class Main:
def __init__(self):
self.input_folder = None
self.output_folder = None
self.output_file_path = None
self.batch_size = None
self.overwrite_output_model = None
self.model_path = None
self.model_name = None
self.keras_model = None
self.onnx_model = None
def get_inputs(self):
self.input_folder = Folder(
get_input_names_for_role("input_folder_id")[0])
output_folder_id = get_output_names_for_role("output_folder_id")[0]
self.output_folder = Folder(output_folder_id)
self.output_file_path = get_recipe_config()['output_model_path']
self.batch_size = int(get_recipe_config()['batch_size'])
if not get_recipe_config()['show_batch_size']:
self.batch_size = -1
self.overwrite_output_model = get_recipe_config(
)['overwrite_output_model']
self.model_path = get_recipe_config()['model_path']
self.model_name = os_splitext(os_split(self.model_path)[1])[0]
self.float_32 = get_recipe_config()["float_32"]
def validate(self):
if self.output_folder.get_path_details(
self.output_file_path
)['exists'] and not self.overwrite_output_model:
raise ValueError(
'Output file already exists, check overwrite box or change output path'
)
if not self.output_file_path:
raise ValueError('Output model path can not be blank')
check_keras_version(self.input_folder, self.model_path)
def load_h5_to_keras(self):
self.keras_model = get_keras_model_from_folder(self.input_folder,
self.model_path)
def write_output(self):
with self.output_folder.get_writer(self.output_file_path) as w:
w.write(self.onnx_model.SerializeToString())
def run(self):
self.get_inputs()
self.validate()
self.load_h5_to_keras()
self.onnx_model = convert_from_keras_to_onnx(self.keras_model,
self.batch_size,
self.float_32)
self.write_output()
class Main:
def __init__(self):
self.folder = None
self.output_file_path = None
self.batch_size = None
self.overwrite_output_model = None
self.model = None
self.keras_model = None
self.onnx_model = None
self.float_32 = None
def get_inputs(self):
self.folder = Folder(get_output_names_for_role("folder_id")[0])
self.output_file_path = get_recipe_config()['output_model_path']
self.overwrite_output_model = get_recipe_config(
)['overwrite_output_model']
self.batch_size = int(get_recipe_config()['batch_size'])
if not get_recipe_config()['show_batch_size']:
self.batch_size = -1
self.model = Model(get_input_names_for_role("saved_model_id")[0])
self.float_32 = get_recipe_config()["float_32"]
def validation(self):
if self.folder.get_path_details(
self.output_file_path
)['exists'] and not self.overwrite_output_model:
raise ValueError(
'Output file already exists, check overwrite box or change output path'
)
if not self.output_file_path:
raise ValueError('Output model path can not be blank')
def write_output(self):
with self.folder.get_writer(self.output_file_path) as w:
w.write(self.onnx_model.SerializeToString())
def run(self):
self.get_inputs()
self.validation()
self.keras_model = get_keras_model_from_saved_model(
default_project_key(), self.model)
self.onnx_model = convert_from_keras_to_onnx(self.keras_model,
self.batch_size,
self.float_32)
self.write_output()
class MyRunnable(Runnable):
"""The base interface for a Python runnable"""
def __init__(self, project_key, config, plugin_config):
"""
:param project_key: the project in which the runnable executes
:param config: the dict of the configuration of the object
:param plugin_config: contains the plugin settings
"""
self.project_key = project_key
self.config = config
self.plugin_config = plugin_config
self.folder = Folder(self._get_folder_id(),
project_key=self.project_key)
self.model = Model(self.config.get('saved_model_id'),
project_key=self.project_key)
self.model.list_versions()
self.output_file_path = self._get_output_file_path()
self.overwrite_output_model = self.config.get('overwrite_output_model')
self.batch_size = self._get_batch_size()
self.float_32 = self.config.get('float_32')
def _get_batch_size(self):
batch_size = int(self.config.get('batch_size'))
if not self.config.get('show_batch_size'):
batch_size = -1
return batch_size
def _get_folder_id(self):
folder_id = self.config.get('folder_id', '')
if not folder_id:
raise ValueError('Output folder can not be blank')
return folder_id
def _get_output_file_path(self):
output_file_path = self.config.get('output_model_path', '')
if not output_file_path:
raise ValueError('Output model path can not be blank')
return output_file_path
def get_progress_target(self):
return None
def run(self, progress_callback):
"""
Gets a saved model, converts it with keras2onnx, saves it back in the folder, builds a url for the download
"""
keras_model = get_keras_model_from_saved_model(self.project_key,
self.model)
onnx_model = convert_from_keras_to_onnx(keras_model, self.batch_size,
self.float_32)
self._write_onnx_model_to_folder(onnx_model)
return self._build_download_url()
def _write_onnx_model_to_folder(self, onnx_model):
if self.folder.get_path_details(
self.output_file_path
)['exists'] and not self.overwrite_output_model:
raise ValueError(
'Output file already exists, check overwrite box or change output path'
)
with self.folder.get_writer(self.output_file_path) as w:
w.write(onnx_model.SerializeToString())
def _build_download_url(self):
return "/dip/api/managedfolder/download-item/?contextProjectKey={}&projectKey={}&obdId={}&path=%2F{}".format(
self.project_key, self.project_key, self.folder.get_id(),
self.output_file_path)