.. _sec_kaggle_cifar10:
Classificação de Imagens (CIFAR-10) no Kaggle
=============================================
Até agora, temos usado o pacote ``data`` do Gluon para obter diretamente
conjuntos de dados de imagem no formato tensor. Na prática, entretanto,
os conjuntos de dados de imagem geralmente existem no formato de
arquivos de imagem. Nesta seção, começaremos com os arquivos de imagem
originais e organizaremos, leremos e converteremos os arquivos para o
formato tensor passo a passo.
Realizamos um experimento no conjunto de dados CIFAR-10 em
:numref:`sec_image_augmentation`. Este é um dado importante definido
no campo de visão do computador. Agora, vamos aplicar o conhecimento que
aprendemos em as seções anteriores para participar da competição Kaggle,
que aborda problemas de classificação de imagens CIFAR-10. O endereço da
competição na web é
https://www.kaggle.com/c/cifar-10
:numref:`fig_kaggle_cifar10` mostra as informações na página da
competição. Para enviar os resultados, primeiro registre uma conta no
site do Kaggle.
.. _fig_kaggle_cifar10:
.. figure:: ../img/kaggle-cifar10.png
:width: 600px
Informações da página da web do concurso de classificação de imagens
CIFAR-10. O conjunto de dados da competição pode ser acessado
clicando na guia “Dados”.
Primeiro, importe os pacotes ou módulos necessários para a competição.
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import collections
import math
import os
import shutil
import pandas as pd
from mxnet import gluon, init, npx
from mxnet.gluon import nn
from d2l import mxnet as d2l
npx.set_np()
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import collections
import math
import os
import shutil
import pandas as pd
import torch
import torchvision
from torch import nn
from d2l import torch as d2l
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#@save
d2l.DATA_HUB['cifar10_tiny'] = (d2l.DATA_URL + 'kaggle_cifar10_tiny.zip',
'2068874e4b9a9f0fb07ebe0ad2b29754449ccacd')
# If you use the full dataset downloaded for the Kaggle competition, set
# `demo` to False
demo = True
if demo:
data_dir = d2l.download_extract('cifar10_tiny')
else:
data_dir = '../data/cifar-10/'
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Downloading ../data/kaggle_cifar10_tiny.zip from http://d2l-data.s3-accelerate.amazonaws.com/kaggle_cifar10_tiny.zip...
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#@save
d2l.DATA_HUB['cifar10_tiny'] = (d2l.DATA_URL + 'kaggle_cifar10_tiny.zip',
'2068874e4b9a9f0fb07ebe0ad2b29754449ccacd')
# If you use the full dataset downloaded for the Kaggle competition, set
# `demo` to False
demo = True
if demo:
data_dir = d2l.download_extract('cifar10_tiny')
else:
data_dir = '../data/cifar-10/'
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Downloading ../data/kaggle_cifar10_tiny.zip from http://d2l-data.s3-accelerate.amazonaws.com/kaggle_cifar10_tiny.zip...
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#@save
def read_csv_labels(fname):
"""Read fname to return a name to label dictionary."""
with open(fname, 'r') as f:
# Skip the file header line (column name)
lines = f.readlines()[1:]
tokens = [l.rstrip().split(',') for l in lines]
return dict(((name, label) for name, label in tokens))
labels = read_csv_labels(os.path.join(data_dir, 'trainLabels.csv'))
print('# training examples:', len(labels))
print('# classes:', len(set(labels.values())))
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# training examples: 1000
# classes: 10
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#@save
def read_csv_labels(fname):
"""Read fname to return a name to label dictionary."""
with open(fname, 'r') as f:
# Skip the file header line (column name)
lines = f.readlines()[1:]
tokens = [l.rstrip().split(',') for l in lines]
return dict(((name, label) for name, label in tokens))
labels = read_csv_labels(os.path.join(data_dir, 'trainLabels.csv'))
print('# training examples:', len(labels))
print('# classes:', len(set(labels.values())))
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# training examples: 1000
# classes: 10
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#@save
def copyfile(filename, target_dir):
"""Copy a file into a target directory."""
