diff --git a/experiments/toy_exp/configs.py b/experiments/toy_exp/configs.py
index e234ac1..27f4b0a 100644
--- a/experiments/toy_exp/configs.py
+++ b/experiments/toy_exp/configs.py
@@ -1,350 +1,350 @@
#!/usr/bin/env python
# Copyright 2018 Division of Medical Image Computing, German Cancer Research Center (DKFZ).
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
import sys
import os
sys.path.append(os.path.dirname(os.path.realpath(__file__)))
import numpy as np
from default_configs import DefaultConfigs
class configs(DefaultConfigs):
def __init__(self, server_env=None):
#########################
# Preprocessing #
#########################
self.root_dir = '/home/gregor/datasets/toy_mdt'
#########################
# I/O #
#########################
# one out of [2, 3]. dimension the model operates in.
self.dim = 2
# one out of ['mrcnn', 'retina_net', 'retina_unet', 'detection_unet', 'ufrcnn'].
- self.model = 'retina_unet'
+ self.model = 'retina_net'
DefaultConfigs.__init__(self, self.model, server_env, self.dim)
# int [0 < dataset_size]. select n patients from dataset for prototyping.
self.select_prototype_subset = None
self.hold_out_test_set = True
self.n_train_data = 2500
# choose one of the 3 toy experiments described in https://arxiv.org/pdf/1811.08661.pdf
# one of ['donuts_shape', 'donuts_pattern', 'circles_scale'].
toy_mode = 'donuts_shape_noise'
# path to preprocessed data.
self.input_df_name = 'info_df.pickle'
self.pp_name = os.path.join(toy_mode, 'train')
self.pp_data_path = os.path.join(self.root_dir, self.pp_name)
self.pp_test_name = os.path.join(toy_mode, 'test')
self.pp_test_data_path = os.path.join(self.root_dir, self.pp_test_name)
# settings for deployment in cloud.
if server_env:
# path to preprocessed data.
pp_root_dir = '/datasets/datasets_ramien/toy_exp/data'
self.pp_name = os.path.join(toy_mode, 'train')
self.pp_data_path = os.path.join(pp_root_dir, self.pp_name)
self.pp_test_name = os.path.join(toy_mode, 'test')
self.pp_test_data_path = os.path.join(pp_root_dir, self.pp_test_name)
self.select_prototype_subset = None
#########################
# Data Loader #
#########################
# select modalities from preprocessed data
self.channels = [0]
self.n_channels = len(self.channels)
# patch_size to be used for training. pre_crop_size is the patch_size before data augmentation.
self.pre_crop_size_2D = [320, 320]
self.patch_size_2D = [320, 320]
self.patch_size = self.patch_size_2D if self.dim == 2 else self.patch_size_3D
self.pre_crop_size = self.pre_crop_size_2D if self.dim == 2 else self.pre_crop_size_3D
# ratio of free sampled batch elements before class balancing is triggered
# (>0 to include "empty"/background patches.)
self.batch_sample_slack = 0.2
# set 2D network to operate in 3D images.
self.merge_2D_to_3D_preds = False
# feed +/- n neighbouring slices into channel dimension. set to None for no context.
self.n_3D_context = None
if self.n_3D_context is not None and self.dim == 2:
self.n_channels *= (self.n_3D_context * 2 + 1)
#########################
# Architecture #
#########################
self.start_filts = 48 if self.dim == 2 else 18
self.end_filts = self.start_filts * 4 if self.dim == 2 else self.start_filts * 2
self.res_architecture = 'resnet50' # 'resnet101' , 'resnet50'
self.norm = None # one of None, 'instance_norm', 'batch_norm'
- self.weight_decay = 1e-4
+ self.weight_decay = 1e-5
# one of 'xavier_uniform', 'xavier_normal', or 'kaiming_normal', None (=default = 'kaiming_uniform')
self.weight_init = None
#########################
# Schedule / Selection #
#########################
- self.num_epochs = 11
+ self.num_epochs = 20
self.num_train_batches = 100 if self.dim == 2 else 200
self.batch_size = 20 if self.dim == 2 else 8
self.do_validation = True
# decide whether to validate on entire patient volumes (like testing) or sampled patches (like training)
# the former is morge accurate, while the latter is faster (depending on volume size)
self.val_mode = 'val_patient' # one of 'val_sampling' , 'val_patient'
if self.val_mode == 'val_patient':
self.max_val_patients = None # if 'None' iterates over entire val_set once.
