# 13.14. Dog Breed Identification (ImageNet Dogs) on Kaggle¶ Open the notebook in Colab Open the notebook in Colab Open the notebook in Colab Open the notebook in SageMaker Studio Lab

In this section, we will practice the dog breed identification problem on Kaggle. The web address of this competition is https://www.kaggle.com/c/dog-breed-identification

In this competition, 120 different breeds of dogs will be recognized. In fact, the dataset for this competition is a subset of the ImageNet dataset. Unlike the images in the CIFAR-10 dataset in Section 13.13, the images in the ImageNet dataset are both higher and wider in varying dimensions. Fig. 13.14.1 shows the information on the competition’s webpage. You need a Kaggle account to submit your results.

Fig. 13.14.1 The dog breed identification competition website. The competition dataset can be obtained by clicking the “Data” tab.

import os
from mxnet import autograd, gluon, init, npx
from mxnet.gluon import nn
from d2l import mxnet as d2l

npx.set_np()

import os
import torch
import torchvision
from torch import nn
from d2l import torch as d2l


## 13.14.1. Obtaining and Organizing the Dataset¶

The competition dataset is divided into a training set and a test set, which contain 10222 and 10357 JPEG images of three RGB (color) channels, respectively. Among the training dataset, there are 120 breeds of dogs such as Labradors, Poodles, Dachshunds, Samoyeds, Huskies, Chihuahuas, and Yorkshire Terriers.

After logging into Kaggle, you can click on the “Data” tab on the competition webpage shown in Fig. 13.14.1 and download the dataset by clicking the “Download All” button. After unzipping the downloaded file in ../data, you will find the entire dataset in the following paths:

• ../data/dog-breed-identification/labels.csv

• ../data/dog-breed-identification/sample_submission.csv

• ../data/dog-breed-identification/train

• ../data/dog-breed-identification/test

You may have noticed that the above structure is similar to that of the CIFAR-10 competition in Section 13.13, where folders train/ and test/ contain training and testing dog images, respectively, and labels.csv contains the labels for the training images. Similarly, to make it easier to get started, we provide a small sample of the dataset mentioned above: train_valid_test_tiny.zip. If you are going to use the full dataset for the Kaggle competition, you need to change the demo variable below to False.

#@save
d2l.DATA_HUB['dog_tiny'] = (d2l.DATA_URL + 'kaggle_dog_tiny.zip',

# If you use the full dataset downloaded for the Kaggle competition, change
# the variable below to False
demo = True
if demo:
else:
data_dir = os.path.join('..', 'data', 'dog-breed-identification')

#@save
d2l.DATA_HUB['dog_tiny'] = (d2l.DATA_URL + 'kaggle_dog_tiny.zip',

# If you use the full dataset downloaded for the Kaggle competition, change
# the variable below to False
demo = True
if demo:
else:
data_dir = os.path.join('..', 'data', 'dog-breed-identification')


### 13.14.1.2. Organizing the Dataset¶

We can organize the dataset similarly to what we did in Section 13.13, namely splitting out a validation set from the original training set, and moving images into subfolders grouped by labels.

The reorg_dog_data function below reads the training data labels, splits out the validation set, and organizes the training set.

def reorg_dog_data(data_dir, valid_ratio):
d2l.reorg_train_valid(data_dir, labels, valid_ratio)
d2l.reorg_test(data_dir)

batch_size = 32 if demo else 128
valid_ratio = 0.1
reorg_dog_data(data_dir, valid_ratio)

def reorg_dog_data(data_dir, valid_ratio):
d2l.reorg_train_valid(data_dir, labels, valid_ratio)
d2l.reorg_test(data_dir)

batch_size = 32 if demo else 128
valid_ratio = 0.1
reorg_dog_data(data_dir, valid_ratio)


## 13.14.2. Image Augmentation¶

Recall that this dog breed dataset is a subset of the ImageNet dataset, whose images are larger than those of the CIFAR-10 dataset in Section 13.13. The following lists a few image augmentation operations that might be useful for relatively larger images.

