def __init__(self):
parser = get_lm_generator_parser()
self.args = parser.parse_args()
print(self.args)
if self.args.temperature < 1e-3:
parser.error("--temperature has to be greater or equal 1e-3")
seed_torch(self.args)
def __init__(self):
parser = get_lm_evaluator_parser()
self.args = parser.parse_args()
print(self.args)
seed_torch(self.args)
self.vocab = Vocab(self.args.vocab_file)
self.ntokens = self.vocab.get_size()
self.data = LMEvalDataset(os.path.join(self.args.data),
self.args.vocab_file)
self.out = open(self.args.outf, "w")
def __init__(self):
parser = get_parser()
self.args = parser.parse_args()
self.args.gpu = self.args.gpu and torch.cuda.is_available()
seed_torch(self.args)
if self.args.experiment_name is None:
self.args.experiment_name = get_experiment_name(self.args)
self.resuming = os.path.exists(os.path.join(self.args.logs_dir, self.args.experiment_name + '.log'))
self.checkpoint = Checkpoint(self)
self.num_classes = 2
self.meter = Meter(self.num_classes)
self.writer = Logger(self.args)
self.writer.write(self.args)
self.timer = Timer()
self.load_datasets()
self.weights = self.get_imbalance()
def __init__(self):
parser = get_lm_parser()
self.args = parser.parse_args()
seed_torch(self.args)
print("Loading datasets")
self.train_data = LMDataset(os.path.join(self.args.data, 'train.tsv'),
self.args.vocab_file)
print("Train dataset loaded")
self.val_data = LMDataset(os.path.join(self.args.data, 'valid.tsv'),
self.args.vocab_file)
print("Val dataset loaded")
self.test_data = LMDataset(os.path.join(self.args.data, 'test.tsv'),
self.args.vocab_file)
print("Test dataset loaded")
self.args.vocab_size = self.train_data.get_vocab_size()
self.args.gpu = self.args.gpu and torch.cuda.is_available()
print("Created dataloaders")
self.train_loader = batchify(self.train_data.get_tokens(),
self.args.batch_size, self.args)
self.val_loader = batchify(self.val_data.get_tokens(),
self.args.batch_size, self.args)
self.test_loader = batchify(self.test_data.get_tokens(),
self.args.batch_size, self.args)
if self.args.experiment_name is None:
self.args.experiment_name = get_lm_experiment_name(self.args)
self.checkpoint = Checkpoint(self)
self.writer = Logger(self.args)
self.writer.write(self.args)
self.timer = Timer()
def test(args):
vocab_path = args.vocab_file
dataset_path = args.dataset_path
gpu = args.gpu
vocab = Vocab(vocab_path, True)
dataset = AcceptabilityDataset(args, dataset_path, vocab)
seed_torch(args)
if gpu:
model = torch.load(args.model_file)
embedding = torch.load(args.embedding_file)
else:
model = torch.load(args.model_file,
map_location=lambda storage, loc: storage)
embedding = torch.load(args.embedding_file,
map_location=lambda storage, loc: storage)
loader = torch.utils.data.DataLoader(dataset,
batch_size=1,
pin_memory=gpu,
shuffle=False)
meter = Meter(2)
model.eval()
embedding.eval()
outputs = []
for data in loader:
x, y, _ = data
x, y = Variable(x).long(), Variable(y)
if gpu:
x = x.cuda()
y = y.cuda()
x = embedding(x)
output = model(x)
if type(output) == tuple:
output = output[0]
out_float = output.squeeze()
output = (out_float > 0.5).long()
# outputs.append(int(output))
outputs.append(float(out_float))
if not gpu:
output = output.unsqueeze(0)
meter.add(output.data, y.data)
print("Matthews %.5f, Accuracy: %.5f" %
(meter.matthews(), meter.accuracy()))
if args.output_file != None:
out_file = open(args.output_file, "w")
for x in outputs:
out_file.write(str(x) + "\n")
out_file.close()