From 1a0fdf8c6dd706e55dd2e5924289f55d1f405148 Mon Sep 17 00:00:00 2001 From: Felix Benning Date: Sat, 11 May 2024 19:05:28 +0200 Subject: [PATCH] remove wandb --- benchmarking/classification/__main__.py | 52 +-- benchmarking/classification/cifar/data.py | 103 ++++++ benchmarking/classification/classifier.py | 3 +- benchmarking/classification/mnist/data.py | 4 +- poetry.lock | 431 +--------------------- pyproject.toml | 1 - 6 files changed, 125 insertions(+), 469 deletions(-) diff --git a/benchmarking/classification/__main__.py b/benchmarking/classification/__main__.py index 6885485..509d85b 100644 --- a/benchmarking/classification/__main__.py +++ b/benchmarking/classification/__main__.py @@ -18,7 +18,6 @@ def train_classic( problem, - trainer: L.Trainer, opt: optim.Optimizer, hyperparameters: Dict[str, Any], ): @@ -27,20 +26,12 @@ def train_classic( data: L.LightningDataModule = problem["dataset"](batch_size=problem["batch_size"]) classifier = Classifier(problem["model"](), optimizer=opt, **hyperparameters) - # wandb.init( - # project="pyrfd", - # config={ - # "dataset": problem["dataset"].__name__, - # "model": problem["model"].__name__, - # "batch_size": problem["batch_size"], - # "optimizer": opt.__name__, - # "hyperparameters": hyperparameters, - # }, - # ) + trainer = trainer_from_problem(problem, opt_name=opt.__name__) trainer.fit(classifier, data) + trainer.test(classifier, data) -def train_rfd(problem, trainer: L.Trainer, cov_string, covariance_model): +def train_rfd(problem, cov_string, covariance_model): """Train a Classifier with RFD""" data: L.LightningDataModule = problem["dataset"](batch_size=problem["batch_size"]) data.prepare_data() @@ -57,49 +48,41 @@ def train_rfd(problem, trainer: L.Trainer, cov_string, covariance_model): problem["model"](), optimizer=RFD, covariance_model=covariance_model ) - # wandb.init( - # project="pyrfd", - # config={ - # "dataset": problem["dataset"].__name__, - # "model": problem["model"].__name__, - # "batch_size": problem["batch_size"], - # "optimizer": "RFD", - # "covariance": cov_string, - # }, - # ) + trainer = trainer_from_problem(problem, opt_name=f"RFD({cov_string})") trainer.fit(classifier, data) + trainer.test(classifier, data) -def main(): - problem = { - "dataset": MNIST, - "model": CNN3, - "batch_size": 100, - } - +def trainer_from_problem(problem, opt_name): problem_id = f"{problem['dataset'].__name__}_{problem['model'].__name__}_{problem['batch_size']}" - tlogger = TensorBoardLogger("logs", name=problem_id) - csvlogger = CSVLogger("logs", name=problem_id) + tlogger = TensorBoardLogger("logs/TensorBoard", name=problem_id + opt_name) + csvlogger = CSVLogger("logs", name=problem_id + opt_name) tlogger.log_hyperparams(params={"batch_size": problem["batch_size"]}) csvlogger.log_hyperparams(params={"batch_size": problem["batch_size"]}) - trainer = L.Trainer( + return L.Trainer( max_epochs=2, log_every_n_steps=1, logger=[tlogger, csvlogger], ) + +def main(): + problem = { + "dataset": MNIST, + "model": CNN3, + "batch_size": 100, + } + train_rfd( problem, - trainer, cov_string="SquaredExponential", covariance_model=covariance.SquaredExponential(), ) train_classic( problem, - trainer, opt=optim.SGD, hyperparameters={ "lr": 1e-3, @@ -109,7 +92,6 @@ def main(): train_classic( problem, - trainer, opt=optim.Adam, hyperparameters={ "lr": 1e-3, diff --git a/benchmarking/classification/cifar/data.py b/benchmarking/classification/cifar/data.py index e69de29..cff0827 100644 --- a/benchmarking/classification/cifar/data.py +++ b/benchmarking/classification/cifar/data.py @@ -0,0 +1,103 @@ +""" CIFAR Data Modules """ + +import lightning as L +import torch +from torch.utils.data import random_split, DataLoader + +from torchvision.datasets import CIFAR10 as CIFAR10Dataset, CIFAR100 as CIFAR100Dataset +from torchvision import transforms + +class CIFAR10(L.LightningDataModule): + """Represents the CIFAR10 dataset. + """ + + def __init__(self, data_dir: str=" ./data", batch_size: int = 100) -> None: + super().