Folders and files
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train.py: train one model (eg. beta-vae, IWAE, bivae) on one specific hyperparamter config
- E.g. Train BiVAE on osmnx_roads data of the following cities, with images of bgcolors
nohup python train.py --model_name="bivae" \ --latent_dim=10 --hidden_dims 32 64 128 256 --adv_dim 32 32 32 --adv_weight 1.0 \ --data_root="/data/hayley-old/osmnx_data/images" \ --data_name="osmnx_roads" \ --cities 'la' 'charlotte' 'vegas' 'boston' 'paris' \ 'amsterdam' 'shanghai' 'seoul' 'chicago' 'manhattan' \ 'berlin' 'montreal' 'rome' \ --bgcolors "k" "r" "g" "b" "y" --n_styles=5 \ --zooms 14 \ --gpu_id=2 --max_epochs=300 --terminate_on_nan=True \ -lr 3e-4 -bs 32 \ --log_root="/data/hayley-old/Tenanbaum2000/lightning_logs/2021-05-18/" &
- E.g.: Train BIVAE on Rotated MNIST of optionally specified subset (given as a filepath to .npy file containing the indices from the original Training MNIST data)
## Specify which indices to use among the MNIST -- comparable to DIVA's experiments ## change 0 to anything inbtw 0,...,9 nohup python train.py --model_name="bivae" \ --latent_dim=128 --hidden_dims 32 64 64 64 --adv_dim 32 32 32 \ --data_name="multi_rotated_mnist" --angles -45 0 45 --n_styles=3 \ --selected_inds_fp='/data/hayley-old/Tenanbaum2000/data/Rotated-MNIST/supervised_inds_0.npy' \ --gpu_id=2
- E.g.: Train Bivae on multi styles of maptiles from specified cities
# Train BiVAE on Multi Maptiles MNIST nohup python train.py --model_name="bivae" \ --latent_dim=10 --hidden_dims 32 64 128 256 --adv_dim 32 32 32 --adv_weight 15.0 \ --data_name="multi_maptiles" \ --cities la paris \ --styles CartoVoyagerNoLabels StamenTonerBackground --n_styles=3 \ --zooms 14 \ --gpu_id=2 --max_epochs=400 --terminate_on_nan=True \ -lr 3e-4 -bs 32 \ --log_root="/data/hayley-old/Tenanbaum2000/lightning_logs/2021-01-23/" &
- tune_asha.py: Use Tune's AsyncHyperBandScheduler to search hyperparameter space more efficiently. Use --tune_metric to specify the value of tune.run's metric argument, e.g. --tune_metric loss for
- tune_asha_with_beta_scheduler.py:
- `
- tune_asha_mnists.py
- tune_asha_osmnx_roads.py