13.1. Image Augmentation¶ Open the notebook in SageMaker Studio Lab
In Section 7.1, we mentioned that large datasets are a prerequisite for the success of deep neural networks in various applications. Image augmentation generates similar but distinct training examples after a series of random changes to the training images, thereby expanding the size of the training set. Alternatively, image augmentation can be motivated by the fact that random tweaks of training examples allow models to less rely on certain attributes, thereby improving their generalization ability. For example, we can crop an image in different ways to make the object of interest appear in different positions, thereby reducing the dependence of a model on the position of the object. We can also adjust factors such as brightness and color to reduce a model’s sensitivity to color. It is probably true that image augmentation was indispensable for the success of AlexNet at that time. In this section we will discuss this widely used technique in computer vision.
%matplotlib inline
from mxnet import autograd, gluon, image, init, np, npx
from mxnet.gluon import nn
from d2l import mxnet as d2l
npx.set_np()
%matplotlib inline
import torch
import torchvision
from torch import nn
from d2l import torch as d2l
13.1.1. Common Image Augmentation Methods¶
In our investigation of common image augmentation methods, we will use the following \(400\times 500\) image an example.
d2l.set_figsize()
img = image.imread('../img/cat1.jpg')
d2l.plt.imshow(img.asnumpy());
d2l.set_figsize()
img = d2l.Image.open('../img/cat1.jpg')
d2l.plt.imshow(img);
Most image augmentation methods have a certain degree of randomness. To
make it easier for us to observe the effect of image augmentation, next
we define an auxiliary function apply. This function runs the image
augmentation method aug multiple times on the input image img
and shows all the results.
def apply(img, aug, num_rows=2, num_cols=4, scale=1.5):
Y = [aug(img) for _ in range(num_rows * num_cols)]
d2l.show_images(Y, num_rows, num_cols, scale=scale)
def apply(img, aug, num_rows=2, num_cols=4, scale=1.5):
Y = [aug(img) for _ in range(num_rows * num_cols)]
d2l.show_images(Y, num_rows, num_cols, scale=scale)
13.1.1.1. Flipping and Cropping¶
Flipping the image left and right usually does not change the category
of the object. This is one of the earliest and most widely used methods
of image augmentation. Next, we use the transforms module to create
the RandomFlipLeftRight instance, which flips an image left and
right with a 50% chance.
apply(img, gluon.data.vision.transforms.RandomFlipLeftRight())
Flipping up and down is not as common as flipping left and right. But at
least for this example image, flipping up and down does not hinder
recognition. Next, we create a RandomFlipTopBottom instance to flip
an image up and down with a 50% chance.
apply(img, gluon.data.vision.transforms.RandomFlipTopBottom())
Flipping the image left and right usually does not change the category
of the object. This is one of the earliest and most widely used methods
of image augmentation. Next, we use the transforms module to create
the RandomHorizontalFlip instance, which flips an image left and
right with a 50% chance.
apply(img, torchvision.transforms.RandomHorizontalFlip())
Flipping up and down is not as common as flipping left and right. But at
least for this example image, flipping up and down does not hinder
recognition. Next, we create a RandomVerticalFlip instance to flip
an image up and down with a 50% chance.
apply(img, torchvision.transforms.RandomVerticalFlip())
In the example image we used, the cat is in the middle of the image, but this may not be the case in general. In Section 6.5, we explained that the pooling layer can reduce the sensitivity of a convolutional layer to the target position. In addition, we can also randomly crop the image to make objects appear in different positions in the image at different scales, which can also reduce the sensitivity of a model to the target position.
