add double-margin contrastive loss layer

[jonbakerfish]

Migrate and reimplement the double-margin contrastive loss from https://bitbucket.org/jonbakerfish/caffe. For consistency (see #2308), this implementation is slightly different from the above repo.
The bitbucket one implements the loss in the following form:
L = y * max(d^2 - margin1, 0) + (1-y) * max(margin2 - d^2, 0)
whereas this PR implements:
L = y * max(d - margin1, 0)^2 + (1-y) * max(margin2 - d, 0)^2

This loss has been used in the following works:
[1] Lin et al. DeepHash: Getting Regularization, Depth and Fine-Tuning Right. CoRR, 2015
[2] Sadeghi et al. VISALOGY: Answering Visual Analogy Questions. In NIPS, 2015.
[3] Cao et al. Quartet-net Learning for Visual Instance Retrieval. In MM, 2016.