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Model Compiler - README updates (#418)
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* Model Compiler README updates

* Update README.md
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@LakshmiKumar23
LakshmiKumar23 authored Jan 12, 2021
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Expand Up @@ -22,20 +22,42 @@ MIVisionX allows hundreds of different [OpenVX](https://www.khronos.org/registry

<p align="center"><img width="100%" src="../docs/images/runtime.png" /></p>

## Pre-requisites
### Pre-requisites

* Ubuntu `16.04` / `18.04` or CentOS `7.5` / `7.6`
* [MIVisionX](https://github.com/GPUOpen-ProfessionalCompute-Libraries/MIVisionX#build--install-mivisionx) - Install MIVisionX
* [MIVisionX](https://github.com/GPUOpen-ProfessionalCompute-Libraries/MIVisionX#build--install-mivisionx) - Install MIVisionX
* python3
```
% apt-get install protobuf-compiler libprotoc-dev
% pip3 install pytz numpy future
```

**Note:** MIVisionX installs model compiler scripts at `/opt/rocm/mivisionx/model_compiler/python/`
**Note:** Model compiler supports Python3. Anyone using python2 might face an issue during the model conversion

#### Caffe

* python3
* numpy
* protobuf
* google

```
% pip3 install google
% pip3 install protobuf
% pip3 install numpy
```


#### ONNX

* python3
* numpy
* onnx

```
% pip install onnx numpy
% pip3 install onnx
% pip3 install numpy
```

**Note:** ONNX Models are available at [ONNX Model Zoo](https://github.com/onnx/models)
Expand All @@ -53,6 +75,7 @@ MIVisionX allows hundreds of different [OpenVX](https://www.khronos.org/registry
**Note:** NNEF Models are available at [NNEF Model Zoo](https://github.com/KhronosGroup/NNEF-Tools/tree/master/models#nnef-model-zoo)
**Note:** NNEF Parser is compatible with python3. Anyone using python2 might face an issue during the model conversion


## Model Compiler Samples - Run Efficient Inference

In this [sample](../samples/model_compiler_samples#mivisionx-model-compiler-samples), we will learn how to run inference efficiently using OpenVX and OpenVX Extensions. The sample will go over each step required to convert a pre-trained neural net model into an OpenVX Graph and run this graph efficiently on any target hardware. In this [sample](../samples/model_compiler_samples#mivisionx-model-compiler-samples), we will also learn about AMD MIVisionX which delivers open source implementation of OpenVX and OpenVX Extensions along with MIVisionX Neural Net Model Compiler & Optimizer.
Expand All @@ -71,27 +94,34 @@ In this [sample](../samples/model_compiler_samples#mivisionx-model-compiler-samp
To convert a pre-trained caffemodel into AMD NNIR model:

```
% python caffe_to_nnir.py <net.caffeModel> <nnirOutputFolder> --input-dims <n,c,h,w> [--verbose <0|1>]
% python3 caffe_to_nnir.py <net.caffeModel> <nnirOutputFolder> --input-dims <n,c,h,w> [OPTIONS]
OPTIONS:
--verbose <0|1> [defualt: 0]
--node_type_append <0|1> [default: 0; appends node type name to output names
```

#### ONNX

To convert an ONNX model into AMD NNIR model:

```
% python onnx_to_nnir.py <model.onnx> <nnirModelFolder> [OPTIONS]
% python3 onnx_to_nnir.py <model.onnx> <nnirModelFolder> [OPTIONS]
OPTIONS:
--input_dims n,c,h,w
--node_type_append 0/1 [default: 0; appends node type name to output names]
--node_type_append <0|1> [default: 0; appends node type name to output names]
```

#### NNEF

To convert a NNEF model into AMD NNIR model:

```
% python3 nnef_to_nnir.py <nnefInputFolder> <nnirOutputFolder>
% python3 nnef_to_nnir.py <nnefInputFolder> <nnirOutputFolder> [OPTIONS]
OPTIONS:
--node_type_append <0|1> [default: 0; appends node type name to output names]
```

