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SlowFast

阮喵喵2023年6月1日大约 9 分钟

SlowFast

参考资料

官方安装依赖清单表

https://github.com/facebookresearch/SlowFast/blob/main/INSTALL.md

window 环境安装

别人是怎么做的？别人曾遇到了什么坑？别人怎么解决的？

在 window10、云平台、linux 等平台下，运行 slowfast 程序的环境是否正确？

linux 环境安装

新建一个全新的 anaconda 环境

conda create -n slowfast python=3.8

安装依赖

pytorch

conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cudatoolkit=11.1 -c pytorch -c conda-forge

fvcore √

参考资料

git clone https://github.com/facebookresearch/fvcore
cd fvcore
python setup.py install

这篇文章称 pip 会下载额外所需的子依赖。根据官方文章所述，这里选择本地克隆项目，然后让 pip 自己安装指定包依赖，并首先索引到本地目录做安装。

git clone https://github.com/facebookresearch/fvcore
pip install -e fvcore

使用正确的克隆地址：

git clone https://github.com/facebookresearch/fvcore.git
pip install -e fvcore

torchvision √

simplejson √

pip install simplejson

GCC >= 4.9 √

PyAV √

conda install av -c conda-forge

这里走清华镜像，故命令为：

conda install av

速度太慢，最终选择 pip

pip install av

iopath √

pip install -U iopath

psutil √

pip install psutil

OpenCV √

pip install opencv-python

pip install tensorboard
pip install pytorchvideo
pip install moviepy
pip install pytorchvideo

fairscale √

git clone https://github.com/facebookresearch/fairscale.git
pip install fairscale

cython √

根据文章所述，这里的命令为：

pip install -U cython

Detectron2

开始逐步执行这些语句

git clone https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI
git clone https://github.com/facebookresearch/detectron2 detectron2_repo

git clone https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI

根据文章所述，这里的命令为：

pip install -U git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI

出错：

ERROR: Could not find a version that satisfies the requirement matplotlib>=2.1.0 (from pycocotools) (from versions: none)
ERROR: No matching distribution found for matplotlib>=2.1.0

尝试换成：

git clone https://github.com/cocodataset/cocoapi.git
cd cocoapi/PythonAPI
pip install -U PythonAPI

pip install -U PythonAPI失败。不存在该依赖包。根据官方文档，改写为：

pip install -e cocoapi/PythonAPI

缺少指定版本依赖，根据官方文档，自主编写命令：

pip install 'matplotlib>=2.1.0'

取消翻墙，使用镜像。顺利完成。

再次执行：

pip install -e cocoapi/PythonAPI

出错。尝试换成：

pip install pycocotools

git clone https://github.com/facebookresearch/detectron2 detectron2_repo

pip install -e detectron2_repo

顺利完成。

$PYTHONPATH

export PYTHONPATH=/path/to/SlowFast/slowfast:$PYTHONPATH

根据参考资料，自主编写设置临时环境变量：

set PTYTHONPATH=slowfast
echo %PYTHONPATH%
set PTYTHONPATH=D:\code\web-dev-work-place\github-desktop-store\SlowFast\slowfast
echo %PYTHONPATH%
set PTYTHONPATH=%PYTHONPATH%;D:\code\web-dev-work-place\github-desktop-store\SlowFast\slowfast
echo %PYTHONPATH%

无法校验是否正确。跳转到下一个阶段。

Build PySlowFast

开始构建

python setup.py build develop

根据文章，更改文件 setup.py。

顺利。无任何错误。

开始运行？

python tools/run_net.py --cfg configs/Kinetics/C2D_8x8_R50.yaml NUM_GPUS 1 TRAIN.BATCH_SIZE 8 SOLVER.BASE_LR 0.0125 DATA.PATH_TO_DATA_DIR path_to_your_data_folder

报错 ImportError: DLL load failed while importing _imaging: 找不到指定的模块

Traceback (most recent call last):
  File "tools/run_net.py", line 6, in <module>
    from slowfast.utils.misc import launch_job
  File "d:\code\web-dev-work-place\github-desktop-store\slowfast\slowfast\utils\misc.py", line 12, in <module>
    import torchvision.io as io
  File "D:\dev-evn\anaconda\envs\slowfast\lib\site-packages\torchvision\__init__.py", line 7, in <module>
    from torchvision import datasets
  File "D:\dev-evn\anaconda\envs\slowfast\lib\site-packages\torchvision\datasets\__init__.py", line 1, in <module>
    from .lsun import LSUN, LSUNClass
  File "D:\dev-evn\anaconda\envs\slowfast\lib\site-packages\torchvision\datasets\lsun.py", line 2, in <module>
    from PIL import Image
  File "D:\dev-evn\anaconda\envs\slowfast\lib\site-packages\PIL\Image.py", line 100, in <module>
    from . import _imaging as core
ImportError: DLL load failed while importing _imaging: 找不到指定的模块。

