A scalable template for PyTorch projects, with examples in Image Segmentation, Object classification, GANs and Reinforcement Learning.
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Implement your PyTorch projects the smart way.
A Scalable template for PyTorch projects, with examples in Image Segmentation, Object classification, GANs and Reinforcement Learning.
Given the nature of deep learning projects, we do not get the chance to think much about the project structure or the code modularity. After working with different deep learning projects and facing problems with files organization and code repetition, we came up with a modular project structure to accommodate any PyTorch project. We also wanted to provide a base for different PyTorch models for the community to build upon.
This is a joint work between Hager Rady and Mo'men AbdelRazek
We are proposing a baseline for any PyTorch project to give you a quick start, where you will get the time to focus on your model's implementation and we will handle the rest. The novelty of this approach lies in:
We are providing a series of tutorials to get your started
This is to ensure that our proposed project structure is compatible with different problems and can handle all the variations related to any of them.
After adding all our examples, the repo has the following structure:
├── agents
| └── dcgan.py
| └── condensenet.py
| └── mnist.py
| └── dqn.py
| └── example.py
| └── base.py
| └── erfnet.py
|
├── configs
| └── dcgan_exp_0.py
| └── condensenet_exp_0.py
| └── mnist_exp_0.py
| └── dqn_exp_0.py
| └── example_exp_0.py
| └── erfnet_exp_0.py
|
├── data
|
├── datasets
| └── cifar10.py
| └── celebA.py
| └── mnist.py
| └── example.py
| └── voc2012.py
|
├── experiments
|
├── graphs
| └── models
| | └── custome_layers
| | | └── denseblock.py
| | | └── layers.py
| | |
| | └── dcgan_discriminator.py
| | └── dcgan_generator.py
| | └── erfnet.py
| | └── erfnet_imagenet.py
| | └── condensenet.py
| | └── mnist.py
| | └── dqn.py
| | └── example.py
| |
| └── losses
| | └── loss.py
|
├── pretrained_weights
|
├── tutorials
|
├── utils
| └── assets
|
├── main.py
└── run.sh
easydict==1.7
graphviz==0.8.4
gym==0.10.5
imageio==2.3.0
matplotlib==2.2.2
numpy==1.14.5
Pillow==5.2.0
scikit-image==0.14.0
scikit-learn==0.19.1
scipy==1.1.0
tensorboardX==1.2
torch==0.4.0
torchvision==0.2.1
tqdm==4.23.4
We are planning to add more examples into our template to include various categories of problems. Next we are going to include the following:
This project is licensed under MIT License - see the LICENSE file for details