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import os
from cnnClassfier.constants import *
from cnnClassfier.utils.common import read_yaml, create_directories
from cnnClassfier.entity.config_entity import (DataIngestionConfig,
PrepareBaseModelConfig,
PrepareCallbacksConfig,
TrainingConfig,
EvaluationConfig)
class ConfigurationManager:
def __init__(
self,
config_filepath = CONFIG_FILE_PATH,
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])
data_ingestion_config = DataIngestionConfig(
root_dir=config.root_dir,
source_URL=config.source_URL,
local_data_file=config.local_data_file,
unzip_dir=config.unzip_dir
)
return data_ingestion_config
def get_prepare_base_model(self) -> PrepareBaseModelConfig:
config = self.config.prepare_base_model
create_directories([config.root_dir])
prepare_base_model_config = PrepareBaseModelConfig(
root_dir=Path(config.root_dir),
base_model_path= Path(config.base_model_path),
updated_base_model_path= Path(config.updated_base_model_path),
params_image_size=self.params.IMAZE_SIZE,
params_learning_rate=self.params.LEARNING_RATE,
params_include_top=self.params.INCLUDE_TOP,
params_weights=self.params.WEIGHTS,
params_classes=self.params.CLASSES
)
return prepare_base_model_config
def get_prepare_callback_config(self) -> PrepareCallbacksConfig:
config = self.config.prepare_callbacks
model_ckpt_dir = os.path.dirname(config.checkpoint_model_filepath)
create_directories([
Path(model_ckpt_dir),
Path(config.tensorboard_root_log_dir)
])
prepare_callback_config = PrepareCallbacksConfig(
root_dir=Path(config.root_dir),
tensorboard_root_log_dir=Path(config.tensorboard_root_log_dir),
checkpoint_model_filepath=Path(config.checkpoint_model_filepath)
)
return prepare_callback_config
def get_training_config(self) -> TrainingConfig:
training = self.config.training
prepare_base_model = self.config.prepare_base_model
params = self.params
training_data = os.path.join(self.config.data_ingestion.unzip_dir, "Chicken-fecal-images")
create_directories([
Path(training.root_dir)
])
training_config = TrainingConfig(
root_dir=Path(training.root_dir),
trained_model_path=Path(training.trained_model_path),
updated_base_model_path=Path(prepare_base_model.updated_base_model_path),
training_data=Path(training_data),
params_epochs=params.EPOCHS,
params_batch_size=params.BATCH_SIZE,
params_is_augmentation=params.AUGMENTATION,
params_image_size=params.IMAZE_SIZE
)
return training_config
def get_validation_config(self) -> EvaluationConfig:
eval_config = EvaluationConfig(
path_of_model="artifacts/training/model.h5",
training_data="artifacts/data_ingestion/Chicken-fecal-images",
all_params=self.params,
params_image_size=self.params.IMAZE_SIZE,
params_batch_size=self.params.BATCH_SIZE
)
return eval_config |