void cleanup() {
if(!is_valid()) throw std::runtime_error( error_ );
for(auto wl : workflow_layer)
di->workflow_item_delete_function( wl );
di->workflow_delete_function( workflow );
}
void cleanup() {
if(!is_valid()) throw std::runtime_error(error_);
for(auto wl : workflow_layer)
di->workflow_item_delete_function(wl);
di->workflow_delete_function(workflow);
for(auto wb : workflow_layer_factor)
if(wb!=nullptr) delete wb;
}
void cleanup(){
if(!is_valid()) throw std::runtime_error(error_);
/* ****************************************************************************************** */
/* Cleanup in memory */
/* ****************************************************************************************** */
std::cout
<< "Cleanup in memory"
<< std::endl
<< "========================================================"
<< std::endl;
di->workflow_item_delete_function(wrkflwi_input);
di->workflow_item_delete_function(wrkflwi_stage_1_conv);
di->workflow_item_delete_function(wrkflwi_stage_1_pool);
di->workflow_item_delete_function(wrkflwi_stage_1_subv);
di->workflow_item_delete_function(wrkflwi_stage_2_conv);
di->workflow_item_delete_function(wrkflwi_stage_2_pool);
di->workflow_item_delete_function(wrkflwi_stage_3_fc);
di->workflow_item_delete_function(wrkflwi_stage_4_fc);
di->workflow_item_delete_function(wrkflwi_softmax);
di->workflow_item_delete_function(wrkflwi_output);
di->workflow_delete_function(workflow);
delete nnwrkld_conv1_weights;
delete nnwrkld_conv1_biases;
delete nnwrkld_conv2_weights;
delete nnwrkld_conv2_biases;
delete nnwrkld_fc1_weights;
delete nnwrkld_fc1_biases;
delete nnwrkld_fc2_weights;
delete nnwrkld_fc2_biases;
delete di;
}
virtual nn_workflow_t *init_test_workflow( nn_device_interface_0_t *_di ) {
if(!is_valid()) throw std::runtime_error( error_ );
for(auto wi : workflow_layer) wi = nullptr;
this->di = _di;
di->workflow_create_function( &workflow, 1, 1 );
// STAGE 0 (input)
{
di->workflow_item_create_function( &workflow_layer[input], 0, nullptr, 1 );
workflow_layer[input]->type = NN_WORK_ITEM_TYPE_INPUT;
workflow_layer[input]->arguments.input.index = 0;
workflow_layer[input]->output_format[0].format = NN_DATA_FORMAT_1D;
workflow_layer[input]->output_format[0].format_1d = { { relu_length } };
}
// STAGE 1 relu
{
nn_workflow_use_descriptor_t inputs_descriptor = { workflow_layer[input], 0 };
di->workflow_item_create_function( &workflow_layer[relu], 1, &inputs_descriptor, 1 );
workflow_layer[relu]->type = NN_WORK_ITEM_TYPE_RELU;
workflow_layer[relu]->output_format[0].format = NN_DATA_FORMAT_1D;
workflow_layer[relu]->output_format[0].format_1d = { { relu_length } };
}
// ------------------------------------------------------------------------------------------
// STAGE 2 output
{
nn_workflow_use_descriptor_t inputs_descriptor = { workflow_layer[relu], 0 };
di->workflow_item_create_function( &workflow_layer[output], 1, &inputs_descriptor, 1 );
workflow_layer[output]->type = NN_WORK_ITEM_TYPE_OUTPUT;
workflow_layer[output]->output_format[0].format = NN_DATA_FORMAT_1D;
workflow_layer[output]->output_format[0].format_3d = { { relu_length } };
}
// -------------------------------------------------------------------------------------------
// END of workflow stages definition
workflow->input[0] = workflow_layer[input];
workflow->output[0] = workflow_layer[output];
// -------------------------------------------------------------------------------------------
return workflow;
}
void cleanup() {
if(!is_valid()) throw std::runtime_error(error_);
for(auto wl : workflow_layer)
di->workflow_item_delete_function(wl);
di->workflow_delete_function(workflow);
for(auto wlwf : workflow_layer_weights_float)
if(wlwf!=nullptr) delete wlwf;
for(auto wlwi : workflow_layer_weights_int16)
if(wlwi!=nullptr) delete wlwi;
for(auto wlbi : workflow_layer_biases_int32)
if(wlbi!=nullptr) delete wlbi;
for(auto wlbf : workflow_layer_biases_float)
if(wlbf!=nullptr) delete wlbf;
if(mean_factor!=nullptr) delete mean_factor;
}
virtual nn_workflow_t *init_workflow(nn_device_interface_0_t *di){
if(!is_valid()) throw std::runtime_error(error_);
this->di = di;
std::cout
<< "--------------------------------------------------------"
<< std::endl
<< "Loading weights and biases"
<< std::endl << std::endl;
// Load weights and biases
auto load_biases_or_weights = [](std::string wb_file_name) {
nn::data<float> *wb_pointer = nn_data_load_from_file_time_measure(wb_file_name);
if(wb_pointer == nullptr) {
std::cerr << "Can't load " << wb_file_name << std::endl;
throw;
}
return wb_pointer;
};
try {
nnwrkld_conv1_weights = load_biases_or_weights("weights_lenet/conv1.nn");
nnwrkld_conv1_biases = load_biases_or_weights("weights_lenet/conv1_bias.nn");
nnwrkld_conv2_weights = load_biases_or_weights("weights_lenet/conv2.nn");
nnwrkld_conv2_biases = load_biases_or_weights("weights_lenet/conv2_bias.nn");
nnwrkld_fc1_weights = load_biases_or_weights("weights_lenet/ip1.nn");
nnwrkld_fc1_biases = load_biases_or_weights("weights_lenet/ip1_bias.nn");
nnwrkld_fc2_weights = load_biases_or_weights("weights_lenet/ip2.nn");
nnwrkld_fc2_biases = load_biases_or_weights("weights_lenet/ip2_bias.nn");
}
catch(...) {
return workflow;
}
std::cout
<< "--------------------------------------------------------" << std::endl
<< "Build of workflow" << std::endl;
di->workflow_create_function(&workflow, 1, 1);
// ------------------------------------------------------------------------------------------
