def main():
global args, best_prec1
args = parser.parse_args()
args.distributed = args.world_size > 1
if args.distributed:
dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
world_size=args.world_size)
# create model
if args.pretrained:
print("=> using pre-trained model '{}'".format(args.arch))
model = models.__dict__[args.arch](pretrained=True)
else:
print("=> creating model '{}'".format(args.arch))
model = models.__dict__[args.arch](low_dim=args.low_dim)
if not args.distributed:
if args.arch.startswith('alexnet') or args.arch.startswith('vgg'):
model.features = torch.nn.DataParallel(model.features)
model.cuda()
else:
model = torch.nn.DataParallel(model).cuda()
else:
model.cuda()
model = torch.nn.parallel.DistributedDataParallel(model)
# Data loading code
traindir = os.path.join(args.data, 'train')
valdir = os.path.join(args.data, 'val')
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_dataset = datasets.ImageFolderInstance(
traindir,
transforms.Compose([
transforms.RandomResizedCrop(224, scale=(0.2,1.)),
transforms.RandomGrayscale(p=0.2),
transforms.ColorJitter(0.4, 0.4, 0.4, 0.4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
]))
if args.distributed:
train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
else:
train_sampler = None
train_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=args.batch_size, shuffle=(train_sampler is None),
num_workers=args.workers, pin_memory=True, sampler=train_sampler)
val_loader = torch.utils.data.DataLoader(
datasets.ImageFolderInstance(valdir, transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize,
])),
batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
# define lemniscate and loss function (criterion)
ndata = train_dataset.__len__()
if args.nce_k > 0:
lemniscate = NCEAverage(args.low_dim, ndata, args.nce_k, args.nce_t, args.nce_m).cuda()
criterion = NCECriterion(ndata).cuda()
else:
lemniscate = LinearAverage(args.low_dim, ndata, args.nce_t, args.nce_m).cuda()
criterion = nn.CrossEntropyLoss().cuda()
optimizer = torch.optim.SGD(model.parameters(), args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'])
lemniscate = checkpoint['lemniscate']
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})"
.format(args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
cudnn.benchmark = True
if args.evaluate:
kNN(0, model, lemniscate, train_loader, val_loader, 200, args.nce_t)
return
for epoch in range(args.start_epoch, args.epochs):
if args.distributed:
train_sampler.set_epoch(epoch)
adjust_learning_rate(optimizer, epoch)
# train for one epoch
train(train_loader, model, lemniscate, criterion, optimizer, epoch)
# evaluate on validation set
prec1 = NN(epoch, model, lemniscate, train_loader, val_loader)
# 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(),
'lemniscate': lemniscate,
'best_prec1': best_prec1,
'optimizer' : optimizer.state_dict(),
}, is_best)
# evaluate KNN after last epoch
kNN(0, model, lemniscate, train_loader, val_loader, 200, args.nce_t)
and callable(models.__dict__[name]))
model = models.__dict__['resnet18'](low_dim=128)
checkpoint = torch.load('lemniscate_resnet18.pth.tar')
model = torch.nn.DataParallel(model).cuda()
model.load_state_dict(checkpoint['state_dict'])
model.eval()
#load query data set from root/datas/val1
traindir = os.path.join('datas', 'val1')
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_dataset = datasets.ImageFolderInstance(
traindir,
transforms.Compose([
transforms.Resize((224,224)),
transforms.ToTensor(),
normalize,
]))
train_loader = torch.utils.data.DataLoader(
train_dataset)
# compute the cosin similarity with perfect_500k dataset for each feature vector in query data set
for i, (input, _, index) in enumerate(train_loader):
#test_index=the image ID of query image
test_index=train_dataset.imgs[i][0].split('/')[3].split('.')[0]
print test_index
# measure data loading time
index = index.cuda(async=True)
input_var = torch.autograd.Variable(input)
def main(args):
# _size = 32
_size = 224
# fix random seeds
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
np.random.seed(args.seed)
best_prec1 = 0
# load model
print('==> Building model..')
