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

fixup

Browse source
This commit is contained in:
Starbeamrainbowlabs 2022-11-24 19:02:58 +00:00
parent e4bea89c89
commit afc1cdcf02
Signed by: sbrl
GPG key ID: 1BE5172E637709C2
1 changed files with 2 additions and 3 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

5
aimodel/src/subcommands/rainfall_stats.py
View file

@ -19,8 +19,8 @@ def parse_args():
parser = argparse.ArgumentParser(description="Output water depth image segmentation maps using a given pretrained mono model.")
# parser.add_argument("--config", "-c", help="Filepath to the TOML config file to load.", required=True)
parser.add_argument("--input", "-i", help="Path to input directory containing the .tfrecord(.gz) files to predict for. If a single file is passed instead, then only that file will be converted.", required=True)
parser.add_argument("--reads-multiplier", help="Optional. The multiplier for the number of files we should read from at once. Defaults to 0. When using this start with 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. SETTING THIS WILL SCRAMBLE THE ORDER OF THE DATASET.")
parser.add_argument("--batch-size", help="Optional. The batch size to calculate statistics with. Can be larger than normal since we don't have a model loaded. Default: 1024")
parser.add_argument("--reads-multiplier", help="Optional. The multiplier for the number of files we should read from at once. Defaults to 0. When using this start with 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. SETTING THIS WILL SCRAMBLE THE ORDER OF THE DATASET.", type=int)
parser.add_argument("--batch-size", help="Optional. The batch size to calculate statistics with. Can be larger than normal since we don't have a model loaded. Default: 1024", type=int)
return parser
def run(args):
@ -30,7 +30,6 @@ def run(args):
if (not hasattr(args, "batch_size")) or args.batch_size == None:
args.batch_size = 1024
sys.stderr.write(f"\n\n>>> This is TensorFlow {tf.__version__}\n\n\n")
# Note that if using a directory of input files, the output order is NOT GUARANTEED TO BE THE SAME. In fact, it probably won't be (see dataset_mono for more details).