* update anakin-engine interfaces for content-dnn

test=develop

* support only-gpu mode of Anakin

modify eltwise parse

test=develop

* modification for thread-safe

test=develop

* Integrated template instance

test=develop

* increase template parameters

test=develop

* support MLU predictor

test=develop

* update anakin cmake files

test=develop

* update TargetWrapper::set_device

* update the initialization of anakin subgraph

test=develop

* use the default constructor of base class

test=develop

* cherry-pick commit from 88770542

* cherry-pick commit from 3f0b97df

* cherry-pick from 16691:Anakin subgraph support yolo_v3 and faster-rcnn

(cherry picked from commit 8643dbc2)

* Cherry-Pick from 16662 : Anakin subgraph cpu support

(cherry picked from commit 7ad182e1)

* Cherry-pick from 1662, 16797.. : add anakin int8 support

(cherry picked from commit e14ab180)

* Cherry-pick from 16813 : change singleton to graph RegistBlock
test=release/1.4

(cherry picked from commit 4b9fa423)

* Cherry Pick : 16837 Support ShuffleNet and MobileNet-v2

Support ShuffleNet and MobileNet-v2, test=release/1.4

(cherry picked from commit a6fb066f)

* Cherry-pick : anakin subgraph add opt config layout argument #16846
test=release/1.4

(cherry picked from commit 8121b3ec)

* 1. add shuffle_channel_detect

(cherry picked from commit 6efdea89)

* update shuffle_channel op convert, test=release/1.4

(cherry picked from commit e4726a06)

* Modify symbol export rules

test=develop

2. refine trt code
test=develop

test=develop

test=develop

* Add Seq2BatchFunctor, which will be used in WarpCTCOp.

* Implement WrapCTCFunctor and WrapCTCKernel.

* Add unittest of warpctc_op.

* Modify the check_output inferface in python unittest framework to allow check a subset of outputs.

* Use absolute offset lod in warpctc_op and related functors.

* Refine the comments of warpctc_op.

* The new python unittest supports checking a subset of the outputs, so revoke the previous change.

* Rename the transform from LoDTensor to Tensor with shape [max_sequence_length, num_sequences, sequence_width] to PaddingSequenceFunctor.

* Update to the newest codes.

* Rename the PaddingSequenceFunctor to PaddingLoDTensorFunctor and remove the computation of dimensions out of the functos.