base_datamgr = miniImageNet_few_shot.SetDataManager(
image_size, n_query=n_query, **train_few_shot_params)
base_loader = base_datamgr.get_data_loader(aug=params.train_aug)
# use unlabeled data from these novel domains for adversarial domain adaptation
# TODO: since the data is unlabeled, we need to modify data manager / data loader
if params.dataset == "ChestX":
target_datamgr = Chest_few_shot.SetDataManager(
image_size, n_query=n_query, **train_few_shot_params)
elif params.dataset == "EuroSAT":
target_datamgr = EuroSAT_few_shot.SetDataManager(
image_size, n_query=n_query, **train_few_shot_params)
elif params.dataset == "ISIC2018":
target_datamgr = ISIC_few_shot.SetDataManager(
image_size, n_query=n_query, **train_few_shot_params)
elif params.dataset == "CropDiseases":
target_datamgr = CropDisease_few_show.SetDataManager(
image_size, n_query=n_query, **train_few_shot_params)
else:
raise ValueError('Unknown dataset')
target_loader = target_datamgr.get_data_loader(novel_file,
aug=params.train_aug)
if params.adversarial or params.adaptFinetune:
# TODO: check argv
target_datamgr = SetDataManager(image_size,
n_query=n_query,
iter_num = 600
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
few_shot_params = dict(n_way=params.test_n_way, n_support=params.n_shot)
models_to_use = params.models_to_use
finetune_each_model = params.fine_tune_all_models
##################################################################
dataset_names = ["ISIC", "EuroSAT", "CropDisease", "Chest"]
novel_loaders = []
datamgr = ISIC_few_shot.SetDataManager(image_size,
n_eposide=iter_num,
n_query=15,
**few_shot_params)
novel_loader = datamgr.get_data_loader(aug=False)
novel_loaders.append(novel_loader)
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)
novel_loaders.append(novel_loader)
datamgr = CropDisease_few_shot.SetDataManager(image_size,
n_eposide=iter_num,
n_query=15,
**few_shot_params)
n_query=n_query,
mode="train",
**train_few_shot_params)
base_loader = datamgr.get_data_loader(aug=params.train_aug)
#the above line waste a lot of time
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)
#=========== ISIC or EuroSAT ========
few_shot_params = dict(n_way=params.test_n_way,
n_support=params.n_shot)
if params.method == 'mytpnadaptisic':
print("init mytpnadaptisic")
datamgr = ISIC_few_shot.SetDataManager(image_size,
n_eposide=600,
n_query=n_query,
**few_shot_params)
elif params.method == 'mytpnadapteurosat':
print("init mytpnadapteurosat")
datamgr = EuroSAT_few_shot.SetDataManager(image_size,
n_eposide=600,
n_query=n_query,
**few_shot_params)
else:
raise ValueError('Unknown method')
novel_loader = datamgr.get_data_loader(aug=False)
#=====================================
#======= load pretrained model and ckpt =======
model = MyTPN_Adapt(model_dict[params.model], **train_few_shot_params)