def check_call(self, gpu, label_key, args, kwargs, model_args,
model_kwargs, metrics_fun, compute_metrics):
init_kwargs = {'label_key': label_key}
if metrics_fun is not None:
init_kwargs['metrics_fun'] = metrics_fun
link = Regressor(chainer.Link(), **init_kwargs)
if gpu:
xp = cuda.cupy
link.to_gpu()
else:
xp = numpy
link.compute_metrics = compute_metrics
y = chainer.Variable(self.y)
link.predictor = mock.MagicMock(return_value=y)
loss = link(*args, **kwargs)
link.predictor.assert_called_with(*model_args, **model_kwargs)
assert hasattr(link, 'y')
assert link.y is not None
assert hasattr(link, 'loss')
xp.testing.assert_allclose(link.loss.data, loss.data)
assert hasattr(link, 'metrics')
if compute_metrics:
assert link.metrics is not None
else:
assert link.metrics is None
def test_report_key(self, metrics_fun, compute_metrics):
repo = chainer.Reporter()
link = Regressor(predictor=DummyPredictor(), metrics_fun=metrics_fun)
link.compute_metrics = compute_metrics
repo.add_observer('target', link)
with repo:
observation = {}
with reporter.report_scope(observation):
link(self.x, self.t)
# print('observation ', observation)
actual_keys = set(observation.keys())
if compute_metrics:
if metrics_fun is None:
assert set(['target/loss']) == actual_keys
elif isinstance(metrics_fun, dict):
assert set(['target/loss', 'target/user_key']) == actual_keys
elif callable(metrics_fun):
assert set(['target/loss', 'target/metrics']) == actual_keys
else:
raise TypeError()
else:
assert set(['target/loss']) == actual_keys