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# src/vitClassifier/config/configuration.py
from vitClassifier.constants import CONFIG_FILE_PATH, PARAMS_FILE_PATH # <-- THIS IMPORT IS THE FIX
from vitClassifier.utils.common import read_yaml, create_directories
from vitClassifier.entity.config_entity import (DataIngestionConfig,
DataTransformationConfig,
TrainingConfig,
EvaluationConfig)
from pathlib import Path
import os
class ConfigurationManager:
def __init__(self, config_filepath=None, params_filepath=None):
# If no path is provided when creating an instance, use the imported constants
if config_filepath is None:
config_filepath = CONFIG_FILE_PATH
if params_filepath is None:
params_filepath = PARAMS_FILE_PATH
self.config = read_yaml(config_filepath)
self.params = read_yaml(params_filepath)
create_directories([self.config.artifacts_root])
def get_data_ingestion_config(self) -> DataIngestionConfig:
config = self.config.data_ingestion
create_directories([config.root_dir])
return DataIngestionConfig(
root_dir=Path(config.root_dir),
source_kaggle_dataset_id=config.source_kaggle_dataset_id,
unzip_dir=Path(config.unzip_dir),
train_df_path=Path(config.train_df_path),
test_df_path=Path(config.test_df_path),
val_df_path=Path(config.val_df_path)
)
def get_data_transformation_config(self) -> DataTransformationConfig:
config = self.config.data_transformation
create_directories([config.root_dir])
return DataTransformationConfig(
root_dir=Path(config.root_dir),
train_data_path=Path(config.train_data_path),
test_data_path=Path(config.test_data_path),
val_data_path=Path(config.val_data_path),
train_dataset_path=Path(config.train_dataset_path),
test_dataset_path=Path(config.test_dataset_path),
val_dataset_path=Path(config.val_dataset_path)
)
def get_training_config(self) -> TrainingConfig:
training = self.config.model_training
params = self.params
create_directories([Path(training.root_dir)])
return TrainingConfig(
root_dir=Path(training.root_dir),
trained_model_path=Path(training.trained_model_path),
model_name=training.model_name,
train_dataset_path=Path(training.train_dataset_path),
val_dataset_path=Path(training.val_dataset_path),
learning_rate=params.LEARNING_RATE,
batch_size=params.BATCH_SIZE,
epochs=params.EPOCHS,
weight_decay=params.WEIGHT_DECAY,
warmup_steps=params.WARMUP_STEPS,
)
def get_evaluation_config(self) -> EvaluationConfig:
eval_config = self.config.model_evaluation
return EvaluationConfig(
path_of_model=Path(eval_config.model_path),
test_dataset_path=Path(eval_config.test_dataset_path),
mlflow_uri=eval_config.mlflow_uri,
all_params=self.params,
batch_size=self.params.BATCH_SIZE,
metrics_file_name=Path(eval_config.metrics_file_name) # <--- MAKE SURE THIS LINE EXISTS
) |