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2023 安装 facebookresearch slowfast 自定义数据集训练 yolo数据集转ava数据集_slowfast数据集

作者：笔触狂放9 | 2024-05-15 22:16:12

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slowfast数据集

目录

0 前言

facebookresearch SlowFast ：https://github.com/facebookresearch/SlowFast

b站视频：https://www.bilibili.com/video/BV1Uh4y1q7R2/

1 准备

平台：Autodl：https://www.autodl.com/
环境：
PyTorch 1.8.1
Python 3.8(ubuntu18.04)
Cuda 11.1

在这里插入图片描述
需要先将这两个权重下载到AI平台的：/root/slowfastFile

model_final_280758.pkl：https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_FPN_3x/137849458/model_final_280758.pkl

SLOWFAST_32x2_R101_50_50.pkl：https://dl.fbaipublicfiles.com/pyslowfast/model_zoo/ava/SLOWFAST_32x2_R101_50_50.pkl

2 安装与运行

2.1 基础环境

pip install 'git+https://gitee.com/YFwinston/fvcore'

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pip install simplejson

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conda install av -c conda-forge -y

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conda install x264 ffmpeg -c conda-forge -y

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pip install -U iopath

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pip install psutil

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pip install opencv-python

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// pip install torchvision

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pip install tensorboard

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pip install moviepy

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pip install pytorchvideo

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pip install 'git+https://gitee.com/YFwinston/fairscale'

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2.2 detectron2_repo 安装

// pip install -U torch torchvision cython
pip install -U  cython

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pip install -U 'git+https://gitee.com/YFwinston/fvcore.git' 'git+https://gitee.com/YFwinston/cocoapi.git#subdirectory=PythonAPI'

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git clone https://gitee.com/YFwinston/detectron2 detectron2_repo

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pip install typing-extensions==4.3.0

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pip install -e detectron2_repo

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pip install pillow
pip install pyyaml

pip install scipy
pip install pandas
pip install scikit-learn


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2.3 slowfast 安装

git clone https://gitee.com/YFwinston/slowfast

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export PYTHONPATH=/root/slowfast:$PYTHONPATH

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cd slowfast
python setup.py build develop
cd /root


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cd /root/slowfast/demo/AVA
cp /root/autodl-nas/slowfast/ava.json ./
cp /root/autodl-nas/slowfast/SLOWFAST_32x2_R101_50_50s.yaml ./
cd /root


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cd /root

git clone https://gitee.com/YFwinston/pytorchvideo.git
cd pytorchvideo
pip install -e .
cd /root

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2.4 相关文件

2.4.1 ava.json

然后在/root/slowfast/demo/AVA下面的ava.json写入：

{"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}


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2.4.2 SLOWFAST_32x2_R101_50_50s.yaml

然后在/root/slowfast/demo/AVA下面的SLOWFAST_32x2_R101_50_50s.yaml 写入：

TRAIN:
  ENABLE: False
  DATASET: ava
  BATCH_SIZE: 1
  EVAL_PERIOD: 1
  CHECKPOINT_PERIOD: 1
  AUTO_RESUME: True
  CHECKPOINT_FILE_PATH: /root/autodl-nas/slowfast/SLOWFAST_32x2_R101_50_50.pkl  #path to pretrain model
  CHECKPOINT_TYPE: pytorch
DATA:
  NUM_FRAMES: 16
  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: 1
DATA_LOADER:
  NUM_WORKERS: 1
  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: "demo/AVA/ava.json" # Add local label file path here.
  INPUT_VIDEO: "/root/autodl-nas/slowfast/1.mp4"
  OUTPUT_FILE: "/root/autodl-nas/slowfast/1_1.mp4"
  DETECTRON2_CFG: "COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml"
  DETECTRON2_WEIGHTS: "/root/autodl-nas/slowfast/model_final_280758.pkl"

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2.5 demo测试

在/home/slowfast/demo/中传入一个视频：1.mp4
执行：

cd /root/slowfast/
python tools/run_net.py --cfg demo/AVA/SLOWFAST_32x2_R101_50_50s.yaml

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2.6 相关报错

2.6.1 报错1

This maybe due to another process holding this lock file. If you are sure no other Matplotlib process is running, remove this file and try again.

