static vx_status VX_CALLBACK validateSoftmaxLayer(vx_node node, const vx_reference parameters[], vx_uint32 num, vx_meta_format metas[])
{
// check tensor dimensions
vx_enum type;
vx_size num_dims;
vx_size input_dims[4], output_dims[4];
ERROR_CHECK_STATUS(vxQueryTensor((vx_tensor)parameters[0], VX_TENSOR_NUMBER_OF_DIMS, &num_dims, sizeof(num_dims)));
ERROR_CHECK_STATUS(vxQueryTensor((vx_tensor)parameters[0], VX_TENSOR_DATA_TYPE, &type, sizeof(type)));
if(num_dims != 4) return VX_ERROR_INVALID_DIMENSION;
if(type != VX_TYPE_FLOAT32) return VX_ERROR_INVALID_TYPE;
ERROR_CHECK_STATUS(vxQueryTensor((vx_tensor)parameters[0], VX_TENSOR_DIMS, input_dims, sizeof(input_dims)));
ERROR_CHECK_STATUS(vxQueryTensor((vx_tensor)parameters[1], VX_TENSOR_NUMBER_OF_DIMS, &num_dims, sizeof(num_dims)));
ERROR_CHECK_STATUS(vxQueryTensor((vx_tensor)parameters[1], VX_TENSOR_DATA_TYPE, &type, sizeof(type)));
if(num_dims != 4) return VX_ERROR_INVALID_DIMENSION;
if(type != VX_TYPE_FLOAT32) return VX_ERROR_INVALID_TYPE;
ERROR_CHECK_STATUS(vxQueryTensor((vx_tensor)parameters[1], VX_TENSOR_DIMS, output_dims, sizeof(output_dims)));
if(output_dims[3] != input_dims[3]) return VX_ERROR_INVALID_DIMENSION;
if(output_dims[2] != input_dims[2]) return VX_ERROR_INVALID_DIMENSION;
if(output_dims[1] != input_dims[1]) return VX_ERROR_INVALID_DIMENSION;
if(output_dims[0] != input_dims[0]) return VX_ERROR_INVALID_DIMENSION;
// output tensor configuration
type = VX_TYPE_FLOAT32;
num_dims = 4;
ERROR_CHECK_STATUS(vxSetMetaFormatAttribute(metas[1], VX_TENSOR_DATA_TYPE, &type, sizeof(type)));
ERROR_CHECK_STATUS(vxSetMetaFormatAttribute(metas[1], VX_TENSOR_NUMBER_OF_DIMS, &num_dims, sizeof(num_dims)));
ERROR_CHECK_STATUS(vxSetMetaFormatAttribute(metas[1], VX_TENSOR_DIMS, output_dims, sizeof(output_dims)));
return VX_SUCCESS;
}
static vx_status VX_CALLBACK validateTensorToImageKernel(vx_node node, const vx_reference parameters[], vx_uint32 num, vx_meta_format metas[])
{
// check input configuration
vx_enum type;
vx_size num_dims, input_dims[4] = { 1, 1, 1, 1 };
ERROR_CHECK_STATUS(vxQueryTensor((vx_tensor)parameters[0], VX_TENSOR_DATA_TYPE, &type, sizeof(type)));
ERROR_CHECK_STATUS(vxQueryTensor((vx_tensor)parameters[0], VX_TENSOR_NUMBER_OF_DIMS, &num_dims, sizeof(num_dims)));
if (num_dims != 4)
return VX_ERROR_INVALID_DIMENSION;
if (type != VX_TYPE_FLOAT32)
return VX_ERROR_INVALID_TYPE;
ERROR_CHECK_STATUS(vxQueryTensor((vx_tensor)parameters[0], VX_TENSOR_DIMS, input_dims, sizeof(input_dims[0])*num_dims));
if ((input_dims[2] != 3 && input_dims[2] != 1) || ((input_dims[0] & 3) != 0))
return VX_ERROR_INVALID_DIMENSION;
vx_enum scalar_type;
ERROR_CHECK_STATUS(vxQueryScalar((vx_scalar)parameters[2], VX_SCALAR_TYPE, &scalar_type, sizeof(scalar_type)));
if(scalar_type != VX_TYPE_FLOAT32)
return VX_ERROR_INVALID_TYPE;
ERROR_CHECK_STATUS(vxQueryScalar((vx_scalar)parameters[3], VX_SCALAR_TYPE, &scalar_type, sizeof(scalar_type)));
if(scalar_type != VX_TYPE_FLOAT32)
return VX_ERROR_INVALID_TYPE;
ERROR_CHECK_STATUS(vxQueryScalar((vx_scalar)parameters[4], VX_SCALAR_TYPE, &scalar_type, sizeof(scalar_type)));
if(scalar_type != VX_TYPE_BOOL)
return VX_ERROR_INVALID_TYPE;
// set output image configuration
vx_uint32 width = (vx_uint32)input_dims[0];
vx_uint32 height = (vx_uint32)(input_dims[1]*input_dims[3]);
vx_df_image format = (input_dims[2] == 3) ? VX_DF_IMAGE_RGB : VX_DF_IMAGE_U8;
ERROR_CHECK_STATUS(vxSetMetaFormatAttribute(metas[1], VX_IMAGE_WIDTH, &width, sizeof(width)));
ERROR_CHECK_STATUS(vxSetMetaFormatAttribute(metas[1], VX_IMAGE_HEIGHT, &height, sizeof(height)));
ERROR_CHECK_STATUS(vxSetMetaFormatAttribute(metas[1], VX_IMAGE_FORMAT, &format, sizeof(format)));
return VX_SUCCESS;
}
static vx_status VX_CALLBACK uninitializeSoftmaxLayer(vx_node node, const vx_reference *parameters, vx_uint32 num)
{
SoftmaxLayerLocalData * data = NULL;
ERROR_CHECK_STATUS(vxQueryNode(node, VX_NODE_LOCAL_DATA_PTR, &data, sizeof(data)));
ERROR_CHECK_MIOPEN_STATUS(miopenDestroyTensorDescriptor(data->input_desc));
ERROR_CHECK_MIOPEN_STATUS(miopenDestroyTensorDescriptor(data->output_desc));
if (data) {
ERROR_CHECK_STATUS(releaseGraphHandle(node, data->handle));
delete data;
}
return VX_SUCCESS;
}
static vx_status VX_CALLBACK processSoftmaxLayer(vx_node node, const vx_reference * parameters, vx_uint32 num)
{
SoftmaxLayerLocalData * data = NULL;
ERROR_CHECK_STATUS(vxQueryNode(node, VX_NODE_LOCAL_DATA_PTR, &data, sizeof(data)));
miopenHandle_t miopenHandle = data->handle->miopen_handle;
ERROR_CHECK_STATUS(vxQueryTensor((vx_tensor)parameters[0], VX_TENSOR_BUFFER_OPENCL, &data->input_mem, sizeof(data->input_mem)));
ERROR_CHECK_STATUS(vxQueryTensor((vx_tensor)parameters[1], VX_TENSOR_BUFFER_OPENCL, &data->output_mem, sizeof(data->output_mem)));
ERROR_CHECK_STATUS(miopenSoftmaxForward(miopenHandle, &data->alpha, data->input_desc, data->input_mem, &data->beta, data->output_desc, data->output_mem));
return VX_SUCCESS;
}
////////
// The user kernel validator callback should check to make sure that all the input
// parameters have correct data types and set meta format for the output parameters.
// The input parameters to be validated are:
// parameter #0 -- input tensor of format VX_TYPE_INT16
// The output parameters that requires setting meta format is:
// parameter #1 -- output tebsor of format VX_TYPE_INT16 with the same dimensions as input
// TODO:********
// 1. Query the input tensor for the dimensions and format.
// 2. Check to make sure that the input tensor format is VX_TYPE_INT16.
// 3. Set the required output tensor meta data as following:
// - output tensor dimensions should be same as input tensor
// - output tensor format should be VX_TYPE_INT16
// - output tensor fixed-point position can be whatever the user requested
// * query the output tensor for the fixed-point position value
// * set the same value in output tensor meta data
vx_status VX_CALLBACK tensor_cos_validator( vx_node node,
const vx_reference parameters[], vx_uint32 num,
vx_meta_format metas[] )
{
// parameter #0 -- query dimensions and format
vx_size num_of_dims;
ERROR_CHECK_STATUS( vxQueryTensor( ( vx_tensor )parameters[0], VX_TENSOR_NUMBER_OF_DIMS, &num_of_dims, sizeof( num_of_dims ) ) );
if( num_of_dims > 4 ) // sanity check to avoid stack corruption with querying VX_TENSOR_DIMS below
{
return VX_ERROR_INVALID_DIMENSION;
}
vx_size dims[4];
ERROR_CHECK_STATUS( vxQueryTensor( ( vx_tensor )parameters[0], VX_TENSOR_DIMS, &dims, num_of_dims * sizeof(vx_size) ) );
vx_enum data_type;
ERROR_CHECK_STATUS( vxQueryTensor( ( vx_tensor )parameters[0], VX_TENSOR_DATA_TYPE, &data_type, sizeof( data_type ) ) );
// parameter #0 -- check input tensor format to be VX_TYPE_INF16
if( data_type != VX_TYPE_INT16 )
{
return VX_ERROR_INVALID_FORMAT;
}
// parameter #1 -- query fixed-point position
vx_uint8 fixed_point_pos;
ERROR_CHECK_STATUS( vxQueryTensor( ( vx_tensor )parameters[1], VX_TENSOR_FIXED_POINT_POSITION, &fixed_point_pos, sizeof( fixed_point_pos ) ) );
// parameter #1 -- set required output tensor meta data
ERROR_CHECK_STATUS( vxSetMetaFormatAttribute( metas[1], VX_TENSOR_NUMBER_OF_DIMS, &num_of_dims, sizeof( num_of_dims ) ) );
ERROR_CHECK_STATUS( vxSetMetaFormatAttribute( metas[1], VX_TENSOR_DIMS, &dims, sizeof( dims ) ) );
ERROR_CHECK_STATUS( vxSetMetaFormatAttribute( metas[1], VX_TENSOR_DATA_TYPE, &data_type, sizeof( data_type ) ) );
ERROR_CHECK_STATUS( vxSetMetaFormatAttribute( metas[1], VX_TENSOR_FIXED_POINT_POSITION, &fixed_point_pos, sizeof( fixed_point_pos ) ) );
return VX_SUCCESS;
}
static vx_status VX_CALLBACK processTensorAddition(vx_node node, const vx_reference * parameters, vx_uint32 num)
{
TensorAddLocalData * data = NULL;
ERROR_CHECK_STATUS(vxQueryNode(node, VX_NODE_LOCAL_DATA_PTR, &data, sizeof(data)));
miopenHandle_t miopenHandle = data->handle->miopen_handle;
ERROR_CHECK_STATUS(vxQueryTensor((vx_tensor)parameters[0], VX_TENSOR_BUFFER_OPENCL, &data->input1_mem, sizeof(data->input1_mem)));
ERROR_CHECK_STATUS(vxQueryTensor((vx_tensor)parameters[1], VX_TENSOR_BUFFER_OPENCL, &data->input2_mem, sizeof(data->input2_mem)));
ERROR_CHECK_STATUS(vxQueryTensor((vx_tensor)parameters[3], VX_TENSOR_BUFFER_OPENCL, &data->output_mem, sizeof(data->output_mem)));
//miopen elementwise addition call.
ERROR_CHECK_MIOPEN_STATUS(miopenOpTensor(miopenHandle, data->operation, &data->alpha1, data->input1, data->input1_mem, &data->alpha2, data->input2, data->input2_mem, &data->beta, data->output, data->output_mem));
return VX_SUCCESS;
}
////////
// The node creation interface for the "app.userkernels.tensor_cos" kernel.
