Update README.md - mivisionx_inference_analyzer (#501)

* Update README.md

* Readme Updates - Codacy fix

Co-authored-by: Kiriti Gowda <[email protected]>

Author: LakshmiKumar23

apps/mivisionx_inference_analyzer/README.md

@@ -52,19 +52,19 @@ MIVisionX provides developers with [docker images](https://hub.docker.com/u/mivi
 
 * Start docker with display
 
-``` 
-% sudo docker pull mivisionx/ubuntu-16.04:latest
-% xhost +local:root
-% sudo docker run -it --device=/dev/kfd --device=/dev/dri --cap-add=SYS_RAWIO --device=/dev/mem --group-add video --network host --env DISPLAY=unix$DISPLAY --privileged --volume $XAUTH:/root/.Xauthority --volume /tmp/.X11-unix/:/tmp/.X11-unix mivisionx/ubuntu-16.04:latest
-```
+  ``` 
+  % sudo docker pull mivisionx/ubuntu-16.04:latest
+  % xhost +local:root
+  % sudo docker run -it --device=/dev/kfd --device=/dev/dri --cap-add=SYS_RAWIO --device=/dev/mem --group-add video --network host --env DISPLAY=unix$DISPLAY --privileged --volume $XAUTH:/root/.Xauthority --volume /tmp/.X11-unix/:/tmp/.X11-unix mivisionx/ubuntu-16.04:latest
+  ```
 
 * Test display with MIVisionX sample
 
-``` 
-% export PATH=$PATH:/opt/rocm/mivisionx/bin
-% export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/rocm/mivisionx/lib
-% runvx /opt/rocm/mivisionx/samples/gdf/canny.gdf
-```
+  ``` 
+  % export PATH=$PATH:/opt/rocm/mivisionx/bin
+  % export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/rocm/mivisionx/lib
+  % runvx /opt/rocm/mivisionx/samples/gdf/canny.gdf
+  ```
 
 * Run [Samples](#samples)
 
@@ -73,7 +73,7 @@ MIVisionX provides developers with [docker images](https://hub.docker.com/u/mivi
 ### Command Line Interface (CLI)
 
 ``` 
-usage: python mivisionx_inference_analyzer.py 	[-h]
+usage: python3 mivisionx_inference_analyzer.py 	[-h]
 												--model_format MODEL_FORMAT 
                                        			--model_name MODEL_NAME 
                                        			--model MODEL 
@@ -115,7 +115,7 @@ usage: python mivisionx_inference_analyzer.py 	[-h]
 ### Graphical User Interface (GUI)
 
