sbrl-github/research-rainfallradar
1
0
Fork 0
mirror of https://github.com/sbrl/research-rainfallradar synced 2024-11-22 01:12:59 +00:00
Code Issues Projects Releases Packages Wiki Activity

add moar env vars

Browse source
This commit is contained in:
Starbeamrainbowlabs 2023-01-12 18:54:39 +00:00
parent 0d41bbba94
commit 176dc022a0
Signed by: sbrl
GPG key ID: 1BE5172E637709C2
2 changed files with 11 additions and 4 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

4
aimodel/slurm-TEST-deeplabv3p-rainfall.job
View file

@ -32,6 +32,8 @@ show_help() {
echo -e " PATH_COLOURMAP The path to the colourmap for predictive purposes." >&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 " STEPS_PER_EPOCH The number of steps to consider an epoch. Defaults to None, which means use the entire dataset." >&2;
echo -e " EPOCHS The number of epochs to train for." >&2;
echo -e " PREDICT_COUNT The number of items from the (SCRAMBLED) dataset to make a prediction for." >&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;
@ -57,7 +59,7 @@ DIR_OUTPUT="output/$(date -u --rfc-3339=date)_${CODE}";
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;
export IMAGE_SIZE BATCH_SIZE DIR_RAINFALLWATER PATH_HEIGHTMAP PATH_COLOURMAP STEPS_PER_EPOCH DIR_OUTPUT PATH_CHECKPOINT EPOCHS;
echo ">>> Installing requirements";
conda run -n py38 pip install -q -r requirements.txt;

11
aimodel/src/deeplabv3_plus_test_rainfall.py
View file

@ -25,6 +25,9 @@ DIR_RAINFALLWATER = os.environ["DIR_RAINFALLWATER"]
PATH_HEIGHTMAP = os.environ["PATH_HEIGHTMAP"]
PATH_COLOURMAP = os.environ["PATH_COLOURMAP"]
STEPS_PER_EPOCH = int(os.environ["STEPS_PER_EPOCH"]) if "STEPS_PER_EPOCH" in os.environ else None
EPOCHS = int(os.environ["EPOCHS"]) if "EPOCHS" in os.environ else 25
PREDICT_COUNT = int(os.environ["PREDICT_COUNT"]) if "PREDICT_COUNT" in os.environ else 4
DIR_OUTPUT=os.environ["DIR_OUTPUT"] if "DIR_OUTPUT" in os.environ else f"output/{datetime.utcnow().date().isoformat()}_deeplabv3plus_rainfall_TEST"
@ -39,8 +42,10 @@ logger.info(f"> DIR_RAINFALLWATER {DIR_RAINFALLWATER}")
logger.info(f"> PATH_HEIGHTMAP {PATH_HEIGHTMAP}")
logger.info(f"> PATH_COLOURMAP {PATH_COLOURMAP}")
logger.info(f"> STEPS_PER_EPOCH {STEPS_PER_EPOCH}")
logger.info(f"> EPOCHS {EPOCHS}")
logger.info(f"> DIR_OUTPUT {DIR_OUTPUT}")
logger.info(f"> PATH_CHECKPOINT {PATH_CHECKPOINT}")
logger.info(f"> PREDICT_COUNT {PREDICT_COUNT}")
dataset_train, dataset_validate = dataset_mono(
@ -153,7 +158,7 @@ if PATH_CHECKPOINT is None:
logger.info(">>> Beginning training")
history = model.fit(dataset_train,
validation_data=dataset_validate,
epochs=25,
epochs=EPOCHS,
callbacks=[
tf.keras.callbacks.CSVLogger(
filename=os.path.join(DIR_OUTPUT, "metrics.tsv"),
@ -287,13 +292,13 @@ def get_from_batched(dataset, count):
plot_predictions(
os.path.join(DIR_OUTPUT, "predict_train_$$.png"),
get_from_batched(dataset_train, 4),
get_from_batched(dataset_train, PREDICT_COUNT),
colormap,
model=model
)
plot_predictions(
os.path.join(DIR_OUTPUT, "predict_validate_$$.png"),
get_from_batched(dataset_validate, 4),
get_from_batched(dataset_validate, PREDICT_COUNT),
colormap,
model=model
)