* Fix indexing error in nms kernel

Here it is indexing a cuda tensor with CPU indices. It used to work, but 
after https://github.com/pytorch/pytorch/commit/006505bb8f9dcf0f38b32518308d071c8a1ccec6 it results in memory corruption.

* Use the device of other tensors

* add force json option

* fix the same issue as #185

* bug fix

* cityscapes config

* update paths catalog

* discard config change

* organize code for more-datasets

* use better representation for coco-style datasets

* rename coco-style config

* remove import

* chmod 644

* make the config more verbose

* update readme

* rename

* chmod

* make pixel indexes 0-based for bounding box in pascal voc dataset

* replacing all instances of torch.distributed.deprecated with torch.distributed

* replacing all instances of torch.distributed.deprecated with torch.distributed

* readme and predictor.py

* format modification and readme

In maskrcnn_benchmark/modeling/roi_heads/mask_head/inference.py
change line 125-126 to
w = int(box[2] - box[0] + TO_REMOVE)
h = int(box[3] - box[1] + TO_REMOVE)

* fix the same issue as #185

* add fix from the previous change

* Dockerfile with jupyter notebook support

* add instruction of building docker with jupyter

* upload dockerfile with jupyter support

* remove Dockerfile-jupyter

* remove jupyter_notebook_config.py

* update INSTALL.md

* Dockerfile with jupyter notebook support

* add instruction of building docker with jupyter

* upload dockerfile with jupyter support

* remove Dockerfile-jupyter

* remove jupyter_notebook_config.py

* update INSTALL.md

* apply changes in 1123

* update INSTALL.md

* update install.md

add description of the password
add -v flag

* clean up

* remove tensorflow copyright

* add support for pascal voc dataset and evaluate

* optimization for adding voc dataset

* make inference.py dataset-agnostic; add use_difficult option to voc dataset

* handle voc difficult objects correctly

* Remove dependency on lxml plus minor improvements

* More cleanups

* More comments and improvements

* Lint fix

* Move configs to their own folder

* add opencv support to Dockerfile

cv2 is used by predictor.py, so it's required

* install opencv-python from conda

* Add an option to postprocess masks during inference

* Fix COCO evaluation to resize masks ony if needed.

* Fix casting

* Fix minor issues in paste_mask_in_image

* Cast mask to uint8

* Make Masker batch compatible

* Remove warnings and stylistic changes

* fix some minor issues in dockerfile


RUN echo 'debconf debconf/frontend select Noninteractive' | debconf-set-selections
fixes
debconf: unable to initialize frontend: Dialog
debconf: (TERM is not set, so the dialog frontend is not usable.)
debconf: falling back to frontend: Readline
debconf: unable to initialize frontend: Readline
debconf: (This frontend requires a controlling tty.)
debconf: falling back to frontend: Teletype
dpkg-preconfigure: unable to re-open stdin: 

RUN apt-get update -y && apt-get install -y apt-utils
fixes
debconf: delaying package configuration, since apt-utils is not installed

* remove redundant command

* add -y to conda install and pip install

* remove -y from pip install

* added Dockerfile

* intructions for installation with docker

* INSTALL.md: added note to inform nvidia-docker only works with Linux

* dockerfile nccl2 hotfix

* INSTALL.md update

* removes explicit install of NCCL2 as it is included in the CUDA image

* adding registry to hook custom building blocks

* adding customizable rpn head

* support customizable c2 weight loading

* added Dockerfile

* intructions for installation with docker

* adds nccl to dockerfile

* Update Dockerfile

* Update INSTALL.md

## Bug
When I repeatedly invoke build_detection_model function to build the model, I will get the error: `AttributeError: 'ResNet' object has no attribute 'layer1'`. 

## To Reproduce
```
model = build_detection_model(cfg)
model = build_detection_model(cfg)
```

## The reason
The variable `ResNet50StagesTo4` is a global generator expression, so when we build the model secondly, the code executes to Line 82 `for stage_spec in stage_specs:` and `stage_specs` will return empty leading to do not add any stage. Finally, `self._freeze_backbone` try to freeze the backbone by executing `m = getattr(self, "layer" + str(stage_index))` firstly. At the moment, it will throw the AttributeError `AttributeError: 'ResNet' object has no attribute 'layer1'`.

I guess you want to define ResNet50StagesTo4 as the tuple, so I try to fix by add tuple type qualifier.

## The solution
Add the tuple type to `ResNet50StagesTo5`, `ResNet50StagesTo4`, `ResNet50FPNStagesTo5`, `ResNet101FPNStagesTo5`.

I do not know whether there are similar bug existing, so you need to review my solution. Thank you!

* quick fix for area

* Update bounding_box.py

use cuda version torch.randperm to avoid copy from gpu to cpu and a fatal bug in multi-thread cpu version

Replace BoxList argument size with image_size.

Replace baseline with benchmark

Fix BoxList constructor call in README.md