尝试升级

pip install -U pillow

报错 ImportError: cannot import name 'cat_all_gather' from 'pytorchvideo.layers.distributed'

Traceback (most recent call last):
  File "tools/run_net.py", line 6, in <module>
    from slowfast.utils.misc import launch_job
  File "d:\code\web-dev-work-place\github-desktop-store\slowfast\slowfast\utils\misc.py", line 19, in <module>
    import slowfast.utils.logging as logging
  File "d:\code\web-dev-work-place\github-desktop-store\slowfast\slowfast\utils\logging.py", line 15, in <module>
    import slowfast.utils.distributed as du
  File "d:\code\web-dev-work-place\github-desktop-store\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' (D:\dev-evn\anaconda\envs\slowfast\lib\site-packages\pytorchvideo\layers\distributed.py)

根据issue，处理方式为

git clone https://github.com/facebookresearch/pytorchvideo.git
cd pytorchvideo
pip install -e .

根据上述的操作方式，这里改写为以下命令：

git clone https://github.com/facebookresearch/pytorchvideo.git
pip install -e pytorchvideo

报错 ModuleNotFoundError: No module named 'scipy'

Traceback (most recent call last):
  File "tools/run_net.py", line 6, in <module>
    from slowfast.utils.misc import launch_job
  File "d:\code\web-dev-work-place\github-desktop-store\slowfast\slowfast\utils\misc.py", line 21, in <module>
    from slowfast.datasets.utils import pack_pathway_output
  File "d:\code\web-dev-work-place\github-desktop-store\slowfast\slowfast\datasets\__init__.py", line 4, in <module>
    from .ava_dataset import Ava  # noqa
  File "d:\code\web-dev-work-place\github-desktop-store\slowfast\slowfast\datasets\ava_dataset.py", line 10, in <module>
    from . import transform as transform
  File "d:\code\web-dev-work-place\github-desktop-store\slowfast\slowfast\datasets\transform.py", line 14, in <module>
    from scipy.ndimage import gaussian_filter
ModuleNotFoundError: No module named 'scipy'

根据报错，自主安装依赖：

pip install scipy

ModuleNotFoundError: No module named 'sklearn'

Traceback (most recent call last):
  File "tools/run_net.py", line 9, in <module>
    from demo_net import demo
  File "D:\code\web-dev-work-place\github-desktop-store\SlowFast\tools\demo_net.py", line 10, in <module>
    from slowfast.visualization.async_predictor import AsyncDemo, AsyncVis
  File "d:\code\web-dev-work-place\github-desktop-store\slowfast\slowfast\visualization\async_predictor.py", line 12, in <module>
    from slowfast.visualization.predictor import Predictor
  File "d:\code\web-dev-work-place\github-desktop-store\slowfast\slowfast\visualization\predictor.py", line 15, in <module>
    from slowfast.visualization.utils import process_cv2_inputs
  File "d:\code\web-dev-work-place\github-desktop-store\slowfast\slowfast\visualization\utils.py", line 8, in <module>
    from sklearn.metrics import confusion_matrix
ModuleNotFoundError: No module named 'sklearn'

自主编写的命令

pip install sklearn

根据文章得知，sklearn 是 scikit-learn 的缩写，应该改为：

pip install scikit-learn

RuntimeError: Distributed package doesn't have NCCL built in

注意到 window 必须要用 gloo，linux 用 nccl。

try 1

git clone --recursive https://github.com/pytorch/pytorch
cd pytorch
pip install numpy ninja pyyaml mkl mkl-include setuptools cmake cffi

这里仅且克隆，但是不安装了。发现 git clone pytorch 会下载太多的子模块。很容易导致后续的失败。这里放弃了。

try 2

import torch
torch.cuda.is_available()
import torch
torch.__version__
# '1.8.0+cu111'
torch.cuda.nccl.is_available(torch.randn(1).cuda())
# True
torch.cuda.nccl.version()

运行了上述代码，为 false：

>>> import torch
>>> torch.__version__
'1.8.0'
>>> torch.cuda.nccl.is_available(torch.randn(1).cuda())
D:\dev-evn\anaconda\envs\slowfast\lib\site-packages\torch\cuda\nccl.py:16: UserWarning: PyTorch is not compiled with NCCL support
  warnings.warn('PyTorch is not compiled with NCCL support')
False
>>> torch.cuda.nccl.version()
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "D:\dev-evn\anaconda\envs\slowfast\lib\site-packages\torch\cuda\nccl.py", line 36, in version
    return torch._C._nccl_version()
AttributeError: module 'torch._C' has no attribute '_nccl_version'

