train.py

import torch
import torch.nn as nn

from learning_to_learn import Learning_to_learn_global_training
from LSTM_Optimizee_Model import LSTM_Optimizee_Model
from hand_optimizer.handcraft_optimizer import Hand_Optimizee_Model
from DataSet.KYLBERG import KYLBERG
import config

opt = config.parse_opt()
opt.batchsize_para=opt.category_num*opt.sample_num
print(opt)

LSTM_Optimizee = LSTM_Optimizee_Model(opt, opt.DIM, opt.DIM, opt.DIM, batchsize_data=opt.batchsize_data, batchsize_para=opt.batchsize_para).cuda()
if opt.loadpretrain == True:
	checkpoint = torch.load(opt.prepath)
	LSTM_Optimizee.load_state_dict(checkpoint,strict=False)
	checkpoint2 = torch.load(opt.prepath2)
	LSTM_Optimizee.load_state_dict(checkpoint,strict=False)

Hand_Optimizee = Hand_Optimizee_Model(opt.hand_optimizer_lr).cuda()

print('pretrain finsist')

train_mnist = KYLBERG(opt.datapath, train=True)


train_loader = torch.utils.data.DataLoader(
        train_mnist, batch_size=opt.batchsize_data,shuffle=True, drop_last=False, num_workers=0)


global_loss_list ,flag = Learning_to_learn_global_training(opt,Hand_Optimizee,LSTM_Optimizee,train_loader)