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Convert ONNX models into AMD NNIR and OpenVX Code

This tool converts ONNX models to AMD NNIR format and OpenVX code.

You need https://github.com/GPUOpen-ProfessionalCompute-Libraries/amdovx-modules to be able to build and run the generated OpenVX code.

Dependencies

  • numpy
  • onnx (0.2.1+)

How to use?

To convert an ONNX model into AMD NNIR model:

% python onnx2nnir.py model.pb nnirModelFolder

To convert a caffemodel into AMD NNIR model:

% python caffe2nnir.py <net.caffeModel> <nnirOutputFolder> --input-dims n,c,h,w [--verbose 0|1]

To update batch size in AMD NNIR model:

% python nnir-update.py --batch-size N nnirModelFolder nnirModelFolderN

To fuse operations in AMD NNIR model (like batch normalization into convolution):

% python nnir-update.py --fuse-ops 1 nnirModelFolderN nnirModelFolderFused

To workaround groups using slice and concat operations in AMD NNIR model:

% python nnir-update.py --slice-groups 1 nnirModelFolderFused nnirModelFolderSliced

To convert an AMD NNIR model into OpenVX C code:

% python --help

Usage: python nnir2openvx.py [OPTIONS] <nnirInputFolder> <outputFolder>

  OPTIONS:
    --argmax UINT8                    -- argmax at the end with 8-bit output
    --argmax UINT16                   -- argmax at the end with 16-bit output
    --argmax <fileNamePrefix>rgb.txt  -- argmax at the end with RGB color mapping using LUT
    --argmax <fileNamePrefix>rgba.txt -- argmax at the end with RGBA color mapping using LUT
    --help                            -- show this help message

  LUT File Format (RGB): 8-bit R G B values one per each label in text format
    R0 G0 B0
    R1 G1 B1
    ...

  LUT File Format (RGBA): 8-bit R G B A values one per each label in text format
    R0 G0 B0 A0
    R1 G1 B1 A1
    ...

Here are few examples of OpenVX C code generation

Generate OpenVX and test code that can be used dump and compare raw tensor data:

% python nnir2openvx.py nnirInputFolderFused openvxCodeFolder
% mkdir openvxCodeFolder/build
% cd openvxCodeFolder/build
% cmake ..
% make
% ./anntest

Usage: anntest <weights.bin> [<input-data-file(s)> [<output-data-file(s)>]]]

   <input-data-file>: is filename to initialize tensor
     .jpg or .png: decode and initialize for 3 channel tensors
         (use %04d in fileName to when batch-size > 1: batch index starts from 0)
     other: initialize tensor with raw data from the file

   <output-data-file>[,<reference-for-compare>,<maxErrorLimit>,<rmsErrorLimit>]:
     <referece-to-compare> is raw tensor data for comparision
     <maxErrorLimit> is max absolute error allowed
     <rmsErrorLimit> is max RMS error allowed
     <output-data-file> is filename for saving output tensor data
       '-' to ignore
       other: save raw tensor into the file

% ./anntest ../weights.bin input.f32 output.f32,reference.f32,1e-6,1e-9
...

Generate OpenVX and test code with argmax that can be used dump and compare 16-bit argmax output tensor:

% python nnir2openvx.py --argmax UINT16 nnirInputFolderFused openvxCodeFolder
% mkdir openvxCodeFolder/build
% cd openvxCodeFolder/build
% cmake ..
% make
% ./anntest

Usage: anntest <weights.bin> [<input-data-file(s)> [<output-data-file(s)>]]]

   <input-data-file>: is filename to initialize tensor
     .jpg or .png: decode and initialize for 3 channel tensors
         (use %04d in fileName to when batch-size > 1: batch index starts from 0)
     other: initialize tensor with raw data from the file

   <output-data-file>[,<reference-for-compare>,<percentErrorLimit>]:
     <referece-to-compare> is raw tensor data of argmax output for comparision
     <percentMismatchLimit> is max mismatch (percentage) allowed
     <output-data-file> is filename for saving output tensor data
       '-' to ignore
       other: save raw tensor into the file

% ./anntest ../weights.bin input-%04d.png output.u16,reference.u16,0.01
...

Generate OpenVX and test code with argmax and LUT that is designed for semantic segmentation use cases. You can dump output in raw format or PNGs and additionally compare with reference data in raw format.

% python nnir2openvx.py --argmax lut-rgb.txt nnirInputFolderFused openvxCodeFolder
% mkdir openvxCodeFolder/build
% cd openvxCodeFolder/build
% cmake ..
% make
% ./anntest

Usage: anntest <weights.bin> [<input-data-file(s)> [<output-data-file(s)>]]]

   <input-data-file>: is filename to initialize tensor
     .jpg or .png: decode and initialize for 3 channel tensors
         (use %04d in fileName to when batch-size > 1: batch index starts from 0)
     other: initialize tensor with raw data from the file

   <output-data-file>[,<reference-for-compare>,<percentErrorLimit>]:
     <referece-to-compare> is raw tensor data of LUT output for comparision
     <percentMismatchLimit> is max mismatch (percentage) allowed
     <output-data-file> is filename for saving output tensor data
       .png: save LUT output as PNG file(s)
         (use %04d in fileName when batch-size > 1: batch index starts from 0)
       '-' to ignore
       other: save raw tensor into the file

% ./anntest ../weights.bin input-%04d.png output.rgb,reference.rgb,0.01
...
% ./anntest ../weights.bin input-%04d.png output-%04d.png,reference.rgb,0.01
...

Currently supported

Models

Support the below models from https://github.com/onnx/models

  • resnet
  • inception
  • alexnet
  • densenet
  • sqeezenet

Operators

Supported ONNX operators are:

  • Conv
  • Relu
  • MaxPool
  • AveragePool
  • GlobalAveragePool
  • LRN
  • BatchNormalization
  • Concat
  • Sum
  • Add
  • Sub
  • Mul
  • Softmax
  • Dropout

License

Copyright (c) 2018 Advanced Micro Devices, Inc. All rights reserved.

Use of this source code is governed by the MIT License that can be found in the LICENSE file.