def test(model_file, test_file, device=-1):
context = utils.Saver.load_context(model_file)
if context.seed is not None:
utils.set_random_seed(context.seed, device)
test_dataset = context.loader.load(test_file, train=False, bucketing=True)
kwargs = dict(context)
if context.model_config is not None:
kwargs.update(context.model_config)
model = _build_parser(**dict(kwargs))
chainer.serializers.load_npz(model_file, model)
if device >= 0:
chainer.cuda.get_device_from_id(device).use()
model.to_gpu(device)
pbar = training.listeners.ProgressBar(lambda n: tqdm(total=n))
pbar.init(len(test_dataset))
evaluator = Evaluator(model, context.loader.rel_map, test_file,
logging.getLogger())
utils.chainer_train_off()
for batch in test_dataset.batch(context.batch_size,
colwise=True,
shuffle=False):
xs, ts = batch[:-1], batch[-1]
ys = model.forward(*xs)
evaluator.on_batch_end({'train': False, 'xs': xs, 'ys': ys, 'ts': ts})
pbar.update(len(ts))
evaluator.on_epoch_validate_end({})
