def get_loader(self, subset):
if self.name == "miniimagenet":
dataset = MiniImageNet(subset)
labels = dataset.labels
elif self.name == "omniglot":
dataset = Omniglot(root="data/", download=True,
transform=transforms.ToTensor(),
background=subset == 'train')
labels = list(map(lambda x: x[1], dataset._flat_character_images))
else:
raise ValueError
sampler = CategoriesSamplerMult(
labels,
n_batches=self.n_batches if subset == 'train' else 400,
ways=dict(train=self.train_ways, valid=self.valid_ways)[subset],
n_images=self.shots + self.queries,
n_combinations=2)
return DataLoader(dataset=dataset, batch_sampler=sampler,
num_workers=8, pin_memory=True)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', default='0')
parser.add_argument('--load', default='./save/proto-1/max-acc.pth')
parser.add_argument('--batch', type=int, default=2000)
parser.add_argument('--way', type=int, default=5)
parser.add_argument('--shot', type=int, default=1)
parser.add_argument('--query', type=int, default=30)
parser.add_argument('--folds', type=int, default=2)
args = parser.parse_args()
pprint(vars(args))
set_gpu(args.gpu)
dataset = MiniImageNet('test')
sampler = CategoriesSampler(dataset.label,
args.batch, args.way, args.folds * args.shot + args.query)
loader = DataLoader(dataset, batch_sampler=sampler,
num_workers=8, pin_memory=True)
model = Convnet().cuda()
model.load_state_dict(torch.load(args.load))
model.eval()
ave_acc = Averager()
s_label = torch.arange(args.train_way).repeat(args.shot).view(args.shot * args.train_way)
s_onehot = torch.zeros(s_label.size(0), 20)
s_onehot = s_onehot.scatter_(1, s_label.unsqueeze(dim=1), 1).cuda()
for i, batch in enumerate(loader, 1):
parser.add_argument('--save-epoch', type=int, default=20)
parser.add_argument('--shot', type=int, default=1)
parser.add_argument('--query', type=int, default=15)
parser.add_argument('--train-way', type=int, default=5)
parser.add_argument('--test-way', type=int, default=5)
parser.add_argument('--save-path', default='./save/proto-1')
parser.add_argument('--gpu', default='0')
parser.add_argument('--base_model', default='resnet18')
parser.add_argument('--use_CTM', type=int, default=1)
args = parser.parse_args()
pprint(vars(args))
set_gpu(args.gpu)
ensure_path(args.save_path)
trainset = MiniImageNet('train')
train_sampler = CategoriesSampler(trainset.label, 100, args.train_way,
args.shot + args.query)
train_loader = DataLoader(dataset=trainset,
batch_sampler=train_sampler,
num_workers=8,
pin_memory=True)
valset = MiniImageNet('val')
val_sampler = CategoriesSampler(valset.label, 400, args.test_way,
args.shot + args.query)
val_loader = DataLoader(dataset=valset,
batch_sampler=val_sampler,
num_workers=8,
pin_memory=True)
parser.add_argument('--query', type=int, default=5)
parser.add_argument('--query_val', type=int, default=15)
parser.add_argument('--n_base_class', type=int, default=80)
parser.add_argument('--train-way', type=int, default=20)
parser.add_argument('--test-way', type=int, default=5)
parser.add_argument('--save-path', default='./save/proto-5shot')
parser.add_argument('--gpu', default='1')
args = parser.parse_args()
logname = 'baseline'
logfile = open(osp.join(args.save_path, logname + '.txt'), 'w+')
pprint(vars(args))
set_gpu(args.gpu)
trainset = MiniImageNet('trainvaltest')
train_sampler = CategoriesSampler_train_100way(trainset.label, 100,
args.train_way, args.shot,
args.query,
args.n_base_class)
train_loader = DataLoader(dataset=trainset,
batch_sampler=train_sampler,
num_workers=8,
pin_memory=True)
#valset = MiniImageNet('test')
valset = MiniImageNet('trainvaltest')
val_sampler = CategoriesSampler_val_100way(valset.label, 400,
args.test_way, args.shot,
args.query_val)
val_loader = DataLoader(dataset=valset,
def get_data(dataset, dset_dir, image_size, model, phase, unsupervised, spc,
batch_size, workers):
dataset = dataset.lower()
if dataset == 'omniglot_original':
dset_dir = os.path.join(dset_dir, 'omniglot')
transformations = get_transform(dataset, image_size, model)
if phase == 'train':
train_data = Omniglot(
root=dset_dir,
phase='background',
unsupervised=unsupervised,
spc=spc,
pre_transform=transformations['pre_transform'],
transform=transformations['transform'],
post_transform=transformations['post_transform'])
data_loader = DataLoader(train_data,
batch_size=batch_size,
shuffle=True,
num_workers=int(workers))
else:
test_data = Omniglot(
root=dset_dir,
phase='evaluation',
spc=20,
pre_transform=transformations['test_transform'])