# net = models.__dict__['ResNet18'](low_dim=args.low_dim)
net = models.__dict__['alexnet'](out=args.low_dim)
net = torch.nn.DataParallel(net, device_ids=range(torch.cuda.device_count()))
checkpoint = torch.load(args.model)
net.load_state_dict(checkpoint['net'])
# model = load_model(args.model)
model = net
model.cuda()
cudnn.benchmark = True
# freeze the features layers
for param in model.parameters(): #features.
param.requires_grad = False
# define loss function (criterion) and optimizer
criterion = nn.CrossEntropyLoss().cuda()
# data loading code
# traindir = os.path.join(args.data, 'train')
# valdir = os.path.join(args.data, 'val')
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
transform_train = transforms.Compose([
transforms.Resize(size=_size),
transforms.RandomResizedCrop(size=_size, scale=(0.2, 1.)),
transforms.ColorJitter(0.4, 0.4, 0.4, 0.4),
transforms.RandomGrayscale(p=0.2),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
])
if args.tencrops:
transform_test = transforms.Compose([
transforms.Resize(size=_size),
transforms.Lambda(lambda crops: torch.stack([normalize(transforms.ToTensor()(crop)) for crop in crops])),
])
else:
transform_test = transforms.Compose([
transforms.Resize(size=_size),
transforms.CenterCrop(_size),
transforms.ToTensor(),
normalize,
])
trainset = cifar_datasets.ImageFolderInstance('/data2/zyf/ImageNet/ILSVRC2012-100/train',
transform=transform_train, two_crop=True)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=args.batch_size, shuffle=True, num_workers=8)
testset = cifar_datasets.ImageFolderInstance('/data2/zyf/ImageNet/ILSVRC2012-100/val',
transform=transform_test)
testloader = torch.utils.data.DataLoader(testset, batch_size=100, shuffle=False, num_workers=8)
# logistic regression
reglog = AlexnetRegLog(args.conv, len(trainset.classes)).cuda()
optimizer = torch.optim.SGD(
filter(lambda x: x.requires_grad, reglog.parameters()),
args.lr,
momentum=args.momentum,
weight_decay=10 ** args.weight_decay
)
for epoch in range(args.epochs):
end = time.time()
# train for one epoch
train(trainloader, model, reglog, criterion, optimizer, epoch, forward_alexnet)
# evaluate on validation set
prec1, prec5, loss = validate(testloader, model, reglog, criterion, forward_alexnet)
writer.add_scalar('acc', prec1, epoch)
# loss_log.log(loss)
# prec1_log.log(prec1)
# prec5_log.log(prec5)
# remember best prec@1 and save checkpoint
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
if is_best:
filename = 'pcf200_model_best.pth.tar'
print('best acc: ' + str(prec1))
else:
filename = 'pcf200_checkpoint.pth.tar'
torch.save({
'epoch': epoch + 1,
'arch': 'net',
'state_dict': model.state_dict(),
'prec5': prec5,
'best_prec1': best_prec1,
'optimizer': optimizer.state_dict(),
}, os.path.join(args.exp, filename))
def main():
global args, best_prec1
args = parser.parse_args()
# Initialize distributed processing
args.distributed = args.world_size > 1
if args.distributed:
dist.init_process_group(backend=args.dist_backend,
init_method=args.dist_url,
world_size=args.world_size)
# create model
if args.pretrained:
print("=> using pre-trained model '{}'".format(args.arch))
model = models.__dict__[args.arch](pretrained=True,
low_dim=args.low_dim)
else:
print("=> creating model '{}'".format(args.arch))
model = models.__dict__[args.arch](low_dim=args.low_dim)
if not args.distributed:
if args.arch.startswith('alexnet') or args.arch.startswith('vgg'):
model.features = torch.nn.DataParallel(model.features)
model.cuda()
else:
model = torch.nn.DataParallel(model).cuda()
else:
model.cuda()
model = torch.nn.parallel.DistributedDataParallel(model)
# Data loading code
traindir = os.path.join(args.data, 'train')
