raw2logit / figures /train.sh
willis
reorganize
0220054
#!/bin/bash
# # Parametrized Training
# 100 epochs, frozen_processor: http://deplo-mlflo-1ssxo94f973sj-890390d809901dbf.elb.eu-central-1.amazonaws.com/#/experiments/49/runs/2803f44514e34a0f87d591520706e876
# model_uri="s3://mlflow-artifacts-601883093460/49/2803f44514e34a0f87d591520706e876/artifacts/model"
# used for training current model to 100% train and 80% val accuracy
# python train.py \
# --experiment_name parametrized \
# --classifier_uri "${model_uri}" \
# --run_name par_full_kurt \
# --dataset Microscopy \
# --lr 1e-5 \
# --epochs 50 \
# --freeze_classifier \
# --freeze_processor \
# # Adversarial Training
# python train.py \
# --experiment_name adversarial \
# --run_name adv_frozen_processor \
# --classifier_uri "${model_uri}" \
# --dataset Microscopy \
# --adv_training \
# --lr 1e-3 \
# --epochs 7 \
# --freeze_classifier \
# --track_processing \
# --track_every_epoch \
# --log_model=False \
# --adv_aux_weight=0.1 \
# --adv_aux_loss "l2" \
# --adv_aux_weight=2e-5 \
# --adv_aux_weight=2e-5 \
# --adv_aux_weight=1.9e-5 \
# Cross pipeline training (Segmentation/Classification)
# Static Pipeline Script
# datasets="Microscopy Drone DroneSegmentation"
datasets="DroneSegmentation"
augmentations="weak strong none"
demosaicings="bilinear malvar2004 menon2007"
sharpenings="sharpening_filter unsharp_masking"
denoisings="median_denoising gaussian_denoising"
for augment in $augmentations
do
for data in $datasets
do
for demosaicing in $demosaicings
do
for sharpening in $sharpenings
do
for denoising in $denoisings
do
python train.py \
--experiment_name ABtesting \
--run_name "$data"_"$demosaicing"_"$sharpening"_"$denoising"_"$augment" \
--dataset "$data" \
--batch_size 4 \
--lr 1e-5 \
--epochs 100 \
--sp_debayer "$demosaicing" \
--sp_sharpening "$sharpening" \
--sp_denoising "$denoising" \
--processing_mode "static" \
--augmentation "$augment" \
--n_split 5 \
done
done
done
done
done