// This user kernel example expects parameters in the following order:
// parameter #0 -- input tensor of format VX_TYPE_INT16
// parameter #1 -- output tensor of format VX_TYPE_INT16
//
// TODO:********
// 1. Use vxGetKernelByEnum API to get a kernel object from USER_KERNEL_TENSOR_COS.
// Note that you need to use vxGetContext API to get the context from a graph object.
// 2. Use vxCreateGenericNode API to create a node from the kernel object.
// 3. Use vxSetParameterByIndex API to set node arguments.
// 4. Release the kernel object that are not needed any more.
// 5. Use ERROR_CHECK_OBJECT and ERROR_CHECK_STATUS macros for error detection.
vx_node userTensorCosNode( vx_graph graph,
vx_tensor input,
vx_tensor output )
{
vx_context context = vxGetContext( ( vx_reference ) graph );
vx_kernel kernel = vxGetKernelByEnum( context, USER_KERNEL_TENSOR_COS );
ERROR_CHECK_OBJECT( kernel );
vx_node node = vxCreateGenericNode( graph, kernel );
ERROR_CHECK_OBJECT( node );
ERROR_CHECK_STATUS( vxSetParameterByIndex( node, 0, ( vx_reference ) input ) );
ERROR_CHECK_STATUS( vxSetParameterByIndex( node, 1, ( vx_reference ) output ) );
ERROR_CHECK_STATUS( vxReleaseKernel( &kernel ) );
return node;
}
vx_status publishSoftmaxLayer(vx_context context)
{
// add kernel to the context with callbacks
vx_kernel kernel = vxAddUserKernel(context, "org.khronos.nn_extension.softmax_layer", VX_KERNEL_SOFTMAX_LAYER, processSoftmaxLayer, 2, validateSoftmaxLayer, initializeSoftmaxLayer, uninitializeSoftmaxLayer);
ERROR_CHECK_OBJECT(kernel);
// enable OpenCL buffer access since the kernel_f callback uses OpenCL buffers instead of host accessible buffers
vx_bool enableBufferAccess = vx_true_e;
ERROR_CHECK_STATUS(vxSetKernelAttribute(kernel, VX_KERNEL_ATTRIBUTE_AMD_OPENCL_BUFFER_ACCESS_ENABLE, &enableBufferAccess, sizeof(enableBufferAccess)));
// set kernel parameters
ERROR_CHECK_STATUS(vxAddParameterToKernel(kernel, 0, VX_INPUT, VX_TYPE_TENSOR, VX_PARAMETER_STATE_REQUIRED));
ERROR_CHECK_STATUS(vxAddParameterToKernel(kernel, 1, VX_OUTPUT, VX_TYPE_TENSOR, VX_PARAMETER_STATE_REQUIRED));
// finalize and release kernel object
ERROR_CHECK_STATUS(vxFinalizeKernel(kernel));
ERROR_CHECK_STATUS(vxReleaseKernel(&kernel));
return VX_SUCCESS;
}
////////
// User kernels needs to be registered with every OpenVX context before use in a graph.
//
// TODO:********
// 1. Use vxAddUserKernel API to register "app.userkernels.tensor_cos" with
// kernel enumeration = USER_KERNEL_TENSOR_COS, numParams = 2, and
// all of the user kernel callback functions you implemented above.
// 2. Use vxAddParameterToKernel API to specify direction, data_type, and
// state of all 2 parameters to the kernel. Look into the comments of
// userTensorCosNode function (above) to details about the order of
// kernel parameters and their types.
// 3. Use vxFinalizeKernel API to make the kernel ready to use in a graph.
// Note that the kernel object is still valid after this call.
// So you need to call vxReleaseKernel before returning from this function.
vx_status registerUserKernel( vx_context context )
{
vx_kernel kernel = vxAddUserKernel( context,
"app.userkernels.tensor_cos",
USER_KERNEL_TENSOR_COS,
tensor_cos_host_side_function,
2, // numParams
tensor_cos_validator,
NULL,
NULL );
ERROR_CHECK_OBJECT( kernel );
ERROR_CHECK_STATUS( vxAddParameterToKernel( kernel, 0, VX_INPUT, VX_TYPE_TENSOR, VX_PARAMETER_STATE_REQUIRED ) ); // input
ERROR_CHECK_STATUS( vxAddParameterToKernel( kernel, 1, VX_OUTPUT, VX_TYPE_TENSOR, VX_PARAMETER_STATE_REQUIRED ) ); // output
ERROR_CHECK_STATUS( vxFinalizeKernel( kernel ) );
ERROR_CHECK_STATUS( vxReleaseKernel( &kernel ) );
vxAddLogEntry( ( vx_reference ) context, VX_SUCCESS, "OK: registered user kernel app.userkernels.tensor_cos\n" );
return VX_SUCCESS;
}
static vx_status VX_CALLBACK initializeSoftmaxLayer(vx_node node, const vx_reference *parameters, vx_uint32 num)
{
SoftmaxLayerLocalData * data = new SoftmaxLayerLocalData;
memset(data, 0, sizeof(*data));
ERROR_CHECK_STATUS(createGraphHandle(node, &data->handle));
//Parameters input and output.
vx_size input_dims[4], output_dims[4];
ERROR_CHECK_STATUS(vxQueryTensor((vx_tensor)parameters[0], VX_TENSOR_DIMS, input_dims, sizeof(input_dims)));
ERROR_CHECK_STATUS(vxQueryTensor((vx_tensor)parameters[1], VX_TENSOR_DIMS, output_dims, sizeof(output_dims)));
ERROR_CHECK_MIOPEN_STATUS(miopenCreateTensorDescriptor(&data->input_desc));
ERROR_CHECK_MIOPEN_STATUS(miopenCreateTensorDescriptor(&data->output_desc));
ERROR_CHECK_MIOPEN_STATUS(miopenSet4dTensorDescriptor(data->input_desc, miopenFloat, input_dims[3], input_dims[2], input_dims[1], input_dims[0]));
ERROR_CHECK_MIOPEN_STATUS(miopenSet4dTensorDescriptor(data->output_desc, miopenFloat, output_dims[3], output_dims[2], output_dims[1], output_dims[0]));
ERROR_CHECK_STATUS(vxQueryTensor((vx_tensor)parameters[0], VX_TENSOR_BUFFER_OPENCL, &data->input_mem, sizeof(data->input_mem)));
ERROR_CHECK_STATUS(vxQueryTensor((vx_tensor)parameters[1], VX_TENSOR_BUFFER_OPENCL, &data->output_mem, sizeof(data->output_mem)));
data->alpha = 1;
data->beta = 0;
#if ENABLE_DEBUG_PRINT_DIMS
std::cout << "softmax input " << input_dims[3] << " " << input_dims[2] << " " << input_dims[1] << " " << input_dims[0] << " ";
std::cout << "output " << output_dims[3] << " " << output_dims[2] << " " << output_dims[1] << " " << output_dims[0] << std::endl;
#endif
ERROR_CHECK_STATUS(vxSetNodeAttribute(node, VX_NODE_LOCAL_DATA_PTR, &data, sizeof(data)));
return VX_SUCCESS;
}
//! \brief The kernel publisher.
vx_status publishImageToTensorConvert(vx_context context)
{
vx_kernel kernel = vxAddUserKernel(context, "com.amd.nn_extension.convert_image_to_tensor", VX_KERNEL_CONVERT_IMAGE_TO_TENSOR_AMD, host_kernel, 5, validateImageToTensorKernel, nullptr, nullptr);
ERROR_CHECK_OBJECT(kernel);
amd_kernel_query_target_support_f query_target_support_f = query_target_support;
amd_kernel_opencl_codegen_callback_f opencl_codegen_callback_f = opencl_codegen;
ERROR_CHECK_STATUS(vxSetKernelAttribute(kernel, VX_KERNEL_ATTRIBUTE_AMD_QUERY_TARGET_SUPPORT, &query_target_support_f, sizeof(query_target_support_f)));
ERROR_CHECK_STATUS(vxSetKernelAttribute(kernel, VX_KERNEL_ATTRIBUTE_AMD_OPENCL_CODEGEN_CALLBACK, &opencl_codegen_callback_f, sizeof(opencl_codegen_callback_f)));
// set kernel parameters.
ERROR_CHECK_STATUS(vxAddParameterToKernel(kernel, 0, VX_INPUT, VX_TYPE_IMAGE, VX_PARAMETER_STATE_REQUIRED));
ERROR_CHECK_STATUS(vxAddParameterToKernel(kernel, 1, VX_OUTPUT, VX_TYPE_TENSOR, VX_PARAMETER_STATE_REQUIRED));
ERROR_CHECK_STATUS(vxAddParameterToKernel(kernel, 2, VX_INPUT, VX_TYPE_SCALAR, VX_PARAMETER_STATE_REQUIRED));
ERROR_CHECK_STATUS(vxAddParameterToKernel(kernel, 3, VX_INPUT, VX_TYPE_SCALAR, VX_PARAMETER_STATE_REQUIRED));
ERROR_CHECK_STATUS(vxAddParameterToKernel(kernel, 4, VX_INPUT, VX_TYPE_SCALAR, VX_PARAMETER_STATE_REQUIRED));
// finalize and release kernel object.
ERROR_CHECK_STATUS(vxFinalizeKernel(kernel));
ERROR_CHECK_STATUS(vxReleaseKernel(&kernel));
return VX_SUCCESS;
}
static vx_status VX_CALLBACK initializeTensorAddition(vx_node node, const vx_reference *parameters, vx_uint32 num)
{
TensorAddLocalData * data = new TensorAddLocalData;
memset(data, 0, sizeof(*data));
ERROR_CHECK_STATUS(createGraphHandle(node, &data->handle));
//initialize input and output tensor descriptors.
vx_size input1_dims[4], input2_dims[4], output_dims[4];
ERROR_CHECK_STATUS(vxQueryTensor((vx_tensor)parameters[0], VX_TENSOR_DIMS, input1_dims, sizeof(input1_dims)));
ERROR_CHECK_STATUS(vxQueryTensor((vx_tensor)parameters[1], VX_TENSOR_DIMS, input2_dims, sizeof(input2_dims)));
ERROR_CHECK_STATUS(vxQueryTensor((vx_tensor)parameters[3], VX_TENSOR_DIMS, output_dims, sizeof(output_dims)));
ERROR_CHECK_MIOPEN_STATUS(miopenCreateTensorDescriptor(&data->input1));
ERROR_CHECK_MIOPEN_STATUS(miopenCreateTensorDescriptor(&data->input2));
ERROR_CHECK_MIOPEN_STATUS(miopenCreateTensorDescriptor(&data->output));
ERROR_CHECK_MIOPEN_STATUS(miopenSet4dTensorDescriptor(data->input1, miopenFloat, input1_dims[3], input1_dims[2], input1_dims[1], input1_dims[0]));
ERROR_CHECK_MIOPEN_STATUS(miopenSet4dTensorDescriptor(data->input2, miopenFloat, input2_dims[3], input2_dims[2], input2_dims[1], input2_dims[0]));
ERROR_CHECK_MIOPEN_STATUS(miopenSet4dTensorDescriptor(data->output, miopenFloat, output_dims[3], output_dims[2], output_dims[1], output_dims[0]));
//scaling parameters.
data->alpha1 = 1;
data->alpha2 = 1;
data->beta = 0;
data->operation = miopenTensorOpAdd;
//input and output memory.