valdir = os.path.join(args.data, 'val')
normalize = transforms.Normalize(
mean=[0.485, 0.456, 0.406], # ImageNet stats
std=[0.229, 0.224, 0.225])
# normalize = transforms.Normalize(mean=[0.234, 0.191, 0.159], # xView stats
# std=[0.173, 0.143, 0.127])
print("Creating datasets")
cj = args.color_jit
train_dataset = datasets.ImageFolderInstance(
traindir,
transforms.Compose([
transforms.Resize((224, 224)),
# transforms.Grayscale(3),
# transforms.ColorJitter(cj, cj, cj, cj), #transforms.ColorJitter(0.4, 0.4, 0.4, 0.4),
transforms.RandomHorizontalFlip(),
transforms.RandomVerticalFlip(),
transforms.RandomRotation(45),
transforms.ToTensor(),
normalize,
]))
if args.distributed:
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset)
elif args.balanced_sampling:
print("Using balanced sampling")
# Here's where we compute the weights for WeightedRandomSampler
class_counts = {v: 0 for v in train_dataset.class_to_idx.values()}
for path, ndx in train_dataset.samples:
class_counts[ndx] += 1
total = float(np.sum([v for v in class_counts.values()]))
class_probs = [
class_counts[ndx] / total for ndx in range(len(class_counts))
]
# make a list of class probabilities corresponding to the entries in train_dataset.samples
reciprocal_weights = [
class_probs[idx]
for i, (_, idx) in enumerate(train_dataset.samples)
]
# weights are the reciprocal of the above
weights = (1 / torch.Tensor(reciprocal_weights))
train_sampler = torch.utils.data.sampler.WeightedRandomSampler(
weights, len(train_dataset), replacement=True)
else:
#if args.red_data is < 1, then the training is done with a subsamle of the total data. Otherwise it's the total data.
data_size = len(train_dataset)
sub_index = np.random.randint(0, data_size,
round(args.red_data * data_size))
sub_index.sort()
train_sampler = torch.utils.data.sampler.SubsetRandomSampler(sub_index)
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=(train_sampler is None),
num_workers=args.workers,
pin_memory=True,
sampler=train_sampler)
print("Training on", len(train_dataset.imgs),
"images. Training batch size:", args.batch_size)
if len(train_dataset.imgs) % args.batch_size != 0:
print(
"Warning: batch size doesn't divide the # of training images so ",
len(train_dataset.imgs) % args.batch_size,
"images will be skipped per epoch.")
print("If you don't want to skip images, use a batch size in:",
get_factors(len(train_dataset.imgs)))
val_dataset = datasets.ImageFolderInstance(
valdir,
transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
normalize,
]))
val_bs = [
factor for factor in get_factors(len(val_dataset)) if factor < 500
][-1]
val_bs = 100
val_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=val_bs,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
print("Validating on", len(val_dataset), "images. Validation batch size:",
val_bs)
# define lemniscate and loss function (criterion)
ndata = train_dataset.__len__()
if args.nce_k > 0:
lemniscate = NCEAverage(args.low_dim, ndata, args.nce_k, args.nce_t,
args.nce_m)
criterion = NCECriterion(ndata).cuda()
else:
lemniscate = LinearAverage(args.low_dim, ndata, args.nce_t,
args.nce_m).cuda()
criterion = nn.CrossEntropyLoss().cuda()
optimizer = torch.optim.SGD(model.parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
model.load_state_dict(checkpoint['state_dict'])
optimizer = FP16_Optimizer(optimizer,
static_loss_scale=args.static_loss,
verbose=False)
optimizer.load_state_dict(checkpoint['optimizer'])
args.start_epoch = checkpoint['epoch']
# best_prec1 = checkpoint['best_prec1']
lemniscate = checkpoint['lemniscate']
if args.select_load:
pred = checkpoint['prediction']
print("=> loaded checkpoint '{}' (epoch {}, best_prec1 )".format(
args.resume,
