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vfdev-5 3da2e09c9b Added antialias flag to interpolate (CPU only, bilinear) (#65142) 
Summary:
Description:
- Added antialias flag to interpolate (CPU only)
  - forward and backward for bilinear mode
  - added tests

### Benchmarks

<details>
<summary>
Forward pass, CPU. PTH interpolation vs PIL
</summary>

Cases:
- PTH RGB 3 Channels, float32 vs PIL RGB uint8 (apply vs pears)
- PTH 1 Channel, float32 vs PIL 1 Channel Float

Code: https://gist.github.com/vfdev-5/b173761a567f2283b3c649c3c0574112

```
# OMP_NUM_THREADS=1 python bench_interp_aa_vs_pillow.py

Torch config: PyTorch built with:
  - GCC 9.3
  - C++ Version: 201402
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - CPU capability usage: AVX2
  - CUDA Runtime 11.1
  - NVCC architecture flags: -gencode;arch=compute_75,code=sm_75
  - CuDNN 8.0.5
  - Build settings: BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, CXX_COMPILER=/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_PYTORCH_QNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.10.0, USE_CUDA=1, USE_CUDNN=1, USE_EIGEN_FOR_BLAS=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=OFF, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=0, USE_OPENMP=ON,

Num threads: 1
[------------------------ Downsampling: torch.Size([1, 3, 906, 438]) -> (320, 196) ------------------------]
                                                  |  Reference, PIL 8.3.2, mode: RGB  |  1.10.0a0+git1e87d91
1 threads: -------------------------------------------------------------------------------------------------
      channels_first contiguous torch.float32     |                2.9                |          3.1
      channels_last non-contiguous torch.float32  |                2.6                |          3.6

Times are in milliseconds (ms).

[------------------------ Downsampling: torch.Size([1, 3, 906, 438]) -> (460, 220) ------------------------]
                                                  |  Reference, PIL 8.3.2, mode: RGB  |  1.10.0a0+git1e87d91
1 threads: -------------------------------------------------------------------------------------------------
      channels_first contiguous torch.float32     |                3.4                |          4.0
      channels_last non-contiguous torch.float32  |                3.4                |          4.8

Times are in milliseconds (ms).

[------------------------ Downsampling: torch.Size([1, 3, 906, 438]) -> (120, 96) -------------------------]
                                                  |  Reference, PIL 8.3.2, mode: RGB  |  1.10.0a0+git1e87d91
1 threads: -------------------------------------------------------------------------------------------------
      channels_first contiguous torch.float32     |                1.6                |          1.8
      channels_last non-contiguous torch.float32  |                1.6                |          1.9

Times are in milliseconds (ms).

[----------------------- Downsampling: torch.Size([1, 3, 906, 438]) -> (1200, 196) ------------------------]
                                                  |  Reference, PIL 8.3.2, mode: RGB  |  1.10.0a0+git1e87d91
1 threads: -------------------------------------------------------------------------------------------------
      channels_first contiguous torch.float32     |                9.0                |          11.3
      channels_last non-contiguous torch.float32  |                8.9                |          12.5

Times are in milliseconds (ms).

[----------------------- Downsampling: torch.Size([1, 3, 906, 438]) -> (120, 1200) ------------------------]
                                                  |  Reference, PIL 8.3.2, mode: RGB  |  1.10.0a0+git1e87d91
1 threads: -------------------------------------------------------------------------------------------------
      channels_first contiguous torch.float32     |                2.1                |          1.8
      channels_last non-contiguous torch.float32  |                2.1                |          3.4

Times are in milliseconds (ms).

[--------------- Downsampling: torch.Size([1, 1, 906, 438]) -> (320, 196) --------------]
                                 |  Reference, PIL 8.3.2, mode: F  |  1.10.0a0+git1e87d91
1 threads: ------------------------------------------------------------------------------
       contiguous torch.float32  |               1.2               |          1.0

Times are in milliseconds (ms).

[--------------- Downsampling: torch.Size([1, 1, 906, 438]) -> (460, 220) --------------]
                                 |  Reference, PIL 8.3.2, mode: F  |  1.10.0a0+git1e87d91
1 threads: ------------------------------------------------------------------------------
       contiguous torch.float32  |               1.4               |          1.3

Times are in milliseconds (ms).

[--------------- Downsampling: torch.Size([1, 1, 906, 438]) -> (120, 96) ---------------]
                                 |  Reference, PIL 8.3.2, mode: F  |  1.10.0a0+git1e87d91
1 threads: ------------------------------------------------------------------------------
       contiguous torch.float32  |              719.9              |         599.9

Times are in microseconds (us).

[-------------- Downsampling: torch.Size([1, 1, 906, 438]) -> (1200, 196) --------------]
                                 |  Reference, PIL 8.3.2, mode: F  |  1.10.0a0+git1e87d91
1 threads: ------------------------------------------------------------------------------
       contiguous torch.float32  |               3.7               |          3.5

Times are in milliseconds (ms).

[-------------- Downsampling: torch.Size([1, 1, 906, 438]) -> (120, 1200) --------------]
                                 |  Reference, PIL 8.3.2, mode: F  |  1.10.0a0+git1e87d91
1 threads: ------------------------------------------------------------------------------
       contiguous torch.float32  |              834.4              |         605.7

Times are in microseconds (us).

