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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.
Major features:
-
A universal and powerful runner:
- 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).
- Easily compatible with models from popular algorithm libraries such as TIMM, TorchVision, and Detectron2.
-
Open architecture with unified interfaces:
- Handles different algorithm tasks with unified APIs, e.g., implement a method and apply it to all compatible models.
- 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.
-
Customizable training process:
- Defines the training process just like playing with Legos.
- Provides rich components and strategies.
- Complete controls on the training process with different levels of APIs.
v0.5.0 was released in 2023-01-20.
Highlights:
- Add
BaseInferencerto provide a general inference interface - Provide
ReduceOnPlateauParamSchedulerto adjust learning rate by metric - Deprecate support for Python3.6
Read Changelog for more details.
Before installing MMEngine, please ensure that PyTorch has been successfully installed following the official guide.
Install MMEngine
pip install -U openmim
mim install mmengineVerify the installation
python -c 'from mmengine.utils.dl_utils import collect_env;print(collect_env())'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
modeis "loss", and theforwardmethod should return adictcontaining the key "loss". - During validation, the value of
modeis "predict", and the forward method should return results containing both predictions and labels.
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, labelsBuild Datasets
Next, we need to create Datasets and DataLoaders for training and validation. In this case, we simply use built-in datasets supported in TorchVision.
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.
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.
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
runner.train()Advanced tutorials
Examples
Common Usage
Design
Migration guide
We appreciate all contributions to improve MMEngine. Please refer to CONTRIBUTING.md for the contributing guideline.
This project is released under the Apache 2.0 license.
- MIM: MIM installs OpenMMLab packages.
- MMCV: OpenMMLab foundational library for computer vision.
- MMEval: A unified evaluation library for multiple machine learning libraries.
- MMClassification: OpenMMLab image classification toolbox and benchmark.
- MMDetection: OpenMMLab detection toolbox and benchmark.
- MMDetection3D: OpenMMLab's next-generation platform for general 3D object detection.
- MMRotate: OpenMMLab rotated object detection toolbox and benchmark.
- MMYOLO: OpenMMLab YOLO series toolbox and benchmark.
- MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark.
- MMOCR: OpenMMLab text detection, recognition, and understanding toolbox.
- MMPose: OpenMMLab pose estimation toolbox and benchmark.
- MMHuman3D: OpenMMLab 3D human parametric model toolbox and benchmark.
- MMSelfSup: OpenMMLab self-supervised learning toolbox and benchmark.
- MMRazor: OpenMMLab model compression toolbox and benchmark.
- MMFewShot: OpenMMLab fewshot learning toolbox and benchmark.
- MMAction2: OpenMMLab's next-generation action understanding toolbox and benchmark.
- MMTracking: OpenMMLab video perception toolbox and benchmark.
- MMFlow: OpenMMLab optical flow toolbox and benchmark.
- MMEditing: OpenMMLab image and video editing toolbox.
- MMGeneration: OpenMMLab image and video generative models toolbox.
- MMDeploy: OpenMMLab model deployment framework.