checkpoint['epoch'])) #, checkpoint['best_prec1']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
# optionally fine-tune a model trained on a different dataset
elif args.fine_tune:
print("=> loading checkpoint '{}'".format(args.fine_tune))
checkpoint = torch.load(args.fine_tune)
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
optimizer = FP16_Optimizer(optimizer,
static_loss_scale=args.static_loss,
verbose=False)
print("=> loaded checkpoint '{}' (epoch {})".format(
args.fine_tune, checkpoint['epoch']))
else:
optimizer = FP16_Optimizer(optimizer,
static_loss_scale=args.static_loss,
verbose=False)
# Optionally recompute memory. If fine-tuning, then we must recompute memory
if args.recompute_memory or args.fine_tune:
# Aaron - Experiments show that iterating over torch.utils.data.DataLoader will skip the last few
# unless the batch size evenly divides size of the data set. This shouldn't be the case
# according to documentation, there's even a flag for drop_last, but it's not working
# compute a good batch size for re-computing memory
memory_bs = [
factor for factor in get_factors(len(train_loader.dataset))
if factor < 500
][-1]
print("Recomputing memory using", train_dataset.root,
"with a batch size of", memory_bs)
transform_bak = train_loader.dataset.transform
train_loader.dataset.transform = val_loader.dataset.transform
temploader = torch.utils.data.DataLoader(
train_loader.dataset,
batch_size=memory_bs,
shuffle=False,
num_workers=train_loader.num_workers,
pin_memory=True)
lemniscate.memory = torch.zeros(len(train_loader.dataset),
args.low_dim).cuda()
model.eval()
with torch.no_grad():
for batch_idx, (inputs, targets,
indexes) in enumerate(tqdm.tqdm(temploader)):
batchSize = inputs.size(0)
features = model(inputs)
lemniscate.memory[batch_idx * batchSize:batch_idx * batchSize +
batchSize, :] = features.data
train_loader.dataset.transform = transform_bak
model.train()
cudnn.benchmark = True
if args.evaluate:
kNN(model, lemniscate, train_loader, val_loader, args.K, args.nce_t)
return
begin_train_time = datetime.datetime.now()
# my_knn(model, lemniscate, train_loader, val_loader, args.K, args.nce_t, train_dataset, val_dataset)
if args.tsne:
labels = idx_to_name(train_dataset, args.graph_labels)
tsne(lemniscate, args.tsne, labels)
if args.pca:
labels = idx_to_name(train_dataset, args.graph_labels)
pca(lemniscate, labels)
if args.view_knn:
my_knn(model, lemniscate, train_loader, val_loader, args.K, args.nce_t,
train_dataset, val_dataset)
if args.kmeans:
kmeans, yi = kmean(lemniscate, args.kmeans, 500, args.K, train_dataset)
D, I = kmeans.index.search(lemniscate.memory.data.cpu().numpy(), 1)
cent_group = {}
data_cent = {}
for n, i in enumerate(I):
if i[0] not in cent_group.keys():
cent_group[i[0]] = []
cent_group[i[0]].append(n)
data_cent[n] = i[0]
train_sampler = torch.utils.data.sampler.SubsetRandomSampler(
cent_group[0])
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.batch_size,
shuffle=(train_sampler is None),
num_workers=args.workers,
pin_memory=True,
sampler=train_sampler)
# lemniscate = NCEAverage(args.low_dim, ndata, args.nce_k, args.nce_t, args.nce_m)
# criterion = NCECriterion(ndata).cuda()
# lemniscate = NCEAverage(args.low_dim, ndata, args.nce_k, args.nce_t, args.nce_m)
if args.tsne_grid:
tsne_grid(val_loader, model)
if args.h_cluster:
for size in range(2, 3):
# size = 20
kmeans, topk = kmean(lemniscate, size, 500, 10, train_dataset)
respred = torch.tensor([]).cuda()
lab, idx = [[] for i in range(2)]
num = 0
'''
for p,index,label in pred:
respred = torch.cat((respred,p))
if num == 0:
lab = label
else:
lab += label
idx.append(index)
num+=1
'''
h_cluster(lemniscate, train_dataset, kmeans, topk,
