shuffle=False, num_workers=int(opt.workers)) #################################################################################### # Build the Model #################################################################################### n_words = dataset_val.vocab_size ques_length = dataset_val.ques_length ans_length = dataset_val.ans_length + 1 his_length = ques_length+dataset_val.ans_length itow = dataset_val.itow img_feat_size = 512 netE = _netE(opt.model, opt.ninp, opt.nhid, opt.nlayers, opt.dropout, img_feat_size) netW = model._netW(n_words, opt.ninp, opt.dropout) netD = model._netD(opt.model, opt.ninp, opt.nhid, opt.nlayers, n_words, opt.dropout) critD = model.nPairLoss(opt.nhid, 2) if opt.model_path != '': # load the pre-trained model. netW.load_state_dict(checkpoint['netW']) netE.load_state_dict(checkpoint['netE']) netD.load_state_dict(checkpoint['netD']) print('Loading model Success!') if opt.cuda: # ship to cuda, if has GPU netW.cuda(), netE.cuda(), netD.cuda() critD.cuda() n_neg = 100 #################################################################################### # Some Functions
shuffle=False, num_workers=int(opt.workers)) #################################################################################### # Build the Model #################################################################################### n_neg = opt.negative_sample vocab_size = dataset.vocab_size ques_length = dataset.ques_length ans_length = dataset.ans_length + 1 his_length = dataset.ans_length + dataset.ques_length itow = dataset.itow img_feat_size = 512 netE = _netE(opt.model, opt.ninp, opt.nhid, opt.nlayers, opt.dropout, img_feat_size) netW = model._netW(vocab_size, opt.ninp, opt.dropout) netD = model._netD(opt.model, opt.ninp, opt.nhid, opt.nlayers, vocab_size, opt.dropout) critD =model.nPairLoss(opt.ninp, opt.margin) if opt.model_path != '': # load the pre-trained model. netW.load_state_dict(checkpoint['netW']) netE.load_state_dict(checkpoint['netE']) netD.load_state_dict(checkpoint['netD']) if opt.cuda: # ship to cuda, if has GPU netW.cuda(), netE.cuda(), netD.cuda(), critD.cuda() #################################################################################### # training model #################################################################################### def train(epoch):