389 lines
18 KiB
Markdown
389 lines
18 KiB
Markdown
<div align="center">
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<img src="https://user-images.githubusercontent.com/58739961/187154444-fce76639-ac8d-429b-9354-c6fac64b7ef8.jpg" width="600"/>
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<div> </div>
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<div align="center">
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<b><font size="5">OpenMMLab website</font></b>
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<sup>
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<a href="https://openmmlab.com">
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<i><font size="4">HOT</font></i>
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</a>
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</sup>
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<b><font size="5">OpenMMLab platform</font></b>
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<sup>
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<a href="https://platform.openmmlab.com">
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<i><font size="4">TRY IT OUT</font></i>
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</a>
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</sup>
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</div>
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<div> </div>
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[](https://pypi.org/project/mmengine/)
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[](https://pypi.org/project/mmengine)
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[](https://github.com/open-mmlab/mmengine/blob/main/LICENSE)
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[](https://github.com/open-mmlab/mmengine/issues)
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[](https://github.com/open-mmlab/mmengine/issues)
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[📘Documentation](https://mmengine.readthedocs.io/en/latest/) |
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[🛠️Installation](https://mmengine.readthedocs.io/en/latest/get_started/installation.html) |
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[🤔Reporting Issues](https://github.com/open-mmlab/mmengine/issues/new/choose)
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</div>
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<div align="center">
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English | [简体中文](README_zh-CN.md)
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</div>
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<div align="center">
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<a href="https://openmmlab.medium.com/" style="text-decoration:none;">
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<img src="https://user-images.githubusercontent.com/25839884/219255827-67c1a27f-f8c5-46a9-811d-5e57448c61d1.png" width="3%" alt="" /></a>
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<img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
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<a href="https://discord.com/channels/1037617289144569886/1073056342287323168" style="text-decoration:none;">
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<img src="https://user-images.githubusercontent.com/25839884/218347213-c080267f-cbb6-443e-8532-8e1ed9a58ea9.png" width="3%" alt="" /></a>
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<img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
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<a href="https://twitter.com/OpenMMLab" style="text-decoration:none;">
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<img src="https://user-images.githubusercontent.com/25839884/218346637-d30c8a0f-3eba-4699-8131-512fb06d46db.png" width="3%" alt="" /></a>
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<img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
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<a href="https://www.youtube.com/openmmlab" style="text-decoration:none;">
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<img src="https://user-images.githubusercontent.com/25839884/218346691-ceb2116a-465a-40af-8424-9f30d2348ca9.png" width="3%" alt="" /></a>
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<img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
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<a href="https://space.bilibili.com/1293512903" style="text-decoration:none;">
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<img src="https://user-images.githubusercontent.com/25839884/219026751-d7d14cce-a7c9-4e82-9942-8375fca65b99.png" width="3%" alt="" /></a>
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<img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
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<a href="https://www.zhihu.com/people/openmmlab" style="text-decoration:none;">
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<img src="https://user-images.githubusercontent.com/25839884/219026120-ba71e48b-6e94-4bd4-b4e9-b7d175b5e362.png" width="3%" alt="" /></a>
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</div>
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## What's New
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v0.8.4 was released on 2023-08-03.
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Highlights:
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- Support enabling `efficient_conv_bn_eval` for efficient convolution and batch normalization. See [save memory on gpu](https://mmengine.readthedocs.io/en/latest/common_usage/save_gpu_memory.html#save-memory-on-gpu) for more details
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- Add an [example](./examples/llama2/) to finetune Llama2.
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- Support training with [FSDP](https://pytorch.org/tutorials/intermediate/FSDP_adavnced_tutorial.html?highlight=fsdp) and [DeepSpeed](https://www.deepspeed.ai/). Refer to the [Training Large Models](https://mmengine.readthedocs.io/en/latest/common_usage/large_model_training.html) for more detailed usages.
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- Introduce the pure Python style configuration file:
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- Support navigating to base configuration file in IDE
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- Support navigating to base variable in IDE
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- Support navigating to source code of class in IDE
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- Support inheriting two configuration files containing the same field
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- Load the configuration file without other third-party requirements
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Refer to the [tutorial](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta) for more detailed usages.
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Read [Changelog](./docs/en/notes/changelog.md#v083-08032023) for more details.
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## Table of Contents
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- [Introduction](#introduction)
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- [Installation](#installation)
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- [Get Started](#get-started)
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- [Learn More](#learn-more)
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- [Contributing](#contributing)
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- [Citation](#citation)
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- [License](#license)
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- [Ecosystem](#ecosystem)
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- [Projects in OpenMMLab](#projects-in-openmmlab)
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## Introduction
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MMEngine is a foundational library for training deep learning models based on PyTorch. It provides a solid engineering foundation and frees developers from writing redundant codes on workflows. 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.
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Major features:
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1. **A universal and powerful runner**:
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- Supports training different tasks with a small amount of code, e.g., ImageNet can be trained with only 80 lines of code (400 lines of the original PyTorch example).