size) #, respred, lab, idx)
# axis_explore(lemniscate, train_dataset)
# kmeans_opt(lemniscate, 5)
if args.select:
if not args.select_load:
pred = []
if args.select_size:
size = int(args.select_size * ndata)
else:
size = round(ndata / 100.0)
sub_sample = np.random.randint(0, ndata, size=size)
train_sampler = torch.utils.data.sampler.SubsetRandomSampler(
sub_sample)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.batch_size,
shuffle=(train_sampler is None),
num_workers=args.workers,
pin_memory=True,
sampler=train_sampler)
pred = div_train(train_loader, model, 0, pred)
pred_features = []
pred_labels = []
pred_idx = []
for inst in pred:
feat, idx, lab = list(inst)
pred_features.append(feat)
pred_labels.append(lab)
pred_idx.append(idx.data.cpu())
if args.select_save:
save_checkpoint(
{
'epoch': args.start_epoch,
'arch': args.arch,
'state_dict': model.state_dict(),
'prediction': pred,
'lemniscate': lemniscate,
'optimizer': optimizer.state_dict(),
}, 'select.pth.tar')
min_idx = selection(pred_features, pred_idx, train_dataset,
args.select_num, args.select_thresh)
train_sampler = torch.utils.data.sampler.SubsetRandomSampler(min_idx)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.batch_size,
shuffle=(train_sampler is None),
num_workers=args.workers,
pin_memory=True,
sampler=train_sampler)
lemniscate = NCEAverage(args.low_dim, ndata, 20, args.nce_t,
args.nce_m)
optimizer = torch.optim.SGD(model.parameters(),
0.1,
momentum=0.1,
weight_decay=0.00001)
optimizer = FP16_Optimizer(optimizer,
static_loss_scale=args.static_loss,
verbose=False)
for epoch in range(50):
if args.distributed:
train_sampler.set_epoch(epoch)
adjust_learning_rate(optimizer, epoch)
if epoch % 1 == 0:
save_checkpoint({
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'lemniscate': lemniscate,
'optimizer': optimizer.state_dict(),
})
train(train_loader, model, lemniscate, criterion, optimizer, epoch)
train_sampler = torch.utils.data.sampler.SubsetRandomSampler(sub_index)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.batch_size,
shuffle=(train_sampler is None),
num_workers=args.workers,
pin_memory=True,
sampler=train_sampler)
lemniscate = NCEAverage(args.low_dim, ndata, args.nce_k, args.nce_t,
args.nce_m)
optimizer = torch.optim.SGD(model.parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
optimizer = FP16_Optimizer(optimizer,
static_loss_scale=args.static_loss,
verbose=False)
if args.kmeans_opt:
kmeans_opt(lemniscate, 500)
for epoch in range(args.start_epoch, args.epochs):
if args.distributed:
train_sampler.set_epoch(epoch)
adjust_learning_rate(optimizer, epoch)
if epoch % 1 == 0:
# evaluate on validation set
#prec1 = NN(epoch, model, lemniscate, train_loader, train_loader) # was evaluating on train
# prec1 = kNN(model, lemniscate, train_loader, val_loader, args.K, args.nce_t)
# prec1 really should be renamed to prec5 as kNN now returns top5 score, but
# it won't be backward's compatible as earlier models were saved with "best_prec1"
# 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(),
'lemniscate': lemniscate,
# 'best_prec1': best_prec1,
'optimizer': optimizer.state_dict(),
}) # , is_best)
# train for one epoch
train(train_loader, model, lemniscate, criterion, optimizer, epoch)
# kmeans,cent = kmeans()
# group_train(train_loader, model, lemniscate, criterion, optimizer, epoch, kmeans, cent)
# print elapsed time
end_train_time = datetime.datetime.now()
d = end_train_time - begin_train_time
print(
"Trained for %d epochs. Elapsed time: %s days, %.2dh: %.2dm: %.2ds" %
(len(range(args.start_epoch, args.epochs)), d.days, d.seconds // 3600,
(d.seconds // 60) % 60, d.seconds % 60))
def main(args):
# Data
print('==> Preparing data..')
