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import tensorflow as tf
from EmotionRecognition import logger
from EmotionRecognition.entity.config_entity import ModelEvaluationConfig
from pathlib import Path
import mlflow
import mlflow.keras
from EmotionRecognition.utils.common import save_json
class ModelEvaluation:
def __init__(self, config: ModelEvaluationConfig, params: dict):
self.config = config
self.params = params
def get_validation_dataset(self):
"""
Loads the prepared validation/test dataset from disk.
"""
data_params = self.params.DATA_PARAMS
val_ds = tf.keras.utils.image_dataset_from_directory(
self.config.test_data_dir,
labels='inferred',
label_mode='categorical',
class_names=data_params.CLASSES,
image_size=data_params.IMAGE_SIZE,
interpolation='nearest',
batch_size=data_params.BATCH_SIZE,
shuffle=False,
color_mode='grayscale' # <--- THIS IS THE FIX
)
def preprocess(image, label):
image = tf.image.grayscale_to_rgb(image)
image = tf.cast(image, tf.float32) / 255.0
return image, label
return val_ds.map(preprocess, num_parallel_calls=tf.data.AUTOTUNE).prefetch(tf.data.AUTOTUNE)
def evaluate_and_log(self):
logger.info("Preparing validation dataset for evaluation...")
val_ds = self.get_validation_dataset()
logger.info("Loading full trained model from disk...")
model = tf.keras.models.load_model(str(self.config.trained_model_path))
logger.info("Evaluating model...")
score = model.evaluate(val_ds)
scores = {"loss": score[0], "accuracy": score[1]}
logger.info(f"Evaluation scores: {scores}")
save_json(path=self.config.metrics_file_name, data=scores)
logger.info("Starting MLflow logging...")
mlflow.set_tracking_uri(self.config.mlflow_uri)
mlflow.set_experiment("Emotion Recognition Experiment")
with mlflow.start_run():
mlflow.log_params(self.params.DATA_PARAMS)
mlflow.log_params(self.params.TRAINING_PARAMS)
mlflow.log_metrics(scores)
mlflow.keras.log_model(model, "model")
logger.info("MLflow logging complete.") |