Torch interoperability

In this example, we will follow the Torch tutorial : https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html. And run the network with N2D2 instead of Torch.

You can find the full python script here torch_example.py.

Example

Firstly, we import the same libraries as in the tutorial plus our pytorch_to_n2d2 and n2d2 libraries.

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
import n2d2
import pytorch_to_n2d2

We then still follow the tutorial and add the code to load the data and we define the Network.

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


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=2)

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)

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



# functions to show an image


def imshow(img, img_path):
        img = img / 2 + 0.5     # unnormalize
        cpu_img = img.cpu()
        npimg = cpu_img.numpy()
        plt.imshow(np.transpose(npimg, (1, 2, 0)))
        plt.savefig(img_path)


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

Here we begin to add our code, we intialize the Torch Network and we pass it to the pytorch_to_n2d2.wrap() method. This will give us a torch.nn.Module which run N2D2 and that we will use instead of the Torch Network.

torch_net = Net()
# specify that we want to use CUDA.
n2d2.global_variables.default_model = "Frame_CUDA"
# creating a model which run with N2D2 backend.
net = pytorch_to_n2d2.wrap(torch_net, (batch_size, 3, 32, 32))

criterion = nn.CrossEntropyLoss()
# Reminder : We define an optimizer, but it will not be used to optimized N2D2 parameters.
# If you want to change the optimizer of N2D2 refer to the N2D2 solver.
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

And that is it ! From this point, we can follow again the tutorial provided by PyTorch and we have a script ready to run. You can compare the N2D2 and the torch version by commenting the code we added and renaming torch_net into net.

for epoch in range(2):  # loop over the dataset multiple times
e_t = time()
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('[%d, %5d] loss: %.3f' %
                (epoch + 1, i + 1, running_loss / 2000))
        running_loss = 0.0
print(f"Expoch {epoch} : {time()-e_t}")
print('Finished Training')

dataiter = iter(testloader)
images, labels = dataiter.next()
images = images.to(device)
labels = labels.to(device)
# print images
imshow(torchvision.utils.make_grid(images), "torch_inference.png")
print('GroundTruth: ', ' '.join('%5s' % classes[labels[j]] for j in range(4)))
outputs = net(images)

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

print('Predicted: ', ' '.join('%5s' % classes[predicted[j]]
                        for j in range(4)))