Metadata-Version: 2.1
Name: mmengine
Version: 0.2.0
Summary: Engine of OpenMMLab projects
Home-page: https://github.com/open-mmlab/mmengine
Author: MMEngine Authors
Author-email: openmmlab@gmail.com
License: UNKNOWN
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        English | [简体中文](README_zh-CN.md)
        
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        ## Introduction
        
        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:
        
        1. **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
        
        2. **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.
        
        3. **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.
        
        ## What's New
        
        v0.2.0 was released in 2022-10-11.
        
        1. Refactor FileIO to provide a more unified interface without BC breaking.
        2. Add the base model for test time augmentation.
        3. Support converting `BN` to `SyncBN` during distributed data-parallel training.
        4. Add SMDDP backend and support running on AWS.
        
        Read [Changelog](./docs/en/notes/changelog.md#v0.2.0 "11/10/2022") for more details.
        
        ## Installation
        
        Before installing MMEngine, please ensure that PyTorch has been successfully installed following the [official guide](https://pytorch.org/get-started/locally/).
        
        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.
        
        <details>
        <summary>Build Models</summary>
        
        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
        ```
        
        </details>
        
        <details>
        <summary>Build Datasets</summary>
        
        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)
                                        ])))
        ```
        
        </details>
        
        <details>
        <summary>Build Metrics</summary>
        
        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)
        ```
        
        </details>
        
        <details>
        <summary>Build a Runner</summary>
        
        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 wapper 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),
        )
        ```
        
        </details>
        
        <details>
        <summary>Launch Training</summary>
        
        ```python
        runner.train()
        ```
        
        </details>
        
        ## Contributing
        
        We appreciate all contributions to improve MMEngine. Please refer to [CONTRIBUTING.md](CONTRIBUTING.md) for the contributing guideline.
        
        ## License
        
        This project is released under the [Apache 2.0 license](LICENSE).
        
        ## Projects in OpenMMLab
        
        - [MIM](https://github.com/open-mmlab/mim): MIM installs OpenMMLab packages.
        - [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision.
        - [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab image classification toolbox and benchmark.
        - [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark.
        - [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.
        - [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab rotated object detection toolbox and benchmark.
        - [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark.
        - [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab text detection, recognition, and understanding toolbox.
        - [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.
        - [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 3D human parametric model toolbox and benchmark.
        - [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab self-supervised learning toolbox and benchmark.
        - [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab model compression toolbox and benchmark.
        - [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab fewshot learning toolbox and benchmark.
        - [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark.
        - [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark.
        - [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark.
        - [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox.
        - [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab image and video generative models toolbox.
        - [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab model deployment framework.
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Topic :: Utilities
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: all
Provides-Extra: tests
