super-gradients/tests/unit_tests/lr_cooldown_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
from super_gradients.training.models import LeNet
from super_gradients.training.utils.callbacks import TestLRCallback


class LRCooldownTest(unittest.TestCase):
    def test_lr_cooldown_with_lr_scheduling(self):
        # Define Model
        net = LeNet()
        trainer = Trainer("lr_warmup_test")

        lrs = []
        phase_callbacks = [TestLRCallback(lr_placeholder=lrs)]

        train_params = {
            "max_epochs": 7,
            "cosine_final_lr_ratio": 0.2,
            "lr_mode": "cosine",
            "lr_cooldown_epochs": 2,
            "lr_warmup_epochs": 3,
            "initial_lr": 1,
            "loss": "cross_entropy",
            "optimizer": "SGD",
            "criterion_params": {},
            "optimizer_params": {"weight_decay": 1e-4, "momentum": 0.9},
            "train_metrics_list": [Accuracy()],
            "valid_metrics_list": [Accuracy()],
            "metric_to_watch": "Accuracy",
            "greater_metric_to_watch_is_better": True,
            "ema": False,
            "phase_callbacks": phase_callbacks,
        }

        expected_lrs = [0.25, 0.5, 0.75, 0.9236067977499791, 0.4763932022500211, 0.4763932022500211, 0.4763932022500211]
        trainer.train(
            model=net,
            training_params=train_params,
            train_loader=classification_test_dataloader(dataset_size=5, batch_size=4),
            valid_loader=classification_test_dataloader(dataset_size=5, batch_size=4),
        )

        # ALTHOUGH NOT SEEN IN HERE, THE 4TH EPOCH USES LR=1, SO THIS IS THE EXPECTED LIST AS WE COLLECT
        # THE LRS AFTER THE UPDATE
        self.assertListEqual(lrs, expected_lrs)