// STAGE 0 (input)
// output: 28x28x3
{
di->workflow_item_create_function(&wrkflwi_input, 0, nullptr, 1);
wrkflwi_input->type = NN_WORK_ITEM_TYPE_INPUT;
wrkflwi_input->arguments.input.index = 0;
wrkflwi_input->output_format[0].format = NN_DATA_FORMAT_2D;
wrkflwi_input->output_format[0].format_3d ={ { img_size, img_size} };
}
// ------------------------------------------------------------------------------------------
// STAGE 01
// convo: 5x5 stride 1x1; no-activation; output: 24x24x20
// maxpool: 2x2 stride 2x2;
// output: 12x12x20
{
nn_workflow_use_descriptor_t inputs_descriptor = { wrkflwi_input, 0 };
di->workflow_item_create_function(&wrkflwi_stage_1_conv, 1, &inputs_descriptor, 1);
wrkflwi_stage_1_conv->type = NN_WORK_ITEM_TYPE_CONVOLUTION;
wrkflwi_stage_1_conv->name = "c1";
wrkflwi_stage_1_conv->arguments.forward_convolution.padding = NN_PADDING_MODE_DATA_OR_ZERO;
wrkflwi_stage_1_conv->arguments.forward_convolution.activation.function = NN_ACTIVATION_FUNCTION_NONE;
// We have weights, biases for 20 filters , but we want to have for four more filters so lets add padding
wrkflwi_stage_1_conv->arguments.forward_convolution.weights = nn_data_extend_weights_by_padding(nnwrkld_conv1_weights,1,24);
wrkflwi_stage_1_conv->arguments.forward_convolution.biases = nn_data_extend_biases_by_padding(nnwrkld_conv1_biases,24);
wrkflwi_stage_1_conv->arguments.forward_convolution.center_offset[0] = 0;
wrkflwi_stage_1_conv->arguments.forward_convolution.center_offset[1] = 0;
wrkflwi_stage_1_conv->arguments.forward_convolution.stride[0] = 1;
wrkflwi_stage_1_conv->arguments.forward_convolution.stride[1] = 1;
wrkflwi_stage_1_conv->output_format[0].format = NN_DATA_FORMAT_3D;
// It should be 20 output FM , but we do support only case when output FM number is divisble by 8
wrkflwi_stage_1_conv->output_format[0].format_3d ={ { 24, 24, 24 } };
}
{
nn_workflow_use_descriptor_t inputs_descriptor = { wrkflwi_stage_1_conv, 0 };
di->workflow_item_create_function(&wrkflwi_stage_1_pool, 1, &inputs_descriptor, 1);
wrkflwi_stage_1_pool->type = NN_WORK_ITEM_TYPE_POOLING;
wrkflwi_stage_1_pool->name = "p1";
wrkflwi_stage_1_pool->arguments.forward_pooling.mode = NN_POOLING_MODE_MAX;
wrkflwi_stage_1_pool->arguments.forward_pooling.size[0] = 2;
wrkflwi_stage_1_pool->arguments.forward_pooling.size[1] = 2;
wrkflwi_stage_1_pool->arguments.forward_pooling.stride[0] = 2;
wrkflwi_stage_1_pool->arguments.forward_pooling.stride[1] = 2;
wrkflwi_stage_1_pool->output_format[0].format = NN_DATA_FORMAT_3D;
wrkflwi_stage_1_pool->output_format[0].format_3d ={ { 12, 12, 24 } };
}
// view
{
nn_workflow_use_descriptor_t inputs_descriptor = { wrkflwi_stage_1_pool, 0 };
di->workflow_item_create_function(&wrkflwi_stage_1_subv, 1, &inputs_descriptor, 1); // view
wrkflwi_stage_1_subv->type = NN_WORK_ITEM_TYPE_VIEW;
wrkflwi_stage_1_subv->arguments.view.origin[0] = 0;
wrkflwi_stage_1_subv->arguments.view.origin[1] = 0;
wrkflwi_stage_1_subv->arguments.view.origin[2] = 0;
wrkflwi_stage_1_subv->output_format[0].format = NN_DATA_FORMAT_3D;
wrkflwi_stage_1_subv->output_format[0].format_3d ={ { 12, 12, 20 } };
}
// ------------------------------------------------------------------------------------------
// STAGE 02
// convo: 5x5 stride 1x1; no-activation; output: 8x8x50
// maxpool: 2x2 stride 2x2;
// output: 4x4x50
// convolution 2
{
nn_workflow_use_descriptor_t inputs_descriptor = { wrkflwi_stage_1_subv, 0 };
di->workflow_item_create_function(&wrkflwi_stage_2_conv, 1, &inputs_descriptor, 1);
wrkflwi_stage_2_conv->type = NN_WORK_ITEM_TYPE_CONVOLUTION;
wrkflwi_stage_2_conv->name = "c2";
wrkflwi_stage_2_conv->arguments.forward_convolution.activation.function = NN_ACTIVATION_FUNCTION_NONE;
wrkflwi_stage_2_conv->arguments.forward_convolution.padding = NN_PADDING_MODE_DATA_OR_ZERO;
wrkflwi_stage_2_conv->arguments.forward_convolution.weights = nn_data_extend_weights_by_padding(nnwrkld_conv2_weights,20,56);
wrkflwi_stage_2_conv->arguments.forward_convolution.biases = nn_data_extend_biases_by_padding(nnwrkld_conv2_biases,56);
wrkflwi_stage_2_conv->arguments.forward_convolution.center_offset[0] = 0;
wrkflwi_stage_2_conv->arguments.forward_convolution.center_offset[1] = 0;
wrkflwi_stage_2_conv->arguments.forward_convolution.stride[0] = 1;
wrkflwi_stage_2_conv->arguments.forward_convolution.stride[1] = 1;
wrkflwi_stage_2_conv->output_format[0].format = NN_DATA_FORMAT_3D;
// It should be 50 output FM , but we do support only case when output FM number is divisble by 8
wrkflwi_stage_2_conv->output_format[0].format_3d ={ { 8, 8, 56 } };
}
// maxpool: 2x2 stride 2x2;
{
nn_workflow_use_descriptor_t inputs_descriptor = { wrkflwi_stage_2_conv, 0 };
di->workflow_item_create_function(&wrkflwi_stage_2_pool, 1, &inputs_descriptor, 1); // pooling
wrkflwi_stage_2_pool->type = NN_WORK_ITEM_TYPE_POOLING;
wrkflwi_stage_2_pool->name = "p2";
wrkflwi_stage_2_pool->arguments.forward_pooling.mode = NN_POOLING_MODE_MAX;
wrkflwi_stage_2_pool->arguments.forward_pooling.size[0] = 2;
wrkflwi_stage_2_pool->arguments.forward_pooling.size[1] = 2;
wrkflwi_stage_2_pool->arguments.forward_pooling.stride[0] = 2;
wrkflwi_stage_2_pool->arguments.forward_pooling.stride[1] = 2;
wrkflwi_stage_2_pool->output_format[0].format = NN_DATA_FORMAT_3D;
wrkflwi_stage_2_pool->output_format[0].format_3d ={ { 4, 4, 56 } };
}
// ------------------------------------------------------------------------------------------