_resize = 256
_size = 224
transform_train = transforms.Compose([
transforms.Resize(size=_resize),
transforms.RandomResizedCrop(size=_size, scale=(0.2, 1.)),
transforms.ColorJitter(0.4, 0.4, 0.4, 0.4),
transforms.RandomGrayscale(p=0.2),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
])
transform_test = transforms.Compose([
transforms.Resize(size=_resize),
transforms.CenterCrop(_size), ###
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
])
trainset = datasets.ImageFolderInstance('/data2/zyf/ImageNet/ILSVRC2012-100/train',
transform=transform_train, two_crop=True)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=args.batch_size, shuffle=True, num_workers=16)
testset = datasets.ImageFolderInstance('/data2/zyf/ImageNet/ILSVRC2012-100/val',
transform=transform_test)
testloader = torch.utils.data.DataLoader(testset, batch_size=100, shuffle=False, num_workers=16)
classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
ndata = trainset.__len__()
print('trainset length: ' + str(ndata))
print('==> Building model..')
# net = models.__dict__['ResNet18'](low_dim=args.low_dim)
# net = models.__dict__['resnet18'](low_dim=args.low_dim)
net = models.__dict__['alexnet'](out=args.low_dim)
# net2 = models.__dict__['appendnet'](low_dim=args.low_dim)
device = 'cuda' if torch.cuda.is_available() else 'cpu'
if device == 'cuda':
net = torch.nn.DataParallel(net, device_ids=range(torch.cuda.device_count()))
# net2 = torch.nn.DataParallel(net2, device_ids=range(torch.cuda.device_count()))
cudnn.benchmark = True
# define loss function: inner product loss within each mini-batch
# criterion = BatchCriterion(args.batch_m, args.batch_t, args.batch_size, ndata)
criterion = ICRcriterion()
# define loss function: inner product loss within each mini-batch
uel_criterion = BatchCriterion(args.batch_m, args.batch_t, args.batch_size, ndata)
net.to(device)
# net2.to(device)
criterion.to(device)
uel_criterion.to(device)
best_acc = 0 # best test accuracy
start_epoch = 0 # start from epoch 0 or last checkpoint epoch
cluster_ratio = args.cluster_ratio
if args.test_only or len(args.resume) > 0:
cluster_ratio = args.cluster_ratio
# Load checkpoint.
model_path = 'checkpoint/' + args.resume
print('==> Resuming from checkpoint..')
assert os.path.isdir(args.model_dir), 'Error: no checkpoint directory found!'
checkpoint = torch.load(model_path)
net.load_state_dict(checkpoint['net'])
best_acc = checkpoint['acc']
start_epoch = checkpoint['epoch']
# define leminiscate
if args.test_only and len(args.resume) > 0:
trainFeatures, feature_index = compute_feature(trainloader, net, len(trainset), args)
lemniscate = LinearAverage(torch.tensor(trainFeatures), args.low_dim, ndata, args.nce_t, args.nce_m)
else:
lemniscate = LinearAverage(torch.tensor([]), args.low_dim, ndata, args.nce_t, args.nce_m)
lemniscate.to(device)
# define optimizer
optimizer = torch.optim.SGD(net.parameters(), lr=args.lr, momentum=0.9, weight_decay=5e-4)
# optimizer2 = torch.optim.SGD(net2.parameters(), lr=args.lr, momentum=0.9, weight_decay=5e-4)
# test acc
if args.test_only:
acc = kNN(0, net, trainloader, testloader, 200, args.batch_t, ndata, low_dim=args.low_dim)
exit(0)
if len(args.resume) > 0:
best_acc = best_acc
start_epoch = start_epoch + 1
else:
best_acc = 0 # best test accuracy
start_epoch = 0 # start from epoch 0 or last checkpoint epoch
icr2 = ICRDiscovery(ndata)
# init_cluster_num = 20000
for round in range(5):
for epoch in range(start_epoch, 200):
#### get Features
# trainFeatures are trainloader features and shuffle=True, so feature_index is match data
trainFeatures, feature_index = compute_feature(trainloader, net, len(trainset), args)
if round == 0:
y = -1 * math.log10(ndata-5) / 200 * epoch + math.log10(ndata-5)
cluster_num = int(math.pow(10, y))
if cluster_num <= args.nmb_cluster:
cluster_num = args.nmb_cluster
print('cluster number: ' + str(cluster_num))
###clustering algorithm to use
# faiss cluster
deepcluster = clustering.__dict__[args.clustering](int(cluster_num))
#### Features to clustering
clustering_loss = deepcluster.cluster(trainFeatures, feature_index, verbose=args.verbose)
L = np.array(deepcluster.images_lists)
image_dict = deepcluster.images_dict
print('create ICR ...')
