Yes, except for those transformations that are meant for data-augmentation, all other transformation steps should be applied to the test data as well.
Specifically, this transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) applied to your training data will sort of mean-center and standardize the image channels. Therefore, if you do not apply this transformation to your test images, the train data and test data will have different data distributions.
Data augmentation transformations include RandomHorizontalFlip, RandomCrop and so on, which do not need to be applied in the evaluation phase.