* Add capability to train scale-variant MMOD models

* Review fixes: change bool scale_invariant to strongly typed enum, etc

* Add serialization and deserialization of assumed_input_layer_type

* Fix code formatting

* Rename things as per review feedback

* Review fix: move enum use_image_pyramid outside mmod_options

* Continue execution with net, if deserialization of shape predictor fails

* Revert "Continue execution with net, if deserialization of shape predictor fails"

This reverts commit 8ea4482c043b5b98b97ed5b78bfc6916a1e2a453.

output 2 more statistics, which are the mean absolute error and the standard
deviation of the absolute error.  This means these functions now return 4D
rather than 2D vectors.

I also made test_regression_function() take a non-const reference to the
regression function so that DNN objects can be tested.

* Remove explicit specification of library path in dlib.cmake
Enables side-by-side multi configuration build on windows

* Add dlib_LIBS

For backwards compatability

* This fix suggested by davisking make unit tests more reliable. Fix issue https://github.com/davisking/dlib/issues/925

* This fix suggested by davisking make unit tests more reliable. Fix issue https://github.com/davisking/dlib/issues/925

0.5*MSE.  The only thing this effects is the logging messages that print during
training, which were confusing since the reported loss was half the size you
would expect.

atomics.  This makes the timing code a lot more precise.

minutes in the output.

as input layer specifications.  This will create input tensors with K
channels.

be smaller.  Instead, they now behave like std::vector in that they just change
their nominal size but keep the same memory, only reallocating if they are
resized to something larger than their underlying memory block.

This change makes some uses of dlib faster, in particular, running networks on
a large set of images of differing sizes will now run faster since there won't
be any GPU reallocations, which are notoriously slow.

when we switched everything to std::shared_ptr.  Turns out std::shared_ptr has
some surprising limitations.  This change fixes a bug where the program crashes or hangs
sometimes during program shutdown.

included in the edge graph.  If it isn't then the output labels from
chinese_whispers would be missing faces in this degenerate case.  So basically this fixes a bug
where chinese_whispers(), when called from python, would sometimes return a labels array
that doesn't include labels for all the inputs.