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,
)
def test_default_transformer_optim_loop_logging_smoke(self):
asset = self.load_asset(
path.join("optim", "default_transformer_optim_loop"))
image_loader = asset.input.image_loader
optim_logger = optim.OptimLogger()
log_fn = optim.default_transformer_optim_log_fn(optim_logger,
len(image_loader),
log_freq=1)
with self.assertLogs(optim_logger.logger, "INFO"):
optim.default_transformer_optim_loop(
image_loader,
asset.input.transformer,
asset.input.criterion,
asset.input.criterion_update_fn,
logger=optim_logger,
log_fn=log_fn,
)
def test_default_transformer_optim_loop_logging_smoke(caplog,
optim_asset_loader):
asset = optim_asset_loader("default_transformer_optim_loop")
image_loader = asset.input.image_loader
criterion = asset.input.criterion
make_torch_ge_1_6_compatible(image_loader, criterion)
optim_logger = optim.OptimLogger()
log_fn = optim.default_transformer_optim_log_fn(optim_logger,
len(image_loader),
log_freq=1)
with asserts.assert_logs(caplog, logger=optim_logger):
optim.default_transformer_optim_loop(
image_loader,
asset.input.transformer,
criterion,
asset.input.criterion_update_fn,
logger=optim_logger,
log_fn=log_fn,
)