In the code below, we randomly crop an area with an area of \(10\% \sim 100\%\) of the original area each time, and the ratio of width to height of this area is randomly selected from \(0.5 \sim 2\). Then, the width and height of the region are both scaled to 200 pixels. Unless otherwise specified, the random number between \(a\) and \(b\) in this section refers to a continuous value obtained by random and uniform sampling from the interval \([a, b]\).
shape_aug = gluon.data.vision.transforms.RandomResizedCrop(
(200, 200), scale=(0.1, 1), ratio=(0.5, 2))
apply(img, shape_aug)
shape_aug = torchvision.transforms.RandomResizedCrop(
(200, 200), scale=(0.1, 1), ratio=(0.5, 2))
apply(img, shape_aug)
13.1.1.2. Changing Colors¶
Another augmentation method is changing colors. We can change four aspects of the image color: brightness, contrast, saturation, and hue. In the example below, we randomly change the brightness of the image to a value between 50% (\(1-0.5\)) and 150% (\(1+0.5\)) of the original image.
apply(img, gluon.data.vision.transforms.RandomBrightness(0.5))
apply(img, torchvision.transforms.ColorJitter(
brightness=0.5, contrast=0, saturation=0, hue=0))
Similarly, we can randomly change the hue of the image.
apply(img, gluon.data.vision.transforms.RandomHue(0.5))
apply(img, torchvision.transforms.ColorJitter(
brightness=0, contrast=0, saturation=0, hue=0.5))
We can also create a RandomColorJitter instance and set how to
randomly change the brightness, contrast, saturation, and
hue of the image at the same time.
color_aug = gluon.data.vision.transforms.RandomColorJitter(
brightness=0.5, contrast=0.5, saturation=0.5, hue=0.5)
apply(img, color_aug)
color_aug = torchvision.transforms.ColorJitter(
brightness=0.5, contrast=0.5, saturation=0.5, hue=0.5)
apply(img, color_aug)
13.1.1.3. Combining Multiple Image Augmentation Methods¶
In practice, we will combine multiple image augmentation methods. For
example, we can combine the different image augmentation methods defined
above and apply them to each image via a Compose instance.
augs = gluon.data.vision.transforms.Compose([
gluon.data.vision.transforms.RandomFlipLeftRight(), color_aug, shape_aug])
apply(img, augs)
augs = torchvision.transforms.Compose([
torchvision.transforms.RandomHorizontalFlip(), color_aug, shape_aug])
apply(img, augs)
13.1.2. Training with Image Augmentation¶
Let us train a model with image augmentation. Here we use the CIFAR-10 dataset instead of the Fashion-MNIST dataset that we used before. This is because the position and size of the objects in the Fashion-MNIST dataset have been normalized, while the color and size of the objects in the CIFAR-10 dataset have more significant differences. The first 32 training images in the CIFAR-10 dataset are shown below.
d2l.show_images(gluon.data.vision.CIFAR10(
train=True)[0:32][0], 4, 8, scale=0.8);
all_images = torchvision.datasets.CIFAR10(train=True, root="../data",
download=True)
d2l.show_images([all_images[i][0] for i in range(32)], 4, 8, scale=0.8);
Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ../data/cifar-10-python.tar.gz
100.0%
Extracting ../data/cifar-10-python.tar.gz to ../data
In order to obtain definitive results during prediction, we usually only
apply image augmentation to training examples, and do not use image
augmentation with random operations during prediction. Here we only use
the simplest random left-right flipping method. In addition, we use a
ToTensor instance to convert a minibatch of images into the format
required by the deep learning framework, i.e., 32-bit floating point
numbers between 0 and 1 with the shape of (batch size, number of
channels, height, width).
train_augs = gluon.data.vision.transforms.Compose([
gluon.data.vision.transforms.RandomFlipLeftRight(),
gluon.data.vision.transforms.ToTensor()])
test_augs = gluon.data.vision.transforms.Compose([
gluon.data.vision.transforms.ToTensor()])
Next, we define an auxiliary function to facilitate reading the image
and applying image augmentation. The transform_first function
provided by Gluon’s datasets applies image augmentation to the first
element of each training example (image and label), i.e., the image. For
a detailed introduction to DataLoader, please refer to
Section 3.5.
def load_cifar10(is_train, augs, batch_size):
return gluon.data.DataLoader(
gluon.data.vision.CIFAR10(train=is_train).transform_first(augs),
batch_size=batch_size, shuffle=is_train,
num_workers=d2l.get_dataloader_workers())
train_augs = torchvision.transforms.Compose([
torchvision.transforms.RandomHorizontalFlip(),
torchvision.transforms.ToTensor()])
test_augs = torchvision.transforms.Compose([
torchvision.transforms.ToTensor()])
Next, we define an auxiliary function to facilitate reading the image
and applying image augmentation. The transform argument provided by
PyTorch’s dataset applies augmentation to transform the images. For a
detailed introduction to DataLoader, please refer to
Section 3.5.
