Data

Tools

Indexes

Applications

Experiments

Open Source Science

open-mmlab__mmengine

https://github.com/swe-gym-raw/open-mmlab__mmengine

Science Score: 44.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
  • ○
    Academic email domains
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (7.1%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

Basic Info
  • Host: GitHub
  • Owner: SWE-Gym-Raw
  • License: apache-2.0
  • Language: Python
  • Default Branch: main
  • Size: 4.38 MB
Statistics
  • Stars: 0
  • Watchers: 0
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created about 1 year ago · Last pushed about 1 year ago
Metadata Files
Readme Contributing License Code of conduct Citation

README.md

 
OpenMMLab website HOT      OpenMMLab platform TRY IT OUT
 
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/mmengine)](https://pypi.org/project/mmengine/) [![pytorch](https://img.shields.io/badge/pytorch-1.6~2.1-yellow)](#installation) [![PyPI](https://img.shields.io/pypi/v/mmengine)](https://pypi.org/project/mmengine) [![license](https://img.shields.io/github/license/open-mmlab/mmengine.svg)](https://github.com/open-mmlab/mmengine/blob/main/LICENSE) [Introduction](#introduction) | [Installation](#installation) | [Get Started](#get-started) | [📘Documentation](https://mmengine.readthedocs.io/en/latest/) | [🤔Reporting Issues](https://github.com/open-mmlab/mmengine/issues/new/choose)
English | [简体中文](README_zh-CN.md)

What's New

v0.10.5 was released on 2024-9-11.

Highlights:

  • Support custom artifact_location in MLflowVisBackend #1505
  • Enable exclude_frozen_parameters for DeepSpeedEngine._zero3_consolidated_16bit_state_dict #1517

Read Changelog for more details.

Introduction

MMEngine is a foundational library for training deep learning models based on PyTorch. It serves as the training engine of all OpenMMLab codebases, which support hundreds of algorithms in various research areas. Moreover, MMEngine is also generic to be applied to non-OpenMMLab projects. Its highlights are as follows:

Integrate mainstream large-scale model training frameworks

Supports a variety of training strategies

Provides a user-friendly configuration system

Covers mainstream training monitoring platforms

Installation

Supported PyTorch Versions | MMEngine | PyTorch | Python | | ------------------ | ------------ | -------------- | | main | >=1.6 \<=2.1 | >=3.8, \<=3.11 | | >=0.9.0, \<=0.10.4 | >=1.6 \<=2.1 | >=3.8, \<=3.11 |

Before installing MMEngine, please ensure that PyTorch has been successfully installed following the official guide.

Install MMEngine

bash pip install -U openmim mim install mmengine

Verify the installation

bash python -c 'from mmengine.utils.dl_utils import collect_env;print(collect_env())'

Get Started

Taking the training of a ResNet-50 model on the CIFAR-10 dataset as an example, we will use MMEngine to build a complete, configurable training and validation process in less than 80 lines of code.

