import torch

import torchvision

import torchvision.transforms as transforms

transform = transforms.Compose(

    [transforms.ToTensor(),

     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

batch_size = 4

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=0)

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=0)

classes = ('plane', 'car', 'bird', 'cat',

           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')

import matplotlib.pyplot as plt

import numpy as np

# functions to show an image

def imshow(img):

    img = img / 2 + 0.5     # unnormalize

    npimg = img.numpy()

    plt.imshow(np.transpose(npimg, (1, 2, 0)))

    plt.show()

# get some random training images

dataiter = iter(trainloader)

images, labels = dataiter.next()

# show images

imshow(torchvision.utils.make_grid(images))

# print labels

print(' '.join(f'{classes[labels[j]]:5s}' for j in range(batch_size)))

import torch.nn as nn

import torch.nn.functional as F

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

net = Net()

import torch.optim as optim

criterion = nn.CrossEntropyLoss()

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

for epoch in range(2):  # loop over the dataset multiple times

    running_loss = 0.0

    for i, data in enumerate(trainloader, 0):

        # get the inputs; data is a list of [inputs, labels]

        inputs, labels = data

        # 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 = dataiter.next()

# print images

imshow(torchvision.utils.make_grid(images))

print('GroundTruth: ', ' '.join(f'{classes[labels[j]]:5s}' for j in range(4)))

net = Net()

net.load_state_dict(torch.load(PATH))

outputs = net(images)

outputs

_, 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:

        images, labels = data

        # calculate outputs by running images through the network

        outputs = net(images)

        # 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

        outputs = net(images)

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

        # collect the correct predictions for each class

        for label, prediction in zip(labels, predictions):

            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} %')

device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')

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

print(device)

net.to(device)

inputs, labels = data[0].to(device), data[1].to(device)