update #1
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Fix dispatch breakage
* support for any one cuda device * Revert "support for any one cuda device" This reverts commit 0197e4e. * 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
* 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)
* fixes to dockerfile * replaces local installation by git clone
…cker (#612) * add a FORCE_CUDA flag Following discussion [here](#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
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 tqdm in line32 : RUN pip install ninja yacs cython matplotlib opencv-python tqdm
* 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
* fix py2 * fix py2
* 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 color jitter augmentation * fix spelling
* 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
* 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
* 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
…ME.md' (#701) * 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'
* Add ImportError to generic except * Black formatter * OpenCV is no longer optional * Update README w.r.t. FPN_POST_NMS_PER_BATCH
* Update SigmoidFocalLoss_cuda.cu line 120 converted to long to support windows10 * Update SigmoidFocalLoss_cuda.cu removed old line and converted to long to support windows10 * Update SigmoidFocalLoss_cuda.cu Converted to long to support windows10 * Update INSTALL.md Step_By_Step Guide installation on windows * Update INSTALL.md updated Clone of architecture to be the one of this repo which has the fixes needed for Windows10 * Update INSTALL.md How to check what cuda version to install with pytorch * Update INSTALL.md
* Implement multi-scale testing(bbox aug) like Detectron. * Add comment. * Fix missing cfg after merge.
* fix the bug in segmentation_mask when initializing BinaryMaskList with an empty list * Add empty binary mask handling * Strict indexing in BinaryMaskList - raise error for empty indexing
remove `_C.MODEL.BACKBONE.USE_GN = False`, since it is not used in the code. ``` liliang@liliang-ubuntu:~/liliang_learning/maskrcnn-benchmark/maskrcnn_benchmark$ grep -r "USE_GN" Binary file config/__pycache__/defaults.cpython-37.pyc matches config/defaults.py:_C.MODEL.BACKBONE.USE_GN = False config/defaults.py:_C.MODEL.FPN.USE_GN = False config/defaults.py:_C.MODEL.ROI_BOX_HEAD.USE_GN = False config/defaults.py:_C.MODEL.ROI_MASK_HEAD.USE_GN = False Binary file modeling/backbone/__pycache__/backbone.cpython-37.pyc matches modeling/backbone/backbone.py: cfg.MODEL.FPN.USE_GN, cfg.MODEL.FPN.USE_RELU modeling/backbone/backbone.py: cfg.MODEL.FPN.USE_GN, cfg.MODEL.FPN.USE_RELU Binary file modeling/roi_heads/mask_head/__pycache__/roi_mask_feature_extractors.cpython-37.pyc matches modeling/roi_heads/mask_head/roi_mask_feature_extractors.py: use_gn = cfg.MODEL.ROI_MASK_HEAD.USE_GN Binary file modeling/roi_heads/box_head/__pycache__/roi_box_feature_extractors.cpython-37.pyc matches modeling/roi_heads/box_head/roi_box_feature_extractors.py: use_gn = cfg.MODEL.ROI_BOX_HEAD.USE_GN modeling/roi_heads/box_head/roi_box_feature_extractors.py: use_gn = cfg.MODEL.ROI_BOX_HEAD.USE_GN ```
* Add requests for compatibility with the demo notebook * Add requests for compatibility with the demo notebook
* do not split strings so that format() works as expected * address flake8 indentation issue
Fixed missing line importing F (torchvision.transforms.functional) used in line 43
now can inference on cpu by setting MODEL.DEVICE cpu
* keep the resize function the same in test time the same with training time * add vertical flip
…f the images during training` (#836) * Update setup.py * add a FORCE_CUDA flag Following discussion [here](#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 * Update trainer.py * Update trainer.py * Update trainer.py * Update trainer.py * Update trainer.py * Update setup.py
* Merge branch 'master' of /home/braincreator/projects/maskrcnn-benchmark with conflicts. * update Dockerfile * save config in output dir * replace string format with os.path.join
* Merge branch 'master' of /home/braincreator/projects/maskrcnn-benchmark with conflicts. * update Dockerfile * save labels to output dir * save labels on main process only
* Add mixed precision training instruction Highlight the model support for mixed precision training and add instruction to enable it on README * Add link to in-depth MP training guide
* 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 * Revert "Pr150" * Add RLE size check for SegmentationMask
Fix typo, highlight words in python language, add hyperlink
From https://github.com/facebookresearch/maskrcnn-benchmark/blob/master/INSTALL.md: 1. additionally install tqdm, cudatoolkit, apex 2. add FORCE_CUDA argument
* print pretty evaluation results * remove `name` attribute from Datasets * add a newline before printing results
* Added panoptic segmentation demo * Removed overlong notebook outputs
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