clarify instructions regarding use of windows for SNPE work.

.vscode/launch.json

@@ -261,7 +261,14 @@
             "type": "python",
             "request": "launch",
             "program": "${file}",
-            "console": "integratedTerminal"
+            "console": "integratedTerminal",
+            "cwd": "D:\\git\\microsoft\\archai\\archai\\tasks\\face_segmentation",
+            "args":[
+                "--dataset_dir", "C:\\datasets\\FaceSynthetics",
+                "--output_dir", "d:\\temp\\face_segmentation",
+                "--search_config", "confs\\snp_search.yaml",
+                "--serial_training"
+            ]
         }
     ]
 }

tasks/face_segmentation/snpe/docker/quantizer/setup.ps1

@@ -173,8 +173,11 @@ $rc = &"$env:windir\system32\where" kubectl.exe 2>&1
 print("where kubectl.exe => $rc")
 if ($rc.ToString().Contains("Could not find files")) {
     Write-Host "kubectl not found, skipping kubectl setup."
-    Write-Host "To build the quantizer docker image on windows you should install the Docker Desktop for Windows"
-    Write-Host "and set it to Linux container mode."
+    if ($IsWindows){
+        Write-Host "You can build the quantizer docker image on Windows if you install the Docker Desktop for Windows"
+        Write-Host "and set it to Linux container mode and you can manage your Azure Kubernetes cluster using kubectl if"
+        Write-Host "you enable the docker desktop Kubernetes support under Settings."
+    }
 }
 else
 {

tasks/face_segmentation/snpe/snpe/convert_onnx.sh

@@ -1,37 +0,0 @@
-#!/bin/bash
-
-MODEL_NAME="model"
-
-if [ "$1" == "--help" ] ; then
-    echo "### Usage: convert_onnx.sh [model_name]"
-    echo "Converts the given onnx model to .dlc then quantizes it."
-    echo "Default model path is 'model/model.onnx'."
-    exit 1
-fi 
-
-if [ "$1" != "" ]; then
-   MODEL_NAME=$1
-fi   
-
-if [ ! -f "model/${MODEL_NAME}.onnx" ]; then
-    echo "### Model does not exist: model/${MODEL_NAME}.onnx"
-    exit 1
-fi
-
-mkdir -p ./snpe_models
-
-# pb 2 dlc
-snpe-onnx-to-dlc \
-    -i "model/${MODEL_NAME}.onnx" \
-    -d input_0 "1,3,256,256" \
-    --input_layout input_0 NCHW \
-    --out_node "output_0" \
-    -o "snpe_models/${MODEL_NAME}.dlc" \
-    #--debug
-
-# quantize
-snpe-dlc-quantize \
-    --input_dlc "snpe_models/${MODEL_NAME}.dlc" \
-    --input_list "data/quant/input_list.txt" \
-    --output_dlc "snpe_models/${MODEL_NAME}.quant.dlc" \
-    --use_enhanced_quantizer

tasks/face_segmentation/snpe/snpe/readme.md

@@ -11,16 +11,12 @@ The best way is using `azcopy`.  You could put them in a datasets folder,
 for example: `d:\datasets\FaceSynthetics`.  Then set your `INPUT_DATASET` environment
 variable pointing to this folder.
 
-1. **Install Android NDK**. You need a working version of `adb` in your PATH.  If you are
-on Windows you can use the VS 2022 installer to install the
-mobile development package which includes the Android NDK
-and `adb` lives in `C:\Microsoft\AndroidSDK\25\platform-tools\`.
-If you are on Linux, just download the zip file from [https://developer.android.com/ndk/downloads/](https://developer.android.com/ndk/downloads/) and unzip it then you can set your `ANDROID_NDK_ROOT` environment variable pointing to the folder containing the unzipped bits.
+1. **Install Android NDK**. You need a working version of `adb` in your PATH.
+Just download the zip file from [https://developer.android.com/ndk/downloads/](https://developer.android.com/ndk/downloads/)
+and unzip it then you can set your `ANDROID_NDK_ROOT` environment variable pointing to the folder containing the
+unzipped bits.
 
-1. **Check Device USB**.  Check you can run `adb shell` to connect to your Snapdragon
-over USB. (You won't be able to do this on WSL2, but you
-can do it from Windows).  So the `convert_onnx.sh` that uses
-the SNPE SDK runs in Ubuntu, but everything else can run on the Windows side.
+1. **Check Device USB**.  Check you can run `adb shell` to connect to your Snapdragon over USB.
 You may need to run `sudo usermod -aG plugdev $LOGNAME`.
 
 1. **Install SNPE SDK on Ubuntu 18.04**.
@@ -67,20 +63,24 @@ bits.  If you plan to use Qualcomm hardware devices then set the `SNPE_ANDROID_R
     - data/quant - the image dataset for quantizing the model
     - snpe_models/model.quant.dlc - the quantized model
 
-1. **Prepare data**. Run `python create_data.py --help`, this scripts creates data for both quantization and test and puts it in  your local experiment folder under `data/test` and `data/quant`.  For example:
+1. **Prepare data**. Run `python create_data.py --help`, this scripts creates data for both
+   quantization and test and puts it in  your local experiment folder under `data/test` and
+   `data/quant`.  For example:
 
     ```
     python create_data.py --input ~/datasets/FaceSynthetics --count 1000 --dim 256
     ```
 
-1. **Convert and quantize model**. You can use `test_snpe.py` to convert a .onnx model to .dlc and quantize it.  For
-example:
+1. **Convert and quantize model**. You can use `test_snpe.py` to convert a .onnx model to .dlc and
+quantize it.  For example:
     ```
     python test_snpe.py --quantize --model model.onnx
     ```
-This can take about 10 minutes depending on the size of your quantization data set and the size of your model.
+This can take about 10 minutes depending on the size of your quantization data set and the size of
+your model.
 
-1. **Run test images on device**. You can use `test_snpe.py` to test your quantized model on a Qualcomm 888 dev board. You can find the dev board id using `adb devices`:
+1. **Run test images on device**. You can use `test_snpe.py` to test your quantized model on a
+Qualcomm 888 dev board. You can find the device id using `adb devices`:
     ```
     python test_snpe.py --device e6dc0375 --images ./data/test --model model.onnx --dlc ./snpe_models/model.quant.dlc
     ```