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- #
- # Trains an MNIST digit recognizer using PyTorch Lightning,
- # and uses Mlflow to log metrics, params and artifacts
- # NOTE: This example requires you to first install
- # pytorch-lightning (using pip install pytorch-lightning)
- # and mlflow (using pip install mlflow).
- #
- # pylint: disable=arguments-differ
- # pylint: disable=unused-argument
- # pylint: disable=abstract-method
- import pytorch_lightning as pl
- import mlflow.pytorch
- import os
- import torch
- from argparse import ArgumentParser
- from pytorch_lightning.callbacks.early_stopping import EarlyStopping
- from pytorch_lightning.callbacks import ModelCheckpoint
- from pytorch_lightning.callbacks import LearningRateMonitor
- from pytorch_lightning.metrics.functional import accuracy
- from torch.nn import functional as F
- from torch.utils.data import DataLoader, random_split
- from torchvision import datasets, transforms
- class MNISTDataModule(pl.LightningDataModule):
- def __init__(self, **kwargs):
- """
- Initialization of inherited lightning data module
- """
- super(MNISTDataModule, self).__init__()
- self.df_train = None
- self.df_val = None
- self.df_test = None
- self.train_data_loader = None
- self.val_data_loader = None
- self.test_data_loader = None
- self.args = kwargs
- # transforms for images
- self.transform = transforms.Compose(
- [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
- )
- def setup(self, stage=None):
- """
- Downloads the data, parse it and split the data into train, test, validation data
- :param stage: Stage - training or testing
- """
- self.df_train = datasets.MNIST(
- "dataset", download=True, train=True, transform=self.transform
- )
- self.df_train, self.df_val = random_split(self.df_train, [55000, 5000])
- self.df_test = datasets.MNIST(
- "dataset", download=True, train=False, transform=self.transform
- )
- def create_data_loader(self, df):
- """
- Generic data loader function
- :param df: Input tensor
- :return: Returns the constructed dataloader
- """
- return DataLoader(
- df, batch_size=self.args["batch_size"], num_workers=self.args["num_workers"],
- )
- def train_dataloader(self):
- """
- :return: output - Train data loader for the given input
- """
- return self.create_data_loader(self.df_train)
- def val_dataloader(self):
- """
- :return: output - Validation data loader for the given input
- """
- return self.create_data_loader(self.df_val)
- def test_dataloader(self):
- """
- :return: output - Test data loader for the given input
- """
- return self.create_data_loader(self.df_test)
- class LightningMNISTClassifier(pl.LightningModule):
- def __init__(self, **kwargs):
- """
- Initializes the network
- """
- super(LightningMNISTClassifier, self).__init__()
- # mnist images are (1, 28, 28) (channels, width, height)
- self.optimizer = None
- self.scheduler = None
- self.layer_1 = torch.nn.Linear(28 * 28, 128)
- self.layer_2 = torch.nn.Linear(128, 256)
- self.layer_3 = torch.nn.Linear(256, 10)
- self.args = kwargs
- @staticmethod
- def add_model_specific_args(parent_parser):
- parser = ArgumentParser(parents=[parent_parser], add_help=False)
- parser.add_argument(
- "--batch_size",
- type=int,
- default=64,
- metavar="N",
- help="input batch size for training (default: 64)",
- )
- parser.add_argument(
- "--num_workers",
- type=int,
- default=3,
- metavar="N",
- help="number of workers (default: 3)",
- )
- parser.add_argument(
- "--lr", type=float, default=0.001, metavar="LR", help="learning rate (default: 0.001)",
- )
- return parser
- def forward(self, x):
- """
- :param x: Input data
- :return: output - mnist digit label for the input image
- """
- batch_size = x.size()[0]
- # (b, 1, 28, 28) -> (b, 1*28*28)
- x = x.view(batch_size, -1)
- # layer 1 (b, 1*28*28) -> (b, 128)
- x = self.layer_1(x)
- x = torch.relu(x)
- # layer 2 (b, 128) -> (b, 256)
- x = self.layer_2(x)
- x = torch.relu(x)
- # layer 3 (b, 256) -> (b, 10)
- x = self.layer_3(x)
- # probability distribution over labels
