base_datamgr = SetDataManager(image_size, n_query = n_query, n_eposide = 100, **train_few_shot_params)
test_few_shot_params = dict(n_way = params.test_n_way, n_support = params.n_shot)
val_datamgr = SetDataManager(image_size, n_query = n_query, n_eposide = 100, **test_few_shot_params)
val_loader = val_datamgr.get_data_loader( val_file, aug = False)
val_loader_nd = val_datamgr.get_data_loader( val_file, aug = False)
model = MAML(params, tf_path=params.tf_dir)
model.cuda()
# resume training
start_epoch = params.start_epoch
stop_epoch = params.stop_epoch
if params.resume != '':
resume_file = get_resume_file('%s/checkpoints/%s'%(params.save_dir, params.resume), params.resume_epoch)
if resume_file is not None:
start_epoch = model.resume(resume_file)
print(' resume the training with at {} epoch (model file {})'.format(start_epoch, params.resume))
else:
raise ValueError('No resume file')
# load pre-trained feature encoder
else:
if params.warmup == 'scratch':
pass
elif params.warmup == 'gg3b0':
raise Exception('Must provide pre-trained feature-encoder file using --warmup option!')
else:
model.model.feature.load_state_dict(load_warmup_state('%s/checkpoints/%s'%(params.save_dir, params.warmup), params.method), strict=False)
# training
print('\n--- start the training ---')
train(base_datamgr, datasets, val_loader, val_loader_nd, model, start_epoch, stop_epoch, params)
# statics
print('\n--- get statics ---')
for i in range(n_exp):
acc_all = np.empty((n_task, 2))
base_data_loader = datamgr.get_data_loader(base_loadfile, aug=False)
test_data_loader = datamgr.get_data_loader(test_loadfile, aug=False)
base_data_generator = iter(base_data_loader)
test_data_generator = iter(test_data_loader)
task_pbar = tqdm(range(n_task))
for j in task_pbar:
base_task = next(base_data_generator)[0]
test_task = next(test_data_generator)[0]
n_sub_query = params.n_sub_query
_ = model.resume(modelfile)
if False:
base_acc, test_acc = test_uni(base_task, test_task, model,
n_iter, n_sub_query, params)
else:
base_acc, test_acc = test(base_task, test_task, model, n_iter,
n_sub_query, params)
acc_all[j] = [base_acc, test_acc]
acc_mean = np.mean(acc_all, axis=0)
acc_std = np.std(acc_all, axis=0)
base_acc, test_acc = acc_mean
base_ci, test_ci = 1.96 * acc_std / np.sqrt(n_task)
print(