data_loader = DataLoader(test_data,
batch_size=batch_size,
shuffle=False,
num_workers=int(workers))
elif dataset == 'omniglot_aug':
dset_dir = os.path.join(dset_dir, 'omniglot')
transformations = get_transform(dataset, image_size, model)
if phase == 'train':
train_data = Omniglot(
root=dset_dir,
phase=phase,
unsupervised=unsupervised,
spc=spc,
pre_transform=transformations['pre_transform'],
transform=transformations['transform'],
post_transform=transformations['post_transform'])
data_loader = DataLoader(train_data,
batch_size=batch_size,
shuffle=True,
num_workers=int(workers))
else:
test_data = Omniglot(
root=dset_dir,
phase=phase,
spc=20,
pre_transform=transformations['test_transform'])
data_loader = DataLoader(test_data,
batch_size=batch_size,
shuffle=False,
num_workers=int(workers))
elif dataset == 'miniimagenet':
dset_dir = os.path.join(dset_dir, 'miniImagenet')
transformations = get_transform(dataset, image_size, model)
if phase == 'train':
train_data = MiniImageNet(
root=dset_dir,
phase=phase,
unsupervised=unsupervised,
spc=spc,
pre_transform=transformations['pre_transform'],
transform=transformations['transform'],
post_transform=transformations['post_transform'])
data_loader = DataLoader(train_data,
batch_size=batch_size,
shuffle=True,
num_workers=int(workers))
else:
test_data = MiniImageNet(
root=dset_dir,
phase=phase,
spc=600,
pre_transform=transformations['test_transform'])
data_loader = DataLoader(test_data,
batch_size=batch_size,
shuffle=False,
num_workers=int(workers))
else:
raise NotImplementedError
return data_loader
parser.add_argument('--shot', type=int, default=5)
parser.add_argument('--query', type=int, default=15)
parser.add_argument('--train-way', type=int, default=5)
parser.add_argument('--test-way', type=int, default=5)
parser.add_argument('--save-path', default='./save/psn-model')
parser.add_argument(
'--data-path',
default='./datapath') # need to change to your data path
parser.add_argument('--gpu', default='0')
args = parser.parse_args()
pprint(vars(args))
set_gpu(args.gpu)
#ensure_path(args.save_path)
trainset = MiniImageNet('train', args.data_path)
train_sampler = CategoriesSampler(
trainset.label,
100, #100 episodes
args.train_way,
args.shot + args.query)
train_loader = DataLoader(dataset=trainset,
batch_sampler=train_sampler,
num_workers=8,
pin_memory=True)
valset = MiniImageNet('val', args.data_path)
val_sampler = CategoriesSampler(
valset.label,
400, #400 episodes
args.test_way,
noise = torch.distributions.Normal(loc=0, scale=.02)
# trainset = MiniImageNet('train')
# train_sampler = CategoriesSampler(trainset.label, 100,
# args.train_way, 2*args.shot + args.query)
# train_loader = DataLoader(dataset=trainset, batch_sampler=train_sampler,
# num_workers=args.num_workers, pin_memory=True)
ssdata = SSMiniImageNet()
ss_sampler = CategoriesSampler(ssdata.slabel, 100, args.train_way,
2 * args.shot + args.query)
ss_loader = DataLoader(dataset=ssdata,
batch_sampler=ss_sampler,
num_workers=args.num_workers,
pin_memory=True)
valset = MiniImageNet('val')
val_sampler = CategoriesSampler(valset.label, 400, args.test_way,
args.shot + args.query)
val_loader = DataLoader(dataset=valset,
batch_sampler=val_sampler,
num_workers=args.num_workers,
pin_memory=True)
model = Convnet().cuda()
if args.load is not 'na':
print('Loading Model')
model.load_state_dict(torch.load(args.load))
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=20,
parser = argparse.ArgumentParser()
parser.add_argument('--max-epoch', type=int, default=200)
parser.add_argument('--save-epoch', type=int, default=20)
parser.add_argument('--shot', type=int, default=1)
parser.add_argument('--query', type=int, default=15)
parser.add_argument('--train-way', type=int, default=30)
parser.add_argument('--test-way', type=int, default=5)
parser.add_argument('--save-path', default='./save/proto-1')
parser.add_argument('--gpu', default='0')
parser.add_argument('--folds', type=int, default=2)
args = parser.parse_args()
pprint(vars(args))
#set_gpu(args.gpu)
ensure_path(args.save_path)
testset = MiniImageNet('test')
test_sampler = CategoriesSampler(testset.label,
args.batch, args.way, args.folds * args.shot + args.query)
test_loader = DataLoader(testset, batch_sampler=test_sampler,
num_workers=args.num_workers, pin_memory=True)
trainset = MiniImageNet('train')
train_sampler = CategoriesSampler(trainset.label, 100,
args.train_way, args.shot + args.query)
train_loader = DataLoader(dataset=trainset, batch_sampler=train_sampler,