- x = torch.log_softmax(x, dim=1)
- return x
- def cross_entropy_loss(self, logits, labels):
- """
- Initializes the loss function
- :return: output - Initialized cross entropy loss function
- """
- return F.nll_loss(logits, labels)
- def training_step(self, train_batch, batch_idx):
- """
- Training the data as batches and returns training loss on each batch
- :param train_batch: Batch data
- :param batch_idx: Batch indices
- :return: output - Training loss
- """
- x, y = train_batch
- logits = self.forward(x)
- loss = self.cross_entropy_loss(logits, y)
- return {"loss": loss}
- def validation_step(self, val_batch, batch_idx):
- """
- Performs validation of data in batches
- :param val_batch: Batch data
- :param batch_idx: Batch indices
- :return: output - valid step loss
- """
- x, y = val_batch
- logits = self.forward(x)
- loss = self.cross_entropy_loss(logits, y)
- return {"val_step_loss": loss}
- def validation_epoch_end(self, outputs):
- """
- Computes average validation accuracy
- :param outputs: outputs after every epoch end
- :return: output - average valid loss
- """
- avg_loss = torch.stack([x["val_step_loss"] for x in outputs]).mean()
- self.log("val_loss", avg_loss, sync_dist=True)
- def test_step(self, test_batch, batch_idx):
- """
- Performs test and computes the accuracy of the model
- :param test_batch: Batch data
- :param batch_idx: Batch indices
- :return: output - Testing accuracy
- """
- x, y = test_batch
- output = self.forward(x)
- _, y_hat = torch.max(output, dim=1)
- test_acc = accuracy(y_hat.cpu(), y.cpu())
- return {"test_acc": test_acc}
- def test_epoch_end(self, outputs):
- """
- Computes average test accuracy score
- :param outputs: outputs after every epoch end
- :return: output - average test loss
- """
- avg_test_acc = torch.stack([x["test_acc"] for x in outputs]).mean()
- self.log("avg_test_acc", avg_test_acc)
- def prepare_data(self):
- """
- Prepares the data for training and prediction
- """
- return {}
- def configure_optimizers(self):
- """
- Initializes the optimizer and learning rate scheduler
- :return: output - Initialized optimizer and scheduler
- """
- self.optimizer = torch.optim.Adam(self.parameters(), lr=self.args["lr"])
- self.scheduler = {
- "scheduler": torch.optim.lr_scheduler.ReduceLROnPlateau(
- self.optimizer, mode="min", factor=0.2, patience=2, min_lr=1e-6, verbose=True,
- ),
- "monitor": "val_loss",
- }
- return [self.optimizer], [self.scheduler]
- if __name__ == "__main__":
- parser = ArgumentParser(description="PyTorch Autolog Mnist Example")
- # Early stopping parameters
- parser.add_argument(
- "--es_monitor", type=str, default="val_loss", help="Early stopping monitor parameter"
- )
- parser.add_argument("--es_mode", type=str, default="min", help="Early stopping mode parameter")
- parser.add_argument(
- "--es_verbose", type=bool, default=True, help="Early stopping verbose parameter"
- )
- parser.add_argument(
- "--es_patience", type=int, default=3, help="Early stopping patience parameter"
- )
- parser = pl.Trainer.add_argparse_args(parent_parser=parser)
- parser = LightningMNISTClassifier.add_model_specific_args(parent_parser=parser)
- mlflow.pytorch.autolog()
- args = parser.parse_args()
- dict_args = vars(args)
- if "accelerator" in dict_args:
- if dict_args["accelerator"] == "None":
- dict_args["accelerator"] = None
- model = LightningMNISTClassifier(**dict_args)
- dm = MNISTDataModule(**dict_args)
- dm.prepare_data()
- dm.setup(stage="fit")
- early_stopping = EarlyStopping(
- monitor=dict_args["es_monitor"],
- mode=dict_args["es_mode"],
- verbose=dict_args["es_verbose"],
- patience=dict_args["es_patience"],
- )
- checkpoint_callback = ModelCheckpoint(
- filepath=os.getcwd(), save_top_k=1, verbose=True, monitor="val_loss", mode="min", prefix="",
- )
- lr_logger = LearningRateMonitor()
- trainer = pl.Trainer.from_argparse_args(
- args, callbacks=[lr_logger, early_stopping], checkpoint_callback=checkpoint_callback
- )
- trainer.fit(model, dm)
- trainer.test()
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