| 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 |
|
|
| 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 |
| ) |
|
|
| @staticmethod |
| 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]}) |
| |
| 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") |
|
|
