super-gradients/tests/unit_tests/train_with_precise_bn_test.py

import unittest

from super_gradients import Trainer, \
    ClassificationTestDatasetInterface
from super_gradients.training.metrics import Accuracy, Top5
from super_gradients.training.models import ResNet18


class TrainWithPreciseBNTest(unittest.TestCase):
    """
    Unit test for training with precise_bn.
    """

    def test_train_with_precise_bn_explicit_size(self):
        trainer = Trainer("test_train_with_precise_bn_explicit_size", model_checkpoints_location='local')
        dataset_params = {"batch_size": 10}
        dataset = ClassificationTestDatasetInterface(dataset_params=dataset_params)
        trainer.connect_dataset_interface(dataset)

        net = ResNet18(num_classes=5, arch_params={})
        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,
                        "precise_bn": True, "precise_bn_batch_size": 100}
        trainer.train(model=net, training_params=train_params)

    def test_train_with_precise_bn_implicit_size(self):
        trainer = Trainer("test_train_with_precise_bn_implicit_size", model_checkpoints_location='local')
        dataset_params = {"batch_size": 10}
        dataset = ClassificationTestDatasetInterface(dataset_params=dataset_params)
        trainer.connect_dataset_interface(dataset)

        net = ResNet18(num_classes=5, arch_params={})
        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,
                        "precise_bn": True}
        trainer.train(model=net, training_params=train_params)


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