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v0.10.5 was released on 2024-9-11.
Highlights:
artifact_location in MLflowVisBackend #1505exclude_frozen_parameters for DeepSpeedEngine._zero3_consolidated_16bit_state_dict #1517Read Changelog for more details.
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
| 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
pip install -U openmim
mim install mmengine
Verify 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.
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.
mode is "loss", and the forward method should return a dict containing the key "loss".mode is "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, labels
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)
])))
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)
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),
)
runner.train()
We appreciate all contributions to improve MMEngine. Please refer to CONTRIBUTING.md for the contributing guideline.
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}
}
This project is released under the Apache 2.0 license.
FAQs
Engine of OpenMMLab projects
We found that mmengine demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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