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.

A few change to prepare for batch inference:

* Modify the return type of batch non max suppression to be tuple of tensors
  so it can be reused for both stages of faster rcnn without any confusion
  in the semantics implied the the keys used to represent the tensors.
* Allow dynamic number of anchors (boxes) in addition to dynamic batch size.
* Remove a redundant dynamic batch size test.

* Change dictionnaries iteritems() to items()

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