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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