【slowfast 训练自己的数据集】自定义动作，制作自己的数据集，使用预训练模型进行训练，并检测其结果

你的名字 2022-09-03 05:23 652阅读 0赞

### 目录 ###

*  [前言][Link 1]
 *  [一，视频的处理][Link 2]
 *   *  [1.1 视频准备][1.1]
     *  [1.2 切割视频为图片][1.2]
     *  [1.3 使用faster rcnn自动框人][1.3 _faster rcnn]
     *  [1.4 via标注图片][1.4 via]
 *  [二，数据集文件][Link 3]
 *   *  [2.1 数据集文件总览][2.1]
     *  [2.2 annotations][]
     *   *  [2.2.1 ava\_train\_v2.2.csv][2.2.1 ava_train_v2.2.csv]
         *  [2.2.2 ava\_val\_v2.2.csv][2.2.2 ava_val_v2.2.csv]
         *  [2.2.3 ava\_val\_excluded\_timestamps\_v2.2.csv][2.2.3 ava_val_excluded_timestamps_v2.2.csv]
         *  [2.2.4 ava\_action\_list\_v2.2\_for\_activitynet\_2019.pbtxt][2.2.4 ava_action_list_v2.2_for_activitynet_2019.pbtxt]
         *  [2.2.5 ava\_detection\_train\_boxes\_and\_labels\_include\_negative\_v2.2.csv][2.2.5 ava_detection_train_boxes_and_labels_include_negative_v2.2.csv]
         *  [2.2.6 ava\_detection\_val\_boxes\_and\_labels.csv][2.2.6 ava_detection_val_boxes_and_labels.csv]
     *  [2.3 frame\_lists][2.3 frame_lists]
     *   *  [2.3.1 train.csv][]
         *  [2.3.2 val.csv][]
     *  [2.4 frames][]
 *  [三，slowfast 训练自己的数据集][slowfast]
 *   *  [3.1 预训练模型][3.1]
     *  [3.2 配置文件][3.2]
     *  [3.3 训练过程][3.3]
 *  [四 检测模型效果][Link 4]
 *   *  [4.2 创建json文件][4.2 _json]
     *  [4.2 创建yaml文件][4.2 _yaml]
     *  [4.3 修改ava\_helper.py][4.3 _ava_helper.py]
     *  [4.4 运行][4.4]

# 前言 #

终于到了这一步了，看了很久很久的slowfast，这次终于用slowfast训练了自己的数据集（只是用了个非常小的，非常小的数据集跑了一下），并且格式修改过程是手动修改的，训练过程所需要的yaml文件也是手动写出来的（后面会改成程序自动修改）

在看这篇训练自己的数据集博客之前，我觉得有必要了解一下之前我写一些博客：  
的slowfast的训练，slowfast的减少数据集

1, 【SlowFast复现】SlowFast Networks for Video Recognition复现代码 使用自己的视频进行demo检测: [https://blog.csdn.net/sinat\_28371057/article/details/119429163][https_blog.csdn.net_sinat_28371057_article_details_119429163]

2:【Faster RCNN & detectron2】detectron2实现Faster RCNN目标检测:[https://blog.csdn.net/sinat\_28371057/article/details/119429199][https_blog.csdn.net_sinat_28371057_article_details_119429199]

3，【faster rcnn 实现via的自动框人】使用detectron2中faster rcnn 算法生成人的坐标，将坐标导入via(VGG Image Annotator)中，实现自动框选出人的区域:[https://blog.csdn.net/sinat\_28371057/article/details/119429214][https_blog.csdn.net_sinat_28371057_article_details_119429214]

4，【ffmpeg裁剪视频faster rcnn自动检测 via】全自动实现ffmpeg将视频切割为图片帧，再使用faster rcnn将图片中的人检测出来，最后将检测结果转化为via可识别的csv格式:[https://blog.csdn.net/sinat\_28371057/article/details/119429241][https_blog.csdn.net_sinat_28371057_article_details_119429241]

5，【slowfast复现 训练】训练过程 制作ava数据集 复现 SlowFast Networks for Video Recognition 训练 train:[https://blog.csdn.net/sinat\_28371057/article/details/119429261][https_blog.csdn.net_sinat_28371057_article_details_119429261]

6，【slowfast 减少ava数据集】将ava数据集缩小到2个，对数据集做训练，然后进行检测，为训练自己的数据集做准备:[https://blog.csdn.net/sinat\_28371057/article/details/119429286][https_blog.csdn.net_sinat_28371057_article_details_119429286]

