def run_synthetic(main, tmp_root, extra_flags=None, synth=True, max_train=1):
"""Performs a minimal run of a model.
This function is intended to test for syntax errors throughout a model. A
very limited run is performed using synthetic data.
Args:
main: The primary function used to exercise a code path. Generally this
function is "<MODULE>.main(argv)".
tmp_root: Root path for the temp directory created by the test class.
extra_flags: Additional flags passed by the caller of this function.
synth: Use synthetic data.
max_train: Maximum number of allowed training steps.
"""
extra_flags = [] if extra_flags is None else extra_flags
model_dir = tempfile.mkdtemp(dir=tmp_root)
args = [sys.argv[0], "--model_dir", model_dir, "--train_epochs", "1",
"--epochs_between_evals", "1"] + extra_flags
if synth:
args.append("--use_synthetic_data")
if max_train is not None:
args.extend(["--max_train_steps", str(max_train)])
try:
flags_core.parse_flags(argv=args)
main(flags.FLAGS)
finally:
if os.path.exists(model_dir):
shutil.rmtree(model_dir)
def run_end_to_end(main: Callable[[Any], None],
extra_flags: Optional[Iterable[str]] = None,
model_dir: Optional[str] = None):
"""Runs the classifier trainer end-to-end."""
extra_flags = [] if extra_flags is None else extra_flags
args = [sys.argv[0], '--model_dir', model_dir] + extra_flags
flags_core.parse_flags(argv=args)
main(flags.FLAGS)
def test_benchmark_setting(self):
defaults = dict(
hooks=["LoggingMetricHook"],
benchmark_log_dir="/tmp/12345",
gcp_project="project_abc",
)
flags_core.set_defaults(**defaults)
flags_core.parse_flags()
for key, value in defaults.items():
assert flags.FLAGS.get_flag_value(name=key, default=None) == value
def test_get_nondefault_flags_as_str(self):
defaults = dict(clean=True,
data_dir="abc",
hooks=["LoggingTensorHook"],
stop_threshold=1.5,
use_synthetic_data=False)
flags_core.set_defaults(**defaults)
flags_core.parse_flags()
expected_flags = ""
self.assertEqual(flags_core.get_nondefault_flags_as_str(),
expected_flags)
flags.FLAGS.clean = False
expected_flags += "--noclean"
self.assertEqual(flags_core.get_nondefault_flags_as_str(),
expected_flags)
flags.FLAGS.data_dir = "xyz"
expected_flags += " --data_dir=xyz"
self.assertEqual(flags_core.get_nondefault_flags_as_str(),
expected_flags)
flags.FLAGS.hooks = ["aaa", "bbb", "ccc"]
expected_flags += " --hooks=aaa,bbb,ccc"
self.assertEqual(flags_core.get_nondefault_flags_as_str(),
expected_flags)
flags.FLAGS.stop_threshold = 3.
expected_flags += " --stop_threshold=3.0"
self.assertEqual(flags_core.get_nondefault_flags_as_str(),
expected_flags)
flags.FLAGS.use_synthetic_data = True
expected_flags += " --use_synthetic_data"
self.assertEqual(flags_core.get_nondefault_flags_as_str(),
expected_flags)
# Assert that explicit setting a flag to its default value does not cause it
# to appear in the string
flags.FLAGS.use_synthetic_data = False
expected_flags = expected_flags[:-len(" --use_synthetic_data")]
self.assertEqual(flags_core.get_nondefault_flags_as_str(),
expected_flags)
def test_default_setting(self):
"""Test to ensure fields exist and defaults can be set.
"""
defaults = dict(data_dir="dfgasf",
model_dir="dfsdkjgbs",
train_epochs=534,
epochs_between_evals=15,
batch_size=256,
hooks=["LoggingTensorHook"],
num_parallel_calls=18,
inter_op_parallelism_threads=5,
intra_op_parallelism_threads=10,
data_format="channels_first")
flags_core.set_defaults(**defaults)
flags_core.parse_flags()
for key, value in defaults.items():
assert flags.FLAGS.get_flag_value(name=key, default=None) == value
def test_parse_dtype_info(self):
for dtype_str, tf_dtype, loss_scale in [["fp16", tf.float16, 128],
["fp32", tf.float32, 1]]:
flags_core.parse_flags([__file__, "--dtype", dtype_str])
self.assertEqual(flags_core.get_tf_dtype(flags.FLAGS), tf_dtype)
self.assertEqual(flags_core.get_loss_scale(flags.FLAGS),
loss_scale)
flags_core.parse_flags(
[__file__, "--dtype", dtype_str, "--loss_scale", "5"])
self.assertEqual(flags_core.get_loss_scale(flags.FLAGS), 5)
flags_core.parse_flags(
[__file__, "--dtype", dtype_str, "--loss_scale", "dynamic"])
self.assertEqual(flags_core.get_loss_scale(flags.FLAGS), "dynamic")
with self.assertRaises(SystemExit):
flags_core.parse_flags([__file__, "--dtype", "int8"])
with self.assertRaises(SystemExit):
flags_core.parse_flags(
[__file__, "--dtype", "fp16", "--loss_scale", "abc"])
def test_parse_dtype_info(self):
flags_core.parse_flags([__file__, "--dtype", "fp16"])
self.assertEqual(flags_core.get_tf_dtype(flags.FLAGS), tf.float16)
self.assertEqual(
flags_core.get_loss_scale(flags.FLAGS, default_for_fp16=2), 2)
flags_core.parse_flags(
[__file__, "--dtype", "fp16", "--loss_scale", "5"])
self.assertEqual(
flags_core.get_loss_scale(flags.FLAGS, default_for_fp16=2), 5)
flags_core.parse_flags(
[__file__, "--dtype", "fp16", "--loss_scale", "dynamic"])
self.assertEqual(
flags_core.get_loss_scale(flags.FLAGS, default_for_fp16=2),
"dynamic")
flags_core.parse_flags([__file__, "--dtype", "fp32"])
self.assertEqual(flags_core.get_tf_dtype(flags.FLAGS), tf.float32)
self.assertEqual(
flags_core.get_loss_scale(flags.FLAGS, default_for_fp16=2), 1)
flags_core.parse_flags(
[__file__, "--dtype", "fp32", "--loss_scale", "5"])
self.assertEqual(
flags_core.get_loss_scale(flags.FLAGS, default_for_fp16=2), 5)
with self.assertRaises(SystemExit):
flags_core.parse_flags([__file__, "--dtype", "int8"])
with self.assertRaises(SystemExit):
flags_core.parse_flags(
[__file__, "--dtype", "fp16", "--loss_scale", "abc"])