ERROR_CHECK_STATUS(vxQueryTensor((vx_tensor)parameters[0], VX_TENSOR_BUFFER_OPENCL, &data->input1_mem, sizeof(data->input1_mem)));
ERROR_CHECK_STATUS(vxQueryTensor((vx_tensor)parameters[1], VX_TENSOR_BUFFER_OPENCL, &data->input2_mem, sizeof(data->input2_mem)));
ERROR_CHECK_STATUS(vxQueryTensor((vx_tensor)parameters[3], VX_TENSOR_BUFFER_OPENCL, &data->output_mem, sizeof(data->output_mem)));
#if ENABLE_DEBUG_PRINT_DIMS
std::cout << "tensor_add input1 " << input1_dims[3] << " " << input1_dims[2] << " " << input1_dims[1] << " " << input1_dims[0] << " ";
std::cout << "tensor_add input2 " << input2_dims[3] << " " << input2_dims[2] << " " << input2_dims[1] << " " << input2_dims[0] << " ";
std::cout << "tensor_add output " << output_dims[3] << " " << output_dims[2] << " " << output_dims[1] << " " << output_dims[0] << std::endl;
#endif
ERROR_CHECK_STATUS(vxSetNodeAttribute(node, VX_NODE_LOCAL_DATA_PTR, &data, sizeof(data)));
return VX_SUCCESS;
}
vx_status publishROIPoolingLayer(vx_context context)
{
// add kernel to the context with callbacks
vx_kernel kernel = vxAddUserKernel(context, "org.khronos.nn_extension.roi_pooling_layer", VX_KERNEL_ROI_POOLING_LAYER, processROIPoolingLayer, 4, validateROIPoolingLayer, initializeROIPoolingLayer, uninitializeROIPoolingLayer);
ERROR_CHECK_OBJECT(kernel);
// set kernel parameters
ERROR_CHECK_STATUS(vxAddParameterToKernel(kernel, 0, VX_INPUT, VX_TYPE_TENSOR, VX_PARAMETER_STATE_REQUIRED));
ERROR_CHECK_STATUS(vxAddParameterToKernel(kernel, 1, VX_INPUT, VX_TYPE_TENSOR, VX_PARAMETER_STATE_REQUIRED));
ERROR_CHECK_STATUS(vxAddParameterToKernel(kernel, 2, VX_INPUT, VX_TYPE_SCALAR, VX_PARAMETER_STATE_REQUIRED));
ERROR_CHECK_STATUS(vxAddParameterToKernel(kernel, 3, VX_OUTPUT, VX_TYPE_TENSOR, VX_PARAMETER_STATE_REQUIRED));
// finalize and release kernel object
ERROR_CHECK_STATUS(vxFinalizeKernel(kernel));
ERROR_CHECK_STATUS(vxReleaseKernel(&kernel));
return VX_SUCCESS;
}
vx_status publishTensorAdd(vx_context context)
{
// add kernel to the context with callbacks
vx_kernel kernel = vxAddUserKernel(context, "org.khronos.openvx.tensor_add", VX_KERNEL_TENSOR_ADD, processTensorAddition, 4, validateTensorAddition, initializeTensorAddition, uninitializeTensorAddition);
ERROR_CHECK_OBJECT(kernel);
// enable OpenCL buffer access since the kernel_f callback uses OpenCL buffers instead of host accessible buffers
vx_bool enableBufferAccess = vx_true_e;
ERROR_CHECK_STATUS(vxSetKernelAttribute(kernel, VX_KERNEL_ATTRIBUTE_AMD_OPENCL_BUFFER_ACCESS_ENABLE, &enableBufferAccess, sizeof(enableBufferAccess)));
// set kernel parameters
ERROR_CHECK_STATUS(vxAddParameterToKernel(kernel, 0, VX_INPUT, VX_TYPE_TENSOR, VX_PARAMETER_STATE_REQUIRED));
ERROR_CHECK_STATUS(vxAddParameterToKernel(kernel, 1, VX_INPUT, VX_TYPE_TENSOR, VX_PARAMETER_STATE_REQUIRED));
ERROR_CHECK_STATUS(vxAddParameterToKernel(kernel, 2, VX_INPUT, VX_TYPE_SCALAR, VX_PARAMETER_STATE_REQUIRED));
ERROR_CHECK_STATUS(vxAddParameterToKernel(kernel, 3, VX_OUTPUT, VX_TYPE_TENSOR, VX_PARAMETER_STATE_REQUIRED));
// finalize and release kernel object
ERROR_CHECK_STATUS(vxFinalizeKernel(kernel));
ERROR_CHECK_STATUS(vxReleaseKernel(&kernel));
return VX_SUCCESS;
}
////////
// main() has all the OpenVX application code for this exercise.
// Command-line usage:
// % exercise3 [<video-sequence>|<camera-device-number>]
// When neither video sequence nor camera device number is specified,
// it defaults to the video sequence in "PETS09-S1-L1-View001.avi".
int main( int argc, char * argv[] )
{
// Get default video sequence when nothing is specified on command-line and
// instantiate OpenCV GUI module for reading input RGB images and displaying
// the image with OpenVX results
const char * video_sequence = argv[1];
CGuiModule gui( video_sequence );
// Try grab first video frame from the sequence using cv::VideoCapture
// and check if video frame is available
if( !gui.Grab() )
{
printf( "ERROR: input has no video\n" );
return 1;
}
////////
// Set the application configuration parameters. Note that input video
// sequence is an 8-bit RGB image with dimensions given by gui.GetWidth()
// and gui.GetHeight(). The parameters for the tensors are:
// tensor_dims - 3 dimensions of tensor [3 x <width> x <height>]
// tensor_input_fixed_point_pos - fixed-point position for input tensor
// tensor_output_fixed_point_pos - fixed-point position for output tensor
vx_uint32 width = gui.GetWidth();
vx_uint32 height = gui.GetHeight();
vx_size tensor_dims[3] = { width, height, 3 }; // 3 channels (RGB)
vx_uint8 tensor_input_fixed_point_pos = 5; // Q10.5: input[-128..127] will be mapped to -4..3.96875
vx_uint8 tensor_output_fixed_point_pos = 7; // Q8.7: output[-1..1] will be mapped to -128 to 128
////////
// Create the OpenVX context and make sure returned context is valid and
// register the log_callback to receive messages from OpenVX framework.
vx_context context = vxCreateContext();
ERROR_CHECK_OBJECT( context );
vxRegisterLogCallback( context, log_callback, vx_false_e );
////////
// Register user kernels with the context.
//
// TODO STEP 05:********
// 1. Register user kernel with context by calling your implementation of "registerUserKernel()".
// ERROR_CHECK_STATUS( registerUserKernel( context ) );
////////
// Create OpenVX tensor objects for input and output
//
// TODO STEP 06:********
// 1. Create tensor objects using tensor_dims, tensor_input_fixed_point_pos, and
// tensor_output_fixed_point_pos
// vx_tensor input_tensor = vxCreateTensor( context, 3, tensor_dims, VX_TYPE_INT16, tensor_input_fixed_point_pos );
// vx_tensor output_tensor = vxCreateTensor( context, /* Fill in parameters */ );
// ERROR_CHECK_OBJECT( input_tensor );
// ERROR_CHECK_OBJECT( output_tensor );
////////
// Create, build, and verify the graph with user kernel node.
//
// TODO STEP 07:********
// 1. Build a graph with just one node created using userTensorCosNode()
// vx_graph graph = vxCreateGraph( context );
// ERROR_CHECK_OBJECT( graph );
// vx_node cos_node = userTensorCosNode( graph, /* Fill in parameters */ );
// ERROR_CHECK_OBJECT( cos_node );
// ERROR_CHECK_STATUS( vxReleaseNode( &cos_node ) );
// ERROR_CHECK_STATUS( vxVerifyGraph( graph ) );
////////
// Process the video sequence frame by frame until the end of sequence or aborted.
cv::Mat bgrMatForOutputDisplay( height, width, CV_8UC3 );
for( int frame_index = 0; !gui.AbortRequested(); frame_index++ )
{
////////
// Copy input RGB frame from OpenCV into input_tensor with UINT8 to Q10.5 (INT16) conversion.
// In order to do this, vxMapTensorPatch API (see "vx_ext_amd.h").
//
// TODO STEP 08:********
// 1. Use vxMapTensorPatch API for access to input tensor object for writing
// 2. Copy UINT8 data from OpenCV RGB image to tensor object
// 3. Use vxUnmapTensorPatch API to return control of buffer back to framework
vx_uint8 * cv_rgb_image_buffer = gui.GetBuffer();
vx_size rgb_stride = gui.GetStride();
// vx_size zeros[3] = { 0 };
// vx_size tensor_stride[3];
// vx_map_id map_id;
// vx_uint8 * buf;
// ERROR_CHECK_STATUS( vxMapTensorPatch( input_tensor,
// 3, /* Fill in parameters */
// &map_id, tensor_stride,
// (void **)&buf, VX_WRITE_ONLY, VX_MEMORY_TYPE_HOST, 0 ) );
// for( vx_size c = 0; c < 3; c++ )
// {
// for( vx_size y = 0; y < height; y++ )
// {
// const vx_uint8 * img = cv_rgb_image_buffer + y * rgb_stride + c;
// vx_int16 * inp = (vx_int16 *)(buf + y * tensor_stride[1] + c * tensor_stride[2]);
// for( vx_size x = 0; x < width; x++ )
// {
// // convert 0..255 to Q10.5 [-4..3.96875 range] fixed-point format
// inp[x] = (vx_int16)img[x * 3] - 128;
// }
// }
// }
// ERROR_CHECK_STATUS( vxUnmapTensorPatch( input_tensor, map_id ) );
////////
// Now that input tensor is ready, just run the graph.
//
// TODO STEP 09:********
// 1. Call vxProcessGraph to execute the tensor_cos kernel in graph
// ERROR_CHECK_STATUS( vxProcessGraph( graph ) );
////////
// Display the output tensor object as RGB image
//
// TODO STEP 10:********
// 1. Use vxMapTensorPatch API for access to output tensor object for reading
// 2. Copy tensor object data into OpenCV RGB image
// 3. Use vxUnmapTensorPatch API to return control of buffer back to framework
// ERROR_CHECK_STATUS( vxMapTensorPatch( output_tensor,
// 3, zeros, tensor_dims,
// &map_id, tensor_stride,
// (void **)&buf, VX_WRITE_ONLY, VX_MEMORY_TYPE_HOST, 0 ) );
// vx_uint8 * cv_bgr_image_buffer = bgrMatForOutputDisplay.data;
// vx_size bgr_stride = bgrMatForOutputDisplay.step;
// for( vx_size c = 0; c < 3; c++ )
// {
// for( vx_size y = 0; y < height; y++ )
// {
// const vx_int16 * out = (const vx_int16 *)(buf + y * tensor_stride[1] + c * tensor_stride[2]);
// vx_uint8 * img = cv_bgr_image_buffer + y * bgr_stride + (2 - c); // (2 - c) for RGB to BGR conversion
// for( vx_size x = 0; x < width; x++ )
// {
// // scale convert Q8.7 [-1..1 range] fixed-point format to 0..255 with saturation
// vx_int16 value = out[x] + 128;
// value = value > 255 ? 255 : value; // saturation needed
// img[x * 3] = (vx_uint8)value;
// }
// }
// }
//#if ENABLE_DISPLAY
// cv::imshow( "Cosine", bgrMatForOutputDisplay );
//#endif
// ERROR_CHECK_STATUS( vxUnmapTensorPatch( output_tensor, map_id ) );
////////
// Display the results and grab the next input RGB frame for the next iteration.
char text[128];
sprintf( text, "Keyboard ESC/Q-Quit SPACE-Pause [FRAME %d] [fixed_point_pos input:%d output:%d]", frame_index, tensor_input_fixed_point_pos, tensor_output_fixed_point_pos );
gui.DrawText( 0, 16, text );
gui.Show();
if( !gui.Grab() )
{
// Terminate the processing loop if the end of sequence is detected.
gui.WaitForKey();
break;
}
}
////////********
// To release an OpenVX object, you need to call vxRelease<Object> API which takes a pointer to the object.
// If the release operation is successful, the OpenVX framework will reset the object to NULL.
//
// TODO STEP 11:****
// 1. Release graph and tensor objects
// ERROR_CHECK_STATUS( vxReleaseGraph( &graph ) );
// ERROR_CHECK_STATUS( vxReleaseTensor( &input_tensor ) );
// ERROR_CHECK_STATUS( vxReleaseTensor( &output_tensor ) );
ERROR_CHECK_STATUS( vxReleaseContext( &context ) );
return 0;
}
////////
// main() has all the OpenVX application code for this exercise.