# 一，视频的处理 #

这里使用的数据集就是自己定义的数据集了，大家做什么领域的就用什么视频，比如有人要做手势识别的，就用手势的。有人要做人摔倒的，那就找人摔倒的视频。

我这里就用一个简单的视频，人在说话的视频。视频不多，就2个3秒的视频（这里是我用来做例子的，如果真实情况，2个视频3秒视频是远远不够的）。

## 1.1 视频准备 ##

准备2个3秒的视频，最好里面人少一点，减少标注的难度。下图是我选择的几个3秒的视频（为了快速，我这里只选择其中2个）  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1doaWZmZVlG_size_16_color_FFFFFF_t_70]  
下面是使用ffmpeg将一段长视频裁剪为3秒视频的命令。

ffmpeg -ss 00:00:00.0 -to 00:00:3.0 -i "../cutVideo2/1/1.mp4" "../shortVideoTrain/A.mp4"
    ffmpeg -ss 00:00:03.0 -to 00:00:06.0 -i "../cutVideo2/1/1.mp4" "../shortVideoTrain/B.mp4"
    ffmpeg -ss 00:00:06.0 -to 00:00:09.0 -i "../cutVideo2/1/1.mp4" "../shortVideoTrain/C.mp4"
    ffmpeg -ss 00:00:09.0 -to 00:00:12.0 -i "../cutVideo2/1/1.mp4" "../shortVideoTrain/D.mp4"
    ffmpeg -ss 00:00:12.0 -to 00:00:15.0 -i "../cutVideo2/1/1.mp4" "../shortVideoTrain/E.mp4"

## 1.2 切割视频为图片 ##

这里需要把3秒的视频按2种方式切割

**第一种**是，把视频按照每秒1帧来裁剪，这样裁剪的目的是用来标注，因为ava数据集就是1秒1帧的标志。

命令如下：

#切割图片，每秒1帧
    IN_DATA_DIR="../shortVideoTrain/"
    OUT_DATA_DIR="../shortVideoTrainCut/"
    
    if [[ ! -d "${OUT_DATA_DIR}" ]]; then
      echo "${OUT_DATA_DIR} doesn't exist. Creating it.";
      mkdir -p ${OUT_DATA_DIR}
    fi
    
    for video in $(ls -A1 -U ${IN_DATA_DIR}/*)
    do
      video_name=${video##*/}
    
      if [[ $video_name = *".webm" ]]; then
        video_name=${video_name::-5}
      else
        video_name=${video_name::-4}
      fi
    
      out_video_dir=${OUT_DATA_DIR}/${video_name}/
      mkdir -p "${out_video_dir}"
    
      out_name="${out_video_dir}/${video_name}_%06d.jpg"
    
      ffmpeg -i "${video}" -r 1 -q:v 1 "${out_name}"
    done

切割结果如下（3秒的视频被切割为5张）：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1doaWZmZVlG_size_16_color_FFFFFF_t_70 1]

![在这里插入图片描述][20210411103348734.png]

**第二种**是，把视频按照每秒30帧来裁剪，这样裁剪的目的是，用于slowfast的训练，因为slowfast，在slow流里，1秒会采集到15帧，在fast流里，1秒会采集到2帧。

命令如下：

#切割图片，每秒30帧
    IN_DATA_DIR="../shortVideoTrain/"
    OUT_DATA_DIR="../shortVideoFrames/"
    
    if [[ ! -d "${OUT_DATA_DIR}" ]]; then
      echo "${OUT_DATA_DIR} doesn't exist. Creating it.";
      mkdir -p ${OUT_DATA_DIR}
    fi
    
    for video in $(ls -A1 -U ${IN_DATA_DIR}/*)
    do
      video_name=${video##*/}
    
      if [[ $video_name = *".webm" ]]; then
        video_name=${video_name::-5}
      else
        video_name=${video_name::-4}
      fi
    
      out_video_dir=${OUT_DATA_DIR}/${video_name}/
      mkdir -p "${out_video_dir}"
    
      out_name="${out_video_dir}/${video_name}_%06d.jpg"
    
      ffmpeg -i "${video}" -r 30 -q:v 1 "${out_name}"
    done

切割结果如下（3秒的视频被切割为90张）：

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1doaWZmZVlG_size_16_color_FFFFFF_t_70 2]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1doaWZmZVlG_size_16_color_FFFFFF_t_70 3]

## 1.3 使用faster rcnn自动框人 ##

其中这一步可以自己框人，因为2个视频就10张图片，但是，之后的数据集，应该是上万张，而且一张图片可能多达40个人，自己手动标注会很累的，所以这里采用我之前写的博客里的方法，使用detectron2里的faster rcnn 实现人的自动框出。

