research-rainfallradar/aimodel/src/lib/ai/model_rainfallwater_mono.py

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import math
from loguru import logger
import tensorflow as tf
from .components.convnext import make_convnext
from .components.convnext_inverse import do_convnext_inverse
from .components.LayerStack2Image import LayerStack2Image
def model_rainfallwater_mono(metadata, shape_water_out, model_arch_enc="convnext_xtiny", model_arch_dec="convnext_i_xtiny", feature_dim=512, batch_size=64, water_bins=2):
"""Makes a new rainfall / waterdepth mono model.
Args:
metadata (dict): A dictionary of metadata about the dataset to use to build the model with.
shape_water_out (int[]): The width and height (in that order) that should dictate the output shape of the segmentation head. CURRENTLY NOT USED.
model_arch (str, optional): The architecture code for the underlying (inverted) ConvNeXt model. Defaults to "convnext_i_xtiny".
batch_size (int, optional): The batch size. Reduce to save memory. Defaults to 64.
water_bins (int, optional): The number of classes that the water depth output oft he segmentation head should be binned into. Defaults to 2.
Returns:
tf.keras.Model: The new model, freshly compiled for your convenience! :D
"""
rainfall_channels, rainfall_width, rainfall_height = metadata["rainfallradar"] # shape = [channels, width, height]
out_water_width, out_water_height = shape_water_out
layer_input = tf.keras.layers.Input(
shape=(rainfall_width, rainfall_height, rainfall_channels)
)
# ENCODER
layer_next = make_convnext(
input_shape = (rainfall_width, rainfall_height, rainfall_channels),
classifier_activation = tf.nn.relu, # this is not actually a classifier, but rather a feature encoder
num_classes = feature_dim, # size of the feature dimension, see the line above this one
arch_name = model_arch_enc
)(layer_input)
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print("ENCODER output_shape", layer_next.shape)
# BOTTLENECK
layer_next = tf.keras.layers.Dense(name="cns.stage.bottleneck.dense2", units=feature_dim)(layer_input)
layer_next = tf.keras.layers.Activation(name="cns.stage.bottleneck.gelu2", activation="gelu")(layer_next)
layer_next = tf.keras.layers.LayerNormalization(name="cns.stage.bottleneck.norm2", epsilon=1e-6)(layer_next)
layer_next = tf.keras.layers.Dropout(name="cns.stage.bottleneck.dropout", rate=0.1)(layer_next)
# DECODER
layer_next = LayerStack2Image(target_width=4, target_height=4)(layer_next)
# layer_next = tf.keras.layers.Reshape((4, 4, math.floor(feature_dim_in/(4*4))), name="cns.stable_begin.reshape")(layer_next)
layer_next = tf.keras.layers.Dense(name="cns.stage.begin.dense2", units=feature_dim)(layer_next)
layer_next = tf.keras.layers.Activation(name="cns.stage_begin.relu2", activation="gelu")(layer_next)
layer_next = tf.keras.layers.LayerNormalization(name="cns.stage_begin.norm2", epsilon=1e-6)(layer_next)
layer_next = do_convnext_inverse(layer_next, arch_name=model_arch_dec)
# 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, activation="gelu")(layer_next)
layer_next = tf.keras.layers.Conv2D(water_bins, activation="gelu", kernel_size=1, padding="same")(layer_next)
layer_next = tf.keras.layers.Softmax(axis=-1)(layer_next)
model = tf.keras.Model(
inputs = layer_input,
outputs = layer_next
)
model.compile(
optimizer="Adam",
loss=tf.keras.losses.CategoricalCrossentropy(),
metrics=[tf.keras.metrics.SparseCategoricalAccuracy()]
)
return model