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) # x*weight^T + bias <--> y
print('w: ', linear.weight) # (out_features, in_features)
print('b: ', linear.bias) # out_features
# Build loss function and optimizer.
criterion = nn.MSELoss(reduction='elementwise_mean') # mean square error
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(one forward and backward).
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 pipline #
# ================================================================== #
# 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 batch_idx, (image, labels) in enumerate(train_loader, 0):
# Training code should be written here.
pass
# ================================================================== #
# 5. Input pipline for custom dataset #
# ================================================================== #
# You should your 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
# xy = np.loadtxt('../../data/diabets.csv.gz')
# self.len = xy.shape[0]
# self.x_data = torch.from_numpy(xy[:, 0:-1])
# self.y_data = torch.from_numpy(xy[:, [-1]])
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
# return self.x_data[index], self.y_data[index]
def __len__(self):
# You should change 0 to the total size of your dataset.
return 0
# return self.len
# You can then use the prebuilt data loader.
custom_dataset = CustomDataset()
train_loader = torch.utils.data.DataLoader(dataset=custom_dataset,
batch_size=32,
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'))
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')
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
# Hyper-parameters
input_size = 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, 28*28)
# 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)
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')
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')
