def decode(self, data):
offset = Offset()
self.src = unpack_int(data, offset)
self.dst = unpack_int(data, offset)
self.key = bytes(unpack_raw_data(data, offset))
self.data = unpack_raw_data(data, offset)
self.check()
return self
def deseralize_pubkeys(self, data: bytearray, offset: Offset = None):
offset = offset or Offset()
num_keys = unpack_int(data, offset)
for _ in range(num_keys):
id = unpack_int(data, offset)
new_key = Key().deseralize_pubkey(data, offset)
self[id] = new_key
return self
def deseralize_keys(self, data: bytearray, offset: Offset = None):
offset = offset or Offset()
num_keys = unpack_int(data, offset)
for _ in range(num_keys):
new_key = Key()
new_key.deseralize_key(data, offset)
self.append(new_key)
return self
def decompress(self, data: bytearray) -> [torch.Tensor]:
result = []
offset = Offset(0)
num_layers = unpack_int(data, offset)
if isinstance(self.compressors, _TensorCompressor):
compressors = [self.compressors] * num_layers
elif isinstance(self.compressors, list):
assert (len(self.compressors) == num_layers)
compressors = self.compressors
for i in range(num_layers):
result.append(compressors[i].decompress(data, offset))
return result
def decompress(self, data: bytearray) -> [torch.Tensor]:
result = []
result_types = []
offset = Offset(0)
# get number of tensors
num_tensors = unpack_int(data, offset)
# re-construct the tensor list
for i in range(num_tensors):
dtype = bytes(unpack_fixed_width_data(data, offset, 1))
result_types.append(self.type_reverse_map[dtype])
shape = unpack_tensor_shape(data, offset)
result.append(torch.zeros(shape))
# restore compressed data
compressed_data = self.compressors.decompress(data, offset)
torch.nn.utils.vector_to_parameters(compressed_data, result)
# restore types
for i in range(len(result)):
result[i] = result[i].to(dtype=result_types[i])
return result