def export_as_deliverable_model(
output_dir,
tensorflow_saved_model=None,
converter_for_request: Union[None, Callable] = None,
converter_for_response: Union[None, Callable] = None,
keras_saved_model=None,
keras_h5_model=None,
meta_content_id="algorithmId-corpusId-configId-runId",
vocabulary_lookup_table=None,
tag_lookup_table=None,
padding_parameter=None,
addition_model_dependency=None,
custom_object_dependency=None,
):
# check parameters
assert any(
[tensorflow_saved_model, keras_saved_model, keras_h5_model]
), "one and only one of [tensorflow_saved_model, keras_saved_model, keras_h5_model] must be set up"
assert (
sum(
int(bool(i)) for i in
[tensorflow_saved_model, keras_saved_model, keras_h5_model]) == 1
), "one and only one of [tensorflow_saved_model, keras_saved_model, keras_h5_model] must be set up"
# default value
addition_model_dependency = ([] if addition_model_dependency is None else
addition_model_dependency)
custom_object_dependency = ([] if custom_object_dependency is None else
custom_object_dependency)
# setup main object
deliverable_model_builder = DeliverableModelBuilder(output_dir)
# metadata builder
metadata_builder = MetadataBuilder()
meta_content = MetaContent(meta_content_id)
metadata_builder.set_meta_content(meta_content)
metadata_builder.save()
# processor builder
processor_builder = ProcessorBuilder()
decode_processor = BILUOEncodeProcessor()
decoder_processor_handle = processor_builder.add_processor(
decode_processor)
lookup_processor = LookupProcessor()
pad_processor = PadProcessor(padding_parameter=padding_parameter)
if vocabulary_lookup_table:
lookup_processor.add_vocabulary_lookup_table(vocabulary_lookup_table)
if tag_lookup_table:
lookup_processor.add_tag_lookup_table(tag_lookup_table)
if vocabulary_lookup_table or tag_lookup_table:
lookup_processor_handle = processor_builder.add_processor(
lookup_processor)
pad_processor_handle = processor_builder.add_processor(pad_processor)
# # pre process: encoder > [lookup] > [pad]
processor_builder.add_preprocess(decoder_processor_handle)
if vocabulary_lookup_table or tag_lookup_table:
processor_builder.add_preprocess(lookup_processor_handle)
if vocabulary_lookup_table or tag_lookup_table:
processor_builder.add_preprocess(pad_processor_handle)
# # post process: lookup > encoder
processor_builder.add_postprocess(decoder_processor_handle)
if vocabulary_lookup_table or tag_lookup_table:
processor_builder.add_postprocess(lookup_processor_handle)
processor_builder.save()
# model builder
model_builder = ModelBuilder()
model_builder.append_dependency(addition_model_dependency)
model_builder.set_custom_object_dependency(custom_object_dependency)
if converter_for_request:
model_builder.add_converter_for_request(converter_for_request)
if converter_for_response:
model_builder.add_converter_for_response(converter_for_response)
if tensorflow_saved_model:
model_builder.add_tensorflow_saved_model(tensorflow_saved_model)
elif keras_saved_model:
model_builder.add_keras_saved_model(keras_saved_model)
else:
model_builder.add_keras_h5_model(keras_h5_model)
model_builder.save()
# compose all the parts
deliverable_model_builder.add_processor(processor_builder)
deliverable_model_builder.add_metadata(metadata_builder)
deliverable_model_builder.add_model(model_builder)
metadata = deliverable_model_builder.save()
return metadata
def mt_export_as_deliverable_model(
output_dir,
tensorflow_saved_model=None,
converter_for_request: Union[None, Callable] = None,
converter_for_response: Union[None, Callable] = None,
keras_saved_model=None,
keras_h5_model=None,
meta_content_id="algorithmId-corpusId-configId-runId",
lookup_tables: Dict = None,
padding_parameter=None,
addition_model_dependency=None,
custom_object_dependency=None,
):
# check parameters
assert any(
[tensorflow_saved_model, keras_saved_model, keras_h5_model]
), "one and only one of [tensorflow_saved_model, keras_saved_model, keras_h5_model] must be set up"
assert (
sum(
int(bool(i)) for i in
[tensorflow_saved_model, keras_saved_model, keras_h5_model]) == 1
), "one and only one of [tensorflow_saved_model, keras_saved_model, keras_h5_model] must be set up"
# default value
addition_model_dependency = ([] if addition_model_dependency is None else
addition_model_dependency)
custom_object_dependency = ([] if custom_object_dependency is None else
custom_object_dependency)
# setup main object
deliverable_model_builder = DeliverableModelBuilder(output_dir)
# metadata builder
metadata_builder = MetadataBuilder()
meta_content = MetaContent(meta_content_id)
metadata_builder.set_meta_content(meta_content)
metadata_builder.save()
# processor builder
vocabulary_lookup_table = lookup_tables['vocab_lookup']
tag_lookup_table = lookup_tables['tag_lookup']
label_lookup_table = lookup_tables['label_lookup']
processor_builder = ProcessorBuilder()
decode_processor = BILUOEncodeProcessor()
decoder_processor_handle = processor_builder.add_processor(
decode_processor)
pad_processor = PadProcessor(padding_parameter=padding_parameter)
pad_processor_handle = processor_builder.add_processor(pad_processor)
vocab_lookup_processor = LookupProcessor(vocabulary_lookup_table)
vocab_lookup_processor_handle = processor_builder.add_processor(
vocab_lookup_processor)
tag_lookup_processor = LookupProcessor(tag_lookup_table)
tag_lookup_processor_handle = processor_builder.add_processor(
tag_lookup_processor)
label_lookup_processor = LookupProcessor(
label_lookup_table, **{
"post_input_key": 'cls',
"post_output_key": 'cls'
})
label_lookup_processor_handle = processor_builder.add_processor(
label_lookup_processor)
# # pre process: encoder[memory text] > lookup[str -> num] > pad[to fixed length]
processor_builder.add_preprocess(decoder_processor_handle)
processor_builder.add_preprocess(vocab_lookup_processor_handle)
processor_builder.add_preprocess(pad_processor_handle)
# # post process: lookup[num -> str] > encoder
processor_builder.add_postprocess(tag_lookup_processor_handle)
processor_builder.add_postprocess(label_lookup_processor_handle)
processor_builder.add_postprocess(decoder_processor_handle)
processor_builder.save()
# model builder
model_builder = ModelBuilder()
model_builder.append_dependency(addition_model_dependency)
model_builder.set_custom_object_dependency(custom_object_dependency)
if converter_for_request:
model_builder.add_converter_for_request(converter_for_request)
if converter_for_response:
model_builder.add_converter_for_response(converter_for_response)
if tensorflow_saved_model:
model_builder.add_tensorflow_saved_model(tensorflow_saved_model)
elif keras_saved_model:
model_builder.add_keras_saved_model(keras_saved_model)
else:
model_builder.add_keras_h5_model(keras_h5_model)
model_builder.save()
# compose all the parts
deliverable_model_builder.add_processor(processor_builder)
deliverable_model_builder.add_metadata(metadata_builder)
deliverable_model_builder.add_model(model_builder)
metadata = deliverable_model_builder.save()
return metadata