OpenVX Neural Network Extension Library (vx_nn)

vx_nn is an OpenVX Neural Network extension module. This implementation supports only floating-point tensor data type and does not support 8-bit and 16-bit fixed-point data types specified in the OpenVX specification.

List of supported tensor and neural network layers:

Layer name	Function	Kernel name
Activation	vxActivationLayer	org.khronos.nn_extension.activation_layer
Argmax	vxArgmaxLayerNode	com.amd.nn_extension.argmax_layer
Batch Normalization	vxBatchNormalizationLayer	com.amd.nn_extension.batch_normalization_layer
Cast	vxCastLayer	com.amd.nn_extension.cast_layer
Concat	vxConcatLayer	com.amd.nn_extension.concat_layer
Convolution	vxConvolutionLayer	org.khronos.nn_extension.convolution_layer
Crop	vxCropLayer	com.amd.nn_extension.crop_layer
CropAndResize	vxCropAndResizeLayer	com.amd.nn_extension.crop_and_resize_layer
Deconvolution	vxDeconvolutionLayer	org.khronos.nn_extension.deconvolution_layer
Detection Output	vxDetectionOutputLayer	com.amd.nn_extension.detection_output
Fully Connected	vxFullyConnectedLayer	org.khronos.nn_extension.fully_connected_layer
Gather	vxGatherLayer	com.amd.nn_extension.gather_layer
Local Response Normalization	vxNormalizationLayer	org.khronos.nn_extension.normalization_layer
Non Max Suppression	vxNMSLayer	com.amd.nn_extension.nms_layer
Permute	vxPermuteLayer	com.amd.nn_extension.permute_layer
Pooling	vxPoolingLayer	org.khronos.nn_extension.pooling_layer
Prior Box	vxPriorBoxLayer	com.amd.nn_extension.prior_box_layer
Reduce Min	vxReduceMinLayer	com.amd.nn_extension.reduce_min_layer
ROI Pooling	vxROIPoolingLayer	org.khronos.nn_extension.roi_pooling_layer
Scale	vxScaleLayer	com.amd.nn_extension.scale_layer
Slice	vxSliceLayer	com.amd.nn_extension.slice_layer
Softmax	vxSoftmaxLayer	org.khronos.nn_extension.softmax_layer
Tensor Add	vxTensorAddNode	org.khronos.openvx.tensor_add
Tensor Compare	vxTensorCompareNode	com.amd.nn_extension.tensor_compare
Tensor Convert Depth	vxTensorConvertDepthNode	org.khronos.openvx.tensor_convert_depth
Tensor Convert from Image	vxConvertImageToTensorNode	com.amd.nn_extension.convert_image_to_tensor
Tensor Convert to Image	vxConvertTensorToImageNode	com.amd.nn_extension.convert_tensor_to_image
Tensor Exponential	vxTensorExpNode	com.amd.nn_extension.tensor_exp
Tensor Log	vxTensorLogNode	com.amd.nn_extension.tensor_log
Tensor Matrix Multiply	vxTensorMatrixMultiplyNode	org.khronos.openvx.tensor_matrix_multiply
Tensor Max	vxTensorMaxNode	com.amd.nn_extension.tensor_max
Tensor Min	vxTensorMinNode	com.amd.nn_extension.tensor_min
Tensor Multiply	vxTensorMultiplyNode	org.khronos.openvx.tensor_multiply
Tensor Subtract	vxTensorSubtractNode	org.khronos.openvx.tensor_subtract
Tile	vxTileLayer	com.amd.nn_extension.tile_layer
TopK	vxTopKLayer	com.amd.nn_extension.topk_layer
Upsample Nearest Neighborhood	vxUpsampleNearestLayer	com.amd.nn_extension.upsample_nearest_layer

Example 1: Convert an image to a tensor of type float32

Use the below GDF with RunVX.

import vx_nn

data input = image:32,32,RGB2
data output = tensor:4,{32,32,3,1},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 input.png
node com.amd.nn_extension.convert_image_to_tensor input output a b reverse_channel_order
write output input.f32

Example 2: 2x2 Upsample a tensor of type float32

Use the below GDF with RunVX.

import vx_nn

data input = tensor:4,{80,80,3,1},VX_TYPE_FLOAT32,0
data output = tensor:4,{160,160,3,1},VX_TYPE_FLOAT32,0

read input tensor.f32
node com.amd.nn_extension.upsample_nearest_layer input output
write output upsample.f32