Windows 11 安装 SlowFast

参考链接

Win10配置SlowFast全过程并使用slowfast进行视频行为识别检测
【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
AVA时空检测数据集下载—AVA_Actions&AVA_Kinetics

软件版本

conda Python 3.8
VS Code 1.66.2

数据集下载：

Download-AVA_Kinetics-and-AVA_Actions

硬件环境

RTX 3070

具体步骤

Python 环境

conda create - slowfast python=3.8
conda activate slowfast

PyTorch

conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch

基础类

pip install 'git+https://github.com/facebookresearch/fvcore'
pip install 'git+https://github.com/facebookresearch/fairscale'
pip install simplejson
pip install -U iopath
pip install psutil tensorboard opencv-python moviepy pytorchvideo

Detectron2

将 ninja.exe 放入 C:\Windows\System32 文件夹中。

git clone https://github.com/facebookresearch/detectron2.git
python -m pip install -e detectron2

SlowFast

将setup.py中的 PIL 改为 Pillow 。

python setup.py build develop

运行Demo

conda install pywin32

测试

在demo/AVA下建立 ava.json 文件，输入如下内容：

{"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
}

在官网 ModelZoo 下载模型权重文件

修改 demo/AVA/SLOWFAST_32x2_R101_50_50.yaml 文件，将CHECKPOINT_FILE_PATH、LABEL_FILE_PATH分别修改为上述两个文件的路径（为了避免出错，最好为绝对路径）；添加输入和输出视频路径INPUT_VIDEO、OUTPUT_FILE；并进行如下所示的注释。

# TENSORBOARD:
#   MODEL_VIS:
#     TOPK: 2
# WEBCAM: 0

最终文件如下所示：

TRAIN:ENABLE: FalseDATASET: avaBATCH_SIZE: 16EVAL_PERIOD: 1CHECKPOINT_PERIOD: 1AUTO_RESUME: TrueCHECKPOINT_FILE_PATH: "D:/slowfast/demo/models/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: "D:/slowfast/demo/AVA/ava.json"# Add local label file path here.INPUT_VIDEO: "D:/slowfast/demo/AVA/1.mp4"OUTPUT_FILE: "D:/slowfast/demo/AVA/1_output.mp4"# WEBCAM: 0DETECTRON2_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

将数据集中的任意视频命名为1.mp4，在slowfast根目录下输入如下命令即可。

python .\tools\run_net.py --cfg .\demo\AVA\SLOWFAST_32x2_R101_50_50.yaml