if self.val_mode == 'val_sampling':
self.num_val_batches = 50
# set dynamic_lr_scheduling to True to apply LR scheduling with below settings.
self.dynamic_lr_scheduling = True
self.lr_decay_factor = 0.5
self.scheduling_patience = int(self.num_train_batches * self.batch_size / 2400)
self.scheduling_criterion = 'malignant_ap'
self.scheduling_mode = 'min' if "loss" in self.scheduling_criterion else 'max'
#########################
# Testing / Plotting #
#########################
# set the top-n-epochs to be saved for temporal averaging in testing.
self.save_n_models = 5
self.test_n_epochs = 5
# set a minimum epoch number for saving in case of instabilities in the first phase of training.
self.min_save_thresh = 0 if self.dim == 2 else 0
self.report_score_level = ['patient', 'rois'] # choose list from 'patient', 'rois'
self.class_dict = {1: 'benign', 2: 'malignant'} # 0 is background.
self.patient_class_of_interest = 2 # patient metrics are only plotted for one class.
self.ap_match_ious = [0.1] # list of ious to be evaluated for ap-scoring.
self.model_selection_criteria = ['benign_ap', 'malignant_ap'] # criteria to average over for saving epochs.
self.min_det_thresh = 0.1 # minimum confidence value to select predictions for evaluation.
# threshold for clustering predictions together (wcs = weighted cluster scoring).
# needs to be >= the expected overlap of predictions coming from one model (typically NMS threshold).
# if too high, preds of the same object are separate clusters.
self.wcs_iou = 1e-5
self.plot_prediction_histograms = True
self.plot_stat_curves = False
#########################
# Data Augmentation #
#########################
self.da_kwargs={
'do_elastic_deform': True,
'alpha':(0., 1500.),
'sigma':(30., 50.),
'do_rotation':True,
'angle_x': (0., 2 * np.pi),
'angle_y': (0., 0),
'angle_z': (0., 0),
'do_scale': True,
'scale':(0.8, 1.1),
'random_crop':False,
'rand_crop_dist': (self.patch_size[0] / 2. - 3, self.patch_size[1] / 2. - 3),
'border_mode_data': 'constant',
'border_cval_data': 0,
'order_data': 1
}
if self.dim == 3:
self.da_kwargs['do_elastic_deform'] = False
self.da_kwargs['angle_x'] = (0, 0.0)
self.da_kwargs['angle_y'] = (0, 0.0) #must be 0!!
self.da_kwargs['angle_z'] = (0., 2 * np.pi)
#########################
# Add model specifics #
#########################
{'detection_unet': self.add_det_unet_configs,
'mrcnn': self.add_mrcnn_configs,
'ufrcnn': self.add_mrcnn_configs,
'ufrcnn_surrounding': self.add_mrcnn_configs,
'retina_net': self.add_mrcnn_configs,
'retina_unet': self.add_mrcnn_configs,
'prob_detector': self.add_mrcnn_configs,
}[self.model]()
def add_det_unet_configs(self):
self.learning_rate = [1e-4] * self.num_epochs
# aggregation from pixel perdiction to object scores (connected component). One of ['max', 'median']
self.aggregation_operation = 'max'
# max number of roi candidates to identify per image (slice in 2D, volume in 3D)
self.n_roi_candidates = 3 if self.dim == 2 else 8
# loss mode: either weighted cross entropy ('wce'), batch-wise dice loss ('dice), or the sum of both ('dice_wce')
self.seg_loss_mode = 'dice_wce'
# if <1, false positive predictions in foreground are penalized less.
self.fp_dice_weight = 1 if self.dim == 2 else 1
self.wce_weights = [1, 1, 1]
self.detection_min_confidence = self.min_det_thresh
# if 'True', loss distinguishes all classes, else only foreground vs. background (class agnostic).
self.class_specific_seg_flag = True
self.num_seg_classes = 3 if self.class_specific_seg_flag else 2
self.head_classes = self.num_seg_classes
def add_mrcnn_configs(self):