Build Models First, we need to define a **model** which 1) inherits from `BaseModel` and 2) accepts an additional argument `mode` in the `forward` method, in addition to those arguments related to the dataset. - During training, the value of `mode` is "loss", and the `forward` method should return a `dict` containing the key "loss". - During validation, the value of `mode` is "predict", and the forward method should return results containing both predictions and labels. ```python import torch.nn.functional as F import torchvision from mmengine.model import BaseModel class MMResNet50(BaseModel): def __init__(self): super().__init__() self.resnet = torchvision.models.resnet50() def forward(self, imgs, labels, mode): x = self.resnet(imgs) if mode == 'loss': return {'loss': F.cross_entropy(x, labels)} elif mode == 'predict': return x, labels ```
Build Datasets Next, we need to create **Dataset**s and **DataLoader**s for training and validation. In this case, we simply use built-in datasets supported in TorchVision. ```python import torchvision.transforms as transforms from torch.utils.data import DataLoader norm_cfg = dict(mean=[0.491, 0.482, 0.447], std=[0.202, 0.199, 0.201]) train_dataloader = DataLoader(batch_size=32, shuffle=True, dataset=torchvision.datasets.CIFAR10( 'data/cifar10', train=True, download=True, transform=transforms.Compose([ transforms.RandomCrop(32, padding=4), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize(**norm_cfg) ]))) val_dataloader = DataLoader(batch_size=32, shuffle=False, dataset=torchvision.datasets.CIFAR10( 'data/cifar10', train=False, download=True, transform=transforms.Compose([ transforms.ToTensor(), transforms.Normalize(**norm_cfg) ]))) ```
Build Metrics To validate and test the model, we need to define a **Metric** called accuracy to evaluate the model. This metric needs to inherit from `BaseMetric` and implements the `process` and `compute_metrics` methods. ```python from mmengine.evaluator import BaseMetric class Accuracy(BaseMetric): def process(self, data_batch, data_samples): score, gt = data_samples # Save the results of a batch to `self.results` self.results.append({ 'batch_size': len(gt), 'correct': (score.argmax(dim=1) == gt).sum().cpu(), }) def compute_metrics(self, results): total_correct = sum(item['correct'] for item in results) total_size = sum(item['batch_size'] for item in results) # Returns a dictionary with the results of the evaluated metrics, # where the key is the name of the metric return dict(accuracy=100 * total_correct / total_size) ```
Build a Runner Finally, we can construct a **Runner** with previously defined `Model`, `DataLoader`, and `Metrics`, with some other configs, as shown below. ```python from torch.optim import SGD from mmengine.runner import Runner runner = Runner( model=MMResNet50(), work_dir='./work_dir', train_dataloader=train_dataloader, # a wrapper to execute back propagation and gradient update, etc. optim_wrapper=dict(optimizer=dict(type=SGD, lr=0.001, momentum=0.9)), # set some training configs like epochs train_cfg=dict(by_epoch=True, max_epochs=5, val_interval=1), val_dataloader=val_dataloader, val_cfg=dict(), val_evaluator=dict(type=Accuracy), ) ```
Launch Training ```python runner.train() ```

Learn More

Tutorials - [Runner](https://mmengine.readthedocs.io/en/latest/tutorials/runner.html) - [Dataset and DataLoader](https://mmengine.readthedocs.io/en/latest/tutorials/dataset.html) - [Model](https://mmengine.readthedocs.io/en/latest/tutorials/model.html) - [Evaluation](https://mmengine.readthedocs.io/en/latest/tutorials/evaluation.html) - [OptimWrapper](https://mmengine.readthedocs.io/en/latest/tutorials/optim_wrapper.html) - [Parameter Scheduler](https://mmengine.readthedocs.io/en/latest/tutorials/param_scheduler.html) - [Hook](https://mmengine.readthedocs.io/en/latest/tutorials/hook.html)
Advanced tutorials - [Registry](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/registry.html) - [Config](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html) - [BaseDataset](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/basedataset.html) - [Data Transform](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/data_transform.html) - [Weight Initialization](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/initialize.html) - [Visualization](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/visualization.html) - [Abstract Data Element](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/data_element.html) - [Distribution Communication](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/distributed.html) - [Logging](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/logging.html) - [File IO](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/fileio.html) - [Global manager (ManagerMixin)](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/manager_mixin.html) - [Use modules from other libraries](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/cross_library.html) - [Test Time Agumentation](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/test_time_augmentation.html)
Examples - [Train a GAN](https://mmengine.readthedocs.io/en/latest/examples/train_a_gan.html)
Common Usage - [Resume Training](https://mmengine.readthedocs.io/en/latest/common_usage/resume_training.html) - [Speed up Training](https://mmengine.readthedocs.io/en/latest/common_usage/speed_up_training.html) - [Save Memory on GPU](https://mmengine.readthedocs.io/en/latest/common_usage/save_gpu_memory.html)
Design - [Hook](https://mmengine.readthedocs.io/en/latest/design/hook.html) - [Runner](https://mmengine.readthedocs.io/en/latest/design/runner.html) - [Evaluation](https://mmengine.readthedocs.io/en/latest/design/evaluation.html) - [Visualization](https://mmengine.readthedocs.io/en/latest/design/visualization.html) - [Logging](https://mmengine.readthedocs.io/en/latest/design/logging.html) - [Infer](https://mmengine.readthedocs.io/en/latest/design/infer.html)
Migration guide - [Migrate Runner from MMCV to MMEngine](https://mmengine.readthedocs.io/en/latest/migration/runner.html) - [Migrate Hook from MMCV to MMEngine](https://mmengine.readthedocs.io/en/latest/migration/hook.html) - [Migrate Model from MMCV to MMEngine](https://mmengine.readthedocs.io/en/latest/migration/model.html) - [Migrate Parameter Scheduler from MMCV to MMEngine](https://mmengine.readthedocs.io/en/latest/migration/param_scheduler.html) - [Migrate Data Transform to OpenMMLab 2.0](https://mmengine.readthedocs.io/en/latest/migration/transform.html)

Contributing

We appreciate all contributions to improve MMEngine. Please refer to CONTRIBUTING.md for the contributing guideline.

