super-gradients/tests/unit_tests/factories_test.py

import unittest

import torch

from super_gradients import Trainer
from super_gradients.common.decorators.factory_decorator import resolve_param
from super_gradients.common.factories.activations_type_factory import ActivationsTypeFactory
from super_gradients.common.object_names import Models
from super_gradients.training import models
from super_gradients.training.dataloaders.dataloaders import classification_test_dataloader
from super_gradients.training.losses import LabelSmoothingCrossEntropyLoss
from super_gradients.training.metrics import Accuracy, Top5
from torch import nn


class FactoriesTest(unittest.TestCase):
    def test_training_with_factories(self):
        trainer = Trainer("test_train_with_factories")
        net = models.get(Models.RESNET18, num_classes=5)
        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": "torch.optim.ASGD",  # use an optimizer by factory
            "criterion_params": {},
            "optimizer_params": {"lambd": 0.0001, "alpha": 0.75},
            "train_metrics_list": ["Accuracy", "Top5"],  # use a metric by factory
            "valid_metrics_list": ["Accuracy", "Top5"],  # use a metric by factory
            "metric_to_watch": "Accuracy",
            "greater_metric_to_watch_is_better": True,
        }

        trainer.train(model=net, training_params=train_params, train_loader=classification_test_dataloader(), valid_loader=classification_test_dataloader())

        self.assertIsInstance(trainer.train_metrics.Accuracy, Accuracy)
        self.assertIsInstance(trainer.valid_metrics.Top5, Top5)
        self.assertIsInstance(trainer.optimizer, torch.optim.ASGD)

    def test_training_with_factories_with_typos(self):
        trainer = Trainer("test_train_with_factories_with_typos")
        net = models.get("Resnet___18", num_classes=5)
        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": "crossEnt_ropy",
            "optimizer": "AdAm_",  # use an optimizer by factory
            "criterion_params": {},
            "train_metrics_list": ["accur_acy", "Top_5"],  # use a metric by factory
            "valid_metrics_list": ["aCCuracy", "Top5"],  # use a metric by factory
            "metric_to_watch": "Accurac_Y",
            "greater_metric_to_watch_is_better": True,
        }

        trainer.train(model=net, training_params=train_params, train_loader=classification_test_dataloader(), valid_loader=classification_test_dataloader())

        self.assertIsInstance(trainer.train_metrics.Accuracy, Accuracy)
        self.assertIsInstance(trainer.valid_metrics.Top5, Top5)
        self.assertIsInstance(trainer.optimizer, torch.optim.Adam)
        self.assertIsInstance(trainer.criterion, LabelSmoothingCrossEntropyLoss)

    def test_activations_factory(self):
        class DummyModel(nn.Module):
            @resolve_param("activation_in_head", ActivationsTypeFactory())
            def __init__(self, activation_in_head):
                super().__init__()
                self.activation_in_head = activation_in_head()

        model = DummyModel(activation_in_head="leaky_relu")
        self.assertIsInstance(model.activation_in_head, nn.LeakyReLU)


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