// Command-line usage:
// % solution_exercise2 [<video-sequence>|<camera-device-number>]
// When neither video sequence nor camera device number is specified,
// it defaults to the video sequence in "PETS09-S1-L1-View001.avi".
int main( int argc, char * argv[] )
{
// Get default video sequence when nothing is specified on command-line and
// instantiate OpenCV GUI module for reading input RGB images and displaying
// the image with OpenVX results.
const char * video_sequence = argv[1];
CGuiModule gui( video_sequence );
// Try to grab the first video frame from the sequence using cv::VideoCapture
// and check if a video frame is available.
if( !gui.Grab() )
{
printf( "ERROR: input has no video\n" );
return 1;
}
////////
// Set the application configuration parameters. Note that input video
// sequence is an 8-bit RGB image with dimensions given by gui.GetWidth()
// and gui.GetHeight(). The parameters for the Harris corners algorithm are:
// max_keypoint_count - maximum number of keypoints to track
// harris_strength_thresh - minimum threshold score to keep a corner
// (computed using the normalized Sobel kernel)
// harris_min_distance - radial L2 distance for non-max suppression
// harris_k_sensitivity - sensitivity threshold k from the Harris-Stephens
// harris_gradient_size - window size for gradient computation
// harris_block_size - block window size used to compute the
// Harris corner score
// lk_pyramid_levels - number of pyramid levels for LK optical flow
// lk_termination - can be VX_TERM_CRITERIA_ITERATIONS or
// VX_TERM_CRITERIA_EPSILON or
// VX_TERM_CRITERIA_BOTH
// lk_epsilon - error for terminating the algorithm
// lk_num_iterations - number of iterations
// lk_use_initial_estimate - turn on/off use of initial estimates
// lk_window_dimension - size of window on which to perform the algorithm
vx_uint32 width = gui.GetWidth();
vx_uint32 height = gui.GetHeight();
vx_size max_keypoint_count = 10000;
vx_float32 harris_strength_thresh = 0.0005f;
vx_float32 harris_min_distance = 5.0f;
vx_float32 harris_k_sensitivity = 0.04f;
vx_int32 harris_gradient_size = 3;
vx_int32 harris_block_size = 3;
vx_uint32 lk_pyramid_levels = 6;
vx_float32 lk_pyramid_scale = VX_SCALE_PYRAMID_HALF;
vx_enum lk_termination = VX_TERM_CRITERIA_BOTH;
vx_float32 lk_epsilon = 0.01f;
vx_uint32 lk_num_iterations = 5;
vx_bool lk_use_initial_estimate = vx_false_e;
vx_uint32 lk_window_dimension = 6;
////////
// Create the OpenVX context and make sure the returned context is valid and
// register the log_callback to receive messages from OpenVX framework.
vx_context context = vxCreateContext();
ERROR_CHECK_OBJECT( context );
vxRegisterLogCallback( context, log_callback, vx_false_e );
////////
// Create OpenVX image object for input RGB image.
vx_image input_rgb_image = vxCreateImage( context, width, height, VX_DF_IMAGE_RGB );
ERROR_CHECK_OBJECT( input_rgb_image );
////////********
// OpenVX optical flow functionality requires pyramids of the current input
// image and the previous image. It also requires keypoints that correspond
// to the previous pyramid and will output updated keypoints into
// another keypoint array. To be able to toggle between the current and
// the previous buffers, you need to use OpenVX delay objects and vxAgeDelay().
// Create OpenVX pyramid and array object exemplars and create OpenVX delay
// objects for both to hold two of each. Note that the exemplar objects are not
// needed once the delay objects are created.
//
// TODO STEP 01:********
// 1. Use vxCreatePyramid API to create a pyramid exemplar with the
// same dimensions as the input image, VX_DF_IMAGE_U8 as image format,
// lk_pyramid_levels as levels, and lk_pyramid_scale as scale.
// We gave code for this in comments.
// 2. Use vxCreateArray API to create an array exemplar with
// keypoint data type with num_keypoint_count as capacity.
// You need to add missing parameters to code in comments.
// 3. Use vxCreateDelay API to create delay objects for pyramid and
// keypoint array using the exemplars created using the two steps above.
// Use 2 delay slots for both of the delay objects.
// We gave code for one in comments; do similar for the other.
// 4. Release the pyramid and keypoint array exemplar objects.
// We gave code for one in comments; do similar for the other.
// 5. Use ERROR_CHECK_OBJECT/STATUS macros for proper error checking.
// We gave few error checks; do similar for the others.
// vx_pyramid pyramidExemplar = vxCreatePyramid( context, lk_pyramid_levels,
// lk_pyramid_scale, width, height, VX_DF_IMAGE_U8 );
// ERROR_CHECK_OBJECT( pyramidExemplar );
// vx_delay pyramidDelay = vxCreateDelay( context, ( vx_reference )pyramidExemplar, 2 );
// ERROR_CHECK_OBJECT( pyramidDelay );
// ERROR_CHECK_STATUS( vxReleasePyramid( &pyramidExemplar ) );
// vx_array keypointsExemplar = vxCreateArray( /* Fill in parameters */ );
// vx_delay keypointsDelay = vxCreateDelay( /* Fill in parameters */ );
////////********
// An object from a delay slot can be accessed using vxGetReferenceFromDelay API.
// You need to use index = 0 for the current object and index = -1 for the previous object.
//
// TODO STEP 02:********
// 1. Use vxGetReferenceFromDelay API to get the current and previous
// pyramid objects from pyramid delay object. Note that you need
// to typecast the vx_reference object to vx_pyramid.
// We gave code for one in comments; do similar for the other.
// 2. Similarly, get the current and previous keypoint array objects from
// the keypoint delay object.
// We gave code for one in comments; do similar for the other.
// 3. Use ERROR_CHECK_OBJECT for proper error checking.
// We gave one error check; do similar for the others.
// vx_pyramid currentPyramid = ( vx_pyramid ) vxGetReferenceFromDelay( pyramidDelay, 0 );
// vx_pyramid previousPyramid = ( vx_pyramid ) vxGetReferenceFromDelay( /* Fill in parameters */ );
// vx_array currentKeypoints = ( vx_array ) vxGetReferenceFromDelay( /* Fill in parameters */ );
// vx_array previousKeypoints = ( vx_array ) vxGetReferenceFromDelay( keypointsDelay, -1 );
// ERROR_CHECK_OBJECT( currentPyramid );
////////********
// Harris and optical flow algorithms require their own graph objects.
// The Harris graph needs to extract gray scale image out of input RGB,
// compute an initial set of keypoints, and compute an initial pyramid for use
// by the optical flow graph.
//
// TODO STEP 03:********
// 1. Create two graph objects: one for the Harris corner detector and
// the other for feature tracking using optical flow using the
// vxCreateGraph API.
// We gave code for one graph; do similar for the other.
// 2. Use ERROR_CHECK_OBJECT to check the objects.
// We gave one error check; do similar for the other.
// vx_graph graphHarris = vxCreateGraph( context );
// vx_graph graphTrack = /* Fill in here */;
// ERROR_CHECK_OBJECT( graphHarris );
////////********
// Harris and pyramid computation expect input to be an 8-bit image.
// Given that input is an RGB image, it is best to extract a gray image
// from RGB image, which requires two steps:
// - perform RGB to IYUV color conversion
// - extract Y channel from IYUV image
// This requires two intermediate OpenVX image objects. Since you don't
// need to access these objects from the application, they can be virtual
// objects that can be created using the vxCreateVirtualImage API.
//
// TODO STEP 04:********
// 1. Create an IYUV image and a U8 image (for Y channel) with the same
// dimensions as the input RGB image. Note that the image formats for
// IYUV and U8 images are VX_DF_IMAGE_IYUV and VX_DF_IMAGE_U8.
// Note that virtual objects are specific to a graph, so you
// need to create two sets, one for each graph.
// We gave one fully in comments and you need to fill in missing
// parameters for the others.
// 2. Use ERROR_CHECK_OBJECT to check the objects.
// We gave one error check in comments; do similar for others.
// vx_image harris_yuv_image = vxCreateVirtualImage( graphHarris, width, height, VX_DF_IMAGE_IYUV );
// vx_image harris_luma_image = vxCreateVirtualImage( graphHarris, /* Fill in parameters */ );
// vx_image opticalflow_yuv_image = vxCreateVirtualImage( graphTrack, /* Fill in parameters */ );
// vx_image opticalflow_luma_image = vxCreateVirtualImage( /* Fill in parameters */ );
// ERROR_CHECK_OBJECT( harris_yuv_image );
////////********
// The Harris corner detector and optical flow nodes (see "VX/vx_nodes.h")
// take strength_thresh, min_distance, sensitivity, epsilon,
// num_iterations, and use_initial_estimate parameters as scalar
// data objects. So, you need to create scalar objects with the corresponding
// configuration parameters.
//
// TODO STEP 05:********
// 1. Create scalar data objects of VX_TYPE_FLOAT32 for strength_thresh,
// min_distance, sensitivity, and epsilon. Set their
// initial values to harris_strength_thresh, harris_min_distance,
// harris_k_sensitivity, and lk_epsilon.
// We gave code full code for one scalar in comments; fill in
// missing arguments for other ones.
// 2. Similarly, create scalar objects for num_iterations and
// use_initial_estimate with initial values: lk_num_iterations and
// lk_use_initial_estimate. Make sure to use proper data types for
// these parameters.
// We gave code full code for one scalar in comments; fill in
// missing arguments for the other.
// 3. Use ERROR_CHECK_OBJECT to check proper creation of objects.
// We gave the error check for one scalar; do similar for other 5 scalars.
// vx_scalar strength_thresh = NULL; // vxCreateScalar( context, VX_TYPE_FLOAT32, &harris_strength_thresh );
// vx_scalar min_distance = NULL; // vxCreateScalar( context, /* Fill in parameters */ );
// vx_scalar sensitivity = NULL; // vxCreateScalar( /* Fill in parameters */ );
// vx_scalar epsilon = NULL; // vxCreateScalar( /* Fill in parameters */ );
// vx_scalar num_iterations = NULL; // vxCreateScalar( context, VX_TYPE_UINT32, /* Fill in parameter */ );
// vx_scalar use_initial_estimate = NULL; // vxCreateScalar( context, VX_TYPE_BOOL, &lk_use_initial_estimate );
// ERROR_CHECK_OBJECT( strength_thresh );
////////********
// Now all the objects have been created for building the graphs.
// First, build a graph that performs Harris corner detection and initial pyramid computation.
// See "VX/vx_nodes.h" for APIs how to add nodes into a graph.
//
// TODO STEP 06:********
// 1. Use vxColorConvertNode and vxChannelExtractNode APIs to get gray
// scale image for Harris and Pyramid computation from the input
// RGB image. Add these nodes into Harris graph.
// We gave code in comments with a missing parameter for you to fill in.
// 2. Use vxGaussianPyramidNode API to add pyramid computation node.
// You need to use the current pyramid from the pyramid delay object.
// We gave code in comments with a missing parameter for you to fill in.
// 3. Use vxHarrisCornersNode API to add a Harris corners node.
// You need to use the current keypoints from keypoints delay object.
// We gave code in comments with few missing parameters for you to fill in.
// 4. Use ERROR_CHECK_OBJECT to check proper creation of objects.
// 5. Release node and virtual objects immediately since the graph
// retains references to them.
// 6. Call vxVerifyGraph to check for any errors in the graph.
// Fill in missing parameter in commented code.
