PyTorchVideo教程01快速实现视频动作识别

Posted 2022-08-06 CV-杨帆

目录

• 1 PyTorchVideo介绍
• • 1.1 参考资料：
  • 1.2 介绍
• 3 GPU平台
• 4 安装pytorchvideo
• 5 demo演示

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