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- Easily compatible with models from popular algorithm libraries such as TIMM, TorchVision, and Detectron2.
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2. **Open architecture with unified interfaces**:
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- Handles different algorithm tasks with unified APIs, e.g., implement a method and apply it to all compatible models.
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- Provides a unified abstraction for upper-level algorithm libraries, which supports various back-end devices such as Nvidia CUDA, Mac MPS, AMD, MLU, and more for model training.
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3. **Customizable training process**:
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- Defines the training process just like playing with Legos.
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- Provides rich components and strategies.
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- Complete controls on the training process with different levels of APIs.
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## Installation
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Before installing MMEngine, please ensure that PyTorch has been successfully installed following the [official guide](https://pytorch.org/get-started/locally/).
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Install MMEngine
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```bash
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pip install -U openmim
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mim install mmengine
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```
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Verify the installation
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```bash
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python -c 'from mmengine.utils.dl_utils import collect_env;print(collect_env())'
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```
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## Get Started
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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.
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<details>
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<summary>Build Models</summary>
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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.
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- During training, the value of `mode` is "loss", and the `forward` method should return a `dict` containing the key "loss".
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- During validation, the value of `mode` is "predict", and the forward method should return results containing both predictions and labels.
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```python
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import torch.nn.functional as F
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import torchvision
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from mmengine.model import BaseModel
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class MMResNet50(BaseModel):
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def __init__(self):
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super().__init__()
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self.resnet = torchvision.models.resnet50()
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def forward(self, imgs, labels, mode):
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x = self.resnet(imgs)
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if mode == 'loss':
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return {'loss': F.cross_entropy(x, labels)}
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elif mode == 'predict':
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return x, labels
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```
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</details>
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<details>
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<summary>Build Datasets</summary>
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Next, we need to create **Dataset**s and **DataLoader**s for training and validation.
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In this case, we simply use built-in datasets supported in TorchVision.
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```python
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import torchvision.transforms as transforms
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from torch.utils.data import DataLoader
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norm_cfg = dict(mean=[0.491, 0.482, 0.447], std=[0.202, 0.199, 0.201])
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train_dataloader = DataLoader(batch_size=32,
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shuffle=True,
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dataset=torchvision.datasets.CIFAR10(
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'data/cifar10',
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train=True,
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download=True,
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transform=transforms.Compose([
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transforms.RandomCrop(32, padding=4),
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transforms.RandomHorizontalFlip(),
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transforms.ToTensor(),
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transforms.Normalize(**norm_cfg)
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])))
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val_dataloader = DataLoader(batch_size=32,
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shuffle=False,
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dataset=torchvision.datasets.CIFAR10(
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'data/cifar10',
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train=False,
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download=True,
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transform=transforms.Compose([
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transforms.ToTensor(),
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transforms.Normalize(**norm_cfg)
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])))
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```
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</details>
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<details>
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<summary>Build Metrics</summary>
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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.
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```python
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from mmengine.evaluator import BaseMetric
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class Accuracy(BaseMetric):
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def process(self, data_batch, data_samples):
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score, gt = data_samples
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# Save the results of a batch to `self.results`
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self.results.append({
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'batch_size': len(gt),
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'correct': (score.argmax(dim=1) == gt).sum().cpu(),
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})
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def compute_metrics(self, results):
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total_correct = sum(item['correct'] for item in results)
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total_size = sum(item['batch_size'] for item in results)
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# Returns a dictionary with the results of the evaluated metrics,
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# where the key is the name of the metric
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return dict(accuracy=100 * total_correct / total_size)
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```
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</details>
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<details>
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<summary>Build a Runner</summary>
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Finally, we can construct a **Runner** with previously defined `Model`, `DataLoader`, and `Metrics`, with some other configs, as shown below.
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```python
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from torch.optim import SGD
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from mmengine.runner import Runner
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runner = Runner(
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model=MMResNet50(),
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work_dir='./work_dir',
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train_dataloader=train_dataloader,
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# a wrapper to execute back propagation and gradient update, etc.
