* add mask fpn r50 2x & minor fix for MODEL ZOO doc (#2747)

* cherry-pick bug fix for release/1.5

* fix mask eval for multi-batch (#2772)

* cherry-pick bug fix for release/1.5

* fix faster rcnn use im_shape (#2779)

* cherry-pick bug fix for release/1.5

* refine bbox_normalize in infer.py (#2781)

* refine bbox_normalize in infer.py

* add is_bbox_normalize

* rename _forward to build

* check callable

* cherry-pick bug fix for release/1.5

* Prevent module instance from modifying global config (#2790)

some of the global config values are reference type, e.g., objects, lists or
dicts, if the created module instances modify those values (see FPN and
spatial_scale), global config will reflect these changes, and instances of the
same class created later will inherit the changed values

* Fix command line parsing for non module options (#2839)

* fix save_inference_model cannot find feed vars (#2842)

* fix save_inference_model cannot find feed vars

* remove comment

* fix format

* add comment for pruned var

* add log for prune

* Fix train+eval in PaddleDetection(#2847)

* resolve conflict merge error

* reset data_feed

* Rename object_detection to PaddleDetection
* Small fix for doc

* Clean up config files

- reorder: batch_size -> data_set -> sample_transforms -> batch_transforms -> worker -> other
- unify bool values, lower case true/false
- remove `null` values
- remove `shuffle` settings, covered by default values

* Update ResNet50 pretrained weight url

* Remove `use_padded_im_info` settings, covered by default value

* Add GC in train.py by defalut and change data co dataset in configs
* Update docs/INSTALL.md
* Enable build_strategy.enable_inplace = True

* add VOC visualize

* fixn ssd_mobilenet_v1_voc.yml

* use default label

* clean TestFeed dataset config

* fix voc default label

* fix format

* fix as review

* revert voc default

* use defult label for all

* enable batch size != 1

* Fix `norm_decay` in config files

should be float value now

* Clean up a bit

mostly max line length related

* refine infer with args

* remove samples

* fix as review

* refine code

* refine args

* move get_test_images to infer.py

* add visualize log

* fix images = []

* fix args

* refine infer.py

* fix yolov3_r34.yml

* Unified object detection framework based on PaddlePaddle.
* Include algo: Faster, Mask, FPN, Cascade, RetinaNet, Yolo v3, SSD.