// STAGE 03
// full: ReLU
// output: 500
{
nn_workflow_use_descriptor_t inputs_descriptor = { wrkflwi_stage_2_pool, 0 };
di->workflow_item_create_function(&wrkflwi_stage_3_fc, 1, &inputs_descriptor, 1);
wrkflwi_stage_3_fc->type = NN_WORK_ITEM_TYPE_FULLY_CONNECTED;
wrkflwi_stage_3_fc->name = "fc1";
wrkflwi_stage_3_fc->arguments.forward_fully_connected.activation.function = NN_ACTIVATION_FUNCTION_RELU;
// Generated weights if taken from caffe , are in 2D format while we need them in 4d format
nn::data<float>* nnwrkld_fc1_converted_weights = nn_data_convert_weights_2D_to_4D(nnwrkld_fc1_weights,
4,
4,
50,
nnwrkld_fc1_weights->size[1]);
// release original weights
delete nnwrkld_fc1_weights;
// Extend weights' depth of FC layer to match extended weights input
nnwrkld_fc1_weights = nn_data_extend_weights_by_padding(nnwrkld_fc1_converted_weights,56,nnwrkld_fc1_converted_weights->size[3]);
delete nnwrkld_fc1_converted_weights;
nnwrkld_fc1_converted_weights = nullptr;
wrkflwi_stage_3_fc->arguments.forward_fully_connected.weights = nnwrkld_fc1_weights;
wrkflwi_stage_3_fc->arguments.forward_fully_connected.biases = nnwrkld_fc1_biases;
wrkflwi_stage_3_fc->output_format[0].format = NN_DATA_FORMAT_1D;
wrkflwi_stage_3_fc->output_format[0].format_1d ={ { 500 } };
}
// ------------------------------------------------------------------------------------------
// STAGE 04
// full: ;
// output: 10
{
nn_workflow_use_descriptor_t inputs_descriptor = { wrkflwi_stage_3_fc, 0 };
di->workflow_item_create_function(&wrkflwi_stage_4_fc, 1, &inputs_descriptor, 1);
wrkflwi_stage_4_fc->type = NN_WORK_ITEM_TYPE_FULLY_CONNECTED;
wrkflwi_stage_4_fc->name = "fc2";
wrkflwi_stage_4_fc->arguments.forward_fully_connected.activation.function = NN_ACTIVATION_FUNCTION_NONE;
wrkflwi_stage_4_fc->arguments.forward_fully_connected.weights = nnwrkld_fc2_weights;
wrkflwi_stage_4_fc->arguments.forward_fully_connected.biases = nnwrkld_fc2_biases;
wrkflwi_stage_4_fc->output_format[0].format = NN_DATA_FORMAT_1D;
wrkflwi_stage_4_fc->output_format[0].format_1d ={ { 10 } };
}
// ------------------------------------------------------------------------------------------
// STAGE 05 (softmax)
// output: 10
{
nn_workflow_use_descriptor_t inputs_descriptor = { wrkflwi_stage_4_fc, 0 };
di->workflow_item_create_function(&wrkflwi_softmax, 1, &inputs_descriptor, 1);
wrkflwi_softmax->type = NN_WORK_ITEM_TYPE_SOFTMAX;
wrkflwi_softmax->output_format[0].format = NN_DATA_FORMAT_1D;
wrkflwi_softmax->output_format[0].format_1d ={ { 10 } };
}
// ------------------------------------------------------------------------------------------
// STAGE 6 (output)
// output: 10
{
nn_workflow_use_descriptor_t inputs_descriptor = { wrkflwi_softmax, 0 };
di->workflow_item_create_function(&wrkflwi_output, 1, &inputs_descriptor, 1);
wrkflwi_output->type = NN_WORK_ITEM_TYPE_OUTPUT;
wrkflwi_output->output_format[0].format = NN_DATA_FORMAT_1D;
wrkflwi_output->output_format[0].format_1d ={ { 10 } };
}
// -------------------------------------------------------------------------------------------
// END of workflow stages definition
// -------------------------------------------------------------------------------------------
workflow->input[0] = wrkflwi_input;
workflow->output[0] = wrkflwi_output;
// -------------------------------------------------------------------------------------------
return workflow;
}
virtual nn_workflow_t *init_test_workflow(nn_device_interface_0_t *_di) {
if(!is_valid()) throw std::runtime_error(error_);
this->di = _di;
// load nn:data factors (weights and biases) for successive layers
mean_factor = nn_data_load_from_file("weights_caffenet/imagenet_mean.nnd");
workflow_layer_weights_float[conv1_factor] = nn_data_load_from_file("weights_caffenet/conv1.nnd");
workflow_layer_biases_float[conv1_factor] = nn_data_load_from_file("weights_caffenet/conv1_bias.nnd");
workflow_layer_weights_float[conv2_1_factor] = nn_data_load_from_file("weights_caffenet/conv2_g1.nnd");
workflow_layer_biases_float[conv2_1_factor] = nn_data_load_from_file("weights_caffenet/conv2_bias_g1.nnd");
workflow_layer_weights_float[conv2_2_factor] = nn_data_load_from_file("weights_caffenet/conv2_g2.nnd");
workflow_layer_biases_float[conv2_2_factor] = nn_data_load_from_file("weights_caffenet/conv2_bias_g2.nnd");
workflow_layer_weights_float[conv3_factor] = nn_data_load_from_file("weights_caffenet/conv3.nnd");
workflow_layer_biases_float[conv3_factor] = nn_data_load_from_file("weights_caffenet/conv3_bias.nnd");
workflow_layer_weights_float[conv4_1_factor] = nn_data_load_from_file("weights_caffenet/conv4_g1.nnd");
workflow_layer_biases_float[conv4_1_factor] = nn_data_load_from_file("weights_caffenet/conv4_bias_g1.nnd");
workflow_layer_weights_float[conv4_2_factor] = nn_data_load_from_file("weights_caffenet/conv4_g2.nnd");
workflow_layer_biases_float[conv4_2_factor] = nn_data_load_from_file("weights_caffenet/conv4_bias_g2.nnd");
workflow_layer_weights_float[conv5_1_factor] = nn_data_load_from_file("weights_caffenet/conv5_g1.nnd");
workflow_layer_biases_float[conv5_1_factor] = nn_data_load_from_file("weights_caffenet/conv5_bias_g1.nnd");
workflow_layer_weights_float[conv5_2_factor] = nn_data_load_from_file("weights_caffenet/conv5_g2.nnd");
workflow_layer_biases_float[conv5_2_factor] = nn_data_load_from_file("weights_caffenet/conv5_bias_g2.nnd");
workflow_layer_weights_float[fc6_factor] = nn_data_load_from_file("weights_caffenet/fc6.nnd");
