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
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+import torch
+import torch.nn as nn
+import torchvision
+import torchvision.transforms as transforms
+
+
+# Device configuration
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+# Hyper-parameters 
+input_size = 784
+hidden_size = 500
+num_classes = 10
+num_epochs = 5
+batch_size = 100
+learning_rate = 0.001
+
+# MNIST dataset 
+train_dataset = torchvision.datasets.MNIST(root='../../data', 
+                                           train=True, 
+                                           transform=transforms.ToTensor(),  
+                                           download=True)
+
+test_dataset = torchvision.datasets.MNIST(root='../../data', 
+                                          train=False, 
+                                          transform=transforms.ToTensor())
+
+# Data loader
+train_loader = torch.utils.data.DataLoader(dataset=train_dataset, 
+                                           batch_size=batch_size, 
+                                           shuffle=True)
+
+test_loader = torch.utils.data.DataLoader(dataset=test_dataset, 
+                                          batch_size=batch_size, 
+                                          shuffle=False)
+
+# Fully connected neural network with one hidden layer
+class NeuralNet(nn.Module):
+    def __init__(self, input_size, hidden_size, num_classes):
+        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(input_size, hidden_size, num_classes).to(device)
+
+# Loss and optimizer
+criterion = nn.CrossEntropyLoss()
+optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)  
+
+# Train the model
+total_step = len(train_loader)
+for epoch in range(num_epochs):
+    for i, (images, labels) in enumerate(train_loader):  
+        # Move tensors to the configured device
+        images = images.reshape(-1, 28*28).to(device)
+        labels = labels.to(device)
+        
+        # Forward pass
+        outputs = model(images)
+        loss = criterion(outputs, labels)
+        
+        # Backward and optimize
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+        
+        if (i+1) % 100 == 0:
+            print ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}' 
+                   .format(epoch+1, num_epochs, i+1, total_step, loss.item()))
+
+# Test the model
+# In test phase, we don't need to compute gradients (for memory efficiency)
+with torch.no_grad():
+    correct = 0
+    total = 0
+    for images, labels in test_loader:
+        images = images.reshape(-1, 28*28).to(device)
+        labels = labels.to(device)
+        outputs = model(images)
+        _, predicted = torch.max(outputs.data, 1)
+        total += labels.size(0)
+        correct += (predicted == labels).sum().item()
+
+    print('Accuracy of the network on the 10000 test images: {} %'.format(100 * correct / total))
+
+# Save the model checkpoint
+torch.save(model.state_dict(), 'model.ckpt')
@@ -0,0 +1,55 @@
+import torch
+import torch.nn as nn
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+# Hyper-parameters
+input_size = 1
+output_size = 1
+num_epochs = 60
+learning_rate = 0.001
+
+# Toy dataset
+x_train = np.array([[3.3], [4.4], [5.5], [6.71], [6.93], [4.168], 
+                    [9.779], [6.182], [7.59], [2.167], [7.042], 
+                    [10.791], [5.313], [7.997], [3.1]], dtype=np.float32)
+
+y_train = np.array([[1.7], [2.76], [2.09], [3.19], [1.694], [1.573], 
+                    [3.366], [2.596], [2.53], [1.221], [2.827], 
+                    [3.465], [1.65], [2.904], [1.3]], dtype=np.float32)
+
+# Linear regression model
+model = nn.Linear(input_size, output_size)
+
+# Loss and optimizer
+criterion = nn.MSELoss()
+optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)  
+
+# Train the model
+for epoch in range(num_epochs):
+    # Convert numpy arrays to torch tensors
+    inputs = torch.from_numpy(x_train)
+    targets = torch.from_numpy(y_train)
+
+    # Forward pass
+    outputs = model(inputs)
+    loss = criterion(outputs, targets)
+    
+    # Backward and optimize
+    optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()
+    
+    if (epoch+1) % 5 == 0:
+        print ('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))
+
+# Plot the graph
+predicted = model(torch.from_numpy(x_train)).detach().numpy()
+plt.plot(x_train, y_train, 'ro', label='Original data')
+plt.plot(x_train, predicted, label='Fitted line')
+plt.legend()
+plt.show()
+
+# Save the model checkpoint
