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import os
from box.exceptions import BoxValueError
import yaml
from EmotionRecognition import logger
import json
from ensure import ensure_annotations
from box import ConfigBox
from pathlib import Path
from typing import Any
import json
import tensorflow as tf
@ensure_annotations
def read_yaml(path_to_yaml: Path) -> ConfigBox:
"""reads yaml file and returns
Args:
path_to_yaml (str): path like input
Raises:
ValueError: if yaml file is empty
e: empty file
Returns:
ConfigBox: ConfigBox type
"""
try:
with open(path_to_yaml) as yaml_file:
content = yaml.safe_load(yaml_file)
logger.info(f"yaml file: {path_to_yaml} loaded successfully")
return ConfigBox(content)
except BoxValueError:
raise ValueError("yaml file is empty")
except Exception as e:
raise e
@ensure_annotations
def create_directories(path_to_directories: list, verbose=True):
"""create list of directories
Args:
path_to_directories (list): list of path of directories
ignore_log (bool, optional): ignore if multiple dirs is to be created. Defaults to False.
"""
for path in path_to_directories:
os.makedirs(path, exist_ok=True)
if verbose:
logger.info(f"created directory at: {path}")
def save_json(path: Path, data: dict):
with open(path, "w") as f:
json.dump(data, f, indent=4)
logger.info(f"json file saved at: {path}")
def create_mobilenetv2_model(input_shape, num_classes, dropout_rate, is_training=True): # <--- ADD ARGUMENT
"""
Builds the MobileNetV2 model with our custom head.
This centralized function ensures consistency.
"""
base_model = tf.keras.applications.MobileNetV2(
input_shape=input_shape, include_top=False, weights='imagenet'
)
inputs = tf.keras.Input(shape=input_shape)
# --- CRITICAL CHANGE ---
# Pass the is_training flag to the base model call
x = base_model(inputs, training=is_training)
# --- END CHANGE ---
x = tf.keras.layers.GlobalAveragePooling2D()(x)
x = tf.keras.layers.Dense(128, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.01))(x)
x = tf.keras.layers.Dropout(dropout_rate)(x)
outputs = tf.keras.layers.Dense(num_classes, activation='softmax')(x)
model = tf.keras.Model(inputs, outputs)
return model |