#!/usr/bin/env bash
#SBATCH -J DeepRain
#SBATCH -N 1
#SBATCH -n 14
#SBATCH --gres=gpu:1
#SBATCH -o %j.%N.%a.deeplab-rainfall.out.log
#SBATCH -e %j.%N.%a.deeplab-rainfall.err.log
#SBATCH -p gpu
#SBATCH --no-requeue
#SBATCH --time=5-00:00:00
#SBATCH --mem=30000
# ---> in MiB
# no-requeue: ref https://support.hull.ac.uk/tas/public/ssp/content/detail/incident?unid=652db7ac6e73485c9f7658db78b2b628

module load utilities/multi
module load readline/7.0
module load gcc/10.2.0
module load cuda/11.5.0
export XLA_FLAGS="--xla_gpu_cuda_data_dir=/home/ViperAppsFiles/cuda/11.5.0"; # weird... this wasn't needed before?

module load python/anaconda/4.6/miniconda/3.7


show_help() {
	echo -e "Trains a TEST DeepLabv3+ model using rainfall radar and water depth data." >&2;
	echo -e "" >&2;
	echo -e "Usage:" >&2;
	echo -e "    sbatch slurm-TEST-deeplabv3p-rainfall.job" >&2;
	echo -e "" >&2;
	echo -e "....where:" >&2;
	echo -e "    IMAGE_SIZE=128		Optional. Sets the size of the 'images' that the DeepLabV3+ model will work with." >&2;
	echo -e "    BATCH_SIZE=64		Optional. Sets the batch size to train the model with." >&2;
	echo -e "    DIR_RAINFALLWATER	The path to the directory the .tfrecord files containing the rainfall radar / water depth data." >&2;
	echo -e "    PATH_HEIGHTMAP 	The path to the heightmap jsonl file to read in." >&2;
	echo -e "    PATH_COLOURMAP 	The path to the colourmap for predictive purposes." >&2;
	echo -e "    PARALLEL_READS		Multiplier for the number of files to read in parallel. 1 = number of CPU cores available. Very useful on high-read-latency systems (e.g. HPC like Viper) to avoid starving the GPU of data. WILL MANGLE THE ORDERING OF DATA. Set to 0 to disable and read data sequentially. WILL ONLY NOT MANGLE DATA IF PREDICT_AS_ONE IS SET. Defaults to 1.5." >&2;
	echo -e "    STEPS_PER_EPOCH	The number of steps to consider an epoch. Defaults to None, which means use the entire dataset." >&2;
	echo -e "    NO_REMOVE_ISOLATED_PIXELS	Set to any value to avoid the engine from removing isolated pixels - that is, water pixels with no other surrounding pixels, either side to side to diagonally." >&2;
	echo -e "    EPOCHS				The number of epochs to train for." >&2;
	echo -e "    LOSS               The loss function to use. Default: cross-entropy (possible values: cross-entropy, cross-entropy-dice)." >&2;
	echo -e "    DICE_LOG_COSH      When in cross-entropy-dice mode, in addition do loss = cel + log(cosh(dice_loss)) instead of just loss = cel + dice_loss." >&2;
	echo -e "    WATER_THRESHOLD    The threshold to cut water off at when training, in metres. Default: 0.1" >&2;
	echo -e "    PATH_CHECKPOINT 	The path to a checkpoint to load. If specified, a model will be loaded instead of being trained." >&2;
	echo -e "    LEARNING_RATE      The learning rate to use. Default: 0.001." >&2;
	echo -e "    UPSAMPLE           How much to upsample by at the beginning of the model. A value of disables upscaling. Default: 2." >&2;
	echo -e "    STEPS_PER_EXECUTION How many steps to perform before surfacing from the GPU to e.g. do callbacks. Default: 16." >&2;
	echo -e "    RANDSEED           The random seed to use when shuffling filepaths. Default: unset, which means use a random value." >&2;
	echo -e "    JIT_COMPILE        Set to any value to compile the model with XLA." >&2;
	echo -e "    PREDICT_COUNT		The number of items from the (SCRAMBLED) dataset to make a prediction for." >&2;
	echo -e "    PREDICT_AS_ONE		[prediction only] Set to any value to avoid splitting the input dataset into training/validation and instead treat it as a single dataset. Default: False (treat it as training/validation)" >&2;
	echo -e "    POSTFIX			Postfix to append to the output dir (auto calculated)." >&2;
	echo -e "    ARGS				Optional. Any additional arguments to pass to the python program." >&2;
	echo -e "" >&2;
	echo -e "It is strongly advised that all filepaths do NOT contain spaces." >&2;
	echo -e "" >&2;
	echo -e "The code used to identify the run is taken automatically from the filename of the config file." >&2;
	exit;
}

DIR_RAINFALLWATER="${DIR_RAINFALLWATER:-$HOME/rainfallwater_records_tfrecord}";
PATH_HEIGHTMAP="${PATH_HEIGHTMAP:-$HOME/data/terrain50-nimrodsized.json.gz}";
PATH_COLOURMAP="${PATH_COLOURMAP:-$HOME/data/instance-level-human-parsing/instance-level_human_parsing/human_colormap.mat}";

CODE="deeplabv3+_rainfall";

if [[ -n "${POSTFIX}" ]]; then
	echo -e ">>> Applying postfix of ${POSTFIX}" >&2;
	CODE="${CODE}_${POSTFIX}";
fi

DIR_OUTPUT="${DIR_OUTPUT:-output/$(date -u --rfc-3339=date)_${CODE}}";

mkdir -p "${DIR_OUTPUT}";

echo -e ">>> NOW: $(date)";
echo -e ">>> DIR_OUTPUT: ${DIR_OUTPUT}";
echo -e ">>> Additional args: ${ARGS}";

export PATH=$HOME/software/bin:$PATH;
export IMAGE_SIZE BATCH_SIZE DIR_RAINFALLWATER PATH_HEIGHTMAP PATH_COLOURMAP STEPS_PER_EPOCH DIR_OUTPUT PATH_CHECKPOINT EPOCHS PREDICT_COUNT NO_REMOVE_ISOLATED_PIXELS LOSS LEARNING_RATE DICE_LOG_COSH WATER_THRESHOLD UPSAMPLE STEPS_PER_EXECUTION JIT_COMPILE RANDSEED PREDICT_AS_ONE;

echo ">>> Installing requirements";
conda run -n py38 pip install -q -r requirements.txt;
echo ">>> Training model";
#shellcheck disable=SC2016
/usr/bin/env time -v conda run -n py38 bash -c 'src/deeplabv3_plus_test_rainfall.py >>"${DIR_OUTPUT}/experiment.${SLURM_JOB_ID}.out.log" 2>>"${DIR_OUTPUT}/experiment.${SLURM_JOB_ID}.err.log"; echo "[slurm_runner] EXIT_CODE: $?" >>"${DIR_OUTPUT}/experiment.${SLURM_JOB_ID}.out.log";';
echo ">>> exited with code $?";