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