def eval(model_filename, clean_data_filename):
x_test, y_test = data_loader(clean_data_filename)
x_test = data_preprocess(x_test)
bd_model = keras.models.load_model(model_filename)
class_accu = eval_loaded_model(bd_model, x_test, y_test)
return class_accu
"""
Eval script
"""
import keras
import sys
from PIL import Image
from eval import data_preprocess
import numpy as np
model_path = './models/anonymous_2_bd_net.h5'
rep_model_path = './models/anonymous_2_bd_net_repaired.h5'
img = np.array(Image.open(sys.argv[1]))
img = np.expand_dims(img, 0) # bs, sx, sy, ch
img = data_preprocess(img)
orig_model = keras.models.load_model(model_path)
rep_model = keras.models.load_model(rep_model_path)
orig_pred = np.argmax(orig_model.predict(img), axis=1)
rep_pred = np.argmax(rep_model.predict(img), axis=1)
if orig_pred == rep_pred:
print(orig_pred)
else:
print(-1)
def data_preprocess_and_load(datapath):
test_x, test_y = data_loader(datapath)
return data_preprocess(test_x), test_y