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PENCIL.py
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PENCIL.py
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import argparse
import shutil
import time
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torchvision.transforms as transforms
import models
import numpy as np
from PIL import Image
import os
import os.path
import sys
import resnet
if sys.version_info[0] == 2:
import cPickle as pickle
else:
import pickle
model_names = sorted(name for name in models.__dict__
if name.islower() and not name.startswith("__")
and callable(models.__dict__[name]))
parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
parser.add_argument('--arch', '-a', metavar='ARCH', default='preact_resnet32',
choices=model_names,
help='model architecture: ' +
' | '.join(model_names) +
' (default: resnet18)')
parser.add_argument('-j', '--workers', default=32, type=int, metavar='N',
help='number of data loading workers (default: 4)')
parser.add_argument('--start-epoch', default=0, type=int, metavar='N',
help='manual epoch number (useful on restarts)')
parser.add_argument('-b', '--batch-size', default=128, type=int,
metavar='N', help='mini-batch size (default: 256)')
parser.add_argument('--lr', '--learning-rate', default=0.06, type=float,
metavar='H-P', help='initial learning rate')
parser.add_argument('--lr2', '--learning-rate2', default=0.2, type=float,
metavar='H-P', help='initial learning rate of stage3')
parser.add_argument('--alpha', default=0.4, type=float,
metavar='H-P', help='the coefficient of Compatibility Loss')
parser.add_argument('--beta', default=0.1, type=float,
metavar='H-P', help='the coefficient of Entropy Loss')
parser.add_argument('--lambda1', default=600, type=int,
metavar='H-P', help='the value of lambda')
parser.add_argument('--stage1', default=70, type=int,
metavar='H-P', help='number of epochs utill stage1')
parser.add_argument('--stage2', default=200, type=int,
metavar='H-P', help='number of epochs utill stage2')
parser.add_argument('--epochs', default=320, type=int, metavar='H-P',
help='number of total epochs to run')
parser.add_argument('--datanum', default=45000, type=int,
metavar='H-P', help='number of train dataset samples')
parser.add_argument('--classnum', default=10, type=int,
metavar='H-P', help='number of train dataset classes')
parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
help='momentum')
parser.add_argument('--weight-decay', '--wd', default=1e-4, type=float,
metavar='W', help='weight decay (default: 1e-4)')
parser.add_argument('--print-freq', '-p', default=50, type=int,
metavar='N', help='print frequency (default: 10)')
parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true',
help='evaluate model on validation set')
parser.add_argument('--pretrained', default=False,dest='pretrained', action='store_true',
help='use pre-trained model')
parser.add_argument('--world-size', default=1, type=int,
help='number of distributed processes')
parser.add_argument('--dist-url', default='tcp://224.66.41.62:23456', type=str,
help='url used to set up distributed training')
parser.add_argument('--dist-backend', default='gloo', type=str,
help='distributed backend')
parser.add_argument('--gpu', dest='gpu', default='0', type=str,
help='select gpu')
parser.add_argument('--dir', dest='dir', default='', type=str, metavar='PATH',
help='model dir')
best_prec1 = 0
class CIFAR10(torch.utils.data.Dataset):
base_folder = 'cifar-10-batches-py'
url = "https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz"
filename = "cifar-10-python.tar.gz"
tgz_md5 = 'c58f30108f718f92721af3b95e74349a'
train_list = [
['data_batch_1', 'c99cafc152244af753f735de768cd75f'],
['data_batch_2', 'd4bba439e000b95fd0a9bffe97cbabec'],
['data_batch_3', '54ebc095f3ab1f0389bbae665268c751'],
['data_batch_4', '634d18415352ddfa80567beed471001a'],
['data_batch_5', '482c414d41f54cd18b22e5b47cb7c3cb'],
]
# val_dataset is from data_batch_5
val_list = [
['val_batch', '482c414d41f54cd18b22e5b47cb7c3cb'],
]
test_list = [
['test_batch', '40351d587109b95175f43aff81a1287e'],
]
def __init__(self, root, train=0,
transform=None, target_transform=None):
self.root = os.path.expanduser(root)
self.transform = transform
self.target_transform = target_transform
self.train = train # training set or test set
# now load the picked numpy arrays
if self.train == 0:
self.train_data = []
self.train_labels = []
for fentry in self.train_list:
f = fentry[0]
file = os.path.join(self.root, self.base_folder, f)
