def test_export_constant(self):
FOO = 1 # pylint: disable=invalid-name,unused-variable
api_util.mm_export('foo.FOO').export_constant(__name__, 'FOO')
self.assertLen(api_util.NAME_TO_SYMBOL, 1)
expected = api_util.Symbol('foo.FOO', ['foo', 'FOO'], None, '__main__',
'FOO')
self.assertEqual(api_util.NAME_TO_SYMBOL['foo.FOO'], expected)
def test_call_global_function(self):
def test_func():
"""Func to test export."""
pass
exportor = api_util.mm_export('foo.bar.test_func')
ret_func = exportor(test_func)
self.assertEqual(ret_func, test_func)
func = api_util.NAME_TO_SYMBOL['foo.bar.test_func']
self.assertEqual(func.gen_import(), 'from __main__ import test_func')
self.assertEqual(func.get_package_name(), 'foo.bar')
exportor = api_util.mm_export('fn')
exportor(test_func)
func = api_util.NAME_TO_SYMBOL['fn']
self.assertEqual(func.gen_import(),
'from __main__ import test_func as fn')
self.assertEqual(func.get_package_name(), '')
tflite_model = converter.convert()
with tf.io.gfile.GFile(tflite_filepath, 'wb') as f:
f.write(tflite_model)
efficientdet_lite0_spec = functools.partial(
EfficientDetModelSpec,
model_name='efficientdet-lite0',
uri='https://tfhub.dev/tensorflow/efficientdet/lite0/feature-vector/1',
)
efficientdet_lite0_spec.__doc__ = util.wrap_doc(
EfficientDetModelSpec,
'Creates EfficientDet-Lite0 model spec. See also: `tflite_model_maker.object_detector.EfficientDetSpec`.'
)
mm_export('object_detector.EfficientDetLite0Spec').export_constant(
__name__, 'efficientdet_lite0_spec')
efficientdet_lite1_spec = functools.partial(
EfficientDetModelSpec,
model_name='efficientdet-lite1',
uri='https://tfhub.dev/tensorflow/efficientdet/lite1/feature-vector/1',
)
efficientdet_lite1_spec.__doc__ = util.wrap_doc(
EfficientDetModelSpec,
'Creates EfficientDet-Lite1 model spec. See also: `tflite_model_maker.object_detector.EfficientDetSpec`.'
)
mm_export('object_detector.EfficientDetLite1Spec').export_constant(
__name__, 'efficientdet_lite1_spec')
efficientdet_lite2_spec = functools.partial(
EfficientDetModelSpec,
do_train: Whether to run training.
Returns:
An instance based on ObjectDetector.
"""
model_spec = ms.get(model_spec)
if epochs is not None:
model_spec.config.num_epochs = epochs
if batch_size is not None:
model_spec.config.batch_size = batch_size
if train_whole_model:
model_spec.config.var_freeze_expr = None
if compat.get_tf_behavior() not in model_spec.compat_tf_versions:
raise ValueError('Incompatible versions. Expect {}, but got {}.'.format(
model_spec.compat_tf_versions, compat.get_tf_behavior()))
object_detector = cls(model_spec, train_data.label_map, train_data)
if do_train:
tf.compat.v1.logging.info('Retraining the models...')
object_detector.train(train_data, validation_data, epochs, batch_size)
else:
object_detector.create_model()
return object_detector
# Shortcut function.
create = ObjectDetector.create
mm_export('object_detector.create').export_constant(__name__, 'create')