compute pixelwise gradients w.r.t. to classes
classify + localize, with notebook

- define nets for classification and gradient backprop on imagenet 1k
- given an image, classify then compute pixelwise gradients w.r.t.
  top activation
- demo in notebook
drop deprecated compute_gradients()
input + output pd data frames, adopt compute_feats_grads
support classification / classification + gradient localization use

- accept localize_def flag/param for the gradient backpropagation /
  localization net
- configure localization (when given)
- compute pixewise gradients w.r.t. top activation (when configured for
  localization)
fix redundancy and hardcoding of batch size
compute gradients to order (default to top activation), store complete

Compute the pixelwise gradient w.r.t. the classes in the `grad_classes`
field of the input dataframe and fallback to the top activation if the
field is empty.

Store the signed gradient at every pixel/channel (downstream processing
can max/sum/whatever).
compute gradients w.r.t. class score itself, with only one forward pass

- drop softmax loss + label input from backprop localization net
- run single forward pass
- for backprop, set top loss for the classes to localize and run the
  backward passes