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dlr: add env var for water thresholding

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Starbeamrainbowlabs 2023-03-14 20:18:39 +00:00
parent c5fc62c411
commit 623208ba6d
Signed by: sbrl
GPG key ID: 1BE5172E637709C2
2 changed files with 4 additions and 2 deletions
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3
aimodel/slurm-TEST-deeplabv3p-rainfall.job
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@ -38,6 +38,7 @@ show_help() {
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 checkcpoint 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 " PREDICT_COUNT The number of items from the (SCRAMBLED) dataset to make a prediction for." >&2;
@ -70,7 +71,7 @@ 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;
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;
echo ">>> Installing requirements";
conda run -n py38 pip install -q -r requirements.txt;

3
aimodel/src/deeplabv3_plus_test_rainfall.py
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@ -49,6 +49,7 @@ EPOCHS = int(os.environ["EPOCHS"]) if "EPOCHS" in os.environ else 50
LOSS = os.environ["LOSS"] if "LOSS" in os.environ else "cross-entropy-dice"
DICE_LOG_COSH = True if "DICE_LOG_COSH" in os.environ else False
LEARNING_RATE = float(os.environ["LEARNING_RATE"]) if "LEARNING_RATE" in os.environ else 0.001
WATER_THRESHOLD = float(os.environ["WATER_THRESHOLD"]) if "WATER_THRESHOLD" in os.environ else 0.1
DIR_OUTPUT=os.environ["DIR_OUTPUT"] if "DIR_OUTPUT" in os.environ else f"output/{datetime.utcnow().date().isoformat()}_deeplabv3plus_rainfall_TEST"
@ -76,7 +77,7 @@ for env_name in [ "BATCH_SIZE","NUM_CLASSES", "DIR_RAINFALLWATER", "PATH_HEIGHTM
dataset_train, dataset_validate = dataset_mono(
dirpath_input=DIR_RAINFALLWATER,
batch_size=BATCH_SIZE,
water_threshold=0.1,
water_threshold=WATER_THRESHOLD,
rainfall_scale_up=2, # done BEFORE cropping to the below size
output_size=IMAGE_SIZE,
input_size="same",