os.makedirs(target_dir, exist_ok=True)
shutil.copy(filename, target_dir)
#@save
def reorg_train_valid(data_dir, labels, valid_ratio):
# The number of examples of the class with the least examples in the
# training dataset
n = collections.Counter(labels.values()).most_common()[-1][1]
# The number of examples per class for the validation set
n_valid_per_label = max(1, math.floor(n * valid_ratio))
label_count = {}
for train_file in os.listdir(os.path.join(data_dir, 'train')):
label = labels[train_file.split('.')[0]]
fname = os.path.join(data_dir, 'train', train_file)
# Copy to train_valid_test/train_valid with a subfolder per class
copyfile(fname, os.path.join(data_dir, 'train_valid_test',
'train_valid', label))
if label not in label_count or label_count[label] < n_valid_per_label:
# Copy to train_valid_test/valid
copyfile(fname, os.path.join(data_dir, 'train_valid_test',
'valid', label))
label_count[label] = label_count.get(label, 0) + 1
else:
# Copy to train_valid_test/train
copyfile(fname, os.path.join(data_dir, 'train_valid_test',
'train', label))
return n_valid_per_label
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#@save
def copyfile(filename, target_dir):
"""Copy a file into a target directory."""
os.makedirs(target_dir, exist_ok=True)
shutil.copy(filename, target_dir)
#@save
def reorg_train_valid(data_dir, labels, valid_ratio):
# The number of examples of the class with the least examples in the
# training dataset
n = collections.Counter(labels.values()).most_common()[-1][1]
# The number of examples per class for the validation set
n_valid_per_label = max(1, math.floor(n * valid_ratio))
label_count = {}
for train_file in os.listdir(os.path.join(data_dir, 'train')):
label = labels[train_file.split('.')[0]]
fname = os.path.join(data_dir, 'train', train_file)
# Copy to train_valid_test/train_valid with a subfolder per class
copyfile(fname, os.path.join(data_dir, 'train_valid_test',
'train_valid', label))
if label not in label_count or label_count[label] < n_valid_per_label:
# Copy to train_valid_test/valid
copyfile(fname, os.path.join(data_dir, 'train_valid_test',
'valid', label))
label_count[label] = label_count.get(label, 0) + 1
else:
# Copy to train_valid_test/train
copyfile(fname, os.path.join(data_dir, 'train_valid_test',
'train', label))
return n_valid_per_label
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#@save
def reorg_test(data_dir):
for test_file in os.listdir(os.path.join(data_dir, 'test')):
copyfile(os.path.join(data_dir, 'test', test_file),
os.path.join(data_dir, 'train_valid_test', 'test',
'unknown'))
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#@save
def reorg_test(data_dir):
for test_file in os.listdir(os.path.join(data_dir, 'test')):
copyfile(os.path.join(data_dir, 'test', test_file),
os.path.join(data_dir, 'train_valid_test', 'test',
'unknown'))
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def reorg_cifar10_data(data_dir, valid_ratio):
labels = read_csv_labels(os.path.join(data_dir, 'trainLabels.csv'))
reorg_train_valid(data_dir, labels, valid_ratio)
reorg_test(data_dir)
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def reorg_cifar10_data(data_dir, valid_ratio):
labels = read_csv_labels(os.path.join(data_dir, 'trainLabels.csv'))
reorg_train_valid(data_dir, labels, valid_ratio)
reorg_test(data_dir)
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batch_size = 4 if demo else 128
valid_ratio = 0.1
reorg_cifar10_data(data_dir, valid_ratio)
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batch_size = 4 if demo else 128
valid_ratio = 0.1
reorg_cifar10_data(data_dir, valid_ratio)
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transform_train = gluon.data.vision.transforms.Compose([
# Magnify the image to a square of 40 pixels in both height and width
gluon.data.vision.transforms.Resize(40),
# Randomly crop a square image of 40 pixels in both height and width to
# produce a small square of 0.64 to 1 times the area of the original
# image, and then shrink it to a square of 32 pixels in both height and
# width
gluon.data.vision.transforms.RandomResizedCrop(32, scale=(0.64, 1.0),
ratio=(1.0, 1.0)),
gluon.data.vision.transforms.RandomFlipLeftRight(),
gluon.data.vision.transforms.ToTensor(),
# Normalize each channel of the image
gluon.data.vision.transforms.Normalize([0.4914, 0.4822, 0.4465],
[0.2023, 0.1994, 0.2010])])
