Codebase for the paper titled "Continual learning with local module selection"

This repository contains the codebase for the paper Continual Learning via Local Module Composition.

Setting up the environemnt

Create a new conda environment and install the requirements.

conda create --name ENV python=3.7
conda activate ENV
pip install -r requirements.txt
pip install -e Utils/ctrl/
pip install Utils/nngeometry/

CTrL Benchmark

All experiments were run on Nvidia Quadro RTX 8000 GPUs. To run CTrL experiments use the following comands for different streams:

Stream S^-

LMC (task agnostic)

python main_transfer.py --activate_after_str_oh=0 --momentum_bn 0.1 --track_running_stats_bn 1 --pr_name lmc_cr --shuffle_test 0 --init_oh=none --task_sequence s_minus --momentum_bn_decoder=0.1 --activation_structural=sigmoid --deviation_threshold=4 --depth=4 --epochs=100 --fix_layers_below_on_addition=0 --hidden_size=64 --lr=0.001 --mask_str_loss=1 --module_init=mean --multihead=gated_linear --normalize_oh=1 --optmize_structure_only_free_modules=1 --projection_layer_oh=0 --projection_phase_length=20 --reg_factor=10  --running_stats_steps=100 --str_prior_factor=1 --str_prior_temp=0.1 --structure_inv=ae --structure_inv_oh=linear_no_act --task_agnostic_test=1 --temp=0.1 --wdecay=0.001

(test acc. 0.6863, 15 modules)

MNTDP (task aware)

python main_transfer_mntdp.py --momentum_bn 0.1 --pr_name lmc_cr --copy_batchstats 1 --track_running_stats_bn 1 --task_sequence s_minus --gating MNTDP --shuffle_test 0 --epochs 100 --lr 1e-3 --wdecay 1e-3

(test acc. 0.667, 12 modules)

Stream S⁺

LMC

python main_transfer.py --activate_after_str_oh=0 --activation_structural=sigmoid --deviation_threshold=1.5 --early_stop_complete=0 --pr_name lmc_cr --epochs=100 --epochs_str_only_after_addition=1 --hidden_size=64 --init_oh=none --init_runingstats_on_addition=1 --keep_bn_in_eval_after_freeze=1 --lr=0.001 --module_init=most_likely --momentum_bn=0.1 --momentum_bn_decoder=0.1 --multihead=gated_linear --normalize_oh=1 --optmize_structure_only_free_modules=1 --projection_layer_oh=0 --projection_phase_length=5 --reg_factor=10 --running_stats_steps=100 --str_prior_factor=1 --str_prior_temp=0.1 --structure_inv=ae --structure_inv_oh=linear_no_act --task_agnostic_test=1 --task_sequence=s_plus --temp=1 --wdecay=0.001

(test acc. 0.6244, 22 modules)

MNTDP (task aware)

python main_transfer_mntdp.py --momentum_bn 0.1 --pr_name lmc_cr --copy_batchstats 1 --track_running_stats_bn 1 --task_sequence s_plus --gating MNTDP --shuffle_test 0 --epochs 100 --lr 1e-3 --wdecay 1e-3 --regenerate_seed 0

(test acc. 0.609, 18 modules)

Stream Sⁱⁿ

LMC

python main_transfer.py --activate_after_str_oh=0 --momentum_bn 0.1 --track_running_stats_bn 1 --pr_name lmc_cr --shuffle_test 0 --init_oh=none --task_sequence s_in --momentum_bn_decoder=0.1 --activation_structural=sigmoid --deviation_threshold=4 --depth=4 --epochs=100 --fix_layers_below_on_addition=0 --hidden_size=64 --lr=0.001 --mask_str_loss=1 --module_init=most_likely --multihead=gated_linear --normalize_oh=1 --optmize_structure_only_free_modules=1 --projection_layer_oh=0 --projection_phase_length=20 --reg_factor=10  --running_stats_steps=100 --str_prior_factor=1 --str_prior_temp=0.1 --structure_inv=ae --structure_inv_oh=linear_no_act --task_agnostic_test=1 --temp=0.1 --wdecay=0.001

(test acc. 0.7081, 21 modules)

MNTDP (task aware)

python main_transfer_mntdp.py --momentum_bn 0.1 --pr_name lmc_cr --copy_batchstats 1 --track_running_stats_bn 1 --task_sequence s_in --gating MNTDP --shuffle_test 0 --epochs 100 --lr 1e-3 --wdecay 1e-3 --regenerate_seed 0

(test acc. 0.6646, 15 modules)