2.6.1 解决1

pip install --upgrade matplotlib

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2.6.2 报错2

ModuleNotFoundError: No module named ‘torch._six’

2.6.2 解决2

 vim /root/slowfast/slowfast/datasets/multigrid_helper.py

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if TORCH_MAJOR >= 1 and TORCH_MINOR >= 8:
    _int_classes = int
else:
    int_classes = int
    _int_classes = int
    #from torch._six import int_classes as _int_classes

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2.6.3 报错3

报错：

    ERROR: Command errored out with exit status 1:
     command: /root/miniconda3/bin/python -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '"'"'/root/detectron2_repo/setup.py'"'"'; __file__='"'"'/root/detectron2_repo/setup.py'"'"';f = getattr(tokenize, '"'"'open'"'"', open)(__file__) if os.path.exists(__file__) else io.StringIO('"'"'from setuptools import setup; setup()'"'"');code = f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' egg_info --egg-base /tmp/pip-pip-egg-info-yoefyzdp
         cwd: /root/detectron2_repo/
    Complete output (13 lines):
    Traceback (most recent call last):
      File "<string>", line 1, in <module>
      File "/root/detectron2_repo/setup.py", line 10, in <module>
        import torch
      File "/root/miniconda3/lib/python3.8/site-packages/torch/__init__.py", line 1429, in <module>
        from torch import optim as optim
      File "/root/miniconda3/lib/python3.8/site-packages/torch/optim/__init__.py", line 8, in <module>
        from .adadelta import Adadelta
      File "/root/miniconda3/lib/python3.8/site-packages/torch/optim/adadelta.py", line 4, in <module>
        from .optimizer import (Optimizer, _use_grad_for_differentiable, _default_to_fused_or_foreach,
      File "/root/miniconda3/lib/python3.8/site-packages/torch/optim/optimizer.py", line 23, in <module>
        from typing_extensions import ParamSpec, Self, TypeAlias
    ImportError: cannot import name 'Self' from 'typing_extensions' (/root/miniconda3/lib/python3.8/site-packages/typing_extensions.py)
    ----------------------------------------
WARNING: Discarding file:///root/detectron2_repo. Command errored out with exit status 1: python setup.py egg_info Check the logs for full command output.
ERROR: Command errored out with exit status 1: python setup.py egg_info Check the logs for full command output.

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在这里插入图片描述

2.6.3 解决3

pip install typing-extensions==4.3.0

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2.6.4 报错4

Traceback (most recent call last):
  File "tools/run_net.py", line 6, in <module>
    from slowfast.utils.misc import launch_job
  File "/root/slowfast/slowfast/utils/misc.py", line 19, in <module>
    import slowfast.utils.logging as logging
  File "/root/slowfast/slowfast/utils/logging.py", line 15, in <module>
    import slowfast.utils.distributed as du
  File "/root/slowfast/slowfast/utils/distributed.py", line 12, in <module>
    from pytorchvideo.layers.distributed import (  # noqa
ImportError: cannot import name 'cat_all_gather' from 'pytorchvideo.layers.distributed' (/root/miniconda3/lib/python3.8/site-packages/pytorchvideo/layers/distributed.py)

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在这里插入图片描述

2.6.4 解决4

cd /root

git clone https://gitee.com/YFwinston/pytorchvideo.git
cd pytorchvideo
pip install -e .
cd /root

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3 yolo数据集转ava数据集

https://github.com/Whiffe/SCB-dataset/tree/main/yolo2ava

python yolo2ava.py --yolo_path /root/5k_HRW_yolo_Dataset --ava_path /root/autodl-tmp/SCB-ava-Dataset4

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执行后的目录结构
SCB-ava-Dataset4
├── annotations
│ ├── ava_action_list_v2.2_for_activitynet_2019.pbtxt
│ ├── ava_included_timestamps_v2.2.txt
│ ├── ava_test_excluded_timestamps_v2.2.csv
│ ├── ava_train_excluded_timestamps_v2.2.csv
│ ├── ava_train_v2.2.csv
│ ├── ava_val_excluded_timestamps_v2.2.csv
│ ├── ava_val_v2.2.csv
│ └── person_box_67091280_iou90
│ ├── ava_detection_train_boxes_and_labels_include_negative_v2.2.csv
│ └── ava_detection_val_boxes_and_labels.csv
├── frame_lists
│ ├── train.csv
│ └── val.csv
└── frames
├── 0001001
│ ├── 0001001_000001.png
│ ├── 0001001_000002.png
│ ├── …
│
├── 0001002
│ ├── 0001002_000001.png
│ ├── 0001002_000002.png
│ ├── …