transform_train = gluon.data.vision.transforms.Compose([
# Randomly crop the image to obtain an image with an area of 0.08 to 1 of
# the original area and height-to-width ratio between 3/4 and 4/3. Then,
# scale the image to create a new 224 x 224 image
gluon.data.vision.transforms.RandomResizedCrop(224, scale=(0.08, 1.0),
ratio=(3.0/4.0, 4.0/3.0)),
gluon.data.vision.transforms.RandomFlipLeftRight(),
# Randomly change the brightness, contrast, and saturation
gluon.data.vision.transforms.RandomColorJitter(brightness=0.4,
contrast=0.4,
saturation=0.4),
gluon.data.vision.transforms.RandomLighting(0.1),
gluon.data.vision.transforms.ToTensor(),
# Standardize each channel of the image
gluon.data.vision.transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])])

transform_train = torchvision.transforms.Compose([
# Randomly crop the image to obtain an image with an area of 0.08 to 1 of
# the original area and height-to-width ratio between 3/4 and 4/3. Then,
# scale the image to create a new 224 x 224 image
torchvision.transforms.RandomResizedCrop(224, scale=(0.08, 1.0),
ratio=(3.0/4.0, 4.0/3.0)),
torchvision.transforms.RandomHorizontalFlip(),
# Randomly change the brightness, contrast, and saturation
torchvision.transforms.ColorJitter(brightness=0.4,
contrast=0.4,
saturation=0.4),
torchvision.transforms.ToTensor(),
# Standardize each channel of the image
torchvision.transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])])


During prediction, we only use image preprocessing operations without randomness.

transform_test = gluon.data.vision.transforms.Compose([
gluon.data.vision.transforms.Resize(256),
# Crop a 224 x 224 square area from the center of the image
gluon.data.vision.transforms.CenterCrop(224),
gluon.data.vision.transforms.ToTensor(),
gluon.data.vision.transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])])

transform_test = torchvision.transforms.Compose([
torchvision.transforms.Resize(256),
# Crop a 224 x 224 square area from the center of the image
torchvision.transforms.CenterCrop(224),
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])])


As in Section 13.13, we can read the organized dataset consisting of raw image files.

train_ds, valid_ds, train_valid_ds, test_ds = [
gluon.data.vision.ImageFolderDataset(
os.path.join(data_dir, 'train_valid_test', folder))
for folder in ('train', 'valid', 'train_valid', 'test')]

train_ds, train_valid_ds = [torchvision.datasets.ImageFolder(
os.path.join(data_dir, 'train_valid_test', folder),
transform=transform_train) for folder in ['train', 'train_valid']]

valid_ds, test_ds = [torchvision.datasets.ImageFolder(
os.path.join(data_dir, 'train_valid_test', folder),
transform=transform_test) for folder in ['valid', 'test']]


Below we create data iterator instances the same way as in Section 13.13.

train_iter, train_valid_iter = [gluon.data.DataLoader(
dataset.transform_first(transform_train), batch_size, shuffle=True,
last_batch='discard') for dataset in (train_ds, train_valid_ds)]

valid_ds.transform_first(transform_test), batch_size, shuffle=False,

test_ds.transform_first(transform_test), batch_size, shuffle=False,
last_batch='keep')

train_iter, train_valid_iter = [torch.utils.data.DataLoader(
dataset, batch_size, shuffle=True, drop_last=True)
for dataset in (train_ds, train_valid_ds)]

drop_last=True)

drop_last=False)


## 13.14.4. Fine-Tuning a Pretrained Model¶

Again, the dataset for this competition is a subset of the ImageNet dataset. Therefore, we can use the approach discussed in Section 13.2 to select a model pretrained on the full ImageNet dataset and use it to extract image features to be fed into a custom small-scale output network. High-level APIs of deep learning frameworks provide a wide range of models pretrained on the ImageNet dataset. Here, we choose a pretrained ResNet-34 model, where we simply reuse the input of this model’s output layer (i.e., the extracted features). Then we can replace the original output layer with a small custom output network that can be trained, such as stacking two fully-connected layers. Different from the experiment in Section 13.2, the following does not retrain the pretrained model used for feature extraction. This reduces training time and memory for storing gradients.

Recall that we standardized images using the means and standard deviations of the three RGB channels for the full ImageNet dataset. In fact, this is also consistent with the standardization operation by the pretrained model on ImageNet.

def get_net(devices):
finetune_net = gluon.model_zoo.vision.resnet34_v2(pretrained=True)
# Define a new output network
finetune_net.output_new = nn.HybridSequential(prefix='')
# There are 120 output categories
# Initialize the output network
finetune_net.output_new.initialize(init.Xavier(), ctx=devices)
# Distribute the model parameters to the CPUs or GPUs used for computation
finetune_net.collect_params().reset_ctx(devices)
return finetune_net

def get_net(devices):
finetune_net = nn.Sequential()
finetune_net.features = torchvision.models.resnet34(pretrained=True)
# Define a new output network (there are 120 output categories)
finetune_net.output_new = nn.Sequential(nn.Linear(1000, 256),
nn.ReLU(),
nn.Linear(256, 120))
# Move the model to devices
finetune_net = finetune_net.to(devices[0])
# Freeze parameters of feature layers
for param in finetune_net.features.parameters():
return finetune_net


