ShallowMind

A self-use config-based training tool for deep learning

Amazed and inspired by MMSegmentation, I want my Deep Learning project could be simplified to a single 'config.py' (probably a specific dataset/model class), and do not need to worried about training. So I decide to make a config-based training tool based on Pytorch-lightning with disentangle modules.

Now support:

• Custom Model Class
  • Base/General architecture like
    • BaseEncoderDecoder (for Classification/Regression)
    • BaseVAE (for Recognition/Generation)
    • BaseGAN (for Generation)
    • BaseDQN (for Reinforcement Learning)
  • Specific architecture like FiringRateEncoder/NeuralEncoder/SLDiscoEncoder
  • Backbone,
    • Embedding layers like Base/Linear/Convolutional/PositionalEncoding
    • MLP/BaseConvNet/LSTM/Transformer/TCN/Timm_models/NeuralPredictors
  • Head like MLP/Poolers/ConvTransposeHead
• Custom Dataset Class
  • Support public dataset like TorchVision/Gym/NetSim/LiNGAM
  • Single dataset and Multiple datasets concatenation
  • Pipeline for augmentations from Albumentations, Tsaug, etc
• Various Optimizers and Schedulers
  • Warmup like Linear, Cosine
  • Optimizers like Adam, AdamW, SGD, etc (support multiple simultaneous optimizers)
  • Schedulers like OneCycleLR, CosineAnnealingLR, etc (support multiple simultaneous schedulers)
• Various Loss and Metrics
  • Multi loss fusion like CrossEntropy, BCE, FocalLoss, etc (support weighted multi loss/auxiliary loss)
  • Multi metric mainly based on torchmetrics
• Logging and Checkpointing
• Distributed Training
• Simple api train/infer for use

Feel free to combine the existed components to build your own model, or write your special one.
(E.x. BaseEncoderDecoder(Embedding(Conv)+Transformer+BaseHead) == ViT; BaseEncoderDecoder(Timm_models+BaseHead) == Classic Image Classification Model; BaseEncoderDecoder(LSTM/Transformer/TCN+BaseHead) == Sequence Prediction Model) ... )

Will expand it with my own projects!

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Installation

python >= 3.9

$ cd shallowmind && pip install -e .

Usage

Demo image classification task on CIFAR10 with the ResNet50 backbone from Timm Models

$ cd shallowmind && python api/train.py --config configs/image_classification_example.py

Function used to load the trained checkpoint

from shallomwind.api.infer import prepare_inference
# di: correponding datainterface object in the config.py
# mi: correponding model object in the config.py (loaded checkpoint weight)
di, mi = prepare_inference(config_path, checkpoint_path)

Then you can either use trainer from pytorch-lightning to do following things on checkpoints

from pytorch_lightning import Trainer
trainer = Trainer(gpus=1)
trainer.test(mi, di.test_dataloader())

or write a naive inference loop

for batch in di.test_dataloader():
    mi.eval()
    with torch.no_grad():
        output = mi(batch)

Example

• ResNet50 on CIFAR10 in 100 epochs from a scratch 94.11% Top1 Acc
• ViT on CIFAR10 in 300 epochs from a scratch
• VAE on MNIST
• GAN on MNIST
• DQN on CartPole