def efficientdet_lite4_spec(**kwargs):
args = util.dict_with_default(default_dict=dict(
model_name='efficientdet-lite4',
uri='https://tfhub.dev/tensorflow/efficientdet/lite4/feature-vector/1'
),
**kwargs)
return EfficientDetModelSpec(**args)
def resnet_50_spec(**kwargs):
args = util.dict_with_default(default_dict=dict(
uri='https://tfhub.dev/google/imagenet/resnet_v2_50/feature_vector/4',
compat_tf_versions=2,
name='resnet_50'),
**kwargs)
return ImageModelSpec(**args)
def efficientnet_lite0_spec(**kwargs):
"""Model specification for EfficientNet-Lite0."""
args = util.dict_with_default(default_dict=dict(
uri='https://tfhub.dev/tensorflow/efficientnet/lite0/feature-vector/2',
compat_tf_versions=[1, 2],
name='efficientnet_lite0'),
**kwargs)
return ImageModelSpec(**args)
def mobilenet_v2_spec(**kwargs):
args = util.dict_with_default(default_dict=dict(
uri=
'https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4',
compat_tf_versions=2,
name='mobilenet_v2'),
**kwargs)
return ImageModelSpec(**args)
def efficientnet_lite3_spec(**kwargs):
args = util.dict_with_default(
default_dict=dict(
uri='https://tfhub.dev/tensorflow/efficientnet/lite3/feature-vector/2',
compat_tf_versions=[1, 2],
input_image_shape=[280, 280],
name='efficientnet_lite3'),
**kwargs)
return ImageModelSpec(**args)
def efficientnet_lite2_spec(**kwargs):
"""Model specification for EfficientNet-Lite2."""
args = util.dict_with_default(default_dict=dict(
uri='https://tfhub.dev/tensorflow/efficientnet/lite2/feature-vector/2',
compat_tf_versions=[1, 2],
input_image_shape=[260, 260],
name='efficientnet_lite2'),
**kwargs)
args.update(**kwargs)
return ImageModelSpec(**args)
def mobilebert_classifier_spec(**kwargs):
"""Model specification for MobileBERT in the text classification task."""
args = util.dict_with_default(default_dict=dict(
uri=
'https://tfhub.dev/google/mobilebert/uncased_L-24_H-128_B-512_A-4_F-4_OPT/1',
is_tf2=False,
distribution_strategy='off',
name='MobileBert',
default_batch_size=48),
**kwargs)
return BertClassifierModelSpec(**args)
def mobilebert_qa_spec(**kwargs):
"""Model specification for MobileBERT in the question answer task."""
args = util.dict_with_default(default_dict=dict(
uri=
'https://tfhub.dev/google/mobilebert/uncased_L-24_H-128_B-512_A-4_F-4_OPT/1',
is_tf2=False,
distribution_strategy='off',
learning_rate=4e-05,
name='MobileBert',
default_batch_size=32),
**kwargs)
return BertQAModelSpec(**args)
def mobilebert_classifier_spec(**kwargs):
"""Model specification for MobileBERT in the text classification task."""
args = util.dict_with_default(default_dict=dict(
uri=
'https://tfhub.dev/google/mobilebert/uncased_L-24_H-128_B-512_A-4_F-4_OPT/1',
is_tf2=False,
distribution_strategy='off',
convert_from_saved_model_tf2=True,
name='MobileBert',
tflite_input_name=_MOBILEBERT_TFLITE_INPUT_NAME,
default_batch_size=48),
**kwargs)
return BertClassifierModelSpec(**args)
def mobilebert_qa_squad_spec(**kwargs):
"""Model specification for MobileBERT that already retrained on SQuAD1.1."""
args = util.dict_with_default(default_dict=dict(
uri=
'https://tfhub.dev/google/mobilebert/uncased_L-24_H-128_B-512_A-4_F-4_OPT/squadv1/1',
is_tf2=False,
distribution_strategy='off',
learning_rate=4e-05,
name='MobileBert',
init_from_squad_model=True,
default_batch_size=32),
**kwargs)
return BertQAModelSpec(**args)
def mobilebert_qa_spec(**kwargs):
"""Model specification for MobileBERT in the question answer task."""
args = util.dict_with_default(default_dict=dict(
uri=
'https://tfhub.dev/google/mobilebert/uncased_L-24_H-128_B-512_A-4_F-4_OPT/1',
is_tf2=False,
distribution_strategy='off',
convert_from_saved_model_tf2=True,
learning_rate=4e-05,
name='MobileBert',
tflite_input_name=_MOBILEBERT_TFLITE_INPUT_NAME,
tflite_output_name=_MOBILEBERT_TFLITE_OUTPUT_NAME,
default_batch_size=32),
**kwargs)
return BertQAModelSpec(**args)
