mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2026-04-23 00:17:25 +08:00
CSWYF3634076
9286403570
* support v1 loader * remove useless code * remove useless * [Model] support Qwen3VL images success * [Model] support Qwen3VL rope_3d * [Model] support Qwen3VL remove log * [Model] support Qwen3VL RL * [Model] support Qwen3VL tp * [Model] support Qwen3VL video * [Model] support Qwen3VL fix ernievl * [Model] support Qwen3VL fix get_image_boundaries.cc array out of bounds * [Model] support Qwen3VL fix multi card * [Model] support Qwen3VL file close * [Model] support Qwen3VL fix ce * [Model] support Qwen3VL fix unittest * [Model] support Qwen3VL add unittest --------- Co-authored-by: Ayakouji <yuhongh@qq.com>
101 lines
4.0 KiB
Python
101 lines
4.0 KiB
Python
"""
|
|
# Copyright (c) 2025 PaddlePaddle Authors. 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.
|
|
"""
|
|
|
|
from typing import Optional, Union
|
|
|
|
import numpy as np
|
|
|
|
from fastdeploy.utils import data_processor_logger
|
|
|
|
from .image_processor import ceil_by_factor, floor_by_factor
|
|
|
|
|
|
def sample_frames(
|
|
frame_factor: int,
|
|
min_frames: int,
|
|
max_frames: int,
|
|
metadata: Optional[dict] = None,
|
|
fps: Optional[Union[int, float]] = -1,
|
|
num_frames: Optional[int] = -1,
|
|
):
|
|
"""
|
|
Sample frames from video according to specified criteria.
|
|
|
|
Args:
|
|
frame_factor: Ensure sampled frames are multiples of this factor
|
|
min_frames: Minimum number of frames to sample
|
|
max_frames: Maximum number of frames to sample
|
|
metadata: Video metadata containing fps information
|
|
fps: Target frames per second for sampling
|
|
num_frames: Exact number of frames to sample
|
|
|
|
Returns:
|
|
np.ndarray: Sampled video frames
|
|
|
|
Raises:
|
|
ValueError: If both fps and num_frames are specified,
|
|
or if required metadata is missing,
|
|
or if requested frames exceed available frames
|
|
"""
|
|
if fps > 0 and num_frames > 0:
|
|
raise ValueError("`num_frames` and `fps` are mutually exclusive arguments, please use only one!")
|
|
|
|
total_num_frames = metadata["num_of_frame"]
|
|
|
|
# If num_frames is not given but fps is, calculate num_frames from fps
|
|
if num_frames > 0:
|
|
num_frames = round(num_frames / frame_factor) * frame_factor
|
|
elif fps > 0:
|
|
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"
|
|
)
|
|
# max_frames = math.floor(min(max_frames, total_num_frames) / frame_factor) * frame_factor
|
|
min_frames = ceil_by_factor(min_frames, frame_factor)
|
|
max_frames = floor_by_factor(min(max_frames, total_num_frames), frame_factor)
|
|
|
|
num_frames = total_num_frames / metadata["fps"] * fps
|
|
|
|
if num_frames > total_num_frames:
|
|
data_processor_logger.warning(f"smart_nframes: nframes[{num_frames}] > total_frames[{total_num_frames}]")
|
|
|
|
num_frames = min(min(max(num_frames, min_frames), max_frames), total_num_frames)
|
|
num_frames = floor_by_factor(num_frames, frame_factor)
|
|
|
|
if num_frames > total_num_frames:
|
|
raise ValueError(
|
|
f"Video can't be sampled. The inferred `num_frames={num_frames}` exceeds `total_num_frames={total_num_frames}`. "
|
|
"Decrease `num_frames` or `fps` for sampling."
|
|
)
|
|
|
|
# Hack code ensures that num_frames can always be divided by 4
|
|
# due to sched/resource_manager_v1.py 中 grid_thw.extend([[2, h, w]] * (t // 2))
|
|
if num_frames > 2 and num_frames % 4 != 0:
|
|
num_frames = (num_frames // 4) * 4 # 向下取整到 4 的倍数
|
|
total_num_frames = (total_num_frames // 4) * 4
|
|
num_frames = min(min(max(num_frames, min_frames), max_frames), total_num_frames)
|
|
|
|
# Calculate frame indices based on sampling strategy
|
|
if num_frames > 0:
|
|
# Evenly spaced sampling for target frame count
|
|
indices = np.arange(0, total_num_frames, total_num_frames / num_frames).astype(np.int32)
|
|
else:
|
|
# Keep all frames if no sampling requested
|
|
indices = np.arange(0, total_num_frames).astype(np.int32)
|
|
|
|
return indices
|