+torch.save(model.state_dict(), 'model.ckpt')
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+import torch
+import torch.nn as nn
+import torchvision
+import torchvision.transforms as transforms
+
+
+# Hyper-parameters 
+input_size = 28 * 28    # 784
+num_classes = 10
+num_epochs = 5
+batch_size = 100
+learning_rate = 0.001
+
+# MNIST dataset (images and labels)
+train_dataset = torchvision.datasets.MNIST(root='../../data', 
+                                           train=True, 
+                                           transform=transforms.ToTensor(),
+                                           download=True)
+
+test_dataset = torchvision.datasets.MNIST(root='../../data', 
+                                          train=False, 
+                                          transform=transforms.ToTensor())
+
+# Data loader (input pipeline)
+train_loader = torch.utils.data.DataLoader(dataset=train_dataset, 
+                                           batch_size=batch_size, 
+                                           shuffle=True)
+
+test_loader = torch.utils.data.DataLoader(dataset=test_dataset, 
+                                          batch_size=batch_size, 
+                                          shuffle=False)
+
+# Logistic regression model
+model = nn.Linear(input_size, num_classes)
+
+# Loss and optimizer
+# nn.CrossEntropyLoss() computes softmax internally
+criterion = nn.CrossEntropyLoss()  
+optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)  
+
+# Train the model
+total_step = len(train_loader)
+for epoch in range(num_epochs):
+    for i, (images, labels) in enumerate(train_loader):
+        # Reshape images to (batch_size, input_size)
+        images = images.reshape(-1, input_size)
+        
+        # Forward pass
+        outputs = model(images)
+        loss = criterion(outputs, labels)
+        
+        # Backward and optimize
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+        
+        if (i+1) % 100 == 0:
+            print ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}' 
+                   .format(epoch+1, num_epochs, i+1, total_step, loss.item()))
+
+# Test the model
+# In test phase, we don't need to compute gradients (for memory efficiency)
+with torch.no_grad():
+    correct = 0
+    total = 0
+    for images, labels in test_loader:
+        images = images.reshape(-1, input_size)
+        outputs = model(images)
+        _, predicted = torch.max(outputs.data, 1)
+        total += labels.size(0)
+        correct += (predicted == labels).sum()
+
+    print('Accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total))
+
+# Save the model checkpoint
+torch.save(model.state_dict(), 'model.ckpt')
@@ -0,0 +1,189 @@
+import torch 
+import torchvision
+import torch.nn as nn
+import numpy as np
+import torchvision.transforms as transforms
+
+
+# ================================================================== #
+#                         Table of Contents                          #
+# ================================================================== #
+
+# 1. Basic autograd example 1               (Line 25 to 39)
+# 2. Basic autograd example 2               (Line 46 to 83)
+# 3. Loading data from numpy                (Line 90 to 97)
+# 4. Input pipline                          (Line 104 to 129)
+# 5. Input pipline for custom dataset       (Line 136 to 156)
+# 6. Pretrained model                       (Line 163 to 176)
+# 7. Save and load model                    (Line 183 to 189) 
+
+
+# ================================================================== #
+#                     1. Basic autograd example 1                    #
+# ================================================================== #
+
+# Create tensors.
+x = torch.tensor(1., requires_grad=True)
+w = torch.tensor(2., requires_grad=True)
+b = torch.tensor(3., requires_grad=True)
+
+# Build a computational graph.
+y = w * x + b    # y = 2 * x + 3
+
+# Compute gradients.
+y.backward()
+
+# Print out the gradients.
+print(x.grad)    # x.grad = 2 
+print(w.grad)    # w.grad = 1 
+print(b.grad)    # b.grad = 1 
+
+
+# ================================================================== #
+#                    2. Basic autograd example 2                     #
+# ================================================================== #
+
+# Create tensors of shape (10, 3) and (10, 2).
+x = torch.randn(10, 3)
+y = torch.randn(10, 2)
+
+# Build a fully connected layer.
+linear = nn.Linear(3, 2)
+print ('w: ', linear.weight)
+print ('b: ', linear.bias)
+
+# Build loss function and optimizer.