# learning rate is a list with one entry per epoch.
self.learning_rate = [1e-4] * self.num_epochs
# disable mask head loss. (e.g. if no pixelwise annotations available)
self.frcnn_mode = False
# disable the re-sampling of mask proposals to original size for speed-up.
# since evaluation is detection-driven (box-matching) and not instance segmentation-driven (iou-matching),
# mask-outputs are optional.
self.return_masks_in_val = True
self.return_masks_in_test = False
# set number of proposal boxes to plot after each epoch.
self.n_plot_rpn_props = 0 if self.dim == 2 else 0
# number of classes for head networks: n_foreground_classes + 1 (background)
self.head_classes = 3
# seg_classes hier refers to the first stage classifier (RPN)
self.num_seg_classes = 2 # foreground vs. background
# feature map strides per pyramid level are inferred from architecture.
self.backbone_strides = {'xy': [4, 8, 16, 32], 'z': [1, 2, 4, 8]}
# anchor scales are chosen according to expected object sizes in data set. Default uses only one anchor scale
# per pyramid level. (outer list are pyramid levels (corresponding to BACKBONE_STRIDES), inner list are scales per level.)
self.rpn_anchor_scales = {'xy': [[8], [16], [32], [64]], 'z': [[2], [4], [8], [16]]}
# choose which pyramid levels to extract features from: P2: 0, P3: 1, P4: 2, P5: 3.
self.pyramid_levels = [0, 1, 2, 3]
# number of feature maps in rpn. typically lowered in 3D to save gpu-memory.
self.n_rpn_features = 512 if self.dim == 2 else 128
# anchor ratios and strides per position in feature maps.
self.rpn_anchor_ratios = [0.5, 1., 2.]
self.rpn_anchor_stride = 1
# Threshold for first stage (RPN) non-maximum suppression (NMS): LOWER == HARDER SELECTION
self.rpn_nms_threshold = 0.7 if self.dim == 2 else 0.7
# loss sampling settings.
self.rpn_train_anchors_per_image = 2 #per batch element
self.train_rois_per_image = 2 #per batch element
self.roi_positive_ratio = 0.5
self.anchor_matching_iou = 0.7
# factor of top-k candidates to draw from per negative sample (stochastic-hard-example-mining).
# poolsize to draw top-k candidates from will be shem_poolsize * n_negative_samples.
self.shem_poolsize = 10
self.pool_size = (7, 7) if self.dim == 2 else (7, 7, 3)
self.mask_pool_size = (14, 14) if self.dim == 2 else (14, 14, 5)
self.mask_shape = (28, 28) if self.dim == 2 else (28, 28, 10)
self.rpn_bbox_std_dev = np.array([0.1, 0.1, 0.1, 0.2, 0.2, 0.2])
self.bbox_std_dev = np.array([0.1, 0.1, 0.1, 0.2, 0.2, 0.2])
self.window = np.array([0, 0, self.patch_size[0], self.patch_size[1]])
self.scale = np.array([self.patch_size[0], self.patch_size[1], self.patch_size[0], self.patch_size[1]])
if self.dim == 2:
self.rpn_bbox_std_dev = self.rpn_bbox_std_dev[:4]
self.bbox_std_dev = self.bbox_std_dev[:4]
self.window = self.window[:4]
self.scale = self.scale[:4]
# pre-selection in proposal-layer (stage 1) for NMS-speedup. applied per batch element.
self.pre_nms_limit = 3000 if self.dim == 2 else 6000
# n_proposals to be selected after NMS per batch element. too high numbers blow up memory if "detect_while_training" is True,
# since proposals of the entire batch are forwarded through second stage in as one "batch".
self.roi_chunk_size = 800 if self.dim == 2 else 600
self.post_nms_rois_training = 500 if self.dim == 2 else 75
self.post_nms_rois_inference = 500
# Final selection of detections (refine_detections)
self.model_max_instances_per_batch_element = 10 if self.dim == 2 else 30 # per batch element and class.
self.detection_nms_threshold = 1e-5 # needs to be > 0, otherwise all predictions are one cluster.
self.model_min_confidence = 0.1
if self.dim == 2:
self.backbone_shapes = np.array(
[[int(np.ceil(self.patch_size[0] / stride)),
int(np.ceil(self.patch_size[1] / stride))]
for stride in self.backbone_strides['xy']])
else:
self.backbone_shapes = np.array(
[[int(np.ceil(self.patch_size[0] / stride)),
int(np.ceil(self.patch_size[1] / stride)),
int(np.ceil(self.patch_size[2] / stride_z))]
for stride, stride_z in zip(self.backbone_strides['xy'], self.backbone_strides['z']
)])
if self.model == 'ufrcnn':
self.operate_stride1 = True
self.class_specific_seg_flag = True
self.num_seg_classes = 3 if self.class_specific_seg_flag else 2
self.frcnn_mode = True
if self.model == 'retina_net' or self.model == 'retina_unet' or self.model == 'prob_detector':
