Deep-Learning-Project-Template

PyTorch Project Template

A simple and well designed structure is essential for any Deep Learning project, so after a lot practice and contributing in pytorch projects here's a pytorch project template that combines simplicity, best practice for folder structure and good OOP design. The main idea is that there's much same stuff you do every time when you start your pytorch project, so wrapping all this shared stuff will help you to change just the core idea every time you start a new pytorch project.

So, here’s a simple pytorch template that help you get into your main project faster and just focus on your core (Model Architecture, Training Flow, etc)

In order to decrease repeated stuff, we recommend to use a high-level library. You can write your own high-level library or you can just use some third-part libraries such as ignite, fastai, mmcv … etc. This can help you write compact but full-featured training loops in a few lines of code. Here we use ignite to train mnist as an example.

Requirements

• yacs (Yet Another Configuration System)
• PyTorch (An open source deep learning platform)
• ignite (High-level library to help with training neural networks in PyTorch)

Table Of Contents

• In a Nutshell
• In Details
• Future Work
• Contributing
• Acknowledgments

In a Nutshell

In a nutshell here's how to use this template, so for example assume you want to implement ResNet-18 to train mnist, so you should do the following:

• In modeling folder create a python file named whatever you like, here we named it example_model.py . In modeling/__init__.py file, you can build a function named build_model to call your model

from .example_model import ResNet18

def build_model(cfg):
    model = ResNet18(cfg.MODEL.NUM_CLASSES)
    return model

• In engine folder create a model trainer function and inference function. In trainer function, you need to write the logic of the training process, you can use some third-party library to decrease the repeated stuff.

# trainer
def do_train(cfg, model, train_loader, val_loader, optimizer, scheduler, loss_fn):
 """
 implement the logic of epoch:
 -loop on the number of iterations in the config and call the train step
 -add any summaries you want using the summary
 """
pass

# inference
def inference(cfg, model, val_loader):
"""
implement the logic of the train step
- run the tensorflow session
- return any metrics you need to summarize
 """
pass

• In tools folder, you create the train.py . In this file, you need to get the instances of the following objects "Model", "DataLoader”, “Optimizer”, and config

# create instance of the model you want
model = build_model(cfg)

# create your data generator
train_loader = make_data_loader(cfg, is_train=True)
val_loader = make_data_loader(cfg, is_train=False)

# create your model optimizer
optimizer = make_optimizer(cfg, model)

• Pass the all these objects to the function do_train , and start your training

# here you train your model
do_train(cfg, model, train_loader, val_loader, optimizer, None, F.cross_entropy)

You will find a template file and a simple example in the model and trainer folder that shows you how to try your first model simply.

In Details

…

Future Work

Contributing

Any kind of enhancement or contribution is welcomed.

Acknowledgments