Before calculating the loss, we first obtain the input of the pretrained model’s output layer, i.e., the extracted feature. Then we use this feature as the input for our small custom output network to calculate the loss.

loss = gluon.loss.SoftmaxCrossEntropyLoss()

def evaluate_loss(data_iter, net, devices):
l_sum, n = 0.0, 0
for features, labels in data_iter:
X_shards, y_shards = d2l.split_batch(features, labels, devices)
output_features = [net.features(X_shard) for X_shard in X_shards]
outputs = [net.output_new(feature) for feature in output_features]
ls = [loss(output, y_shard).sum() for output, y_shard
in zip(outputs, y_shards)]
l_sum += sum([float(l.sum()) for l in ls])
n += labels.size
return l_sum / n

loss = nn.CrossEntropyLoss(reduction='none')

def evaluate_loss(data_iter, net, devices):
l_sum, n = 0.0, 0
for features, labels in data_iter:
features, labels = features.to(devices[0]), labels.to(devices[0])
outputs = net(features)
l = loss(outputs, labels)
l_sum += l.sum()
n += labels.numel()
return l_sum / n


## 13.14.5. Defining the Training Function¶

We will select the model and tune hyperparameters according to the model’s performance on the validation set. The model training function train only iterates parameters of the small custom output network.

def train(net, train_iter, valid_iter, num_epochs, lr, wd, devices, lr_period,
lr_decay):
# Only train the small custom output network
trainer = gluon.Trainer(net.output_new.collect_params(), 'sgd',
{'learning_rate': lr, 'momentum': 0.9, 'wd': wd})
num_batches, timer = len(train_iter), d2l.Timer()
legend = ['train loss']
if valid_iter is not None:
legend.append('valid loss')
animator = d2l.Animator(xlabel='epoch', xlim=[1, num_epochs],
legend=legend)
for epoch in range(num_epochs):
metric = d2l.Accumulator(2)
if epoch > 0 and epoch % lr_period == 0:
trainer.set_learning_rate(trainer.learning_rate * lr_decay)
for i, (features, labels) in enumerate(train_iter):
timer.start()
X_shards, y_shards = d2l.split_batch(features, labels, devices)
output_features = [net.features(X_shard) for X_shard in X_shards]
outputs = [net.output_new(feature)
for feature in output_features]
ls = [loss(output, y_shard).sum() for output, y_shard
in zip(outputs, y_shards)]
for l in ls:
l.backward()
trainer.step(batch_size)
metric.add(sum([float(l.sum()) for l in ls]), labels.shape[0])
timer.stop()
if (i + 1) % (num_batches // 5) == 0 or i == num_batches - 1:
animator.add(epoch + (i + 1) / num_batches,
(metric[0] / metric[1], None))
if valid_iter is not None:
valid_loss = evaluate_loss(valid_iter, net, devices)
measures = f'train loss {metric[0] / metric[1]:.3f}'
if valid_iter is not None:
measures += f', valid loss {valid_loss:.3f}'
print(measures + f'\n{metric[1] * num_epochs / timer.sum():.1f}'
f' examples/sec on {str(devices)}')

def train(net, train_iter, valid_iter, num_epochs, lr, wd, devices, lr_period,
lr_decay):
# Only train the small custom output network
net = nn.DataParallel(net, device_ids=devices).to(devices[0])
trainer = torch.optim.SGD((param for param in net.parameters()
momentum=0.9, weight_decay=wd)
scheduler = torch.optim.lr_scheduler.StepLR(trainer, lr_period, lr_decay)
num_batches, timer = len(train_iter), d2l.Timer()
legend = ['train loss']
if valid_iter is not None:
legend.append('valid loss')
animator = d2l.Animator(xlabel='epoch', xlim=[1, num_epochs],
legend=legend)
for epoch in range(num_epochs):
metric = d2l.Accumulator(2)
for i, (features, labels) in enumerate(train_iter):
timer.start()
features, labels = features.to(devices[0]), labels.to(devices[0])
output = net(features)
l = loss(output, labels).sum()
l.backward()
trainer.step()
timer.stop()
if (i + 1) % (num_batches // 5) == 0 or i == num_batches - 1:
animator.add(epoch + (i + 1) / num_batches,
(metric[0] / metric[1], None))
measures = f'train loss {metric[0] / metric[1]:.3f}'
if valid_iter is not None:
valid_loss = evaluate_loss(valid_iter, net, devices)
scheduler.step()
if valid_iter is not None:
measures += f', valid loss {valid_loss:.3f}'
print(measures + f'\n{metric[1] * num_epochs / timer.sum():.1f}'
f' examples/sec on {str(devices)}')