这里就不说怎么自动标注了，上面两篇博客写的很清楚自动标注的过程。

自动标注后，会有生成一个csv文件，如下图所示：  
![在这里插入图片描述][20210411111839397.png]  
打开这个文件，把里面的 单引号 ’ 全部删掉（可以替换为空），结果如下：

filename,file_size,file_attributes,region_count,region_id,region_shape_attributes,region_attributes
    A_000001.jpg,33338,"""{}""",3,0,"{""name"": ""rect"", ""x"": 285, ""y"": 93, ""width"": 108, ""height"": 284}","""{}"""
    A_000001.jpg,33338,"""{}""",3,1,"{""name"": ""rect"", ""x"": 537, ""y"": 357, ""width"": 50, ""height"": 46}","""{}"""
    A_000001.jpg,33338,"""{}""",3,2,"{""name"": ""rect"", ""x"": 238, ""y"": 361, ""width"": 52, ""height"": 42}","""{}"""
    A_000002.jpg,33430,"""{}""",3,0,"{""name"": ""rect"", ""x"": 535, ""y"": 361, ""width"": 55, ""height"": 42}","""{}"""
    A_000002.jpg,33430,"""{}""",3,1,"{""name"": ""rect"", ""x"": 285, ""y"": 95, ""width"": 106, ""height"": 268}","""{}"""
    A_000002.jpg,33430,"""{}""",3,2,"{""name"": ""rect"", ""x"": 238, ""y"": 361, ""width"": 52, ""height"": 42}","""{}"""
    A_000003.jpg,33416,"""{}""",3,0,"{""name"": ""rect"", ""x"": 328, ""y"": 96, ""width"": 105, ""height"": 152}","""{}"""
    A_000003.jpg,33416,"""{}""",3,1,"{""name"": ""rect"", ""x"": 234, ""y"": 360, ""width"": 52, ""height"": 43}","""{}"""
    A_000003.jpg,33416,"""{}""",3,2,"{""name"": ""rect"", ""x"": 543, ""y"": 367, ""width"": 52, ""height"": 36}","""{}"""
    A_000004.jpg,34544,"""{}""",3,0,"{""name"": ""rect"", ""x"": 328, ""y"": 94, ""width"": 110, ""height"": 152}","""{}"""
    A_000004.jpg,34544,"""{}""",3,1,"{""name"": ""rect"", ""x"": 232, ""y"": 363, ""width"": 52, ""height"": 40}","""{}"""
    A_000004.jpg,34544,"""{}""",3,2,"{""name"": ""rect"", ""x"": 541, ""y"": 365, ""width"": 52, ""height"": 38}","""{}"""
    A_000005.jpg,34433,"""{}""",4,0,"{""name"": ""rect"", ""x"": 328, ""y"": 93, ""width"": 106, ""height"": 155}","""{}"""
    A_000005.jpg,34433,"""{}""",4,1,"{""name"": ""rect"", ""x"": 233, ""y"": 364, ""width"": 53, ""height"": 39}","""{}"""
    A_000005.jpg,34433,"""{}""",4,2,"{""name"": ""rect"", ""x"": 367, ""y"": 354, ""width"": 54, ""height"": 48}","""{}"""
    A_000005.jpg,34433,"""{}""",4,3,"{""name"": ""rect"", ""x"": 543, ""y"": 369, ""width"": 52, ""height"": 34}","""{}"""

## 1.4 via标注图片 ##

打开via，导入图片，导入csv，结果如下：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1doaWZmZVlG_size_16_color_FFFFFF_t_70 4]

接下来，要做的是把一些没用的框给去掉，比如说上面图片中，只有脑袋的检测结果，这些就是不需要的，去掉，去掉后结果如下：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1doaWZmZVlG_size_16_color_FFFFFF_t_70 5]

注意，这里不打标签，不打标签，因为slowfast所需要的标签格式和via的标签格式不同，这里在后面会手动填入标签，在我之后的博客里，会把via和slowfast的数据集格式的转换博客补上。

我们处理好上面所说的之后，导出csv文件，如下图：  
![在这里插入图片描述][20210411113918666.png]

filename,file_size,file_attributes,region_count,region_id,region_shape_attributes,region_attributes
    A_000001.jpg,33338,"{""0"":""{"",""1"":""}""}",1,0,"{""name"":""rect"",""x"":285,""y"":93,""width"":108,""height"":284}","{""0"":""{"",""1"":""}""}"
    A_000002.jpg,33430,"{""0"":""{"",""1"":""}""}",1,0,"{""name"":""rect"",""x"":285,""y"":95,""width"":106,""height"":268}","{""0"":""{"",""1"":""}""}"
    A_000003.jpg,33416,"{""0"":""{"",""1"":""}""}",1,0,"{""name"":""rect"",""x"":328,""y"":96,""width"":105,""height"":152}","{""0"":""{"",""1"":""}""}"
    A_000004.jpg,34544,"{""0"":""{"",""1"":""}""}",1,0,"{""name"":""rect"",""x"":328,""y"":94,""width"":110,""height"":152}","{""0"":""{"",""1"":""}""}"
    A_000005.jpg,34433,"{""0"":""{"",""1"":""}""}",1,0,"{""name"":""rect"",""x"":328,""y"":93,""width"":106,""height"":155}","{""0"":""{"",""1"":""}""}"