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可以用下面的命令查看当前路径的文件占了多少空间

du -sh

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4 训练

3.1 训练的相关配置文件

3.1.1 SLOWFAST_32x2_R101_50_50s.yaml

configs/AVA/c2/SLOWFAST_32x2_R101_50_50s.yaml

TRAIN:
  ENABLE: True
  DATASET: ava
  BATCH_SIZE: 4
  EVAL_PERIOD: 1
  CHECKPOINT_PERIOD: 1
  AUTO_RESUME: True
  CHECKPOINT_FILE_PATH: /root/autodl-nas/slowfast/SLOWFAST_32x2_R101_50_50.pkl 
  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]
  PATH_TO_DATA_DIR: '/root/autodl-tmp/SCB-ava-Dataset4'
DETECTION:
  ENABLE: True
  ALIGNED: False

AVA:
  FRAME_DIR: '/root/autodl-tmp/SCB-ava-Dataset4/frames'
  FRAME_LIST_DIR: '/root/autodl-tmp/SCB-ava-Dataset4/frame_lists'
  ANNOTATION_DIR: '/root/autodl-tmp/SCB-ava-Dataset4/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"]
  FULL_TEST_ON_VAL: True

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: True
  DATASET: ava
  BATCH_SIZE: 8
DATA_LOADER:
  NUM_WORKERS: 2
  PIN_MEMORY: True
NUM_GPUS: 1
NUM_SHARDS: 1
RNG_SEED: 0
OUTPUT_DIR: '/root/autodl-tmp/'



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3.1.2 ava_action_list_v2.2_for_activitynet_2019.pbtxt

SCB-ava-Dataset4/annotations/ava_action_list_v2.2_for_activitynet_2019.pbtxt

item {
  name: "hand-raising"
  id: 1
}
item {
  name: "reading"
  id: 2
}
item {
  name: "writing"
  id: 3
}

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3.1.3 ava_included_timestamps_v2.2.txt

SCB-ava-Dataset4/annotations/ava_included_timestamps_v2.2.txt

0002

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3.1.4 ava_train/test_excluded_timestamps_v2.2.csv

空
SCB-ava-Dataset4/annotations/ava_test_excluded_timestamps_v2.2.csv
SCB-ava-Dataset4/annotations/ava_train_excluded_timestamps_v2.2.csv

ava_helper.py

ava默认是从902帧开始，我们可以从第1帧开始，不过需要修改代码，位置在slowfast/slowfast/datasets/ava_helper.py，
AVA_VALID_FRAMES = range(2, 3)
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3.2 训练

python tools/run_net.py \
  --cfg configs/AVA/c2/SLOWFAST_32x2_R101_50_50s.yaml 

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3.3 相关报错

3.3.1 报错1

报错：
Traceback (most recent call last):
File “tools/run_net.py”, line 52, in
main()
File “tools/run_net.py”, line 27, in main
launch_job(cfg=cfg, init_method=args.init_method, func=train)
File “/root/slowfast/slowfast/utils/misc.py”, line 430, in launch_job
func(cfg=cfg)
File “/root/slowfast/tools/train_net.py”, line 681, in train
train_epoch(
File “/root/slowfast/tools/train_net.py”, line 276, in train_epoch
del inputs
UnboundLocalError: local variable ‘inputs’ referenced before assignment
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3.3.1 解决1

解决：
https://github.com/facebookresearch/SlowFast/issues/547
在这里插入图片描述
更改batch_size的大小，报错是因为batch_size大于视频数量

3.3.2 报错2

报错：
Traceback (most recent call last):
File “tools/run_net.py”, line 52, in
main()
File “tools/run_net.py”, line 27, in main
launch_job(cfg=cfg, init_method=args.init_method, func=train)
File “/root/slowfast/slowfast/utils/misc.py”, line 430, in launch_job
func(cfg=cfg)
File “/root/slowfast/tools/train_net.py”, line 681, in train
train_epoch(
File “/root/slowfast/tools/train_net.py”, line 110, in train_epoch
optim.set_lr(optimizer, lr)
File “/root/slowfast/slowfast/models/optimizer.py”, line 267, in set_lr
param_group[“lr”] = new_lr * param_group[“layer_decay”]
KeyError: ‘layer_decay’

在这里插入图片描述

3.3.2 解决2

解决：
https://github.com/facebookresearch/SlowFast/issues/654

slowfast/slowfast/models/optimizer.py
在这里插入图片描述


def set_lr(optimizer, new_lr):
    """
    Sets the optimizer lr to the specified value.
    Args:
        optimizer (optim): the optimizer using to optimize the current network.
        new_lr (float): the new learning rate to set.
    """
    for param_group in optimizer.param_groups:
        try:
            param_group["lr"] = new_lr * param_group["layer_decay"]
        except:
            param_group["lr"] = new_lr * 1.0



