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import os
import argparse
from comet_ml import Experiment
from src.utils.config_loader import Config
from src.utils import config_loader
from src.utils.data_utils import print_title
from src.utils.script_utils import validate_config
import importlib
from pathlib import Path
from dotenv import load_dotenv
load_dotenv()
def train(args):
config_file_path = args.config_file
config = Config(config_file_path)
# validate config
validate_config(config)
# set config globally
config_loader.config = config
# now load the model
Model = importlib.import_module(f"src.{config.task}.model.models.{config.model}").Model
model_dir = os.path.join("models",config.task,config.model)
os.makedirs(model_dir,exist_ok=True)
model_save_path = os.path.join(model_dir,"model.weights.h5")
experiment = Experiment(
api_key=os.environ["COMET_API_KEY"],
project_name="image-colorization",
workspace="anujpanthri",
auto_histogram_activation_logging=True,
auto_histogram_epoch_rate=True,
auto_histogram_gradient_logging=True,
auto_histogram_weight_logging=True,
auto_param_logging=True,
)
model = Model(experiment=experiment)
model.train()
model.save(model_save_path)
# log model to comet
if "LOCAL_SYSTEM" not in os.environ:
experiment.log_model(f"{config.task}_{config.dataset}_{config.model}",model_save_path)
# evaluate model
print_title("\nEvaluating Model")
metrics = model.evaluate()
print("Model Evaluation Metrics:",metrics)
experiment.end()
def main():
parser = argparse.ArgumentParser(description="train model based on config yaml file")
parser.add_argument("config_file",type=str)
args = parser.parse_args()
train(args)
if __name__=="__main__":
main() |