Home » Tutorials » PyTorch » Training a Classifier

Training a Classifier

An adaptation of training a classifier tutorial using Habana Gaudi AI processors.

In this tutorial, we will train a Convolutional Neural Network (CNN) to classify images. We will start with CPU training, and then continue to make the needed changes for training the model on Gaudi HPU.

Handling data

Generally, when you have to deal with image, text, audio or video data, you can use standard python packages that load data into a numpy array. Then you can convert this array into a torch.*Tensor.

  • For images, packages such as Pillow, OpenCV are useful
  • For audio, packages such as scipy and librosa
  • For text, either raw Python or Cython based loading, or NLTK and SpaCy are useful

Specifically for vision, we have created a package called torchvision, that has data loaders for common datasets such as Imagenet, CIFAR10, MNIST, etc. and data transformers for images, viz., torchvision.datasets and torch.utils.data.DataLoader.

This provides a huge convenience and avoids writing boilerplate code.

For this tutorial, we will use the CIFAR10 dataset. It has the classes: ‘airplane’, ‘automobile’, ‘bird’, ‘cat’, ‘deer’, ‘dog’, ‘frog’, ‘horse’, ‘ship’, ‘truck’. The images in CIFAR-10 are of size 3x32x32, i.e. 3-channel color images of 32×32 pixels in size.

cifar

Training an image classifier

We will do the following steps in order:

  1. Load and normalize the CIFAR10 training and test datasets using torchvision
  2. Define a Convolutional Neural Network
  3. Define a loss function
  4. Train the network on the training data
  5. Test the network on the test data

The first step:

  1. Load and normalize CIFAR10

Using torchvision, it’s extremely easy to load CIFAR10.

%matplotlib inline
import torch
import torchvision
import torchvision.transforms as transforms

The output of torchvision datasets are PILImage images of range [0, 1]. We transform them to Tensors of normalized range [-1, 1].

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

batch_size = 64

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')
Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data\cifar-10-python.tar.gz Extracting ./data\cifar-10-python.tar.gz to ./data Files already downloaded and verified
Code language: JavaScript (javascript)

Let us show some of the training images, for fun.

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('%5s' % classes[labels[j]] for j in range(batch_size)))
Habana - Image grid training
car car frog frog dog cat plane horse cat horse cat plane frog truck deer plane car car car ship frog truck car frog car car car bird car cat frog frog ship dog bird bird frog deer ship deer ship ship bird car cat deer frog ship deer ship bird bird deer cat ship ship ship plane frog car car bird plane frog
  1. Define a Convolutional Neural Network

Copy the neural network from the Neural Networks section before and modify it to take 3-channel images (instead of 1-channel images as it was defined).

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()
  1. Define a Loss function and optimizer

Let’s use a Classification Cross-Entropy loss and SGD with momentum.

import torch.optim as optim

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

This is when things start to get interesting. We simply have to loop over our data iterator, and feed the inputs to the network and optimize.

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 % 200 == 199:    # print every 200 mini-batches
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 200))
            running_loss = 0.0

print('Finished Training')
[1, 200] loss: 2.303 [1, 400] loss: 2.299 [1, 600] loss: 2.293 [2, 200] loss: 2.245 [2, 400] loss: 2.129 [2, 600] loss: 2.017 Finished Training
Code language: CSS (css)

Let’s quickly save our trained model:

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

See here <https://pytorch.org/docs/stable/notes/serialization.html>_ for more details on saving PyTorch models.

  1. Test the network on the test data

We have trained the network for 2 passes over the training dataset. But we need to check if the network has learnt anything at all.

We will check this by predicting the class label that the neural network outputs, and checking it against the ground-truth. If the prediction is correct, we add the sample to the list of correct predictions.

Okay, first step. Let us display an image from the test set to get familiar.

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

# print images
imshow(torchvision.utils.make_grid(images))
print('GroundTruth: ', ' '.join('%5s' % classes[labels[j]] for j in range(4)))
Habana - Image grid training
GroundTruth: cat ship ship plane
Code language: HTTP (http)

Next, let’s load back in our saved model (note: saving and re-loading the model wasn’t necessary here, we only did it to illustrate how to do so):

net = Net()
net.load_state_dict(torch.load(PATH))
<All keys matched successfully>
Code language: HTML, XML (xml)

Okay, now let us see what the neural network thinks these examples above are:

outputs = net(images)

The outputs are energies for the 10 classes. The higher the energy for a class, the more the network thinks that the image is of the particular class. So, let’s get the index of the highest energy:

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

print('Predicted: ', ' '.join('%5s' % classes[predicted[j]]
                              for j in range(4)))
Predicted: cat ship ship ship
Code language: HTTP (http)

The results seem pretty good.

