* 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

* add GroupNorm

* add GroupNorm -- sort out yaml files

* use torch.nn.GroupNorm instead, replace 'use_gn' with 'conv_block' and use 'BaseStem'&'Bottleneck' to simply codes

* modification on 'group_norm' and 'conv_with_kaiming_uniform' function

* modification on yaml files in configs/gn_baselines/ and reduce the amount of indentation and code duplication

* use 'kaiming_uniform' to initialize resnet, disable gn after fc layer, and add dilation into ResNetHead

* agnostic-regression for bbox

* please set 'STRIDE_IN_1X1' to be 'False' when backbone use GN

* add README.md for GN

* add dcn from mmdetection

* add documentation for finetuning cityscapes

* add documentation for finetuning cityscapes

* add documentation for finetuning cityscapes

* add 'once_differentiable' for dcn and modify 'configs/cityscapes/README.md'

* 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

* add GroupNorm

* add GroupNorm -- sort out yaml files

* use torch.nn.GroupNorm instead, replace 'use_gn' with 'conv_block' and use 'BaseStem'&'Bottleneck' to simply codes

* modification on 'group_norm' and 'conv_with_kaiming_uniform' function

* modification on yaml files in configs/gn_baselines/ and reduce the amount of indentation and code duplication

* use 'kaiming_uniform' to initialize resnet, disable gn after fc layer, and add dilation into ResNetHead

* agnostic-regression for bbox

* please set 'STRIDE_IN_1X1' to be 'False' when backbone use GN

* add README.md for GN

* add dcn from mmdetection

* add documentation for finetuning cityscapes

* add documentation for finetuning cityscapes

* add documentation for finetuning cityscapes

* Initial multi-precision training

Adds fp16 support via apex.amp
Also switches communication to apex.DistributedDataParallel

* Add Apex install to dockerfile

* Fixes from @fmassa review

Added support to tools/test_net.py
SOLVER.MIXED_PRECISION -> DTYPE \in {float32, float16}
apex.amp not installed now raises ImportError

* Remove extraneous apex DDP import

* Move to new amp API

* proposals from RPN per image during training

* README

* Update README for setting FPN_POST_NMS_TOP_N_TRAIN

* Update README.md

* removing extra space change

* add color jitter augmentation

* fix spelling

* 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

* add GroupNorm

* add GroupNorm -- sort out yaml files

* use torch.nn.GroupNorm instead, replace 'use_gn' with 'conv_block' and use 'BaseStem'&'Bottleneck' to simply codes

* modification on 'group_norm' and 'conv_with_kaiming_uniform' function

* modification on yaml files in configs/gn_baselines/ and reduce the amount of indentation and code duplication

* use 'kaiming_uniform' to initialize resnet, disable gn after fc layer, and add dilation into ResNetHead

* agnostic-regression for bbox

* please set 'STRIDE_IN_1X1' to be 'False' when backbone use GN

* add README.md for GN

* add dcn from mmdetection

https://gist.github.com/botcs/a59f3f59e22e5df93e3e5e4f86718af3

* fix py2

* fix py2

* 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

* 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

* add GroupNorm

* add GroupNorm -- sort out yaml files

* use torch.nn.GroupNorm instead, replace 'use_gn' with 'conv_block' and use 'BaseStem'&'Bottleneck' to simply codes

* modification on 'group_norm' and 'conv_with_kaiming_uniform' function

* modification on yaml files in configs/gn_baselines/ and reduce the amount of indentation and code duplication

* use 'kaiming_uniform' to initialize resnet, disable gn after fc layer, and add dilation into ResNetHead

* agnostic-regression for bbox

* please set 'STRIDE_IN_1X1' to be 'False' when backbone use GN

* add README.md for GN

There were two `RESNETS` sections, which overrode each other, leading to error messages like: 

```
RuntimeError: Error(s) in loading state_dict for GeneralizedRCNN:
	size mismatch for backbone.fpn.fpn_inner1.weight: copying a param with shape torch.Size([256, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([1024, 256, 1, 1]).
...
	size mismatch for roi_heads.mask.feature_extractor.mask_fcn1.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 1024, 3, 3]).
```

This just combines them back into a single section, while maintaining all param values. That got the model running again for me.