'dampnet_full_class', 'dampnet_full_sparse', 'protonet_damp',
'maml', 'relationnet', 'dampnet_full', 'dampnet', 'protonet',
'gnnnet', 'gnnnet_maml', 'metaoptnet', 'gnnnet_normalized',
'gnnnet_neg_margin'
]:
n_query = max(
1, int(16 * params.test_n_way / params.train_n_way)
) #if test_n_way is smaller than train_n_way, reduce n_query to keep batch size small
train_few_shot_params = dict(n_way=params.train_n_way,
n_support=params.n_shot)
test_few_shot_params = dict(n_way=params.test_n_way,
n_support=params.n_shot)
if params.dataset == "miniImageNet":
print("loading")
datamgr = miniImageNet_few_shot.SetDataManager(
image_size, n_query=n_query, **train_few_shot_params)
base_loader = datamgr.get_data_loader(aug=params.train_aug)
#datamgr = miniImageNet_few_shot.SimpleDataManager(image_size, batch_size = 64)
#data_loader = datamgr.get_data_loader(aug = False )
print("BYE")
else:
raise ValueError('Unknown dataset')
if params.method == 'protonet':
model = ProtoNet(model_dict[params.model], **train_few_shot_params)
elif params.method == 'protonet_damp':
model = protonet_damp.ProtoNet(model_dict[params.model],
**train_few_shot_params)
elif params.method == 'relationnet':
n_pseudo = 100
##################################################################
# loading dataset
pretrained_dataset = "miniImageNet"
dataset_names = ["EuroSAT", "ISIC"]
novel_loaders = []
if task == 'fsl':
freeze_backbone = True
dataset_names = ["miniImageNet"]
print("Loading mini-ImageNet")
datamgr = miniImageNet_few_shot.SetDataManager(image_size,
n_eposide=iter_num,
n_query=15,
mode="test",
**few_shot_params)
novel_loader = datamgr.get_data_loader(aug=False)
novel_loaders.append(novel_loader)
else:
freeze_backbone = params.freeze_backbone
dataset_names = ["EuroSAT", "ISIC"]
print("Loading EuroSAT")
datamgr = EuroSAT_few_shot.SetDataManager(image_size,
n_eposide=iter_num,
n_query=15,
**few_shot_params)
novel_loader = datamgr.get_data_loader(aug=False)
params.logit_scale)
elif params.method in ['protonet', 'myprotonet']:
n_query = max(
1, int(16 * params.test_n_way / params.train_n_way)
) #if test_n_way is smaller than train_n_way, reduce n_query to keep batch size small
train_few_shot_params = dict(n_way=params.train_n_way,
n_support=params.n_shot)
test_few_shot_params = dict(n_way=params.test_n_way,
n_support=params.n_shot)
if params.dataset == "miniImageNet":
datamgr = miniImageNet_few_shot.SetDataManager(
image_size,
n_query=n_query,
mode="train",
**train_few_shot_params)
base_loader = datamgr.get_data_loader(aug=params.train_aug)
val_datamgr = miniImageNet_few_shot.SetDataManager(
image_size,
n_query=n_query,
mode="val",
**test_few_shot_params)
val_loader = val_datamgr.get_data_loader(aug=False)
else:
raise ValueError('Unknown dataset')
if params.method == 'protonet':
model = ProtoNet(model_dict[params.model], **train_few_shot_params)