GitLab

* fix a little bug about resume

When resuming, we need to start from the last epoch not 0.

* the second way for resuming

the second way for resuming

* scriptability checks

* tests upds

* linter upds

* linter upds

* upds

* tuple list changes

* linter updates

Bugfix on GroupedBatchSampler for corner case where there are not enough examples in a category to form a batch (#1677)

* Make R-CNN models less verbose in script mode

* Fix typo in warning message

* remove windows skips from video_utils tests, now that they pass

* replace lambda in videoclips in order to be pickled on windows and update tests

* out_place checks

* lint ups

* tgz updates

* tgz updates

* tgz updates

* Update KeypointRCNN weights with correct file

* Fix model

* Fix

* add scriptable transform: center_crop

* add test: center_crop

* add scriptable transform: five_crop

* add scriptable transform: five_crop

* add scriptable transform: fix minor issues

The current link to the dataset is dead. The change links to the dataset's author's personal page, which describe the dataset and is also referenced at https://github.com/fyu/lsun.

* Add Deformable Convolution operation.

This adds the deformable convolution operation, as described in Deformable Convolutional Networks (https://arxiv.org/abs/1703.06211).

- The code is based on https://github.com/open-mmlab/mmdetection/blob/master/mmdet/ops/dcn/src/deform_conv_cuda.cpp ; the whole code was modified and refactored to remove redundancies and increase clarity, and to adapt it to torchvision.

- The CPU part is a direct copy of the CUDA code; it might make sense to do follow-up adjustments in the CPU code to simplify it / optimize it, or to reuse functionality between CPU and CUDA..

- We also add tests (with a non-trivial set of parameters); they can be made more robust by randomizing the parameters and executing multiple times.

* Update DeformConv to be more consistent w/ Conv2d

* rename some variables and arguments to match Conv2d;
* add optional bias;
* add weight, offset and bias as module parameters;
* remove the n_para...

* Add tests for results in script vs eager mode

This copies some logic from `test_jit.py` to check that a TorchScript'ed
model's outputs are the same as outputs from the model in eager mode.

To support differences in TorchScript / eager mode outputs, an
`unwrapper` function can be provided per-model.

* Fix inception, use PYTORCH_TEST_WITH_SLOW

* Update

* Remove assertNestedTensorObjectsEqual

* Add PYTORCH_TEST_WITH_SLOW to CircleCI config

* Add MaskRCNN unwrapper

* fix prec args

* Remove CI changes

* update

* Update

* remove expect changes

* Fix tolerance bug

* Fix breakages

* Fix quantized resnet

* Fix merge errors and simplify code

* DeepLabV3 has been fixed

* Temporarily disable jit compilation

* Generalised for custom dataset

* Typo, redundant code, sensible default

* Args for name of train and val dir

* almost working...

* respond to comments

* add empty tensor op, handle different output types in generalized rcnn

* clean ups

* address comments

* more changes

* it's working!

* torchscript bugs

* add script/ eager test

* eval script model

* fix flake

* division import

* py2 compat

* update test, fix arange bug

* import division statement

* fix linter

* fixes

* changes needed for JIT master

* cleanups

* remove imagelist_to

* requested changes

* Make FPN backwards-compatible and torchscript compatible

We remove support for feature channels=0, but support for it was already a bit limited

* Fix ONNX regression

* code changes to enable onnx export for keypoint rcnn

* add import

* fix copy paste error

* disable test

* disable profiling

* Update test_onnx.py

* remove changes that induced BC

* Re-enable tests that have been disabled

* Remove outdated comment

* Remove outdated comment