super-gradients/tests/unit_tests/train_logging_test.py

import unittest
from super_gradients import SgModel, \
    ClassificationTestDatasetInterface
from super_gradients.training.metrics import Accuracy, Top5
from super_gradients.training.models import ResNet18
import os
import logging
from super_gradients.common.abstractions.abstract_logger import get_logger
import shutil


class SgTrainerLoggingTest(unittest.TestCase):
    def test_train_logging(self):
        model = SgModel("test_train_with_full_log", model_checkpoints_location='local')
        dataset_params = {"batch_size": 10}
        dataset = ClassificationTestDatasetInterface(dataset_params=dataset_params)
        model.connect_dataset_interface(dataset)

        net = ResNet18(num_classes=5, arch_params={})
        model.build_model(net)
        train_params = {"max_epochs": 2, "lr_updates": [1], "lr_decay_factor": 0.1, "lr_mode": "step",
                        "lr_warmup_epochs": 0, "initial_lr": 0.1, "loss": "cross_entropy", "optimizer": "SGD",
                        "criterion_params": {}, "optimizer_params": {"weight_decay": 1e-4, "momentum": 0.9},
                        "train_metrics_list": [Accuracy(), Top5()], "valid_metrics_list": [Accuracy(), Top5()],
                        "loss_logging_items_names": ["Loss"], "metric_to_watch": "Accuracy",
                        "greater_metric_to_watch_is_better": True,
                        "save_full_train_log": True}

        model.train(train_params)

        logfile_path = model.log_file.replace('.txt', 'full_train_log.log')
        assert os.path.exists(logfile_path) and os.path.getsize(logfile_path) > 0

        root_logger_handlers = logging.root.handlers
        assert any(isinstance(handler, logging.handlers.RotatingFileHandler) and handler.baseFilename == logfile_path for handler in root_logger_handlers)
        assert any(isinstance(handler, logging.StreamHandler) and handler.name == 'console' for handler in root_logger_handlers)

    def test_logger_with_non_existing_deci_logs_dir(self):
        user_dir = os.path.expanduser(r"~")
        logs_dir_path = os.path.join(user_dir, 'non_existing_deci_logs_dir')
        if os.path.exists(logs_dir_path):
            shutil.rmtree(logs_dir_path)

        module_name = 'super_gradients.trainer.sg_model'

        _ = get_logger(module_name, training_log_path=None, logs_dir_path=logs_dir_path)
        root_logger_handlers = logging.root.handlers
        assert any(isinstance(handler, logging.StreamHandler) and handler.name == 'console' for handler in root_logger_handlers)


if __name__ == '__main__':
    unittest.main()