Library structure¶
Introduction¶
Eztorch as a library has a specific organization for how and where the files are created. Names of folders and files should be informative of their content.
General structure¶
The General tree structure of Eztorch is the following:
eztorch
├── docs
├── eztorch
├── run
└── tests
Doc¶
In docs/, you will find source documentation files about Eztorch such as in this file. We aim to make Eztorch accessible to anyone.
Eztorch¶
The folder eztorch/ contains the actual library that we will detail below.
Run¶
The folder run/ contains various scripts to make training or evaluation. These scripts thanks a Hydra configuration should perform a task agnostically to specific classes or architectures and be able to generalize to any relevant Hydra configuration.
Tests¶
The folder tests/ contains various tests to ensure that components in Eztorch properly work.
As a general rule of thumb, the best would be to test individual components every time they are added, which is not the case for now.
Library structure¶
The tree structure of the library is the following:
eztorch/eztorch
├── callbacks
├── configs
├── datamodules
├── datasets
├── evaluation
├── losses
├── models
├── optimizers
├── schedulers
├── transforms
├── utils
└── visualization
Callbacks¶
The callbacks/ folder contains callbacks to feed the trainer based on Pytorch-Lightning.
Datamodules¶
The datamodules/ folder contains datamodules for various image and video datasets.
Datasets¶
The datasets/ folder contains datasets tools for various image and video datasets.
Among these tools, there are:
Datasets for videos, for example
datasets/hmdb51.py.Wrapper around datasets, for example
datasets/dict_dataset.py.Other tools such as clip samplers for video clip generation in
datasets/clip_samplers/.
Evaluation¶
The evaluation/ folder contains the tools for evaluation networks.
For now, there are:
Linear classifier evaluation for SSL protocol.
Testing time augmentations functions to average predictions of several augmentations.
Losses¶
The losses/ folder contains individual component losses.
Models¶
The models/ folder contains models and tools to configure the various models supported by Eztorch for images and videos.
It follows the following structure:
eztorch/eztorch/models
├── heads
├── modules
├── siamese
└── trunks
Heads¶
The heads/ folder contains various heads for your models.
Heads are the part of your models that should specialize to a task:
Linear heads for classification task.
MLP heads for self-supervised learning projections and predictions.
…
Modules¶
The modules/ folder contains layers or modules for your models that accomplish a specific action.
It could be gathering tensors, or splitting batch normalization layers in parts, …
Siamese¶
The siamese/ folder contains siamese models for self-supervised learning.
Currently implemented methods are:
MoCo (v1, v2, v3)
ReSSL
SCE
SimCLR
Trunks¶
The trunks/ folder contains the trunks, backbone, or encoders, used in Eztorch for Image and Video modalities.
Timm and Pytorchvideo models are also available.
Optimizers¶
The optimizers/ folder contains the custom optimizers (such as LARS) but also factories to easily instantiate them.
Schedulers¶
The schedulers/ folder contains the custom schedulers (such as Cosine Annealing) but also factories to easily instantiate them.
Transforms¶
The transforms/ folder contains all custom transforms defined by Eztorch for images and videos.
Transforms from Torchvision or Kornia are also available. The latter permits GPU transforms.
Utils¶
The utils/ folder contains all utils functions to help Eztorch functionalities and allow various training tricks.
Visualization¶
The visualization/ contains tools to visualize data.