I’ll help where I can. I might be wrong on a few points.

Ideally imagenet mean/std shouldn’t be too different from your mean/std. So doing anything should be fine. If you are freezing initial layers, you should use imagenet mean/std. Exact normalisation to (0, 1) mean/std is not required anymore because of batch norm.
Just on train. It should be as if model never knew about test data.
Again shouldn’t matter.
Just calculate mean/std of pixels on a decently sized random sample of original images.
About mean, you’re right. I’m not sure if caffe doesn’t have std.

Sasank.