__init__() + self.data_dir = data_dir + self.batch_size = batch_size + # self.transform = transforms.Compose() + + def prepare_data(self) -> None: + # download + CIFAR10Dataset(self.data_dir, train=True, download=True) + CIFAR10Dataset(self.data_dir, train=False, download=True) + + # pylint: disable=attribute-defined-outside-init + def setup(self, stage: str): + # Assign train/val datasets for use in dataloaders + if stage == "fit": + cifar10_full = CIFAR10Dataset(self.data_dir, train=True) + self.train_data, self.validation_data = random_split( + cifar10_full, [0.9, 0.1], generator=torch.Generator().manual_seed(42) + ) + + # Assign test dataset for use in dataloader(s) + if stage == "test": + self.test_data = CIFAR10Dataset( + self.data_dir, train=False + ) + + if stage == "predict": + self.prediction_data = CIFAR10Dataset( + self.data_dir, train=False + ) + + def train_dataloader(self): + return DataLoader(self.train_data, batch_size=self.batch_size, shuffle=True) + + def val_dataloader(self): + return DataLoader(self.validation_data, batch_size=self.batch_size) + + def test_dataloader(self): + return DataLoader(self.test_data, batch_size=self.batch_size) + + def predict_dataloader(self): + return DataLoader(self.prediction_data, batch_size=self.batch_size) + + +class CIFAR100(L.LightningDataModule): + """Represents the CIFAR10 dataset. + """ + + def __init__(self, data_dir: str=" ./data", batch_size: int = 100) -> None: + super().__init__() + self.data_dir = data_dir + self.batch_size = batch_size + # self.transform = transforms.Compose() + + def prepare_data(self) -> None: + # download + CIFAR100Dataset(self.data_dir, train=True, download=True) + CIFAR100Dataset(self.data_dir, train=False, download=True) + + # pylint: disable=attribute-defined-outside-init + def setup(self, stage: str): + # Assign train/val datasets for use in dataloaders + if stage == "fit": + cifar100_full = CIFAR100Dataset(self.data_dir, train=True) + self.train_data, self.validation_data = random_split( + cifar100_full, [0.9, 0.1], generator=torch.Generator().manual_seed(42) + ) + + # Assign test dataset for use in dataloader(s) + if stage == "test": + self.test_data = CIFAR100Dataset( + self.data_dir, train=False + ) + + if stage == "predict": + self.prediction_data = CIFAR100Dataset( + self.data_dir, train=False + ) + + def train_dataloader(self): + return DataLoader(self.train_data, batch_size=self.batch_size, shuffle=True) + + def val_dataloader(self): + return DataLoader(self.validation_data, batch_size=self.batch_size) + + def test_dataloader(self): + return DataLoader(self.test_data, batch_size=self.batch_size) + + def predict_dataloader(self): + return DataLoader(self.prediction_data, batch_size=self.batch_size) diff --git a/benchmarking/classification/classifier.py b/benchmarking/classification/classifier.py index 6abfbf2..e4b351e 100644 --- a/benchmarking/classification/classifier.py +++ b/benchmarking/classification/classifier.py @@ -19,7 +19,8 @@ def __init__(self, model, optimizer, **hyperameters): self.optimizer = optimizer self.model = model self.hyperparemeters = hyperameters - self.save_hyperparameters() + self.save_hyperparameters(ignore=["model", "optimizer", "covariance_model"]) + # pylint: disable=arguments-differ def training_step(self, batch, *args, **kwargs): diff --git a/benchmarking/classification/mnist/data.py b/benchmarking/classification/mnist/data.py index 840078e..8ba904c 100644 --- a/benchmarking/classification/mnist/data.py +++ b/benchmarking/classification/mnist/data.py @@ -36,7 +36,7 @@ def setup(self, stage: str): if stage == "fit": mnist_full = MNISTDataset(self.data_dir, train=True, transform=self.transform) self.train_data, self.validation_data = random_split( - mnist_full, [55000, 5000], generator=torch.Generator().manual_seed(42) + mnist_full, [0.8, 0.2], generator=torch.Generator().manual_seed(42) ) # Assign test dataset for use in dataloader(s) @@ -91,7 +91,7 @@ def setup(self, stage: str): if stage == "fit": mnist_full = FashionMNISTDataset(self.data_dir, train=True, transform=self.transform) self.train_data, self.validation_data = random_split( - 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