Mmcv runner. runner import load_checkpoint from mmdet.

Mmcv runner Runner¶ The runner class is designed to manage the training. register_module class Fp16OptimizerHook (OptimizerHook): """FP16 optimizer hook (mmcv's implementation). mmcv-lite: lite, without CUDA ops but all other features, similar to mmcv<1. mmcv. Runner object can be built from config by ``runner = Runner. x, which were removed at PR #2179, PR #2216, PR #2217. apis import inference_detector, show_result_pyplot from mmdet. hooks. x to MMCV v2. 1. Args: dataloader (DataLoader): A PyTorch dataloader, whose dataset has implemented ``evaluate`` function. import os. engine, mmcv. Runner object can be built from config by runner = Runner. utils module during the upgrade from MMCV v1. Implementing customized runners is also allowed to meet customized needs. It takes longer time to build. This hook will regularly perform evaluation in a given interval when performing in distributed environment. import numbers from abc import ABCMeta, abstractmethod from typing . Installation¶. Finally, we can construct a Runner with previously defined Model, DataLoader, MMCV: OpenMMLab foundational library for computer vision. engine and mmcv. runner, mmcv. runner import load_checkpoint from mmdet. All classes in mmcv. API reference table¶. runner. @classmethod def _get_checkpoint_loader (cls, path): """Finds a loader that supports the given path. Scale the loss value. Args: path (str): checkpoint path Returns: callable: checkpoint loader """ for p in cls. All rights reserved. Learn how to use runner class to manage the training process with less code and more flexibility. by_epoch and self. See full list on github. Module, metaclass = ABCMeta): """Base module for all modules in openmmlab. The main features are as listed: Support EpochBasedRunner and IterBasedRunner for different scenarios. 0. def register_training_hooks (self, lr_config, optimizer_config = None, checkpoint_config = None, log_config = None, momentum_config = None, timer_config = dict (type Sep 13, 2022 · 本文主要分析了 Runner 的作用、特性和具体实现，熟悉 Runner 部分对于理解 OpenMMLab 开源框架有很大帮助。 更好理解本文，可先阅读 MMCV 和 MMDetection 前系列解读文章。 Jan 3, 2014 · Runner ¶ The runner class is designed to manage the training. utils (eg Config and Registry) and many functions, removed in PR #2217. start (int | None, optional): Evaluation starting epoch. Falls back to the local loader if no other loader is found. device modules, and all classes and most of the functions in the mmcv. """ if not self. _should_evaluate (runner): # Because the priority of EvalHook is higher than LoggerHook, the # training log and the evaluating log are mixed. path as osp import platform import shutil import time import API reference table¶. Jan 3, 2014 · def after_train_iter (self, runner): """Called after every training iter to evaluate the results. Then, we’ll introduce the difference in the instantiation of Runner between MMCV and MMEngine in detail. ``BaseModule`` is a wrapper of ``torch. _schemes: # use regular match to handle some cases that where the prefix of # loader has a prefix. HOOKS. Enable extensibility through various hooks, including hooks defined in MMCV and customized ones class DistEvalHook (EvalHook): """Distributed evaluation hook. There are two versions of MMCV: mmcv: comprehensive, with full features and various CUDA ops out of box. fileio, mmcv. from_cfg(cfg)`` where the ``cfg`` usually contains training, validation, and test-related configurations to build corresponding components. nn. In the runner, all the distinct modules - whether visible ones like model and dataset, or obscure ones like logging, distributed environment and random seed - are getting organized and scheduled. The steps of fp16 optimizer is as follows. epoch_based_runner. Mar 19, 2022 · import mmcv from mmcv. fileio module, removed in PR #2179. from_cfg(cfg) where the cfg usually contains training, validation, and test-related configurations to build corresponding components. path as osp import platform import shutil import time import Welcome to MMCV’s documentation!¶ You can switch between Chinese and English documents in the lower-left corner of the layout. logger. We usually use the same config to launch training, testing, and validation tasks. The runner is the “manager” of all modules in MMEngine. Build a Runner. FileIO module from mmengine will be used wherever required. Take MMDet as an example, the differences between training scripts in MMCV and MMEngine are as follows: [docs] class BaseRunner(metaclass=ABCMeta): """The base class of Runner, a training helper for PyTorch. It eases the training process with less code demanded from users while staying flexible and configurable. parallel, mmcv. base. Compare EpochBasedRunner and IterBasedRunner for different scenarios, and customize your own runner with hooks. Due to the removal of the mmcv. models import build_detector config class BaseModule (nn. Only a few functions related to mmcv are reserved. Source code for mmcv. com Firstly, we will introduce how to migrate the entry point of training from MMCV to MMEngine, to simplify and unify the training script. device, removed in PR #2216. Module`` with additional functionality of parameter initialization. # Copyright (c) OpenMMLab. hljwu wqhgpw mnti pufk pwlhe uwtsw wdm gftj ojaw iuxyde