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transform_train = torchvision.transforms.Compose([
# Magnify the image to a square of 40 pixels in both height and width
torchvision.transforms.Resize(40),
# Randomly crop a square image of 40 pixels in both height and width to
# produce a small square of 0.64 to 1 times the area of the original
# image, and then shrink it to a square of 32 pixels in both height and
# width
torchvision.transforms.RandomResizedCrop(32, scale=(0.64, 1.0),
ratio=(1.0, 1.0)),
torchvision.transforms.RandomHorizontalFlip(),
torchvision.transforms.ToTensor(),
# Normalize each channel of the image
torchvision.transforms.Normalize([0.4914, 0.4822, 0.4465],
[0.2023, 0.1994, 0.2010])])
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transform_test = gluon.data.vision.transforms.Compose([
gluon.data.vision.transforms.ToTensor(),
gluon.data.vision.transforms.Normalize([0.4914, 0.4822, 0.4465],
[0.2023, 0.1994, 0.2010])])
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transform_test = torchvision.transforms.Compose([
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize([0.4914, 0.4822, 0.4465],
[0.2023, 0.1994, 0.2010])])
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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']]
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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']]
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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_iter = gluon.data.DataLoader(
valid_ds.transform_first(transform_test), batch_size, shuffle=False,
last_batch='discard')
test_iter = gluon.data.DataLoader(
test_ds.transform_first(transform_test), batch_size, shuffle=False,
last_batch='keep')
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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)]
valid_iter = torch.utils.data.DataLoader(valid_ds, batch_size, shuffle=False,
drop_last=True)
test_iter = torch.utils.data.DataLoader(test_ds, batch_size, shuffle=False,
drop_last=False)
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class Residual(nn.HybridBlock):
def __init__(self, num_channels, use_1x1conv=False, strides=1, **kwargs):
super(Residual, self).__init__(**kwargs)
self.conv1 = nn.Conv2D(num_channels, kernel_size=3, padding=1,
strides=strides)
self.conv2 = nn.Conv2D(num_channels, kernel_size=3, padding=1)
if use_1x1conv:
self.conv3 = nn.Conv2D(num_channels, kernel_size=1,
strides=strides)
else:
self.conv3 = None
self.bn1 = nn.BatchNorm()
self.bn2 = nn.BatchNorm()
def hybrid_forward(self, F, X):
Y = F.npx.relu(self.bn1(self.conv1(X)))
Y = self.bn2(self.conv2(Y))
if self.conv3:
X = self.conv3(X)
return F.npx.relu(Y + X)
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def resnet18(num_classes):
net = nn.HybridSequential()
net.add(nn.Conv2D(64, kernel_size=3, strides=1, padding=1),
nn.BatchNorm(), nn.Activation('relu'))
def resnet_block(num_channels, num_residuals, first_block=False):
blk = nn.HybridSequential()
for i in range(num_residuals):
if i == 0 and not first_block:
blk.add(Residual(num_channels, use_1x1conv=True, strides=2))
else:
blk.add(Residual(num_channels))
return blk
net.add(resnet_block(64, 2, first_block=True),
resnet_block(128, 2),
resnet_block(256, 2),
resnet_block(512, 2))
net.add(nn.GlobalAvgPool2D(), nn.Dense(num_classes))
return net
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def get_net(devices):
num_classes = 10
net = resnet18(num_classes)
net.initialize(ctx=devices, init=init.Xavier())
return net
loss = gluon.loss.SoftmaxCrossEntropyLoss()
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def get_net():
num_classes = 10
# PyTorch doesn't have the notion of hybrid model
net = d2l.resnet18(num_classes, 3)
return net
loss = nn.CrossEntropyLoss(reduction="none")
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def train(net, train_iter, valid_iter, num_epochs, lr, wd, devices, lr_period,
lr_decay):
trainer = gluon.Trainer(net.collect_params(), 'sgd',
{'learning_rate': lr, 'momentum': 0.9, 'wd': wd})
num_batches, timer = len(train_iter), d2l.Timer()
animator = d2l.Animator(xlabel='epoch', xlim=[1, num_epochs],
legend=['train loss', 'train acc', 'valid acc'])
for epoch in range(num_epochs):
metric = d2l.Accumulator(3)
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()
l, acc = d2l.train_batch_ch13(
net, features, labels.astype('float32'), loss, trainer,
