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‎bin/clip/main_small.sh

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# Ensures that all data is downloaded
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# echo "Ensures that all data is downloaded."
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# `dirname $0`/../clip/download_data.sh "$@"
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echo "Ensures that all data is downloaded."
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`dirname $0`/../clip/download_data.sh "$@"
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wait
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‎bin/mnist/augmnist_aug.sh

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#!/usr/bin/env bash
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experiment="augmnist_aug"
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notes="
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**Goal**: Understand the impact of the choice of augmentation on a simple setting,
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"
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# parses special mode for running the script
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source `dirname $0`/../utils.sh
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# define all the arguments modified or added to `conf`. If they are added use `+`
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kwargs="
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experiment=$experiment
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timeout=$time
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is_only_feat=False
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featurizer=neural_feat
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architecture@encoder=resnet18
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data@data_pred=mnist_aug
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checkpoint@checkpoint_feat=bestTrainLoss
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rate=H_hyper
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trainer.max_epochs=100
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$add_kwargs
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"
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# every arguments that you are sweeping over
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kwargs_multi="
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distortion=VIC,BINCE
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data@data_feat=mnist_aug_less,mnist_aug_sup,mnist_aug
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featurizer.loss.beta=0.003,0.01,0.03,0.1,0.3,1,3
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seed=1
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"
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# every arguments that you are sweeping over
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kwargs_multi="
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data@data_feat=mnist_aug_less,mnist_aug_sup,mnist_aug
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seed=1
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"
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if [ "$is_plot_only" = false ] ; then
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for kwargs_dep in "distortion=BINCE featurizer.loss.beta=1e-7,1e-6,1e-5,1e-4,1e-3"
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#"distortion=VIC featurizer.loss.beta=1e-5,1e-4,1e-3,1e-2,0.1,1" "distortion=BINCE featurizer.loss.beta=1e-7,1e-6,1e-5,1e-4,1e-3,1e-2"
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do
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python "$main" +hydra.job.env_set.WANDB_NOTES="\"${notes}\"" $kwargs $kwargs_multi $kwargs_dep -m &
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sleep 3
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done
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fi
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wait
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col_val_subset=""
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rate_cols="['test/feat/rate']"
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distortion_cols="['test/feat/distortion','test/feat/online_loss','test/feat/online_err','test/pred/loss','test/pred/err']"
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compare="datafeat"
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data="merged" # want to access both ther featurizer data and the predictor data
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python utils/aggregate.py \
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experiment=$experiment \
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$col_val_subset \
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+plot_all_RD_curves.folder_col="dist" \
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+plot_all_RD_curves.data="${data}" \
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+plot_all_RD_curves.rate_cols="${rate_cols}" \
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+plot_all_RD_curves.distortion_cols="${distortion_cols}" \
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+plot_all_RD_curves.hue=$compare \
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+plot_all_RD_curves.plot_config_kwargs.font_scale=1 \
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+summarize_RD_curves.data="${data}" \
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+summarize_RD_curves.rate_cols="${rate_cols}" \
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+summarize_RD_curves.distortion_cols="${distortion_cols}" \
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+summarize_RD_curves.mse_cols="${distortion_cols}" \
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agg_mode=[summarize_metrics,plot_all_RD_curves,summarize_RD_curves]
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col_val_subset=""
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rate_cols="['test/feat/rate']"
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distortion_cols="['test/pred/err']"
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compare="datafeat"
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data="merged" # want to access both ther featurizer data and the predictor data
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python utils/aggregate.py \
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experiment=$experiment \
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$col_val_subset \
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kwargs.prfx="single" \
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+plot_all_RD_curves.folder_col="dist" \
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+plot_all_RD_curves.data="${data}" \
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+plot_all_RD_curves.rate_cols="${rate_cols}" \
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+plot_all_RD_curves.distortion_cols="${distortion_cols}" \
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+plot_all_RD_curves.hue=$compare \
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+plot_all_RD_curves.plot_config_kwargs.font_scale=1 \
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agg_mode=[plot_all_RD_curves]

