Py之fvcore：fvcore库的简介、安装、使用方法之详细攻略

Py之fvcore：fvcore库的简介、安装、使用方法之详细攻略

目录

fvcore库的简介

fvcore库的安装

fvcore库的使用方法

1、基础用法

fvcore库的简介

        fvcore是一个轻量级的核心库，它提供了在各种计算机视觉框架(如Detectron2)中共享的最常见和最基本的功能。这个库基于Python 3.6+和PyTorch。这个库中的所有组件都经过了类型注释、测试和基准测试。Facebook 的人工智能实验室即FAIR的计算机视觉组负责维护这个库。

github地址：https://github.com/facebookresearch/fvcore

fvcore库的安装

pip install -U 'git+https://github.com/facebookresearch/fvcore'

fvcore库的使用方法

1、基础用法

  

   

    

     

    
    

     

      """Configs."""
     
    
   

    

     

    
    

     

      from fvcore.common.config import CfgNode
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # -----------------------------------------------------------------------------
     
    
   

    

     

    
    

     

      # Config definition
     
    
   

    

     

    
    

     

      # -----------------------------------------------------------------------------
     
    
   

    

     

    
    

     

      _C = CfgNode()
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # ---------------------------------------------------------------------------- #
     
    
   

    

     

    
    

     

      # Batch norm options
     
    
   

    

     

    
    

     

      # ---------------------------------------------------------------------------- #
     
    
   

    

     

    
    

     

      _C.BN = CfgNode()
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # BN epsilon.
     
    
   

    

     

    
    

     

      _C.BN.EPSILON = 1e-5
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # BN momentum.
     
    
   

    

     

    
    

     

      _C.BN.MOMENTUM = 0.1
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # Precise BN stats.
     
    
   

    

     

    
    

     

      _C.BN.USE_PRECISE_STATS = False
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # Number of samples use to compute precise bn.
     
    
   

    

     

    
    

     

      _C.BN.NUM_BATCHES_PRECISE = 200
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # Weight decay value that applies on BN.
     
    
   

    

     

    
    

     

      _C.BN.WEIGHT_DECAY = 0.0
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # ---------------------------------------------------------------------------- #
     
    
   

    

     

    
    

     

      # Training options.
     
    
   

    

     

    
    

     

      # ---------------------------------------------------------------------------- #
     
    
   

    

     

    
    

     

      _C.TRAIN = CfgNode()
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # If True Train the model, else skip training.
     
    
   

    

     

    
    

     

      _C.TRAIN.ENABLE = True
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # Dataset.
     
    
   

    

     

    
    

     

      _C.TRAIN.DATASET = "kinetics"
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # Total mini-batch size.
     
    
   

    

     

    
    

     

      _C.TRAIN.BATCH_SIZE = 64
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # Evaluate model on test data every eval period epochs.
     
    
   

    

     

    
    

     

      _C.TRAIN.EVAL_PERIOD = 1
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # Save model checkpoint every checkpoint period epochs.
     
    
   

    

     

    
    

     

      _C.TRAIN.CHECKPOINT_PERIOD = 1
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # Resume training from the latest checkpoint in the output directory.
     
    
   

    

     

    
    

     

      _C.TRAIN.AUTO_RESUME = True
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # Path to the checkpoint to load the initial weight.
     
    
   

    

     

    
    

     

      _C.TRAIN.CHECKPOINT_FILE_PATH = ""
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # Checkpoint types include `caffe2` or `pytorch`.
     
    
   

    

     

    
    

     

      _C.TRAIN.CHECKPOINT_TYPE = "pytorch"
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # If True, perform inflation when loading checkpoint.
     
    
   

    

     

    
    

     

      _C.TRAIN.CHECKPOINT_INFLATE = False
     
    
  
 

文章来源: yunyaniu.blog.csdn.net，作者：一个处女座的程序猿，版权归原作者所有，如需转载，请联系作者。

原文链接：yunyaniu.blog.csdn.net/article/details/103881195