// vx_node nodesHarris[] =
// {
// vxColorConvertNode( graphHarris, input_rgb_image, harris_yuv_image ),
// vxChannelExtractNode( graphHarris, /* Fill in parameter */, VX_CHANNEL_Y, harris_luma_image ),
// vxGaussianPyramidNode( graphHarris, /* Fill in parameter */, currentPyramid ),
// vxHarrisCornersNode( graphHarris, /* Fill in missing parameters */, currentKeypoints, NULL )
// };
// for( vx_size i = 0; i < sizeof( nodesHarris ) / sizeof( nodesHarris[0] ); i++ )
// {
// ERROR_CHECK_OBJECT( nodesHarris[i] );
// ERROR_CHECK_STATUS( vxReleaseNode( &nodesHarris[i] ) );
// }
// ERROR_CHECK_STATUS( vxReleaseImage( &harris_yuv_image ) );
// ERROR_CHECK_STATUS( vxReleaseImage( &harris_luma_image ) );
// ERROR_CHECK_STATUS( vxVerifyGraph( /* Fill in parameter */ ) );
////////********
// Now, build a graph that performs pyramid computation and feature
// tracking using optical flow.
//
// TODO STEP 07:********
// 1. Use vxColorConvertNode and vxChannelExtractNode APIs to get a gray
// scale image for Harris and Pyramid computation from the input
// RGB image. Add these nodes into Harris graph.
// We gave the code in comments for color convert node; do similar
// one for the channel extract node.
// 2. Use vxGaussianPyramidNode API to add pyramid computation node.
// You need to use the current pyramid from the pyramid delay object.
// Most of the code is given in the comments; fill in the missing parameter.
// 3. Use vxOpticalFlowPyrLKNode API to add an optical flow node. You need to
// use the current and previous keypoints from the keypoints delay object.
// Fill in the missing parameters in commented code.
// 4. Use ERROR_CHECK_OBJECT to check proper creation of objects.
// 5. Release node and virtual objects immediately since the graph
// retains references to them.
// 6. Call vxVerifyGraph to check for any errors in the graph.
// Fill in the missing parameter in commented code.
// vx_node nodesTrack[] =
// {
// vxColorConvertNode( graphTrack, input_rgb_image, opticalflow_yuv_image ),
// vxChannelExtractNode( graphTrack, /* Fill in parameters */ ),
// vxGaussianPyramidNode( graphTrack, /* Fill in parameter */, currentPyramid ),
// vxOpticalFlowPyrLKNode( graphTrack, /* Fill in parameters */ )
// };
// for( vx_size i = 0; i < sizeof( nodesTrack ) / sizeof( nodesTrack[0] ); i++ )
// {
// ERROR_CHECK_OBJECT( nodesTrack[i] );
// ERROR_CHECK_STATUS( vxReleaseNode( &nodesTrack[i] ) );
// }
// ERROR_CHECK_STATUS( vxReleaseImage( &opticalflow_yuv_image ) );
// ERROR_CHECK_STATUS( vxReleaseImage( &opticalflow_luma_image ) );
// ERROR_CHECK_STATUS( vxVerifyGraph( /* Fill in parameter */ ) );
////////
// Process the video sequence frame by frame until the end of sequence or aborted.
for( int frame_index = 0; !gui.AbortRequested(); frame_index++ )
{
////////
// Copy the input RGB frame from OpenCV to OpenVX.
// In order to do this, you need to use vxAccessImagePatch and vxCommitImagePatch APIs.
// See "VX/vx_api.h" for the description of these APIs.
vx_rectangle_t cv_rgb_image_region;
cv_rgb_image_region.start_x = 0;
cv_rgb_image_region.start_y = 0;
cv_rgb_image_region.end_x = width;
cv_rgb_image_region.end_y = height;
vx_imagepatch_addressing_t cv_rgb_image_layout;
cv_rgb_image_layout.stride_x = 3;
cv_rgb_image_layout.stride_y = gui.GetStride();
vx_uint8 * cv_rgb_image_buffer = gui.GetBuffer();
ERROR_CHECK_STATUS( vxAccessImagePatch( input_rgb_image, &cv_rgb_image_region, 0,
&cv_rgb_image_layout, ( void ** )&cv_rgb_image_buffer, VX_WRITE_ONLY ) );
ERROR_CHECK_STATUS( vxCommitImagePatch( input_rgb_image, &cv_rgb_image_region, 0,
&cv_rgb_image_layout, cv_rgb_image_buffer ) );
////////********
// Now that input RGB image is ready, just run a graph.
// Run Harris at the beginning to initialize the previous keypoints.
//
// TODO STEP 08:********
// 1. Run a graph using vxProcessGraph API. Select Harris graph
// if the frame_index == 0 (i.e., the first frame of the video
// sequence), otherwise, select the feature tracking graph.
// 2. Use ERROR_CHECK_STATUS for error checking.
////////********
// To mark the keypoints in display, you need to access the output
// keypoint array and draw each item on the output window using gui.DrawArrow().
//
// TODO STEP 09:********
// 1. Use vxGetReferenceFromDelay API to get the current and previous
// keypoints array objects from the keypoints delay object.
// Make sure to typecast the vx_reference object to vx_array.
// We gave one for the previous previous keypoint array in comments;
// do a similar one for the current keypoint array.
// 2. OpenVX array object has an attribute that keeps the current
// number of items in the array. The name of the attribute is
// VX_ARRAY_ATTRIBUTE_NUMITEMS and its value is of type vx_size.
// Use vxQueryArray API to get number of keypoints in the
// current keypoint array data object, representing number of
// corners detected in the input RGB image.
// IMPORTANT: Read number of items into "num_corners"
// because this variable is displayed by code segment below.
// We gave most part of this statement in comment; just fill in the
// missing parameter.
// 3. The data items in output keypoint array are of type
// vx_keypoint_t (see "VX/vx_types.h"). To access the array
// buffer, use vxAccessArrayRange with start index = 0,
// end index = number of items in the array, and usage mode =
// VX_READ_ONLY. Note that the stride returned by this access
// call is not guaranteed to be sizeof(vx_keypoint_t).
// Also make sure that num_corners is > 0, because
// vxAccessArrayRange expects end index > 0.
// We gave the code for previous keypoint array in comment;
// do similar one for the current keypoint array.
// 4. For each item in the keypoint buffer, use vxArrayItem to
// access an individual keypoint and draw a marker at (x,y)
// using gui.DrawArrow() if tracking_status field of keypoint
// is non-zero. Also count number of keypoints with
// non-zero tracking_status into "num_tracking" variable.
// We gave most of the code; fill in the missing parameters and uncomment.
// 5. Hand the control of output keypoint buffer over back to
// OpenVX framework by calling vxCommitArrayRange API.
// We gave the code for previous keypoint array in comment;
// do similar one for the current keypoint array.
// 6. Use ERROR_CHECK_STATUS for error checking.
vx_size num_corners = 0, num_tracking = 0;
// previousKeypoints = ( vx_array )vxGetReferenceFromDelay( keypointsDelay, -1 );
// currentKeypoints = ( vx_array )vxGetReferenceFromDelay( /* Fill in parameters */ );
// ERROR_CHECK_OBJECT( currentKeypoints );
// ERROR_CHECK_OBJECT( previousKeypoints );
// ERROR_CHECK_STATUS( vxQueryArray( previousKeypoints, /* Fill in parameter */, &num_corners, sizeof( num_corners ) ) );
if( num_corners > 0 )
{
vx_size kp_old_stride, kp_new_stride;
vx_keypoint_t * kp_old_buf = NULL, * kp_new_buf = NULL;
// ERROR_CHECK_STATUS( vxAccessArrayRange( previousKeypoints, 0, num_corners,
// &kp_old_stride, ( void ** ) &kp_old_buf, VX_READ_ONLY ) );
// ERROR_CHECK_STATUS( vxAccessArrayRange( /* Fill in parameters */ );
for( vx_size i = 0; i < num_corners; i++ )
{
// vx_keypoint_t * kp_old = &vxArrayItem( vx_keypoint_t, kp_old_buf, i, kp_old_stride );
// vx_keypoint_t * kp_new = &vxArrayItem( /* Fill in parameters */ );
// if( kp_new->tracking_status )
// {
// num_tracking++;
// gui.DrawArrow( kp_old->x, kp_old->y, kp_new->x, kp_new->y );
// }
}
// ERROR_CHECK_STATUS( vxCommitArrayRange( previousKeypoints, 0, num_corners, kp_old_buf ) );
// ERROR_CHECK_STATUS( vxCommitArrayRange( /* Fill in parameters */ ) );
}
////////********
// Flip the current and previous pyramid and keypoints in the delay objects.
//
// TODO STEP 10:********
// 1. Use vxAgeDelay API to flip the current and previous buffers in delay objects.
// You need to call vxAgeDelay for both two delay objects.
// 2. Use ERROR_CHECK_STATUS for error checking.
// ERROR_CHECK_STATUS( vxAgeDelay( /* Fill in parameter */ ) );
// ERROR_CHECK_STATUS( vxAgeDelay( /* Fill in parameter */ ) );
////////
// Display the results and grab the next input RGB frame for the next iteration.
char text[128];
sprintf( text, "Keyboard ESC/Q-Quit SPACE-Pause [FRAME %d]", frame_index );
gui.DrawText( 0, 16, text );
sprintf( text, "Number of Corners: %d [tracking %d]", ( int )num_corners, ( int )num_tracking );
gui.DrawText( 0, 36, text );
gui.Show();
if( !gui.Grab() )
{
// Terminate the processing loop if the end of sequence is detected.
gui.WaitForKey();
break;
}
}
////////********
// Query graph performance using VX_GRAPH_ATTRIBUTE_PERFORMANCE and print timing
// in milliseconds. Note that time units of vx_perf_t fields are nanoseconds.
//
// TODO STEP 11:********
// 1. Use vxQueryGraph API with VX_GRAPH_ATTRIBUTE_PERFORMANCE to query graph performance.
// We gave the attribute query for one graph in comments. Do the same for the second graph.
// 2. Print the average and min execution times in milliseconds. Use the printf in comments.
// vx_perf_t perfHarris = { 0 }, perfTrack = { 0 };
// ERROR_CHECK_STATUS( vxQueryGraph( graphHarris, VX_GRAPH_ATTRIBUTE_PERFORMANCE, &perfHarris, sizeof( perfHarris ) ) );
// ERROR_CHECK_STATUS( vxQueryGraph( /* Fill in parameters here for get performance of the other graph */ );
// printf( "GraphName NumFrames Avg(ms) Min(ms)\n"
// "Harris %9d %7.3f %7.3f\n"
// "Track %9d %7.3f %7.3f\n",
// ( int )perfHarris.num, ( float )perfHarris.avg * 1e-6f, ( float )perfHarris.min * 1e-6f,
// ( int )perfTrack.num, ( float )perfTrack.avg * 1e-6f, ( float )perfTrack.min * 1e-6f );
////////********
// Release all the OpenVX objects created in this exercise, and make the context as the last one to release.
// To release an OpenVX object, you need to call vxRelease<Object> API which takes a pointer to the object.
// If the release operation is successful, the OpenVX framework will reset the object to NULL.
//
// TODO STEP 12:********
// 1. For releasing all other objects use vxRelease<Object> APIs.
// You have to release 2 graph objects, 1 image object, 2 delay objects,
// 6 scalar objects, and 1 context object.
// 2. Use ERROR_CHECK_STATUS for error checking.