# 二，数据集文件 #

在第一部分，我们已经拿到了2个图片文件的标注csv，接下来我们就手动把数据集的相关文件写了。

## 2.1 数据集文件总览 ##

`annotations`  
—`person_box_67091280_iou90`  
——`ava_detection_train_boxes_and_labels_include_negative_v2.2.csv`  
——`ava_detection_val_boxes_and_labels.csv`  
—`ava_action_list_v2.2_for_activitynet_2019.pbtxt`  
—`ava_val_excluded_timestamps_v2.2.csv`  
—`ava_train_v2.2.csv`  
—`ava_val_v2.2.csv`

`frame_lists`  
—`train.csv`  
—`val.csv`

`frames`  
—`A`  
——`A_000001.jpg`  
——`A_0000012.jpg`  
…  
——`A_000090.jpg`  
—`B`  
——`B_000001.jpg`  
——`B_0000012.jpg`  
…  
——`B_000090.jpg`

## 2.2 annotations ##

### 2.2.1 ava\_train\_v2.2.csv ###

A,1,0.395,0.230,0.545,0.933,1,0
    A,2,0.395,0.235,0.543,0.898,1,0
    A,3,0.455,0.237,0.601,0.613,1,0
    A,4,0.455,0.232,0.608,0.608,1,0
    A,5,0.455,0.230,0.602,0.613,1,0

### 2.2.2 ava\_val\_v2.2.csv ###

B,1,0.455,0.227,0.601,0.613,1,0
    B,2,0.455,0.227,0.601,0.611,1,0
    B,3,0.455,0.225,0.601,0.611,1,0
    B,4,0.455,0.230,0.602,0.616,1,0
    B,5,0.455,0.227,0.604,0.611,1,0

### 2.2.3 ava\_val\_excluded\_timestamps\_v2.2.csv ###

这是空文件

### 2.2.4 ava\_action\_list\_v2.2\_for\_activitynet\_2019.pbtxt ###

item {
      name: "talk"
      id: 1
    }

### 2.2.5 ava\_detection\_train\_boxes\_and\_labels\_include\_negative\_v2.2.csv ###

A,1,0.395,0.230,0.545,0.933,1,0.996382
    A,2,0.395,0.235,0.543,0.898,1,0.996382
    A,3,0.455,0.237,0.601,0.613,1,0.996382
    A,4,0.455,0.232,0.608,0.608,1,0.996382
    A,5,0.455,0.230,0.602,0.613,1,0.996382

### 2.2.6 ava\_detection\_val\_boxes\_and\_labels.csv ###

B,1,0.455,0.227,0.601,0.613,,0.995518
    B,2,0.455,0.227,0.601,0.611,,0.995518
    B,3,0.455,0.225,0.601,0.611,,0.995518
    B,4,0.455,0.230,0.602,0.616,,0.995518
    B,5,0.455,0.227,0.604,0.611,,0.995518

