src file added

config/__init__.py

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+import toml
+import os 
+
+config_file:str = os.path.join(os.path.dirname(__file__),'config.toml')
+
+CONFIG = toml.load(config_file)

config/config.toml

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+batch_size = 128
+shuffle    = true 
+num_workers= 0
+pin_memory = false
+
+[optimizer]
+lr = 0.1
+momentum = 0.9
+
+[scheduler]
+step_size = 15
+gamma = 0.1
+
+
+[training]
+num_epochs = 15
+
+
+[data]
+dir_path = 'C:\Users\muthu\GitHub\DATA 📁\'
+
+[model]
+dropout_rate = 0.01
+bias = false

data/__init__.py

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+from .data import LitMNISTDataModule

data/data.py

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+from typing import Callable,List,Any
+from pathlib import Path
+
+from lightning import LightningDataModule
+from lightning.pytorch.utilities.types import TRAIN_DATALOADERS,EVAL_DATALOADERS
+import torch
+from torch.utils.data import DataLoader,random_split
+from torchvision import transforms
+from torchvision.datasets import MNIST
+
+device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
+
+class LitMNISTDataModule(LightningDataModule):
+    def __init__(
+            self,
+            data_dir:Path = Path('.'),
+            batch_size:int = 32,
+            num_workers:int = 0,
+            test_transform:Callable =  transforms.Compose([transforms.ToTensor(),transforms.Normalize(mean=(.1307,),std=(.3081,))]),
+            train_transform:Callable = transforms.Compose([transforms.ToTensor(),transforms.Normalize(mean=(.1307,),std=(.3081,))])
+    ) -> None:
+        super().__init__()
+        self.data_dir:Path = data_dir
+        self.batch_size:int = batch_size
+        self.num_workers:int = num_workers
+        self.test_transform:Callable = test_transform
+        self.train_transform:Callable = train_transform
+        self.save_hyperparameters()
+
+    def prepare_data(self) -> None:
+        MNIST(self.data_dir,train=True,download=True)
+        MNIST(self.data_dir,train=False,download=True)
+    
+    def setup(self, stage: str=None) -> None:
+        if stage=="fit" or stage is None:
+            _mnist_full = MNIST(self.data_dir,train=True,transform=self.train_transform)
+            self.mnist_train, self.mnist_val = random_split(_mnist_full,[.9,.1],generator=torch.Generator(device))
+        
+        if stage=='test' or stage is None: 
+            self.mnist_test = MNIST(self.data_dir,train=False, transform=self.test_transform)
+
+    
+    def train_dataloader(self) -> TRAIN_DATALOADERS:
+        return DataLoader(self.mnist_train,batch_size=self.batch_size,num_workers=self.num_workers,collate_fn=None,shuffle=True,generator= torch.Generator(device) )
+    
+    def val_dataloader(self) -> EVAL_DATALOADERS:
+        return DataLoader(self.mnist_val,batch_size=self.batch_size,num_workers=self.num_workers,collate_fn=None,shuffle=False,generator= torch.Generator(device))
+    
+    def test_dataloader(self) -> EVAL_DATALOADERS:
+        return DataLoader(self.mnist_test,batch_size=self.batch_size,num_workers=self.num_workers,collate_fn=None,shuffle=False,generator= torch.Generator(device))
+