mirror of
https://github.com/sbrl/research-rainfallradar
synced 2024-10-05 19:34:07 +00:00
80 lines
3.9 KiB
Python
80 lines
3.9 KiB
Python
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
|
|
from .components.LossCrossentropy import LossCrossentropy
|
|
|
|
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_height, rainfall_width = metadata["rainfallradar"] # shape = [channels, height, weight]
|
|
# BUG: We somehow *still* have the rainfall radar data transposed incorrectly! I have no idea how this happened. dataset_mono fixes it with (another) transpose
|
|
|
|
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("DEBUG:model ENCODER output_shape", layer_next.shape)
|
|
|
|
# BOTTLENECK
|
|
layer_next = tf.keras.layers.Dense(name="cns.stage.bottleneck.dense2", units=feature_dim)(layer_next)
|
|
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)
|
|
|
|
print("DEBUG:model BOTTLENECK:stack2image output_shape", layer_next.shape)
|
|
|
|
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=LossCrossentropy(batch_size=batch_size),
|
|
# loss=tf.keras.losses.CategoricalCrossentropy(),
|
|
metrics=[tf.keras.metrics.CategoricalAccuracy()]
|
|
)
|
|
|
|
return model
|