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save_accuracy.py
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import torch
import util
import numpy as np
from models.load_model import load_model
from util import generate_folder_name
from test import accuracy2
from util_classes import get_save_name, require_domain_knowledge
import os
import json
import sys
def get_ranks(args):
# Load the data and make it global
global x_attack, y_attack, dk_plain, key_guesses
x_attack, y_attack, key_guesses, real_key, dk_plain = load_data(args)
model_params = {}
map_accuracy = {}
folder = "{}/{}/".format(args.models_path, generate_folder_name(args))
model_params.update({"max_pool": args.max_pool})
for channel_size in args.channels:
model_params.update({"channel_size": channel_size})
for layers in args.layers:
model_params.update({"num_layers": layers})
for kernel in args.kernels:
model_params.update({"kernel_size": kernel})
# Calculate the accuracy
mean_acc = 0.0
no_data = False
for run in range(args.runs):
model_path = '{}/model_r{}_{}.pt'.format(
folder,
run,
get_save_name(args.network_name, model_params))
if not os.path.exists(model_path):
print(util.BColors.WARNING + f"Path {model_path} does not exists" + util.BColors.ENDC)
no_data = True
break
print('path={}'.format(model_path))
model = load_model(args.network_name, model_path)
model.eval()
print("Using {}".format(model))
model.to(args.device)
# Calculate predictions
if require_domain_knowledge(args.network_name):
_, acc = accuracy2(model, x_attack, y_attack, dk_plain)
else:
_, acc = accuracy2(model, x_attack, y_attack, None)
print('Accuracy: {} - {}%'.format(acc, acc * 100))
acc = acc * 100
mean_acc = mean_acc + acc
if not no_data:
mean_acc = mean_acc / float(args.runs)
map_accuracy.update({f"c_{channel_size}_l{layers}_k{kernel}": mean_acc})
print(util.BColors.WARNING + f"Mean accuracy {mean_acc}" + util.BColors.ENDC)
if args.noise_level >= 0:
acc_filename = f"{folder}/acc_{args.network_name}_noise{args.noise_level}.json"
else:
acc_filename = f"{folder}/acc_{args.network_name}.json"
print(acc_filename)
with open(acc_filename, "w") as acc_file:
acc_file.write(json.dumps(map_accuracy))
def load_data(args):
_x_attack, _y_attack, _real_key, _dk_plain, _key_guesses = None, None, None, None, None
###################
# Load the traces #
###################
loader = util.load_data_set(args.data_set)
total_x_attack, total_y_attack, plain = loader({'use_hw': args.use_hw,
'traces_path': args.traces_path,
'raw_traces': args.raw_traces,
'start': args.train_size + args.validation_size,
'size': args.attack_size,
'domain_knowledge': True,
'use_noise_data': args.use_noise_data,
'data_set': args.data_set,
'noise_level': args.noise_level})
if plain is not None:
_dk_plain = torch.from_numpy(plain).cuda()
print('Loading key guesses')
####################################
# Load the key guesses and the key #
####################################
data_set_name = str(args.data_set)
_key_guesses = util.load_csv('{}/{}/Value/key_guesses_ALL_transposed.csv'.format(
args.traces_path,
data_set_name),
delimiter=' ',
dtype=np.int,
start=args.train_size + args.validation_size,
size=args.attack_size)
_real_key = util.load_csv('{}/{}/secret_key.csv'.format(args.traces_path, data_set_name),
dtype=np.int)
_x_attack = total_x_attack
_y_attack = total_y_attack
return _x_attack, _y_attack, _key_guesses, _real_key, _dk_plain
def run_load(model, l2_penal, noise_level=-1.0, data_set=util.DataSet.RANDOM_DELAY_NORMALIZED):
args = util.EmptySpace()
args.use_hw = False
args.data_set = data_set
# args.traces_path = "/tudelft.net/staff-bulk/ewi/insy/CYS/spicek/student-datasets/"
# args.models_path = "/tudelft.net/staff-bulk/ewi/insy/CYS/spicek/rtubbing/"
args.traces_path = "/media/rico/Data/TU/thesis/data/"
args.models_path = "/media/rico/Data/TU/thesis/runs3/"
args.raw_traces = True
args.train_size = 40000
args.validation_size = 1000
args.attack_size = 9000
args.use_noise_data = True if noise_level > 0 else False
args.epochs = 75
args.batch_size = 100
args.lr = 0.0001
args.l2_penalty = float(l2_penal)
args.init_weights = "kaiming"
args.noise_level = float(noise_level)
args.type_network = 'HW' if args.use_hw else 'ID'
args.device = torch.device("cuda")
args.network_name = model
args.subkey_index = 2
args.unmask = True
args.desync = 0
args.spread_factor = 1
args.runs = 5
# args.kernels = [20]
# args.layers = [5]
args.kernels = [100, 50, 25, 20, 15, 17, 10, 7, 5, 3]
args.layers = [1, 2, 3, 4, 5, 6, 7, 8, 9]
args.channels = [32]
args.max_pool = 4
get_ranks(args)
if __name__ == "__main__":
if len(sys.argv) == 1:
run_load("VGGNumLayers2", l2_penal=0.005)
elif len(sys.argv) == 2:
run_load(sys.argv[1], sys.argv[2], data_set=util.DataSet.ASCAD_NORMALIZED)
elif len(sys.argv) == 3:
for n_level in [0.0, 0.1, 0.25, 0.5, 0.75, 1.0]:
run_load(sys.argv[1], sys.argv[2], n_level,
data_set=util.DataSet.RANDOM_DELAY_NORMALIZED)
else:
print(sys.argv)
run_load(model=sys.argv[1],
l2_penal=sys.argv[2],
noise_level=sys.argv[3],
data_set=util.DataSet.from_string(sys.argv[4]))