```

</details>

Code is moved from torchvision: https://github.com/pytorch/vision/pull/4208

Pull Request resolved: https://github.com/pytorch/pytorch/pull/65142

Reviewed By: mrshenli

Differential Revision: D32432405

Pulled By: jbschlosser

fbshipit-source-id: b66c548347f257c522c36105868532e8bc1d4c6d
2021-11-17 09:10:15 -08:00
..
__init__.py [ONNX] Remove the argument use_external_data_format of export() method entirely. (#67080) (#67811) 2021-11-15 17:20:04 -08:00
operators.py
symbolic_caffe2.py [ONNX] use consistent quoting for string literals (#57757) (#58695) 2021-05-27 12:06:42 -07:00
symbolic_helper.py [ONNX] Use human readable enum for dtype scalars (#66822) (#67807) 2021-11-08 14:37:05 -08:00
symbolic_opset7.py [ONNX] Fix remainder export (#64230) (#64578) 2021-09-30 21:08:54 -07:00
symbolic_opset8.py Added antialias flag to interpolate (CPU only, bilinear) (#65142) 2021-11-17 09:10:15 -08:00
symbolic_opset9.py Added antialias flag to interpolate (CPU only, bilinear) (#65142) 2021-11-17 09:10:15 -08:00
symbolic_opset10.py Added antialias flag to interpolate (CPU only, bilinear) (#65142) 2021-11-17 09:10:15 -08:00
symbolic_opset11.py Added antialias flag to interpolate (CPU only, bilinear) (#65142) 2021-11-17 09:10:15 -08:00
symbolic_opset12.py [ONNX] Support opset 15 (#67121) (#67805) 2021-11-08 14:37:00 -08:00
symbolic_opset13.py [ONNX] Use human readable enum for dtype scalars (#66822) (#67807) 2021-11-08 14:37:05 -08:00
symbolic_opset14.py [ONNX] Fix for batchnorm training op mode (#52758) (#62760) 2021-08-18 13:29:07 -07:00
symbolic_opset15.py [ONNX] Support opset 15 (#67121) (#67805) 2021-11-08 14:37:00 -08:00
symbolic_registry.py Allow registration of custom symbolics for prim namespace (#64460) (#66139) 2021-10-08 07:41:06 -07:00
utils.py [ONNX] Remove the argument use_external_data_format of export() method entirely. (#67080) (#67811) 2021-11-15 17:20:04 -08:00