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