def save_and_export(
config: PyTextConfig,
task: Task_Deprecated,
metric_channels: Optional[List[Channel]] = None,
) -> None:
print("\n=== Saving model to: " + config.save_snapshot_path)
meta = None
tensorizers = None
if hasattr(task, "data_handler"):
meta = task.data_handler.metadata_to_save()
else:
tensorizers = task.data.tensorizers
save(config, task.model, meta, tensorizers=tensorizers)
if config.export_list:
export_configs = config.export_list
elif config.export:
export_configs = [config.export]
else:
export_configs = []
print("No export options.")
for export_config in export_configs:
model = reload_model_for_multi_export(config)
if model is None:
model = task.model
if export_config.export_caffe2_path:
task.export(
model,
export_config.export_caffe2_path,
metric_channels,
export_config.export_onnx_path,
)
elif export_config.export_torchscript_path:
try:
task.torchscript_export(
model=model,
export_path=export_config.export_torchscript_path,
export_config=export_config,
)
except (RuntimeError, TypeError) as e:
print("Ran into error: {}".format(", ".join(e.args)))
traceback.print_exception(*sys.exc_info())
print(
f"The torchscript model at {export_config.export_torchscript_path} could not be saved, skipping for now."
)
elif export_config.export_lite_path:
task.lite_export(
model=model,
export_path=export_config.export_lite_path,
export_config=export_config,
)
else:
print("No model to export.")
def save_and_export(
config: PyTextConfig,
task: Task_Deprecated,
metric_channels: Optional[List[Channel]] = None,
) -> None:
print("\n=== Saving model to: " + config.save_snapshot_path)
meta = None
tensorizers = None
if hasattr(task, "data_handler"):
meta = task.data_handler.metadata_to_save()
else:
tensorizers = task.data.tensorizers
save(config, task.model, meta, tensorizers=tensorizers)
if config.export_list:
export_configs = config.export_list
elif config.export:
export_configs = [config.export]
else:
export_configs = []
print("No export options.")
for export_config in export_configs:
if export_config.export_caffe2_path:
task.export(
task.model,
export_config.export_caffe2_path,
metric_channels,
export_config.export_onnx_path,
)
elif export_config.export_torchscript_path:
task.torchscript_export(
model=task.model,
export_path=export_config.export_torchscript_path,
export_config=export_config,
)
elif export_config.export_lite_path:
task.lite_export(
model=task.model,
export_path=export_config.export_lite_path,
export_config=export_config,
)
else:
print("No model to export.")
def save_and_export(
config: PyTextConfig,
task: Task_Deprecated,
metric_channels: Optional[List[Channel]] = None,
) -> None:
print("\n=== Saving model to: " + config.save_snapshot_path)
meta = None
tensorizers = None
if hasattr(task, "data_handler"):
meta = task.data_handler.metadata_to_save()
else:
tensorizers = task.data.tensorizers
save(config, task.model, meta, tensorizers=tensorizers)
export_config = config.export
if export_config.export_caffe2_path:
task.export(
task.model,
export_config.export_caffe2_path,
metric_channels,
export_config.export_onnx_path,
)
if export_config.export_torchscript_path:
task.torchscript_export(
model=task.model,
export_path=export_config.export_torchscript_path,
quantize=export_config.torchscript_quantize,
inference_interface=export_config.inference_interface,
accelerate=export_config.accelerate,
seq_padding_control=export_config.seq_padding_control,
batch_padding_control=export_config.batch_padding_control,
)
if export_config.export_lite_path:
task.lite_export(
model=task.model,
export_path=export_config.export_lite_path,
quantize=export_config.torchscript_quantize,
inference_interface=export_config.inference_interface,
accelerate=export_config.accelerate,
seq_padding_control=export_config.seq_padding_control,
batch_padding_control=export_config.batch_padding_control,
)