* add autoaugment, which is validated on faster-rcnn

* split yolov3 loss

* Polish Reader to simplify preprocessing logic.
#  sample_transforms-> make batch -> batch_transforms in Reader.
* Clean some code
* Imporve yolov3_r50vd_dcn_obj365_pretrained_coco 41.4 to 41.8.
* Update all configs.

Fix hang bug in parallel_map.py when multiprocessing mode used and some consumer exit abnormally (#106)

* Optimize data transforms for yolo training

* Simplify and add docstring

* update code & doc

* add multi-scale test

* Add face detection configs and main code.
* Integration multi-scale evaluation.

* add CropImageWithDataAnchorSampling
* Refine bbox sampling code
* Update comments
* fix import and variable problem

* fix rcnn eval on voc

* update comment

* fix bug in sharedmemory
* fix bug in sharedmemory

* Add full configs example & refine doc

* refine docs

* add_ssd_config_demo

# Add VGG-SSD on VOC and COCO dataset.
# Add config and model zoo. 
# Refine bbox2out and draw_bbox:
    - Add bbox de-normalize in bbox2out function.
    - Remove bbox de-normalize in draw_bbox.

* fix yolov3 voc eval in train

* fix comment

* add voc_eval and yolo_darknet_voc

* add yolov3_darknet_voc in MODEL_ZOO

* fix default im_size

* fix MODEL_ZOO note

* fix is_bbox_normalized

* extract map to map_utils.py

* update yolov3_dorknet_voc mixup

* add yolov3_r34_voc

* add yolov3_mobilenet_v1_voc

* fix drop empty in VAL mode

* use cfg.num_classes

* assert metric valid

* enable difficulty can be None

* add comment for bbox_eval

* num_classes in retinanet

* Fix ResizeImage and data transform for SSD
* Fix code style

* Rename object_detection to PaddleDetection
* Small fix for doc