devices, d2l.split_batch)
metric.add(l, acc, 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[2], metric[1] / metric[2],
None))
if valid_iter is not None:
valid_acc = d2l.evaluate_accuracy_gpus(net, valid_iter,
d2l.split_batch)
animator.add(epoch + 1, (None, None, valid_acc))
if valid_iter is not None:
print(f'loss {metric[0] / metric[2]:.3f}, '
f'train acc {metric[1] / metric[2]:.3f}, '
f'valid acc {valid_acc:.3f}')
else:
print(f'loss {metric[0] / metric[2]:.3f}, '
f'train acc {metric[1] / metric[2]:.3f}')
print(f'{metric[2] * num_epochs / timer.sum():.1f} examples/sec '
f'on {str(devices)}')
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def train(net, train_iter, valid_iter, num_epochs, lr, wd, devices, lr_period,
lr_decay):
trainer = torch.optim.SGD(net.parameters(), lr=lr, 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()
animator = d2l.Animator(xlabel='epoch', xlim=[1, num_epochs],
legend=['train loss', 'train acc', 'valid acc'])
net = nn.DataParallel(net, device_ids=devices).to(devices[0])
for epoch in range(num_epochs):
net.train()
metric = d2l.Accumulator(3)
for i, (features, labels) in enumerate(train_iter):
timer.start()
l, acc = d2l.train_batch_ch13(net, features, labels,
loss, trainer, devices)
metric.add(l, acc, 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[2], metric[1] / metric[2],
None))
if valid_iter is not None:
valid_acc = d2l.evaluate_accuracy_gpu(net, valid_iter)
animator.add(epoch + 1, (None, None, valid_acc))
scheduler.step()
if valid_iter is not None:
print(f'loss {metric[0] / metric[2]:.3f}, '
f'train acc {metric[1] / metric[2]:.3f}, '
f'valid acc {valid_acc:.3f}')
else:
print(f'loss {metric[0] / metric[2]:.3f}, '
f'train acc {metric[1] / metric[2]:.3f}')
print(f'{metric[2] * num_epochs / timer.sum():.1f} examples/sec '
f'on {str(devices)}')
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devices, num_epochs, lr, wd = d2l.try_all_gpus(), 5, 0.1, 5e-4
lr_period, lr_decay, net = 50, 0.1, get_net(devices)
net.hybridize()
train(net, train_iter, valid_iter, num_epochs, lr, wd, devices, lr_period,
lr_decay)
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loss nan, train acc 0.102, valid acc 0.100
131.6 examples/sec on [gpu(0), gpu(1)]
.. figure:: output_kaggle-cifar10_42a34e_132_1.svg
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devices, num_epochs, lr, wd = d2l.try_all_gpus(), 5, 0.1, 5e-4
lr_period, lr_decay, net = 50, 0.1, get_net()
train(net, train_iter, valid_iter, num_epochs, lr, wd, devices, lr_period,
lr_decay)
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loss 2.522, train acc 0.105, valid acc 0.100
116.4 examples/sec on [device(type='cuda', index=0), device(type='cuda', index=1)]
.. figure:: output_kaggle-cifar10_42a34e_135_1.svg
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net, preds = get_net(devices), []
net.hybridize()
train(net, train_valid_iter, None, num_epochs, lr, wd, devices, lr_period,
lr_decay)
for X, _ in test_iter:
y_hat = net(X.as_in_ctx(devices[0]))
preds.extend(y_hat.argmax(axis=1).astype(int).asnumpy())
sorted_ids = list(range(1, len(test_ds) + 1))
sorted_ids.sort(key=lambda x: str(x))
df = pd.DataFrame({'id': sorted_ids, 'label': preds})
df['label'] = df['label'].apply(lambda x: train_valid_ds.synsets[x])
df.to_csv('submission.csv', index=False)
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loss nan, train acc 0.102
138.0 examples/sec on [gpu(0), gpu(1)]
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net, preds = get_net(), []
train(net, train_valid_iter, None, num_epochs, lr, wd, devices, lr_period,
lr_decay)
for X, _ in test_iter:
y_hat = net(X.to(devices[0]))
preds.extend(y_hat.argmax(dim=1).type(torch.int32).cpu().numpy())
sorted_ids = list(range(1, len(test_ds) + 1))
sorted_ids.sort(key=lambda x: str(x))
df = pd.DataFrame({'id': sorted_ids, 'label': preds})
df['label'] = df['label'].apply(lambda x: train_valid_ds.classes[x])
df.to_csv('submission.csv', index=False)
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loss 2.501, train acc 0.097
133.7 examples/sec on [device(type='cuda', index=0), device(type='cuda', index=1)]
.. figure:: output_kaggle-cifar10_42a34e_144_1.svg
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`Discussões `__
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`Discussões `__
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