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No need for a CLI arg for feature_dim_in - metadata should contain this

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Starbeamrainbowlabs 2022-10-13 17:37:16 +01:00
parent e201372252
commit f12e6ab905
Signed by: sbrl
GPG key ID: 1BE5172E637709C2
2 changed files with 18 additions and 5 deletions
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21
aimodel/src/lib/ai/model_rainfallwater_segmentation.py
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@ -5,9 +5,24 @@ import tensorflow as tf
from .components.convnext_inverse import do_convnext_inverse
def model_rainfallwater_segmentation(metadata, feature_dim_in, shape_water_out, model_arch="convnext_i_xtiny", batch_size=64, summary_file=None):
def model_rainfallwater_segmentation(metadata, shape_water_out, model_arch="convnext_i_xtiny", batch_size=64, summary_file=None, water_bins=2):
"""Makes a new rainfall / waterdepth segmentation head model.
Args:
metadata (dict): A dictionary of metadata about the dataset to use to build the model with.
feature_dim_in (int): The size of the feature dimension
shape_water_out (_type_): _description_
model_arch (str, optional): _description_. Defaults to "convnext_i_xtiny".
batch_size (int, optional): _description_. Defaults to 64.
summary_file (_type_, optional): _description_. Defaults to None.
water_bins (int, optional): _description_. Defaults to 2.
Returns:
_type_: _description_
"""
out_water_width, out_water_height = shape_water_out
feature_dim_in = metadata["rainfallradar"][0]
layer_input = tf.keras.layers.Input(
shape=(feature_dim_in)
@ -31,7 +46,7 @@ def model_rainfallwater_segmentation(metadata, feature_dim_in, shape_water_out,
# TODO: An attention layer here instead of a dense layer, with a skip connection perhaps?
logger.warning("Warning: TODO implement attention from https://ieeexplore.ieee.org/document/9076883")
layer_next = tf.keras.layers.Dense(32)(layer_next)
layer_next = tf.keras.layers.Conv2D(1, kernel_size=1, activation="softmax", padding="same")(layer_next)
layer_next = tf.keras.layers.Conv2D(water_bins, kernel_size=1, activation="softmax", padding="same")(layer_next)
model = tf.keras.Model(
inputs = layer_input,

2
aimodel/src/subcommands/train.py
View file

@ -14,7 +14,6 @@ def parse_args():
# 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 pretrain with", required=True)
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)
parser.add_argument("--feature-dim", help="The size of the input feature dimension of the model [default: 512].", type=int)
parser.add_argument("--batch-size", help="Sets the batch size [default: 64].", type=int)
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) files at once. Set to a higher number of systems with high read latency to avoid starving the GPU of data.")
parser.add_argument("--water-size", help="The width and height of the square of pixels that the model will predict. Smaller values crop the input more [default: 100].", type=int)
@ -63,7 +62,6 @@ def run(args):
ai = RainfallWaterSegmenter(
dir_output=args.output,
batch_size=args.batch_size,
feature_dim_in=args.feature_dim,
model_arch=args.arch,
metadata = read_metadata(args.input),