# implement extra anchor-scales according to retina-net publication.
self.rpn_anchor_scales['xy'] = [[ii[0], ii[0] * (2 ** (1 / 3)), ii[0] * (2 ** (2 / 3))] for ii in
self.rpn_anchor_scales['xy']]
self.rpn_anchor_scales['z'] = [[ii[0], ii[0] * (2 ** (1 / 3)), ii[0] * (2 ** (2 / 3))] for ii in
self.rpn_anchor_scales['z']]
self.n_anchors_per_pos = len(self.rpn_anchor_ratios) * 3
self.n_rpn_features = 256 if self.dim == 2 else 64
# pre-selection of detections for NMS-speedup. per entire batch.
self.pre_nms_limit = 10000 if self.dim == 2 else 50000
# anchor matching iou is lower than in Mask R-CNN according to https://arxiv.org/abs/1708.02002
self.anchor_matching_iou = 0.5
# if 'True', seg loss distinguishes all classes, else only foreground vs. background (class agnostic).
self.num_seg_classes = 3 if self.class_specific_seg_flag else 2
if self.model == 'retina_unet':
self.operate_stride1 = True
diff --git a/shell_scripts/cluster_runner_meddec.sh b/shell_scripts/cluster_runner_meddec.sh
index ef036be..703e1a4 100644
--- a/shell_scripts/cluster_runner_meddec.sh
+++ b/shell_scripts/cluster_runner_meddec.sh
@@ -1,65 +1,65 @@
#!/bin/bash
#Usage:
# -->not true?: this script has to be started from the same directory the python files called below lie in (e.g. exec.py lies in meddetectiontkit).
# part of the slurm-job name you pass to sbatch will be the experiment folder's name.
# you need to pass 3 positional arguments to this script (cluster_runner_..sh #1 #2 #3):
# -#1 source directory in which main source code (framework) is located (e.g. medicaldetectiontoolkit/)
# -#2 the exp_dir where job-specific code was copied before by create_exp and exp results are safed by exec.py
# -#3 absolute path to dataset-specific code in source dir
# -#4 mode to run
# -#5 folds to run on
source_dir=${1}
exp_dir=${2}
dataset_abs_path=${3}
mode=${4}
folds=${5}
resume=$6
#known problem: trap somehow does not execute the rm -r tmp_dir command when using scancel on job
#trap clean_up EXIT KILL TERM ABRT QUIT
job_dir=/ssd/ramien/${LSB_JOBID}
tmp_dir_data=${job_dir}/data
mkdir $tmp_dir_data
tmp_dir_cache=${job_dir}/cache
mkdir $tmp_dir_cache
CUDA_CACHE_PATH=$tmp_dir_cache
export CUDA_CACHE_PATH
#data must not lie permantly on nodes' ssd, only during training time
#needs to be named with the SLURM_JOB_ID to not be automatically removed
#can permanently lie on /datasets drive --> copy from there before every experiment
#files on datasets are saved as npz (compressed) --> use data_manager.py to copy and unpack into .npy; is done implicitly in exec.py
#(tensorboard --logdir ${exp_dir}/.. --port 1337 || echo "tboard startup failed")& # || tensorboard --logdir ${exp_dir}/.. --port 1338)&
#tboard_pid=$!
#clean_up() {
# rm -rf ${job_dir};
#}
export OMP_NUM_THREADS=1 # this is a work-around fix for batchgenerators to deal with numpy-inherent multi-threading.
launch_opts="${source_dir}/exec.py --use_stored_settings --server_env --exp_source ${dataset_abs_path} --data_dest ${tmp_dir_data} --exp_dir ${exp_dir} --mode ${mode}"
if [ ! -z "${resume}" ]; then
- launch_opts=${launch_opts} --resume
+ launch_opts="${launch_opts} --resume"
echo "Resuming from checkpoint(s)."
fi
if [ ! -z "${folds}" ]; then
- launch_opts=${launch_opts} --folds ${folds}
+ launch_opts="${launch_opts} --folds ${folds}"
fi
echo "submitting with ${launch_opts}"
python ${launch_opts}
diff --git a/shell_scripts/job_starter.sh b/shell_scripts/job_starter.sh
index 4e5add3..d3ca0b4 100644
--- a/shell_scripts/job_starter.sh
+++ b/shell_scripts/job_starter.sh
@@ -1,193 +1,193 @@
#!/bin/bash
#wrapper for cluster_runner_....sh which copies job-specific, frequently changing files (e.g. configs.py) before the actual sbatch job
#is submitted since the job might pend in queue before execution --> hazard of job-specific files being unintentionally changed during queue wait time.
