In [1]:
import torch
import torch.nn as nn
import torchvision.datasets as dsets
import torchvision.transforms as transforms
from torch.autograd import Variable
print(torch.__version__)
# CIFAR-10 Dataset
# Image Processing
transform = transforms.Compose([
transforms.Scale(40), # pytorch 3.0에선 Resize라는 것이 추가
transforms.RandomHorizontalFlip(),
# 주어진 PIL Image를 임의로 0.5 확률로 가로로 뒤집음
transforms.RandomCrop(32),
transforms.ToTensor()])
train_dataset = dsets.CIFAR10(root='./data/',
train=True,
transform=transform,
download=True)
test_dataset = dsets.CIFAR10(root='./data/',
train=False,
transform=transforms.ToTensor())
# Data Loader (Input Pipeline)
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=100,
shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
batch_size=100,
shuffle=False)
In [2]:
def conv3x3(in_channels, out_channels, stride=1):
return nn.Conv2d(in_channels, out_channels, kernel_size=3,
stride=stride, padding=1, bias=False)
In [6]:
class ResidualBlock(nn.Module):
def __init__(self, in_channels, out_channels, stride=1, downsample=None):
super(ResidualBlock, self).__init__()
self.conv1 = conv3x3(in_channels, out_channels, stride)
self.bn1 = nn.BatchNorm2d(out_channels)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(out_channels, out_channels)
self.bn2 = nn.BatchNorm2d(out_channels)
self.downsample = downsample
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
In [20]:
class ResNet(nn.Module):
def __init__(self, block, layers, num_classes=10):
super(ResNet, self).__init__()
self.in_channels = 16
self.conv = conv3x3(3, 16)
self.bn = nn.BatchNorm2d(16)
self.relu = nn.ReLU(inplace=True)
self.layer1 = self.make_layer(block, 16, layers[0])
self.layer2 = self.make_layer(block, 32, layers[0], 2)
self.layer3 = self.make_layer(block, 64, layers[1], 2)
self.avg_pool = nn.AvgPool2d(8)
self.fc = nn.Linear(64, num_classes)
def make_layer(self, block, out_channels, blocks, stride=1):
downsample = None
if (stride != 1) or (self.in_channels != out_channels):
downsample = nn.Sequential(
conv3x3(self.in_channels, out_channels, stride=stride),
nn.BatchNorm2d(out_channels))
layers = []
layers.append(block(self.in_channels, out_channels, stride, downsample))
self.in_channels = out_channels
for i in range(1, blocks):
layers.append(block(out_channels, out_channels))
return nn.Sequential(*layers)
def forward(self, x):
out = self.conv(x)
out = self.bn(out)
out = self.relu(out)
out = self.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
out = self.avg_pool(out)
out = out.view(out.size(0), -1)
out = self.fc(out)
return out
In [21]:
resnet = ResNet(ResidualBlock, [2, 2, 2, 2])
In [ ]:
%%time
criterion = nn.CrossEntropyLoss()
lr = 0.001
optimizer = torch.optim.Adam(resnet.parameters(), lr=lr)
# Training
for epoch in range(80):
for i, (images, labels) in enumerate(train_loader):
images = Variable(images)
labels = Variable(labels)
# Forward + Backward + Optimize
optimizer.zero_grad()
outputs = resnet(images)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
if (i+1) % 100 == 0:
print ("Epoch [%d/%d], Iter [%d/%d] Loss: %.4f" %(epoch+1, 80, i+1, 500, loss.data[0]))
# Decaying Learning Rate
if (epoch+1) % 20 == 0:
lr /= 3
optimizer = torch.optim.Adam(resnet.parameters(), lr=lr)
In [ ]:
correct = 0
total = 0
for images, labels in test_loader:
images = Variable(images)
outputs = resnet(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum()
print('Accuracy of the model on the test images: %d %%' % (100 * correct / total))