## 2.3 frame\_lists ##

### 2.3.1 train.csv ###

original_vido_id video_id frame_id path labels
    A 0 0 A/A_000001.jpg ""
    A 0 1 A/A_000002.jpg ""
    A 0 2 A/A_000003.jpg ""
    A 0 3 A/A_000004.jpg ""
    A 0 4 A/A_000005.jpg ""
    A 0 5 A/A_000006.jpg ""
    A 0 6 A/A_000007.jpg ""
    A 0 7 A/A_000008.jpg ""
    A 0 8 A/A_000009.jpg ""
    A 0 9 A/A_000010.jpg ""
    A 0 10 A/A_000011.jpg ""
    A 0 11 A/A_000012.jpg ""
    A 0 12 A/A_000013.jpg ""
    A 0 13 A/A_000014.jpg ""
    A 0 14 A/A_000015.jpg ""
    A 0 15 A/A_000016.jpg ""
    A 0 16 A/A_000017.jpg ""
    A 0 17 A/A_000018.jpg ""
    A 0 18 A/A_000019.jpg ""
    A 0 19 A/A_000020.jpg ""
    A 0 20 A/A_000021.jpg ""
    A 0 21 A/A_000022.jpg ""
    A 0 22 A/A_000023.jpg ""
    A 0 23 A/A_000024.jpg ""
    A 0 24 A/A_000025.jpg ""
    A 0 25 A/A_000026.jpg ""
    A 0 26 A/A_000027.jpg ""
    A 0 27 A/A_000028.jpg ""
    A 0 28 A/A_000029.jpg ""
    A 0 29 A/A_000030.jpg ""
    A 0 30 A/A_000031.jpg ""
    A 0 31 A/A_000032.jpg ""
    A 0 32 A/A_000033.jpg ""
    A 0 33 A/A_000034.jpg ""
    A 0 34 A/A_000035.jpg ""
    A 0 35 A/A_000036.jpg ""
    A 0 36 A/A_000037.jpg ""
    A 0 37 A/A_000038.jpg ""
    A 0 38 A/A_000039.jpg ""
    A 0 39 A/A_000040.jpg ""
    A 0 40 A/A_000041.jpg ""
    A 0 41 A/A_000042.jpg ""
    A 0 42 A/A_000043.jpg ""
    A 0 43 A/A_000044.jpg ""
    A 0 44 A/A_000045.jpg ""
    A 0 45 A/A_000046.jpg ""
    A 0 46 A/A_000047.jpg ""
    A 0 47 A/A_000048.jpg ""
    A 0 48 A/A_000049.jpg ""
    A 0 49 A/A_000050.jpg ""
    A 0 50 A/A_000051.jpg ""
    A 0 51 A/A_000052.jpg ""
    A 0 52 A/A_000053.jpg ""
    A 0 53 A/A_000054.jpg ""
    A 0 54 A/A_000055.jpg ""
    A 0 55 A/A_000056.jpg ""
    A 0 56 A/A_000057.jpg ""
    A 0 57 A/A_000058.jpg ""
    A 0 58 A/A_000059.jpg ""
    A 0 59 A/A_000060.jpg ""
    A 0 60 A/A_000061.jpg ""
    A 0 61 A/A_000062.jpg ""
    A 0 62 A/A_000063.jpg ""
    A 0 63 A/A_000064.jpg ""
    A 0 64 A/A_000065.jpg ""
    A 0 65 A/A_000066.jpg ""
    A 0 66 A/A_000067.jpg ""
    A 0 67 A/A_000068.jpg ""
    A 0 68 A/A_000069.jpg ""
    A 0 69 A/A_000070.jpg ""
    A 0 70 A/A_000071.jpg ""
    A 0 71 A/A_000072.jpg ""
    A 0 72 A/A_000073.jpg ""
    A 0 73 A/A_000074.jpg ""
    A 0 74 A/A_000075.jpg ""
    A 0 75 A/A_000076.jpg ""
    A 0 76 A/A_000077.jpg ""
    A 0 77 A/A_000078.jpg ""
    A 0 78 A/A_000079.jpg ""
    A 0 79 A/A_000080.jpg ""
    A 0 80 A/A_000081.jpg ""
    A 0 81 A/A_000082.jpg ""
    A 0 82 A/A_000083.jpg ""
    A 0 83 A/A_000084.jpg ""
    A 0 84 A/A_000085.jpg ""
    A 0 85 A/A_000086.jpg ""
    A 0 86 A/A_000087.jpg ""
    A 0 87 A/A_000088.jpg ""
    A 0 88 A/A_000089.jpg ""
    A 0 89 A/A_000090.jpg ""

### 2.3.2 val.csv ###

originBl_vido_id video_id frBme_id pBth lBbels
    B 1 0 B/B_000001.jpg ""
    B 1 1 B/B_000002.jpg ""
    B 1 2 B/B_000003.jpg ""
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## 2.4 frames ##

这里的存放我们在第一部分切割的图片，1秒切割30张的那个文件：  
![在这里插入图片描述][20210411134306619.png]

# 三，slowfast 训练自己的数据集 #

在第一部分和第二部分，我们把需要准备的东西都准备好了，下一步就是在slowfast修改训练所需要的代码。

## 3.1 预训练模型 ##

最好使用预训练模型，这样可以缩短训练的一个时间，我用的预训练模型如下图

[模型下载官网][Link 5]，[预训练模型下载链接][Link 6]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1doaWZmZVlG_size_16_color_FFFFFF_t_70 6]  
下载下来后，放在文件夹`/SlowFast/configs/AVA/c2/`下面，如下图：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1doaWZmZVlG_size_16_color_FFFFFF_t_70 7]

## 3.2 配置文件 ##

在`/SlowFast/configs/AVA/`下创建一个新的yaml文件：`SLOWFAST_32x2_R50_SHORT5.yaml`，如下图：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1doaWZmZVlG_size_16_color_FFFFFF_t_70 8]  
代码如下：

