The onnx model(which is transfor by export_onnx.py) out put is differ from pytoch model

[Genlk]

def image_preprocess(img_path):
img = cv2.imread(img_path).astype("float32")/255
# mean = [103.53, 116.28, 123.675] # Image net values
# std = [57.375, 57.12, 58.395]
mean = [113.533554, 118.14172, 123.63607]
std = [21.405144, 21.405144, 21.405144]
mean = np.array(mean, dtype=np.float32).reshape(1, 1, 3) / 255
std = np.array(std, dtype=np.float32).reshape(1, 1, 3) / 255
img = (img - mean) / std
img = np.transpose(img, (2, 0, 1))
img = np.expand_dims(img, axis=0)
return img

def test_onnx_model(onnx_model,img_path=None):
if img_path is None:
img_path = "path for img"
imgdata = image_preprocess(img_path)
sess = rt.InferenceSession(onnx_model)
input_name = sess.get_inputs()[0].name
output_detect_name = sess.get_outputs()[0].name
pred_onnx0= sess.run([output_detect_name], {input_name: imgdata})
print("outputs:")
print(np.array(pred_onnx0))