def to_proto(self):
d = {
'train_py': self.script_locations.train_py,
'eval_py': self.script_locations.eval_py,
'export_py': self.script_locations.export_py,
'train_restart_dir': self.train_options.train_restart_dir,
'sync_interval': self.train_options.sync_interval,
'do_monitoring': self.train_options.do_monitoring,
'do_eval': self.train_options.do_eval,
'replace_model': self.train_options.replace_model,
'debug': self.debug,
'training_data_uri': self.training_data_uri,
'training_output_uri': self.training_output_uri,
'model_uri': self.model_uri,
'fine_tune_checkpoint_name': self.fine_tune_checkpoint_name,
'tfdl_config': self.tfdl_config
}
conf = json_format.ParseDict(d, BackendConfigMsg.TFDeeplabConfig())
msg = BackendConfigMsg(backend_type=rv.TF_DEEPLAB,
tf_deeplab_config=conf)
if self.pretrained_model_uri:
msg.MergeFrom(
BackendConfigMsg(
pretrained_model_uri=self.pretrained_model_uri))
return msg
def to_proto(self):
d = {
'sync_interval': self.train_options.sync_interval,
'do_monitoring': self.train_options.do_monitoring,
'replace_model': self.train_options.replace_model,
'model_main_py': self.script_locations.model_main_uri,
'export_py': self.script_locations.export_uri,
'training_data_uri': self.training_data_uri,
'training_output_uri': self.training_output_uri,
'model_uri': self.model_uri,
'debug': self.debug,
'fine_tune_checkpoint_name': self.fine_tune_checkpoint_name,
'tfod_config': self.tfod_config
}
conf = json_format.ParseDict(
d, BackendConfigMsg.TFObjectDetectionConfig())
msg = BackendConfigMsg(
backend_type=rv.TF_OBJECT_DETECTION,
tf_object_detection_config=conf)
if self.pretrained_model_uri:
msg.MergeFrom(
BackendConfigMsg(
pretrained_model_uri=self.pretrained_model_uri))
return msg
def to_proto(self):
struct = struct_pb2.Struct()
struct['sync_interval'] = self.train_options.sync_interval
struct['do_monitoring'] = self.train_options.do_monitoring
struct['replace_model'] = self.train_options.replace_model
struct['model_uri'] = self.model_uri
struct['debug'] = self.debug
struct['training_data_uri'] = self.training_data_uri
struct['training_output_uri'] = self.training_output_uri
msg = BackendConfigMsg(backend_type=self.backend_type,
custom_config=struct)
if self.pretrained_model_uri:
msg.MergeFrom(
BackendConfigMsg(
pretrained_model_uri=self.pretrained_model_uri))
return msg
def to_proto(self):
struct = struct_pb2.Struct()
struct['scenes'] = list(
map(lambda x: json_format.MessageToDict(x.to_proto()),
self.scenes))
struct['batch_size'] = self.batch_size
struct['epochs'] = self.epochs
struct['epoch_size'] = self.epoch_size
struct['epoch_save_rate'] = self.epoch_save_rate
struct['training_output_uri'] = self.training_output_uri
struct['model_uri'] = self.model_uri
msg = BackendConfigMsg(backend_type=self.backend_type,
custom_config=struct)
if self.pretrained_model_uri:
msg.MergeFrom(
BackendConfigMsg(
pretrained_model_uri=self.pretrained_model_uri))
return msg
def to_proto(self):
d = {
'sync_interval': self.train_options.sync_interval,
'do_monitoring': self.train_options.do_monitoring,
'replace_model': self.train_options.replace_model,
'training_data_uri': self.training_data_uri,
'training_output_uri': self.training_output_uri,
'model_uri': self.model_uri,
'debug': self.debug,
'kc_config': self.kc_config
}
conf = json_format.ParseDict(
d, BackendConfigMsg.KerasClassificationConfig())
msg = BackendConfigMsg(backend_type=rv.KERAS_CLASSIFICATION,
keras_classification_config=conf)
if self.pretrained_model_uri:
msg.MergeFrom(
BackendConfigMsg(
pretrained_model_uri=self.pretrained_model_uri))
return msg