‎bin/mnist/augmnist_aug_old.sh

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#!/usr/bin/env bash
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experiment="augmnist_aug"
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notes="
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**Goal**: Understand the impact of the choice of augmentation on a simple setting,
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"
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8+
# parses special mode for running the script
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source `dirname $0`/../utils.sh
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# define all the arguments modified or added to `conf`. If they are added use `+`
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kwargs="
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experiment=$experiment
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timeout=$time
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is_only_feat=False
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featurizer=neural_feat
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architecture@encoder=resnet18
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data@data_pred=mnist_aug
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checkpoint@checkpoint_feat=bestTrainLoss
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rate=H_hyper
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trainer.max_epochs=100
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$add_kwargs
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"
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# every arguments that you are sweeping over
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kwargs_multi="
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distortion=VIC,BINCE
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data@data_feat=mnist_aug_less,mnist_aug_sup,mnist_aug
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featurizer.loss.beta=0.003,0.01,0.03,0.1,0.3,1,3
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seed=1
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"
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if [ "$is_plot_only" = false ] ; then
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for kwargs_dep in ""
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do
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#python "$main" +hydra.job.env_set.WANDB_NOTES="\"${notes}\"" $kwargs $kwargs_multi $kwargs_dep -m &
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sleep 3
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done
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fi
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wait
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col_val_subset=""
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rate_cols="['test/feat/rate']"
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distortion_cols="['test/feat/distortion','test/feat/online_loss','test/feat/online_err','test/pred/loss','test/pred/err']"
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compare="datafeat"
55+
data="merged" # want to access both ther featurizer data and the predictor data
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python utils/aggregate.py \
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experiment=$experiment \
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$col_val_subset \
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+plot_all_RD_curves.folder_col="dist" \
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+plot_all_RD_curves.data="${data}" \
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+plot_all_RD_curves.rate_cols="${rate_cols}" \
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+plot_all_RD_curves.distortion_cols="${distortion_cols}" \
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+plot_all_RD_curves.hue=$compare \
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+plot_all_RD_curves.plot_config_kwargs.font_scale=1 \
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+summarize_RD_curves.data="${data}" \
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+summarize_RD_curves.rate_cols="${rate_cols}" \
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+summarize_RD_curves.distortion_cols="${distortion_cols}" \
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+summarize_RD_curves.mse_cols="${distortion_cols}" \
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agg_mode=[summarize_metrics,plot_all_RD_curves,summarize_RD_curves]
70+
71+
72+
col_val_subset=""
73+
rate_cols="['test/feat/rate']"
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distortion_cols="['test/pred/err']"
75+
compare="datafeat"
76+
data="merged" # want to access both ther featurizer data and the predictor data
77+
python utils/aggregate.py \
78+
experiment=$experiment \
79+
$col_val_subset \
80+
kwargs.prfx="single" \
81+
+plot_all_RD_curves.folder_col="dist" \
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+plot_all_RD_curves.data="${data}" \
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+plot_all_RD_curves.rate_cols="${rate_cols}" \
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+plot_all_RD_curves.distortion_cols="${distortion_cols}" \
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+plot_all_RD_curves.hue=$compare \
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+plot_all_RD_curves.plot_config_kwargs.font_scale=1 \
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agg_mode=[plot_all_RD_curves]
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‎bin/mnist/augmnist_aug_warm.sh

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#!/usr/bin/env bash
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experiment="augmnist_aug_warm"
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notes="
5+
**Goal**: Understand the impact of the choice of augmentation on a simple setting,
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"
7+
8+
# parses special mode for running the script
9+
source `dirname $0`/../utils.sh
10+
11+
# define all the arguments modified or added to `conf`. If they are added use `+`
12+
kwargs="
13+
experiment=$experiment
14+
timeout=$time
15+
is_only_feat=False
16+
featurizer=neural_feat
17+
architecture@encoder=resnet18
18+
data@data_pred=mnist_aug
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checkpoint@checkpoint_feat=bestTrainLoss
20+
rate=H_hyper
21+
trainer.max_epochs=100
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rate.kwargs.warmup_k_epoch=5
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$add_kwargs
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"
25+
26+
27+
# every arguments that you are sweeping over
28+
kwargs_multi="
29+
data@data_feat=mnist_aug
30+
seed=1
31+
"
32+
33+
if [ "$is_plot_only" = false ] ; then
34+
for kwargs_dep in "distortion=VIC featurizer.loss.beta=1e-5,1e-4,1e-3,1e-2,0.1,1" "distortion=BINCE featurizer.loss.beta=1e-7,1e-6,1e-5,1e-4,1e-3,1e-2"
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do
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37+
python "$main" +hydra.job.env_set.WANDB_NOTES="\"${notes}\"" $kwargs $kwargs_multi $kwargs_dep -m &
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39+
sleep 3
40+
41+
done
42+
fi
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45+
wait
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col_val_subset=""
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rate_cols="['test/feat/rate']"
50+
distortion_cols="['test/feat/distortion','test/feat/online_loss','test/feat/online_err','test/pred/loss','test/pred/err']"
51+
compare="datafeat"
52+
data="merged" # want to access both ther featurizer data and the predictor data
53+
python utils/aggregate.py \
54+
experiment=$experiment \
55+
$col_val_subset \
56+
+plot_all_RD_curves.folder_col="dist" \
57+
+plot_all_RD_curves.data="${data}" \
58+
+plot_all_RD_curves.rate_cols="${rate_cols}" \
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+plot_all_RD_curves.distortion_cols="${distortion_cols}" \
60+
+plot_all_RD_curves.hue=$compare \
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+plot_all_RD_curves.plot_config_kwargs.font_scale=1 \
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+summarize_RD_curves.data="${data}" \
63+
+summarize_RD_curves.rate_cols="${rate_cols}" \
64+
+summarize_RD_curves.distortion_cols="${distortion_cols}" \
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+summarize_RD_curves.mse_cols="${distortion_cols}" \
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agg_mode=[summarize_metrics,plot_all_RD_curves,summarize_RD_curves]
67+
68+
69+
col_val_subset=""
70+
rate_cols="['test/feat/rate']"
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distortion_cols="['test/pred/err']"
72+
compare="datafeat"
73+
data="merged" # want to access both ther featurizer data and the predictor data
74+
python utils/aggregate.py \
75+
experiment=$experiment \
76+
$col_val_subset \
77+
kwargs.prfx="single" \
78+
+plot_all_RD_curves.folder_col="dist" \
79+
+plot_all_RD_curves.data="${data}" \
80+
+plot_all_RD_curves.rate_cols="${rate_cols}" \
81+
+plot_all_RD_curves.distortion_cols="${distortion_cols}" \
82+
+plot_all_RD_curves.hue=$compare \
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+plot_all_RD_curves.plot_config_kwargs.font_scale=1 \
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agg_mode=[plot_all_RD_curves]

