add lightning code, finetuning whisper, recommender system neural collaborative filtering

ML/Pytorch/pytorch_lightning/7. Callbacks/dataset.py

@@ -0,0 +1,59 @@
+import torch
+import torch.nn.functional as F
+import torchvision.datasets as datasets
+import torchvision.transforms as transforms
+from torch import nn, optim
+from torch.utils.data import DataLoader
+from torch.utils.data import random_split
+import pytorch_lightning as pl
+
+
+class MnistDataModule(pl.LightningDataModule):
+    def __init__(self, data_dir, batch_size, num_workers):
+        super().__init__()
+        self.data_dir = data_dir
+        self.batch_size = batch_size
+        self.num_workers = num_workers
+
+    def prepare_data(self):
+        datasets.MNIST(self.data_dir, train=True, download=True)
+        datasets.MNIST(self.data_dir, train=False, download=True)
+
+    def setup(self, stage):
+        entire_dataset = datasets.MNIST(
+            root=self.data_dir,
+            train=True,
+            transform=transforms.ToTensor(),
+            download=False,
+        )
+        self.train_ds, self.val_ds = random_split(entire_dataset, [50000, 10000])
+        self.test_ds = datasets.MNIST(
+            root=self.data_dir,
+            train=False,
+            transform=transforms.ToTensor(),
+            download=False,
+        )
+
+    def train_dataloader(self):
+        return DataLoader(
+            self.train_ds,
+            batch_size=self.batch_size,
+            num_workers=self.num_workers,
+            shuffle=True,
+        )
+
+    def val_dataloader(self):
+        return DataLoader(
+            self.val_ds,
+            batch_size=self.batch_size,
+            num_workers=self.num_workers,
+            shuffle=False,
+        )
+
+    def test_dataloader(self):
+        return DataLoader(
+            self.test_ds,
+            batch_size=self.batch_size,
+            num_workers=self.num_workers,
+            shuffle=False,
+        )