## 13.14.6. Training and Validating the Model¶

Now we can train and validate the model. The following hyperparameters are all tunable. For example, the number of epochs can be increased. Because lr_period and lr_decay are set to 2 and 0.9, respectively, the learning rate of the optimization algorithm will be multiplied by 0.9 after every 2 epochs.

devices, num_epochs, lr, wd = d2l.try_all_gpus(), 10, 5e-3, 1e-4
lr_period, lr_decay, net = 2, 0.9, get_net(devices)
net.hybridize()
train(net, train_iter, valid_iter, num_epochs, lr, wd, devices, lr_period,
lr_decay)

train loss 0.908, valid loss 0.791
103.3 examples/sec on [gpu(0), gpu(1)]

devices, num_epochs, lr, wd = d2l.try_all_gpus(), 10, 1e-4, 1e-4
lr_period, lr_decay, net = 2, 0.9, get_net(devices)
train(net, train_iter, valid_iter, num_epochs, lr, wd, devices, lr_period,
lr_decay)

train loss 1.275, valid loss 1.238
578.6 examples/sec on [device(type='cuda', index=0), device(type='cuda', index=1)]


## 13.14.7. Classifying the Testing Set and Submitting Results on Kaggle¶

Similar to the final step in Section 13.13, in the end all the labeled data (including the validation set) are used for training the model and classifying the testing set. We will use the trained custom output network for classification.

net = get_net(devices)
net.hybridize()
train(net, train_valid_iter, None, num_epochs, lr, wd, devices, lr_period,
lr_decay)

preds = []
for data, label in test_iter:
output_features = net.features(data.as_in_ctx(devices[0]))
output = npx.softmax(net.output_new(output_features))
preds.extend(output.asnumpy())
ids = sorted(os.listdir(
os.path.join(data_dir, 'train_valid_test', 'test', 'unknown')))
with open('submission.csv', 'w') as f:
f.write('id,' + ','.join(train_valid_ds.synsets) + '\n')
for i, output in zip(ids, preds):
f.write(i.split('.')[0] + ',' + ','.join(
[str(num) for num in output]) + '\n')

train loss 0.797
149.7 examples/sec on [gpu(0), gpu(1)]

net = get_net(devices)
train(net, train_valid_iter, None, num_epochs, lr, wd, devices, lr_period,
lr_decay)

preds = []
for data, label in test_iter:
output = torch.nn.functional.softmax(net(data.to(devices[0])), dim=0)
preds.extend(output.cpu().detach().numpy())
ids = sorted(os.listdir(
os.path.join(data_dir, 'train_valid_test', 'test', 'unknown')))
with open('submission.csv', 'w') as f:
f.write('id,' + ','.join(train_valid_ds.classes) + '\n')
for i, output in zip(ids, preds):
f.write(i.split('.')[0] + ',' + ','.join(
[str(num) for num in output]) + '\n')

train loss 1.199
870.3 examples/sec on [device(type='cuda', index=0), device(type='cuda', index=1)]


The above code will generate a submission.csv file to be submitted to Kaggle in the same way described in Section 4.10.

## 13.14.8. Summary¶

• Images in the ImageNet dataset are larger (with varying dimensions) than CIFAR-10 images. We may modify image augmentation operations for tasks on a different dataset.

• To classify a subset of the ImageNet dataset, we can leverage pre-trained models on the full ImageNet dataset to extract features and only train a custom small-scale output network. This will lead to less computational time and memory cost.

## 13.14.9. Exercises¶

1. When using the full Kaggle competition dataset, what results can you achieve when you increase batch_size (batch size) and num_epochs (number of epochs) while setting some other hyperparameters as lr = 0.01, lr_period = 10, and lr_decay = 0.1?

2. Do you get better results if you use a deeper pretrained model? How do you tune hyperparameters? Can you further improve the results?