workflow_layer_biases_float[fc6_factor] = nn_data_load_from_file("weights_caffenet/fc6_bias.nnd");
workflow_layer_weights_float[fc7_factor] = nn_data_load_from_file("weights_caffenet/fc7.nnd");
workflow_layer_biases_float[fc7_factor] = nn_data_load_from_file("weights_caffenet/fc7_bias.nnd");
workflow_layer_weights_float[fc8_factor] = nn_data_load_from_file("weights_caffenet/fc8.nnd");
workflow_layer_biases_float[fc8_factor] = nn_data_load_from_file("weights_caffenet/fc8_bias.nnd");
for (auto wlwf : workflow_layer_weights_float)
if (wlwf == nullptr)
throw std::runtime_error("error: one or more of file with weights was not loaded");
for (auto wlbf : workflow_layer_biases_float)
if (wlbf == nullptr)
throw std::runtime_error("error: one or more of file with biases was not loaded");
di->workflow_create_function(&workflow,1,1);
// { c1 c2_1 c2_2 c3 c4_1 c4_2 c5_1 c5_2 fc6 fc7 fc8 }
const size_t nnwrkld_accumulator_fraction[last_factor+1] = { 16, 19, 17, 22, 22, 22, 23, 22, 24, 26, 24 };
const size_t nnwrkld_output_fraction[last_factor+1] = { 3, 7, 7, 6, 7, 7, 8, 8, 10, 12, 26 };
const size_t nnwrkld_weights_float_fraction[last_factor+1] = { 16, 16, 14, 15, 16, 16, 16, 15, 16, 16, 12 };
const size_t nnwrkld_biases_float_fraction[last_factor+1] = {nnwrkld_accumulator_fraction[conv1_factor],
nnwrkld_accumulator_fraction[conv2_1_factor],
nnwrkld_accumulator_fraction[conv2_2_factor],
nnwrkld_accumulator_fraction[conv3_factor],
nnwrkld_accumulator_fraction[conv4_1_factor],
nnwrkld_accumulator_fraction[conv4_2_factor],
nnwrkld_accumulator_fraction[conv5_1_factor],
nnwrkld_accumulator_fraction[conv5_2_factor],
nnwrkld_accumulator_fraction[fc6_factor],
nnwrkld_accumulator_fraction[fc7_factor],
nnwrkld_accumulator_fraction[fc8_factor]
};
for(auto i = 0; i<=last_factor;++i) {
workflow_layer_weights_int16[i] = new nn::data<int16_t>(static_cast<const size_t*>(workflow_layer_weights_float[i]->size),workflow_layer_weights_float[i]->dimension);
workflow_layer_biases_int32[i] = new nn::data<int32_t>(static_cast<const size_t*>(workflow_layer_biases_float[i]->size),workflow_layer_biases_float[i]->dimension);
nn_data_convert_float_to_int16_fixedpoint(workflow_layer_weights_float[i],workflow_layer_weights_int16[i],1 << nnwrkld_weights_float_fraction[i]);
nn_data_convert_float_to_int32_fixedpoint(workflow_layer_biases_float[i],workflow_layer_biases_int32[i],1 << nnwrkld_biases_float_fraction[i]);
}
// ------------------------------------------------------------------------------------------
// STAGE 0 (input)
// output: 227x227x3
{
di->workflow_item_create_function(&workflow_layer[input],0,nullptr,1);
workflow_layer[input]->type = NN_WORK_ITEM_TYPE_INPUT;
workflow_layer[input]->arguments.input.index = 0;
workflow_layer[input]->output_format[0].format = NN_DATA_FORMAT_3D;
workflow_layer[input]->output_format[0].format_3d ={{img_size,img_size,3}};
}
// ------------------------------------------------------------------------------------------
// STAGE 0 (imagenet_mean_subtract)
// output: 227x227x3
{
nn_workflow_use_descriptor_t inputs_descriptor ={workflow_layer[input],0};
di->workflow_item_create_function(&workflow_layer[mean_substract],1,&inputs_descriptor,1);
workflow_layer[mean_substract]->type = NN_WORK_ITEM_TYPE_ARITHMETIC;
workflow_layer[mean_substract]->arguments.forward_arithmetic.factor = mean_factor;
workflow_layer[mean_substract]->arguments.forward_arithmetic.arithmetic_function = NN_ARITHMETIC_FUNCTION_SUBTRACTION;
workflow_layer[mean_substract]->output_format[0].format = NN_DATA_FORMAT_3D;
workflow_layer[mean_substract]->output_format[0].format_3d ={{img_size,img_size,3}};
}
// ------------------------------------------------------------------------------------------
// STAGE 0 Convert float to int16
//
{
nn_workflow_use_descriptor_t inputs_descriptor = { workflow_layer[mean_substract], 0 };
di->workflow_item_create_function(&workflow_layer[convert], 1, &inputs_descriptor, 1);
workflow_layer[convert]->type = NN_WORK_ITEM_TYPE_CONVERT_FLOAT_TO_INT16_FIXEDPOINT;
workflow_layer[convert]->arguments.forward_convert_float_to_int16_fixedpoint.output_fraction = 0;
workflow_layer[convert]->output_format[0].format = NN_DATA_FORMAT_3D;
workflow_layer[convert]->output_format[0].format_3d = nn_output_format_3d{ { img_size, img_size, 4 } };
}
// ------------------------------------------------------------------------------------------
// STAGE 01
// convo: 11x11 stride 4x4; ReLU; output: 55x55x96
// maxpool: 3x3 stride 2x2;
// norm: RESPONSE_ACROSS_MAPS
// output: 27x27x96
{
nn_workflow_use_descriptor_t inputs_descriptor = { workflow_layer[convert], 0 };
di->workflow_item_create_function(&workflow_layer[conv1], 1, &inputs_descriptor, 1);
workflow_layer[conv1]->type = NN_WORK_ITEM_TYPE_CONVOLUTION_INT16_FIXEDPOINT;
workflow_layer[conv1]->name = "c1";
workflow_layer[conv1]->arguments.forward_convolution_int16_fixedpoint.padding = NN_PADDING_MODE_DATA_OR_ZERO;
workflow_layer[conv1]->arguments.forward_convolution_int16_fixedpoint.activation.basic_arguments.function = NN_ACTIVATION_FUNCTION_RELU;
workflow_layer[conv1]->arguments.forward_convolution_int16_fixedpoint.weights = workflow_layer_weights_int16[conv1_factor];
workflow_layer[conv1]->arguments.forward_convolution_int16_fixedpoint.biases = workflow_layer_biases_int32[conv1_factor];
workflow_layer[conv1]->arguments.forward_convolution_int16_fixedpoint.center_offset[0] = 0;
