model_dir + "/" + weights_fname,
".\nThese are to be used as input for party which owns the model\n",
)
DumpTFMtData.save_weights(optimized_graph_def, sess, feed_dict,
weights_fname, scaling_factor)
weights_path = os.path.join(model_dir, weights_fname)
os.chdir(cwd)
return weights_path
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--config",
required=True,
type=str,
help="Path to the config file")
args = parser.parse_args()
return args
if __name__ == "__main__":
args = parse_args()
params = parse_config.get_params(args.config)
compile(
params["model_name"],
params["input_tensors"],
params["output_tensors"],
params["scale"],
params["save_weights"],
)
with open(filename, "r") as f:
for line in f:
match = matcher.fullmatch(line.rstrip())
if match:
unsigned_number = int(match.group(0))
number = (unsigned_number if
(unsigned_number < 2**(bitlength - 1)) else
unsigned_number - 2**bitlength)
scaled_array.append(float(number) / (2**scale))
return np.array(scaled_array)
if __name__ == "__main__":
if len(sys.argv) < 3:
print("Usage: python get_output.py mpc_output.txt config.json")
output_fname = sys.argv[1]
config_name = sys.argv[2]
params = parse_config.get_params(config_name)
scale = 12 if params["scale"] is None else params["scale"]
if params["bitlength"] is None:
if target == "SCI":
bitlength = 63
else:
bitlength = 64
else:
bitlength = params["bitlength"]
model_name = os.path.splitext(params["model_name"])[0]
np_arr = convert_raw_output_to_np(output_fname, bitlength, scale)
np.save(model_name + "_output", np_arr)
print("Output dumped as np array in " + model_name + "_output.npy")