Stream S^out

LMC

python main_transfer.py --activate_after_str_oh=0 --momentum_bn 0.1 --track_running_stats_bn 1 --pr_name lmc_cr --shuffle_test 0 --init_oh=none --task_sequence s_out --momentum_bn_decoder=0.1 --activation_structural=sigmoid --deviation_threshold=4 --depth=4 --epochs=100 --fix_layers_below_on_addition=0 --hidden_size=64 --lr=0.001 --mask_str_loss=1 --module_init=mean --multihead=gated_linear --normalize_oh=1 --optmize_structure_only_free_modules=1 --projection_layer_oh=0 --projection_phase_length=20 --reg_factor=10  --running_stats_steps=100 --str_prior_factor=1 --str_prior_temp=0.1 --structure_inv=ae --structure_inv_oh=linear_no_act --task_agnostic_test=1 --temp=0.1 --wdecay=0.001

(test acc. 0.5849, 15 modules)

MNTDP (task aware)

python main_transfer_mntdp.py --momentum_bn 0.1 --pr_name lmc_cr --copy_batchstats 1 --track_running_stats_bn 1 --task_sequence s_out --gating MNTDP --shuffle_test 0 --epochs 100 --lr 1e-3 --wdecay 0 --regenerate_seed 0

(test acc. 0.6567, 11 modules)

Stream S^pl

LMC

python main_transfer.py --activate_after_str_oh=0 --activation_structural=sigmoid --pr_name lmc_cr --deviation_threshold=1.5 --early_stop_complete=0 --epochs=100 --hidden_size=64 --init_oh=none --init_runingstats_on_addition=0 --keep_bn_in_eval_after_freeze=1 --lr=0.001 --module_init=most_likely --momentum_bn=0.1 --momentum_bn_decoder=0.1 --multihead=gated_linear --normalize_oh=1 --optmize_structure_only_free_modules=1 --projection_layer_oh=0 --projection_phase_length=10 --reg_factor=10 --running_stats_steps=100 --str_prior_factor=1 --str_prior_temp=0.1 --structure_inv=ae --structure_inv_oh=linear_no_act --task_agnostic_test=1 --task_sequence=s_pl --temp=1 --regenerate_seed 0 --wdecay=0.001

(test acc. 0.6241, 19 modules)

MNTDP (task aware)

python main_transfer_mntdp.py --momentum_bn 0.1 --pr_name lmc_cr --copy_batchstats 1 --track_running_stats_bn 1 --task_sequence s_pl --gating MNTDP --shuffle_test 0 --epochs 100 --lr 1e-3 --wdecay 1e-4 --regenerate_seed 0

(test acc. 0.6391, 18 modules)

Stream S^long30 -- 30 tasks

LMC (task aware)

python main_transfer.py --activate_after_str_oh=0 --activation_structural=sigmoid --deviation_threshold=1.5 --epochs=50 --hidden_size=64 --init_oh=none --keep_bn_in_eval_after_freeze=1 --lr=0.001 --module_init=most_likely --momentum_bn_decoder=0.1 --multihead=gated_linear --n_tasks=100 --normalize_oh=1 --optmize_structure_only_free_modules=1 --projection_layer_oh=0 --projection_phase_length=5 --reg_factor=1 --running_stats_steps=50 --seed=180 --str_prior_factor=1 --str_prior_temp=0.01 --structure_inv=ae --structure_inv_oh=linear_no_act --task_agnostic_test=0 --task_sequence=s_long30 --temp=1 --wdecay=0.001

(test acc. 62.44, 50 modules)

MNTDP (task aware)

python main_transfer_mntdp.py --epochs=50 --hidden_size=64 --lr=0.001 --module_init=most_likely --multihead=gated_linear --n_tasks=100 --seed=180 --task_sequence=s_long30 --wdecay=0.001

(test acc. 64.58, 64 modules)

Stream S^long -- 100 tasks

LMC (task aware)

python main_transfer.py --activate_after_str_oh=0 --activation_structural=sigmoid --deviation_threshold=4 --epochs=100 --hidden_size=64 --init_oh=none --keep_bn_in_eval_after_freeze=1 --lr=0.001 --module_init=most_likely --momentum_bn_decoder=0.1 --multihead=gated_linear --n_tasks=100 --normalize_oh=1 --optmize_structure_only_free_modules=1 --projection_layer_oh=0 --projection_phase_length=5 --reg_factor=1 --running_stats_steps=50 --seed=180 --str_prior_factor=1 --str_prior_temp=0.01 --structure_inv=ae --structure_inv_oh=linear_no_act --task_agnostic_test=0 --task_sequence=s_long --temp=1 --pr_name s_long_cr --wdecay=0