def load_cifar10(is_train, augs, batch_size):
dataset = torchvision.datasets.CIFAR10(root="../data", train=is_train,
transform=augs, download=True)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
shuffle=is_train, num_workers=d2l.get_dataloader_workers())
return dataloader
13.1.2.1. Multi-GPU Training¶
We train the ResNet-18 model from Section 7.6 on the CIFAR-10 dataset. Recall the introduction to multi-GPU training in Section 12.6. In the following, we define a function to train and evaluate the model using multiple GPUs.
#@save
def train_batch_ch13(net, features, labels, loss, trainer, devices,
split_f=d2l.split_batch):
"""Train for a minibatch with mutiple GPUs (defined in Chapter 13)."""
X_shards, y_shards = split_f(features, labels, devices)
with autograd.record():
pred_shards = [net(X_shard) for X_shard in X_shards]
ls = [loss(pred_shard, y_shard) for pred_shard, y_shard
in zip(pred_shards, y_shards)]
for l in ls:
l.backward()
# The `True` flag allows parameters with stale gradients, which is useful
# later (e.g., in fine-tuning BERT)
trainer.step(labels.shape[0], ignore_stale_grad=True)
train_loss_sum = sum([float(l.sum()) for l in ls])
train_acc_sum = sum(d2l.accuracy(pred_shard, y_shard)
for pred_shard, y_shard in zip(pred_shards, y_shards))
return train_loss_sum, train_acc_sum
#@save
def train_ch13(net, train_iter, test_iter, loss, trainer, num_epochs,
devices=d2l.try_all_gpus(), split_f=d2l.split_batch):
"""Train a model with mutiple GPUs (defined in Chapter 13)."""
timer, num_batches = d2l.Timer(), len(train_iter)
animator = d2l.Animator(xlabel='epoch', xlim=[1, num_epochs], ylim=[0, 1],
legend=['train loss', 'train acc', 'test acc'])
for epoch in range(num_epochs):
# Sum of training loss, sum of training accuracy, no. of examples,
# no. of predictions
metric = d2l.Accumulator(4)
for i, (features, labels) in enumerate(train_iter):
timer.start()
l, acc = train_batch_ch13(
net, features, labels, loss, trainer, devices, split_f)
metric.add(l, acc, labels.shape[0], labels.size)
timer.stop()
if (i + 1) % (num_batches // 5) == 0 or i == num_batches - 1:
animator.add(epoch + (i + 1) / num_batches,
(metric[0] / metric[2], metric[1] / metric[3],
None))
test_acc = d2l.evaluate_accuracy_gpus(net, test_iter, split_f)
animator.add(epoch + 1, (None, None, test_acc))
print(f'loss {metric[0] / metric[2]:.3f}, train acc '
f'{metric[1] / metric[3]:.3f}, test acc {test_acc:.3f}')
print(f'{metric[2] * num_epochs / timer.sum():.1f} examples/sec on '
f'{str(devices)}')
#@save
def train_batch_ch13(net, X, y, loss, trainer, devices):
"""Train for a minibatch with mutiple GPUs (defined in Chapter 13)."""
if isinstance(X, list):
# Required for BERT fine-tuning (to be covered later)
X = [x.to(devices[0]) for x in X]
else:
X = X.to(devices[0])
y = y.to(devices[0])
net.train()
trainer.zero_grad()
pred = net(X)
l = loss(pred, y)
l.sum().backward()
trainer.step()
train_loss_sum = l.sum()
train_acc_sum = d2l.accuracy(pred, y)
return train_loss_sum, train_acc_sum
#@save
def train_ch13(net, train_iter, test_iter, loss, trainer, num_epochs,
devices=d2l.try_all_gpus()):
"""Train a model with mutiple GPUs (defined in Chapter 13)."""
