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目录
1 PyTorchVideo介绍
PyTorchVideo是Facebook2021年4月份发布，主要针对视频深度学习应用。
b站：https://www.bilibili.com/video/BV1QT411j7M3
1.1 参考资料：
pytorchvideo官网：https://pytorchvideo.org/
pytorchvideo Github：https://github.com/facebookresearch/pytorchvideo
Tutorials：https://pytorchvideo.org/docs/tutorial_torchhub_inference
深入浅出PyTorch：8.3 PyTorchVideo简介
PyTorchVideo: 针对视频深度学习，你想要的它都有：https://zhuanlan.zhihu.com/p/390909705
PyTorchVideo: A Deep Learning Library for Video Understanding:https://arxiv.org/pdf/2111.09887.pdf
1.2 介绍
近几年来，随着传播媒介和视频平台的发展，视频正在取代图片成为下一代的主流媒体，这也使得有关视频的深度学习模型正在获得越来越多的关注。
然而，有关视频的深度学习模型仍然有着许多缺点：
计算资源耗费更多，并且没有高质量的 model zoo，不能像图片一样进行迁移学习和论文复现。
数据集处理较麻烦，但没有一个很好的视频处理工具。
随着多模态越来越流行，亟需一个工具来处理其他模态。
除此之外，还有部署优化等问题，为了解决这些问题，Meta推出了PyTorchVideo深度学习库（包含组件如Figure 1所示）。PyTorchVideo 是一个专注于视频理解工作的深度学习库。PytorchVideo 提供了加速视频理解研究所需的可重用、模块化和高效的组件。PyTorchVideo 是使用PyTorch开发的，支持不同的深度学习视频组件，如视频模型、视频数据集和视频特定转换。
正文开始之前先放一个demo，PyTorchVideo通过模型部署优化模组(accelerator)率先实现了移动端的实时视频动作识别（基于X3D模型），未来视频模型跑在移动端不再是梦想。
PyTorchVideo 移动端的实时视频动作识别
PyTorchVideo A deep learning library for video understanding
3 GPU平台
极链AI：https://cloud.videojj.com/auth/register?inviter=18452&activityChannel=student_invite
镜像快速搭建
4 安装pytorchvideo
cd /home pip install pytorchvideo wget https://dl.fbaipublicfiles.com/pyslowfast/dataset/class_names/kinetics_classnames.json wget https://dl.fbaipublicfiles.com/pytorchvideo/projects/archery.mp4
如果archery.mp4无法下载，可以先下载好，然后上传，视频资源我已经上传到了阿里云盘：
https://www.aliyundrive.com/s/xjzfmH3uoFB
我在csdn上也上传了视频资源：archery.mp4 行为识别 pytorchvideo demo演示视频（行为识别）
5 demo演示
需要提前准备好一个视频
开始搭建（使用Notebook，主要是查看中间的步骤）
import torch
import json
from torchvision.transforms import Compose, Lambda
from torchvision.transforms._transforms_video import (
CenterCropVideo,
NormalizeVideo,
)
from pytorchvideo.data.encoded_video import EncodedVideo
from pytorchvideo.transforms import (
ApplyTransformToKey,
ShortSideScale,
UniformTemporalSubsample,
UniformCropVideo
)
from typing import Dict
# Device on which to run the model
# Set to cuda to load on GPU
device = "cpu"
# Pick a pretrained model and load the pretrained weights
model_name = "slowfast_r50"
model = torch.hub.load("facebookresearch/pytorchvideo", model=model_name, pretrained=True)
# Set to eval mode and move to desired device
model = model.to(device)
model = model.eval()
with open("kinetics_classnames.json", "r") as f:
kinetics_classnames = json.load(f)
# Create an id to label name mapping
kinetics_id_to_classname = {
}
for k, v in kinetics_classnames.items():
kinetics_id_to_classname[v] = str(k).replace('"', "")
####################
# SlowFast transform
####################
side_size = 256
mean = [0.45, 0.45, 0.45]
std = [0.225, 0.225, 0.225]
crop_size = 256
num_frames = 32
sampling_rate = 2
frames_per_second = 30
alpha = 4
class PackPathway(torch.nn.Module):
"""
Transform for converting video frames as a list of tensors.
"""
def __init__(self):
super().__init__()
def forward(self, frames: torch.Tensor):
fast_pathway = frames
# Perform temporal sampling from the fast pathway.
slow_pathway = torch.index_select(
frames,
1,
torch.linspace(
0, frames.shape[1] - 1, frames.shape[1] // alpha
).long(),
)
frame_list = [slow_pathway, fast_pathway]
return frame_list
transform = ApplyTransformToKey(
key="video",
transform=Compose(
[
UniformTemporalSubsample(num_frames),
Lambda(lambda x: x/255.0),
NormalizeVideo(mean, std),
ShortSideScale(
size=side_size
),
CenterCropVideo(crop_size),
PackPathway()
]
),
)
# The duration of the input clip is also specific to the model.
clip_duration = (num_frames * sampling_rate)/frames_per_second
# Load the example video video_path = "archery.mp4" # Select the duration of the clip to load by specifying the start and end duration # The start_sec should correspond to where the action occurs in the video start_sec = 0 end_sec = start_sec + clip_duration # Initialize an EncodedVideo helper class video = EncodedVideo.from_path(video_path) # Load the desired clip video_data = video.get_clip(start_sec=start_sec, end_sec=end_sec) # Apply a transform to normalize the video input video_data = transform(video_data) # Move the inputs to the desired device inputs = video_data["video"] inputs = [i.to(device)[None, ...] for i in inputs]
# Pass the input clip through the model preds = model(inputs)
# Get the predicted classes
post_act = torch.nn.Softmax(dim=1)
preds = post_act(preds)
pred_classes = preds.topk(k=5).indices
# Map the predicted classes to the label names
pred_class_names = [kinetics_id_to_classname[int(i)] for i in pred_classes[0]]
print("Predicted labels: %s" % ", ".join(pred_class_names))
处理结果：
Predicted labels: archery, throwing axe, playing paintball, disc golfing, riding or walking with horse
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