@@ -22,6 +22,7 @@ from tests.unit_tests import (
     CrashTipTest,
 )
 from tests.end_to_end_tests import TestTrainer
+from tests.unit_tests.local_ckpt_head_replacement_test import LocalCkptHeadReplacementTest
 from tests.unit_tests.phase_delegates_test import ContextMethodsTest
 from tests.unit_tests.quantization_utility_tests import QuantizationUtilityTest
 from tests.unit_tests.random_erase_test import RandomEraseTest
@@ -107,6 +108,7 @@ class CoreUnitTestSuiteRunner:
         self.unit_tests_suite.addTest(self.test_loader.loadTestsFromModule(CallTrainAfterTestTest))
         self.unit_tests_suite.addTest(self.test_loader.loadTestsFromModule(TestDetectionOutputAdapter))
         self.unit_tests_suite.addTest(self.test_loader.loadTestsFromModule(ConfigInspectTest))
+        self.unit_tests_suite.addTest(self.test_loader.loadTestsFromModule(LocalCkptHeadReplacementTest))
 
     def _add_modules_to_end_to_end_tests_suite(self):
         """

import unittest

from super_gradients.training import Trainer, models
from super_gradients.training.dataloaders.dataloaders import classification_test_dataloader
from super_gradients.training.metrics import Accuracy, Top5
from super_gradients.training.utils.utils import check_models_have_same_weights
import os


class LocalCkptHeadReplacementTest(unittest.TestCase):
    def test_local_ckpt_head_replacement(self):
        train_params = {
            "max_epochs": 1,
            "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()],
            "metric_to_watch": "Accuracy",
            "greater_metric_to_watch_is_better": True,
        }

        # Define Model
        net = models.get("resnet18", num_classes=5)
        trainer = Trainer("test_resume_training")
        trainer.train(model=net, training_params=train_params, train_loader=classification_test_dataloader(), valid_loader=classification_test_dataloader())
        ckpt_path = os.path.join(trainer.checkpoints_dir_path, "ckpt_latest.pth")

        net2 = models.get("resnet18", num_classes=10, checkpoint_num_classes=5, checkpoint_path=ckpt_path)
        self.assertFalse(check_models_have_same_weights(net, net2))

        net.linear = None
        net2.linear = None
        self.assertTrue(check_models_have_same_weights(net, net2))