def test_weight(self):
self.summary.add(1., 0.5)
self.summary.add(2., numpy.array(0.4))
self.summary.add(3., torch.from_numpy(numpy.array(0.3)))
mean = self.summary.compute_mean().array
val = (1 * 0.5 + 2 * 0.4 + 3 * 0.3) / (0.5 + 0.4 + 0.3)
testing.assert_allclose(mean, val)
def test_weight(self):
self.summary.add({'a': (1., 0.5)})
self.summary.add({'a': (2., numpy.array(0.4))})
self.summary.add({'a': (3., torch.from_numpy(numpy.array(0.3)))})
mean = self.summary.compute_mean()
val = (1 * 0.5 + 2 * 0.4 + 3 * 0.3) / (0.5 + 0.4 + 0.3)
testing.assert_allclose(mean['a'], val)
with self.assertRaises(ValueError):
self.summary.add({'a': (4., numpy.array([0.5]))})
with self.assertRaises(ValueError):
self.summary.add({'a': (4., torch.from_numpy(numpy.array([0.5])))})
def _run_trainer(self, extension, expect, optimizer=None):
if optimizer is None:
optimizer = self.optimizer
extension.initialize(self.trainer)
actual = []
for _ in range(self.warmup_iter + 2):
self.trainer.updater.update()
actual.append(optimizer.param_groups[0]['x'])
if self.trigger(self.trainer):
extension(self.trainer)
testing.assert_allclose(actual[0], expect[0])
testing.assert_allclose(actual[self.warmup_iter - 1], expect[1])
testing.assert_allclose(actual[self.warmup_iter], expect[2])
testing.assert_allclose(actual[self.warmup_iter + 1], expect[3])
def test_basic(self, backend_config):
self.summary.add(backend_config.get_tensor(numpy.array(1, 'f')))
self.summary.add(backend_config.get_tensor(numpy.array(-2, 'f')))
mean = self.summary.compute_mean()
testing.assert_allclose(mean, numpy.array(-0.5, 'f'))
mean, std = self.summary.make_statistics()
testing.assert_allclose(mean, numpy.array(-0.5, 'f'))
testing.assert_allclose(std, numpy.array(1.5, 'f'))
def test_float(self):
self.summary.add(1.)
self.summary.add(2.)
self.summary.add(3.)
mean = self.summary.compute_mean()
testing.assert_allclose(mean, 2.)
mean, std = self.summary.make_statistics()
testing.assert_allclose(mean, 2.)
testing.assert_allclose(std, numpy.sqrt(2. / 3.))
def test_serialize_backward_compat(self):
with tempfile.NamedTemporaryFile(delete=False) as f:
# old version does not save anything
torch.save(dict(dummy=0), f.name)
with testing.assert_warns(UserWarning):
self.summary.load_state_dict(torch.load(f.name))
self.summary.add(2.)
self.summary.add(3.)
mean = self.summary.compute_mean()
testing.assert_allclose(mean, 2.5)
mean, std = self.summary.make_statistics()
testing.assert_allclose(mean, 2.5)
testing.assert_allclose(std, 0.5)
def check(self, summary, data):
mean = summary.compute_mean()
self.assertEqual(set(mean.keys()), set(data.keys()))
for name in data.keys():
m = sum(data[name]) / float(len(data[name]))
testing.assert_allclose(mean[name], m)
stats = summary.make_statistics()
self.assertEqual(
set(stats.keys()),
set(data.keys()).union(name + '.std' for name in data.keys()))
for name in data.keys():
m = sum(data[name]) / float(len(data[name]))
s = numpy.sqrt(
sum(x * x for x in data[name]) / float(len(data[name]))
- m * m)
testing.assert_allclose(stats[name], m)
testing.assert_allclose(stats[name + '.std'], s)
def check_serialize(self, value1, value2, value3):
self.summary.add(value1)
self.summary.add(value2)
summary = pytorch_trainer.reporter.Summary()
testing.save_and_load_pth(self.summary, summary)
summary.add(value3)
expected_mean = (value1 + value2 + value3).to(dtype=torch.float) / 3.
expected_std = ((value1 ** 2 + value2 ** 2 + value3 ** 2)
.to(dtype=torch.float) / 3. - expected_mean ** 2).sqrt()
mean = summary.compute_mean()
testing.assert_allclose(mean, expected_mean)
mean, std = summary.make_statistics()
testing.assert_allclose(mean, expected_mean)
testing.assert_allclose(std, expected_std)
