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] print("RAINFALL channels", rainfall_channels, "width", rainfall_width, "height", rainfall_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) 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