# MIVisionX ONNX Model Validation

 ## Usage:

        MIVisionX-WinML-Validate.exe [options]  --m <ONNX.model full path>
                                                --i <model input tensor name>
                                                --o <model output tensor name>
                                                --s <output tensor size in (n,c,h,w)>
                                                --l <label.txt full path>
                                                --f <image frame full path>
                                                --d <Learning Model Device Kind <DirectXHighPerformance>> [optional]

## MIVisionX ONNX Model Validation Parameters

        --m/--model                     -- onnx model full path [required]
        --i/--inputName                 -- model input tensor name [required]
        --o/--outputName                -- model output tensor name [required]
        --s/--outputSize                -- model output tensor size <n,c,h,w> [required]
        --l/--label                     -- label.txt file full path [required]
        --f/--imageFrame                -- imageFrame.png file full path [required]
        --d/--deviceKind                -- Learning Model Device Kind <0-4> [optional]
                                         0 - Default
                                         1 - Cpu
                                         2 - DirectX
	                                     3 - DirectXHighPerformance
                                         4 - DirectXMinPower

## MIVisionX ONNX Model Validation Options

        --h/--help      -- Show full help

## Sample

Get ONNX models from [ONNX Model Zoo](https://github.com/onnx/models)

### Sample - SqeezeNet

* Download the [SqueezeNet](https://s3.amazonaws.com/download.onnx/models/opset_8/squeezenet.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; 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 the MIVisionX WinML Validation
* Use --d 0 if only CPU available, else use --d 3 for GPU inference

````
        MIVisionX-WinML-Validate.exe [options]  --m \full-path-to-model\model.onnx
                                                --i data_0
                                                --o softmaxout_1
                                                --s 1,1000,1,1
                                                --l \full-path-to-labels\Labels.txt 
                                                --f \full-path-to-labels\car.JPEG
                                                --d 3 [optional]
````