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

import math

from loguru import logger
import tensorflow as tf

from .components.convnext_inverse import do_convnext_inverse

def model_rainfallwater_segmentation(metadata, feature_dim_in, shape_water_out, batch_size=64, summary_file=None):
	out_water_width, out_water_height = shape_water_out
	
	
	layer_input = tf.keras.layers.Input(
		shape=(feature_dim_in)
	)
	
	# BEGIN
	layer_next = tf.keras.layers.Dense(name="cns.stage.begin.dense", units=feature_dim_in)(layer_input)
	layer_next = tf.keras.layers.LayerNormalisation(name="stage_begin.norm", epsilon=1e-6)(layer_next)
	layer_next = tf.keras.layers.ReLU(name="stage_begin.relu")(layer_next)
	
	layer_next = do_convnext_inverse(layer_next, arch_name="convnext_i_tiny")
	
	# TODO: An attention layer here instead of a dense layer, with a skip connection perhaps?
	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)
	
	model = tf.keras.Model(
		inputs = layer_input,
		outputs = layer_next
	)
	
	model.compile(
		optimizer="Adam",
		loss=tf.keras.losses.SparseCategoricalCrossentropy(),
		metrics=["accuracy"]
	)
	
	return model