def save_pattern(pattern, mask, y_target):
# create result dir
if not os.path.exists(RESULT_DIR):
os.mkdir(RESULT_DIR)
img_filename = ('%s/%s' % (RESULT_DIR, IMG_FILENAME_TEMPLATE %
('pattern', y_target)))
#utils_backdoor.dump_image(pattern, img_filename, 'png')
#print("before write pattern: ", pattern.shape)
pattern = np.transpose(pattern, (1, 2, 0)) * 255.
#print("before write after transpose: ", pattern.shape)
cv2.imwrite(img_filename, pattern)
img_filename = ('%s/%s' % (RESULT_DIR, IMG_FILENAME_TEMPLATE %
('mask', y_target)))
#print("before save mask: ", np.expand_dims(mask, axis=2))
# utils_backdoor.dump_image(np.expand_dims(mask, axis=2) * 255,
# img_filename,
# 'png')
mask = np.transpose(mask, (1, 2, 0))
utils_backdoor.dump_image(mask * 255., img_filename, 'png')
# fusion = np.multiply(pattern, np.expand_dims(mask, axis=2))
fusion = np.multiply(pattern, mask)
img_filename = ('%s/%s' % (RESULT_DIR, IMG_FILENAME_TEMPLATE %
('fusion', y_target)))
utils_backdoor.dump_image(fusion, img_filename, 'png')
pass
def save_pattern(pattern, mask, y_target):
# create result dir
if not os.path.exists(RESULT_DIR):
os.mkdir(RESULT_DIR)
img_filename = ('%s/%s' % (RESULT_DIR, IMG_FILENAME_TEMPLATE %
('pattern', y_target)))
utils_backdoor.dump_image(pattern, img_filename, 'png')
img_filename = ('%s/%s' % (RESULT_DIR, IMG_FILENAME_TEMPLATE %
('mask', y_target)))
if CHANNELS_FIRST:
utils_backdoor.dump_image(
np.expand_dims(mask, axis=0) * 255, img_filename, 'png')
else:
utils_backdoor.dump_image(
np.expand_dims(mask, axis=2) * 255, img_filename, 'png')
if CHANNELS_FIRST:
fusion = np.multiply(pattern, np.expand_dims(mask, axis=0))
else:
fusion = np.multiply(pattern, np.expand_dims(mask, axis=2))
img_filename = ('%s/%s' % (RESULT_DIR, IMG_FILENAME_TEMPLATE %
('fusion', y_target)))
utils_backdoor.dump_image(fusion, img_filename, 'png')
pass
def save_tmp_func(self, step):
cur_mask = K.eval(self.mask_upsample_tensor)
cur_mask = cur_mask[0, ..., 0]
img_filename = ('%s/%s' %
(self.tmp_dir, 'tmp_mask_step_%d.png' % step))
utils_backdoor.dump_image(
np.expand_dims(cur_mask, axis=2) * 255, img_filename, 'png')
cur_fusion = K.eval(self.mask_upsample_tensor *
self.pattern_raw_tensor)
cur_fusion = cur_fusion[0, ...]
img_filename = ('%s/%s' %
(self.tmp_dir, 'tmp_fusion_step_%d.png' % step))
utils_backdoor.dump_image(cur_fusion, img_filename, 'png')
pass