#positonal
# -arg #1 identifies the folder name of the dataset-related code (e.g. >toy_exp< or >lidc_exp<) within the code source directory
# -arg #2 is the experiment and first part of the job name,
# optional args and flags:
# -c / --create: (flag) whether to create the exp, i.e., if this is a new start of the exp with configs etc from source dir.
# -f / --folds FOLDS: (option) fold(s) to run on (FOLDS needs to be only one int or string of multiple ints separated by space), default None (-->set to all in config)
# -m / --mode MODE: (option) string, one of "train", "train_test", "test", defaults to "train_test"
# -p / --exp_parent_dir: (option) name of parent_dir rel to dataset folder on cluster. exp_dir is exp_parent_dir/exp_name, if not given defaults to "experiments"
# -q / --queue: (option) which queue (-q parameter for bsub) to send job to. default: gputest. others: gputest-short (max 5h jobs).
# -w / --which: (option) same as argument -m to bsub; host or host list (string separated by space) to send the job to.
# use nodenameXX where XX==nr of node or nodenameXX,nodenameYY,... or nodename[XX-YY]. nodename is e.g. e132-comp.
# --gmem: (option) how much gpu memory to request for job (in gigabytes), defaults to 11.9. Currently, the smaller nodes have 11.9G, the larger ones 31.7G.
# --resume: (flag) only with explicit fold argument, if set, resumes from checkpoint in exp_dir/fold_x/last_state.pth.
# --no_parallel: (flag) if set, folds won't start as parallel jobs on cluster, but run sequentially in one job.
dataset_name="${1}"
exp_name="${2}"
#arguments not passed, e.g. $7 if no seventh argument, are null.
if [ ! -z "${18}" ]; then #-z checks if is null string
echo "Error: Received too many arguments."
exit
fi
#make args optional: move up if some args are missing inbetween
while [ ${#} -gt 2 ]; do
case "${3}" in
-c|--create)
create_exp="c"
shift
;;
-f|--folds)
folds="${4}"
shift; shift
;;
-m|--mode)
mode="${4}"
shift; shift
;;
-p|--exp_parent_dir)
exp_parent_dir="${4}"
shift; shift
;;
-q|--queue)
queue="${4}"
shift; shift
;;
-w|--which)
which="${4}"
shift; shift
;;
-R|--resource)
resource="${4}"
shift; shift
;;
--gmem)
gmem="${4}"
shift; shift
;;
--resume)
resume=true
shift
;;
--no_parallel)
no_parallel=true
shift
;;
*)
echo "Invalid argument/option passed: ${3}"
exit 1
;;
esac
done
# default values
if [ -z ${exp_parent_dir} ]; then
exp_parent_dir="experiments"
fi
if [ -z ${mode} ]; then
mode="train_test"
fi
if [ -z ${queue} ]; then
queue="gputest"
fi
if [ -z ${gmem} ]; then
gmem="11"
fi
root_dir=/home/ramien #assumes /home/ramien exists
prep_node=ramien@e132-comp07 #node used for prep tasks like create_exp
#medicaldetectiontoolkit
source_dir=${root_dir}/mdt-public
dataset_abs_path=${source_dir}/experiments/${dataset_name} #set as second argument passed to this script
exp_parent_dir=/datasets/datasets_ramien/${dataset_name}/${exp_parent_dir}
#exp_parent_dir=/home/gregor/Documents/medicaldetectiontoolkit/datasets/${dataset_name}/experiments #for testing this script
# /dataset is not mounted on log-in/job submission nodes (would maybe make sense, I feel), only on queue gputest's nodes e132-compXX.
#ssh ${prep_node} "mkdir -p ${exp_parent_dir}"
exp_dir=${exp_parent_dir}/${exp_name}
#activate virtualenv that has all the packages:
source_dl="module load python/3.7.0; module load gcc/7.2.0; source ${root_dir}/.virtualenvs/mdt/bin/activate;"
# TODO as long as no fix available: this script needs to be started directly from the prep node. :/ would be nice if (most importantly
-# 'module ...' would also work over ssh, but somehow some commands are not availabe over the ssh-induced shell (even when using it as interactive).