TRAIN:
      ENABLE: True
      DATASET: ava
      BATCH_SIZE: 8 #64
      EVAL_PERIOD: 5
      CHECKPOINT_PERIOD: 1
      AUTO_RESUME: True
      CHECKPOINT_FILE_PATH: '/home/lxn/0yangfan/Slowfast2/SlowFast-master/configs/AVA/c2/SLOWFAST_32x2_R101_50_50.pkl'  #path to pretrain model
      CHECKPOINT_TYPE: caffe2
    DATA:
      NUM_FRAMES: 32
      SAMPLING_RATE: 2
      TRAIN_JITTER_SCALES: [256, 320]
      TRAIN_CROP_SIZE: 224
      TEST_CROP_SIZE: 224
      INPUT_CHANNEL_NUM: [3, 3]
      PATH_TO_DATA_DIR: '/home/lxn/yf_videos/myCustomava'
    DETECTION:
      ENABLE: True
      ALIGNED: True
    AVA:
      FRAME_DIR: '/home/lxn/yf_videos/myCustomava/frames'
      FRAME_LIST_DIR: '/home/lxn/yf_videos/myCustomava/frame_lists'
      ANNOTATION_DIR: '/home/lxn/yf_videos/myCustomava/annotations'
      DETECTION_SCORE_THRESH: 0.8
      TRAIN_PREDICT_BOX_LISTS: [
        "ava_train_v2.2.csv",
        "person_box_67091280_iou90/ava_detection_train_boxes_and_labels_include_negative_v2.2.csv",
      ]
      TEST_PREDICT_BOX_LISTS: ["person_box_67091280_iou90/ava_detection_val_boxes_and_labels.csv"]
      
      
    SLOWFAST:
      ALPHA: 4
      BETA_INV: 8
      FUSION_CONV_CHANNEL_RATIO: 2
      FUSION_KERNEL_SZ: 7
    RESNET:
      ZERO_INIT_FINAL_BN: True
      WIDTH_PER_GROUP: 64
      NUM_GROUPS: 1
      DEPTH: 50
      TRANS_FUNC: bottleneck_transform
      STRIDE_1X1: False
      NUM_BLOCK_TEMP_KERNEL: [[3, 3], [4, 4], [6, 6], [3, 3]]
      SPATIAL_DILATIONS: [[1, 1], [1, 1], [1, 1], [2, 2]]
      SPATIAL_STRIDES: [[1, 1], [2, 2], [2, 2], [1, 1]]
    NONLOCAL:
      LOCATION: [[[], []], [[], []], [[], []], [[], []]]
      GROUP: [[1, 1], [1, 1], [1, 1], [1, 1]]
      INSTANTIATION: dot_product
      POOL: [[[1, 2, 2], [1, 2, 2]], [[1, 2, 2], [1, 2, 2]], [[1, 2, 2], [1, 2, 2]], [[1, 2, 2], [1, 2, 2]]]
    BN:
      USE_PRECISE_STATS: False
      NUM_BATCHES_PRECISE: 200
    SOLVER:
      BASE_LR: 0.1
      LR_POLICY: steps_with_relative_lrs
      STEPS: [0, 10, 15, 20]
      LRS: [1, 0.1, 0.01, 0.001]
      MAX_EPOCH: 20
      MOMENTUM: 0.9
      WEIGHT_DECAY: 1e-7
      WARMUP_EPOCHS: 5.0
      WARMUP_START_LR: 0.000125
      OPTIMIZING_METHOD: sgd
    MODEL:
      NUM_CLASSES: 1
      ARCH: slowfast
      MODEL_NAME: SlowFast
      LOSS_FUNC: bce
      DROPOUT_RATE: 0.5
      HEAD_ACT: sigmoid
    TEST:
      ENABLE: False
      DATASET: ava
      BATCH_SIZE: 8
    DATA_LOADER:
      NUM_WORKERS: 2
      PIN_MEMORY: True
    NUM_GPUS: 1
    NUM_SHARDS: 1
    RNG_SEED: 0
    OUTPUT_DIR: .

有几个需要注意的地方：

1，TRAIN:CHECKPOINT\_FILE\_PATH 就是我们下载的与训练模型的位置  
2，DATA:PATH\_TO\_DATA\_DIR 就是我们第二部分制作的数据集文件  
3，AVA: 下面的路径也是对应第二部分数据集文件对应的地方  
4，MODEL:NUM\_CLASSES: 1 这里是最需要主义的，这里classes必需为1，因为我们只有talk这一个分类。

## 3.3 训练过程 ##

python tools/run_net.py --cfg configs/AVA/SLOWFAST_32x2_R50_SHORT5.yaml

# 四 检测模型效果 #

使用slowfast检测自己的视频可以参考我之前写的一篇博客：

[【SlowFast复现】SlowFast Networks for Video Recognition复现代码 使用自己的视频进行demo检测][SlowFast_SlowFast Networks for Video Recognition_ _demo]

详细过程我就不写了，上面这篇博客写的有。

## 4.2 创建json文件 ##

在目录SlowFast-master/demo/AVA/下创建一个json文件：ava2.json

{"talk": 0}

## 4.2 创建yaml文件 ##

在目录SlowFast-master/demo/AVA/下创建一个yaml文件：SLOWFAST\_32x2\_R101\_50\_50s2.yaml  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1doaWZmZVlG_size_16_color_FFFFFF_t_70 9]

