Comments (1)
I suggest several operators for reference:
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CTC Loss, this OP is supported by mxnet, but before PyTorch 1.0, users must compile by themselves.
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Deformable Convolution (and also DCNV2), Same as 1, users need compiling, and mxnet does not provide CPU implementation version.
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Image wrapAffine based on Homography matrix. If this one is implemented, users can implement their image augmentations like image crop, translation, rotating and even scaling based on GPU.
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Related Issues (20)
- Does these operators respect the asynchronous execution in MXNet? HOT 7
- Not working with MXNet nightly build (1.5.0b20181222) HOT 6
- Lack of comments HOT 2
- [Question] Using types other than float32? HOT 10
- Does it work if custom op and the framework are compiled with different version of GCC? HOT 1
- ROIAlign custom op runs slowly HOT 13
- [Feature request] Gradient check HOT 4
- LICENSE Problem
- Low performance in gpu mode HOT 4
- Not working with multiple processes HOT 13
- Leveraging framework specific math helpers HOT 1
- Build Failed in GCC5. Incomplete type Error. HOT 1
- Question on Example on PyTorch HOT 4
- Question on ATTEN SAMPLER based on MobulaOP HOT 3
- The crash when training model on multiple GPUs HOT 1
- compile error HOT 5
- Is MobulaOP support cupy? HOT 2
- undefined symbol: MXShallowCopyNDArray HOT 2
- Implementation ideas of creating Operator HOT 6
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