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PACTran Metrics

This is the code repository for the paper: PACTran: PAC-Bayesian Metrics for Estimating the Transferability of Pretrained Models to Classification Tasks (https://arxiv.org/abs/2203.05126).

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Introduction

PACTran is a theoretically grounded family of metrics for pretrained model selection and transferability measurement. The family is derived from the optimal PAC-Bayesian bound under the transfer learning setting. It contains three metric instantiations: PACTran-Dirichlet, PACTran-Gamma, PACTran-Gaussian. Our experiments showed that PACTran-Gaussian is a more consistent and effective transferability measure compared to other existing selection methods.

How to use

In the following, we provide the instructions of using the metrics (while using the caltech101 task in the Visual Task Adaptation Benchmark (VTAB) [1] and the Sup-100% checkpoint as an example). Run the following scripts in the parent dir of pactran_metrics.

• Prerequisites:

  • Tensorflow
  • Tensorflow-Hub
  • Tensorflow-datasets
  • Numpy
  • Scipy
  • Scikit-learn

• For penultimate layer feature prediction, run

python -m pactran_metrics.adapt_and_eval \
--hub_module 'https://tfhub.dev/vtab/sup-100/1'  \
--hub_module_signature default \
--finetune_layer default \
--work_dir /tmp/all_features/sup-100/caltech101/ \
--dataset 'caltech101' \
--batch_size 512 \
--batch_size_eval 512 \
--initial_learning_rate 0.001 \
--decay_steps 1500,3000,4500 \
--max_steps 1 \
--run_adaptation=False \
--run_evaluation=False \
--run_prediction=True \
--linear_eval=False

• For whole network finetuning, run

python -m pactran_metrics.adapt_and_eval \
--hub_module 'https://tfhub.dev/vtab/sup-100/1'  \
--hub_module_signature default \
--finetune_layer default \
--work_dir /tmp/all_models/sup-100/caltech101/ \
--dataset 'caltech101' \
--batch_size 512 \
--batch_size_eval 512 \
--initial_learning_rate 0.001 \
--decay_steps 1500,3000,4500 \
--max_steps 5000 \
--run_adaptation \
--use_anil False

• For top-layer only finetuning, run

python -m pactran_metrics.anil_classifier \
--hub_module="https://tfhub.dev/vtab/sup-100/1" \
--dataset="caltech101" \
--work_dir=/tmp/all_results/caltech101 \
--feature_dir=/tmp/all_features

• For metrics estimation, run

python -m pactran_metrics.compute_metrics \
--hub_module="https://tfhub.dev/vtab/sup-100/1" \
--dataset="caltech101" \
--work_dir=/tmp/all_results/caltech101 \
--feature_dir=/tmp/all_features \
--num_examples=2

• All available datasets (in ./data folder):
  • "caltech101"
  • "oxford_flowers102"
  • "patch_camelyon"
  • "sun397"
  • "dmlab"
  • "cifar"
  • "ddsm"
  • "oxford_iiit_pet"
  • "smallnorb"
• All available model URLs:
  • "https://tfhub.dev/vtab/sup-100/1" \
  • "https://tfhub.dev/vtab/sup-rotation-100/1" \
  • "https://tfhub.dev/vtab/sup-exemplar-100/1" \
  • "https://tfhub.dev/vtab/semi-exemplar-10/1" \
  • "https://tfhub.dev/vtab/semi-rotation-10/1" \
  • "https://tfhub.dev/vtab/rotation/1" \
  • "https://tfhub.dev/vtab/exemplar/1" \
  • "https://tfhub.dev/vtab/relative-patch-location/1" \
  • "https://tfhub.dev/vtab/jigsaw/1" \
  • "https://tfhub.dev/vtab/uncond-biggan/1" \
  • "https://tfhub.dev/vtab/cond-biggan/1" \
  • "https://tfhub.dev/vtab/wae-mmd/1" \
  • "https://tfhub.dev/vtab/vae/1" \
  • "https://tfhub.dev/vtab/wae-ukl/1" \
  • "https://tfhub.dev/vtab/wae-gan/1"

Reference:

[1] Zhai, X., Puigcerver, J., Kolesnikov, A., Ruyssen, P., Riquelme, C., Lucic, M., Djolonga, J., Pinto, A.S., Neumann, M., Dosovitskiy, A., et al.: A large-scale study of representation learning with the visual task adaptation benchmark. arXiv preprint arXiv:1910.04867 (2019)