AAM refactoring & benchmark

This PR:

- Removes the `smoothing_pyramid()` possibility for building AAMs. The training images are always rescaled using Gaussian pyramid. The `scaled_shape_models` parameters controls whether the reference frames (shape models) will be scaled or the one of the highest level will be used.
- Adds `pyramid_on_features` parameter. If True, the Gaussian pyramid is extracted from the features image, which is computed at the highest level. If False, the Gaussian pyramid is applied on the intensities image and features are extracted at each level.
- Imports the `benchmark` which currently includes two predefined experiments with alternating inverse compositional: `aam_fastest_alternating` which is based on `scaled_shape_models=True` and `pyramid_on_features=True` and has the smallest computational cost and `aam_best_performance_alternating` which is based on `scaled_shape_models=False` and `pyramid_on_features=True` and returns the best fitting results.

Please test that everything works fine; you can use the benchmark for this! :)

After this PR is merged and everyone is happy, the same changes will be applied to CLMs and SDMs.