Пример #1
0
else:
    # create new folder.
    t = datetime.datetime.now()
    cur_time = '%s-%s-%s' %(t.day, t.month, t.hour)
    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
####################################################################################
Пример #2
0
else:
    # create new folder.
    t = datetime.datetime.now()
    cur_time = '%s-%s-%s' %(t.day, t.month, t.hour)
    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
####################################################################################
Пример #3
0

if __name__ == '__main__':
    args = parse_args()
    os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpuid)
    args.seed = random.randint(1, 10000)
    torch.manual_seed(args.seed)
    torch.cuda.manual_seed(args.seed)
    torch.backends.cudnn.benchmark = True

    batch_size = args.batch_size

    train_dataset = dl.train(input_img_h5=args.input_img_h5,
                             input_imgid=args.input_imgid,
                             input_ques_h5=args.input_ques_h5,
                             input_json=args.input_json,
                             negative_sample=args.negative_sample,
                             num_val=args.num_val,
                             data_split='train')

    eval_dateset = dl.validate(input_img_h5=args.input_img_h5,
                               input_imgid=args.input_imgid,
                               input_ques_h5=args.input_ques_h5,
                               input_json=args.input_json,
                               negative_sample=args.negative_sample,
                               num_val=args.num_val,
                               data_split='val')

    train_loader = torch.utils.data.DataLoader(train_dataset,
                                               batch_size=args.batch_size,
                                               shuffle=True,