198 lines
8.1 KiB
Python
198 lines
8.1 KiB
Python
# coding=utf-8
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# Copyright 2025 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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"""Fast Video processor class for InternS1."""
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from typing import Optional, Union
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from transformers.image_processing_utils import BatchFeature
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from transformers.image_utils import (
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OPENAI_CLIP_MEAN,
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OPENAI_CLIP_STD,
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SizeDict,
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)
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from transformers.processing_utils import Unpack, VideosKwargs
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from transformers.utils import (
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TensorType,
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is_torch_available,
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is_torchvision_available,
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is_torchvision_v2_available,
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is_vision_available,
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)
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from transformers.utils.import_utils import requires
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from transformers.video_processing_utils import BaseVideoProcessor
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from transformers.video_utils import VideoMetadata, group_videos_by_shape, reorder_videos
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if is_torchvision_available():
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if is_torchvision_v2_available():
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from torchvision.transforms.v2 import functional as F
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else:
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from torchvision.transforms import functional as F
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if is_torch_available():
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import torch
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if is_vision_available():
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from transformers.image_utils import PILImageResampling
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class InternS1VideoProcessorInitKwargs(VideosKwargs):
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initial_shift: Union[bool, float, int]
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@requires(backends=("torchvision",))
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class InternS1VideoProcessor(BaseVideoProcessor):
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resample = PILImageResampling.BICUBIC
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image_mean = OPENAI_CLIP_MEAN
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image_std = OPENAI_CLIP_STD
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size = {"height": 384, "width": 384}
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do_resize = True
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do_rescale = True
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do_normalize = True
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do_convert_rgb = True
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initial_shift = True
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do_sample_frames = False # Set to False for BC, recommended to set `True` in new models
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valid_kwargs = InternS1VideoProcessorInitKwargs
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model_input_names = ["pixel_values_videos"]
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def __init__(self, **kwargs: Unpack[InternS1VideoProcessorInitKwargs]):
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super().__init__(**kwargs)
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def sample_frames(
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self,
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video: "torch.Tensor",
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metadata: Optional[Union[VideoMetadata, dict]] = None,
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num_frames: Optional[int] = None,
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fps: Optional[Union[int, float]] = None,
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initial_shift: Optional[Union[bool, float, int]] = None,
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):
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"""
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Default sampling function which uniformly samples the desired number of frames between 0 and total number of frames.
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If `fps` is passed along with metadata, `fps` frames per second are sampled uniformty. Arguments `num_frames`
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and `fps` are mutually exclusive.
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Args:
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video (`torch.Tensor`):
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Video that need to be sampled.
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metadata (`VideoMetadata`, *optional*):
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Metadata of the video containing information about total duration, fps and total number of frames.
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num_frames (`int`, *optional*):
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Maximum number of frames to sample. Defaults to `self.num_frames`.
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fps (`int` or `float`, *optional*):
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Target frames to sample per second. Defaults to `self.fps`.
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initial_shift (`bool`, `float` or `int`, defaults to `self.initial_shift`):
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The initial shift to apply when sampling frames. If `True`, the shift is set so that frames are sampled from the middle of the video.
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Returns:
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torch.Tensor:
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Sampled video frames.
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"""
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num_frames = num_frames if num_frames is not None else self.num_frames
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initial_shift = initial_shift if initial_shift is not None else self.initial_shift
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total_num_frames = video.shape[0]
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# If num_frames is not given but fps is, calculate num_frames from fps
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if num_frames is None and fps is not None:
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if metadata is None:
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raise ValueError(
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"Asked to sample `fps` frames per second but no video metadata was provided which is required when sampling with `fps`. "
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"Please pass in `VideoMetadata` object or use a fixed `num_frames` per input video"
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)
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num_frames = int(total_num_frames / metadata["fps"] * fps)
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if initial_shift is True:
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initial_shift = total_num_frames / num_frames / 2
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if num_frames > total_num_frames:
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raise ValueError(
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f"Video can't be sampled. The `num_frames={num_frames}` exceeds `total_num_frames={total_num_frames}`. "
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)
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indices = torch.arange(initial_shift, total_num_frames, total_num_frames / num_frames).int()
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video = video[indices].contiguous()
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return video
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def _preprocess(
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self,
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videos: list["torch.Tensor"],
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video_metadata: Union[list[VideoMetadata], list[dict]],
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do_convert_rgb: bool,
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do_resize: bool,
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size: SizeDict,
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size_divisor: Optional[int],
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interpolation: Optional["F.InterpolationMode"],
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do_center_crop: bool,
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crop_size: SizeDict,
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do_rescale: bool,
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do_pad: bool,
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rescale_factor: float,
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do_normalize: bool,
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image_mean: Optional[Union[float, list[float]]],
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image_std: Optional[Union[float, list[float]]],
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do_sample_frames: Optional[bool] = None,
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fps: Optional[Union[int, float]] = None,
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num_frames: Optional[int] = None,
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initial_shift: Optional[Union[bool, float, int]] = None,
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return_tensors: Optional[Union[str, TensorType]] = None,
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device: Optional["torch.Tensor"] = None,
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**kwargs
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) -> BatchFeature:
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if do_sample_frames:
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# Sample video frames
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videos = [
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self.sample_frames(video, metadata, fps=fps, num_frames=num_frames, initial_shift=initial_shift)
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for video, metadata in zip(videos, video_metadata)
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]
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# We need to sample frames first before moving to device, if `do_sample_frames=True`. Otherwise
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# moving the whole video incurs high GPU mem usage for long videos
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if device is not None:
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videos = [video.to(device) for video in videos]
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# Group videos by size for batched resizing
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grouped_videos, grouped_videos_index = group_videos_by_shape(videos)
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resized_videos_grouped = {}
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for shape, stacked_videos in grouped_videos.items():
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if do_convert_rgb:
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stacked_videos = self.convert_to_rgb(stacked_videos)
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if do_resize:
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stacked_videos = self.resize(
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stacked_videos, size=size, size_divisor=size_divisor, interpolation=interpolation
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)
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resized_videos_grouped[shape] = stacked_videos
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resized_videos = reorder_videos(resized_videos_grouped, grouped_videos_index)
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# Group videos by size for further processing
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# Needed in case do_resize is False, or resize returns videos with different sizes
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grouped_videos, grouped_videos_index = group_videos_by_shape(resized_videos)
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processed_videos_grouped = {}
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for shape, stacked_videos in grouped_videos.items():
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if do_center_crop:
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stacked_videos = self.center_crop(stacked_videos, crop_size)
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# Fused rescale and normalize
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stacked_videos = self.rescale_and_normalize(
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stacked_videos, do_rescale, rescale_factor, do_normalize, image_mean, image_std
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)
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processed_videos_grouped[shape] = stacked_videos
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processed_videos = reorder_videos(processed_videos_grouped, grouped_videos_index)
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processed_videos = torch.stack(processed_videos, dim=0) if return_tensors else processed_videos
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return BatchFeature(data={"pixel_values_videos": processed_videos}, tensor_type=return_tensors)
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__all__ = ["InternS1VideoProcessor"]
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