GitLab

* Creates a new batch_decode method in SSD Meta architecture that can handle
  dynamic batch size.
* use combined_shapes in _get_feature_maps_spatial_dims method to handle
  dynamic batch image_size.
* Add dynamic batch size tests to check preprocess, predict and postprocess
  methods in SSD Meta architecture.

* Adds a util function to compute a mix of dynamic and static shapes
  preferring static when available.
* Uses batch_multiclass_non_max_suppression function in postprocess_rpn
  instead of looping over static batch shape and performing
  multiclass_non_max_suppression.
* Adds a new helper function _unpad_proposals_and_sample_boxclassifier_batch
  to sample from a batch of tensors possibly containing paddings.
* Tests batch inference with various configurations of static shape via
  unittests.

Add back six dependency and change pbtxt files to reflect new way of using own data to train object recognition models

Also clarifies usage on label maps. In a nutshell, label map IDs
should start at index 1 (and not include 0).

iteritems() -> items()

* make batcher compatible for py3

* make prefetcher and operations in ops compatible for py3

* use six.iteritem

* make all tests in  compatible with py3

* simplify usage of six and modify import order

* add back the space line

For details see our paper:
"Speed/accuracy trade-offs for modern convolutional object detectors."
Huang J, Rathod V, Sun C, Zhu M, Korattikara A, Fathi A, Fischer I,
Wojna Z, Song Y, Guadarrama S, Murphy K, CVPR 2017
https://arxiv.org/abs/1611.10012