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optim_wrapper=dict(optimizer=dict(type=SGD, lr=0.001, momentum=0.9)),
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# set some training configs like epochs
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train_cfg=dict(by_epoch=True, max_epochs=5, val_interval=1),
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val_dataloader=val_dataloader,
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val_cfg=dict(),
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val_evaluator=dict(type=Accuracy),
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)
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```
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</details>
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<details>
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<summary>Launch Training</summary>
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```python
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runner.train()
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```
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</details>
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## Learn More
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<details>
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<summary>Tutorials</summary>
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- [Runner](https://mmengine.readthedocs.io/en/latest/tutorials/runner.html)
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- [Dataset and DataLoader](https://mmengine.readthedocs.io/en/latest/tutorials/dataset.html)
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- [Model](https://mmengine.readthedocs.io/en/latest/tutorials/model.html)
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- [Evaluation](https://mmengine.readthedocs.io/en/latest/tutorials/evaluation.html)
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- [OptimWrapper](https://mmengine.readthedocs.io/en/latest/tutorials/optim_wrapper.html)
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- [Parameter Scheduler](https://mmengine.readthedocs.io/en/latest/tutorials/param_scheduler.html)
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- [Hook](https://mmengine.readthedocs.io/en/latest/tutorials/hook.html)
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</details>
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<details>
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<summary>Advanced tutorials</summary>
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- [Registry](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/registry.html)
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- [Config](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html)
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- [BaseDataset](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/basedataset.html)
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- [Data Transform](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/data_transform.html)
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- [Weight Initialization](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/initialize.html)
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- [Visualization](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/visualization.html)
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- [Abstract Data Element](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/data_element.html)
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- [Distribution Communication](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/distributed.html)
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- [Logging](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/logging.html)
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- [File IO](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/fileio.html)
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- [Global manager (ManagerMixin)](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/manager_mixin.html)
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- [Use modules from other libraries](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/cross_library.html)
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- [Test Time Agumentation](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/test_time_augmentation.html)
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</details>
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<details>
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<summary>Examples</summary>
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- [Train a GAN](https://mmengine.readthedocs.io/en/latest/examples/train_a_gan.html)
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</details>
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<details>
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<summary>Common Usage</summary>
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- [Resume Training](https://mmengine.readthedocs.io/en/latest/common_usage/resume_training.html)
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- [Speed up Training](https://mmengine.readthedocs.io/en/latest/common_usage/speed_up_training.html)
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- [Save Memory on GPU](https://mmengine.readthedocs.io/en/latest/common_usage/save_gpu_memory.html)
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</details>
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<details>
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<summary>Design</summary>
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- [Hook](https://mmengine.readthedocs.io/en/latest/design/hook.html)
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- [Runner](https://mmengine.readthedocs.io/en/latest/design/runner.html)
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- [Evaluation](https://mmengine.readthedocs.io/en/latest/design/evaluation.html)
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- [Visualization](https://mmengine.readthedocs.io/en/latest/design/visualization.html)
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- [Logging](https://mmengine.readthedocs.io/en/latest/design/logging.html)
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- [Infer](https://mmengine.readthedocs.io/en/latest/design/infer.html)
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</details>
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<details>
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<summary>Migration guide</summary>
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- [Migrate Runner from MMCV to MMEngine](https://mmengine.readthedocs.io/en/latest/migration/runner.html)
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- [Migrate Hook from MMCV to MMEngine](https://mmengine.readthedocs.io/en/latest/migration/hook.html)
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- [Migrate Model from MMCV to MMEngine](https://mmengine.readthedocs.io/en/latest/migration/model.html)
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- [Migrate Parameter Scheduler from MMCV to MMEngine](https://mmengine.readthedocs.io/en/latest/migration/param_scheduler.html)
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- [Migrate Data Transform to OpenMMLab 2.0](https://mmengine.readthedocs.io/en/latest/migration/transform.html)
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</details>
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## Contributing
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We appreciate all contributions to improve MMEngine. Please refer to [CONTRIBUTING.md](CONTRIBUTING.md) for the contributing guideline.
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## Citation
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If you find this project useful in your research, please consider cite:
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```
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@article{mmengine2022,
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title = {{MMEngine}: OpenMMLab Foundational Library for Training Deep Learning Models},
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author = {MMEngine Contributors},
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howpublished = {\url{https://github.com/open-mmlab/mmengine}},
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year={2022}
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}
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```
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## License
|
||
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||
This project is released under the [Apache 2.0 license](LICENSE).
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## Ecosystem
|
||
|
||
- [APES: Attention-based Point Cloud Edge Sampling](https://github.com/JunweiZheng93/APES)
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- [DiffEngine: diffusers training toolbox with mmengine](https://github.com/okotaku/diffengine)
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## Projects in OpenMMLab
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- [MIM](https://github.com/open-mmlab/mim): MIM installs OpenMMLab packages.
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- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision.
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- [MMEval](https://github.com/open-mmlab/mmeval): A unified evaluation library for multiple machine learning libraries.
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- [MMPreTrain](https://github.com/open-mmlab/mmpretrain): OpenMMLab pre-training toolbox and benchmark.
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- [MMagic](https://github.com/open-mmlab/mmagic): Open**MM**Lab **A**dvanced, **G**enerative and **I**ntelligent **C**reation toolbox.
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- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark.
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- [MMYOLO](https://github.com/open-mmlab/mmyolo): OpenMMLab YOLO series toolbox and benchmark.
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- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.
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- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab rotated object detection toolbox and benchmark.
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- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark.
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- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.
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- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark.
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- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab text detection, recognition, and understanding toolbox.
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||
- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 3D human parametric model toolbox and benchmark.
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||
- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab self-supervised learning toolbox and benchmark.
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||
- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab fewshot learning toolbox and benchmark.
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||
- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark.
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||
- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark.
|
||
- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab model deployment framework.
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||
- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab model compression toolbox and benchmark.
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- [Playground](https://github.com/open-mmlab/playground): A central hub for gathering and showcasing amazing projects built upon OpenMMLab.
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