mirror of
https://github.com/sbrl/research-rainfallradar
synced 2024-11-14 13:43:02 +00:00
85 lines
2.6 KiB
Python
85 lines
2.6 KiB
Python
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import os
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import io
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import re
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import sys
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import json
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import tensorflow as tf
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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_contrastive import model_rainfallwater_contrastive
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from .helpers import make_callbacks
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from .helpers import summarywriter
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from .components.LayerContrastiveEncoder import LayerContrastiveEncoder
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from .components.LayerCheeseMultipleOut import LayerCheeseMultipleOut
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from .helpers.summarywriter import summarywriter
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class RainfallWaterContraster(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(RainfallWaterContraster, 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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self.model = self.make_model()
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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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summarywriter(self.model, self.filepath_summary)
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writefile(os.path.join(self.dir_output, "params.json"), json.dumps(self.model.get_config()))
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else:
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self.model = self.load_model(filepath_checkpoint)
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@staticmethod
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def from_checkpoint(filepath_checkpoint, filepath_hyperparams):
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hyperparams = json.loads(readfile(filepath_hyperparams))
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return RainfallWaterContraster(filepath_checkpoint=filepath_checkpoint, **hyperparams)
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def make_model(self):
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model = model_rainfallwater_contrastive(batch_size=self.batch_size, **self.kwargs)
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return model
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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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return tf.keras.models.load_model(filepath_checkpoint, custom_objects={
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"LayerContrastiveEncoder": LayerContrastiveEncoder,
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"LayerCheeseMultipleOut": LayerCheeseMultipleOut
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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)
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)
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def embed(self, dataset):
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result = []
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i_batch = -1
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for batch in dataset:
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i_batch += 1
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result_batch = self.model(batch[0])
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# Currently, the left and right should be the same
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left, _ = tf.unstack(result_batch, axis=-2)
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result_batch = tf.unstack(left, axis=0)
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result.extend(result_batch)
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return result
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