* Fix data type of variable in edit distance evaluator.
test=develop

* Add unitest for edit_distance python API.
test=develop

* fix spelling errors; test=develop

* Update API.spec

update md5

* Update API.spec

* change the order of api;test=develop

* Remove layers.detection_map API
* Since uers can use fluid.metrics.DetectionMAP to calculate mAP of current-batch and cumulative-batch. layers.detection_map only can calculate cur-batch mAP.

evaluator.py throws warnings and tell users to use DetectionMAP in metrics.py, but it is wrong in metrics.py.
So refine this API in metrics.py.

test=develop

* "add some api reference"

* "fix based on preview"

* "fix evaluator"

* "remove template doc"

* "add some api reference"

* "fix based on preview"

* "fix evaluator"

* "remove template doc"

1. If all bboxes are not difficult ground truth, the users can not define the data layer for this flag and not the input can be None for detection_map API.
2. Set default value for aspect_ratios in prior_box API.
3. Add more check in prior_box API.

* "add metrics"

* "add fluid metrics"

* "add import guards"

* "show warnings"

* "add demo"

* "fix ci"

* "add some details"

* "fix cci"

* "add demo Python"

* "add metrics"

* Fix mAP evaluator bug.

* Fix bug in detection mAP evaluator.

* Fix unit testing.

* Support to set background label index in detection mAP op.

Implement detection mAP evaluator wrapper and unify label format between SSD loss and mAP evaluator  (#8736)

* Implement mAP evalutor Python interface.

* Fix unit testing and uniy label format between SSD loss and mAP evalutor.

* Update doc.

* Add reset and guard to unique_name

* Move Pod Types from DataType enum to Type enum

* Fixed data_type.h

* Fix type in TensorDesc

* Add comment to framework.proto

* Fixed type in data_type.h

* Updated format of type in data_type.h

* Fix var_desc.h

* Fix op_kernel_type.h

* Fixed data_type_transform_test.cc

* Fix operator.h

* Fixed data_type_transform.cc

* Fixed op_kernel_type_test.cc

* Fix operator.cc

* Fixed data_layout_transform_test.cc

* Fix var_desc.cc

* Fixed assign_value_op.cc

* Fixed assign_value_op.h

* fixed protobuf.cc

* Fix data_layout_transform_test.cc and op_kernel_type_test.cc

* Fixed rnn_memory_helper_op.cc

* Fix progrma_desc_test.cc

* Fixed fill_constant_batch_size_like_op.cc

* Fix operator_test.cc

* Fixed fill_constant_op.cc

* Fixed gaussian_random_op.cc

* Fixed uniform_random_op.cc

* Fixed edit_distance_op.cc

* Fixed fill_constant_batch_size_like_op.cc

* Fixed rnn_memory_helper_op.cc

* Fixed chunk_eval_op.cc

* Fixed assign_value_op.cc

* Fixed assign_value_op.h

* Fixed cast_op.h

* Fixed cast_op.h

* Fix fill constant op

* Fixed clang for assign_value_op.cc

* Fix one_hot_op.h

* Fix one_hot_op.cc

* Fix fill_op.cc

* Fixed sum_op.cc

* Fixed sum_op clang

* Fix uniform_random_op.cc

* Fix gaussian_random_op.cc

* Fix backward.cc

* Fix protobuf.cc

* Fixed prune_test.cc

* Fixed op_registry_test.cc

* Fix data_device_transform_test.cu

* Fix travis error

* Fixed one_hot_op.cu

* Fixed op_registry_test.cc

* Fixed nccl_op.cc

* Fixing python tests

* Revert "Fixing python tests"

This reverts commit fccaa4c5.

* Fixing Pybind to remove data type

* Fixing tensor.py

* Updated the new files:

* Resolve error in merge conflict of fill_constant_batch_size_like_op.cc

2. Fix evaluator using 'reduce_sum' op instead of 'mean' op

* "fix decode bug"

* "follow commnet"

* "fix error"

* "fix hook bug"

* fix based comment

* fix copyright

* fix based on comment

2. Add edit distance evaluator to evaluator.py

* add copyright hook

* add copyright hook

* refine copyright hook

* "test copyright hook"

* fix check style

* fix ci

* Simplize system_allocator and fix GPU_INFO

* Remove main_program/startup_program

* Fix optimizer

* Fix CI

* Follow comments

* Unify fluid submodules to fluid module

Change books just use `import fluid`, not submodules

* Remove g_main_program/g_startup_program

Use default_main_program/default_startup_program instead

* Typo

* Fix CI

Change books just use `import fluid`, not submodules

* Stash

* Stash

* Polish Evaluator

* Merge code

* Revert

* is_training to is_test in dropout op

* handle dropout and batch_norm operator when prune pdesc in testing mode

* handle dropout and batch_norm operator when prune pdesc in testing mode

* add get_inference_program method

* fix dropout op

* fix ci

* test data after each batch training

* refine code

* refine test_book3

* fix ci

* follow comments

* Change all `data_type` in Python to `dtype`

* Change `date_type` in C++ to `dtype`

* Refine

* init commit

* change some dir name