Add scrip ade 100--50.

Author: arthurdouillard

scripts/ade/plop_ade_100-50.sh

@@ -0,0 +1,52 @@
+#!/bin/bash
+
+set -e
+
+start=`date +%s`
+
+START_DATE=$(date '+%Y-%m-%d')
+
+PORT=$((9000 + RANDOM % 1000))
+GPU=0,1
+NB_GPU=2
+
+DATA_ROOT=/path/to/my/ade
+
+DATASET=ade
+TASK=100-10
+NAME=PLOP
+METHOD=FT
+OPTIONS="--checkpoint checkpoints/step/ --pod local --pod_factor 0.001 --pod_logits --pseudo entropy --threshold 0.001 --classif_adaptive_factor --init_balanced"
+
+SCREENNAME="${DATASET}_${TASK}_${NAME} On GPUs ${GPU}"
+
+RESULTSFILE=results/${START_DATE}_${DATASET}_${TASK}_${NAME}.csv
+rm -f ${RESULTSFILE}
+
+echo -ne "\ek${SCREENNAME}\e\\"
+
+echo "Writing in ${RESULTSFILE}"
+
+# If you already trained the model for the first step, you can re-use those weights
+# in order to skip this initial step --> faster iteration on your model
+# Set this variable with the weights path
+# FIRSTMODEL=/path/to/my/first/weights
+# Then, for the first step, append those options:
+# --ckpt ${FIRSTMODEL} --test
+# And for the second step, this option:
+# --step_ckpt ${FIRSTMODEL}
+
+CUDA_VISIBLE_DEVICES=${GPU} python3 -m torch.distributed.launch --master_port ${PORT} --nproc_per_node=${NB_GPU} run.py --date ${START_DATE} --data_root ${DATA_ROOT} --overlap --batch_size 12 --dataset ${DATASET} --name ${NAME} --task ${TASK} --step 0 --lr 0.01 --epochs 60 --method ${METHOD} --opt_level O1 ${OPTIONS}
+CUDA_VISIBLE_DEVICES=${GPU} python3 -m torch.distributed.launch --master_port ${PORT} --nproc_per_node=${NB_GPU} run.py --date ${START_DATE} --data_root ${DATA_ROOT} --overlap --batch_size 12 --dataset ${DATASET} --name ${NAME} --task ${TASK} --step 1 --lr 0.001 --epochs 60 --method ${METHOD} --opt_level O1 ${OPTIONS} --pod_options "{\"switch\": {\"after\": {\"extra_channels\": \"sum\", \"factor\": 0.00001, \"type\": \"local\"}}}"
+CUDA_VISIBLE_DEVICES=${GPU} python3 -m torch.distributed.launch --master_port ${PORT} --nproc_per_node=${NB_GPU} run.py --date ${START_DATE} --data_root ${DATA_ROOT} --overlap --batch_size 12 --dataset ${DATASET} --name ${NAME} --task ${TASK} --step 2 --lr 0.001 --epochs 60 --method ${METHOD} --opt_level O1 ${OPTIONS} --pod_options "{\"switch\": {\"after\": {\"extra_channels\": \"sum\", \"factor\": 0.00001, \"type\": \"local\"}}}"
+CUDA_VISIBLE_DEVICES=${GPU} python3 -m torch.distributed.launch --master_port ${PORT} --nproc_per_node=${NB_GPU} run.py --date ${START_DATE} --data_root ${DATA_ROOT} --overlap --batch_size 12 --dataset ${DATASET} --name ${NAME} --task ${TASK} --step 3 --lr 0.001 --epochs 60 --method ${METHOD} --opt_level O1 ${OPTIONS} --pod_options "{\"switch\": {\"after\": {\"extra_channels\": \"sum\", \"factor\": 0.00001, \"type\": \"local\"}}}"
+CUDA_VISIBLE_DEVICES=${GPU} python3 -m torch.distributed.launch --master_port ${PORT} --nproc_per_node=${NB_GPU} run.py --date ${START_DATE} --data_root ${DATA_ROOT} --overlap --batch_size 12 --dataset ${DATASET} --name ${NAME} --task ${TASK} --step 4 --lr 0.001 --epochs 60 --method ${METHOD} --opt_level O1 ${OPTIONS} --pod_options "{\"switch\": {\"after\": {\"extra_channels\": \"sum\", \"factor\": 0.00001, \"type\": \"local\"}}}"
+CUDA_VISIBLE_DEVICES=${GPU} python3 -m torch.distributed.launch --master_port ${PORT} --nproc_per_node=${NB_GPU} run.py --date ${START_DATE} --data_root ${DATA_ROOT} --overlap --batch_size 12 --dataset ${DATASET} --name ${NAME} --task ${TASK} --step 5 --lr 0.001 --epochs 60 --method ${METHOD} --opt_level O1 ${OPTIONS} --pod_options "{\"switch\": {\"after\": {\"extra_channels\": \"sum\", \"factor\": 0.00001, \"type\": \"local\"}}}"
+python3 average_csv.py ${RESULTSFILE}
+
+echo ${SCREENNAME}
+
+
+end=`date +%s`
+runtime=$((end-start))
+echo "Run in ${runtime}s"