+criterion = nn.MSELoss()
+optimizer = torch.optim.SGD(linear.parameters(), lr=0.01)
+
+# Forward pass.
+pred = linear(x)
+
+# Compute loss.
+loss = criterion(pred, y)
+print('loss: ', loss.item())
+
+# Backward pass.
+loss.backward()
+
+# Print out the gradients.
+print ('dL/dw: ', linear.weight.grad) 
+print ('dL/db: ', linear.bias.grad)
+
+# 1-step gradient descent.
+optimizer.step()
+
+# You can also perform gradient descent at the low level.
+# linear.weight.data.sub_(0.01 * linear.weight.grad.data)
+# linear.bias.data.sub_(0.01 * linear.bias.grad.data)
+
+# Print out the loss after 1-step gradient descent.
+pred = linear(x)
+loss = criterion(pred, y)
+print('loss after 1 step optimization: ', loss.item())
+
+
+# ================================================================== #
+#                     3. Loading data from numpy                     #
+# ================================================================== #
+
+# Create a numpy array.
+x = np.array([[1, 2], [3, 4]])
+
+# Convert the numpy array to a torch tensor.
+y = torch.from_numpy(x)
+
+# Convert the torch tensor to a numpy array.
+z = y.numpy()
+
+
+# ================================================================== #
+#                         4. Input pipeline                           #
+# ================================================================== #
+
+# Download and construct CIFAR-10 dataset.
+train_dataset = torchvision.datasets.CIFAR10(root='../../data/',
+                                             train=True, 
+                                             transform=transforms.ToTensor(),
+                                             download=True)
+
+# Fetch one data pair (read data from disk).
+image, label = train_dataset[0]
+print (image.size())
+print (label)
+
+# Data loader (this provides queues and threads in a very simple way).
+train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
+                                           batch_size=64, 
+                                           shuffle=True)
+
+# When iteration starts, queue and thread start to load data from files.
+data_iter = iter(train_loader)
+
+# Mini-batch images and labels.
+images, labels = data_iter.next()
+
+# Actual usage of the data loader is as below.
+for images, labels in train_loader:
+    # Training code should be written here.
+    pass
+
+
+# ================================================================== #
+#                5. Input pipeline for custom dataset                 #
+# ================================================================== #
+
+# You should build your custom dataset as below.
+class CustomDataset(torch.utils.data.Dataset):
+    def __init__(self):
+        # TODO
+        # 1. Initialize file paths or a list of file names. 
+        pass
+    def __getitem__(self, index):
+        # TODO
+        # 1. Read one data from file (e.g. using numpy.fromfile, PIL.Image.open).
+        # 2. Preprocess the data (e.g. torchvision.Transform).
+        # 3. Return a data pair (e.g. image and label).
+        pass
+    def __len__(self):
+        # You should change 0 to the total size of your dataset.
+        return 0 
+
+# You can then use the prebuilt data loader. 
+custom_dataset = CustomDataset()
+train_loader = torch.utils.data.DataLoader(dataset=custom_dataset,
+                                           batch_size=64, 
+                                           shuffle=True)
+
+
+# ================================================================== #
+#                        6. Pretrained model                         #
+# ================================================================== #
+
+# Download and load the pretrained ResNet-18.
+resnet = torchvision.models.resnet18(pretrained=True)
+
+# If you want to finetune only the top layer of the model, set as below.
+for param in resnet.parameters():
+    param.requires_grad = False
+
+# Replace the top layer for finetuning.
+resnet.fc = nn.Linear(resnet.fc.in_features, 100)  # 100 is an example.
+
+# Forward pass.
+images = torch.randn(64, 3, 224, 224)
+outputs = resnet(images)
+print (outputs.size())     # (64, 100)
+
+
+# ================================================================== #
+#                      7. Save and load the model                    #
+# ================================================================== #
+
+# Save and load the entire model.
+torch.save(resnet, 'model.ckpt')
+model = torch.load('model.ckpt')
+
+# Save and load only the model parameters (recommended).
+torch.save(resnet.state_dict(), 'params.ckpt')
+resnet.load_state_dict(torch.load('params.ckpt'))