Py之fvcore:fvcore庫的簡介、安裝、使用方法之詳細攻略
Py之fvcore:fvcore庫的簡介、安裝、使用方法之詳細攻略

目錄
fvcore庫的簡介
fvcore庫的安裝
fvcore庫的使用方法
1、基礎用法
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框架(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。這個庫中的所有組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地址: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
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版權聲明:本文內容由網絡用戶投稿,版權歸原作者所有,本站不擁有其著作權,亦不承擔相應法律責任。如果您發現本站中有涉嫌抄襲或描述失實的內容,請聯系我們jiasou666@gmail.com 處理,核實后本網站將在24小時內刪除侵權內容。