def _detail_keras_model(worker, model_tuple):
"""
This function converts a serialized model into a local
model.
Args:
modeltuple (bin): serialized obj of Keras model.
It's a tuple where the first value is the binary of the model.
The second is the model id.
Returns:
tf.keras.models.Model: a deserialized Keras model
"""
model_ser, model_id = model_tuple
bio = io.BytesIO(model_ser)
with TemporaryDirectory() as model_location:
with zipfile.ZipFile(bio, 'r', zipfile.ZIP_DEFLATED) as model_file:
# WARNING: zipped archives can potentially deposit extra files onto
# the system, although Python's zipfile offers some protection
# more info: https://docs.python.org/3/library/zipfile.html#zipfile.ZipFile.extractall
# TODO: further investigate security, find better option if needed
model_file.extractall(model_location)
model = tf.keras.models.load_model(model_location)
initialize_object(
hook=syft.tensorflow.hook,
obj=model,
owner=worker,
reinitialize=False,
id=model_id,
init_args=[],
init_kwargs={},
)
return model
def initialize_tensor(hook,
obj,
owner=None,
id=None,
init_args=tuple(),
init_kwargs={}):
"""Initializes the tensor.
Args:
hook: A reference to TorchHook class.
cls: An object to keep track of id, owner and whether it is a native
tensor or a wrapper over pytorch.
is_tensor: A boolean parameter (default False) to indicate whether
it is torch tensor or not.
owner: The owner of the tensor being initialised, leave it blank
to if you have already provided a reference to TorchHook class.
id: The id of tensor, a random id will be generated if there is no id
specified.
"""
initialize_object(
hook,
obj,
owner=owner,
reinitialize=False,
id=id,
init_args=init_args,
init_kwargs=init_kwargs,
)
def new___init__(self, *args, owner=None, id=None, register=True, **kwargs):
initialize_object(
hook=hook_self,
obj=self,
id=id,
reinitialize=not is_tensor,
init_args=args,
init_kwargs=kwargs,
)
def _detail_tf_keras_layers(worker, layer_tuple) -> tf.Tensor:
"""
This function converts a serialized keras layer into a local keras layer
Args:
layer_tuple (bin): serialized obj of TF layer. It's a tuple where
the first value is the ID, the second value is the binary for the
layer object, the third value is the layer weights, and
the fourth value is the batch input shape.
Returns:
tf.Tensor: a deserialized TF tensor
"""
layer_id, layer_bin, weights_bin, batch_input_shape_bin = layer_tuple
layer_dict = syft.serde.serde._detail(worker, layer_bin)
layer = tf.keras.layers.deserialize(layer_dict)
weights = syft.serde.serde._detail(worker, weights_bin)
batch_input_shape = syft.serde.serde._detail(worker, batch_input_shape_bin)
layer.build(batch_input_shape)
layer.set_weights(weights)
initialize_object(
hook=syft.tensorflow.hook,
obj=layer,
owner=worker,
reinitialize=False,
id=layer_id,
init_args=[],
init_kwargs={},
)
return layer