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一#xff0c;准备 1.1代码1.2 环境准备1.3 搭建镜像1.4 配置slowfast环境1.5 ava.json1.6 SLOWFAST_32x2_R101_50_50.yaml1.7 SLOWFAST_32x2_R101_50_50 .pkl二#xff0c;代码运行三 错误解决复现过程视频#xff1a;B站复现视频复现结果 一#xff0c;准备
1.1代…目录
一准备 1.1代码1.2 环境准备1.3 搭建镜像1.4 配置slowfast环境1.5 ava.json1.6 SLOWFAST_32x2_R101_50_50.yaml1.7 SLOWFAST_32x2_R101_50_50 .pkl二代码运行三 错误解决复现过程视频B站复现视频复现结果 一准备
1.1代码
SlowFast官网地址 代码下载
git clone https://github.com/facebookresearch/slowfast这里建议使用码云来下载使用上面的那个命令实在太慢码云下载方法git clone 显著提速解决Github代码拉取速度缓慢问题
下载链接
1.2 环境准备
这里推荐几个网站租用这些网站的GPU然后跑代码因为普通的笔记本搭建图像处理的环境会遇到各种各样的问题。在网站上租用GPU可以使用官方搭建好的镜像如果跑代码或者自己把环境弄乱了就可以释放实例重新再来约等于自己电脑的重装系统但是这个租用的释放和搭建新镜像速度非常快
以下是我推荐的几个网站 1极链AI云平台我用的就是这个有50元的白嫖额度我就是靠这50元的额度跑出来slowfast 2MistGPU
1.3 搭建镜像
第一步在极链AI云平台选择一个机子我选的一般都是4元/小时的。
第二步选择GPU数量我们跑自己的视频一般1个GPU就够了 第三步实例镜像官方给的框架是PyTorch这里也就PyTorchPyTorch的版本官方给的1.3当时官方发布的时候PyTorch最高版本就1.3但是这里的镜像没有1.3所以选择一个比它高的1.4版本。注意python一定要选3.7版本python一定要选3.7版本python一定要选3.7版本3.6版本我是跑出了问题。CUDA只有10.1可选这也是官方镜像的好处在自己电脑上安装CUDA很烦的
备注有读者尝试的时候这里出现问题这是我2021年2月份做出来的读者是4月份尝试的然后报错这里读者采用了pytorch1.4的版本cuda10.2的版本。所以如果版本问题的话尝试把pytorch和cuda版本提高。 第四步按照极链AI云平台的手册指引在Xshell上打开实例。
1.4 配置slowfast环境
按照官网上的步骤来Installation 注意这里的opencv-python就不要执行因为镜像里自带了opencv-python
1.5 ava.json
{bend/bow (at the waist): 0, crawl: 1, crouch/kneel: 2, dance: 3, fall down: 4, get up: 5, jump/leap: 6, lie/sleep: 7, martial art: 8, run/jog: 9, sit: 10, stand: 11, swim: 12, walk: 13, answer phone: 14, brush teeth: 15, carry/hold (an object): 16, catch (an object): 17, chop: 18, climb (e.g., a mountain): 19, clink glass: 20, close (e.g., a door, a box): 21, cook: 22, cut: 23, dig: 24, dress/put on clothing: 25, drink: 26, drive (e.g., a car, a truck): 27, eat: 28, enter: 29, exit: 30, extract: 31, fishing: 32, hit (an object): 33, kick (an object): 34, lift/pick up: 35, listen (e.g., to music): 36, open (e.g., a window, a car door): 37, paint: 38, play board game: 39, play musical instrument: 40, play with pets: 41, point to (an object): 42, press: 43, pull (an object): 44, push (an object): 45, put down: 46, read: 47, ride (e.g., a bike, a car, a horse): 48, row boat: 49, sail boat: 50, shoot: 51, shovel: 52, smoke: 53, stir: 54, take a photo: 55, text on/look at a cellphone: 56, throw: 57, touch (an object): 58, turn (e.g., a screwdriver): 59, watch (e.g., TV): 60, work on a computer: 61, write: 62, fight/hit (a person): 63, give/serve (an object) to (a person): 64, grab (a person): 65, hand clap: 66, hand shake: 67, hand wave: 68, hug (a person): 69, kick (a person): 70, kiss (a person): 71, lift (a person): 72, listen to (a person): 73, play with kids: 74, push (another person): 75, sing to (e.g., self, a person, a group): 76, take (an object) from (a person): 77, talk to (e.g., self, a person, a group): 78, watch (a person): 79}将上面的内容保存在ava.json里面然后保存在/home/slowfast/demo/AVA目录下。
