def test_default_image_optim_log_fn_loss_dict_smoke(self):
class MockOptimLogger:
def __init__(self):
self.msg = None
@contextlib.contextmanager
def environment(self, header):
yield
def message(self, msg):
self.msg = msg
loss_dict = pystiche.LossDict(
(("a", torch.tensor(0.0)), ("b.c", torch.tensor(1.0))))
log_freq = 1
max_depth = 1
optim_logger = MockOptimLogger()
log_fn = optim.default_image_optim_log_fn(optim_logger,
log_freq=log_freq,
max_depth=max_depth)
step = log_freq
log_fn(step, loss_dict)
actual = optim_logger.msg
desired = loss_dict.format(max_depth=max_depth)
self.assertEqual(actual, desired)
def test_default_image_optim_log_fn_other():
optim_logger = optim.OptimLogger()
log_freq = 1
log_fn = optim.default_image_optim_log_fn(optim_logger, log_freq=log_freq)
with pytest.raises(TypeError):
step = log_freq
loss = None
log_fn(step, loss)
def test_default_image_optim_loop_logging_smoke(self):
asset = self.load_asset(path.join("optim", "default_image_optim_loop"))
num_steps = 1
optim_logger = optim.OptimLogger()
log_fn = optim.default_image_optim_log_fn(optim_logger, log_freq=1)
with self.assertLogs(optim_logger.logger, "INFO"):
optim.default_image_optim_loop(
asset.input.image,
asset.input.criterion,
num_steps=num_steps,
log_fn=log_fn,
)
def test_default_image_optim_loop_logging_smoke(caplog, optim_asset_loader):
asset = optim_asset_loader("default_image_optim_loop")
num_steps = 1
optim_logger = optim.OptimLogger()
log_fn = optim.default_image_optim_log_fn(optim_logger, log_freq=1)
with asserts.assert_logs(caplog, logger=optim_logger):
optim.default_image_optim_loop(
asset.input.image,
asset.input.criterion,
num_steps=num_steps,
log_fn=log_fn,
)
def test_default_image_pyramid_optim_loop_logging_smoke(
caplog, optim_asset_loader):
asset = optim_asset_loader("default_image_pyramid_optim_loop")
optim_logger = optim.OptimLogger()
log_freq = max(level.num_steps
for level in asset.input.pyramid._levels) + 1
log_fn = optim.default_image_optim_log_fn(optim_logger, log_freq=log_freq)
with asserts.assert_logs(caplog, logger=optim_logger):
optim.default_image_pyramid_optim_loop(
asset.input.image,
asset.input.criterion,
asset.input.pyramid,
logger=optim_logger,
log_fn=log_fn,
)
def test_default_image_pyramid_optim_loop_logging_smoke(self):
asset = self.load_asset(
path.join("optim", "default_image_pyramid_optim_loop"))
optim_logger = optim.OptimLogger()
log_freq = max(
[level.num_steps for level in asset.input.pyramid._levels]) + 1
log_fn = optim.default_image_optim_log_fn(optim_logger,
log_freq=log_freq)
with self.assertLogs(optim_logger.logger, "INFO"):
optim.default_image_pyramid_optim_loop(
asset.input.image,
asset.input.criterion,
asset.input.pyramid,
logger=optim_logger,
log_fn=log_fn,
)
