Windows 11 安装 SlowFast
软件版本Python 3.8硬件环境RTX 3070具体步骤Python 环境conda create - slowfast python=3.8conda activate slowfastPyTorchconda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch基础类pip install 'git+https
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: False
DATASET: ava
BATCH_SIZE: 16
EVAL_PERIOD: 1
CHECKPOINT_PERIOD: 1
AUTO_RESUME: True
CHECKPOINT_FILE_PATH: "D:/slowfast/demo/models/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: 80
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: "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: 0
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
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
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