diff --git a/amd_openvx_extensions/amd_winml.sln b/amd_openvx_extensions/amd_winml.sln
index f489269e83..dcaf2853e9 100644
--- a/amd_openvx_extensions/amd_winml.sln
+++ b/amd_openvx_extensions/amd_winml.sln
@@ -56,7 +56,7 @@ Global
HideSolutionNode = FALSE
EndGlobalSection
GlobalSection(ExtensibilityGlobals) = postSolution
- SolutionGuid = {3FB58533-A51A-34B5-ACE3-6B1BFD797DB1}
SolutionGuid = {F164F9CB-D02C-4BC0-9122-54E6CEA7DA72}
+ SolutionGuid = {3FB58533-A51A-34B5-ACE3-6B1BFD797DB1}
EndGlobalSection
EndGlobal
diff --git a/amd_openvx_extensions/amd_winml/README.md b/amd_openvx_extensions/amd_winml/README.md
index 1a13c80ff0..41d77f731e 100644
--- a/amd_openvx_extensions/amd_winml/README.md
+++ b/amd_openvx_extensions/amd_winml/README.md
@@ -1,11 +1,11 @@
# AMD WinML Extension
The AMD WinML (vx_winml) is an OpenVX module that implements a mechanism to access WinML functionality as OpenVX kernels. These kernels can be accessed from within OpenVX framework using OpenVX API call [vxLoadKernels](https://www.khronos.org/registry/vx/specs/1.0.1/html/da/d83/group__group__user__kernels.html#gae00b6343fbb0126e3bf0f587b09393a3)(context, "vx_winml").
-
+
WinML extension will allow developers to import a pre-trained ONNX model into an OpenVX graph and add hundreds of different pre & post processing `vision`/`generic`/`user-defined` functions, available in OpenVX and OpenCV interop, to the input and output of the neural net model. This will allow developers to build an end to end application for inference.
-
+
## List of WinML-interop kernels
The following is a list of WinML functions that have been included in the vx_winml module.
diff --git a/amd_openvx_extensions/amd_winml/include/vx_ext_winml.h b/amd_openvx_extensions/amd_winml/include/vx_ext_winml.h
index 6cb573de26..1dc6341734 100644
--- a/amd_openvx_extensions/amd_winml/include/vx_ext_winml.h
+++ b/amd_openvx_extensions/amd_winml/include/vx_ext_winml.h
@@ -54,12 +54,12 @@ extern "C" {
* \retval vx_node A node reference. Any possible errors preventing a successful creation should be checked using \ref vxGetStatus*/
VX_API_ENTRY vx_node VX_API_CALL vxExtWinMLNode_OnnxToMivisionX
(
- vx_graph graph,
- vx_scalar modelLocation,
- vx_scalar inputTensorName,
- vx_scalar outputTensorName,
- vx_tensor inputTensor,
- vx_tensor outputTensor,
+ vx_graph graph,
+ vx_scalar modelLocation,
+ vx_scalar inputTensorName,
+ vx_scalar outputTensorName,
+ vx_tensor inputTensor,
+ vx_tensor outputTensor,
vx_scalar deviceKind
);
diff --git a/amd_openvx_extensions/amd_winml/samples/README.md b/amd_openvx_extensions/amd_winml/samples/README.md
index 8ba56aa3b6..826ac426a8 100644
--- a/amd_openvx_extensions/amd_winml/samples/README.md
+++ b/amd_openvx_extensions/amd_winml/samples/README.md
@@ -8,7 +8,7 @@ Get ONNX models from [ONNX Model Zoo](https://github.com/onnx/models)
* Use [Netron](https://lutzroeder.github.io/netron/) to open the model.onnx
* Look at Model Properties to find Input & Output Tensor Name (data_0 - input; softmaxout_1 - output)
* Look at output tensor dimensions (n,c,h,w - [1,1000,1,1] for softmaxout_1)
-* Use the label file - Labels.txt and sample image - car.JPEG to run samples
+* Use the label file - [data\Labels.txt](data/Labels.txt) and sample image - data\car.JPEG to run samples
### winML-image.gdf - Single Image Inference
@@ -32,7 +32,7 @@ read input_image FULL_PATH_TO\data\car.JPEG
data modelLocation = scalar:STRING,FULL_PATH_TO\squeezenet\model.onnx:view,resultWindow