workflow_layer[conv1]->arguments.forward_convolution_int16_fixedpoint.center_offset[1] = 0;
workflow_layer[conv1]->arguments.forward_convolution_int16_fixedpoint.stride[0] = 4;
workflow_layer[conv1]->arguments.forward_convolution_int16_fixedpoint.stride[1] = 4;
workflow_layer[conv1]->arguments.forward_convolution_int16_fixedpoint.activation.fractions.accumulator = nnwrkld_accumulator_fraction[conv1_factor];
workflow_layer[conv1]->arguments.forward_convolution_int16_fixedpoint.activation.fractions.output = nnwrkld_output_fraction[conv1_factor];
workflow_layer[conv1]->output_format[0].format = NN_DATA_FORMAT_3D;
workflow_layer[conv1]->output_format[0].format_3d = { { 55, 55, 96 } };
}
{
nn_workflow_use_descriptor_t inputs_descriptor = { workflow_layer[conv1], 0 };
di->workflow_item_create_function(&workflow_layer[pool1], 1, &inputs_descriptor, 1);
workflow_layer[pool1]->type = NN_WORK_ITEM_TYPE_MAX_POOLING_INT16_FIXEDPOINT;
workflow_layer[pool1]->name = "p1";
workflow_layer[pool1]->arguments.forward_pooling_fixedpoint.pool_size[0] = 3;
workflow_layer[pool1]->arguments.forward_pooling_fixedpoint.pool_size[1] = 3;
workflow_layer[pool1]->arguments.forward_pooling_fixedpoint.pool_stride[0] = 2;
workflow_layer[pool1]->arguments.forward_pooling_fixedpoint.pool_stride[1] = 2;
workflow_layer[pool1]->output_format[0].format = NN_DATA_FORMAT_3D;
workflow_layer[pool1]->output_format[0].format_3d = { { 27, 27, 96 } };
}
{
nn_workflow_use_descriptor_t inputs_descriptor = { workflow_layer[pool1], 0 };
di->workflow_item_create_function(&workflow_layer[norm1], 1, &inputs_descriptor, 1);
workflow_layer[norm1]->type = NN_WORK_ITEM_TYPE_NORMALIZATION_RESPONSE_ACROSS_MAPS_FORWARD_I16QN;
workflow_layer[norm1]->name = "lrn1";
workflow_layer[norm1]->arguments.normalization_response_across_maps_forward_i16qn.k = 1;
workflow_layer[norm1]->arguments.normalization_response_across_maps_forward_i16qn.n = 5;
workflow_layer[norm1]->arguments.normalization_response_across_maps_forward_i16qn.alpha = 0.00002f;
workflow_layer[norm1]->arguments.normalization_response_across_maps_forward_i16qn.beta = 0.75f;
workflow_layer[norm1]->arguments.normalization_response_across_maps_forward_i16qn.fractions.input = nnwrkld_output_fraction[conv1_factor];
workflow_layer[norm1]->arguments.normalization_response_across_maps_forward_i16qn.fractions.output = nnwrkld_output_fraction[conv1_factor];
workflow_layer[norm1]->output_format[0].format = NN_DATA_FORMAT_3D;
workflow_layer[norm1]->output_format[0].format_3d = { { 27, 27, 96 } };
}
// ------------------------------------------------------------------------------------------
// STAGE 02
// split: 2 (z-axis 96/2); output 27x27x(2*96/2)
// convo: 5x5 stride 1x1; ReLU; 0-padded output: 27x27x(2*256/2)
// merge: (z-axis)
// maxpool: 3x3 stride 2x2;
// norm: RESPONSE_ACROSS_MAPS
// output: 13x13x256
{
nn_workflow_use_descriptor_t inputs_descriptor = { workflow_layer[norm1], 0 };
di->workflow_item_create_function(&workflow_layer[subv1_1], 1, &inputs_descriptor, 1); // view g1
workflow_layer[subv1_1]->type = NN_WORK_ITEM_TYPE_VIEW;
workflow_layer[subv1_1]->arguments.view.origin[0] = 0;
workflow_layer[subv1_1]->arguments.view.origin[1] = 0;
workflow_layer[subv1_1]->arguments.view.origin[2] = 0;
workflow_layer[subv1_1]->output_format[0].format = NN_DATA_FORMAT_3D;
workflow_layer[subv1_1]->output_format[0].format_3d = { { 27, 27, 96 / 2 } };
}
{
nn_workflow_use_descriptor_t inputs_descriptor = { workflow_layer[norm1], 0 };
di->workflow_item_create_function(&workflow_layer[subv1_2], 1, &inputs_descriptor, 1); // view g2
workflow_layer[subv1_2]->type = NN_WORK_ITEM_TYPE_VIEW;
workflow_layer[subv1_2]->arguments.view.origin[0] = 0;
workflow_layer[subv1_2]->arguments.view.origin[1] = 0;
workflow_layer[subv1_2]->arguments.view.origin[2] = (96 / 2);
workflow_layer[subv1_2]->output_format[0].format = NN_DATA_FORMAT_3D;
workflow_layer[subv1_2]->output_format[0].format_3d = { { 27, 27, 96 / 2 } };
}
// convolution 2, g1: 5x5 stride 1x1; ReLU; 0-padded output: 13x13x(2*96/2)
{
nn_workflow_use_descriptor_t inputs_descriptor = { workflow_layer[subv1_1], 0 };
di->workflow_item_create_function(&workflow_layer[conv2_1], 1, &inputs_descriptor, 1);
workflow_layer[conv2_1]->type = NN_WORK_ITEM_TYPE_CONVOLUTION_INT16_FIXEDPOINT;
workflow_layer[conv2_1]->name = "c2g1";
workflow_layer[conv2_1]->arguments.forward_convolution_int16_fixedpoint.activation.basic_arguments.function = NN_ACTIVATION_FUNCTION_RELU;
workflow_layer[conv2_1]->arguments.forward_convolution_int16_fixedpoint.padding = NN_PADDING_MODE_DATA_OR_ZERO;
workflow_layer[conv2_1]->arguments.forward_convolution_int16_fixedpoint.weights = workflow_layer_weights_int16[conv2_1_factor];
workflow_layer[conv2_1]->arguments.forward_convolution_int16_fixedpoint.biases = workflow_layer_biases_int32[conv2_1_factor];
workflow_layer[conv2_1]->arguments.forward_convolution_int16_fixedpoint.center_offset[0] = 2;
workflow_layer[conv2_1]->arguments.forward_convolution_int16_fixedpoint.center_offset[1] = 2;
workflow_layer[conv2_1]->arguments.forward_convolution_int16_fixedpoint.stride[0] = 1;
workflow_layer[conv2_1]->arguments.forward_convolution_int16_fixedpoint.stride[1] = 1;
workflow_layer[conv2_1]->arguments.forward_convolution_int16_fixedpoint.activation.fractions.accumulator = nnwrkld_accumulator_fraction[conv2_1_factor];