# icr = ICRDiscovery(ndata)
# if args.test_only and len(args.resume) > 0:
# icr = cluster_assign(icr, L, trainFeatures, feature_index, trainset,
# cluster_ratio + epoch*((1-cluster_ratio)/250))
icrtime = time.time()
# icr = cluster_assign(epoch, L, trainFeatures, feature_index, 1, 1)
if epoch < args.warm_epoch:
icr = cluster_assign(epoch, L, trainFeatures, feature_index, args.cluster_ratio, 1)
else:
icr = PreScore(epoch, L, image_dict, trainFeatures, feature_index, trainset,
args.high_ratio, args.cluster_ratio, args.alpha, args.beta)
print('calculate ICR time is: {}'.format(time.time() - icrtime))
writer.add_scalar('icr_time', (time.time() - icrtime), epoch + round * 200)
else:
cluster_num = args.nmb_cluster
print('cluster number: ' + str(cluster_num))
###clustering algorithm to use
# faiss cluster
deepcluster = clustering.__dict__[args.clustering](int(cluster_num))
#### Features to clustering
clustering_loss = deepcluster.cluster(trainFeatures, feature_index, verbose=args.verbose)
L = np.array(deepcluster.images_lists)
image_dict = deepcluster.images_dict
print('create ICR ...')
# icr = ICRDiscovery(ndata)
# if args.test_only and len(args.resume) > 0:
# icr = cluster_assign(icr, L, trainFeatures, feature_index, trainset,
# cluster_ratio + epoch*((1-cluster_ratio)/250))
icrtime = time.time()
# icr = cluster_assign(epoch, L, trainFeatures, feature_index, 1, 1)
icr = PreScore(epoch, L, image_dict, trainFeatures, feature_index, trainset,
args.high_ratio, args.cluster_ratio, args.alpha, args.beta)
print('calculate ICR time is: {}'.format(time.time() - icrtime))
writer.add_scalar('icr_time', (time.time() - icrtime), epoch + round * 200)
# else:
# icr = cluster_assign(icr, L, trainFeatures, feature_index, trainset, 0.2 + epoch*0.004)
# print(icr.neighbours)
icr2 = train(epoch, net, optimizer, lemniscate, criterion, uel_criterion, trainloader,
icr, icr2, args.stage_update, args.lr, device, round)
print('----------Evaluation---------')
start = time.time()
acc = kNN(0, net, trainloader, testloader, 200, args.batch_t, ndata, low_dim=args.low_dim)
print("Evaluation Time: '{}'s".format(time.time() - start))
writer.add_scalar('nn_acc', acc, epoch + round * 200)
if acc > best_acc:
print('Saving..')