````
-* Add full path to the data\Labels.txt provided in this folder in line 34
+* Add full path to the [data\Labels.txt](data/Labels.txt) provided in this folder in line 34
````
data labelLocation = scalar:STRING,FULL_PATH_TO\data\Labels.txt
````
@@ -54,7 +54,69 @@ Make the below changes in the `winML-live.gdf` file to run the inference
data modelLocation = scalar:STRING,FULL_PATH_TO\squeezenet\model.onnx:view,resultWindow
````
-* Add full path to the data\Labels.txt provided in this folder in line 25
+* Add full path to the [data\Labels.txt](data/Labels.txt) provided in this folder in line 25
````
data labelLocation = scalar:STRING,FULL_PATH_TO\data\Labels.txt
````
+
+## Sample - FER+ Emotion Recognition
+
+* Download the [FER+ Emotion Recognition](https://onnxzoo.blob.core.windows.net/models/opset_8/emotion_ferplus/emotion_ferplus.tar.gz) ONNX Model
+* Use [Netron](https://lutzroeder.github.io/netron/) to open the model.onnx
+ * Look at Model Properties to find Input & Output Tensor Name (Input3 - input; Plus692_Output_0 - output)
+ * Look at output tensor dimensions (n,c,h,w - [1,8] for Plus692_Output_0)
+* Use the label file - [data/emotions.txt](data/emotions.txt) to run sample
+
+This sample is in [Graph Description Format](../../../utilities/runvx#amd-runvx) (gdf)
+
+#### usage
+````
+runvx.exe -frames:LIVE winML-live-emotions.gdf
+````
+
+**NOTE:**
+Make the below changes in the `winML-live-emotions.gdf` file to run the inference
+
+* Add full path to the FER+ Emotion Recognition ONNX model downloaded in line 16
+````
+data modelLocation = scalar:STRING,FULL_PATH_TO\emotion_ferplus\model.onnx:view,inputImageWindow
+````
+
+* Add full path to the data\emotions.txt provided in this folder in line 25
+````
+data labelLocation = scalar:STRING,FULL_PATH_TO\data\emotions.txt
+````
+
+## Sample - VGG19
+
+* Download the [VGG-19](https://s3.amazonaws.com/download.onnx/models/opset_8/vgg19.tar.gz) ONNX Model
+* Use [Netron](https://lutzroeder.github.io/netron/) to open the model.onnx
+ * Look at Model Properties to find Input & Output Tensor Name (data_0 - input; prob_1 - output)
+ * Look at output tensor dimensions (n,c,h,w - [1,1000] for prob_1)
+* Use the label file - [data/Labels.txt](data/Labels.txt) to run sample
+
+This sample is in [Graph Description Format](../../../utilities/runvx#amd-runvx) (gdf)
+
+#### usage
+````
+runvx.exe -v winML-image-vgg19.gdf
+````
+
+**NOTE:**
+Make the below changes in the `winML-live-vgg19.gdf` file to run the inference
+
+* Add full path to the data\bird.JPEG image provided in this folder in line 11
+````
+read input_image FULL_PATH_TO\data\bird.JPEG
+````
+
+* Add full path to the VGG 19 ONNX model downloaded in line 21
+````
+data modelLocation = scalar:STRING,FULL_PATH_TO\vgg19\model.onnx:view,resultWindow
+````
+
+* Add full path to the [data\Labels.txt](data\Labels.txt) provided in this folder in line 33
+````
+data labelLocation = scalar:STRING,FULL_PATH_TO\data\Labels.txt
+````
+````
diff --git a/amd_openvx_extensions/amd_winml/samples/data/emotions.txt b/amd_openvx_extensions/amd_winml/samples/data/emotions.txt
new file mode 100644
index 0000000000..06cc4e4eab
--- /dev/null
+++ b/amd_openvx_extensions/amd_winml/samples/data/emotions.txt
@@ -0,0 +1,8 @@
+0,neutral
+1,happiness
+2,surprise
+3,sadness
+4,anger
+5,disgust
+6,fear
+7,contempt
\ No newline at end of file