代码如下：

TRAIN:
      ENABLE: False
      DATASET: ava
      BATCH_SIZE: 16
      EVAL_PERIOD: 1
      CHECKPOINT_PERIOD: 1
      AUTO_RESUME: True
      CHECKPOINT_FILE_PATH: '/home/lxn/0yangfan/Slowfast2/SlowFast-master/configs/AVA/c2/SLOWFAST_32x2_R101_50_50.pkl'  #path to pretrain model
      CHECKPOINT_TYPE: pytorch
    DATA:
      NUM_FRAMES: 32
      SAMPLING_RATE: 2
      TRAIN_JITTER_SCALES: [256, 320]
      TRAIN_CROP_SIZE: 224
      TEST_CROP_SIZE: 256
      INPUT_CHANNEL_NUM: [3, 3]
    DETECTION:
      ENABLE: True
      ALIGNED: False
    AVA:
      BGR: False
      DETECTION_SCORE_THRESH: 0.8
      TEST_PREDICT_BOX_LISTS: ["person_box_67091280_iou90/ava_detection_val_boxes_and_labels.csv"]
    SLOWFAST:
      ALPHA: 4
      BETA_INV: 8
      FUSION_CONV_CHANNEL_RATIO: 2
      FUSION_KERNEL_SZ: 5
    RESNET:
      ZERO_INIT_FINAL_BN: True
      WIDTH_PER_GROUP: 64
      NUM_GROUPS: 1
      DEPTH: 101
      TRANS_FUNC: bottleneck_transform
      STRIDE_1X1: False
      NUM_BLOCK_TEMP_KERNEL: [[3, 3], [4, 4], [6, 6], [3, 3]]
      SPATIAL_DILATIONS: [[1, 1], [1, 1], [1, 1], [2, 2]]
      SPATIAL_STRIDES: [[1, 1], [2, 2], [2, 2], [1, 1]]
    NONLOCAL:
      LOCATION: [[[], []], [[], []], [[6, 13, 20], []], [[], []]]
      GROUP: [[1, 1], [1, 1], [1, 1], [1, 1]]
      INSTANTIATION: dot_product
      POOL: [[[2, 2, 2], [2, 2, 2]], [[2, 2, 2], [2, 2, 2]], [[2, 2, 2], [2, 2, 2]], [[2, 2, 2], [2, 2, 2]]]
    BN:
      USE_PRECISE_STATS: False
      NUM_BATCHES_PRECISE: 200
    SOLVER:
      MOMENTUM: 0.9
      WEIGHT_DECAY: 1e-7
      OPTIMIZING_METHOD: sgd
    MODEL:
      NUM_CLASSES: 1
      ARCH: slowfast
      MODEL_NAME: SlowFast
      LOSS_FUNC: bce
      DROPOUT_RATE: 0.5
      HEAD_ACT: sigmoid
    TEST:
      ENABLE: False
      DATASET: ava
      BATCH_SIZE: 8
    DATA_LOADER:
      NUM_WORKERS: 2
      PIN_MEMORY: True
    
    NUM_GPUS: 1
    NUM_SHARDS: 1
    RNG_SEED: 0
    OUTPUT_DIR: .
    #TENSORBOARD:
    #  MODEL_VIS:
    #    TOPK: 2
    DEMO:
      ENABLE: True
      LABEL_FILE_PATH: "/home/lxn/0yangfan/Slowfast2/SlowFast-master/demo/AVA/ava2.json"
      INPUT_VIDEO: "/home/lxn/yf_videos/myVideo15mins/eight.mp4"
      OUTPUT_FILE: "/home/lxn/0yangfan/Slowfast2/SlowFast-master/Voutput/eight2.mp4"
    
      DETECTRON2_CFG: "COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml"
      DETECTRON2_WEIGHTS: detectron2://COCO-Detection/faster_rcnn_R_50_FPN_3x/137849458/model_final_280758.pkl

有几个地方需要注意：

1，TRAIN:CHECKPOINT\_FILE\_PATH 就是第三部分训练自己数据集的模型位置。  
2，MODEL:NUM\_CLASSES: 1 这里我们只有1个动作，所以只有一个分类

## 4.3 修改ava\_helper.py ##

在目录/SlowFast-master/slowfast/datasets/下文件：ava\_helper.py，如下图：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1doaWZmZVlG_size_16_color_FFFFFF_t_70 10]  
只需要一个修改：

#AVA_VALID_FRAMES = range(902, 1799)
    AVA_VALID_FRAMES = range(1, 6)

把原来的 range(902, 1799) 改为 range(1, 6) 这是因为图片的编号改为了1~5.