num_workers=args.num_workers, pin_memory=True)
valset = MiniImageNet('val')
val_sampler = CategoriesSampler(valset.label, 400,
args.test_way, args.shot + args.query)
val_loader = DataLoader(dataset=valset, batch_sampler=val_sampler,
parser.add_argument('--max-epoch', type=int, default=200)
parser.add_argument('--save-epoch', type=int, default=20)
parser.add_argument('--shot', type=int, default=1)
parser.add_argument('--query', type=int, default=15)
parser.add_argument('--train-way', type=int, default=30)
parser.add_argument('--test-way', type=int, default=5)
parser.add_argument('--save-path', default='./save/proto-1')
parser.add_argument('--gpu', default='0')
args = parser.parse_args()
pprint(vars(args))
#set_gpu(args.gpu)
ensure_path(args.save_path)
trainset = MiniImageNet(
'/mnt/lustre/dingmingyu/Research/da_zsl/dataset/mini-imagenet/',
dataset='mini-imagenet',
mode='train')
train_sampler = CategoriesSampler(trainset.label, 100, args.train_way,
args.shot + args.query)
train_loader = DataLoader(dataset=trainset,
batch_sampler=train_sampler,
num_workers=8,
pin_memory=True)
valset = MiniImageNet(
'/mnt/lustre/dingmingyu/Research/da_zsl/dataset/mini-imagenet/',
dataset='mini-imagenet',
mode='val_new_domain')
val_sampler = CategoriesSampler(valset.label, 400, args.test_way,
args.shot + args.query)
val_loader = DataLoader(dataset=valset,
parser.add_argument('--save-path', default='./save/proto-1')
parser.add_argument('--gpu', default='0')
parser.add_argument('--load', default='na')
parser.add_argument('--num_workers', type=int, default=8)
parser.add_argument('--start_epoch', type=int, default=1)
parser.add_argument('--noise', type=int, default=.02)
parser.add_argument('--meta-size', type=int, default=5)
args = parser.parse_args()
pprint(vars(args))
set_gpu(args.gpu)
ensure_path(args.save_path)
writer = SummaryWriter()
# noise_sample = torch.distributions.normal(loc=0, scale=.02)
noise = torch.distributions.Normal(loc=0, scale=.02)
supset = MiniImageNet('sup')
ssdata = SSMiniImageNet()
ss_sampler = CategoriesSampler(ssdata.slabel, 100, args.train_way,
2 * args.shot + args.query)
ss_loader = DataLoader(dataset=ssdata,
batch_sampler=ss_sampler,
num_workers=args.num_workers,
pin_memory=True)
# train_sampler = CategoriesSampler(supset.label, 100,
# args.train_way, 2*args.shot + args.query)
# train_loader = DataLoader(dataset=supset, batch_sampler=train_sampler,
# num_workers=args.num_workers, pin_memory=True)
#
# unsup = MiniImageNet('unsup')
# unsup_sampler = CategoriesSampler(unsup.label, 100,
# args.train_way, args.uquery)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', default='0')
parser.add_argument('--load', default='./save/proto-1/max-acc.pth')
parser.add_argument('--batch', type=int, default=2000)
parser.add_argument('--way', type=int, default=5)
parser.add_argument('--shot', type=int, default=1)
parser.add_argument('--query', type=int, default=30)
args = parser.parse_args()
pprint(vars(args))
set_gpu(args.gpu)
dataset = MiniImageNet(
'/mnt/lustre/dingmingyu/Research/da_zsl/dataset/mini-imagenet/',
dataset='mini-imagenet',
mode='test_new_domain_fsl')
sampler = CategoriesSampler(dataset.label, args.batch, args.way,
args.shot + args.query)
loader = DataLoader(dataset,
batch_sampler=sampler,
num_workers=8,
pin_memory=True)
model = Convnet().cuda()
model.load_state_dict(torch.load(args.load))
model.eval()
ave_acc = Averager()
for i, batch in enumerate(loader, 1):
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', default='0')
parser.add_argument('--load', default='./save/psn-model/max-acc.pth')
parser.add_argument('--batch', type=int, default=600)
parser.add_argument('--way', type=int, default=5)
parser.add_argument('--shot', type=int, default=5)
parser.add_argument('--query', type=int, default=15)
parser.add_argument('--data-path', default='/home/csimon/research/data/miniimagenet/split/') # need to change to your data path
args = parser.parse_args()
pprint(vars(args))
set_gpu(args.gpu)
dataset = MiniImageNet('test', args.data_path)
sampler = CategoriesSampler(dataset.label,
args.batch, args.way, args.shot + args.query)
loader = DataLoader(dataset, batch_sampler=sampler,
num_workers=8, pin_memory=True)
model = ConvNet().cuda()
model.load_state_dict(torch.load(args.load))
model.eval()
projection_pro = Projection(shot=2) #subspace dim
ave_acc = Averager()
acc_all=[]
for i, batch in enumerate(loader, 1):
data, _ = [_.cuda() for _ in batch]