(test acc. 63.88, 32 modules)

MNTDP (task aware)

python main_transfer_mntdp.py --momentum_bn 0.1 --n_tasks 100 --hidden_size 64 --searchspace topdown --keep_bn_in_eval_after_freeze 1 --pr_name s_long_cr --copy_batchstats 1 --track_running_stats_bn 1 --wand_notes correct_MNTDP --task_sequence s_long --gating MNTDP --shuffle_test 0 --epochs 50 --lr 1e-3 --wdecay 1e-3

(test acc. 68.92, 142 modules)

OOD generalization experiments

LMC

python main_transfer.py --regenerate_seed 0 --deviation_threshold=8 --epochs=50 --pr_name lmc_cr --hidden_size=64 --keep_bn_in_eval_after_freeze=0 --lr=0.001 --module_init=none --momentum_bn_decoder=0.1 --normalize_data=1 --optmize_structure_only_free_modules=0 --projection_phase_length=10 --no_projection_phase 0 --reg_factor=10 --running_stats_steps=1000 --str_prior_factor=1 --str_prior_temp=0.1 --structure_inv=linear_no_act --task_sequence=s_ood --temp=1 --wdecay=0 --task_agnostic_test=0

EWC

python main_transfer.py --epochs=50 --ewc=1000 --hidden_size=256 --keep_bn_in_eval_after_freeze=0 --lr=0.001 --module_init=none --pr_name lmc_cr --multihead=usual --normalize_data=1  --task_sequence=s_ood --use_structural=0 --wdecay=0 --projection_phase_length=0

MNTDP

python main_transfer_mntdp.py --epochs=50 --regenerate_seed 0 --hidden_size=64 --keep_bn_in_eval_after_freeze=0 --pr_name lmc_cr --lr=0.01 --module_init=none --multihead=usual --normalize_data=1 --task_sequence=s_ood --use_structural=0 --wdecay=0

LMC (no projetion)

python main_transfer.py --regenerate_seed 0 --deviation_threshold=8 --epochs=50 --pr_name lmc_cr --hidden_size=64 --keep_bn_in_eval_after_freeze=0 --lr=0.001 --module_init=none --momentum_bn_decoder=0.1 --normalize_data=1 --optmize_structure_only_free_modules=0 --projection_phase_length=0 --no_projection_phase 1 --reg_factor=10 --running_stats_steps=1000 --str_prior_factor=1 --str_prior_temp=0.1 --structure_inv=linear_no_act --task_sequence=s_ood --temp=1 --wdecay=0

Plug and play (combining independently trained modular learners)

python main_plug_and_play.py --activate_after_str_oh=0 --activation_structural=sigmoid --deviation_threshold=1.5 --early_stop_complete=0 --epochs=100 --epochs_str_only_after_addition=1 --pr_name lmc_cr --hidden_size=64 --init_oh=none --init_runingstats_on_addition=1 --keep_bn_in_eval_after_freeze=1 --lr=0.001 --module_init=mean --momentum_bn=0.1 --momentum_bn_decoder=0.1 --multihead=gated_linear --n_tasks=3 --normalize_oh=1 --optmize_structure_only_free_modules=1 --projection_layer_oh=0 --projection_phase_length=5 --reg_factor=10 --running_stats_steps=10 --str_prior_factor=1 --str_prior_temp=0.1 --structure_inv=ae --structure_inv_oh=linear_no_act --task_agnostic_test=1 --task_sequence=s_pnp_comp --temp=1 --wdecay=0.001

A list of hyperparameters used for other baselines can be found in the baselines.txt file.

Codebase for the paper titled "Continual learning with local module selection"

Related tags

Overview

This repository contains the codebase for the paper Continual Learning via Local Module Composition.

Setting up the environemnt

CTrL Benchmark

OOD generalization experiments

Plug and play (combining independently trained modular learners)

References

Owner

Oleksiy Ostapenko

Machine learning for NeuroImaging in Python

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Implementation of Bottleneck Transformer in Pytorch

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TANL: Structured Prediction as Translation between Augmented Natural Languages

Tutorial on active learning with the Nvidia Transfer Learning Toolkit (TLT).

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Research - dataset and code for 2016 paper Learning a Driving Simulator

TFOD-MASKRCNN - Tensorflow MaskRCNN With Python

PyTorch implementation of the Quasi-Recurrent Neural Network - up to 16 times faster than NVIDIA's cuDNN LSTM

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Improving Machine Translation Systems via Isotopic Replacement

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Over9000 optimizer