch, require_vision
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from transformers.utils import is_torchvision_available, is_vision_available
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from ...test_video_processing_common import VideoProcessingTestMixin, prepare_video_inputs
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if is_vision_available():
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if is_torchvision_available():
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from transformers import InstructBlipVideoVideoProcessor
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class InstructBlipVideoVideoProcessingTester:
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def __init__(
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self,
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parent,
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batch_size=5,
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num_channels=3,
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num_frames=4,
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min_resolution=30,
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max_resolution=80,
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do_resize=True,
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size=None,
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do_normalize=True,
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image_mean=OPENAI_CLIP_MEAN,
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image_std=OPENAI_CLIP_STD,
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do_convert_rgb=True,
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):
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super().__init__()
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size = size if size is not None else {"height": 18, "width": 18}
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self.parent = parent
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self.batch_size = batch_size
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self.num_frames = num_frames
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self.num_channels = num_channels
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self.min_resolution = min_resolution
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self.max_resolution = max_resolution
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self.do_resize = do_resize
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self.size = size
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self.do_normalize = do_normalize
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self.image_mean = image_mean
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self.image_std = image_std
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self.do_convert_rgb = do_convert_rgb
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def prepare_video_processor_dict(self):
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return {
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"do_resize": self.do_resize,
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"size": self.size,
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"do_normalize": self.do_normalize,
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"image_mean": self.image_mean,
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"image_std": self.image_std,
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"do_convert_rgb": self.do_convert_rgb,
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}
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def expected_output_video_shape(self, images):
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return self.num_frames, self.num_channels, self.size["height"], self.size["width"]
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def prepare_video_inputs(self, equal_resolution=False, return_tensors="pil"):
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videos = prepare_video_inputs(
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batch_size=self.batch_size,
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num_frames=self.num_frames,
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num_channels=self.num_channels,
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min_resolution=self.min_resolution,
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max_resolution=self.max_resolution,
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equal_resolution=equal_resolution,
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return_tensors=return_tensors,
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)
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return videos
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@require_torch
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@require_vision
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class InstructBlipVideoProcessingTest(VideoProcessingTestMixin, unittest.TestCase):
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fast_video_processing_class = InstructBlipVideoVideoProcessor if is_torchvision_available() else None
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input_name = "pixel_values"
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def setUp(self):
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super().setUp()
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self.video_processor_tester = InstructBlipVideoVideoProcessingTester(self)
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@property
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def video_processor_dict(self):
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return self.video_processor_tester.prepare_video_processor_dict()
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def test_image_processor_properties(self):
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video_processing = self.fast_video_processing_class(**self.video_processor_dict)
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self.assertTrue(hasattr(video_processing, "do_resize"))
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self.assertTrue(hasattr(video_processing, "size"))
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self.assertTrue(hasattr(video_processing, "do_normalize"))
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self.assertTrue(hasattr(video_processing, "image_mean"))
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self.assertTrue(hasattr(video_processing, "image_std"))
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self.assertTrue(hasattr(video_processing, "do_convert_rgb"))
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def test_video_processor_from_dict_with_kwargs(self):
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video_processor = self.fast_video_processing_class.from_dict(self.video_processor_dict)
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self.assertEqual(video_processor.size, {"height": 18, "width": 18})
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video_processor = self.fast_video_processing_class.from_dict(self.video_processor_dict, size=42)
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self.assertEqual(video_processor.size, {"height": 42, "width": 42})
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