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import tensorflow as tf |
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from urllib.parse import urlparse |
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import mlflow |
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import mlflow.keras |
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from pathlib import Path |
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from kidney_classification.utils.common import save_json |
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from kidney_classification.entity.config_entity import EvaluationConfig |
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class Evaluation: |
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def __init__(self, config: EvaluationConfig): |
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self.config = config |
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self.valid_generator = None |
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def _valid_generator(self): |
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img_height, img_width = self.config.params_image_size[:-1] |
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self.valid_generator = tf.keras.utils.image_dataset_from_directory( |
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self.config.training_data, |
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image_size=(img_height, img_width), |
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validation_split=0.30, |
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subset="validation", |
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seed=123, |
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) |
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self.valid_generator = self.valid_generator.map(lambda x, y: (x / 255, y)) |
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AUTOTUNE = tf.data.AUTOTUNE |
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self.valid_generator = self.valid_generator.cache().prefetch( |
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buffer_size=AUTOTUNE |
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) |
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@staticmethod |
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def load_model(path: Path) -> tf.keras.Model: |
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return tf.keras.models.load_model(path) |
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def evaluation(self): |
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self.model = self.load_model(self.config.path_of_model) |
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self._valid_generator() |
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self.score = self.model.evaluate(self.valid_generator) |
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self.save_score() |
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def save_score(self): |
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scores = {"loss": self.score[0], "accuracy": self.score[1]} |
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save_json(path=Path("scores.json"), data=scores) |
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def log_into_mlflow(self): |
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mlflow.set_registry_uri(self.config.mlflow_uri) |
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tracking_url_type_store = urlparse(mlflow.get_tracking_uri()).scheme |
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with mlflow.start_run(): |
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mlflow.log_params(self.config.all_params) |
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mlflow.log_metrics({"loss": self.score[0], "accuracy": self.score[1]}) |
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if tracking_url_type_store != "file": |
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mlflow.keras.log_model( |
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self.model, "model", registered_model_name="VGG16Model" |
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) |
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else: |
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mlflow.keras.log_model(self.model, "model") |
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