‎bin/mnist/augmnist_stag_step1.sh

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#!/usr/bin/env bash
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experiment="augmnist_stag_step1"
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notes="
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**Goal**: Understand effect of using staggered VS end to end for compression
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"
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8+
# parses special mode for running the script
9+
source `dirname $0`/../utils.sh
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11+
SCRIPT=`realpath $0`
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SCRIPTPATH=`dirname $SCRIPT`
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pretrained_path="$SCRIPTPATH"/../../pretrained
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15+
# define all the arguments modified or added to `conf`. If they are added use `+`
16+
kwargs="
17+
experiment=$experiment
18+
timeout=$time
19+
is_only_feat=True
20+
featurizer=neural_feat
21+
architecture@encoder=resnet18
22+
data@data_pred=mnist_aug
23+
checkpoint@checkpoint_feat=bestTrainLoss
24+
trainer.max_epochs=100
25+
featurizer.loss.beta=1
26+
distortion.factor_beta=1
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rate.factor_beta=1
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rate=lossless
29+
$add_kwargs
30+
"
31+
32+
# every arguments that you are sweeping over
33+
kwargs_multi="
34+
data@data_feat=mnist_aug
35+
seed=1
36+
"
37+
38+
39+
if [ "$is_plot_only" = false ] ; then
40+
for dist in "VIC" "BINCE"
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do
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python "$main" +hydra.job.env_set.WANDB_NOTES="\"${notes}\"" $kwargs $kwargs_multi distortion=$dist paths.pretrained.staggered=$pretrained_path/lossless/$dist -m &
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sleep 3
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done
48+
fi
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‎bin/mnist/augmnist_stag_step2.sh

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#!/usr/bin/env bash
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3+
experiment="augmnist_stag_step2"
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notes="
5+
**Goal**: Understand effect of using staggered VS end to end for compression
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"
7+
8+
# parses special mode for running the script
9+
source `dirname $0`/../utils.sh
10+
11+
SCRIPT=`realpath $0`
12+
SCRIPTPATH=`dirname $SCRIPT`
13+
pretrained_path="$SCRIPTPATH"/../../pretrained
14+
15+
# define all the arguments modified or added to `conf`. If they are added use `+`
16+
kwargs="
17+
experiment=$experiment
18+
timeout=$time
19+
is_only_feat=False
20+
featurizer=neural_feat
21+
architecture@encoder=resnet18
22+
data@data_pred=mnist_aug
23+
checkpoint@checkpoint_feat=bestTrainLoss
24+
trainer.max_epochs=50
25+
+update_trainer_pred.max_epochs=100
26+
featurizer.loss.beta=1
27+
distortion.factor_beta=1
28+
rate.factor_beta=1
29+
rate=H_hyper
30+
rate.kwargs.is_endToEnd=False
31+
finetune=freezer
32+
distortion=lossy_Z
33+
scheduler@scheduler_feat=unifmultistep1000
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scheduler@scheduler_coder=unifmultistep1000
35+
$add_kwargs
36+
"
37+
38+
# every arguments that you are sweeping over
39+
kwargs_multi="
40+
data@data_feat=mnist_aug
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seed=1
42+
featurizer.loss.beta=1e-2
43+
"
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#VIC
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# beta should now have no impact. What is important is the weight
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47+
48+
if [ "$is_plot_only" = false ] ; then
49+
for dist in "VIC" "BINCE"
50+
do
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52+
python "$main" +hydra.job.env_set.WANDB_NOTES="\"${notes}\"" $kwargs $kwargs_multi paths.pretrained.staggered=$pretrained_path/lossless/$dist -m &
53+
54+
sleep 3
55+
56+
done
57+
fi
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