Summary:
This directory is opted-in to clang-format but is not format-clean. This blocks continuous formatting from being enabled on fbcode, and causes hassle for other codemods that leave inconsistent formatting. This diff runs clang-format, which is widely used and considered safe.
If you are unhappy with the formatting of a particular block, please *accept this diff* and then in a stacked commit undo the change and wrap that code in `// clang-format off` and `// clang-format on`, or `/* clang-format off */` and `/* clang-format on */`.
drop-conflicts
Test Plan: sandcastleit
Reviewed By: jerryzh168
Differential Revision: D22311706
fbshipit-source-id: 1ca59a82e96156a4a5dfad70ba3e64d44c5e762a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19817
A lot of files were depending on the JIT's typesystem
because operator.h depends on function_schema.h. However,
this isn't fundamental to the design. This diff tries to
remove the direct depenency and only includes the c10
wrapper helpers in files where it is required.
Reviewed By: smessmer
Differential Revision: D15112247
fbshipit-source-id: 2c53d83e542c32d9a398c8b60dbf40ab7a1cb0f6
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16723
Removed obsolete argument correct_transform_coords in bbox_transform op.
* It was only for backward compatibility. We should not have models using it now.
Differential Revision: D13937430
fbshipit-source-id: 504bb066137ce408c12dc9dcc2e0a513bad9b7ee
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15103
There are two main optimizations in this diff:
1. We generate all anchors for every single spatial grid first, and then apply
NMS to pick 2000 anchors according to RPN_PRE_NMS_TOP_N. By first sorting the
score and picking the 2000 top ones and then lazily generating only the
corresponding anchors is much faster.
2. Transposing bbox_deltas from (num_anchors * 4, H, W) to
(H, W, num_anchors * 4) was also quite slow - taking about 20ms in the RRPN
case when there are lots of anchors which it's negligible for RPN case (like
0.1 ms). Instead of transponsing, performing all operations in the
(num_anchors, H, W) format speeds things up.
For regular RPN scenario, this gives 5x speedup from 5.84ms to 1.18ms a case
with 35 anchors over a 600x600 image.
For rotated boxes with 245 anchors, the runtime down from 80ms to 27ms per
iter.
Reviewed By: newstzpz
Differential Revision: D13428688
fbshipit-source-id: 6006b332925e01a7c9433ded2ff5dc9e6d96f7d3
Summary:
Some more `ATEN_API` additions for hidden visibility.
Running CI tests to see what fails to link.
cc Yangqing mingzhe09088 ezyang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10624
Reviewed By: mingzhe09088
Differential Revision: D9392728
Pulled By: orionr
fbshipit-source-id: e0f0861496b12c9a4e40c10b6e0c9e0df18e8726
Summary:
Breaking out of #8338
This PR is a workaround for a bug with CUDA9.2 + GCC7.
Here is the error this PR fixed:
.../pytorch/caffe2/operators/elementwise_ops.h: In constructor ‘caffe2::BinaryElementwiseWithArgsOp<InputTypes, Context, Functor, OutputTypeMap>::BinaryElementwiseWithArgsOp(const caffe2::OperatorDef&, caffe2::Workspace*)’:
.../pytorch/caffe2/operators/elementwise_ops.h:106:189: error: ‘GetSingleArgument<bool>’ is not a member of ‘caffe2::BinaryElementwiseWithArgsOp<InputTypes, Context, Functor, OutputTypeMap>’
BinaryElementwiseWithArgsOp(const OperatorDef& operator_def, Workspace* ws)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10510
Reviewed By: orionr
Differential Revision: D9319742
Pulled By: mingzhe09088
fbshipit-source-id: ce59e3db14539f071f3c20301e77ca36a6fc3f81
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9403
In BBoxTransform and GenerateProposal ops, clip_boxes makes sure the bbox fits
within the images. For rotated boxes, this doesn't always make sense as there
could be multiple ways to clip a rotated box within an image boundary.
Moreover, clipping to a horizontal box means we leave out pixels of interest
potentially. Therefore, we clip only boxes with angle almost equal to 0 (with a
specified `angle_thresh` tolerance).
Reviewed By: pjh5
Differential Revision: D8828588
fbshipit-source-id: 39c1eafdb5d39d383780faa0a47e76149145e50c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9056
Closes https://github.com/pytorch/pytorch/pull/9056
Updates bbox_transform for rotated boxes with angle info to normalize the
predicted angle to be within [angle_bound_lo, angle_bound_hi] range.
Reviewed By: pjh5
Differential Revision: D8706240
fbshipit-source-id: f3ee834cf362736136e285f0f8f0c063af94a879
Summary:
Updated bbox_transform op to match detectron training code better.
- Set apply_scale=False and correct_transform_coords=True to match detectron training/inference code.
Reviewed By: wat3rBro
Differential Revision: D6782894
fbshipit-source-id: 053d9847bf2b3c62a535499017a8413d78871ee0
Summary:
- Moved mask-rcnn inference operators to open source caffe2.
- Registered GeneratedProposalsOp as GenerateProposals in addition to GenerateProposalsCPP.
Reviewed By: rbgirshick
Differential Revision: D6747190
fbshipit-source-id: be98d6b56b5b53b13af46e839f5ceaf27f7fddc3
Summary: TSIA - it used to cause build errors.
Reviewed By: pietern
Differential Revision: D6652354
fbshipit-source-id: fd291f662e3793b6d11a7e02e1acc741c027a1fd