state = {
'net': net.state_dict(),
'acc': acc,
'epoch': epoch,
}
if not os.path.isdir(args.model_dir):
os.mkdir(args.model_dir)
torch.save(state, './checkpoint/ckpt_best_round_{}.t7'.format(round))
if epoch < 200:
torch.save(state, './checkpoint/ckpt_200_best_round_{}.t7'.format(round))
if epoch < 300:
torch.save(state, './checkpoint/ckpt_300_best_round_{}.t7'.format(round))
best_acc = acc
state = {
'net': net.state_dict(),
'acc': acc,
'epoch': epoch,
}
if epoch < 200:
torch.save(state, './checkpoint/ckpt_200_last_round_{}.t7'.format(round))
else:
torch.save(state, './checkpoint/ckpt_last_round_{}.t7'.format(round))
if epoch < 300:
torch.save(state, './checkpoint/ckpt_300_last_round_{}.t7'.format(round))
print('[Round]: {} [Epoch]: {} \t accuracy: {}% \t (best acc: {}%)'.format(round, epoch, acc, best_acc))
def main():
global args, best_prec1
args = parser.parse_args()
print(args)
# create model
if args.pretrained:
print("=> using pre-trained model '{}'".format(args.arch))
model = models.__dict__[args.arch](pretrained=True)
else:
print("=> creating model '{}'".format(args.arch))
model = models.__dict__[args.arch](low_dim=args.low_dim)
if args.arch.startswith('alexnet') or args.arch.startswith('vgg'):
model.features = torch.nn.DataParallel(model.features)
model.cuda()
else:
model = torch.nn.DataParallel(model).cuda()
# Data loading code
traindir = os.path.join(args.data, 'train')
valdir = os.path.join(args.data, 'val')
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_dataset = datasets.ImageFolderInstance(
traindir,
transforms.Compose([
transforms.RandomResizedCrop(224, scale=(0.2, 1.)),
transforms.RandomGrayscale(p=0.2),
transforms.ColorJitter(0.4, 0.4, 0.4, 0.4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
]))
train_labels = torch.tensor(train_dataset.targets).long().cuda()
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True,
sampler=None)
val_loader = torch.utils.data.DataLoader(datasets.ImageFolderInstance(
valdir,
transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize,
])),
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
# define lemniscate and loss function (criterion)
ndata = train_dataset.__len__()
lemniscate = LinearAverage(args.low_dim, ndata, args.nce_t,
args.nce_m).cuda()
rlb = ReliableSearch(ndata, args.low_dim, args.threshold_1,
args.threshold_2, args.batch_size).cuda()
criterion = ReliableCrossEntropyLoss().cuda()
optimizer = torch.optim.SGD(model.parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = 0
best_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'])
lemniscate = checkpoint['lemniscate']
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
cudnn.benchmark = True
if args.evaluate:
kNN(0, model, lemniscate, train_loader, val_loader, 200, args.nce_t)
return
for rnd in range(args.start_round, args.rounds):
if rnd > 0:
memory = recompute_memory(model, lemniscate, train_loader,
val_loader, args.batch_size,
args.workers)
num_reliable_1, consistency_1, num_reliable_2, consistency_2 = rlb.update(
memory, train_labels)
print(
'Round [%02d/%02d]\tReliable1: %.12f\tReliable2: %.12f\tConsistency1: %.12f\tConsistency2: %.12f'
% (rnd, args.rounds, num_reliable_1, num_reliable_2,
consistency_1, consistency_2))
for epoch in range(args.start_epoch, args.epochs):
adjust_learning_rate(optimizer, epoch)
# train for one epoch
train(train_loader, model, lemniscate, rlb, criterion, optimizer,
epoch)
# evaluate on validation set
prec1 = NN(epoch, model, lemniscate, train_loader, val_loader)
# 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(),
'lemniscate': lemniscate,
'best_prec1': best_prec1,
'optimizer': optimizer.state_dict(),
#}, is_best, filename='ckpts/%02d-%04d-checkpoint.pth.tar'%(rnd+1, epoch + 1))
},
is_best)
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'lemniscate': lemniscate,
'best_prec1': best_prec1,
'optimizer': optimizer.state_dict(),
},
is_best=False,
filename='ckpts/%02d-checkpoint.pth.tar' % (rnd + 1))
# evaluate KNN after last epoch
top1, top5 = kNN(0, model, lemniscate, train_loader, val_loader, 200,
args.nce_t)
print('Round [%02d/%02d]\tTop1: %.2f\tTop5: %.2f' %
(rnd + 1, args.rounds, top1, top5))