1.6 SLOWFAST_32x2_R101_50_50.yaml
在目录/home/slowfast/demo/AVA中打开SLOWFAST_32x2_R101_50_50.yaml 内容改为如下
TRAIN:ENABLE: FalseDATASET: avaBATCH_SIZE: 16EVAL_PERIOD: 1CHECKPOINT_PERIOD: 1AUTO_RESUME: TrueCHECKPOINT_FILE_PATH: /home/slowfast/configs/AVA/c2/SLOWFAST_32x2_R101_50_50.pkl #path to pretrain modelCHECKPOINT_TYPE: pytorch
DATA:NUM_FRAMES: 32SAMPLING_RATE: 2TRAIN_JITTER_SCALES: [256, 320]TRAIN_CROP_SIZE: 224TEST_CROP_SIZE: 256INPUT_CHANNEL_NUM: [3, 3]
DETECTION:ENABLE: TrueALIGNED: False
AVA:BGR: FalseDETECTION_SCORE_THRESH: 0.8TEST_PREDICT_BOX_LISTS: [person_box_67091280_iou90/ava_detection_val_boxes_and_labels.csv]
SLOWFAST:ALPHA: 4BETA_INV: 8FUSION_CONV_CHANNEL_RATIO: 2FUSION_KERNEL_SZ: 5
RESNET:ZERO_INIT_FINAL_BN: TrueWIDTH_PER_GROUP: 64NUM_GROUPS: 1DEPTH: 101TRANS_FUNC: bottleneck_transformSTRIDE_1X1: FalseNUM_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_productPOOL: [[[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: FalseNUM_BATCHES_PRECISE: 200
SOLVER:MOMENTUM: 0.9WEIGHT_DECAY: 1e-7OPTIMIZING_METHOD: sgd
MODEL:NUM_CLASSES: 80ARCH: slowfastMODEL_NAME: SlowFastLOSS_FUNC: bceDROPOUT_RATE: 0.5HEAD_ACT: sigmoid
TEST:ENABLE: FalseDATASET: avaBATCH_SIZE: 8
DATA_LOADER:NUM_WORKERS: 2PIN_MEMORY: TrueNUM_GPUS: 1
NUM_SHARDS: 1
RNG_SEED: 0
OUTPUT_DIR: .
#TENSORBOARD:
# MODEL_VIS:
# TOPK: 2
DEMO:ENABLE: TrueLABEL_FILE_PATH: /home/slowfast/demo/AVA/ava.jsonINPUT_VIDEO: /home/slowfast/Vinput/2.mp4OUTPUT_FILE: /home/slowfast/Voutput/1.mp4DETECTRON2_CFG: COCO-Detection/faster_rcnn_R_50_FPN_3x.yamlDETECTRON2_WEIGHTS: detectron2://COCO-Detection/faster_rcnn_R_50_FPN_3x/137849458/model_final_280758.pkl下面这一行代码存放的是输入视频的位置当然Vinput这个文件夹是我自己建的 INPUT_VIDEO: /home/slowfast/Vinput/2.mp4下面这一行代码存放的是检测后视频的位置当然Voutput这个文件夹是我自己建的
OUTPUT_FILE: /home/slowfast/Voutput/1.mp41.7 SLOWFAST_32x2_R101_50_50 .pkl
下载页面 下载模型SLOWFAST_32x2_R101_50_50 .pkl 到/home/slowfast/configs/AVA/c2目录下 然后在目录/home/slowfast/demo/AVA下文件SLOWFAST_32x2_R101_50_50.yaml里修改如下
TRAIN:ENABLE: FalseDATASET: avaBATCH_SIZE: 16EVAL_PERIOD: 1CHECKPOINT_PERIOD: 1AUTO_RESUME: TrueCHECKPOINT_FILE_PATH: /home/slowfast/configs/AVA/c2/SLOWFAST_32x2_R101_50_50.pkl #path to pretrain modelCHECKPOINT_TYPE: pytorch二代码运行
在xShell终端中跳转到以下目录
cd /home/slowfast/输入运行命令
python tools/run_net.py --cfg demo/AVA/SLOWFAST_32x2_R101_50_50.yaml最后结果 最后结果上传到了B站上视频链接
三 错误解决
from av._core import time_base, pyav_version as version, library_versions ImportError: libopenh264.so.5: cannot open shared object file: No such file or directory
解决方案参考之前写的解决方案链接 直接再终端运行
conda install x2641!152.20180717 ffmpeg4.0.2 -c conda-forge任何程序错误以及技术疑问或需要解答的请添加
