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#!/bin/bash -l |
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export data_raw="/home/woody/iwi5/iwi5039h/data_raw" |
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export nnUNet_raw_data_base="/home/woody/iwi5/iwi5039h/nnUNet_data/nnUNet_raw_data_base/" |
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export nnUNet_preprocessed="/home/woody/iwi5/iwi5039h/nnUNet_data/nnUNet_preprocessed/" |
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export RESULTS_FOLDER="/home/woody/iwi5/iwi5039h/nnUNet_data/RESULTS_FOLDER" |
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cd nnunet_glacer |
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pwd |
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conda activate nnunet |
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python3 nnunet/dataset_conversion/Task503_Glacier_mtl.py -data_percentage 100 -base $data_raw |
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python3 nnunet/experiment_planning/nnUNet_plan_and_preprocess.py -t 503 -pl3d None -pl2d ExperimentPlanner2D_mtl |
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python3 nnunet/run/run_training.py 2d nnUNetTrainerMTLlate 503 2 -p nnUNetPlans_mtl --disable_postprocessing_on_folds |
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python3 nnunet/inference/predict_simple.py -i $nnUNet_raw_data_base/nnUNet_raw_data/Task503_Glacier_mtl/imagesTs -o $RESULTS_FOLDER/test_predictions/Task503_Glacier_mtl_late/fold_2 -t 503 -m 2d -f 2 -p nnUNetPlans_mtl -tr nnUNetTrainerMTLlate |
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python3 nnunet/dataset_conversion/Task503_Glacier_mtl_reverse.py -i $RESULTS_FOLDER/test_predictions/Task503_Glacier_mtl_late/fold_2 |
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python3 ./evaluate_nnUNet.py --predictions $RESULTS_FOLDER/test_predictions/Task503_Glacier_mtl_late/fold_2/pngs --labels_fronts $data_raw/fronts/test --labels_zones $data_raw/zones/test --sar_images $data_raw/sar_images/test |
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