Let us look at how the network performs on the whole dataset.

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('Accuracy of the network on the 10000 test images: %d %%' % (
    100 * correct / total))
Accuracy of the network on the 10000 test images: 31 %

That looks way better than chance, which is 10% accuracy (randomly picking a class out of 10 classes). Seems like the network learnt something.

Hmmm, what are the classes that performed well, and the classes that did not perform well:

# 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("Accuracy for class {:5s} is: {:.1f} %".format(classname, 
                                                   accuracy))
Accuracy for class plane is: 48.0 % Accuracy for class car is: 62.0 % Accuracy for class bird is: 0.9 % Accuracy for class cat is: 24.2 % Accuracy for class deer is: 25.6 % Accuracy for class dog is: 12.3 % Accuracy for class frog is: 50.2 % Accuracy for class horse is: 42.9 % Accuracy for class ship is: 39.7 % Accuracy for class truck is: 5.2 %
Code language: CSS (css)

Okay, so what next?

How do we run these neural networks on the Habana Processing Unit (HPU, i.e. Gaudi)?

Training on HPU

First let’s we load the necessary libraries for the model and dataloader, just like on CPU:

import torch
import torchvision
import torchvision.transforms as transforms
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim


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

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

batch_size = 64

trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                        download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,
                                          shuffle=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)
Files already downloaded and verified Files already downloaded and verified

Just like how you transfer a Tensor onto the HPU, you transfer the neural net onto the HPU.

Let’s load the necessary libraries to load the Habana module and define our device as the hpu device:

from habana_frameworks.torch.utils.library_loader import load_habana_module
load_habana_module()
device = torch.device("hpu")

# Assuming that we are on a HPU machine, this should print a HPU device:
print(device)
Loading Habana modules from /home/bzhu/builds/latest hpu
Code language: JavaScript (javascript)

And then transfer the model onto the HPU

model = Net().to(device)

Due to HPU perfers RSCK([filter_height, filter_width, input_depth, output_depth]) to KCRS, you need to permute model’s parameters and buffers to RSCK too.

permute_params(model, )
   permute_momentum(optimizer, )

These methods will recursively go over all modules and convert their parameters and buffers to HPU tensors:

def permute_params(model, to_filters_last, lazy_mode):
    with torch.no_grad():
        for name, param in model.named_parameters():
            if(param.ndim == 4):
                if to_filters_last:
                    param.data = param.data.permute((2, 3, 1, 0))  # permute KCRS to RSCK 
                else:
                    param.data = param.data.permute((3, 2, 0, 1))  # permute RSCK to KCRS
    if lazy_mode:
        import habana_frameworks.torch.core as htcore
        htcore.mark_step()

def permute_momentum(optimizer, to_filters_last, lazy_mode):
    # Permute the momentum buffer before using for checkpoint
    for group in optimizer.param_groups:
        for p in group['params']:
            param_state = optimizer.state[p]
            if 'momentum_buffer' in param_state:
                buf = param_state['momentum_buffer']
                if(buf.ndim == 4):
                    if to_filters_last:
                        buf = buf.permute((2,3,1,0))
                    else:
                        buf = buf.permute((3,2,0,1))
                    param_state['momentum_buffer'] = buf

    if lazy_mode:
        import habana_frameworks.torch.core as htcore
        htcore.mark_step()
# define the optimzer and permute parameters
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)

permute_params(model, True, False)
permute_momentum(optimizer, True, False)

Remember that you will have to send the inputs and targets at every step to the HPU too:
inputs, labels = inputs.to(device), labels.to(device)

Below code shows how to handle the inputs and targets during the training on HPU.

for epoch in range(2):  # loop over the dataset multiple times
    model.train()
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        # get the inputs; data is a list of [inputs, labels]
        inputs, labels = data
        # transfer input data to HPU device
        inputs, labels = inputs.to(device), labels.to(device)

        # zero the parameter gradients
        optimizer.zero_grad()

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

        # print statistics
        running_loss += loss.item()
        if i % 200 == 199:    # print every 200 steps
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 200))
            running_loss = 0.0

print('Finished Training')
[1, 200] loss: 2.304 [1, 400] loss: 2.301 [1, 600] loss: 2.296 [2, 200] loss: 2.262 [2, 400] loss: 2.181 [2, 600] loss: 2.074 Finished Training
Code language: CSS (css)

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