 ``` 
-usage: python mivisionx_inference_analyzer.py
+usage: python3 mivisionx_inference_analyzer.py
 ```
 
 <p align="center"><img width="75%" src="../../docs/images/analyzer-4.png" /></p>
@@ -138,23 +138,24 @@ usage: python mivisionx_inference_analyzer.py
 
 * **Step 1:** Clone MIVisionX Inference Analyzer Project
 
-``` 
-	% cd && mkdir sample-1 && cd sample-1
-	% git clone https://github.com/kiritigowda/MIVisionX-inference-analyzer.git
-```
+  ``` 
+  % cd && mkdir sample-1 && cd sample-1
+  % git clone https://github.com/GPUOpen-ProfessionalCompute-Libraries/MIVisionX
+  % cd MIVisionX/apps/mivisionx_inference_analyzer/
+  ```
 
-	**Note:**
+  **Note:**
 
   + MIVisionX needs to be pre-installed
   + MIVisionX Model Compiler & Optimizer scripts are at `/opt/rocm/mivisionx/model_compiler/python/`
   + ONNX model conversion requires ONNX install using `pip install onnx` 	
 
 * **Step 2:** Download pre-trained SqueezeNet ONNX model from [ONNX Model Zoo](https://github.com/onnx/models#open-neural-network-exchange-onnx-model-zoo) - [SqueezeNet Model](https://s3.amazonaws.com/download.onnx/models/opset_8/squeezenet.tar.gz)
 
-``` 
-	% wget https://s3.amazonaws.com/download.onnx/models/opset_8/squeezenet.tar.gz
-	% tar -xvf squeezenet.tar.gz
-```
+  ``` 
+  % wget https://s3.amazonaws.com/download.onnx/models/opset_8/squeezenet.tar.gz
+  % tar -xvf squeezenet.tar.gz
+  ```
 
   **Note:** pre-trained model - `squeezenet/model.onnx`
 
@@ -164,15 +165,15 @@ usage: python mivisionx_inference_analyzer.py
 
   + View inference analyzer usage
 
-``` 
-	% cd ~/sample-1/MIVisionX-inference-analyzer/
-	% python mivisionx_inference_analyzer.py -h
-```
+    ``` 
+    % cd ~/sample-1/MIVisionX-inference-analyzer/
+    % python3 mivisionx_inference_analyzer.py -h
+    ```
 
   + Run SqueezeNet Inference Analyzer
 
     ``` 
-    % python mivisionx_inference_analyzer.py --model_format onnx --model_name SqueezeNet --model ~/sample-1/squeezenet/model.onnx --model_input_dims 3,224,224 --model_output_dims 1000,1,1 --label ./sample/labels.txt --output_dir ~/sample-1/ --image_dir ../../data/images/AMD-tinyDataSet/ --image_val ./sample/AMD-tinyDataSet-val.txt --hierarchy ./sample/hierarchy.csv --replace yes
+    % python3 mivisionx_inference_analyzer.py --model_format onnx --model_name SqueezeNet --model ~/sample-1/squeezenet/model.onnx --model_input_dims 3,224,224 --model_output_dims 1000,1,1 --label ./sample/labels.txt --output_dir ~/sample-1/ --image_dir ../../data/images/AMD-tinyDataSet/ --image_val ./sample/AMD-tinyDataSet-val.txt --hierarchy ./sample/hierarchy.csv --replace yes
     ```
 
 <p align="center"><img width="100%" src="../../docs/images/sample-1-4.png" /></p>
@@ -187,34 +188,36 @@ usage: python mivisionx_inference_analyzer.py
 
 * **Step 1:** Clone MIVisionX Inference Analyzer Project
 
-``` 
-	% cd && mkdir sample-2 && cd sample-2
-	% git clone https://github.com/kiritigowda/MIVisionX-inference-analyzer.git
-```
+  ``` 
+  % cd && mkdir sample-2 && cd sample-2
+  % git clone https://github.com/GPUOpen-ProfessionalCompute-Libraries/MIVisionX
+  % cd MIVisionX/apps/mivisionx_inference_analyzer/
+  ```
 
-	**Note:**
+  **Note:**
 
   + MIVisionX needs to be pre-installed
   + MIVisionX Model Compiler & Optimizer scripts are at `/opt/rocm/mivisionx/model_compiler/python/`
+ 
 * **Step 2:** Download pre-trained VGG 16 caffe model - [VGG_ILSVRC_16_layers.caffemodel](http://www.robots.ox.ac.uk/~vgg/software/very_deep/caffe/VGG_ILSVRC_16_layers.caffemodel)
 
-``` 
-	% wget http://www.robots.ox.ac.uk/~vgg/software/very_deep/caffe/VGG_ILSVRC_16_layers.caffemodel
-```
+  ``` 
+  % wget http://www.robots.ox.ac.uk/~vgg/software/very_deep/caffe/VGG_ILSVRC_16_layers.caffemodel
+  ```
 
 * **Step 3:** Use the command below to run the inference analyzer
 
   + View inference analyzer usage
 
     ``` 
     % cd ~/sample-2/MIVisionX-inference-analyzer/
-    % python mivisionx_inference_analyzer.py -h
+    % python3 mivisionx_inference_analyzer.py -h
     ```
 
   + Run VGGNet-16 Inference Analyzer
 
     ``` 
