Quick Start

• Install Miniconda package manager.
• Create and activate conda environment.

conda env create -f environment.yml
conda activate cs236-dp

NOTE: PyTorch dependency specified in environment.yml uses CUDA 11.1. If CUDA 11.1 is unsupported on your environment, please install PyTorch separately by following the official instructions.

• Download dataset and baseline checkpoints and logs.

python download.py

• Start training using baseline model.

python train.py --name EXPERMENT_NAME

• Evaluate trained models.

python eval.py --ckpt_path PATH_TO_CKPT --im_size RESOLUTION

• Create leaderboard submission submission.pth (upload to Gradescope).

python eval.py --ckpt_path PATH_TO_CKPT --im_size RESOLUTION --submit

• Visualize baseline logs and your experiments using Tensorboard.

tensorboard --logdir out --bind_all

NOTE: Metrics logged during training (e.g. IS, FID, KID) are approximations computed using limited data. Use eval.py to compute accurate metrics.

Baseline Models

The baseline models are Residual SNGANs from Mimicry: Towards the Reproducibility of GAN Research.

Baselines	Baseline-32-150k	Baseline-64-150k	Baseline-32-295k	Baseline-64-295k
Resolution	32x32	64x64	32x32	64x64
Seed	0	0	236	236
Batch Size	64	64	64	64
niter	150k	150k	295k	295k
ndis	5	5	5	5
β1	0	0	0	0
β2	0.9	0.9	0.9	0.9
lr	2e-4	2e-4	2e-4	2e-4
lrdecay	Linear	Linear	Linear	Linear
IS	6.212	7.234	6.326	7.330
FID	42.452	68.360	35.339	62.250
KID	0.02734	0.06240	0.01984	0.05556
Samples

NOTE: Use 150k baselines to benchmark models trained from scratch and use 295k baselines if your models don't involve training from scratch.