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

ML/Pytorch/pytorch_lightning/5. DataModule/simple_fc.py

@@ -0,0 +1,146 @@
+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 tqdm import tqdm
+from torch.utils.data import random_split
+import pytorch_lightning as pl 
+import torchmetrics 
+from torchmetrics import Metric
+
+
+class MyAccuracy(Metric):
+    def __init__(self):
+        super().__init__()
+        self.add_state("total", default=torch.tensor(0), dist_reduce_fx="sum")
+        self.add_state("correct", default=torch.tensor(0), dist_reduce_fx="sum")
+
+    def update(self, preds, target):
+        preds = torch.argmax(preds, dim=1)
+        assert preds.shape == target.shape
+        self.correct += torch.sum(preds == target)
+        self.total += target.numel()
+
+    def compute(self):
+        return self.correct.float() / self.total.float()
+
+
+class NN(pl.LightningModule):
+    def __init__(self, input_size, num_classes):
+        super().__init__()
+        self.fc1 = nn.Linear(input_size, 50)
+        self.fc2 = nn.Linear(50, num_classes)
+        self.loss_fn = nn.CrossEntropyLoss()
+        self.accuracy = torchmetrics.Accuracy(task="multiclass", num_classes=num_classes)
+        self.my_accuracy = MyAccuracy()
+        self.f1_score = torchmetrics.F1Score(task="multiclass", num_classes=num_classes)
+
+    def forward(self, x):
+        x = F.relu(self.fc1(x))
+        x = self.fc2(x)
+        return x
+
+    def training_step(self, batch, batch_idx):
+        loss, scores, y = self._common_step(batch, batch_idx)
+        accuracy = self.my_accuracy(scores, y) 
+        f1_score = self.f1_score(scores, y)
+        self.log_dict({'train_loss': loss, 'train_accuracy': accuracy, 'train_f1_score': f1_score},
+                      on_step=False, on_epoch=True, prog_bar=True)
+        return {'loss': loss, "scores": scores, "y": y}
+    
+    def validation_step(self, batch, batch_idx):
+        loss, scores, y = self._common_step(batch, batch_idx)
+        self.log('val_loss', loss)
+        return loss
+
+    def test_step(self, batch, batch_idx):
+        loss, scores, y = self._common_step(batch, batch_idx)
+        self.log('test_loss', loss)
+        return loss
+
+    def _common_step(self, batch, batch_idx):
+        x, y = batch 
+        x = x.reshape(x.size(0), -1)
+        scores = self.forward(x)
+        loss = self.loss_fn(scores, y)
+        return loss, scores, y
+
+    def predict_step(self, batch, batch_idx):
+        x, y = batch 
+        x = x.reshape(x.size(0), -1)
+        scores = self.forward(x)
+        preds = torch.argmax(scores, dim=1)
+        return preds
+
+    def configure_optimizers(self):
+        return optim.Adam(self.parameters(), lr=0.001)
+
+
+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,
+        )
+
+# Set device cuda for GPU if it's available otherwise run on the CPU
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Hyperparameters
+input_size = 784
+num_classes = 10
+learning_rate = 0.001
+batch_size = 64
+num_epochs = 3
+
+model = NN(input_size=input_size, num_classes=num_classes)
+dm = MnistDataModule(data_dir="dataset/", batch_size=batch_size, num_workers=4)
+trainer = pl.Trainer(accelerator="gpu", devices=1, min_epochs=1, max_epochs=3, precision=16)
+trainer.fit(model, dm)
+trainer.validate(model, dm)
+trainer.test(model, dm)