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3.3.3 报错3

错误：

/root/slowfast/ava_evaluation/metrics.py:41: FutureWarning: In the future `np.bool` will be defined as the corresponding NumPy scalar.
  or labels.dtype != np.bool
Traceback (most recent call last):
  File "tools/run_net.py", line 52, in <module>
    main()
  File "tools/run_net.py", line 27, in main
    launch_job(cfg=cfg, init_method=args.init_method, func=train)
  File "/root/slowfast/slowfast/utils/misc.py", line 430, in launch_job
    func(cfg=cfg)
  File "/root/slowfast/tools/train_net.py", line 748, in train
    eval_epoch(
  File "/root/miniconda3/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
    return func(*args, **kwargs)
  File "/root/slowfast/tools/train_net.py", line 420, in eval_epoch
    val_meter.log_epoch_stats(cur_epoch)
  File "/root/slowfast/slowfast/utils/meters.py", line 235, in log_epoch_stats
    self.finalize_metrics(log=False)
  File "/root/slowfast/slowfast/utils/meters.py", line 208, in finalize_metrics
    self.full_map = evaluate_ava(
  File "/root/slowfast/slowfast/utils/ava_eval_helper.py", line 162, in evaluate_ava
    results = run_evaluation(categories, groundtruth, detections, excluded_keys)
  File "/root/slowfast/slowfast/utils/ava_eval_helper.py", line 238, in run_evaluation
    metrics = pascal_evaluator.evaluate()
  File "/root/slowfast/ava_evaluation/object_detection_evaluation.py", line 307, in evaluate
    ) = self._evaluation.evaluate()
  File "/root/slowfast/ava_evaluation/object_detection_evaluation.py", line 799, in evaluate
    precision, recall = metrics.compute_precision_recall(
  File "/root/slowfast/ava_evaluation/metrics.py", line 41, in compute_precision_recall
    or labels.dtype != np.bool
  File "/root/miniconda3/lib/python3.8/site-packages/numpy/__init__.py", line 305, in __getattr__
    raise AttributeError(__former_attrs__[attr])
AttributeError: module 'numpy' has no attribute 'bool'.
`np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
    https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations

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在这里插入图片描述

3.3.3 解决3

成功解决AttributeError: module ‘numpy‘ has no attribute ‘bool‘.
pip uninstall numpy

pip install numpy==1.23.2

报错：

Traceback (most recent call last):
  File "tools/run_net.py", line 52, in <module>
    main()
  File "tools/run_net.py", line 27, in main
    launch_job(cfg=cfg, init_method=args.init_method, func=train)
  File "/root/slowfast/slowfast/utils/misc.py", line 430, in launch_job
    func(cfg=cfg)
  File "/root/slowfast/tools/train_net.py", line 748, in train
    eval_epoch(
  File "/root/miniconda3/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
    return func(*args, **kwargs)
  File "/root/slowfast/tools/train_net.py", line 420, in eval_epoch
    val_meter.log_epoch_stats(cur_epoch)
  File "/root/slowfast/slowfast/utils/meters.py", line 235, in log_epoch_stats
    self.finalize_metrics(log=False)
  File "/root/slowfast/slowfast/utils/meters.py", line 208, in finalize_metrics
    self.full_map = evaluate_ava(
  File "/root/slowfast/slowfast/utils/ava_eval_helper.py", line 162, in evaluate_ava
    results = run_evaluation(categories, groundtruth, detections, excluded_keys)
  File "/root/slowfast/slowfast/utils/ava_eval_helper.py", line 221, in run_evaluation
    pascal_evaluator.add_single_detected_image_info(
  File "/root/slowfast/ava_evaluation/object_detection_evaluation.py", line 275, in add_single_detected_image_info
    self._evaluation.add_single_detected_image_info(
  File "/root/slowfast/ava_evaluation/object_detection_evaluation.py", line 710, in add_single_detected_image_info
    ) = self.per_image_eval.compute_object_detection_metrics(
  File "/root/slowfast/ava_evaluation/per_image_evaluation.py", line 114, in compute_object_detection_metrics
    scores, tp_fp_labels = self._compute_tp_fp(
  File "/root/slowfast/ava_evaluation/per_image_evaluation.py", line 210, in _compute_tp_fp
    scores, tp_fp_labels = self._compute_tp_fp_for_single_class(
  File "/root/slowfast/ava_evaluation/per_image_evaluation.py", line 310, in _compute_tp_fp_for_single_class
    ) = self._get_overlaps_and_scores_box_mode(
  File "/root/slowfast/ava_evaluation/per_image_evaluation.py", line 253, in _get_overlaps_and_scores_box_mode
    gt_non_group_of_boxlist = np_box_list.BoxList(
  File "/root/slowfast/ava_evaluation/np_box_list.py", line 58, in __init__
    raise ValueError(
ValueError: Invalid box data. data must be a numpy array of N*[y_min, x_min, y_max, x_max]

