super-gradients/tests/unit_tests/loss_loggings_test.py

import unittest

from torch import Tensor
from torchmetrics import Accuracy
import torch
from super_gradients import Trainer
from super_gradients.training import models
from super_gradients.training.dataloaders.dataloaders import classification_test_dataloader


class CriterionWithUnnamedComponents(torch.nn.CrossEntropyLoss):
    def __init__(self):
        super(CriterionWithUnnamedComponents, self).__init__()

    def forward(self, input: Tensor, target: Tensor) -> tuple:
        loss = super(CriterionWithUnnamedComponents, self).forward(input=input, target=target)
        items = torch.cat((loss.unsqueeze(0), loss.unsqueeze(0))).detach()
        return loss, items


class CriterionWithNamedComponents(CriterionWithUnnamedComponents):
    def __init__(self):
        super(CriterionWithNamedComponents, self).__init__()
        self.component_names = ["loss_A", "loss_B"]


class LossLoggingsTest(unittest.TestCase):
    def test_single_item_logging(self):
        trainer = Trainer("test_single_item_logging", model_checkpoints_location='local')
        dataloader = classification_test_dataloader(batch_size=10)

        model = models.get("resnet18", arch_params={"num_classes": 5})
        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": torch.nn.CrossEntropyLoss(),
                        "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}
        trainer.train(model=model, training_params=train_params, train_loader=dataloader, valid_loader=dataloader)
        self.assertListEqual(trainer.loss_logging_items_names, ["CrossEntropyLoss"])

    def test_multiple_unnamed_components_loss_logging(self):
        trainer = Trainer("test_multiple_unnamed_components_loss_logging", model_checkpoints_location='local')
        dataloader = classification_test_dataloader(batch_size=10)

        model = models.get("resnet18", arch_params={"num_classes": 5})
        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": CriterionWithUnnamedComponents(),
                        "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}
        trainer.train(model=model, training_params=train_params, train_loader=dataloader, valid_loader=dataloader)
        self.assertListEqual(trainer.loss_logging_items_names, ["CriterionWithUnnamedComponents/loss_0",
                                                                "CriterionWithUnnamedComponents/loss_1"])

    def test_multiple_named_components_loss_logging(self):
        trainer = Trainer("test_multiple_named_components_loss_logging", model_checkpoints_location='local')
        dataloader = classification_test_dataloader(batch_size=10)

        model = models.get("resnet18", arch_params={"num_classes": 5})
        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": CriterionWithNamedComponents(),
                        "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}
        trainer.train(model=model, training_params=train_params, train_loader=dataloader, valid_loader=dataloader)
        self.assertListEqual(trainer.loss_logging_items_names, ["CriterionWithNamedComponents/loss_A",
                                                                "CriterionWithNamedComponents/loss_B"])


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