Citation

If you find this project useful in your research, please consider cite:

@article{mmengine2022, title = {{MMEngine}: OpenMMLab Foundational Library for Training Deep Learning Models}, author = {MMEngine Contributors}, howpublished = {\url{https://github.com/open-mmlab/mmengine}}, year={2022} }

License

This project is released under the Apache 2.0 license.

Ecosystem

Projects in OpenMMLab

  • MIM: MIM installs OpenMMLab packages.
  • MMCV: OpenMMLab foundational library for computer vision.
  • MMEval: A unified evaluation library for multiple machine learning libraries.
  • MMPreTrain: OpenMMLab pre-training toolbox and benchmark.
  • MMagic: OpenMMLab Advanced, Generative and Intelligent Creation toolbox.
  • MMDetection: OpenMMLab detection toolbox and benchmark.
  • MMYOLO: OpenMMLab YOLO series toolbox and benchmark.
  • MMDetection3D: OpenMMLab's next-generation platform for general 3D object detection.
  • MMRotate: OpenMMLab rotated object detection toolbox and benchmark.
  • MMTracking: OpenMMLab video perception toolbox and benchmark.
  • MMPose: OpenMMLab pose estimation toolbox and benchmark.
  • MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark.
  • MMOCR: OpenMMLab text detection, recognition, and understanding toolbox.
  • MMHuman3D: OpenMMLab 3D human parametric model toolbox and benchmark.
  • MMSelfSup: OpenMMLab self-supervised learning toolbox and benchmark.
  • MMFewShot: OpenMMLab fewshot learning toolbox and benchmark.
  • MMAction2: OpenMMLab's next-generation action understanding toolbox and benchmark.
  • MMFlow: OpenMMLab optical flow toolbox and benchmark.
  • MMDeploy: OpenMMLab model deployment framework.
  • MMRazor: OpenMMLab model compression toolbox and benchmark.
  • Playground: A central hub for gathering and showcasing amazing projects built upon OpenMMLab.

Owner

  • Name: SWE-Gym-Raw
  • Login: SWE-Gym-Raw
  • Kind: organization
  • Email: jingmai@pku.edu.cn

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
  - name: "MMEngine Contributors"
title: "OpenMMLab Foundational Library for Training Deep Learning Models"
date-released: 2022-09-01
url: "https://github.com/open-mmlab/mmengine"
license: Apache-2.0

GitHub Events

Total
  • Push event: 1
  • Fork event: 1
  • Create event: 28
Last Year
  • Push event: 1
  • Fork event: 1
  • Create event: 28

Dependencies

.circleci/docker/Dockerfile docker
  • pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
docker/dev/Dockerfile docker
  • pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
docker/release/Dockerfile docker
  • pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
requirements/docs.txt pypi
  • docutils ==0.18.1
  • myst-parser *
  • opencv-python *
  • sphinx ==6.2.1
  • sphinx-copybutton *
  • sphinx-tabs *
  • sphinx_markdown_tables *
  • tabulate *
  • torch *
  • torchvision *
  • urllib3 <2.0.0
requirements/docs_extra.txt pypi
  • deepspeed *
requirements/runtime.txt pypi
  • addict *
  • matplotlib *
  • numpy *
  • pyyaml *
  • regex *
  • rich *
  • termcolor *
  • yapf *
requirements/runtime_lite.txt pypi
  • addict *
  • numpy *
  • pyyaml *
  • regex *
  • rich *
  • termcolor *
  • yapf *
requirements/tests.txt pypi
  • aim <=3.17.5 test
  • bitsandbytes * test
  • clearml * test
  • coverage * test
  • dadaptation * test
  • dvclive * test
  • lion-pytorch * test
  • lmdb * test
  • mlflow * test
  • parameterized * test
  • pydantic ==1.10.9 test
  • pytest * test
  • transformers * test
requirements/tests_lite.txt pypi
  • lmdb * test
  • parameterized * test
  • pytest * test
requirements.txt pypi
setup.py pypi