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3.3.4 问题4

Traceback (most recent call last):
  File "tools/run_net.py", line 52, in <module>
    main()
  File "tools/run_net.py", line 27, in main
    launch_job(cfg=cfg, init_method=args.init_method, func=train)
  File "/data/SCB/slowfast/slowfast/utils/misc.py", line 430, in launch_job
    func(cfg=cfg)
  File "/data/SCB/slowfast/tools/train_net.py", line 681, in train
    train_epoch(
  File "/data/SCB/slowfast/tools/train_net.py", line 176, in train_epoch
    scaler.step(optimizer)
  File "/root/miniconda3/envs/slowfast/lib/python3.8/site-packages/torch/cuda/amp/grad_scaler.py", line 315, in step
    return optimizer.step(*args, **kwargs)
  File "/root/miniconda3/envs/slowfast/lib/python3.8/site-packages/torch/optim/optimizer.py", line 280, in wrapper
    out = func(*args, **kwargs)
  File "/root/miniconda3/envs/slowfast/lib/python3.8/site-packages/torch/optim/optimizer.py", line 33, in _use_grad
    ret = func(self, *args, **kwargs)
  File "/root/miniconda3/envs/slowfast/lib/python3.8/site-packages/torch/optim/sgd.py", line 76, in step
    sgd(params_with_grad,
  File "/root/miniconda3/envs/slowfast/lib/python3.8/site-packages/torch/optim/sgd.py", line 222, in sgd
    func(params,
  File "/root/miniconda3/envs/slowfast/lib/python3.8/site-packages/torch/optim/sgd.py", line 306, in _multi_tensor_sgd
    torch._foreach_add_(bufs, device_grads, alpha=1 - dampening)
RuntimeError: The size of tensor a (80) must match the size of tensor b (3) at non-singleton dimension 0


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在这里插入图片描述

解决：

这里报错的原因是预训练模型的原因，不应该加载预训练模型，注释TRAIN.CHECKPOINT_FILE_PATH即可
在这里插入图片描述

4 测试

4.1 配置文件

slowfast/configs/AVA/c2/SLOWFAST_32x2_R101_50_50s2.yaml

TRAIN:
  ENABLE: False
  DATASET: ava
  BATCH_SIZE: 4
  EVAL_PERIOD: 1
  CHECKPOINT_PERIOD: 1
  AUTO_RESUME: True
  CHECKPOINT_FILE_PATH: /root/autodl-nas/slowfast/SLOWFAST_32x2_R101_50_50.pkl 
  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]
  PATH_TO_DATA_DIR: '/root/autodl-tmp/SCB-ava-Dataset4'
DETECTION:
  ENABLE: True
  ALIGNED: False

AVA:
  FRAME_DIR: '/root/autodl-tmp/SCB-ava-Dataset4/frames'
  FRAME_LIST_DIR: '/root/autodl-tmp/SCB-ava-Dataset4/frame_lists'
  ANNOTATION_DIR: '/root/autodl-tmp/SCB-ava-Dataset4/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"]
  FULL_TEST_ON_VAL: True

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: True
  DATASET: ava
  BATCH_SIZE: 1
  CHECKPOINT_FILE_PATH: /root/autodl-tmp/slowfast_train/checkpoints/checkpoint_epoch_00034.pyth
DATA_LOADER:
  NUM_WORKERS: 2
  PIN_MEMORY: True
NUM_GPUS: 1
NUM_SHARDS: 1
RNG_SEED: 0
OUTPUT_DIR: '/root/autodl-tmp/'


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4.2 测试

python tools/run_net.py \
  --cfg configs/AVA/c2/SLOWFAST_32x2_R101_50_50s2.yaml  \

