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dlr eo: tidyup

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Starbeamrainbowlabs 2023-03-01 16:47:36 +00:00
parent 69b5ae8838
commit 4fd9feba4f
Signed by: sbrl
GPG key ID: 1BE5172E637709C2
1 changed files with 18 additions and 12 deletions
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30
aimodel/src/deeplabv3_plus_test_rainfall.py
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@ -22,6 +22,13 @@ from lib.ai.components.LossCrossEntropyDice import LossCrossEntropyDice
time_start = datetime.now()
logger.info(f"Starting at {str(datetime.now().isoformat())}")
# ███████ ███ ██ ██ ██ ██ ██████ ██████ ███ ██ ███ ███ ███████ ███ ██ ████████
# ██ ████ ██ ██ ██ ██ ██ ██ ██ ██ ████ ██ ████ ████ ██ ████ ██ ██
# █████ ██ ██ ██ ██ ██ ██ ██████ ██ ██ ██ ██ ██ ██ ████ ██ █████ ██ ██ ██ ██
# ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██
# ███████ ██ ████ ████ ██ ██ ██ ██████ ██ ████ ██ ██ ███████ ██ ████ ██
IMAGE_SIZE = int(os.environ["IMAGE_SIZE"]) if "IMAGE_SIZE" in os.environ else 128 # was 512; 128 is the highest power of 2 that fits the data
BATCH_SIZE = int(os.environ["BATCH_SIZE"]) if "BATCH_SIZE" in os.environ else 64
NUM_CLASSES = 2
@ -39,24 +46,23 @@ DIR_OUTPUT=os.environ["DIR_OUTPUT"] if "DIR_OUTPUT" in os.environ else f"output/
PATH_CHECKPOINT = os.environ["PATH_CHECKPOINT"] if "PATH_CHECKPOINT" in os.environ else None
PREDICT_COUNT = int(os.environ["PREDICT_COUNT"]) if "PREDICT_COUNT" in os.environ else 4
# ~~~
if not os.path.exists(DIR_OUTPUT):
os.makedirs(os.path.join(DIR_OUTPUT, "checkpoints"))
# ~~~
logger.info("DeepLabV3+ rainfall radar TEST")
logger.info(f"> BATCH_SIZE {BATCH_SIZE}")
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"> REMOVE_ISOLATED_PIXELS {REMOVE_ISOLATED_PIXELS} [NO_REMOVE_ISOLATED_PIXELS]")
logger.info(f"> EPOCHS {EPOCHS}")
logger.info(f"> LOSS {LOSS}")
for env_name in [ "BATCH_SIZE","NUM_CLASSES", "DIR_RAINFALLWATER", "PATH_HEIGHTMAP", "PATH_COLOURMAP", "STEPS_PER_EPOCH", "REMOVE_ISOLATED_PIXELS", "EPOCHS", "LOSS", "LEARNING_RATE", "DIR_OUTPUT", "PATH_CHECKPOINT", "PREDICT_COUNT" ]:
logger.info(f"> {env_name} {str(globals()[env_name])}")
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(
dirpath_input=DIR_RAINFALLWATER,