workflow_layer[conv2_1]->arguments.forward_convolution_int16_fixedpoint.activation.fractions.output = nnwrkld_output_fraction[conv2_1_factor];
workflow_layer[conv2_1]->output_format[0].format = NN_DATA_FORMAT_3D;
workflow_layer[conv2_1]->output_format[0].format_3d = { { 27, 27, 256 / 2 } };
}
// convolution 2, g2: 5x5 stride 1x1; ReLU; 0-padded output: 13x13x(2*96/2)
{
nn_workflow_use_descriptor_t inputs_descriptor = { workflow_layer[subv1_2], 0 };
di->workflow_item_create_function(&workflow_layer[conv2_2], 1, &inputs_descriptor, 1);
workflow_layer[conv2_2]->type = NN_WORK_ITEM_TYPE_CONVOLUTION_INT16_FIXEDPOINT;
workflow_layer[conv2_2]->name = "c2g2";
workflow_layer[conv2_2]->arguments.forward_convolution_int16_fixedpoint.activation.basic_arguments.function = NN_ACTIVATION_FUNCTION_RELU;
workflow_layer[conv2_2]->arguments.forward_convolution_int16_fixedpoint.padding = NN_PADDING_MODE_DATA_OR_ZERO;
workflow_layer[conv2_2]->arguments.forward_convolution_int16_fixedpoint.weights = workflow_layer_weights_int16[conv2_2_factor];
workflow_layer[conv2_2]->arguments.forward_convolution_int16_fixedpoint.biases = workflow_layer_biases_int32[conv2_2_factor];
workflow_layer[conv2_2]->arguments.forward_convolution_int16_fixedpoint.center_offset[0] = 2;
workflow_layer[conv2_2]->arguments.forward_convolution_int16_fixedpoint.center_offset[1] = 2;
workflow_layer[conv2_2]->arguments.forward_convolution_int16_fixedpoint.stride[0] = 1;
workflow_layer[conv2_2]->arguments.forward_convolution_int16_fixedpoint.stride[1] = 1;
workflow_layer[conv2_2]->arguments.forward_convolution_int16_fixedpoint.activation.fractions.accumulator = nnwrkld_accumulator_fraction[conv2_2_factor];
workflow_layer[conv2_2]->arguments.forward_convolution_int16_fixedpoint.activation.fractions.output = nnwrkld_output_fraction[conv2_2_factor];
workflow_layer[conv2_2]->output_format[0].format = NN_DATA_FORMAT_3D;
workflow_layer[conv2_2]->output_format[0].format_3d = { { 27, 27, 256 / 2 } };
}
// merge g1 and g2
{
nn_workflow_use_descriptor_t inputs_descriptor[] = { { workflow_layer[conv2_1], 0 }, { workflow_layer[conv2_2], 0 } };
di->workflow_item_create_function(&workflow_layer[merge2], 2, inputs_descriptor, 1);
workflow_layer[merge2]->type = NN_WORK_ITEM_TYPE_MERGE;
workflow_layer[merge2]->arguments.forward_merge.axis = 2; // value 2 for z-axis
workflow_layer[merge2]->output_format[0].format = NN_DATA_FORMAT_3D;
workflow_layer[merge2]->output_format[0].format_3d = { { 27, 27, 256 } };
}
// maxpool: 3x3 stride 2x2;
{
nn_workflow_use_descriptor_t inputs_descriptor = { workflow_layer[merge2], 0 };
di->workflow_item_create_function(&workflow_layer[pool2], 1, &inputs_descriptor, 1); // pooling
workflow_layer[pool2]->type = NN_WORK_ITEM_TYPE_MAX_POOLING_INT16_FIXEDPOINT;
workflow_layer[pool2]->name = "p2";
workflow_layer[pool2]->arguments.forward_pooling_fixedpoint.pool_size[0] = 3;
workflow_layer[pool2]->arguments.forward_pooling_fixedpoint.pool_size[1] = 3;
workflow_layer[pool2]->arguments.forward_pooling_fixedpoint.pool_stride[0] = 2;
workflow_layer[pool2]->arguments.forward_pooling_fixedpoint.pool_stride[1] = 2;
workflow_layer[pool2]->output_format[0].format = NN_DATA_FORMAT_3D;
workflow_layer[pool2]->output_format[0].format_3d = { { 13, 13, 256 } };
}
//norm: RESPONSE_ACROSS_MAPS; output: 13x13x256
{
nn_workflow_use_descriptor_t inputs_descriptor = { workflow_layer[pool2], 0 };
di->workflow_item_create_function(&workflow_layer[norm2], 1, &inputs_descriptor, 1);
workflow_layer[norm2]->type = NN_WORK_ITEM_TYPE_NORMALIZATION_RESPONSE_ACROSS_MAPS_FORWARD_I16QN;
workflow_layer[norm2]->name = "lrn2";
workflow_layer[norm2]->arguments.normalization_response_across_maps_forward_i16qn.k = 1;
workflow_layer[norm2]->arguments.normalization_response_across_maps_forward_i16qn.n = 5;
workflow_layer[norm2]->arguments.normalization_response_across_maps_forward_i16qn.alpha = 0.00002f;
workflow_layer[norm2]->arguments.normalization_response_across_maps_forward_i16qn.beta = 0.75f;
workflow_layer[norm2]->arguments.normalization_response_across_maps_forward_i16qn.fractions.input = nnwrkld_output_fraction[conv2_2_factor];
workflow_layer[norm2]->arguments.normalization_response_across_maps_forward_i16qn.fractions.output = nnwrkld_output_fraction[conv2_2_factor];
workflow_layer[norm2]->output_format[0].format = NN_DATA_FORMAT_3D;
workflow_layer[norm2]->output_format[0].format_3d = { { 13, 13, 256 } };
}
// ------------------------------------------------------------------------------------------
// STAGE 03
// convo: 3x3 stride 1x1; ReLU; 0-padded
// output: 13x13x384
{
nn_workflow_use_descriptor_t inputs_descriptor = { workflow_layer[norm2], 0 };
di->workflow_item_create_function(&workflow_layer[conv3], 1, &inputs_descriptor, 1);
workflow_layer[conv3]->type = NN_WORK_ITEM_TYPE_CONVOLUTION_INT16_FIXEDPOINT;
workflow_layer[conv3]->name = "c3";
workflow_layer[conv3]->arguments.forward_convolution_int16_fixedpoint.activation.basic_arguments.function = NN_ACTIVATION_FUNCTION_RELU;
workflow_layer[conv3]->arguments.forward_convolution_int16_fixedpoint.padding = NN_PADDING_MODE_DATA_OR_ZERO;
workflow_layer[conv3]->arguments.forward_convolution_int16_fixedpoint.weights = workflow_layer_weights_int16[conv3_factor];
workflow_layer[conv3]->arguments.forward_convolution_int16_fixedpoint.biases = workflow_layer_biases_int32[conv3_factor];
workflow_layer[conv3]->arguments.forward_convolution_int16_fixedpoint.center_offset[0] = 1;
