11_gpu.py

import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import datasets, transforms
import time

class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, 3, 1)
        self.conv2 = nn.Conv2d(32, 64, 3, 1)
        self.dropout1 = nn.Dropout2d(0.25)
        self.dropout2 = nn.Dropout2d(0.5)
        self.fc1 = nn.Linear(9216, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = F.relu(x)
        x = self.conv2(x)
        x = F.relu(x)
        x = F.max_pool2d(x, 2)
        x = self.dropout1(x)
        x = torch.flatten(x, 1)
        x = self.fc1(x)
        x = F.relu(x)
        x = self.dropout2(x)
        x = self.fc2(x)
        output = F.log_softmax(x, dim=1)
        return output

def train(train_loader,model,criterion,optimizer,epoch,device):
    model.train()
    t = time.perf_counter()
    for batch_idx, (data, target) in enumerate(train_loader):
        data = data.to(device)
        target = target.to(device)
        output = model(data)
        loss = criterion(output, target)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        if batch_idx % 200 == 0:
            print('Train Epoch: {} [{:>5}/{} ({:.0%})]\tLoss: {:.6f}\t Time:{:.4f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                batch_idx / len(train_loader), loss.data.item(),
                time.perf_counter() - t))
            t = time.perf_counter()

def validate(val_loader,model,criterion,device):
    model.eval()
    val_loss, val_acc = 0, 0
    for data, target in val_loader:
        data = data.to(device)
        target = target.to(device)
        output = model(data)
        loss = criterion(output, target)
        val_loss += loss.item()
        pred = output.data.max(1)[1]
        val_acc += 100. * pred.eq(target.data).cpu().sum() / target.size(0)

    val_loss /= len(val_loader)
    val_acc /= len(val_loader)
    print('\nValidation set: Average loss: {:.4f}, Accuracy: {:.1f}%\n'.format(
        val_loss, val_acc))

def main():
    epochs = 10
    batch_size = 32
    learning_rate = 1.0e-02

    train_dataset = datasets.MNIST('./data',
                                   train=True,
                                   download=True,
                                   transform=transforms.ToTensor())
    val_dataset = datasets.MNIST('./data',
                                 train=False,
                                 transform=transforms.ToTensor())
    train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
                                               batch_size=batch_size,
                                               shuffle=True)
    val_loader = torch.utils.data.DataLoader(dataset=val_dataset,
                                             batch_size=batch_size,
                                             shuffle=False)
    device = torch.device("cuda")
    model = CNN().to(device)
    criterion = nn.CrossEntropyLoss()
    optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)

    for epoch in range(epochs):
        model.train()
        train(train_loader,model,criterion,optimizer,epoch,device)
        validate(val_loader,model,criterion,device)

if __name__ == '__main__':
    main()