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| import tensorflow as tf | |
| from urllib.parse import urlparse | |
| import mlflow | |
| import mlflow.keras | |
| from pathlib import Path | |
| from kidney_classification.utils.common import save_json | |
| from kidney_classification.entity.config_entity import EvaluationConfig | |
| class Evaluation: | |
| def __init__(self, config: EvaluationConfig): | |
| self.config = config | |
| self.valid_generator = None # Initialize to None | |
| def _valid_generator(self): | |
| img_height, img_width = self.config.params_image_size[:-1] | |
| self.valid_generator = tf.keras.utils.image_dataset_from_directory( | |
| self.config.training_data, | |
| image_size=(img_height, img_width), | |
| validation_split=0.30, | |
| subset="validation", | |
| seed=123, | |
| ) | |
| self.valid_generator = self.valid_generator.map(lambda x, y: (x / 255, y)) | |
| AUTOTUNE = tf.data.AUTOTUNE | |
| self.valid_generator = self.valid_generator.cache().prefetch( | |
| buffer_size=AUTOTUNE | |
| ) | |
| def load_model(path: Path) -> tf.keras.Model: | |
| return tf.keras.models.load_model(path) | |
| def evaluation(self): | |
| self.model = self.load_model(self.config.path_of_model) | |
| self._valid_generator() | |
| self.score = self.model.evaluate(self.valid_generator) | |
| self.save_score() | |
| def save_score(self): | |
| scores = {"loss": self.score[0], "accuracy": self.score[1]} | |
| save_json(path=Path("scores.json"), data=scores) | |
| def log_into_mlflow(self): | |
| mlflow.set_registry_uri(self.config.mlflow_uri) | |
| tracking_url_type_store = urlparse(mlflow.get_tracking_uri()).scheme | |
| with mlflow.start_run(): | |
| mlflow.log_params(self.config.all_params) | |
| mlflow.log_metrics({"loss": self.score[0], "accuracy": self.score[1]}) | |
| # Model registry does not work with file store | |
| if tracking_url_type_store != "file": | |
| mlflow.keras.log_model( | |
| self.model, "model", registered_model_name="VGG16Model" | |
| ) | |
| else: | |
| mlflow.keras.log_model(self.model, "model") | |