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手写数字识别

使用了pytorch以及Intel® Optimization for PyTorch，通过优化扩展了 PyTorch，使英特尔硬件的性能进一步提升，让手写数字识别问题更加的快速高效

使用MNIST数据集，该数据集包含了一系列以黑白图像表示的手写数字，每个图像的大小为28x28像素，数据集组成如下：

• 训练集：包含60,000个图像和标签，用于训练模型。
• 测试集：包含10,000个图像和标签，用于测试模型的性能。

每个图像都被标记为0到9之间的一个数字，表示图像中显示的手写数字。这个数据集常常被用来验证图像分类模型的性能，特别是在计算机视觉领域。

参数与包

import os
import torch
import torchvision
import torch.nn as nn
from torchvision import transforms
from torchvision.utils import save_image

import intel_extension_for_pytorch as ipex

# Device configuration
device = torch.device('xpu' if torch.cuda.is_available() else 'cpu')

# Hyper-parameters
latent_size = 64
hidden_size = 256
image_size = 784
num_epochs = 200
batch_size = 100
sample_dir = 'samples'

加载数据

# Create a directory if not exists
if not os.path.exists(sample_dir):
    os.makedirs(sample_dir)

# Image processing
# transform = transforms.Compose([
#                 transforms.ToTensor(),
#                 transforms.Normalize(mean=(0.5, 0.5, 0.5),   # 3 for RGB channels
#                                      std=(0.5, 0.5, 0.5))])
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.5],  # 1 for greyscale channels
                         std=[0.5])])

# MNIST dataset
mnist = torchvision.datasets.MNIST(root='./data/',
                                   train=True,
                                   transform=transform,
                                   download=True)

# Data loader
data_loader = torch.utils.data.DataLoader(dataset=mnist,
                                          batch_size=batch_size,
                                          shuffle=True)

模型

# Discriminator
D = nn.Sequential(
    nn.Linear(image_size, hidden_size),
    nn.LeakyReLU(0.2),
    nn.Linear(hidden_size, hidden_size),
    nn.LeakyReLU(0.2),
    nn.Linear(hidden_size, 1),
    nn.Sigmoid())

# Generator 
G = nn.Sequential(
    nn.Linear(latent_size, hidden_size),
    nn.ReLU(),
    nn.Linear(hidden_size, hidden_size),
    nn.ReLU(),
    nn.Linear(hidden_size, image_size),
    nn.Tanh())

训练过程

# Device setting
D = D.to(device)
G = G.to(device)

# Binary cross entropy loss and optimizer
criterion = nn.BCELoss()
d_optimizer = torch.optim.Adam(D.parameters(), lr=0.0002)
g_optimizer = torch.optim.Adam(G.parameters(), lr=0.0002)

'''
Apply Intel Extension for PyTorch optimization against the model object and optimizer object.
'''
D, d_optimizer = ipex.optimize(D, optimizer=d_optimizer)
G, g_optimizer = ipex.optimize(G, optimizer=g_optimizer)


def denorm(x):
    out = (x + 1) / 2
    return out.clamp(0, 1)


def reset_grad():
    d_optimizer.zero_grad()
    g_optimizer.zero_grad()


# Start training
total_step = len(data_loader)
for epoch in range(num_epochs):
    for i, (images, _) in enumerate(data_loader):
        images = images.reshape(batch_size, -1).to(device)

        # Create the labels which are later used as input for the BCE loss
        real_labels = torch.ones(batch_size, 1).to(device)
        fake_labels = torch.zeros(batch_size, 1).to(device)

        # ================================================================== #
        #                      Train the discriminator                       #
        # ================================================================== #

        # Compute BCE_Loss using real images where BCE_Loss(x, y): - y * log(D(x)) - (1-y) * log(1 - D(x))
        # Second term of the loss is always zero since real_labels == 1
        outputs = D(images)
        d_loss_real = criterion(outputs, real_labels)
        real_score = outputs

        # Compute BCELoss using fake images
        # First term of the loss is always zero since fake_labels == 0
        z = torch.randn(batch_size, latent_size).to(device)
        fake_images = G(z)
        outputs = D(fake_images)
        d_loss_fake = criterion(outputs, fake_labels)
        fake_score = outputs

        # Backprop and optimize
        d_loss = d_loss_real + d_loss_fake
        reset_grad()
        d_loss.backward()
        d_optimizer.step()

        # ================================================================== #
        #                        Train the generator                         #
        # ================================================================== #

        # Compute loss with fake images
        z = torch.randn(batch_size, latent_size).to(device)
        fake_images = G(z)
        outputs = D(fake_images)

        # We train G to maximize log(D(G(z)) instead of minimizing log(1-D(G(z)))
        # For the reason, see the last paragraph of section 3. https://arxiv.org/pdf/1406.2661.pdf
        g_loss = criterion(outputs, real_labels)

        # Backprop and optimize
        reset_grad()
        g_loss.backward()
        g_optimizer.step()

        if (i + 1) % 200 == 0:
            print('Epoch [{}/{}], Step [{}/{}], d_loss: {:.4f}, g_loss: {:.4f}, D(x): {:.2f}, D(G(z)): {:.2f}'
                  .format(epoch, num_epochs, i + 1, total_step, d_loss.item(), g_loss.item(),
                          real_score.mean().item(), fake_score.mean().item()))

    # Save real images
    if (epoch + 1) == 1:
        images = images.reshape(images.size(0), 1, 28, 28)
        save_image(denorm(images), os.path.join(sample_dir, 'real_images.png'))

    # Save sampled images
    fake_images = fake_images.reshape(fake_images.size(0), 1, 28, 28)
    save_image(denorm(fake_images), os.path.join(sample_dir, 'fake_images-{}.png'.format(epoch + 1)))

# Save the model checkpoints 
torch.save(G.state_dict(), 'G.ckpt')
torch.save(D.state_dict(), 'D.ckpt')

结果

oneAPI

import intel_extension_for_pytorch as ipex

# Device configuration
device = torch.device('xpu' if torch.cuda.is_available() else 'cpu')

# Device setting
D = D.to(device)
G = G.to(device)

# Binary cross entropy loss and optimizer
criterion = nn.BCELoss()
d_optimizer = torch.optim.Adam(D.parameters(), lr=0.0002)
g_optimizer = torch.optim.Adam(G.parameters(), lr=0.0002)

'''
Apply Intel Extension for PyTorch optimization against the model object and optimizer object.
'''
D, d_optimizer = ipex.optimize(D, optimizer=d_optimizer)
G, g_optimizer = ipex.optimize(G, optimizer=g_optimizer)