tutorial_code.py

import torch
import torchvision
import torchvision.transforms as transforms

# import matplotlib.pyplot as plt
# import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from time import time


class Net(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = torch.flatten(x, 1)  # flatten all dimensions except batch
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x


if __name__ == "__main__":
    start = time()
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

    net = Net()
    net.to(device)

    # Assuming that we are on a CUDA machine, this should print a CUDA device:
    print(device)

    transform = transforms.Compose(
        [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
    )

    batch_size = 1000

    trainset = torchvision.datasets.CIFAR10(
        root="./data", train=True, download=True, transform=transform
    )
    trainloader = torch.utils.data.DataLoader(
        trainset, batch_size=batch_size, shuffle=True, num_workers=2, drop_last=True
    )

    testset = torchvision.datasets.CIFAR10(
        root="./data", train=False, download=True, transform=transform
    )
    testloader = torch.utils.data.DataLoader(
        testset, batch_size=batch_size, shuffle=False, num_workers=2, drop_last=True
    )

    classes = (
        "plane",
        "car",
        "bird",
        "cat",
        "deer",
        "dog",
        "frog",
        "horse",
        "ship",
        "truck",
    )

    criterion = nn.CrossEntropyLoss()
    optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

    for epoch in range(10000):  # loop over the dataset multiple times
        loop = time()
        print(loop - start)
        print(epoch)

        running_loss = 0.0
        for i, data in enumerate(trainloader, 0):
            # get the inputs; data is a list of [inputs, labels]
            inputs, labels = data[0].to(device), data[1].to(device)

            # zero the parameter gradients
            optimizer.zero_grad()

            # forward + backward + optimize
            outputs = net(inputs)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()

            # print statistics
            running_loss += loss.item()
            if i % 2000 == 1999:  # print every 2000 mini-batches
                print(f"[{epoch + 1}, {i + 1:5d}] loss: {running_loss / 2000:.3f}")
                running_loss = 0.0

    print("Finished Training")

    PATH = "./cifar_net.pth"
    torch.save(net.state_dict(), PATH)

    dataiter = iter(testloader)
    images, labels = next(dataiter)

    net = Net()
    net.load_state_dict(torch.load(PATH))

    outputs = net(images)

    _, predicted = torch.max(outputs, 1)

    print("Predicted: ", " ".join(f"{classes[predicted[j]]:5s}" for j in range(4)))

    correct = 0
    total = 0
    # since we're not training, we don't need to calculate the gradients for our outputs
    with torch.no_grad():
        for data in testloader:
            inputs, labels = data[0], data[1]
            # calculate outputs by running images through the network
            outputs = net(inputs)
            # the class with the highest energy is what we choose as prediction
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()

    print(
        f"Accuracy of the network on the 10000 test images: {100 * correct // total} %"
    )

    # prepare to count predictions for each class
    correct_pred = {classname: 0 for classname in classes}
    total_pred = {classname: 0 for classname in classes}

    # again no gradients needed
    with torch.no_grad():
        for data in testloader:
            images, labels = data[0], data[1]
            outputs = net(images)
            _, predictions = torch.max(outputs, 1)
            # collect the correct predictions for each class
            for label, prediction in zip(labels, predictions):
                # print("test")
                # print(label)
                # print(prediction)
                if label == prediction:
                    correct_pred[classes[label]] += 1
                total_pred[classes[label]] += 1

    # print accuracy for each class
    for classname, correct_count in correct_pred.items():
        accuracy = 100 * float(correct_count) / total_pred[classname]
        print(f"Accuracy for class: {classname:5s} is {accuracy:.1f} %")
    end = time() - start
    print(end)