README.md

PyTorch Lightning Template for Semantic Segmentation

• Getting start for the Semantic Segmentation Competition quickly

Requirements

• Python >= 3.7 (3.8 recommended)
• Pytorch >= 1.7 (conda install pytorch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2 cudatoolkit=10.1 -c pytorch)
• pytorch_lightning
• tqdm
• albumentations
• wandb
• adamp
• opencv-python

Folder Structure

      │
      ├── data/ - abstract base classes
      │   └── train/
      │   │     ├── images/
      │   │     └── masks/
      │   └── val/
      │         ├── images/
      │         └── masks/
      │ 
      ├── saved/ - abstract base classes
      │ 
      ├── datasets.py - main script to start training
      ├── models.py - evaluation of trained model
      ├── test.py - evaluation of trained model
      ├── train.py - evaluation of trained model
      ├── transforms.py - evaluation of trained model
      └── utils.py - evaluation of trained model

Usage

There are Basic features of Trainer of pytorch lightning
Some features what i frequently used are made arguments args

Using Multiple GPU

If you want to train multi-GPU machine, you just add argument --gpus

    python train.py --gpus 4 

Precision

If you want to other precisions, you just add argument --precision

    python train.py --precision 16 # 16, 32, 64, mixed available

Data Augmnetation

Data Augmentation is basically implemented by Albumentations
If you want to add others, append Augmentation in transforms.py

    train_transform = []
    train_transform.append(A.Augmentation())
    
    ...

Optimizer & Scheduler

I implemented 3 optimizers [adam, adamw, adamp] and 2 schedulers [cosine annealing, reducelr] if you want to use other optimizer & scheduler, edit configure_optimizers function in models.py When you use reducelr, essentially implement monitor.

    def configure_optimizers(self):
        if self.args.optimizer == 'adam':
            optimizer = torch.optim.Adam(self.parameters(), lr=self.args.learning_rate)
        elif self.args.optimizer == 'adamw':
            optimizer = torch.optim.AdamW(self.parameters(), lr=self.args.learning_rate)
        elif self.args.optimizer == 'adamp':
            optimizer = AdamP(self.parameters(), lr=self.args.learning_rate, betas=(0.9, 0.999), weight_decay=1e-2)

        if self.args.scheduler == "reducelr":
            scheduler = lr_scheduelr.ReduceLROnPlateau(optimizer, patience=5, factor=0.5, mode="max", verbose=True)
        elif self.args.scheduler == "cosineanneal":
            scheduler = lr_scheduelr.CosineAnnealingWarmRestarts(optimizer, T_0=10, T_mult=1, eta_min=1e-5,
                                                                 last_epoch=-1, verbose=True)

        return {"optimizer": optimizer, "lr_scheduler": scheduler, "monitor": "val/mIoU"}

TODOs

• Implement resume training
• test.py implement
• add feature TTA