# path_data = '~/data'
# train_dataset = l2l.vision.datasets.MiniImagenet(
# root=path_data, mode='train')
# valid_dataset = l2l.vision.datasets.MiniImagenet(
# root=path_data, mode='validation')
# test_dataset = l2l.vision.datasets.MiniImagenet(
# root=path_data, mode='test')
# Hello, CURE_TSR_OG
data_transforms = transforms.Compose(
[transforms.Resize([32, 32]),
transforms.ToTensor()]) #, utils.l2normalize, utils.standardization])
lvl0_train_dir = './CURE_TSR_OG/Real_Train/ChallengeFree/'
lvl5_test_dir = './CURE_TSR_OG/Real_Train/LensBlur-5/'
curetsr_lvl0 = utils.CURETSRDataset(lvl0_train_dir, data_transforms)
curetsr_lvl5 = utils.CURETSRDataset(lvl5_test_dir, data_transforms)
# lvl0_train_dir = './CURE_TSR_Yahan_Shortcut/Real_Train/ChallengeFree/'
# lvl5_test_dir = './CURE_TSR_Yahan_Shortcut/Real_Train/Snow-5/'
# curetsr_lvl0 = datasets.ImageFolder(lvl0_train_dir, transform=data_transforms)
# print("first image, label is ", curetsr_lvl0[0])
# curetsr_lvl5 = datasets.ImageFolder(lvl5_test_dir, transform=data_transforms)
meta_curetsr_lvl0 = l2l.data.MetaDataset(curetsr_lvl0)
meta_curetsr_lvl5 = l2l.data.MetaDataset(curetsr_lvl5)
train_dataset = meta_curetsr_lvl0
valid_dataset = meta_curetsr_lvl0
test_dataset = meta_curetsr_lvl5
def main():
global args
args = parser.parse_args()
traindir = os.path.join(args.data, 'RealChallengeFree/train')
testdir = os.path.join(args.data, 'RealChallengeFree/Test')
train_dataset = utils.CURETSRDataset(
traindir,
transforms.Compose([
transforms.Resize([28, 28]),
transforms.ToTensor(), utils.l2normalize, utils.standardization
]))
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
test_dataset = utils.CURETSRDataset(
testdir,
transforms.Compose([
transforms.Resize([28, 28]),
transforms.ToTensor(), utils.l2normalize, utils.standardization
]))
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
model = models.AutoEncoder()
model = torch.nn.DataParallel(model).cuda()
print("=> creating model %s " % model.__class__.__name__)
criterion = nn.MSELoss().cuda()
savedir = 'AutoEncoder'
checkpointdir = os.path.join('./checkpoints', savedir)
os.makedirs(checkpointdir, exist_ok=True)
print('log directory: %s' % os.path.join('./logs', savedir))
print('checkpoints directory: %s' % checkpointdir)
logger = Logger(os.path.join('./logs/', savedir))
if args.evaluate:
print("=> loading checkpoint ")
checkpoint = torch.load(
os.path.join(checkpointdir, 'model_best.pth.tar'))
model.load_state_dict(checkpoint['AE_state_dict'], strict=False)
modelCNN = models.Net()
modelCNN = torch.nn.DataParallel(modelCNN).cuda()
checkpoint2 = torch.load('./checkpoints/CNN_iter/model_best.pth.tar')
modelCNN.load_state_dict(checkpoint2['state_dict'], strict=False)
evaluate(test_loader, model, modelCNN, criterion)
return
optimizer = torch.optim.Adam(model.parameters(),
args.lr,
weight_decay=args.weight_decay)
cudnn.benchmark = True
timestart = time.time()
if args.finetune:
print("=> loading checkpoint ")
checkpoint = torch.load(
os.path.join(checkpointdir, 'model_best.pth.tar'))
model.load_state_dict(checkpoint['AE_state_dict'], strict=False)
optimizer.load_state_dict(checkpoint['optimizer'])
best_loss = 10e10
# train_accs = []
# test_accs = []
loss_epochs = []
for epoch in range(args.start_epoch, args.epochs):
adjust_learning_rate(optimizer, epoch)
print('\n*** Start Training *** \n')
loss_train = train(train_loader, test_loader, model, criterion,
optimizer, epoch)
print(loss_train)
loss_epochs.append(loss_train)
is_best = loss_train < best_loss
print(best_loss)
best_loss = min(loss_train, best_loss)
info = {
'Loss': loss_train
# 'Testing Accuracy': test_prec1
}
# if not debug:
for tag, value in info.items():
logger.scalar_summary(tag, value, epoch + 1)
if is_best:
best_epoch = epoch + 1
save_checkpoint(
{
'epoch': epoch + 1,
'AE_state_dict': model.state_dict(),
'optimizer': optimizer.state_dict()
}, is_best, checkpointdir)
generate_plots(range(args.start_epoch, args.epochs), loss_epochs)
print('Best epoch: ', best_epoch)
print('Total processing time: %.4f' % (time.time() - timestart))
print('Best loss:', best_loss)
def main():
global args
args = parser.parse_args()
debug = 0 # 0: normal mode 1: debug mode
# Data loading code
# args.data: path to the dataset
traindir = os.path.join(args.data, 'RealChallengeFree/train')
testdir = os.path.join(args.data, 'RealChallengeFree/Test')
train_dataset = utils.CURETSRDataset(
traindir,
transforms.Compose([
transforms.Resize([28, 28]),
transforms.ToTensor(), utils.l2normalize, utils.standardization
]))
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True)
test_dataset = utils.CURETSRDataset(
testdir,
transforms.Compose([
transforms.Resize([28, 28]),
transforms.ToTensor(), utils.l2normalize, utils.standardization
]))
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
model = models.Net()
model = torch.nn.DataParallel(model).cuda()
print("=> creating model %s " % model.__class__.__name__)
savedir = 'CNN_iter'
checkpointdir = os.path.join('./checkpoints', savedir)
if not debug:
os.mkdir(checkpointdir)
print('log directory: %s' % os.path.join('./logs', savedir))
print('checkpoints directory: %s' % checkpointdir)
# Set the logger
if not debug:
logger = Logger(os.path.join('./logs/', savedir))
# 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)
# 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'])
optimizer.load_state_dict(checkpoint['optimizer'])
print(
"=> loaded checkpoint '{}' (epoch {}, best_prec1 @ Source {})".
