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):
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):
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
