Templates for SGN segmentation

Author: schilling40

reproducibility/segmentation_pipeline/apply_unet_SGN_template.sbatch

@@ -0,0 +1,50 @@
+#!/bin/bash
+#SBATCH --job-name=apply-unet-SGN
+#SBATCH -t 01:00:00             # estimated time, adapt to your needs
+
+#SBATCH -p grete:shared         # the partition
+#SBATCH -G A100:1               # For requesting 1 A100 GPU.
+#SBATCH -c 1
+#SBATCH --mem 24G
+#SBATCH -a 0-9
+
+source ~/.bashrc
+micromamba activate micro-sam_gpu
+
+# Print out some info.
+echo "Submitting job with sbatch from directory: ${SLURM_SUBMIT_DIR}"
+echo "Home directory: ${HOME}"
+echo "Working directory: $PWD"
+echo "Current node: ${SLURM_NODELIST}"
+
+# Run the script
+
+SCRIPT_REPO=/user/schilling40/u15000/flamingo-tools
+cd "$SCRIPT_REPO"/flamingo_tools/segmentation/ || exit
+
+export SCRIPT_DIR=$SCRIPT_REPO/scripts
+
+COCHLEA=$1
+
+export INPUT=/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/"$COCHLEA"/MLR214L_Calb1_PV_CR_012_fused.n5
+
+export OUTPUT_FOLDER=/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/predictions/"$COCHLEA"/SGN_v2
+
+export MODEL=/mnt/lustre-grete/usr/u15000/checkpoints/cochlea_distance_unet_SGN_supervised_2025-05-27/
+export PREDICTION_INSTANCES=10
+export INPUT_KEY="setup1/timepoint0/s0"
+
+echo "Input directory: ${INPUT}"
+echo "Output directory: ${OUTPUT_FOLDER}"
+echo "Model: ${MODEL}"
+
+cmd_array=(	'import sys,os;'
+	'sys.path.insert(0,os.environ["SCRIPT_DIR"]);'
+	'import unet_prediction;'
+	'unet_prediction.run_unet_prediction_slurm(input_path=os.environ["INPUT"],'
+	'output_folder=os.environ["OUTPUT_FOLDER"],model_path=os.environ["MODEL"],'
+	'input_key=os.environ["INPUT_KEY"],'
+	'prediction_instances=os.environ["PREDICTION_INSTANCES"])')
+cmd="${cmd_array[*]}"
+python -c "$cmd"
+

reproducibility/segmentation_pipeline/mean_std_SGN_template.sbatch

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+#!/bin/bash
+#SBATCH --job-name=mean-std-SGN
+#SBATCH -t 02:00:00             # estimated time, adapt to your needs
+
+#SBATCH -p standard96s:shared         # the partition
+#SBATCH -A nim00007
+#SBATCH -c 3
+#SBATCH --mem 128G
+
+source ~/.bashrc
+micromamba activate flamingo13
+
+# Run the script
+
+SCRIPT_REPO=/user/schilling40/u15000/flamingo-tools
+cd "$SCRIPT_REPO"/flamingo_tools/segmentation/ || exit
+
+export SCRIPT_DIR=$SCRIPT_REPO/scripts
+
+COCHLEA=$1
+
+export INPUT=/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/"$COCHLEA"/GEK11L_PV_GFP_01_fused.n5
+export OUTPUT_FOLDER=/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/predictions/"$COCHLEA"/SGN_v2
+export SEG_CLASS="sgn"
+export INPUT_KEY="setup0/timepoint0/s0"
+
+if ! [[ -f $OUTPUT_FOLDER ]] ; then
+	mkdir -p "$OUTPUT_FOLDER"
+fi
+
+echo "Input directory: ${INPUT}"
+echo "Output directory: ${OUTPUT_FOLDER}"
+
+cmd_array=(	'import sys,os;'
+	'sys.path.insert(0,os.environ["SCRIPT_DIR"]);'
+	'import unet_prediction;'
+	'unet_prediction.run_unet_prediction_preprocess_slurm(input_path=os.environ["INPUT"],'
+	'input_key=os.environ["INPUT_KEY"],'
+	'output_folder=os.environ["OUTPUT_FOLDER"],seg_class=os.environ["SEG_CLASS"])')
+cmd="${cmd_array[*]}"
+python -c "$cmd"
+

reproducibility/segmentation_pipeline/segment_unet_SGN_template.sbatch

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+#!/bin/bash
+#SBATCH --job-name=segment-unet-SGN_M-LR-000214-L
+#SBATCH --time 10:00:00             # estimated time, adapt to your needs
+
+#SBATCH -p standard96s:shared         # the partition
+#SBATCH -A nim00007
+#SBATCH -c 8
+#SBATCH --mem 400G
+
+source ~/.bashrc
+micromamba activate micro-sam_gpu
+
+# Print out some info.
+echo "Submitting job with sbatch from directory: ${SLURM_SUBMIT_DIR}"
+echo "Home directory: ${HOME}"
+echo "Working directory: $PWD"
+echo "Current node: ${SLURM_NODELIST}"
+
+# Run the script
+#python myprogram.py $SLURM_ARRAY_TASK_ID
+
+SCRIPT_REPO=/user/schilling40/u15000/flamingo-tools
+cd "$SCRIPT_REPO"/flamingo_tools/segmentation/ || exit
+
+COCHLEA=$1
+
+export SCRIPT_DIR=$SCRIPT_REPO/scripts
+export OUTPUT_FOLDER=/mnt/vast-nhr/projects/nim00007/data/moser/cochlea-lightsheet/predictions/"$COCHLEA"/SGN_v2
+export MIN_SIZE=1000
+export BOUNDARY_DISTANCE_THRESHOLD=0.5
+
+echo "Output directory: ${OUTPUT_FOLDER}"
+
+cmd_array=(	'import sys,os;'
+	'sys.path.insert(0,os.environ["SCRIPT_DIR"]);'
+	'import unet_prediction;'
+	'unet_prediction.run_unet_segmentation_slurm(output_folder=os.environ["OUTPUT_FOLDER"],'
+	'min_size=os.environ["MIN_SIZE"])')
+cmd="${cmd_array[*]}"
+python -c "$cmd"
+