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在这里插入图片描述

错误：

Traceback (most recent call last):
  File "tools/run_net.py", line 52, in <module>
    main()
  File "tools/run_net.py", line 37, in main
    launch_job(cfg=cfg, init_method=args.init_method, func=test)
  File "/root/slowfast/slowfast/utils/misc.py", line 430, in launch_job
    func(cfg=cfg)
  File "/root/slowfast/tools/test_net.py", line 252, in test
    test_meter = perform_test(test_loader, model, test_meter, cfg, writer)
  File "/root/miniconda3/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
    return func(*args, **kwargs)
  File "/root/slowfast/tools/test_net.py", line 129, in perform_test
    test_meter.iter_toc()
  File "/root/slowfast/slowfast/utils/meters.py", line 159, in iter_toc
    self.iter_timer.pause()
  File "/root/miniconda3/lib/python3.8/site-packages/fvcore/common/timer.py", line 30, in pause
    raise ValueError("Trying to pause a Timer that is already paused!")
ValueError: Trying to pause a Timer that is already paused!

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在这里插入图片描述

解决：
https://github.com/facebookresearch/SlowFast/issues/599#ref-issue-1441689338
https://github.com/facebookresearch/SlowFast/issues/623

修改test_net.py中的内容，加两组try except

在这里插入图片描述


    if cfg.NUM_GPUS > 1:
        preds, labels, video_idx = du.all_gather([preds, labels, video_idx])
    if cfg.NUM_GPUS:
        preds = preds.cpu()
        labels = labels.cpu()
        video_idx = video_idx.cpu()

    try:
        test_meter.iter_toc()
    except:
        pass
    if not cfg.VIS_MASK.ENABLE:
        # Update and log stats.
        test_meter.update_stats(
            preds.detach(), labels.detach(), video_idx.detach()
        )
    try:
        test_meter.log_iter_stats(cur_iter)
    except:
        test_meter.log_iter_stats(None,cur_iter)

    test_meter.iter_tic()

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报错：

Traceback (most recent call last):
  File "tools/run_net.py", line 52, in <module>
    main()
  File "tools/run_net.py", line 37, in main
    launch_job(cfg=cfg, init_method=args.init_method, func=test)
  File "/root/slowfast/slowfast/utils/misc.py", line 430, in launch_job
    func(cfg=cfg)
  File "/root/slowfast/tools/test_net.py", line 257, in test
    test_meter = perform_test(test_loader, model, test_meter, cfg, writer)
  File "/root/miniconda3/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
    return func(*args, **kwargs)
  File "/root/slowfast/tools/test_net.py", line 166, in perform_test
    test_meter.finalize_metrics()
  File "/root/slowfast/slowfast/utils/meters.py", line 200, in finalize_metrics
    all_ori_boxes = torch.cat(self.all_ori_boxes, dim=0)
RuntimeError: Sizes of tensors must match except in dimension 0. Got 5 and 80 in dimension 1 (The offending index is 1)

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在这里插入图片描述
这个错误是在运行PyTorch代码时出现的，它指的是你在进行张量（tensor）操作时，试图将两个维度（dimension）大小不匹配的张量进行相加或其他操作。

具体来说，错误消息"Sizes of tensors must match except in dimension 0. Got 5 and 80 in dimension 1 (The offending index is 1)"告诉我们，你正在试图将一个在第一个维度大小为5的张量和第一个维度大小为80的张量进行相加。这是不允许的，因为在除第一个维度之外的所有维度上，两个输入张量的大小必须完全相同。

为了解决这个问题，你需要确保你正在操作的两个张量在所有维度上的大小都相同，或者至少在需要进行操作的维度上相同。

例如，如果你有两个大小为(5, 80)和(80, 80)的张量，你不能直接将它们相加，因为它们的第二个维度大小不匹配。你需要确保它们的大小相同，比如通过调整你的数据或者改变你的操作。

如果你能提供更多关于你遇到这个错误的上下文信息（比如你的代码片段和你的数据），我可能能提供更具体的建议。


Traceback (most recent call last):
  File "tools/run_net.py", line 52, in <module>
    main()
  File "tools/run_net.py", line 37, in main
    launch_job(cfg=cfg, init_method=args.init_method, func=test)
  File "/data/SCB/slowfast/slowfast/utils/misc.py", line 430, in launch_job
    func(cfg=cfg)
  File "/data/SCB/slowfast/tools/test_net.py", line 262, in test
    test_meter = perform_test(test_loader, model, test_meter, cfg, writer)
  File "/root/miniconda3/envs/slowfast/lib/python3.8/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
    return func(*args, **kwargs)
  File "/data/SCB/slowfast/tools/test_net.py", line 171, in perform_test
    test_meter.finalize_metrics()
  File "/data/SCB/slowfast/slowfast/utils/meters.py", line 204, in finalize_metrics
    all_ori_boxes = torch.cat(self.all_ori_boxes, dim=0)
RuntimeError: Sizes of tensors must match except in dimension 0. Expected size 5 but got size 3 for tensor number 1 in the list.

