test_dense: write output to file instead of stdout
initial (very(/too?) brittle) test for test_dense mode
test_dense: extend to sort-of-work on networks with padding (by not considering areas influenced by padding)
add nin_imagenet (with-padding) test case using test_dense
boda_base: add dims size function
caffeif/do_predict(): refactor and allow x/y dims with size != in pred layer.

note: still (maybe) todo (1) adjust image annotation to correspond to feature pel used, and (2) deal with lack of softmax at non-prob layers
caffeif: add max_num_disp as stopgap/hack for dealwith with non-prob layer results auto-top-N.

note that the existing prob thresholds are still active, but are generally much less than the activation values for non-prob layers. yikes! i guess you could always set them manually to more reasonable values ...
conv_common: factor out unchecked_out_box_to_in_boxes() function from cap_app
cap_app: use newly-factored-out func
caffeif: inline global get_pipe() into member func wrapper; more progress on do_predict detection flow