format(args.resume, checkpoint['epoch'], best_prec1))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
if args.evaluate:
evaluate(test_loader, model, criterion)
return
cudnn.benchmark = True
timestart = time.time()
best_prec1 = 0
for epoch in range(args.start_epoch, args.epochs):
adjust_learning_rate(optimizer, epoch)
# train for one epoch
print('\n*** Start Training *** \n')
train(train_loader, model, criterion, optimizer, epoch)
# evaluate on validation set
print('\n*** Start Testing *** \n')
test_loss, test_prec1, _ = evaluate(test_loader, model, criterion)
info = {'Testing loss': test_loss, 'Testing Accuracy': test_prec1}
# remember best prec@1 and save checkpoint
is_best = test_prec1 > best_prec1
best_prec1 = max(test_prec1, best_prec1)
if is_best:
best_epoch = epoch + 1
if not debug:
for tag, value in info.items():
logger.scalar_summary(tag, value, epoch + 1)
save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
'last_prec1': test_prec1,
'optimizer': optimizer.state_dict()
}, is_best, checkpointdir)
print('Best epoch: ', best_epoch)
print('Total processing time: %.4f' % (time.time() - timestart))
def cure_tsr_tasksets(
train_ways,
train_samples,
test_ways,
test_samples,
#root,
**kwargs):
"""
Benchmark definition for CURE TSR.
"""
data_transforms = transforms.Compose(
[transforms.Resize([32, 32]),
transforms.ToTensor()]) #, utils.l2normalize, utils.standardization])
lvl0_train_dir = './CURE_TSR_OG/Real_Train/ChallengeFree/'
lvl5_test_dir = './CURE_TSR_OG/Real_Train/Snow-5/'
curetsr_lvl0 = utils.CURETSRDataset(lvl0_train_dir, data_transforms)
curetsr_lvl5 = utils.CURETSRDataset(lvl5_test_dir, data_transforms)
# lvl0_train_dir = './CURE_TSR_Yahan_Shortcut/Real_Train/ChallengeFree/'
# lvl5_test_dir = './CURE_TSR_Yahan_Shortcut/Real_Train/Snow-5/'
# curetsr_lvl0 = datasets.ImageFolder(lvl0_train_dir, transform=data_transforms)
# print("first image, label is ", curetsr_lvl0[0])
# curetsr_lvl5 = datasets.ImageFolder(lvl5_test_dir, transform=data_transforms)
meta_curetsr_lvl0 = l2l.data.MetaDataset(curetsr_lvl0)
meta_curetsr_lvl5 = l2l.data.MetaDataset(curetsr_lvl5)
train_dataset = meta_curetsr_lvl0
validation_dataset = meta_curetsr_lvl0
test_dataset = meta_curetsr_lvl5
classes = list(range(14)) # 14 classes of stop signs
random.shuffle(classes)
train_transforms = [
l2l.data.transforms.FusedNWaysKShots(
train_dataset,
n=train_ways,
k=train_samples,
filter_labels=classes[:8]), # first few classes for training
l2l.data.transforms.LoadData(train_dataset),
l2l.data.transforms.RemapLabels(train_dataset),
l2l.data.transforms.ConsecutiveLabels(train_dataset),
]
validation_transforms = [
l2l.data.transforms.FusedNWaysKShots(
validation_dataset,
n=test_ways,
k=test_samples,
filter_labels=classes[8:14]), # last few classes for val / test
l2l.data.transforms.LoadData(validation_dataset),
l2l.data.transforms.RemapLabels(validation_dataset),
l2l.data.transforms.ConsecutiveLabels(validation_dataset),
]
test_transforms = [
l2l.data.transforms.FusedNWaysKShots(
test_dataset,
n=test_ways,
k=test_samples,
filter_labels=classes[8:14]), # last few classes for val / test
l2l.data.transforms.LoadData(test_dataset),
l2l.data.transforms.RemapLabels(test_dataset),
l2l.data.transforms.ConsecutiveLabels(test_dataset),
]
_datasets = (train_dataset, validation_dataset, test_dataset)
_transforms = (train_transforms, validation_transforms, test_transforms)
return _datasets, _transforms