batch_size=batch_size,
shuffle=False,
num_workers=0)
####################################################################################
# Build the Model
####################################################################################
vocab_size = dataset_val.vocab_size
ques_length = dataset_val.ques_length
ans_length = dataset_val.ans_length + 1
his_length = dataset_val.ans_length + dataset_val.ques_length
itow = dataset_val.itow
img_feat_size = 512
print('init Generative model...')
netG = _netG(opt.model, vocab_size, opt.ninp, opt.nhid, opt.nlayers,
opt.dropout, False)
netE_g = _netE(opt.model, opt.ninp, opt.nhid, opt.nlayers, opt.dropout,
img_feat_size)
netW_g = model._netW(vocab_size, opt.ninp, opt.dropout)
sampler = model.gumbel_sampler()
critG = model.G_loss(opt.ninp)
critLM = model.LMCriterion()
if opt.evalall == False: # opt.model_path_D != '' and opt.model_path_G != '':
print('Loading Generative model...')
netW_g.load_state_dict(checkpoint['netW'])
netE_g.load_state_dict(checkpoint['netE'])
netG.load_state_dict(checkpoint['netG'])
else:
print('Loading Generative model...')
netW_g.load_state_dict(checkpoint['netW_g'])
####################################################################################
# Build the Model
####################################################################################
vocab_size = dataset.vocab_size
ques_length = dataset.ques_length
ans_length = dataset.ans_length + 1
his_length = dataset.ques_length + dataset.ans_length
itow = dataset.itow
img_feat_size = opt.conv_feat_size
netE = _netE(opt.model, opt.ninp, opt.nhid, opt.nlayers, opt.dropout, img_feat_size)
netW = model._netW(vocab_size, opt.ninp, opt.dropout)
netG = _netG(opt.model, vocab_size, opt.ninp, opt.nhid, opt.nlayers, opt.dropout)
critG = model.LMCriterion()
sampler = model.gumbel_sampler()
if opt.cuda:
netW.cuda()
netE.cuda()
netG.cuda()
critG.cuda()
sampler.cuda()
if opt.model_path != '':
netW.load_state_dict(checkpoint['netW'])
netE.load_state_dict(checkpoint['netE'])
netG.load_state_dict(checkpoint['netG'])
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)
netG = _netG(opt.model, n_words, opt.ninp, opt.nhid, opt.nlayers, opt.dropout)
critG = model.LMCriterion()
sampler = model.gumbel_sampler()
if opt.cuda:
netW.cpu()
netE.cpu()
netG.cpu()
critG.cpu()
sampler.cpu()
if opt.model_path != '':
netW.load_state_dict(checkpoint['netW_g'])
netE.load_state_dict(checkpoint['netE_g'])
netG.load_state_dict(checkpoint['netG'])
print('Loading model Success!')
dataloader_val = torch.utils.data.DataLoader(dataset_val, batch_size=opt.batchSize,
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)
netG = _netG(opt.model, n_words, opt.ninp, opt.nhid, opt.nlayers, opt.dropout)
critG = model.LMCriterion()
sampler = model.gumbel_sampler()
if opt.cuda:
netW.cuda()
netE.cuda()
netG.cuda()
critG.cuda()
sampler.cuda()
if opt.model_path != '':
netW.load_state_dict(checkpoint['netW_g'])
netE.load_state_dict(checkpoint['netE_g'])
netG.load_state_dict(checkpoint['netG'])