参考资料的说法无头无尾的。说不清楚到底应该用什么方式处理。都是在分析能不能用，而不是怎么兼容。这个方式不合适，放弃了。

try 3

这篇 issue 给出的解决方案是增加环境变量。设置环境变量 PL_TORCH_DISTRIBUTED_BACKEND=gloo

尝试了。效果不好。正如 issue 所述，效果不好。

try 4

按照同事郭睿的说法，更改代码。这里先选择在自定义配置内改写为 gloo

D:\code\web-dev-work-place\github-desktop-store\SlowFast\build\lib\slowfast\config\custom_config.py

事实上根本判断不出来，到底是应该在那个地方改动，改成 gloo。内容过多。

终止此命令

经过一系列的查询资料。发现在 window 内硬装该项目，太容易暴毙了。环境很不适合。这里考虑参考别人的资料，下载模型权重文件，再看看本地运行效果。

如果情况不好。按照这两个策略来做：

  • 笔记本电脑或者是台式机，本地新建 linux 虚拟机，看看是否可以使用到 gpu。在 linux 环境下，再完成 slowfast 的环境搭建和训练。
  • 用云服务器搭建。

编纂 SLOWFAST_32x2_R101_50_50.yaml

教程的配置：

TRAIN:
  ENABLE: False
  DATASET: ava
  BATCH_SIZE: 16
  EVAL_PERIOD: 1
  CHECKPOINT_PERIOD: 1
  AUTO_RESUME: True
  # 刚刚下载的官方权重文件的路径
  CHECKPOINT_FILE_PATH: "D:/python/video_classify/SlowFast-main/weights/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: "./demo/AVA/ava.json" #刚刚生成的label文件
  INPUT_VIDEO: "./input/1.mp4" #视频输入路径
  OUTPUT_FILE: "./output/1.mp4" #视频输出路径

  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

当前配置：

TRAIN:
  ENABLE: False
  DATASET: ava
  BATCH_SIZE: 16
  EVAL_PERIOD: 1
  CHECKPOINT_PERIOD: 1
  AUTO_RESUME: True
  CHECKPOINT_FILE_PATH: ./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: "./ava.json" #刚刚生成的label文件
  INPUT_VIDEO: "./input/demo.mp4"			#视频输入路径
  OUTPUT_FILE: "./output/demo.mp4"			#视频输出路径

  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

执行命令：

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

_pickle.UnpicklingError: pickle data was truncated

Traceback (most recent call last):
  File "tools/run_net.py", line 57, in <module>
    main()
  File "tools/run_net.py", line 53, in main
    demo(cfg)
  File "D:\code\web-dev-work-place\github-desktop-store\SlowFast\tools\demo_net.py", line 114, in demo
    for task in tqdm.tqdm(run_demo(cfg, frame_provider)):
  File "D:\dev-evn\anaconda\envs\slowfast\lib\site-packages\tqdm\std.py", line 1178, in __iter__
    for obj in iterable:
  File "D:\code\web-dev-work-place\github-desktop-store\SlowFast\tools\demo_net.py", line 59, in run_demo
    model = ActionPredictor(cfg=cfg, async_vis=async_vis)
  File "d:\code\web-dev-work-place\github-desktop-store\slowfast\slowfast\visualization\predictor.py", line 132, in __init__
    self.predictor = Predictor(cfg=cfg, gpu_id=gpu_id)
  File "d:\code\web-dev-work-place\github-desktop-store\slowfast\slowfast\visualization\predictor.py", line 46, in __init__
    cu.load_test_checkpoint(cfg, self.model)
  File "d:\code\web-dev-work-place\github-desktop-store\slowfast\slowfast\utils\checkpoint.py", line 692, in load_test_checkpoint
    load_checkpoint(
  File "d:\code\web-dev-work-place\github-desktop-store\slowfast\slowfast\utils\checkpoint.py", line 298, in load_checkpoint
    checkpoint = torch.load(f, map_location="cpu")
  File "D:\dev-evn\anaconda\envs\slowfast\lib\site-packages\torch\serialization.py", line 593, in load
    return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args)
  File "D:\dev-evn\anaconda\envs\slowfast\lib\site-packages\torch\serialization.py", line 762, in _legacy_load
    magic_number = pickle_module.load(f, **pickle_load_args)
_pickle.UnpicklingError: pickle data was truncated

根据 issue，

python tools/run_net.py --cfg demo/AVA/SLOWFAST_32x2_R101_50_50.yaml long_size=8

无效。

此讨论说明不要再 window 系统内加载数据。而是在 linux 内就加载。

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上次编辑于: 2024/9/20 18:25:20
贡献者: 阮喵喵,ruan-cat