Py之fvcore：fvcore庫的簡介、安裝、使用方法之詳細攻略

fvcore庫的簡介

       fvcore是一個輕量級的核心庫，它提供了在各種計算機視覺架構(如Detectron2)中共享的最常見和最基本的功能。這個庫基于Python 3.6+和PyTorch。這個庫中的所有元件都經過了類型注釋、測試和基準測試。Facebook 的人工智能實驗室即FAIR的計算機視覺組負責維護這個庫。

github位址：

https://github.com/facebookresearch/fvcore

fvcore庫的安裝

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

fvcore庫的使用方法

1、基礎用法

"""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