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minor refactor and addiing callback, metric and loss to experiment
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,85 @@ | ||
| # -*- coding: utf-8 -*- | ||
| """ | ||
| /*************************************************************************** | ||
| segmentation_models_trainer | ||
| ------------------- | ||
| begin : 2020-09-21 | ||
| git sha : $Format:%H$ | ||
| copyright : (C) 2020 by Philipe Borba - Cartographic Engineer @ Brazilian Army | ||
| email : philipeborba at gmail dot com | ||
| ***************************************************************************/ | ||
| /*************************************************************************** | ||
| * * | ||
| * This program is free software; you can redistribute it and/or modify * | ||
| * it under the terms of the GNU General Public License as published by * | ||
| * the Free Software Foundation; either version 2 of the License, or * | ||
| * (at your option) any later version. * | ||
| * * | ||
| **** | ||
| """ | ||
| import tensorflow as tf | ||
| import segmentation_models as sm | ||
| from dataclasses import dataclass | ||
| from dataclasses_jsonschema import JsonSchemaMixin | ||
|
|
||
| @dataclass | ||
| class Loss(JsonSchemaMixin): | ||
| class_name: str | ||
| config: dict | ||
| framework: str | ||
|
|
||
| def __post_init__(self): | ||
| if self.framework == 'sm': | ||
| self.loss_obj = self.get_sm_loss(self.class_name) | ||
| elif self.framework == 'tf.keras': | ||
| identifier = { | ||
| "class_name" : self.class_name, | ||
| "config" : self.config | ||
| } | ||
| self.loss_obj = tf.keras.losses.get(identifier) | ||
| else: | ||
| raise ValueError("Loss not implemented") | ||
|
|
||
|
|
||
| def get_sm_loss(self, name): | ||
| if self.class_name == 'jaccard_loss': | ||
| return sm.losses.JaccardLoss(**self.config) | ||
| elif self.class_name == 'dice_loss': | ||
| return sm.losses.DiceLoss(**self.config) | ||
| elif self.class_name == 'binary_focal_loss': | ||
| return sm.losses.BinaryFocalLoss(**self.config) | ||
| elif self.class_name == 'categorical_focal_loss': | ||
| return sm.losses.CategoricalFocalLoss(**self.config) | ||
| elif self.class_name == 'binary_crossentropy': | ||
| return sm.losses.BinaryCELoss(**self.config) | ||
| elif self.class_name == 'categorical_crossentropy': | ||
| return sm.losses.CategoricalCELoss(**self.config) | ||
| elif self.class_name == 'bce_dice_loss': | ||
| return sm.losses.BinaryCELoss(**self.config) + sm.losses.DiceLoss(**self.config) | ||
| elif self.class_name == 'bce_jaccard_loss': | ||
| return sm.losses.BinaryCELoss(**self.config) + sm.losses.JaccardLoss(**self.config) | ||
| elif self.class_name == 'cce_dice_loss': | ||
| return sm.losses.CategoricalCELoss(**self.config) + sm.losses.DiceLoss(**self.config) | ||
| elif self.class_name == 'cce_jaccard_loss': | ||
| return sm.losses.CategoricalCELoss(**self.config) + sm.losses.JaccardLoss(**self.config) | ||
| elif self.class_name == 'binary_focal_dice_loss': | ||
| return sm.losses.BinaryFocalLoss(**self.config) + sm.losses.DiceLoss(**self.config) | ||
| elif self.class_name == 'binary_focal_jaccard_loss': | ||
| return sm.losses.BinaryFocalLoss(**self.config) + sm.losses.JaccardLoss(**self.config) | ||
| elif self.class_name == 'categorical_focal_dice_loss': | ||
| return sm.losses.CategoricalFocalLoss(**self.config) + sm.losses.DiceLoss(**self.config) | ||
| elif self.class_name == 'categorical_focal_jaccard_loss': | ||
| return sm.losses.CategoricalFocalLoss(**self.config) + sm.losses.JaccardLoss(**self.config) | ||
| else: | ||
| raise ValueError("SM Loss not implemented") | ||
|
|
||
| if __name__ == "__main__": | ||
| import json | ||
| x = Loss( | ||
| class_name='bce_dice_loss', | ||
| config={}, | ||
| framework='sm' | ||
| ) | ||
| print(x.to_json()) | ||
| x |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,84 @@ | ||
| # -*- coding: utf-8 -*- | ||
| """ | ||
| /*************************************************************************** | ||
| segmentation_models_trainer | ||
| ------------------- | ||
| begin : 2020-09-21 | ||
| git sha : $Format:%H$ | ||
| copyright : (C) 2020 by Philipe Borba - Cartographic Engineer @ Brazilian Army | ||
| email : philipeborba at gmail dot com | ||
| ***************************************************************************/ | ||
| /*************************************************************************** | ||
| * * | ||
| * This program is free software; you can redistribute it and/or modify * | ||
| * it under the terms of the GNU General Public License as published by * | ||
| * the Free Software Foundation; either version 2 of the License, or * | ||
| * (at your option) any later version. * | ||
| * * | ||
| **** | ||
| """ | ||
| import tensorflow as tf | ||
| import segmentation_models as sm | ||
| from typing import Any, List | ||
| from dataclasses import dataclass | ||
| from dataclasses_jsonschema import JsonSchemaMixin | ||
|
|
||
| @dataclass | ||
| class Metric(JsonSchemaMixin): | ||
| class_name: str | ||
| config: dict | ||
| framework: str | ||
|
|
||
| def __post_init__(self): | ||
| if self.framework == 'sm': | ||
| self.metric_obj = self.get_sm_metric(self.class_name) | ||
| elif self.framework == 'tf.keras': | ||
| identifier = { | ||
| "class_name" : self.class_name, | ||
| "config" : self.config | ||
| } | ||
| self.metric_obj = tf.keras.metrics.get(identifier) | ||
| else: | ||
| raise ValueError("Metric not implemented") | ||
|
|
||
| def get_sm_metric(self, name): | ||
| if self.class_name == 'iou_score': | ||
| return sm.metrics.iou_score | ||
| elif self.class_name == 'precision': | ||
| return sm.metrics.precision | ||
| elif self.class_name == 'recall': | ||
| return sm.metrics.recall | ||
| elif self.class_name == 'f1_score': | ||
| return sm.metrics.f1_score | ||
| elif self.class_name == 'f2_score': | ||
| return sm.metrics.f2_score | ||
| else: | ||
| raise ValueError("SM metric not implemented") | ||
|
|
||
| @dataclass | ||
| class MetricList(JsonSchemaMixin): | ||
| items: List[Metric] | ||
|
|
||
| def get_tf_objects(self): | ||
| return [ | ||
| i.metric_obj for i in items | ||
| ] | ||
|
|
||
| if __name__ == "__main__": | ||
| import json | ||
| metric_list = [ | ||
| Metric( | ||
| class_name=i, | ||
| config={}, | ||
| framework='sm' | ||
| ) for i in ['iou_score', 'precision', 'recall', 'f1_score', 'f2_score'] | ||
| ] + [ | ||
| Metric( | ||
| class_name=i, | ||
| config={} if i != 'MeanIoU' else {'num_classes':2}, | ||
| framework='tf.keras' | ||
| ) for i in ['LogCoshError', 'KLDivergence', 'MeanIoU'] | ||
| ] | ||
| x=MetricList(metric_list) | ||
| print(x.to_json()) |
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