#model_2_o = copy.deepcopy(model_2)
#model_2_o.load_state_dict(state2_o)
del tmp2
#del tmp2_o
del modelfile2
#del modelfile2_o
ds = False
if params.method in [
"dampnet", "dampnet_full", "dampnet_full_class",
"dampnet_full_sparse"
]:
image_size = 224
datamgr2 = miniImageNet_few_shot.SimpleDataManager(image_size,
batch_size=64)
data_loader2 = datamgr2.get_data_loader(aug=False)
for i, (x, y) in enumerate(data_loader2):
if i % 10 == 0:
print('{:d}/{:d}'.format(i, len(data_loader2)))
x = x.cuda()
x_var = Variable(x)
if params.method != "all":
model = model.cuda()
feats = model(x_var).detach() ##detach compute graph
else:
model_3 = model_3.cuda()
feats = model_3(x_var).detach()
if i == 0:
all_feats = torch.zeros(len(data_loader2), feats.shape[0],
return model
if __name__ == '__main__':
np.random.seed(12)
params = parse_args('train')
image_size = 224
optimization = 'Adam'
if params.method in ['baseline']:
if params.dataset == "miniImageNet":
datamgr = miniImageNet_few_shot.SimpleDataManager(
image_size, batch_size=16, imagejitter=params.imagejitter)
base_loader = datamgr.get_data_loader(aug=params.train_aug,
grayscale=params.grayscale)
elif params.dataset == "CUB":
# base_file = configs.data_dir['CUB'] + 'base.json'
base_datamgr = SimpleDataManager(image_size, batch_size=16)
base_loader = base_datamgr.get_data_loader(base_file,
aug=params.train_aug)
elif params.dataset == "cifar100":
base_datamgr = cifar_few_shot.SimpleDataManager("CIFAR100",
image_size,
batch_size=16)
base_loader = base_datamgr.get_data_loader("base", aug=True)
