Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18512
Ceil and Floor have been supported since version 6 of ONNX: export them using the native onnx ops instead of an Aten op.
Similarly, support for the Where op has been added in version 9, so we don't need to wrap these op in an Aten op.
Reviewed By: houseroad
Differential Revision: D14635130
fbshipit-source-id: d54a2b6e295074a6214b5939b21051a6735c9958
Summary:
Requires https://github.com/onnx/onnx/pull/1377
This PR makes it so that slices with dynamic boundary values can be exported from pytorch and run in caffe2 via ONNX.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11255
Differential Revision: D9790216
Pulled By: jamesr66a
fbshipit-source-id: 6adfcddc5788df4d34d7ca98341077140402a3e2
Summary:
Let's run CI tests to see what fails given the changes that just landed in https://github.com/pytorch/pytorch/pull/10624
cc mingzhe09088 ezyang Yangqing
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10692
Reviewed By: mingzhe09088
Differential Revision: D9423617
Pulled By: orionr
fbshipit-source-id: 3bda1f118d13f8dd8e823727c93167cae747d8cf
Summary:
The broadcast is used by default when the opset version is greater then 6.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10108
Reviewed By: bddppq
Differential Revision: D9176934
Pulled By: houseroad
fbshipit-source-id: b737bd87b0ddc241c657d35856d1273c9950eeba
Summary:
And let Gemm conversion to inspect the input `C` to try converting to FC.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9870
Reviewed By: houseroad
Differential Revision: D9013198
Pulled By: bddppq
fbshipit-source-id: b4c509cfccca238262e1c406b004e66cef256321
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9458
The goal is to support count_include_pad in Caffe2 ONNX backend. This commit contains the first step - support 4-D tensor cases.
AveragePool with count_include_pad can be expressed as PadImage + AveragePool.
Reviewed By: houseroad
Differential Revision: D8852180
fbshipit-source-id: 4db00e9771be7a000a2d92850dfd066d9c9c38bf
* Added support to run ONNX Upsample operator (mode=nearest) in Caffe2
* adding error checks to upsample
* adding error checks to upsample
* adding error checks to upsample
* changing to np.isclose
* Revert onnx submodule update
* still fixing
* Check mappings ONNX -> Caffe2 bear the same argument names
When adding an extra arg to an input ONNX op, if it's not supported in Caffe2, the exporter would just silently pass it to NetDef and ignore it in the implementation. It's pretty error-prone. Caffe2 also has an OpSchema description and we can enforce that all arguments explicitly appear in schema or listed explicitly in Caffe2.
See also https://github.com/caffe2/caffe2/pull/2478
Add test for C2 argument checking
* Some operators do not log arguments, which prevents argument checks.
Invite users to file an issue to fix the schema.
