return args
if __name__ == '__main__':
args = parse_args()
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
torch.backends.cudnn.benchmark = True
batch_size = args.batch_size
eval_dset = dl.validate(input_img_h5=args.input_img_h5,
input_ques_h5=args.input_ques_h5,
input_json=args.input_json,
negative_sample=args.negative_sample,
num_val=args.num_val,
data_split='test')
eval_loader = torch.utils.data.DataLoader(eval_dset,
batch_size=5,
shuffle=False,
num_workers=int(args.workers))
args.vocab_size = eval_dset.vocab_size
args.ques_length = eval_dset.ques_length
args.ans_length = eval_dset.ans_length + 1
args.his_length = eval_dset.ques_length + eval_dset.ans_length
args.seq_length = args.ques_length
constructor = 'build_%s' % args.model
vocab_size = eval_dset.vocab_size
if torch.cuda.is_available() and not opt.cuda:
print(
"WARNING: You have a CUDA device, so you should probably run with --cuda"
)
if opt.model_path != '':
checkpoint = torch.load(opt.model_path)
batch_size = 30
####################################################################################
# Data Loader
####################################################################################
dataset_val = dl.validate(input_img_h5=opt.input_img_h5,
input_ques_h5=opt.input_ques_h5,
input_json=opt.input_json,
negative_sample=opt.negative_sample,
num_val=opt.num_val,
data_split='test')
dataloader_val = torch.utils.data.DataLoader(dataset_val,
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
save_path = os.path.join(opt.outf, opt.decoder + '.' + cur_time)
try:
os.makedirs(save_path)
except OSError:
pass
####################################################################################
# Data Loader
####################################################################################
dataset = dl.train(input_img_h5=opt.input_img_h5, input_ques_h5=opt.input_ques_h5,
input_json=opt.input_json, negative_sample = opt.negative_sample,
num_val = opt.num_val, data_split = 'train')
dataset_val = dl.validate(input_img_h5=opt.input_img_h5, input_ques_h5=opt.input_ques_h5,
input_json=opt.input_json, negative_sample = opt.negative_sample,
num_val = opt.num_val, data_split = 'test')
dataloader = torch.utils.data.DataLoader(dataset, batch_size=opt.batchSize,
shuffle=True, num_workers=int(opt.workers))
dataloader_val = torch.utils.data.DataLoader(dataset_val, batch_size=1,
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