-    % python mivisionx_inference_analyzer.py --model_format caffe --model_name VggNet-16-Caffe --model ~/sample-2/VGG_ILSVRC_16_layers.caffemodel --model_input_dims 3,224,224 --model_output_dims 1000,1,1 --label ./sample/labels.txt --output_dir ~/sample-2/ --image_dir ../../data/images/AMD-tinyDataSet/ --image_val ./sample/AMD-tinyDataSet-val.txt --hierarchy ./sample/hierarchy.csv --replace yes
+    % python3 mivisionx_inference_analyzer.py --model_format caffe --model_name VggNet-16-Caffe --model ~/sample-2/VGG_ILSVRC_16_layers.caffemodel --model_input_dims 3,224,224 --model_output_dims 1000,1,1 --label ./sample/labels.txt --output_dir ~/sample-2/ --image_dir ../../data/images/AMD-tinyDataSet/ --image_val ./sample/AMD-tinyDataSet-val.txt --hierarchy ./sample/hierarchy.csv --replace yes
     ```
 
 <p align="center"><img width="100%" src="../../docs/images/sample-2-2.png" /></p>
@@ -227,41 +230,44 @@ usage: python mivisionx_inference_analyzer.py
 
 * **Step 1:** Clone MIVisionX Inference Analyzer Project
 
-``` 
-	% cd && mkdir sample-3 && cd sample-3
-	% git clone https://github.com/kiritigowda/MIVisionX-inference-analyzer.git
-```
+  ``` 
+  % cd && mkdir sample-3 && cd sample-3
+  % git clone https://github.com/GPUOpen-ProfessionalCompute-Libraries/MIVisionX
+  % cd MIVisionX/apps/mivisionx_inference_analyzer/
+  ```
 
-	**Note:**
+  **Note:**
 
   + MIVisionX needs to be pre-installed
   + MIVisionX Model Compiler & Optimizer scripts are at `/opt/rocm/mivisionx/model_compiler/python/`
   + NNEF model conversion requires [NNEF python parser](https://github.com/KhronosGroup/NNEF-Tools/tree/master/parser#nnef-parser-project) installed
 
 * **Step 2:** Download pre-trained VGG 16 NNEF model
 
-``` 
-	% mkdir ~/sample-3/vgg16
-	% cd ~/sample-3/vgg16
-	% wget https://sfo2.digitaloceanspaces.com/nnef-public/vgg16.onnx.nnef.tgz
-	% tar -xvf vgg16.onnx.nnef.tgz
-```
+  ``` 
+  % mkdir ~/sample-3/vgg16
+  % cd ~/sample-3/vgg16
+  % wget https://sfo2.digitaloceanspaces.com/nnef-public/vgg16.onnx.nnef.tgz
+  % tar -xvf vgg16.onnx.nnef.tgz
+  ```
 
 * **Step 3:** Use the command below to run the inference analyzer
 
   + View inference analyzer usage
 
     ``` 
-	% cd ~/sample-3/MIVisionX-inference-analyzer/
-	% python mivisionx_inference_analyzer.py -h
+    % cd ~/sample-3/MIVisionX-inference-analyzer/
+    % python3 mivisionx_inference_analyzer.py -h
     ```
 
   + Run VGGNet-16 Inference Analyzer
 
     ``` 
-	% python mivisionx_inference_analyzer.py --model_format nnef --model_name VggNet-16-NNEF --model ~/sample-3/vgg16/ --model_input_dims 3,224,224 --model_output_dims 1000,1,1 --label ./sample/labels.txt --output_dir ~/sample-3/ --image_dir ../../data/images/AMD-tinyDataSet/ --image_val ./sample/AMD-tinyDataSet-val.txt --hierarchy ./sample/hierarchy.csv --replace yes
+    % python3 mivisionx_inference_analyzer.py --model_format nnef --model_name VggNet-16-NNEF --model ~/sample-3/vgg16/ --model_input_dims 3,224,224 --model_output_dims 1000,1,1 --label ./sample/labels.txt --output_dir ~/sample-3/ --image_dir ../../data/images/AMD-tinyDataSet/ --image_val ./sample/AMD-tinyDataSet-val.txt --hierarchy ./sample/hierarchy.csv --replace yes
     ```
 
 * **Preprocessing the model:** Use the --add/--multiply option to preprocess the input images
 
-		% python mivisionx_inference_analyzer.py --model_format nnef --model_name VggNet-16-NNEF --model ~/sample-3/vgg16/ --model_input_dims 3,224,224 --model_output_dims 1000,1,1 --label ./sample/labels.txt --output_dir ~/sample-3/ --image_dir ../../data/images/AMD-tinyDataSet/ --image_val ./sample/AMD-tinyDataSet-val.txt --hierarchy ./sample/hierarchy.csv --replace yes --add [-2.1179,-2.0357,-1.8044] --multiply [0.0171,0.0175,0.0174]
+  ```
+  % python3 mivisionx_inference_analyzer.py --model_format nnef --model_name VggNet-16-NNEF --model ~/sample-3/vgg16/ --model_input_dims 3,224,224 --model_output_dims 1000,1,1 --label ./sample/labels.txt --output_dir ~/sample-3/ --image_dir ../../data/images/AMD-tinyDataSet/ --image_val ./sample/AMD-tinyDataSet-val.txt --hierarchy ./sample/hierarchy.csv --replace yes --add [-2.1179,-2.0357,-1.8044] --multiply [0.0171,0.0175,0.0174]
+  ```