+# 'module ...') would also work over ssh, but somehow some commands are not availabe over the ssh-induced shell (even when using it as interactive).
eval ${source_dl}
# ssh: (not working)
#create_cmd="ssh ${prep_node} '${source_dl} python ${source_dir}/exec.py --server_env --mode create_exp --exp_dir ${exp_dir} --exp_source ${dataset_abs_path};'"
# directly from prep node:
create_cmd="python ${source_dir}/exec.py --server_env --mode create_exp --exp_dir ${exp_dir} --exp_source ${dataset_abs_path};"
#if create_exp, check if would overwrite existing exp_dir
if [ ! -z ${create_exp} ] && [ ${create_exp} = "c" ]; then #-n doesnt work as replacement for !-z
if [ -d ${exp_dir} ]; then
echo "Please confirm to overwrite exp ${exp_name} settings, (Y/n): "; read confirmation
if ([ "${confirmation}" = "y" ] || [ "${confirmation}" = "yes" ] || [ "${confirmation}" = "Y" ] || [ -z "${confirmation}" ]); then
echo "Overwriting ${exp_name}"
else
echo "Exiting due to overwrite denial. Adjust options."
exit
fi
fi
#echo "opts: name ${exp_name}, ${source_dir}/exec.py --server_env --mode create_exp --exp_dir ${exp_dir} --exp_source ${dataset_abs_path}"
echo "Creating ${exp_name}"
eval ${create_cmd}
else
if [ ! -d ${exp_dir} ]; then
echo "Experiment directory ${exp_dir} does not exist."
echo "Run create_exp? (Y/n): "; read confirmation
if ([ "${confirmation}" = "y" ] || [ "${confirmation}" = "yes" ] || [ "${confirmation}" = "Y" ] || [ -z "${confirmation}" ]); then
echo "Creating ${exp_name}"
eval ${create_cmd}
fi
fi
fi
#if not create_exp, check if would overwrite existing folds (possibly valuable trained params!)
if [ -z ${create_exp} ] && ([ ${mode} = "train" ] || [ ${mode} = "train_test" ]) && [ -z "${resume}" ]; then
for f in ${folds}; do #if folds is null this check won't apply and folds will be quietly overwritten.
if [ -d ${exp_dir}/fold_${f} ]; then #-d checks if is dir
echo "please confirm to overwrite fold_${f}, (Y/n):"; read confirmation
if ([ "${confirmation}" = "y" ] || [ "${confirmation}" = "yes" ] || [ "${confirmation}" = "Y" ] || [ -z "${confirmation}" ]); then
echo "Overwriting "${exp_name}/fold_${f}
else
echo "Exiting due to overwrite denial. Adjust options."
exit
fi
fi
done
fi
bsub_opts="bsub -N -q ${queue} -gpu num=1:j_exclusive=yes:mode=exclusive_process:gmem=${gmem}G"
if [ ! -z "$resource" ]; then
bsub_opts=$bsub_opts $resource
fi
if [ ! -z ${which} ]; then
bsub_opts="${bsub_opts} -m ${which}"
fi
#----- parallel/separate fold jobs (each fold in a single job) -----------
if [ ! -z "${folds}" ] && [ -z ${no_parallel} ]; then #WHY do i need to convert to string again?
for f in ${folds}; do
out_file=${exp_dir}/logs/fold_${f}_lsf_output.out
bsub_opts="$bsub_opts -J '${dataset_name} ${exp_name} fold ${f} ${mode}' -oo '${out_file}'"
eval "${bsub_opts} sh cluster_runner_meddec.sh ${source_dir} ${exp_dir} ${dataset_abs_path} ${mode} ${f} ${resume}"
done
#----- consecutive folds job (all folds in one single job) -----------
else
if [ ! -z ${resume} ]; then
echo "You need to explicitly specify folds if you would like to resume from a checkpoint. Exiting."
exit
fi
out_file=${exp_dir}/lsf_output.out
bsub_opts="$bsub_opts -J '${dataset_name} ${exp_name} folds ${folds} ${mode}' -oo '${out_file}'"
eval "${bsub_opts} sh cluster_runner_meddec.sh ${source_dir} ${exp_dir} ${dataset_abs_path} ${mode} ${folds} ${resume}"
echo "Started in no parallel, folds:" ${folds}
fi