chore: import upstream snapshot with attribution

2026-07-13 12:37:46 +08:00
commit 041322a27b
45 changed files with 2319 additions and 0 deletions
@@ -0,0 +1,97 @@
+import torch
+import torch.nn as nn
+import torchvision
+from torchvision import transforms
+from logger import Logger
+
+
+# Device configuration
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+# MNIST dataset 
+dataset = torchvision.datasets.MNIST(root='../../data', 
+                                     train=True, 
+                                     transform=transforms.ToTensor(),  
+                                     download=True)
+
+# Data loader
+data_loader = torch.utils.data.DataLoader(dataset=dataset, 
+                                          batch_size=100, 
+                                          shuffle=True)
+
+
+# Fully connected neural network with one hidden layer
+class NeuralNet(nn.Module):
+    def __init__(self, input_size=784, hidden_size=500, num_classes=10):
+        super(NeuralNet, self).__init__()
+        self.fc1 = nn.Linear(input_size, hidden_size) 
+        self.relu = nn.ReLU()
+        self.fc2 = nn.Linear(hidden_size, num_classes)  
+    
+    def forward(self, x):
+        out = self.fc1(x)
+        out = self.relu(out)
+        out = self.fc2(out)
+        return out
+
+model = NeuralNet().to(device)
+
+logger = Logger('./logs')
+
+# Loss and optimizer
+criterion = nn.CrossEntropyLoss()  
+optimizer = torch.optim.Adam(model.parameters(), lr=0.00001)  
+
+data_iter = iter(data_loader)
+iter_per_epoch = len(data_loader)
+total_step = 50000
+
+# Start training
+for step in range(total_step):
+    
+    # Reset the data_iter
+    if (step+1) % iter_per_epoch == 0:
+        data_iter = iter(data_loader)
+
+    # Fetch images and labels
+    images, labels = next(data_iter)
+    images, labels = images.view(images.size(0), -1).to(device), labels.to(device)
+    
+    # Forward pass
+    outputs = model(images)
+    loss = criterion(outputs, labels)
+    
+    # Backward and optimize
+    optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()
+
+    # Compute accuracy
+    _, argmax = torch.max(outputs, 1)
+    accuracy = (labels == argmax.squeeze()).float().mean()
+
+    if (step+1) % 100 == 0:
+        print ('Step [{}/{}], Loss: {:.4f}, Acc: {:.2f}' 
+               .format(step+1, total_step, loss.item(), accuracy.item()))
+
+        # ================================================================== #
+        #                        Tensorboard Logging                         #
+        # ================================================================== #
+
+        # 1. Log scalar values (scalar summary)
+        info = { 'loss': loss.item(), 'accuracy': accuracy.item() }
+
+        for tag, value in info.items():
+            logger.scalar_summary(tag, value, step+1)
+
+        # 2. Log values and gradients of the parameters (histogram summary)
+        for tag, value in model.named_parameters():
+            tag = tag.replace('.', '/')
+            logger.histo_summary(tag, value.data.cpu().numpy(), step+1)
+            logger.histo_summary(tag+'/grad', value.grad.data.cpu().numpy(), step+1)
+
+        # 3. Log training images (image summary)
+        info = { 'images': images.view(-1, 28, 28)[:10].cpu().numpy() }
+
+        for tag, images in info.items():
+            logger.image_summary(tag, images, step+1)