mirror of
https://github.com/sbrl/research-rainfallradar
synced 2024-11-17 07:03:00 +00:00
58 lines
2.3 KiB
Python
58 lines
2.3 KiB
Python
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import sys
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import argparse
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from asyncio.log import logger
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import tensorflow as tf
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from lib.ai.RainfallWaterContraster import RainfallWaterContraster
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from lib.dataset.dataset import dataset
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from lib.dataset.read_metadata import read_metadata
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def parse_args():
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parser = argparse.ArgumentParser(description="Pretrain a contrastive learning model on a directory of rainfall+water .tfrecord.gz files.")
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# parser.add_argument("--config", "-c", help="Filepath to the TOML config file to load.", required=True)
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parser.add_argument("--input", "-i", help="Path to input directory containing the .tfrecord.gz files to pretrain with", required=True)
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parser.add_argument("--output", "-o", help="Path to output directory to write output to (will be automatically created if it doesn't exist)", required=True)
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parser.add_argument("--feature-dim", help="The size of the output feature dimension of the model [default: 200].", type=int)
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parser.add_argument("--batch-size", help="Sets the batch size [default: 64].", type=int)
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parser.add_argument("--reads-multiplier", help="Optional. The multiplier for the number of files we should read from at once. Defaults to 1.5, which means read ceil(NUMBER_OF_CORES * 1.5). Set to a higher number of systems with high read latency to avoid starving the GPU of data.")
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return parser
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def run(args):
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if (not hasattr(args, "batch_size")) or args.batch_size == None:
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args.batch_size = 64
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if (not hasattr(args, "feature_dim")) or args.feature_dim == None:
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args.feature_dim = 200
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if (not hasattr(args, "read_multiplier")) or args.read_multiplier == None:
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args.read_multiplier = 1.5
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# TODO: Validate args here.
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sys.stderr.write(f"\n\n>>> This is TensorFlow {tf.__version__}\n\n\n")
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dataset_train, dataset_validate = dataset(
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dirpath_input=args.input,
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batch_size=args.batch_size,
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)
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dataset_metadata = read_metadata(args.input)
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# for items in dataset_train.repeat(10):
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# print("ITEMS", len(items))
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# print("LEFT", [ item.shape for item in items[0] ])
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# print("ITEMS DONE")
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# exit(0)
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ai = RainfallWaterContraster(
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dir_output=args.output,
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batch_size=args.batch_size,
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feature_dim=args.feature_dim,
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shape_rainfall=dataset_metadata["rainfallradar"],
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shape_water=dataset_metadata["waterdepth"]
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)
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ai.train(dataset_train, dataset_validate)
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