Create inference_single_cvimage.py to inference on the single image.

## A simple class FCOS, mainly to `inference_single_cvimage`.
## 创建一个简单的类 FCOS，用于对单张图像进行推理，减少对 maskrcnn_benchmark 的依赖。

Fix typo: tpye() --> type()

[flake8](http://flake8.pycqa.org) testing of https://github.com/tianzhi0549/FCOS on Python 3.7.1

$ __flake8 . --count --select=E9,F63,F72,F82 --show-source --statistics__
```
./maskrcnn_benchmark/structures/segmentation_mask.py:70:74: F821 undefined name 'tpye'
                "Type of `masks` argument could not be interpreted:%s" % tpye(masks)
                                                                         ^
./docker/docker-jupyter/jupyter_notebook_config.py:5:5: F821 undefined name 'get_config'
c = get_config()
    ^
2     F821 undefined name 'get_config'
2
```
__E901,E999,F821,F822,F823__ are the "_showstopper_" [flake8](http://flake8.pycqa.org) issues that can halt the runtime with a SyntaxError, NameError, etc. These 5 are different from most other flake8 issues which are merely "style violations" -- useful for readability but they do not effect runtime safety.
* F821: undefined name `name`
* F822: undefined name `name` in `__all__`
* F823: local variable name referenced before assignment
* E901: SyntaxError or IndentationError
* E999: SyntaxError -- failed to compile a file into an Abstract Syntax Tree

* support RLE and binary mask

* do not convert to numpy

* be consistent with Detectron

* delete wrong comment

* [WIP] add tests for segmentation_mask

* update tests

* minor change

* Refactored segmentation_mask.py

* Add unit test for segmentation_mask.py

* Add RLE support for BinaryMaskList

* PEP8 black formatting

* Minor patch

* Use internal  that handles 0 channels

* Fix polygon slicing

add tqdm in line32 : RUN pip install ninja yacs cython matplotlib opencv-python tqdm

Fix a bug.
Romove the echo command in line 36: RUN conda install pytorch-nightly cudatoolkit=${CUDA} -c pytorch 
To enable conda installation of pytorch-nightly.

* add a FORCE_CUDA flag

Following discussion [here](https://github.com/facebookresearch/maskrcnn-benchmark/issues/167), this seemed the best solution

* Update Dockerfile

* Update setup.py

* add FORCE_CUDA as an ARG

* 	modified:   docker/Dockerfile
	modified:   setup.py

* small fix to readme of demo

* remove test print

* keep ARG_CUDA

* remove env value and use the one from ARG

* keep same formatting as source

* change proposed by @miguelvr

* Update INSTALL.md

This reverts commit f0318794.

* fixes to dockerfile

* replaces local installation by git clone

* Merge branch 'master' of /home/braincreator/projects/maskrcnn-benchmark with conflicts.

* rolls back the breaking AT dispatch changes (#555)

* revert accidental docker changes

* revert accidental docker changes (2)

* support for any one cuda device

* Revert "support for any one cuda device"

This reverts commit 0197e4e2ef18ec41cc155f3ae2a0face5b77e1e9.

* support runnning for anyone cuda device

* using safe CUDAGuard rather than intrinsic CUDASetDevice

* supplement a header dependency (test passed)

* Support for  arbitrary GPU device.

* Support for arbitrary GPU device.

* add docs for two method to control devices

Fix dispatch breakage

* Added a timer to benchmark model inference time in addition to total runtime.

* Updated FBNet configs and included some baselines benchmark results.

* Added a unit test for detectors.

* Add links to the models

* fix INSTALL.md

* fix PR

* Update INSTALL.md

Using existing get_world_size to prevent AttributeError 'torch.distributed' has no attribute 'is_initialized'. (#511)

* Add new section "Projects using maskrcnn-benchmark".

* Update README.md

update the format.

* Update README.md

* Add coco_2017_train and coco_2017_val

* Update README.md

Add the instructions about COCO_2017

* Update the pip install.
Adding tqdm which is used in engine/inference.py