**Note:** If you want to create NNEF models from pre-trained caffe or tensorflow models, use [NNEF Converter](https://github.com/KhronosGroup/NNEF-Tools) or try NNEF models at [NNEF Model Zoo](https://github.com/KhronosGroup/NNEF-Tools/tree/master/models#nnef-model-zoo)
Expand All @@ -101,33 +131,33 @@ To convert a NNEF model into AMD NNIR model:
To update batch size in AMD NNIR model:

```
% python nnir_update.py --batch-size <N> <nnirModelFolder> <nnirModelFolderN>
% python3 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>
% python3 nnir_update.py --fuse-ops <1> <nnirModelFolderN> <nnirModelFolderFused>
```

To quantize the model to float 16

```
% python nnir_update.py --convert-fp16 <1> <nnirModelFolderN> <nnirModelFolderFused>
% python3 nnir_update.py --convert-fp16 <1> <nnirModelFolderN> <nnirModelFolderFused>
```

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

```
% python nnir_update.py --slice-groups <1> <nnirModelFolderFused> <nnirModelFolderSliced>
% python3 nnir_update.py --slice-groups <1> <nnirModelFolderFused> <nnirModelFolderSliced>
```

### Step 3 - Convert AMD NNIR to OpenVX C code

To convert an AMD NNIR model into OpenVX C code:

```
% python nnir_to_openvx.py --help
% python3 nnir_to_openvx.py --help
Usage: python nnir_to_openvx.py [OPTIONS] <nnirInputFolder> <outputFolder>
Expand Down Expand Up @@ -156,27 +186,27 @@ Usage: python nnir_to_openvx.py [OPTIONS] <nnirInputFolder> <outputFolder>
* Step 1: Convert net.caffemodel into NNIR model using the following command

```
% python caffe_to_nnir.py <net.caffeModel> <nnirOutputFolder> --input-dims n,c,h,w [--verbose 0|1]
% python3 caffe_to_nnir.py <net.caffeModel> <nnirOutputFolder> --input-dims n,c,h,w [--verbose 0|1]
```

* Step 2: Compile NNIR model into OpenVX C code with CMakelists.txt for compiling and building inference library

```
% python nnir_to_openvx.py <nnirModelFolder> <nnirModelOutputFolder>
% python3 nnir_to_openvx.py <nnirModelFolder> <nnirModelOutputFolder>
```

* Step 3: cmake and make the project inside the nnirModelOutputFolder

```
% cd nnirModelOutputFolder
% cmake .
% make
% cd nnirModelOutputFolder
% cmake .
% make
```

* Step 4: Run anntest application for testing the inference with input and output tensor

```
% ./anntest weights.bin
% ./anntest weights.bin
```

* Step 5: The shared C library (libannmodule.so) can be used in any customer application
Expand All @@ -186,7 +216,7 @@ Usage: python nnir_to_openvx.py [OPTIONS] <nnirInputFolder> <outputFolder>
Generate OpenVX and test code that can be used dump and compare raw tensor data:

```
% python nnir_to_openvx.py nnirInputFolderFused openvxCodeFolder
% python3 nnir_to_openvx.py nnirInputFolderFused openvxCodeFolder
% mkdir openvxCodeFolder/build
% cd openvxCodeFolder/build
% cmake ..
Expand Down Expand Up @@ -219,7 +249,7 @@ Usage: anntest <weights.bin> [<input-data-file(s)> [<output-data-file(s)>]]<--ad
Generate OpenVX and test code with argmax that can be used dump and compare 16-bit argmax output tensor:

```
% python nnir_to_openvx.py --argmax UINT16 nnirInputFolderFused openvxCodeFolder
% python3 nnir_to_openvx.py --argmax UINT16 nnirInputFolderFused openvxCodeFolder
% mkdir openvxCodeFolder/build
% cd openvxCodeFolder/build
% cmake ..
Expand Down Expand Up @@ -247,7 +277,7 @@ Usage: anntest <weights.bin> [<input-data-file(s)> [<output-data-file(s)>]]]
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 nnir_to_openvx.py --argmax lut-rgb.txt nnirInputFolderFused openvxCodeFolder
% python3 nnir_to_openvx.py --argmax lut-rgb.txt nnirInputFolderFused openvxCodeFolder
% mkdir openvxCodeFolder/build
% cd openvxCodeFolder/build
% cmake ..
Expand Down

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