解决：

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报错的原因如下：
维度不匹配，
下面内容导致了维度不匹配（test_net.py）

        if not cfg.VIS_MASK.ENABLE:
            # Update and log stats.
            # print("labels.detach():",labels.detach())
            test_meter.update_stats(
                preds.detach(), labels.detach(), video_idx.detach()

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在这里插入图片描述

需要添加配置文件SLOWFAST_32x2_R101_50_50s.yaml中添加如下东西
在这里插入图片描述

VIS_MASK:
  ENABLE: True

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4.2 demo测试

4.2.1 配置文件

slowfast/demo/AVA/SLOWFAST_32x2_R101_50_50s2.yaml

TRAIN:
  ENABLE: False
  DATASET: ava
  BATCH_SIZE: 1
  EVAL_PERIOD: 1
  CHECKPOINT_PERIOD: 1
  AUTO_RESUME: True
  CHECKPOINT_FILE_PATH: /root/slowfast_train/checkpoints/checkpoint_epoch_00034.pyth  #path to pretrain model
  CHECKPOINT_TYPE: pytorch
DATA:
  NUM_FRAMES: 16
  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: 1
DATA_LOADER:
  NUM_WORKERS: 1
  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: "demo/AVA/student_behaviors.json" # Add local label file path here.
  INPUT_VIDEO: "/root/autodl-tmp/0_0_0.mp4"
  OUTPUT_FILE: "/root/autodl-tmp/0_1_1.mp4"
  DETECTRON2_CFG: "COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml"
  DETECTRON2_WEIGHTS: "/root/autodl-nas/slowfast/model_final_280758.pkl"

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4.2.2 student_behaviors.json

slowfast/demo/AVA/student_behaviors.json

{"hand-raising": 0, "reading": 1, "writing": 2}

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4.2.2 demo测试

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

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在这里插入图片描述

5 在非云平台安装SlowFast（本地机器上）

5.1 基础环境

torch版本：
2.0.0+cu117

torchvision版本
0.15.1+cu117

pip install 'git+https://gitee.com/YFwinston/fvcore'

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pip install simplejson

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conda install av -c conda-forge -y

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conda install x264 ffmpeg -c conda-forge -y

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pip install -U iopath

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pip install psutil

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pip install opencv-python

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// pip install torchvision

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pip install tensorboard

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pip install moviepy

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pip install pytorchvideo

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pip install 'git+https://gitee.com/YFwinston/fairscale'

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cd /data/SCB/

git clone https://gitee.com/YFwinston/pytorchvideo.git
cd pytorchvideo
pip install -e .
/data/SCB/

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5.2 detectron2_repo 安装

// pip install -U torch torchvision cython
// pip install -U  cython
pip install cython

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pip install -U 'git+https://gitee.com/YFwinston/fvcore.git' 'git+https://gitee.com/YFwinston/cocoapi.git#subdirectory=PythonAPI'

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git clone https://gitee.com/YFwinston/detectron2 detectron2_repo

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pip install typing-extensions==4.3.0

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pip install -e detectron2_repo

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pip install pillow
pip install pyyaml

pip install scipy
pip install pandas
pip install scikit-learn


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5.3 slowfast 安装

git clone https://gitee.com/YFwinston/slowfast

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export PYTHONPATH=/data/SCB/slowfast:$PYTHONPATH

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cd slowfast
python setup.py build develop
cd ..


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5.4 相关文件

ava.json

cd /data/SCB/slowfast/demo/AVA
vim ava.json


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{"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}


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SLOWFAST_32x2_R101_50_50s.yaml

cd /data/SCB/slowfast/demo/AVA
vim SLOWFAST_32x2_R101_50_50s.yaml

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TRAIN:
  ENABLE: False
  DATASET: ava
  BATCH_SIZE: 1
  EVAL_PERIOD: 1
  CHECKPOINT_PERIOD: 1
  AUTO_RESUME: True
  CHECKPOINT_FILE_PATH: /data/SCB/slowfast/mywork/SLOWFAST_32x2_R101_50_50.pkl  #path to pretrain model
  CHECKPOINT_TYPE: pytorch
DATA:
  NUM_FRAMES: 16
  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: 1
DATA_LOADER:
  NUM_WORKERS: 1
  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: "demo/AVA/ava.json" # Add local label file path here.
  INPUT_VIDEO: "/data/SCB/slowfast/mywork/1.mp4"
  OUTPUT_FILE: "/data/SCB/slowfast/mywork/1_1.mp4"
  DETECTRON2_CFG: "COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml"
  DETECTRON2_WEIGHTS: "/data/SCB/slowfast/mywork/model_final_280758.pkl"