fo = open(file, 'rb')
if sys.version_info[0] == 2:
entry = pickle.load(fo)
else:
entry = pickle.load(fo, encoding='latin1')
self.train_data.append(entry['data'])
if 'labels' in entry:
self.train_labels += entry['labels']
else:
self.train_labels += entry['fine_labels']
fo.close()
self.train_data = np.concatenate(self.train_data)
self.train_data = self.train_data.reshape((45000, 3, 32, 32))
self.train_data = self.train_data.transpose((0, 2, 3, 1)) # convert to HWC
elif self.train == 1:
f = self.test_list[0][0]
file = os.path.join(self.root, self.base_folder, f)
fo = open(file, 'rb')
if sys.version_info[0] == 2:
entry = pickle.load(fo)
else:
entry = pickle.load(fo, encoding='latin1')
self.test_data = entry['data']
if 'labels' in entry:
self.test_labels = entry['labels']
else:
self.test_labels = entry['fine_labels']
fo.close()
self.test_data = self.test_data.reshape((10000, 3, 32, 32))
self.test_data = self.test_data.transpose((0, 2, 3, 1)) # convert to HWC
else:
f = self.val_list[0][0]
file = os.path.join(self.root, self.base_folder, f)
fo = open(file, 'rb')
if sys.version_info[0] == 2:
entry = pickle.load(fo)
else:
entry = pickle.load(fo, encoding='latin1')
self.val_data = entry['data']
if 'labels' in entry:
self.val_labels = entry['labels']
else:
self.val_labels = entry['fine_labels']
fo.close()
self.val_data = self.val_data.reshape((5000, 3, 32, 32))
self.val_data = self.val_data.transpose((0, 2, 3, 1)) # convert to HWC
def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
tuple: (image, target) where target is index of the target class.
"""
if self.train == 0:
img, target = self.train_data[index], self.train_labels[index]
elif self.train == 1:
img, target = self.test_data[index], self.test_labels[index]
else:
img, target = self.val_data[index], self.val_labels[index]
# doing this so that it is consistent with all other datasets
# to return a PIL Image
img = Image.fromarray(img)
if self.transform is not None:
img = self.transform(img)
if self.target_transform is not None:
target = self.target_transform(target)
if self.train == 0:
return img, target, index
else:
return img, target
def __len__(self):
if self.train == 0:
return len(self.train_data)
elif self.train == 1:
return len(self.test_data)
else:
return len(self.val_data)
def main():
global args, best_prec1
args = parser.parse_args()
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
y_file = args.dir + "y.npy"
os.makedirs(args.dir)
os.makedirs(args.dir+'record')
model = resnet.__dict__[args.arch]()
model = torch.nn.DataParallel(model).cuda()
# define loss function (criterion) and optimizer
criterion = nn.CrossEntropyLoss().cuda()
optimizer = torch.optim.SGD(model.parameters(), args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
checkpoint_dir = args.dir + "checkpoint.pth.tar"
modelbest_dir = args.dir + "model_best.pth.tar"
# optionally resume from a checkpoint
if os.path.isfile(checkpoint_dir):
print("=> loading checkpoint '{}'".format(checkpoint_dir))
checkpoint = torch.load(checkpoint_dir)
args.start_epoch = checkpoint['epoch']
# args.start_epoch = 0
best_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})"
.format(checkpoint_dir, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(checkpoint_dir))
cudnn.benchmark = True
# Data loading code
transform1 = transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(32,4),
transforms.ToTensor(),
transforms.Normalize(mean = (0.492, 0.482, 0.446), std = (0.247, 0.244, 0.262)),
])
transform2 = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean = (0.492, 0.482, 0.446), std = (0.247, 0.244, 0.262)),
])
trainset = CIFAR10(root='./', train=0, transform=transform1)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=args.batch_size,
shuffle=True,num_workers=args.workers, pin_memory=True)
testset = CIFAR10(root='./', train=1,transform=transform2)
testloader = torch.utils.data.DataLoader(testset, batch_size=args.batch_size,
shuffle=False,num_workers=args.workers, pin_memory=True)
valset = CIFAR10(root='./', train=2, transform=transform2)
valloader = torch.utils.data.DataLoader(valset, batch_size=args.batch_size,
shuffle=False, num_workers=args.workers, pin_memory=True)
if args.evaluate:
validate(testloader, model, criterion)
return
for epoch in range(args.start_epoch, args.epochs):
adjust_learning_rate(optimizer, epoch)
# train for one epoch
# load y_tilde
if os.path.isfile(y_file):
y = np.load(y_file)
else:
y = []
train(trainloader, model, criterion, optimizer, epoch, y)