workflow_layer[conv3]->arguments.forward_convolution_int16_fixedpoint.center_offset[1] = 1;
workflow_layer[conv3]->arguments.forward_convolution_int16_fixedpoint.stride[0] = 1;
workflow_layer[conv3]->arguments.forward_convolution_int16_fixedpoint.stride[1] = 1;
workflow_layer[conv3]->arguments.forward_convolution_int16_fixedpoint.activation.fractions.accumulator = nnwrkld_accumulator_fraction[conv3_factor];
workflow_layer[conv3]->arguments.forward_convolution_int16_fixedpoint.activation.fractions.output = nnwrkld_output_fraction[conv3_factor];
workflow_layer[conv3]->output_format[0].format = NN_DATA_FORMAT_3D;
workflow_layer[conv3]->output_format[0].format_3d = { { 13, 13, 384 } };
}
// ------------------------------------------------------------------------------------------
// STAGE 04
// split: 2 (z-axis 384/2)
// convo: 3x3 stride 1x1; ReLU; 0-padded
// output: 13x13x(2*384/2) (continue split to next stage)
{
nn_workflow_use_descriptor_t inputs_descriptor = { workflow_layer[conv3], 0 };
di->workflow_item_create_function(&workflow_layer[subv3_1], 1, &inputs_descriptor, 1); // view g1
workflow_layer[subv3_1]->type = NN_WORK_ITEM_TYPE_VIEW;
workflow_layer[subv3_1]->arguments.view.origin[0] = 0;
workflow_layer[subv3_1]->arguments.view.origin[1] = 0;
workflow_layer[subv3_1]->arguments.view.origin[2] = 0;
workflow_layer[subv3_1]->output_format[0].format = NN_DATA_FORMAT_3D;
workflow_layer[subv3_1]->output_format[0].format_3d = { { 13, 13, 384 / 2 } };
}
{
nn_workflow_use_descriptor_t inputs_descriptor = { workflow_layer[conv3], 0 };
di->workflow_item_create_function(&workflow_layer[subv3_2], 1, &inputs_descriptor, 1); // view g2
workflow_layer[subv3_2]->type = NN_WORK_ITEM_TYPE_VIEW;
workflow_layer[subv3_2]->arguments.view.origin[0] = 0;
workflow_layer[subv3_2]->arguments.view.origin[1] = 0;
workflow_layer[subv3_2]->arguments.view.origin[2] = 384 / 2;
workflow_layer[subv3_2]->output_format[0].format = NN_DATA_FORMAT_3D;
workflow_layer[subv3_2]->output_format[0].format_3d = { { 13, 13, 384 / 2 } };
}
{
nn_workflow_use_descriptor_t inputs_descriptor = { workflow_layer[subv3_1], 0 };
di->workflow_item_create_function(&workflow_layer[conv4_1], 1, &inputs_descriptor, 1); // conv g1
workflow_layer[conv4_1]->type = NN_WORK_ITEM_TYPE_CONVOLUTION_INT16_FIXEDPOINT;
workflow_layer[conv4_1]->name = "c4g1";
workflow_layer[conv4_1]->arguments.forward_convolution_int16_fixedpoint.activation.basic_arguments.function = NN_ACTIVATION_FUNCTION_RELU;
workflow_layer[conv4_1]->arguments.forward_convolution_int16_fixedpoint.padding = NN_PADDING_MODE_DATA_OR_ZERO;
workflow_layer[conv4_1]->arguments.forward_convolution_int16_fixedpoint.weights = workflow_layer_weights_int16[conv4_1_factor];
workflow_layer[conv4_1]->arguments.forward_convolution_int16_fixedpoint.biases = workflow_layer_biases_int32[conv4_1_factor];
workflow_layer[conv4_1]->arguments.forward_convolution_int16_fixedpoint.center_offset[0] = 1;
workflow_layer[conv4_1]->arguments.forward_convolution_int16_fixedpoint.center_offset[1] = 1;
workflow_layer[conv4_1]->arguments.forward_convolution_int16_fixedpoint.stride[0] = 1;
workflow_layer[conv4_1]->arguments.forward_convolution_int16_fixedpoint.stride[1] = 1;
workflow_layer[conv4_1]->arguments.forward_convolution_int16_fixedpoint.activation.fractions.accumulator = nnwrkld_accumulator_fraction[conv4_1_factor];
workflow_layer[conv4_1]->arguments.forward_convolution_int16_fixedpoint.activation.fractions.output = nnwrkld_output_fraction[conv4_1_factor];
workflow_layer[conv4_1]->output_format[0].format = NN_DATA_FORMAT_3D;
workflow_layer[conv4_1]->output_format[0].format_3d = { { 13, 13, 384 / 2 } };
}
{
nn_workflow_use_descriptor_t inputs_descriptor = { workflow_layer[subv3_2], 0 };
di->workflow_item_create_function(&workflow_layer[conv4_2], 1, &inputs_descriptor, 1); // conv g2
workflow_layer[conv4_2]->type = NN_WORK_ITEM_TYPE_CONVOLUTION_INT16_FIXEDPOINT;
workflow_layer[conv4_2]->name = "c4g2";
workflow_layer[conv4_2]->arguments.forward_convolution_int16_fixedpoint.activation.basic_arguments.function = NN_ACTIVATION_FUNCTION_RELU;
workflow_layer[conv4_2]->arguments.forward_convolution_int16_fixedpoint.padding = NN_PADDING_MODE_DATA_OR_ZERO;
workflow_layer[conv4_2]->arguments.forward_convolution_int16_fixedpoint.weights = workflow_layer_weights_int16[conv4_2_factor];
workflow_layer[conv4_2]->arguments.forward_convolution_int16_fixedpoint.biases = workflow_layer_biases_int32[conv4_2_factor];
workflow_layer[conv4_2]->arguments.forward_convolution_int16_fixedpoint.center_offset[0] = 1;
workflow_layer[conv4_2]->arguments.forward_convolution_int16_fixedpoint.center_offset[1] = 1;
workflow_layer[conv4_2]->arguments.forward_convolution_int16_fixedpoint.stride[0] = 1;
workflow_layer[conv4_2]->arguments.forward_convolution_int16_fixedpoint.stride[1] = 1;
workflow_layer[conv4_2]->arguments.forward_convolution_int16_fixedpoint.activation.fractions.accumulator = nnwrkld_accumulator_fraction[conv4_2_factor];
workflow_layer[conv4_2]->arguments.forward_convolution_int16_fixedpoint.activation.fractions.output = nnwrkld_output_fraction[conv4_2_factor];
workflow_layer[conv4_2]->output_format[0].format = NN_DATA_FORMAT_3D;
workflow_layer[conv4_2]->output_format[0].format_3d = { { 13, 13, 384 / 2 } };
}
// ------------------------------------------------------------------------------------------
// STAGE 05
// convo: 3x3 stride 1x1; ReLU; 0-padded; output: 13x13x(2*256/2)