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5.5 demo测试

在/data/SCB/slowfast/mywork/中传入一个视频：1.mp4
执行：

cd /data/SCB/slowfast/
python tools/run_net.py --cfg demo/AVA/SLOWFAST_32x2_R101_50_50s.yaml

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在这里插入图片描述

在这里插入图片描述

5.6 训练

训练的配置文件 SLOWFAST_32x2_R101_50_50s.yaml

slowfast/configs/AVA/c2/SLOWFAST_32x2_R101_50_50s.yaml

TRAIN:
  ENABLE: True
  DATASET: ava
  BATCH_SIZE: 8
  EVAL_PERIOD: 1
  CHECKPOINT_PERIOD: 1
  AUTO_RESUME: True
  # CHECKPOINT_FILE_PATH: /data/SCB/slowfast/mywork/SLOWFAST_32x2_R101_50_50.pkl 
  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]
  PATH_TO_DATA_DIR: '/data/SCB/Dataset/SCB-ava-Dataset4'
DETECTION:
  ENABLE: True
  # ENABLE: False
  ALIGNED: False
VIS_MASK:
  ENABLE: True
AVA:
  FRAME_DIR: '/data/SCB/Dataset/SCB-ava-Dataset4/frames'
  FRAME_LIST_DIR: '/data/SCB/Dataset/SCB-ava-Dataset4/frame_lists'
  ANNOTATION_DIR: '/data/SCB/Dataset/SCB-ava-Dataset4/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"]
  FULL_TEST_ON_VAL: True

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:
  BASE_LR: 0.01
  MOMENTUM: 0.9
  WEIGHT_DECAY: 1e-7
  OPTIMIZING_METHOD: sgd
  MAX_EPOCH: 50
MODEL:
  # NUM_CLASSES: 80
  NUM_CLASSES: 3
  ARCH: slowfast
  MODEL_NAME: SlowFast
  # LOSS_FUNC: bce
  LOSS_FUNC: cross_entropy
  # LOSS_FUNC: F_BCE
  DROPOUT_RATE: 0.5
  # HEAD_ACT: sigmoid
TEST:
  ENABLE: True
  DATASET: ava
  BATCH_SIZE: 1
DATA_LOADER:
  NUM_WORKERS: 2
  PIN_MEMORY: True
NUM_GPUS: 1
NUM_SHARDS: 1
RNG_SEED: 0
OUTPUT_DIR: '/data/SCB/slowfast/mywork/out/'


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注意 NUM_GPUS 和 TEST.BATCH_SIZE 要保持一致

训练

python tools/run_net.py \
  --cfg configs/AVA/c2/SLOWFAST_32x2_R101_50_50s.yaml 

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在这里插入图片描述

6 其他

6.1 日志文件修改

在日志文件中添加类似如下内容：

{ 'PascalBoxes_PerformanceByCategory/AP@0.5IOU/hand-raising': 0.9341844125742432,
  'PascalBoxes_PerformanceByCategory/AP@0.5IOU/reading': 0.29780414588177817,
  'PascalBoxes_PerformanceByCategory/AP@0.5IOU/writing': 0.375,
  'PascalBoxes_Precision/mAP@0.5IOU': 0.5356628528186738}


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在"slowfast/slowfast/utils/ava_eval_helper.py"
添加：

    logger.info("AVA eval done in %f seconds." % (time.time() - eval_start))

    logger.info(results)
    
    return results["PascalBoxes_Precision/mAP@0.5IOU"]

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在这里插入图片描述
结果如下：

在这里插入图片描述

相关参考

[深度学习][原创]slowfast的demo跑起来的正确方法

[深度学习][原创]使用labelImg+yolov5完成所有slowfast时空动作检测项目-开山篇

[深度学习][原创]使用labelImg+yolov5完成所有slowfast时空动作检测项目-流程篇

[深度学习][原创]使用labelImg+yolov5完成所有slowfast时空动作检测项目-配置文件篇

[深度学习][原创]使用labelImg+yolov5完成所有slowfast时空动作检测项目-训练测试篇

[深度学习][原创]mmaction2时空动作检测过滤成自己想要的类别

三分钟快速安装 facebookresearch SlowFast

PyTorchVideo SlowFast

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