mirror of
https://github.com/sbrl/research-rainfallradar
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33 lines
No EOL
879 B
Python
33 lines
No EOL
879 B
Python
import math
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from loguru import logger
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import tensorflow as tf
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from .components.convnext_inverse import do_convnext_inverse
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def model_rainfallwater_segmentation(metadata, feature_dim_in, shape_water_out, batch_size=64, summary_file=None):
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layer_input = tf.keras.layers.Input(
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shape=(feature_dim_in)
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)
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# BEGIN
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layer_next = tf.keras.layers.Dense(name="cns.stage.begin.dense")(layer_input)
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layer_next = tf.keras.layers.LayerNormalisation(name="stage_begin.norm", epsilon=1e-6)(layer_next)
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layer_next = tf.keras.layers.ReLU(name="stage_begin.relu")(layer_next)
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layer_next = do_convnext_inverse(layer_next, arch_name="convnext_i_tiny")
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# TODO: Implement projection head here
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model = tf.keras.Model(
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inputs = layer_input,
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outputs = layer_next
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)
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model.compile(
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optimizer="Adam",
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loss="" # TODO: set this to binary cross-entropy loss
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)
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return model |