mirror of
https://github.com/sbrl/research-rainfallradar
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102 lines
No EOL
3 KiB
Python
102 lines
No EOL
3 KiB
Python
import os
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import json
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from loguru import logger
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import tensorflow as tf
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from ..dataset.batched_iterator import batched_iterator
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from ..io.find_paramsjson import find_paramsjson
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from ..io.readfile import readfile
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from ..io.writefile import writefile
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from .model_rainfallwater_mono import model_rainfallwater_mono
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from .helpers import make_callbacks
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from .helpers import summarywriter
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from .components.LayerConvNeXtGamma import LayerConvNeXtGamma
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from .components.LayerStack2Image import LayerStack2Image
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from .components.LossCrossentropy import LossCrossentropy
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from .helpers.summarywriter import summarywriter
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class RainfallWaterMono(object):
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def __init__(self, dir_output=None, filepath_checkpoint=None, epochs=50, batch_size=64, **kwargs):
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super(RainfallWaterMono, self).__init__()
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self.dir_output = dir_output
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self.epochs = epochs
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self.kwargs = kwargs
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self.batch_size = batch_size
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if filepath_checkpoint == None:
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if self.dir_output == None:
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raise Exception("Error: dir_output was not specified, and since no checkpoint was loaded training mode is activated.")
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if not os.path.exists(self.dir_output):
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os.mkdir(self.dir_output)
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self.filepath_summary = os.path.join(self.dir_output, "summary.txt")
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writefile(self.filepath_summary, "") # Empty the file ahead of time
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self.make_model()
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summarywriter(self.model, self.filepath_summary, append=True)
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writefile(os.path.join(self.dir_output, "params.json"), json.dumps(self.get_config()))
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else:
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self.load_model(filepath_checkpoint)
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def get_config(self):
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return {
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"epochs": self.epochs,
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"batch_size": self.batch_size,
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**self.kwargs
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}
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@staticmethod
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def from_checkpoint(filepath_checkpoint, **hyperparams):
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logger.info(f"Loading from checkpoint: {filepath_checkpoint}")
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return RainfallWaterMono(filepath_checkpoint=filepath_checkpoint, **hyperparams)
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def make_model(self):
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self.model = model_rainfallwater_mono(
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batch_size=self.batch_size,
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**self.kwargs
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)
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def load_model(self, filepath_checkpoint):
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"""
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Loads a saved model from the given filename.
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filepath_checkpoint (string): The filepath to load the saved model from.
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"""
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self.model = tf.keras.models.load_model(filepath_checkpoint, custom_objects={
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"LayerConvNeXtGamma": LayerConvNeXtGamma,
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"LayerStack2Image": LayerStack2Image,
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"LossCrossentropy": LossCrossentropy
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})
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def train(self, dataset_train, dataset_validate):
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return self.model.fit(
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dataset_train,
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validation_data=dataset_validate,
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epochs=self.epochs,
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callbacks=make_callbacks(self.dir_output, self.model),
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# steps_per_epoch=10 # For testing
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)
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def embed(self, rainfall_embed):
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rainfall = self.model(rainfall_embed, training=False) # (rainfall_embed, water)
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for step in tf.unstack(rainfall, axis=0):
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yield step
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# def embed_rainfall(self, dataset):
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# result = []
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# for batch in dataset:
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# result_batch = self.model_predict(batch)
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# result.extend(tf.unstack(result_batch, axis=0))
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# return result |