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@didriknielsen didriknielsen released this 22 Sep 16:24
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v1.0.0
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Learn about vigilant mode.

The checkpoints contain model weights, optimizer state, etc.
For details, see the code for checkpoint saving and checkpoint loading.

Usage

  1. Extract checkpoint(s) in YOUR_PATH/survae_flows/experiments/image/log/.
  2. Sample from the models, evaluate the test log-likelihood or continue training using these scripts.

Sampling:

To sample from the models, use the eval_sample.py script.

CIFAR-10:

python eval_sample.py --model YOUR_PATH/survae_flows/experiments/image/log/cifar10_8bit/pool_flow/more/maxpool
python eval_sample.py --model YOUR_PATH/survae_flows/experiments/image/log/cifar10_8bit/pool_flow/more/nonpool

ImageNet 32x32:

python eval_sample.py --model YOUR_PATH/survae_flows/experiments/image/log/imagenet32_8bit/pool_flow/more/maxpool
python eval_sample.py --model YOUR_PATH/survae_flows/experiments/image/log/imagenet32_8bit/pool_flow/more/nonpool

ImageNet 64x64:

python eval_sample.py --model YOUR_PATH/survae_flows/experiments/image/log/imagenet64_8bit/pool_flow/more/maxpool
python eval_sample.py --model YOUR_PATH/survae_flows/experiments/image/log/imagenet64_8bit/pool_flow/more/nonpool

Log-likelihood:

To compute the test log-likelihood for the models, use the eval_loglik.py script.

CIFAR-10:

python eval_loglik.py --model YOUR_PATH/survae_flows/experiments/image/log/cifar10_8bit/pool_flow/more/maxpool --k 1000 --kbs 10
python eval_loglik.py --model YOUR_PATH/survae_flows/experiments/image/log/cifar10_8bit/pool_flow/more/nonpool --k 1000 --kbs 10

ImageNet 32x32:

python eval_loglik.py --model YOUR_PATH/survae_flows/experiments/image/log/imagenet32_8bit/pool_flow/more/maxpool
python eval_loglik.py --model YOUR_PATH/survae_flows/experiments/image/log/imagenet32_8bit/pool_flow/more/nonpool

ImageNet 64x64:

python eval_loglik.py --model YOUR_PATH/survae_flows/experiments/image/log/imagenet64_8bit/pool_flow/more/maxpool
python eval_loglik.py --model YOUR_PATH/survae_flows/experiments/image/log/imagenet64_8bit/pool_flow/more/nonpool

Continue Training:

To continue training for some additional epochs using a new, fixed learning rate, use the train_more.py script.

CIFAR-10:

python train_more.py --model YOUR_PATH/survae_flows/experiments/image/log/cifar10_8bit/pool_flow/more/maxpool --new_epochs 600 --new_lr 1e-5
python train_more.py --model YOUR_PATH/survae_flows/experiments/image/log/cifar10_8bit/pool_flow/more/nonpool --new_epochs 600 --new_lr 1e-5

ImageNet 32x32:

python train_more.py --model YOUR_PATH/survae_flows/experiments/image/log/imagenet32_8bit/pool_flow/more/maxpool --new_epochs 30 --new_lr 1e-5
python train_more.py --model YOUR_PATH/survae_flows/experiments/image/log/imagenet32_8bit/pool_flow/more/nonpool --new_epochs 30 --new_lr 1e-5

ImageNet 64x64:

python train_more.py --model YOUR_PATH/survae_flows/experiments/image/log/imagenet64_8bit/pool_flow/more/maxpool --new_epochs 25 --new_lr 1e-5
python train_more.py --model YOUR_PATH/survae_flows/experiments/image/log/imagenet64_8bit/pool_flow/more/nonpool --new_epochs 25 --new_lr 1e-5
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