super-gradients/tests/unit_tests/load_ema_ckpt_test.py

import unittest
from super_gradients.training import Trainer
from super_gradients.training.dataloaders.dataloaders import classification_test_dataloader
from super_gradients.training.metrics import Accuracy, Top5
from super_gradients.training.utils.callbacks import PhaseCallback, Phase, PhaseContext
from super_gradients.training.utils.utils import check_models_have_same_weights
from super_gradients.training.models import LeNet
from copy import deepcopy


class PreTrainingEMANetCollector(PhaseCallback):
    def __init__(self):
        super(PreTrainingEMANetCollector, self).__init__(phase=Phase.PRE_TRAINING)
        self.net = None

    def __call__(self, context: PhaseContext):
        self.net = deepcopy(context.ema_model)


class LoadCheckpointWithEmaTest(unittest.TestCase):
    def setUp(self) -> None:
        self.train_params = {
            "max_epochs": 2,
            "lr_updates": [1],
            "lr_decay_factor": 0.1,
            "lr_mode": "StepLRScheduler",
            "lr_warmup_epochs": 0,
            "initial_lr": 0.1,
            "loss": "CrossEntropyLoss",
            "optimizer": "SGD",
            "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,
            "ema": True,
        }

    def test_ema_ckpt_reload(self):
        # Define Model
        net = LeNet()
        trainer = Trainer("ema_ckpt_test")
        trainer.train(
            model=net, training_params=self.train_params, train_loader=classification_test_dataloader(), valid_loader=classification_test_dataloader()
        )

        ema_model = trainer.ema_model.ema

        # TRAIN FOR 1 MORE EPOCH AND COMPARE THE NET AT THE BEGINNING OF EPOCH 3 AND THE END OF EPOCH NUMBER 2
        net = LeNet()
        trainer = Trainer("ema_ckpt_test")

        net_collector = PreTrainingEMANetCollector()
        self.train_params["resume"] = True
        self.train_params["max_epochs"] = 3
        self.train_params["phase_callbacks"] = [net_collector]
        trainer.train(
            model=net, training_params=self.train_params, train_loader=classification_test_dataloader(), valid_loader=classification_test_dataloader()
        )

        reloaded_ema_model = net_collector.net.ema

        # ASSERT RELOADED EMA MODEL HAS THE SAME WEIGHTS AS THE EMA MODEL SAVED IN FIRST PART OF TRAINING
        assert check_models_have_same_weights(ema_model, reloaded_ema_model)


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