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main.py
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main.py
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'''Train CIFAR10 with PyTorch.'''
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import torchvision
import torchvision.transforms as transforms
import os
import argparse
from models import ResNet20
from models import ResNet56
from models import ResNet110
from models import ShiftResNet20
from models import ShiftResNet56
from models import ShiftResNet110
from models import DepthwiseResNet20
from models import DepthwiseResNet56
from models import DepthwiseResNet110
from utils import progress_bar
from torch.autograd import Variable
all_models = {
'resnet20': ResNet20,
'shiftresnet20': ShiftResNet20,
'depthwiseresnet20': DepthwiseResNet20,
'resnet56': ResNet56,
'shiftresnet56': ShiftResNet56,
'depthwiseresnet56': DepthwiseResNet56,
'resnet110': ResNet110,
'shiftresnet110': ShiftResNet110,
'depthwiseresnet110': DepthwiseResNet110
}
parser = argparse.ArgumentParser(description='PyTorch CIFAR10 Training')
parser.add_argument('--lr', default=0.1, type=float, help='learning rate')
parser.add_argument('--resume', '-r', action='store_true', help='resume from checkpoint')
parser.add_argument('--batch_size', '-b', default=128, type=int, help='batch size')
parser.add_argument('--arch', '-a', choices=all_models.keys(), default='shiftresnet110', help='neural network architecture')
parser.add_argument('--expansion', '-e', help='Expansion for shift resnet.', default=1, type=float)
parser.add_argument('--reduction', help='Amount to reduce raw resnet model by', default=1.0, type=float)
parser.add_argument('--reduction-mode', choices=('block', 'net', 'depthwise'), help='"block" reduces inner representation for BasicBlock, "net" reduces for all layers', default='net')
parser.add_argument('--dataset', choices=('cifar10', 'cifar100', 'imagenet'), help='Dataset to train and validate on.', default='cifar10')
parser.add_argument('--datadir', help='Folder containing data', default='./data/')
args = parser.parse_args()
use_cuda = torch.cuda.is_available()
best_acc = 0.0 # best test accuracy
start_epoch = 0 # start from epoch 0 or last checkpoint epoch
# Data
print('==> Preparing data..')
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
if args.dataset == 'cifar10':
trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_train)
testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test)
num_classes=10
elif args.dataset == 'cifar100':
trainset = torchvision.datasets.CIFAR100(root='./data', train=True, download=True, transform=transform_train)
testset = torchvision.datasets.CIFAR100(root='./data', train=False, download=True, transform=transform_test)
num_classes = 100
elif args.dataset == 'imagenet':
raise NotImplementedError()
transform_train = transforms.Compose([
transforms.RandomSizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean = [ 0.485, 0.456, 0.406 ],
std = [ 0.229, 0.224, 0.225 ]),
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean = [ 0.485, 0.456, 0.406 ],
std = [ 0.229, 0.224, 0.225 ]),
])
traindir = os.path.join(args.datadir, 'train')
valdir = os.path.join(args.datadir, 'val')
trainset = torchvision.datasets.ImageFolder(traindir, transform_train)
testset = torchvision.datasets.ImageFolder(valdir, transform_test)
num_classes = 1000
trainloader = torch.utils.data.DataLoader(trainset, batch_size=args.batch_size, shuffle=True, num_workers=2)
testloader = torch.utils.data.DataLoader(testset, batch_size=1000, shuffle=False, num_workers=2)
if 'shift' in args.arch:
suffix = '_%s' % args.expansion
elif args.reduction != 1:
suffix = '_%s_%s' % (args.reduction, args.reduction_mode)
else:
suffix = ''
if args.dataset == 'cifar100':
suffix += '_cifar100'
if args.dataset == 'imagenet':
suffix += '_imagenet'
path = './checkpoint/%s%s.t7' % (args.arch, suffix)
print('Using path: %s' % path)
# Model
if args.resume:
# Load checkpoint.
print('==> Resuming from checkpoint.. %s' % path)
assert os.path.isdir('checkpoint'), 'Error: no checkpoint directory found!'
checkpoint = torch.load(path)
net = checkpoint['net']
best_acc = float(checkpoint['acc'])
start_epoch = checkpoint['epoch']
else:
print('==> Building model..')
cls = all_models[args.arch]
assert 'shift' not in args.arch or args.reduction == 1, \
'Only default resnet and depthwise resnet support reductions'
if args.reduction != 1:
print('==> %s with reduction %.2f' % (args.arch, args.reduction))
net = cls(reduction=args.reduction, reduction_mode=args.reduction_mode, num_classes=num_classes)
else:
net = cls(args.expansion, num_classes=num_classes) if 'shift' in args.arch else cls(num_classes=num_classes)
if use_cuda:
net.cuda()
net = torch.nn.DataParallel(
net, device_ids=range(torch.cuda.device_count()))
cudnn.benchmark = True
criterion = nn.CrossEntropyLoss()
def adjust_learning_rate(epoch, lr):
if epoch <= 81: # 32k iterations
return lr
elif epoch <= 122: # 48k iterations
return lr/10
else:
return lr/100
# Training
def train(epoch):
lr = adjust_learning_rate(epoch, args.lr)
optimizer = optim.SGD(net.parameters(), lr=lr, momentum=0.9, weight_decay=5e-4)
print('\nEpoch: %d' % epoch)
net.train()
train_loss = 0
correct = 0
total = 0
for batch_idx, (inputs, targets) in enumerate(trainloader):
if use_cuda:
inputs, targets = inputs.cuda(), targets.cuda()
optimizer.zero_grad()
inputs, targets = Variable(inputs), Variable(targets)
outputs = net(inputs)
loss = criterion(outputs, targets)
loss.backward()
optimizer.step()
train_loss += loss.item() * targets.size(0)
_, predicted = torch.max(outputs.data, 1)
total += targets.size(0)
correct += predicted.eq(targets.data).cpu().sum()
progress_bar(batch_idx, len(trainloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'
% (train_loss/total, 100.*float(correct)/float(total), correct, total))
def test(epoch):
global best_acc
net.eval()
test_loss = 0
correct = 0
total = 0
for batch_idx, (inputs, targets) in enumerate(testloader):
with torch.no_grad():
if use_cuda:
inputs, targets = inputs.cuda(), targets.cuda()
inputs, targets = Variable(inputs), Variable(targets)
outputs = net(inputs)
loss = criterion(outputs, targets)
test_loss += loss.item() * targets.size(0)
_, predicted = torch.max(outputs.data, 1)
total += targets.size(0)
correct += predicted.eq(targets.data).cpu().sum()
progress_bar(batch_idx, len(testloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'
% (test_loss/total, 100.*float(correct)/float(total), correct, total))
# Save checkpoint.
acc = 100.*float(correct)/float(total)
if acc > best_acc:
print('Saving..')
state = {
'net': net.module if use_cuda else net,
'acc': acc,
'epoch': epoch,
}
if not os.path.isdir('checkpoint'):
os.mkdir('checkpoint')
torch.save(state, path)
print('* Saved checkpoint to %s' % path)
best_acc = acc
for epoch in range(start_epoch, 164):
train(epoch)
test(epoch)