If I understand your question correctly, you are asking whether you could load the saved weights of a trained model with the Xception architecture on a a Resnet50 architecture.
Short answer: No
Long answer: Xception and Resnet50 have very different architectures.
Here is a paper comparing multilple CNNs including Xception and Resnet50:
As you can see, the architectures are quiet different between Xception and ResNet50. Thus when you train a model, you are changing the weights and bias of the different layers of the model. If you switch models, and they are similar like let's say VGG16 and VGG19, you could import part of the weights for the layers which are similar between the two models.
As far as I know, I don't know of a function which can do this operation in tensorflow or keras.
But in your case, the architectures are extremely different between Xception and ResNet and there seem not to have any layers in common.
In general, one train a model, save the weights of the training, import back the weights, and use the same model for the next round of training/testing/predictions.
Hope that helps!