README.md

swa-tutorials-pytorch

Stochastic Weight Averaging Tutorials using pytorch. Based on PyTorch 1.6 Official Features (Stochastic Weight Averaging), implement classification codebase using custom dataset.

• author: hoya012
• last update: 2020.10.23

0. Experimental Setup

0-1. Prepare Library

• Need to install PyTorch and Captum

pip install -r requirements.txt

0-2. Download dataset (Kaggle Intel Image Classification)

• Intel Image Classification

This Data contains around 25k images of size 150x150 distributed under 6 categories. {'buildings' -> 0, 'forest' -> 1, 'glacier' -> 2, 'mountain' -> 3, 'sea' -> 4, 'street' -> 5 }

• Make data folder and move dataset into data folder.

1. Baseline Training

• ImageNet Pretrained ResNet-18 from torchvision.models
• Batch Size 256 / Epochs 120 / Initial Learning Rate 0.0001
• Training Augmentation: Resize((256, 256)), RandomHorizontalFlip()
• Adam + Cosine Learning rate scheduling with warmup
• I tried NVIDIA Pascal GPU - GTX 1080 Ti 1 GPU

python main.py --checkpoint_name baseline;

2. Stochastic Weight Averaging Training

In PyTorch 1.6, Stochastic Weight Averaging is very easy to use! Thanks to PyTorch..

• PyTorch's official tutorial's guide

from torch.optim.swa_utils import AveragedModel, SWALR
from torch.optim.lr_scheduler import CosineAnnealingLR

loader, optimizer, model, loss_fn = ...
swa_model = AveragedModel(model)
scheduler = CosineAnnealingLR(optimizer, T_max=100)
swa_start = 5
swa_scheduler = SWALR(optimizer, swa_lr=0.05)

for epoch in range(100):
      for input, target in loader:
          optimizer.zero_grad()
          loss_fn(model(input), target).backward()
          optimizer.step()
      if epoch > swa_start:
          swa_model.update_parameters(model)
          swa_scheduler.step()
      else:
          scheduler.step()

# Update bn statistics for the swa_model at the end
torch.optim.swa_utils.update_bn(loader, swa_model)
# Use swa_model to make predictions on test data 
preds = swa_model(test_input)

• My own implementations

# in main.py
""" define model and learning rate scheduler for stochastic weight averaging """
swa_model = torch.optim.swa_utils.AveragedModel(model)
swa_scheduler = SWALR(optimizer, swa_lr=args.swa_lr)

...  

# in learning/trainer.py
for batch_idx, (inputs, labels) in enumerate(data_loader):
  if not args.decay_type == 'swa':
        self.scheduler.step()
  else:
      if epoch <= args.swa_start:
          self.scheduler.step()

if epoch > args.swa_start and args.decay_type == 'swa':
  self.swa_model.update_parameters(self.model)
  self.swa_scheduler.step()

...

# in main.py
swa_model = swa_model.cpu()
torch.optim.swa_utils.update_bn(train_loader, swa_model)
swa_model = swa_model.cuda() 

Run Script (Command Line)

python main.py --checkpoint_name swa --decay_type swa --swa_start 90 --swa_lr 5e-5;

3. Performance Table

• B : Baseline
• SWA : Stochastic Weight Averaging
  • SWA_{swa_start}_{swa_lr}

Algorithm	Test Accuracy
B	94.10
SWA_90_0.05	80.53
SWA_90_1e-4	94.20
SWA_90_5e-4	93.87
SWA_90_1e-5	94.23
SWA_90_5e-5	94.57
SWA_75_5e-5	94.27
SWA_60_5e-5	94.33

4. Code Reference

• Baseline Code: https://github.com/hoya012/carrier-of-tricks-for-classification-pytorch
• Gradual Warmup Scheduler: https://github.com/ildoonet/pytorch-gradual-warmup-lr
• PyTorch Stochastic Weight Averaging: https://pytorch.org/blog/pytorch-1.6-now-includes-stochastic-weight-averaging