// ERROR_CHECK_STATUS( vxReleaseContext( &context ) );
return 0;
}
static vx_status VX_CALLBACK validateKernel(vx_node node, const vx_reference parameters[], vx_uint32 num, vx_meta_format metas[])
{
// check input configuration
vx_enum type, format;
vx_size num_dims, input_dims[4] = { 1, 1, 1, 1 };
ERROR_CHECK_STATUS(vxQueryTensor((vx_tensor)parameters[0], VX_TENSOR_DATA_TYPE, &type, sizeof(type)));
ERROR_CHECK_STATUS(vxQueryTensor((vx_tensor)parameters[0], VX_TENSOR_NUMBER_OF_DIMS, &num_dims, sizeof(num_dims)));
ERROR_CHECK_STATUS(vxQueryTensor((vx_tensor)parameters[0], VX_TENSOR_DIMS, input_dims, sizeof(input_dims[0])*num_dims));
if ((num_dims != 4 && num_dims != 3) || ((input_dims[0] & 3) != 0))
return VX_ERROR_INVALID_DIMENSION;
if (type != VX_TYPE_FLOAT32)
return VX_ERROR_INVALID_TYPE;
// check output object type and set configuration
ERROR_CHECK_STATUS(vxQueryReference(parameters[1], VX_REFERENCE_TYPE, &type, sizeof(type)));
if (type == VX_TYPE_IMAGE) {
vx_df_image format;
ERROR_CHECK_STATUS(vxQueryImage((vx_image)parameters[1], VX_IMAGE_FORMAT, &format, sizeof(format)));
if(format == VX_DF_IMAGE_U8 && input_dims[2] > 255)
return VX_ERROR_INVALID_FORMAT;
if(format == VX_DF_IMAGE_VIRT)
format = (input_dims[2] < 256) ? VX_DF_IMAGE_U8 : VX_DF_IMAGE_U16;
vx_uint32 width = (vx_uint32)input_dims[0];
vx_uint32 height = (vx_uint32)(input_dims[1]*input_dims[3]);
ERROR_CHECK_STATUS(vxSetMetaFormatAttribute(metas[1], VX_IMAGE_WIDTH, &width, sizeof(width)));
ERROR_CHECK_STATUS(vxSetMetaFormatAttribute(metas[1], VX_IMAGE_HEIGHT, &height, sizeof(height)));
ERROR_CHECK_STATUS(vxSetMetaFormatAttribute(metas[1], VX_IMAGE_FORMAT, &format, sizeof(format)));
}
else if (type == VX_TYPE_TENSOR) {
vx_size output_num_dims, output_dims[4] = { 1, 1, 1, 1 };
ERROR_CHECK_STATUS(vxQueryTensor((vx_tensor)parameters[1], VX_TENSOR_DATA_TYPE, &type, sizeof(type)));
ERROR_CHECK_STATUS(vxQueryTensor((vx_tensor)parameters[1], VX_TENSOR_NUMBER_OF_DIMS, &output_num_dims, sizeof(output_num_dims)));
ERROR_CHECK_STATUS(vxQueryTensor((vx_tensor)parameters[1], VX_TENSOR_DIMS, output_dims, sizeof(output_dims[0])*output_num_dims));
if (output_dims[2] != 1 && output_dims[2] != 2) // top_k must be 1 or 2
return VX_ERROR_INVALID_DIMENSION;
if(type == VX_TYPE_UINT8 && input_dims[2] > 255)
return VX_ERROR_INVALID_FORMAT;
if(type != VX_TYPE_UINT8 && type != VX_TYPE_UINT16 && type != VX_TYPE_INT16)
return VX_ERROR_INVALID_FORMAT;
ERROR_CHECK_STATUS(vxSetMetaFormatAttribute(metas[1], VX_TENSOR_DATA_TYPE, &type, sizeof(type)));
ERROR_CHECK_STATUS(vxSetMetaFormatAttribute(metas[1], VX_TENSOR_NUMBER_OF_DIMS, &output_num_dims, sizeof(output_num_dims)));
ERROR_CHECK_STATUS(vxSetMetaFormatAttribute(metas[1], VX_TENSOR_DIMS, output_dims, sizeof(output_dims[0])*output_num_dims));
}
else
return VX_ERROR_INVALID_PARAMETERS;
return VX_SUCCESS;
}
//! \brief The OpenCL code generator callback.
static vx_status VX_CALLBACK opencl_codegen(
vx_node node, // [input] node
const vx_reference parameters[], // [input] parameters
vx_uint32 num, // [input] number of parameters
bool opencl_load_function, // [input] false: normal OpenCL kernel; true: reserved
char opencl_kernel_function_name[64], // [output] kernel_name for clCreateKernel()
std::string& opencl_kernel_code, // [output] string for clCreateProgramWithSource()
std::string& opencl_build_options, // [output] options for clBuildProgram()
vx_uint32& opencl_work_dim, // [output] work_dim for clEnqueueNDRangeKernel()
vx_size opencl_global_work[], // [output] global_work[] for clEnqueueNDRangeKernel()
vx_size opencl_local_work[], // [output] local_work[] for clEnqueueNDRangeKernel()
vx_uint32& opencl_local_buffer_usage_mask, // [output] reserved: must be ZERO
vx_uint32& opencl_local_buffer_size_in_bytes // [output] reserved: must be ZERO
)
{
// get configuration
vx_size num_dims, input_dims[4] = { 1, 1, 1, 1 }, top_k = 1;
vx_enum output_obj_type, output_data_type = VX_TYPE_UINT16;
ERROR_CHECK_STATUS(vxQueryTensor((vx_tensor)parameters[0], VX_TENSOR_NUMBER_OF_DIMS, &num_dims, sizeof(num_dims)));
ERROR_CHECK_STATUS(vxQueryTensor((vx_tensor)parameters[0], VX_TENSOR_DIMS, input_dims, sizeof(input_dims[0])*num_dims));
ERROR_CHECK_STATUS(vxQueryReference(parameters[1], VX_REFERENCE_TYPE, &output_obj_type, sizeof(output_obj_type)));
if(output_obj_type == VX_TYPE_IMAGE) {
vx_df_image format;
ERROR_CHECK_STATUS(vxQueryImage((vx_image)parameters[1], VX_IMAGE_FORMAT, &format, sizeof(format)));
if(format == VX_DF_IMAGE_U8)
output_data_type = VX_TYPE_UINT8;
else if(format == VX_DF_IMAGE_U16)
output_data_type = VX_TYPE_UINT16;
}
else {
vx_size num_dims_output, output_dims[4] = { 1, 1, 1, 1 };
ERROR_CHECK_STATUS(vxQueryTensor((vx_tensor)parameters[1], VX_TENSOR_NUMBER_OF_DIMS, &num_dims_output, sizeof(num_dims_output)));
ERROR_CHECK_STATUS(vxQueryTensor((vx_tensor)parameters[1], VX_TENSOR_DIMS, output_dims, sizeof(output_dims[0])*num_dims_output));
ERROR_CHECK_STATUS(vxQueryTensor((vx_tensor)parameters[1], VX_TENSOR_DATA_TYPE, &output_data_type, sizeof(output_data_type)));
top_k = output_dims[2];
}
size_t N = input_dims[3];
// compute global work
opencl_work_dim = 3;
opencl_local_work[0] = 8;
opencl_local_work[1] = 8;
opencl_local_work[2] = 1;
opencl_global_work[0] = ((input_dims[0] + 3) / 4 + opencl_local_work[0] - 1) & ~(opencl_local_work[0] - 1);
opencl_global_work[1] = (input_dims[1] + opencl_local_work[1] - 1) & ~(opencl_local_work[1] - 1);
opencl_global_work[2] = N;
// generate OpenCL C code
strcpy(opencl_kernel_function_name, "argmax");
char item[8192];
sprintf(item,
"#pragma OPENCL EXTENSION cl_amd_media_ops : enable\n"
"__kernel __attribute__((reqd_work_group_size(%ld, %ld, 1)))\n" // opencl_local_work[0] opencl_local_work[1]
"void %s(__global uchar * i0_buf, uint i0_offset, uint4 i0_stride, %s)\n"
"{\n"
" uint x = get_global_id(0) * 4;\n"
" uint y = get_global_id(1);\n"
" uint z = get_global_id(2);\n"
" if(x < %ld && y < %ld) {\n"
" i0_buf += i0_offset + z * i0_stride.s3 + y * i0_stride.s1 + x * i0_stride.s0;\n"
" uint4 cmax;\n"
, opencl_local_work[0], opencl_local_work[1], opencl_kernel_function_name
, (output_obj_type == VX_TYPE_IMAGE) ?
"uint o0_width, uint o0_height, __global uchar * o0_buf, uint o0_stride, uint o0_offset" :
"__global uchar * o0_buf, uint o0_offset, uint4 o0_stride"
, input_dims[0], input_dims[1]);
opencl_kernel_code = item;
if(top_k == 2) {
sprintf(item,
" uint4 cmax1;\n"
" float4 f, fmax, fmax1;\n"
" fmax = *(__global float4 *)i0_buf;\n"
" i0_buf += i0_stride.s2; f = *(__global float4 *)i0_buf;\n"
" cmax1.s0 = (f.s0 > fmax.s0) ? 0 : 1;\n"
" cmax.s0 = (f.s0 > fmax.s0) ? 1 : 0;\n"
" fmax1.s0 = (f.s0 > fmax.s0) ? fmax.s0 : f.s0;\n"
" fmax.s0 = (f.s0 > fmax.s0) ? f.s0 : fmax.s0;\n"
" cmax1.s1 = (f.s1 > fmax.s1) ? 0 : 1;\n"
" cmax.s1 = (f.s1 > fmax.s1) ? 1 : 0;\n"
" fmax1.s1 = (f.s1 > fmax.s1) ? fmax.s1 : f.s1;\n"
" fmax.s1 = (f.s1 > fmax.s1) ? f.s1 : fmax.s1;\n"
" cmax1.s2 = (f.s2 > fmax.s2) ? 0 : 1;\n"
" cmax.s2 = (f.s2 > fmax.s2) ? 1 : 0;\n"
" fmax1.s2 = (f.s2 > fmax.s2) ? fmax.s2 : f.s2;\n"
" fmax.s2 = (f.s2 > fmax.s2) ? f.s2 : fmax.s2;\n"
" cmax1.s3 = (f.s3 > fmax.s3) ? 0 : 1;\n"
" cmax.s3 = (f.s3 > fmax.s3) ? 1 : 0;\n"
" fmax1.s3 = (f.s3 > fmax.s3) ? fmax.s3 : f.s3;\n"
" fmax.s3 = (f.s3 > fmax.s3) ? f.s3 : fmax.s3;\n"
" for(uint c = 2; c < %ld; c++) {\n"
" i0_buf += i0_stride.s2; f = *(__global float4 *)i0_buf;\n"
" cmax1.s0 = (f.s0 > fmax.s0) ? cmax.s0 : ((f.s0 > fmax1.s0) ? c : cmax1.s0);\n"
" fmax1.s0 = (f.s0 > fmax.s0) ? fmax.s0 : ((f.s0 > fmax1.s0) ? f.s0 : fmax1.s0);\n"
" cmax.s0 = (f.s0 > fmax.s0) ? c : cmax.s0;\n"
" fmax.s0 = (f.s0 > fmax.s0) ? f.s0 : fmax.s0;\n"
" cmax1.s1 = (f.s1 > fmax.s1) ? cmax.s1 : ((f.s1 > fmax1.s1) ? c : cmax1.s1);\n"
" fmax1.s1 = (f.s1 > fmax.s1) ? fmax.s1 : ((f.s1 > fmax1.s1) ? f.s1 : fmax1.s1);\n"
" cmax.s1 = (f.s1 > fmax.s1) ? c : cmax.s1;\n"
" fmax.s1 = (f.s1 > fmax.s1) ? f.s1 : fmax.s1;\n"
" cmax1.s2 = (f.s2 > fmax.s2) ? cmax.s2 : ((f.s2 > fmax1.s2) ? c : cmax1.s2);\n"
" fmax1.s2 = (f.s2 > fmax.s2) ? fmax.s2 : ((f.s2 > fmax1.s2) ? f.s2 : fmax1.s2);\n"
" cmax.s2 = (f.s2 > fmax.s2) ? c : cmax.s2;\n"
