def predict():
parser = argparse.ArgumentParser()
parser.add_argument('--char-dim', default=8, type=int)
parser.add_argument('--char-channel-width', default=5, type=int)
parser.add_argument('--char-channel-size', default=100, type=int)
parser.add_argument('--context-threshold', default=400, type=int)
parser.add_argument('--dev-batch-size', default=100, type=int)
parser.add_argument('--test-batch-size', default=100, type=int)
parser.add_argument('--dev-file', default='dev-v1.1.json')
parser.add_argument('--test-file', default='test1.json')
parser.add_argument('--dropout', default=0.2, type=float)
parser.add_argument('--epoch', default=12, type=int)
parser.add_argument('--exp-decay-rate', default=0.999, type=float)
parser.add_argument('--gpu', default=0, type=int)
parser.add_argument('--hidden-size', default=200, type=int)
parser.add_argument('--learning-rate', default=0.5, type=float)
parser.add_argument('--print-freq', default=250, type=int)
parser.add_argument('--train-batch-size', default=60, type=int)
parser.add_argument('--train-file', default='train-v1.1.json')
parser.add_argument('--word-dim', default=300, type=int)
args = parser.parse_args()
print('loading SQuAD data...')
current_dir = os.getcwd()
current_dir = os.path.join(current_dir, 'BiDAF')
path = os.path.join(current_dir, 'testing_files')
data = SQuAD(args, path)
setattr(args, 'char_vocab_size', len(data.CHAR.vocab))
setattr(args, 'word_vocab_size', len(data.WORD.vocab))
setattr(args, 'dataset_file', f'testing_files/{args.dev_file}')
setattr(args, 'prediction_file', f'output/prediction{time.time()}.out')
setattr(args, 'model_time', strftime('%H:%M:%S', gmtime()))
print('data loading complete!')
device = torch.device(
f"cuda:{args.gpu}" if torch.cuda.is_available() else "cpu")
model = BiDAF(args, data.WORD.vocab.vectors).to(device)
# load trained the parameters to the model
model_path = os.path.join(current_dir, 'saved_models')
model.load_state_dict(
torch.load(os.path.join(model_path, 'BiDAF_tl_new.pt')))
model.eval() # show the model result
answers = dict() # answers to be saved in dictionary format
with torch.set_grad_enabled(False):
for batch in iter(data.test_iter):
# print(batch)
p1, p2 = model(batch)
# batch_loss = criterion(p1, batch.s_idx) + criterion(p2, batch.e_idx)
# loss += batch_loss.item()
batch_size, c_len = p1.size()
ls = nn.LogSoftmax(dim=1)
mask = (torch.ones(c_len, c_len) *
float('-inf')).to(device).tril(-1).unsqueeze(0).expand(
batch_size, -1, -1)
score = (ls(p1).unsqueeze(2) + ls(p2).unsqueeze(1)) + mask
score, s_idx = score.max(dim=1)
score, e_idx = score.max(dim=1)
s_idx = torch.gather(s_idx, 1, e_idx.view(-1, 1)).squeeze()
for i in range(batch_size):
id = batch.id[i]
answer = batch.c_word[0][i][s_idx[i]:e_idx[i] + 1]
answer = ' '.join(
[data.WORD.vocab.itos[idx] for idx in answer])
answers[id] = answer
print(answers)
return answers
parser.add_argument('--context-threshold', default=400, type=int)
parser.add_argument('--dev-batch-size', default=100, type=int)
parser.add_argument('--dev-file', default='dev-v1.1.json')
parser.add_argument('--dropout', default=0.2, type=float)
parser.add_argument('--epoch', default=12, type=int)
parser.add_argument('--exp-decay-rate', default=0.999, type=float)
parser.add_argument('--gpu', default=0, type=int)
parser.add_argument('--hidden-size', default=100, type=int)
parser.add_argument('--learning-rate', default=0.5, type=float)
parser.add_argument('--print-freq', default=250, type=int)
parser.add_argument('--train-batch-size', default=60, type=int)
parser.add_argument('--train-file', default='train-v1.1.json')
parser.add_argument('--word-dim', default=100, type=int)
args = parser.parse_args()
print('loading SQuAD data...')
data = SQuAD(args)
setattr(args, 'char_vocab_size', len(data.CHAR.vocab))
setattr(args, 'word_vocab_size', len(data.WORD.vocab))
setattr(args, 'dataset_file', f'.data/squad/{args.dev_file}')
setattr(args, 'prediction_file', f'prediction{args.gpu}.out')
setattr(args, 'model_time', strftime('%H:%M:%S', gmtime()))
print('data loading complete!')
device = torch.device(f"cuda:{args.gpu}" if torch.cuda.is_available() else "cpu")
model = BiDAF(args, data.WORD.vocab.vectors).to(device)
# load trained the parameters to the model
# model.load_state_dict(torch.load(r'saved_models/BiDAF_08:03:17.pt'))
model.eval() # show the model result
print(model)