diff --git a/amd_openvx_extensions/amd_winml/samples/winML-image-vgg19.gdf b/amd_openvx_extensions/amd_winml/samples/winML-image-vgg19.gdf
new file mode 100644
index 0000000000..f92e9587b4
--- /dev/null
+++ b/amd_openvx_extensions/amd_winml/samples/winML-image-vgg19.gdf
@@ -0,0 +1,41 @@
+# Get WinML Extension DLL
+import vx_winml
+
+data result_image = image:640,150,RGB2
+data input_image = image:224,224,RGB2
+data input_tensor = tensor:4,{224,224,3,1},VX_TYPE_FLOAT32,0
+data a = scalar:FLOAT32,1.0
+data b = scalar:FLOAT32,0.0
+data reverse_channel_order = scalar:BOOL,0
+
+read input_image FULL_PATH_TO\data\bird.JPEG
+view input_image inputImageWindow
+view result_image resultWindow
+
+# Use convert to tensor node - convert input image to tensor
+node com.winml.convert_image_to_tensor input_image input_tensor a b reverse_channel_order
+
+# write input tensor
+write input_tensor input_tensor.f32
+
+data modelLocation = scalar:STRING,FULL_PATH_TO\data\vgg19\model.onnx:view,resultWindow
+data modelInputName = scalar:STRING,data_0
+data modelOutputName = scalar:STRING,prob_1
+data output = tensor:2,{1000,1},VX_TYPE_FLOAT32,0
+data deviceKind = scalar:INT32,0
+
+# Use Import ONNX Node to run Inference
+node com.winml.onnx_to_mivisionx modelLocation modelInputName modelOutputName input_tensor output deviceKind
+
+# write output tensor
+write output output_tensor.f32
+
+data labelLocation = scalar:STRING,FULL_PATH_TO\data\Labels.txt
+data top1 = scalar:STRING,TOPK:view,resultWindow
+data top2 = scalar:STRING,TOPK:view,resultWindow
+data top3 = scalar:STRING,TOPK:view,resultWindow
+data top4 = scalar:STRING,TOPK:view,resultWindow
+data top5 = scalar:STRING,TOPK:view,resultWindow
+
+# Use get top K label node to display results
+node com.winml.get_top_k_labels output labelLocation top1 top2 top3 top4 top5
\ No newline at end of file
diff --git a/amd_openvx_extensions/amd_winml/samples/winML-live-emotions.gdf b/amd_openvx_extensions/amd_winml/samples/winML-live-emotions.gdf
new file mode 100644
index 0000000000..ebb8099a8d
--- /dev/null
+++ b/amd_openvx_extensions/amd_winml/samples/winML-live-emotions.gdf
@@ -0,0 +1,31 @@
+# Get WinML Extension DLL
+import vx_winml
+
+# Get input from camera: one for display & another for input
+data input_view = image:1080,720,RGB2:camera,0:view,inputImageWindow
+data input_image = image:64,64,U008:camera,0
+
+data input_tensor = tensor:4,{64,64,1,1},VX_TYPE_FLOAT32,0
+data a = scalar:FLOAT32,1.0
+data b = scalar:FLOAT32,0.0
+data reverse_channel_order = scalar:BOOL,0
+
+# Use convert to tensor node - convert input image to tensor
+node com.winml.convert_image_to_tensor input_image input_tensor a b reverse_channel_order
+
+data modelLocation = scalar:STRING,FULL_PATH_TO\emotion_ferplus\model.onnx:view,inputImageWindow
+data modelInputName = scalar:STRING,Input3
+data modelOutputName = scalar:STRING,Plus692_Output_0
+data output = tensor:2,{8,1},VX_TYPE_FLOAT32,0
+data deviceKind = scalar:INT32,0
+
+# Use Import ONNX Node to run Inference
+node com.winml.onnx_to_mivisionx modelLocation modelInputName modelOutputName input_tensor output deviceKind
+
+data labelLocation = scalar:STRING,FULL_PATH_TO\data\emotions.txt
+data top1 = scalar:STRING,INITIALIZE:view,inputImageWindow
+data top2 = scalar:STRING,INITIALIZE:view,inputImageWindow
+data top3 = scalar:STRING,INITIALIZE:view,inputImageWindow
+
+# Use get top K label node to display results
+node com.winml.get_top_k_labels output labelLocation top1 top2 top3
\ No newline at end of file