## 4.4 运行 ##

python tools/run_net.py --cfg demo/AVA/SLOWFAST_32x2_R101_50_50s2.yaml

检测结果如下：

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1doaWZmZVlG_size_16_color_FFFFFF_t_70 11]

[Link 1]: https://blog.csdn.net/WhiffeYF/article/details/115581800#_1
[Link 2]: https://blog.csdn.net/WhiffeYF/article/details/115581800#_19
[1.1]: https://blog.csdn.net/WhiffeYF/article/details/115581800#11__25
[1.2]: https://blog.csdn.net/WhiffeYF/article/details/115581800#12__38
[1.3 _faster rcnn]: https://blog.csdn.net/WhiffeYF/article/details/115581800#13_faster_rcnn_119
[1.4 via]: https://blog.csdn.net/WhiffeYF/article/details/115581800#14_via_151
[Link 3]: https://blog.csdn.net/WhiffeYF/article/details/115581800#_173
[2.1]: https://blog.csdn.net/WhiffeYF/article/details/115581800#21__176
[2.2 annotations]: https://blog.csdn.net/WhiffeYF/article/details/115581800#22_annotations_202
[2.2.1 ava_train_v2.2.csv]: https://blog.csdn.net/WhiffeYF/article/details/115581800#221_ava_train_v22csv_203
[2.2.2 ava_val_v2.2.csv]: https://blog.csdn.net/WhiffeYF/article/details/115581800#222_ava_val_v22csv_211
[2.2.3 ava_val_excluded_timestamps_v2.2.csv]: https://blog.csdn.net/WhiffeYF/article/details/115581800#223_ava_val_excluded_timestamps_v22csv_221
[2.2.4 ava_action_list_v2.2_for_activitynet_2019.pbtxt]: https://blog.csdn.net/WhiffeYF/article/details/115581800#224_ava_action_list_v22_for_activitynet_2019pbtxt_224
[2.2.5 ava_detection_train_boxes_and_labels_include_negative_v2.2.csv]: https://blog.csdn.net/WhiffeYF/article/details/115581800#225_ava_detection_train_boxes_and_labels_include_negative_v22csv_233
[2.2.6 ava_detection_val_boxes_and_labels.csv]: https://blog.csdn.net/WhiffeYF/article/details/115581800#226_ava_detection_val_boxes_and_labelscsv_242
[2.3 frame_lists]: https://blog.csdn.net/WhiffeYF/article/details/115581800#23_frame_lists_252
[2.3.1 train.csv]: https://blog.csdn.net/WhiffeYF/article/details/115581800#231_traincsv_253
[2.3.2 val.csv]: https://blog.csdn.net/WhiffeYF/article/details/115581800#232_valcsv_347
[2.4 frames]: https://blog.csdn.net/WhiffeYF/article/details/115581800#24_frames_442
[slowfast]: https://blog.csdn.net/WhiffeYF/article/details/115581800#slowfast__445
[3.1]: https://blog.csdn.net/WhiffeYF/article/details/115581800#31__448
[3.2]: https://blog.csdn.net/WhiffeYF/article/details/115581800#32__457
[3.3]: https://blog.csdn.net/WhiffeYF/article/details/115581800#33__556
[Link 4]: https://blog.csdn.net/WhiffeYF/article/details/115581800#__563
[4.2 _json]: https://blog.csdn.net/WhiffeYF/article/details/115581800#42_json_570
[4.2 _yaml]: https://blog.csdn.net/WhiffeYF/article/details/115581800#42_yaml_577
[4.3 _ava_helper.py]: https://blog.csdn.net/WhiffeYF/article/details/115581800#43_ava_helperpy_672
[4.4]: https://blog.csdn.net/WhiffeYF/article/details/115581800#44__683
[https_blog.csdn.net_sinat_28371057_article_details_119429163]: https://blog.csdn.net/sinat_28371057/article/details/119429163
[https_blog.csdn.net_sinat_28371057_article_details_119429199]: https://blog.csdn.net/sinat_28371057/article/details/119429199
[https_blog.csdn.net_sinat_28371057_article_details_119429214]: https://blog.csdn.net/sinat_28371057/article/details/119429214
[https_blog.csdn.net_sinat_28371057_article_details_119429241]: https://blog.csdn.net/sinat_28371057/article/details/119429241
[https_blog.csdn.net_sinat_28371057_article_details_119429261]: https://blog.csdn.net/sinat_28371057/article/details/119429261
[https_blog.csdn.net_sinat_28371057_article_details_119429286]: https://blog.csdn.net/sinat_28371057/article/details/119429286
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[20210411111839397.png]: /images/20220829/542023b9b19545f29f554d113566d8ba.png
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[20210411113918666.png]: /images/20220829/759ee05f1c5f4fefbb2fdbe531f89cfc.png
[20210411134306619.png]: /images/20220829/eba5b73904ad48af9895e377adc93d43.png
[Link 5]: https://github.com/facebookresearch/SlowFast/blob/master/MODEL_ZOO.md
[Link 6]: https://dl.fbaipublicfiles.com/pyslowfast/model_zoo/ava/pretrain/SLOWFAST_32x2_R101_50_50.pkl
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[SlowFast_SlowFast Networks for Video Recognition_ _demo]: https://blog.csdn.net/WhiffeYF/article/details/113527759?spm=1001.2014.3001.5501
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