onedl-mmengine 0.10.12

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.

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()

• Runner
• Dataset and DataLoader
• Model
• Evaluation
• OptimWrapper
• Parameter Scheduler
• Hook

• Registry
• Config
• BaseDataset
• Data Transform
• Weight Initialization
• Visualization
• Abstract Data Element
• Distribution Communication
• Logging
• File IO
• Global manager (ManagerMixin)
• Use modules from other libraries
• Test Time Agumentation

• Train a GAN

• Resume Training
• Speed up Training
• Save Memory on GPU

• Hook
• Runner
• Evaluation
• Visualization
• Logging
• Infer

• Migrate Runner from MMCV to MMEngine
• Migrate Hook from MMCV to MMEngine
• Migrate Model from MMCV to MMEngine
• Migrate Parameter Scheduler from MMCV to MMEngine
• Migrate Data Transform to OpenMMLab 2.0