timer, num_batches = d2l.Timer(), len(train_iter)
animator = d2l.Animator(xlabel='epoch', xlim=[1, num_epochs], ylim=[0, 1],
legend=['train loss', 'train acc', 'test acc'])
net = nn.DataParallel(net, device_ids=devices).to(devices[0])
for epoch in range(num_epochs):
# Sum of training loss, sum of training accuracy, no. of examples,
# no. of predictions
metric = d2l.Accumulator(4)
for i, (features, labels) in enumerate(train_iter):
timer.start()
l, acc = train_batch_ch13(
net, features, labels, loss, trainer, devices)
metric.add(l, acc, labels.shape[0], labels.numel())
timer.stop()
if (i + 1) % (num_batches // 5) == 0 or i == num_batches - 1:
animator.add(epoch + (i + 1) / num_batches,
(metric[0] / metric[2], metric[1] / metric[3],
None))
test_acc = d2l.evaluate_accuracy_gpu(net, test_iter)
animator.add(epoch + 1, (None, None, test_acc))
print(f'loss {metric[0] / metric[2]:.3f}, train acc '
f'{metric[1] / metric[3]:.3f}, test acc {test_acc:.3f}')
print(f'{metric[2] * num_epochs / timer.sum():.1f} examples/sec on '
f'{str(devices)}')
Now we can define the train_with_data_aug function to train the
model with image augmentation. This function gets all available GPUs,
uses Adam as the optimization algorithm, applies image augmentation to
the training dataset, and finally calls the train_ch13 function just
defined to train and evaluate the model.
batch_size, devices, net = 256, d2l.try_all_gpus(), d2l.resnet18(10)
net.initialize(init=init.Xavier(), ctx=devices)
def train_with_data_aug(train_augs, test_augs, net, lr=0.001):
train_iter = load_cifar10(True, train_augs, batch_size)
test_iter = load_cifar10(False, test_augs, batch_size)
loss = gluon.loss.SoftmaxCrossEntropyLoss()
trainer = gluon.Trainer(net.collect_params(), 'adam',
{'learning_rate': lr})
train_ch13(net, train_iter, test_iter, loss, trainer, 10, devices)
batch_size, devices, net = 256, d2l.try_all_gpus(), d2l.resnet18(10, 3)
def init_weights(m):
if type(m) in [nn.Linear, nn.Conv2d]:
nn.init.xavier_uniform_(m.weight)
net.apply(init_weights)
def train_with_data_aug(train_augs, test_augs, net, lr=0.001):
train_iter = load_cifar10(True, train_augs, batch_size)
test_iter = load_cifar10(False, test_augs, batch_size)
loss = nn.CrossEntropyLoss(reduction="none")
trainer = torch.optim.Adam(net.parameters(), lr=lr)
train_ch13(net, train_iter, test_iter, loss, trainer, 10, devices)
Let us train the model using image augmentation based on random left-right flipping.
train_with_data_aug(train_augs, test_augs, net)
loss 0.168, train acc 0.941, test acc 0.824
4064.7 examples/sec on [gpu(0), gpu(1)]
train_with_data_aug(train_augs, test_augs, net)
loss 0.192, train acc 0.932, test acc 0.848
5353.8 examples/sec on [device(type='cuda', index=0), device(type='cuda', index=1)]
13.1.3. Summary¶
Image augmentation generates random images based on existing training data to improve the generalization ability of models.
In order to obtain definitive results during prediction, we usually only apply image augmentation to training examples, and do not use image augmentation with random operations during prediction.
Deep learning frameworks provide many different image augmentation methods, which can be applied simultaneously.
13.1.4. Exercises¶
Train the model without using image augmentation:
train_with_data_aug(test_augs, test_augs). Compare training and testing accuracy when using and not using image augmentation. Can this comparative experiment support the argument that image augmentation can mitigate overfitting? Why?Combine multiple different image augmentation methods in model training on the CIFAR-10 dataset. Does it improve test accuracy?
Refer to the online documentation of the deep learning framework. What other image augmentation methods does it also provide?