# evaluate on validation set
prec1 = validate(valloader, model, criterion)
validate(testloader, model, criterion)
# remember best prec@1 and save checkpoint
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
save_checkpoint({
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
'optimizer' : optimizer.state_dict(),
}, is_best,filename=checkpoint_dir,modelbest=modelbest_dir)
def train(train_loader, model, criterion, optimizer, epoch, y):
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
# switch to train mode
model.train()
end = time.time()
# new y is y_tilde after updating
new_y = np.zeros([args.datanum,args.classnum])
for i, (input, target, index) in enumerate(train_loader):
# measure data loading time
data_time.update(time.time() - end)
index = index.numpy()
target1 = target.cuda(async=True)
input_var = torch.autograd.Variable(input)
target_var = torch.autograd.Variable(target1)
# compute output
output = model(input_var)
logsoftmax = nn.LogSoftmax(dim=1).cuda()
softmax = nn.Softmax(dim=1).cuda()
if epoch < args.stage1:
# lc is classification loss
lc = criterion(output, target_var)
# init y_tilde, let softmax(y_tilde) is noisy labels
onehot = torch.zeros(target.size(0), 10).scatter_(1, target.view(-1, 1), 10.0)
onehot = onehot.numpy()
new_y[index, :] = onehot
else:
yy = y
yy = yy[index,:]
yy = torch.FloatTensor(yy)
yy = yy.cuda(async = True)
yy = torch.autograd.Variable(yy,requires_grad = True)
# obtain label distributions (y_hat)
last_y_var = softmax(yy)
lc = torch.mean(softmax(output)*(logsoftmax(output)-torch.log((last_y_var))))
# lo is compatibility loss
lo = criterion(last_y_var, target_var)
# le is entropy loss
le = - torch.mean(torch.mul(softmax(output), logsoftmax(output)))
if epoch < args.stage1:
loss = lc
elif epoch < args.stage2:
loss = lc + args.alpha * lo + args.beta * le
else:
loss = lc
# measure accuracy and record loss
prec1, prec5 = accuracy(output.data, target1, topk=(1, 5))
losses.update(loss.item(), input.size(0))
top1.update(prec1[0], input.size(0))
top5.update(prec5[0], input.size(0))
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
optimizer.step()
if epoch >= args.stage1 and epoch < args.stage2:
lambda1 = args.lambda1
# update y_tilde by back-propagation
yy.data.sub_(lambda1*yy.grad.data)
new_y[index,:] = yy.data.cpu().numpy()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
print('Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
epoch, i, len(train_loader), batch_time=batch_time,
data_time=data_time, loss=losses, top1=top1, top5=top5))
if epoch < args.stage2:
# save y_tilde
y = new_y
y_file = args.dir + "y.npy"
np.save(y_file,y)
y_record = args.dir + "record/y_%03d.npy" % epoch
np.save(y_record,y)
def validate(val_loader, model, criterion):
batch_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
# switch to evaluate mode
model.eval()
end = time.time()
for i, (input, target) in enumerate(val_loader):
target = target.cuda(async=True)
input_var = torch.autograd.Variable(input)
target_var = torch.autograd.Variable(target)
# compute output
output = model(input_var)
loss = criterion(output, target_var)
# measure accuracy and record loss
prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
losses.update(loss.item(), input.size(0))
top1.update(prec1[0], input.size(0))
top5.update(prec5[0], input.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
print('Test: [{0}/{1}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
i, len(val_loader), batch_time=batch_time, loss=losses,
top1=top1, top5=top5))
print(' * Prec@1 {top1.avg:.3f} Prec@5 {top5.avg:.3f}'
.format(top1=top1, top5=top5))
return top1.avg
def save_checkpoint(state, is_best, filename='', modelbest = ''):
torch.save(state, filename)
if is_best:
shutil.copyfile(filename, modelbest)
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def adjust_learning_rate(optimizer, epoch):
"""Sets the learning rate"""
if epoch < args.stage2 :
lr = args.lr
elif epoch < (args.epochs - args.stage2)//3 + args.stage2:
lr = args.lr2
elif epoch < 2 * (args.epochs - args.stage2)//3 + args.stage2:
lr = args.lr2//10
else:
lr = args.lr2//100
for param_group in optimizer.param_groups:
param_group['lr'] = lr
def accuracy(output, target, topk=(1,)):
"""Computes the precision@k for the specified values of k"""
maxk = max(topk)
batch_size = target.size(0)
_ , pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
res.append(correct_k.mul_(100.0 / batch_size))
return res
if __name__ == '__main__':
main()