" fmax.s2 = (f.s2 > fmax.s2) ? f.s2 : fmax.s2;\n"
" cmax1.s3 = (f.s3 > fmax.s3) ? cmax.s3 : ((f.s3 > fmax1.s3) ? c : cmax1.s3);\n"
" fmax1.s3 = (f.s3 > fmax.s3) ? fmax.s3 : ((f.s3 > fmax1.s3) ? f.s3 : fmax1.s3);\n"
" cmax.s3 = (f.s3 > fmax.s3) ? c : cmax.s3;\n"
" fmax.s3 = (f.s3 > fmax.s3) ? f.s3 : fmax.s3;\n"
" }\n"
, input_dims[2]);
opencl_kernel_code += item;
}
else if (top_k == 1) {
sprintf(item,
" cmax = (uint4)0;\n"
" float4 fmax = *(__global float4 *)i0_buf;\n"
" for(uint c = 1; c < %ld; c++) {\n"
" i0_buf += i0_stride.s2;\n"
" float4 f = *(__global float4 *)i0_buf;\n"
" cmax.s0 = (f.s0 > fmax.s0) ? c : cmax.s0;\n"
" fmax.s0 = (f.s0 > fmax.s0) ? f.s0 : fmax.s0;\n"
" cmax.s1 = (f.s1 > fmax.s1) ? c : cmax.s1;\n"
" fmax.s1 = (f.s1 > fmax.s1) ? f.s1 : fmax.s1;\n"
" cmax.s2 = (f.s2 > fmax.s2) ? c : cmax.s2;\n"
" fmax.s2 = (f.s2 > fmax.s2) ? f.s2 : fmax.s2;\n"
" cmax.s3 = (f.s3 > fmax.s3) ? c : cmax.s3;\n"
" fmax.s3 = (f.s3 > fmax.s3) ? f.s3 : fmax.s3;\n"
" }\n"
, input_dims[2]);
opencl_kernel_code += item;
}
if(output_data_type == VX_TYPE_UINT8) {
if(output_obj_type == VX_TYPE_IMAGE) {
sprintf(item, " o0_buf += o0_offset + (z * %ld + y) * o0_stride + x;\n" , input_dims[1]);
opencl_kernel_code += item;
}
else {
opencl_kernel_code +=
" o0_buf += o0_offset + z * o0_stride.s3 + y * o0_stride.s1 + x * o0_stride.s0;\n";
}
opencl_kernel_code +=
" uint imax = cmax.s0 + (cmax.s1 << 8) + (cmax.s2 << 16) + (cmax.s3 << 24);\n"
" *(__global uint *)o0_buf = imax;\n";
if(top_k == 2) {
opencl_kernel_code +=
" uint imax1 = cmax1.s0 + (cmax1.s1 << 8) + (cmax1.s2 << 16) + (cmax1.s3 << 24);\n"
" *(__global uint *)&o0_buf[o0_stride.s2] = imax1;\n";
}
}
else if(output_data_type == VX_TYPE_UINT16) {
if(output_obj_type == VX_TYPE_IMAGE) {
sprintf(item, " o0_buf += o0_offset + (z * %ld + y) * o0_stride + x * 2;\n" , input_dims[1]);
opencl_kernel_code += item;
}
else {
opencl_kernel_code +=
" o0_buf += o0_offset + z * o0_stride.s3 + y * o0_stride.s1 + x * o0_stride.s0;\n";
}
opencl_kernel_code +=
" uint2 imax;\n"
" imax.s0 = cmax.s0 + (cmax.s1 << 16);\n"
" imax.s1 = cmax.s2 + (cmax.s3 << 16);\n"
" *(__global uint2 *)o0_buf = imax;\n";
if(top_k == 2) {
opencl_kernel_code +=
" uint2 imax1;\n"
" imax1.s0 = cmax1.s0 + (cmax1.s1 << 16);\n"
" imax1.s1 = cmax1.s2 + (cmax1.s3 << 16);\n"
" *(__global uint2 *)&o0_buf[o0_stride.s2] = imax1;\n";
}
}
opencl_kernel_code +=
" }\n"
"}\n";
#if ENABLE_DEBUG_PRINT_DIMS
std::cout << "KERNEL argmax_layer output " << input_dims[0] << "x" << input_dims[1] << " " << std::endl;
#endif
return VX_SUCCESS;
}
////////
// User kernel host side function gets called to execute the user kernel node.
// Perform element-wise consine function on input tensor to produce output tensor.
//
// TODO:********
// 1. Get fixed-point position and dimensions of input and output tensors.
// Note that both input and output tensors have same dimensions.
// 2. Access input and output tensor object data using vxMapTensorPatch API.
// 3. Perform element-wise cosine function using fixed-point position.
// 4. Use vxUnmapTensorPatch API to give the data buffers control back to OpenVX framework.
vx_status VX_CALLBACK tensor_cos_host_side_function( vx_node node, const vx_reference * refs, vx_uint32 num )
{
// Get fixed-point position and dimensions of input and output tensors.
// Note that both input and output tensors have same dimensions.
vx_tensor input = ( vx_tensor ) refs[0];
vx_tensor output = ( vx_tensor ) refs[1];
vx_size num_of_dims;
vx_size dims[4] = { 1, 1, 1, 1 };
vx_uint8 input_fixed_point_pos;
vx_uint8 output_fixed_point_pos;
ERROR_CHECK_STATUS( vxQueryTensor( input, VX_TENSOR_NUMBER_OF_DIMS, &num_of_dims, sizeof( num_of_dims ) ) );
ERROR_CHECK_STATUS( vxQueryTensor( input, VX_TENSOR_DIMS, &dims, num_of_dims * sizeof(vx_size) ) );
ERROR_CHECK_STATUS( vxQueryTensor( input, VX_TENSOR_FIXED_POINT_POSITION, &input_fixed_point_pos, sizeof( input_fixed_point_pos ) ) );
ERROR_CHECK_STATUS( vxQueryTensor( output, VX_TENSOR_FIXED_POINT_POSITION, &output_fixed_point_pos, sizeof( output_fixed_point_pos ) ) );
// Access input and output tensor object data using vxMapTensorPatch API.
vx_size zeros[4] = { 0 };
vx_map_id map_input, map_output;
vx_uint8 * buf_input, * buf_output;
vx_size stride_input[4] = { 0 };
vx_size stride_output[4] = { 0 };
ERROR_CHECK_STATUS( vxMapTensorPatch( input,
num_of_dims, zeros, dims,
&map_input, stride_input,
(void **)&buf_input, VX_READ_ONLY, VX_MEMORY_TYPE_HOST, 0 ) );
ERROR_CHECK_STATUS( vxMapTensorPatch( output,
num_of_dims, zeros, dims,
&map_output, stride_output,
(void **)&buf_output, VX_READ_ONLY, VX_MEMORY_TYPE_HOST, 0 ) );
// Perform element-wise cosine function using fixed-point position.
vx_float32 input_to_float_multiplier = 1.0f / (vx_float32)(1 << input_fixed_point_pos);
vx_float32 output_to_int16_multiplier = (vx_float32)(1 << output_fixed_point_pos);
for( vx_size dim3 = 0; dim3 < dims[3]; dim3++)
{
for( vx_size dim2 = 0; dim2 < dims[2]; dim2++)
{
for( vx_size dim1 = 0; dim1 < dims[1]; dim1++)
{
const vx_int16 * ibuf = (const vx_int16 *) (buf_input +
dim3 * stride_input[3] +
dim2 * stride_input[2] +
dim1 * stride_input[1] );
vx_int16 * obuf = (vx_int16 *) (buf_output +
dim3 * stride_output[3] +
dim2 * stride_output[2] +
dim1 * stride_output[1] );
for( vx_size dim0 = 0; dim0 < dims[0]; dim0++)
{
// no saturation done here
vx_int16 ivalue = ibuf[dim0];
vx_int16 ovalue = (vx_int16)(cosf((vx_float32)ivalue * input_to_float_multiplier) * output_to_int16_multiplier + 0.5f);
obuf[dim0] = ovalue;
}
}
}
}
// Use vxUnmapTensorPatch API to give the data buffers control back to OpenVX framework.
ERROR_CHECK_STATUS( vxUnmapTensorPatch( input, map_input ) );
ERROR_CHECK_STATUS( vxUnmapTensorPatch( output, map_output ) );
return VX_SUCCESS;
}
static vx_status VX_CALLBACK validateImageToTensorKernel(vx_node node, const vx_reference parameters[], vx_uint32 num, vx_meta_format metas[])
{
// check input configuration
vx_uint32 width, height;
vx_df_image format;
ERROR_CHECK_STATUS(vxQueryImage((vx_image)parameters[0], VX_IMAGE_WIDTH, &width, sizeof(width)));
ERROR_CHECK_STATUS(vxQueryImage((vx_image)parameters[0], VX_IMAGE_HEIGHT, &height, sizeof(height)));
ERROR_CHECK_STATUS(vxQueryImage((vx_image)parameters[0], VX_IMAGE_FORMAT, &format, sizeof(format)));
if(format != VX_DF_IMAGE_RGB && format != VX_DF_IMAGE_U8)
return VX_ERROR_INVALID_FORMAT;
vx_enum scalar_type;
ERROR_CHECK_STATUS(vxQueryScalar((vx_scalar)parameters[2], VX_SCALAR_TYPE, &scalar_type, sizeof(scalar_type)));
if(scalar_type != VX_TYPE_FLOAT32)
return VX_ERROR_INVALID_TYPE;
ERROR_CHECK_STATUS(vxQueryScalar((vx_scalar)parameters[3], VX_SCALAR_TYPE, &scalar_type, sizeof(scalar_type)));
if(scalar_type != VX_TYPE_FLOAT32)
return VX_ERROR_INVALID_TYPE;
ERROR_CHECK_STATUS(vxQueryScalar((vx_scalar)parameters[4], VX_SCALAR_TYPE, &scalar_type, sizeof(scalar_type)));
if(scalar_type != VX_TYPE_BOOL)
return VX_ERROR_INVALID_TYPE;
// check output dimensions
vx_enum type;
vx_size num_dims, output_dims[4] = { 1, 1, 1, 1 };
ERROR_CHECK_STATUS(vxQueryTensor((vx_tensor)parameters[1], VX_TENSOR_DATA_TYPE, &type, sizeof(type)));
ERROR_CHECK_STATUS(vxQueryTensor((vx_tensor)parameters[1], VX_TENSOR_NUMBER_OF_DIMS, &num_dims, sizeof(num_dims)));
if (type != VX_TYPE_FLOAT32)
return VX_ERROR_INVALID_TYPE;
if (num_dims != 4)
return VX_ERROR_INVALID_DIMENSION;
ERROR_CHECK_STATUS(vxQueryTensor((vx_tensor)parameters[1], VX_TENSOR_DIMS, output_dims, sizeof(output_dims[0])*num_dims));
if ((output_dims[2] != 3 && output_dims[2] != 1) || output_dims[0] != (size_t)width || (output_dims[1] * output_dims[3]) != (size_t)height)
return VX_ERROR_INVALID_DIMENSION;
// set output tensor configuration
ERROR_CHECK_STATUS(vxSetMetaFormatAttribute(metas[1], VX_TENSOR_DATA_TYPE, &type, sizeof(type)));
ERROR_CHECK_STATUS(vxSetMetaFormatAttribute(metas[1], VX_TENSOR_NUMBER_OF_DIMS, &num_dims, sizeof(num_dims)));
ERROR_CHECK_STATUS(vxSetMetaFormatAttribute(metas[1], VX_TENSOR_DIMS, output_dims, sizeof(output_dims)));
return VX_SUCCESS;
}
//! \brief The OpenCL code generator callback.
