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main_grouped_caq.py
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/
main_grouped_caq.py
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import argparse
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
import numpy as np
import math
from dataset_grouped import Dictionary, VQAFeatureDataset_withmask
import caq_model
from train_caq import train
import utils
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--epochs', type=int, default=30)
parser.add_argument('--num_hid', type=int, default=1024)
parser.add_argument('--model', type=str, default='caq_newatt')
parser.add_argument('--output', type=str, default='saved_models/exp0')
parser.add_argument('--batch_size', type=int, default=64)
parser.add_argument('--seed', type=int, default=1111, help='random seed')
args = parser.parse_args()
return args
if __name__ == '__main__':
args = parse_args()
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
torch.backends.cudnn.benchmark = True
dictionary = Dictionary.load_from_file('data/dictionary.pkl')
train_dset = VQAFeatureDataset_withmask('train', dictionary)
eval_dset = VQAFeatureDataset_withmask('val', dictionary)
batch_size = args.batch_size
constructor = 'build_%s' % args.model
model = getattr(caq_model, constructor)(train_dset, args.num_hid).cuda()
model.w_emb.init_embedding('data/glove6b_init_300d.npy')
model = nn.DataParallel(model).cuda()
#seventyfive = list(range(0, int(math.ceil(len(train_dset) * 0.75))))
#trainset_1 = torch.utils.data.Subset(train_dset, seventyfive)
train_loader = DataLoader(train_dset, batch_size, shuffle=True, num_workers=1)
eval_loader = DataLoader(eval_dset, batch_size, shuffle=True, num_workers=1)
train(model, train_loader, eval_loader, args.epochs, args.output)