static vx_status VX_CALLBACK opencl_codegen(
vx_node node, // [input] node
const vx_reference parameters[], // [input] parameters
vx_uint32 num, // [input] number of parameters
bool opencl_load_function, // [input] false: normal OpenCL kernel; true: reserved
char opencl_kernel_function_name[64], // [output] kernel_name for clCreateKernel()
std::string& opencl_kernel_code, // [output] string for clCreateProgramWithSource()
std::string& opencl_build_options, // [output] options for clBuildProgram()
vx_uint32& opencl_work_dim, // [output] work_dim for clEnqueueNDRangeKernel()
vx_size opencl_global_work[], // [output] global_work[] for clEnqueueNDRangeKernel()
vx_size opencl_local_work[], // [output] local_work[] for clEnqueueNDRangeKernel()
vx_uint32& opencl_local_buffer_usage_mask, // [output] reserved: must be ZERO
vx_uint32& opencl_local_buffer_size_in_bytes // [output] reserved: must be ZERO
)
{
// get configuration
vx_df_image format;
vx_size num_dims, input_dims[4] = { 1, 1, 1, 1 };
ERROR_CHECK_STATUS(vxQueryImage((vx_image)parameters[1], VX_IMAGE_FORMAT, &format, sizeof(format)));
ERROR_CHECK_STATUS(vxQueryTensor((vx_tensor)parameters[0], VX_TENSOR_NUMBER_OF_DIMS, &num_dims, sizeof(num_dims)));
ERROR_CHECK_STATUS(vxQueryTensor((vx_tensor)parameters[0], VX_TENSOR_DIMS, input_dims, sizeof(input_dims[0])*num_dims));
vx_uint32 width = (vx_uint32)input_dims[0];
vx_uint32 height = (vx_uint32)input_dims[1];
vx_uint32 N = (vx_uint32)input_dims[3];
// compute global work
vx_uint32 width_div_4 = (width + 3) / 4;
opencl_work_dim = 3;
opencl_local_work[0] = 8;
opencl_local_work[1] = 8;
opencl_local_work[2] = 1;
opencl_global_work[0] = (width_div_4 + opencl_local_work[0] - 1) & ~(opencl_local_work[0] - 1);
opencl_global_work[1] = (height + opencl_local_work[1] - 1) & ~(opencl_local_work[1] - 1);
opencl_global_work[2] = N;
// generate OpenCL C code
strcpy(opencl_kernel_function_name, "tensor_to_image");
if(format == VX_DF_IMAGE_RGB) {
char item[8192];
sprintf(item,
"#pragma OPENCL EXTENSION cl_amd_media_ops : enable\n"
"__kernel __attribute__((reqd_work_group_size(%ld, %ld, 1)))\n" // opencl_local_work[0] opencl_local_work[1]
"void %s(__global uchar * i0_buf, uint i0_offset, uint4 i0_stride, uint o0_width, uint o0_height, __global uchar * o0_buf, uint o0_stride, uint o0_offset, float ka, float kb, uint reverse_channel_order)\n"
"{\n"
" uint x = get_global_id(0) * 4;\n"
" uint y = get_global_id(1);\n"
" uint n = get_global_id(2);\n"
" if(x < %d && y < %d) {\n"
" i0_buf += i0_offset + n * i0_stride.s3 + y * i0_stride.s1 + x * i0_stride.s0;\n"
" float4 r = *(__global float4 *)&i0_buf[reverse_channel_order ? 2 * i0_stride.s2 : 0];\n"
" float4 g = *(__global float4 *)&i0_buf[ i0_stride.s2 ];\n"
" float4 b = *(__global float4 *)&i0_buf[reverse_channel_order ? 0 : 2 * i0_stride.s2];\n"
" r = r * (float4)ka + (float4)kb;\n"
" g = g * (float4)ka + (float4)kb;\n"
" b = b * (float4)ka + (float4)kb;\n"
" uint3 u3;\n"
" u3.s0 = amd_pack((float4)(r.s0, g.s0, b.s0, r.s1));\n"
" u3.s1 = amd_pack((float4)(g.s1, b.s1, r.s2, g.s2));\n"
" u3.s2 = amd_pack((float4)(b.s2, r.s3, g.s3, b.s3));\n"
" vstore3(u3, 0, (__global uint *)&o0_buf[o0_offset + (y + n * %d) * o0_stride + x * 3]);\n"
" }\n"
"}\n"
, opencl_local_work[0], opencl_local_work[1], opencl_kernel_function_name, width, height, height);
opencl_kernel_code = item;
}
else {
char item[8192];
sprintf(item,
"#pragma OPENCL EXTENSION cl_amd_media_ops : enable\n"
"__kernel __attribute__((reqd_work_group_size(%ld, %ld, 1)))\n" // opencl_local_work[0] opencl_local_work[1]
"void %s(__global uchar * i0_buf, uint i0_offset, uint4 i0_stride, uint o0_width, uint o0_height, __global uchar * o0_buf, uint o0_stride, uint o0_offset, float ka, float kb, uint reverse_channel_order)\n"
"{\n"
" uint x = get_global_id(0) * 4;\n"
" uint y = get_global_id(1);\n"
" uint n = get_global_id(2);\n"
" if(x < %d && y < %d) {\n"
" i0_buf += i0_offset + n * i0_stride.s3 + y * i0_stride.s1 + x * i0_stride.s0;\n"
" float4 i = *(__global float4 *)i0_buf;\n"
" i = i * (float4)ka + (float4)kb;\n"
" *(__global uint *)&o0_buf[o0_offset + (y + n * %d) * o0_stride + x] = amd_pack((float4)(i.s0, i.s1, i.s2, i.s3));\n"
" }\n"
"}\n"
, opencl_local_work[0], opencl_local_work[1], opencl_kernel_function_name, width, height, height);
opencl_kernel_code = item;
}
#if ENABLE_DEBUG_PRINT_DIMS
std::cout << "KERNEL tensor_to_image output " << width << "x" << height << " " << N << std::endl;
#endif
return VX_SUCCESS;
}
//! \brief The OpenCL code generator callback.
static vx_status VX_CALLBACK opencl_codegen(
vx_node node, // [input] node
const vx_reference parameters[], // [input] parameters
vx_uint32 num, // [input] number of parameters
bool opencl_load_function, // [input] false: normal OpenCL kernel; true: reserved
char opencl_kernel_function_name[64], // [output] kernel_name for clCreateKernel()
std::string& opencl_kernel_code, // [output] string for clCreateProgramWithSource()
std::string& opencl_build_options, // [output] options for clBuildProgram()
vx_uint32& opencl_work_dim, // [output] work_dim for clEnqueueNDRangeKernel()
vx_size opencl_global_work[], // [output] global_work[] for clEnqueueNDRangeKernel()
vx_size opencl_local_work[], // [output] local_work[] for clEnqueueNDRangeKernel()
vx_uint32& opencl_local_buffer_usage_mask, // [output] reserved: must be ZERO
vx_uint32& opencl_local_buffer_size_in_bytes // [output] reserved: must be ZERO
)
{
// get configuration
vx_uint32 width, height, N;
vx_df_image format;
vx_size num_dims, output_dims[4] = { 1, 1, 1, 1 };
ERROR_CHECK_STATUS(vxQueryImage((vx_image)parameters[0], VX_IMAGE_FORMAT, &format, sizeof(format)));
ERROR_CHECK_STATUS(vxQueryImage((vx_image)parameters[0], VX_IMAGE_WIDTH, &width, sizeof(width)));
ERROR_CHECK_STATUS(vxQueryImage((vx_image)parameters[0], VX_IMAGE_HEIGHT, &height, sizeof(height)));
ERROR_CHECK_STATUS(vxQueryTensor((vx_tensor)parameters[1], VX_TENSOR_NUMBER_OF_DIMS, &num_dims, sizeof(num_dims)));
ERROR_CHECK_STATUS(vxQueryTensor((vx_tensor)parameters[1], VX_TENSOR_DIMS, output_dims, sizeof(output_dims[0])*num_dims));
height = (vx_uint32)output_dims[1];
N = (vx_uint32)output_dims[3];
// generate OpenCL C code and compute global work
strcpy(opencl_kernel_function_name, "image_to_tensor");
if(format == VX_DF_IMAGE_RGB) {
opencl_work_dim = 3;
opencl_local_work[0] = 8;
opencl_local_work[1] = 8;
opencl_local_work[2] = 1;
opencl_global_work[0] = (width + opencl_local_work[0] - 1) & ~(opencl_local_work[0] - 1);
opencl_global_work[1] = (height + opencl_local_work[1] - 1) & ~(opencl_local_work[1] - 1);
opencl_global_work[2] = N;
char item[8192];
sprintf(item,
"#pragma OPENCL EXTENSION cl_amd_media_ops : enable\n"
"__kernel __attribute__((reqd_work_group_size(%ld, %ld, 1)))\n" // opencl_local_work[0] opencl_local_work[1]
"void %s(uint i0_width, uint i0_height, __global uchar * i0_buf, uint i0_stride, uint i0_offset, __global uchar * o0_buf, uint o0_offset, uint4 o0_stride, float ka, float kb, uint reverse_channel_order)\n"
"{\n"
" uint x = get_global_id(0);\n"
" uint y = get_global_id(1);\n"
" uint n = get_global_id(2);\n"
" if(x < %d && y < %d) {\n"
" uint ioffset = i0_offset + (y + n * %d) * i0_stride + x * 3;\n"
" uint2 rgb2 = vload2(0, (__global uint *)&i0_buf[ioffset & ~3]);\n"
" uint rgb = amd_bytealign(rgb2.s1, rgb2.s0, ioffset & 3);\n"
" float r = ka * amd_unpack0(rgb) + kb;\n"
" float g = ka * amd_unpack1(rgb) + kb;\n"
" float b = ka * amd_unpack2(rgb) + kb;\n"
" o0_buf += o0_offset + n * o0_stride.s3 + y * o0_stride.s1 + x * o0_stride.s0;\n"
" *(__global float *)&o0_buf[ 0] = reverse_channel_order ? b : r;\n"
" *(__global float *)&o0_buf[ o0_stride.s2] = g;\n"
" *(__global float *)&o0_buf[2 * o0_stride.s2] = reverse_channel_order ? r : b;\n"
" }\n"
"}\n"
, opencl_local_work[0], opencl_local_work[1], opencl_kernel_function_name, width, height, height);
opencl_kernel_code = item;
}
else if(format == VX_DF_IMAGE_U8) {
opencl_work_dim = 3;
opencl_local_work[0] = 8;
opencl_local_work[1] = 8;
opencl_local_work[2] = 1;
opencl_global_work[0] = ((width+3)/4 + opencl_local_work[0] - 1) & ~(opencl_local_work[0] - 1);
opencl_global_work[1] = (height + opencl_local_work[1] - 1) & ~(opencl_local_work[1] - 1);
opencl_global_work[2] = N;
char item[8192];
sprintf(item,
"#pragma OPENCL EXTENSION cl_amd_media_ops : enable\n"
"__kernel __attribute__((reqd_work_group_size(%ld, %ld, 1)))\n" // opencl_local_work[0] opencl_local_work[1]
"void %s(uint i0_width, uint i0_height, __global uchar * i0_buf, uint i0_stride, uint i0_offset, __global uchar * o0_buf, uint o0_offset, uint4 o0_stride, float a, float b, uint reverse_channel_order)\n"
"{\n"
" uint x = get_global_id(0) * 4;\n"
" uint y = get_global_id(1);\n"
" uint n = get_global_id(2);\n"
" if(x < %d && y < %d) {\n"
" uint u4 = *(__global uint *)&i0_buf[i0_offset + (y + n * %d) * i0_stride + x];\n"
" float p0 = a * amd_unpack0(u4) + b;\n"
" float p1 = a * amd_unpack1(u4) + b;\n"
" float p2 = a * amd_unpack2(u4) + b;\n"
" float p3 = a * amd_unpack3(u4) + b;\n"
" *(__global float4 *)&o0_buf[o0_offset + n * o0_stride.s3 + y * o0_stride.s1 + x * o0_stride.s0] = (float4)(p0 , p1, p2, p3);\n"
" }\n"
"}\n"
, opencl_local_work[0], opencl_local_work[1], opencl_kernel_function_name, width, height, height);
opencl_kernel_code = item;
}
#if ENABLE_DEBUG_PRINT_DIMS
std::cout << "KERNEL image_to_tensor output " << width << " " << height << " " << N << std::endl;
#endif
return VX_SUCCESS;
}