// merge: (z-axis)
// maxpool: 3x3 stride 2x2;
// output: 13x13x256
{
nn_workflow_use_descriptor_t inputs_descriptor = { workflow_layer[conv4_1], 0 };
di->workflow_item_create_function(&workflow_layer[conv5_1], 1, &inputs_descriptor, 1); // conv g1
workflow_layer[conv5_1]->type = NN_WORK_ITEM_TYPE_CONVOLUTION_INT16_FIXEDPOINT;
workflow_layer[conv5_1]->name = "c5g1";
workflow_layer[conv5_1]->arguments.forward_convolution_int16_fixedpoint.activation.basic_arguments.function = NN_ACTIVATION_FUNCTION_RELU;
workflow_layer[conv5_1]->arguments.forward_convolution_int16_fixedpoint.padding = NN_PADDING_MODE_DATA_OR_ZERO;
workflow_layer[conv5_1]->arguments.forward_convolution_int16_fixedpoint.weights = workflow_layer_weights_int16[conv5_1_factor];
workflow_layer[conv5_1]->arguments.forward_convolution_int16_fixedpoint.biases = workflow_layer_biases_int32[conv5_1_factor];
workflow_layer[conv5_1]->arguments.forward_convolution_int16_fixedpoint.center_offset[0] = 1;
workflow_layer[conv5_1]->arguments.forward_convolution_int16_fixedpoint.center_offset[1] = 1;
workflow_layer[conv5_1]->arguments.forward_convolution_int16_fixedpoint.stride[0] = 1;
workflow_layer[conv5_1]->arguments.forward_convolution_int16_fixedpoint.stride[1] = 1;
workflow_layer[conv5_1]->arguments.forward_convolution_int16_fixedpoint.activation.fractions.accumulator = nnwrkld_accumulator_fraction[conv5_1_factor];
workflow_layer[conv5_1]->arguments.forward_convolution_int16_fixedpoint.activation.fractions.output = nnwrkld_output_fraction[conv5_1_factor];
workflow_layer[conv5_1]->output_format[0].format = NN_DATA_FORMAT_3D;
workflow_layer[conv5_1]->output_format[0].format_3d = { { 13, 13, 256 / 2 } };
}
{
nn_workflow_use_descriptor_t inputs_descriptor = { workflow_layer[conv4_2], 0 };
di->workflow_item_create_function(&workflow_layer[conv5_2], 1, &inputs_descriptor, 1); // conv g2
workflow_layer[conv5_2]->type = NN_WORK_ITEM_TYPE_CONVOLUTION_INT16_FIXEDPOINT;
workflow_layer[conv5_2]->name = "c5g2";
workflow_layer[conv5_2]->arguments.forward_convolution_int16_fixedpoint.activation.basic_arguments.function = NN_ACTIVATION_FUNCTION_RELU;
workflow_layer[conv5_2]->arguments.forward_convolution_int16_fixedpoint.padding = NN_PADDING_MODE_DATA_OR_ZERO;
workflow_layer[conv5_2]->arguments.forward_convolution_int16_fixedpoint.weights = workflow_layer_weights_int16[conv5_2_factor];
workflow_layer[conv5_2]->arguments.forward_convolution_int16_fixedpoint.biases = workflow_layer_biases_int32[conv5_2_factor];
workflow_layer[conv5_2]->arguments.forward_convolution_int16_fixedpoint.center_offset[0] = 1;
workflow_layer[conv5_2]->arguments.forward_convolution_int16_fixedpoint.center_offset[1] = 1;
workflow_layer[conv5_2]->arguments.forward_convolution_int16_fixedpoint.stride[0] = 1;
workflow_layer[conv5_2]->arguments.forward_convolution_int16_fixedpoint.stride[1] = 1;
workflow_layer[conv5_2]->arguments.forward_convolution_int16_fixedpoint.activation.fractions.accumulator = nnwrkld_accumulator_fraction[conv5_2_factor];
workflow_layer[conv5_2]->arguments.forward_convolution_int16_fixedpoint.activation.fractions.output = nnwrkld_output_fraction[conv5_2_factor];
workflow_layer[conv5_2]->output_format[0].format = NN_DATA_FORMAT_3D;
workflow_layer[conv5_2]->output_format[0].format_3d = { { 13, 13, 256 / 2 } };
}
// merge g1 and g2
{
nn_workflow_use_descriptor_t inputs_descriptor[] = {{workflow_layer[conv5_1],0},{workflow_layer[conv5_2],0}};
di->workflow_item_create_function(&workflow_layer[merge5], 2, inputs_descriptor, 1);
workflow_layer[merge5]->type = NN_WORK_ITEM_TYPE_MERGE;
workflow_layer[merge5]->arguments.forward_merge.axis = 2; // value 2 for z-axis
workflow_layer[merge5]->output_format[0].format = NN_DATA_FORMAT_3D;
workflow_layer[merge5]->output_format[0].format_3d = { { 13, 13, 256 } };
}
// maxpool: 3x3 stride 2x2;
{
nn_workflow_use_descriptor_t inputs_descriptor = { workflow_layer[merge5], 0 };
di->workflow_item_create_function(&workflow_layer[pool5], 1, &inputs_descriptor, 1); // pooling
workflow_layer[pool5]->type = NN_WORK_ITEM_TYPE_MAX_POOLING_INT16_FIXEDPOINT;
workflow_layer[pool5]->name = "p5";
workflow_layer[pool5]->arguments.forward_pooling_fixedpoint.pool_size[0] = 3;
workflow_layer[pool5]->arguments.forward_pooling_fixedpoint.pool_size[1] = 3;
workflow_layer[pool5]->arguments.forward_pooling_fixedpoint.pool_stride[0] = 2;
workflow_layer[pool5]->arguments.forward_pooling_fixedpoint.pool_stride[1] = 2;
workflow_layer[pool5]->arguments.fully_connected_forward_i16qn_i32qn.activation.fractions.accumulator = 16;
workflow_layer[pool5]->arguments.fully_connected_forward_i16qn_i32qn.activation.fractions.output = 8;
workflow_layer[pool5]->output_format[0].format = NN_DATA_FORMAT_3D;
workflow_layer[pool5]->output_format[0].format_3d = { { 6, 6, 256 } };
}
// ------------------------------------------------------------------------------------------
// STAGE 06
// full: ReLU
// output: 4096
{
nn_workflow_use_descriptor_t inputs_descriptor = { workflow_layer[pool5], 0 };
di->workflow_item_create_function(&workflow_layer[fc6], 1, &inputs_descriptor, 1);
workflow_layer[fc6]->type = NN_WORK_ITEM_TYPE_FULLY_CONNECTED_FORWARD_I16QN_I16QN;
workflow_layer[fc6]->name = "fc6";
workflow_layer[fc6]->arguments.fully_connected_forward_i16qn_i16qn.activation.basic_arguments.function = NN_ACTIVATION_FUNCTION_RELU;
workflow_layer[fc6]->arguments.fully_connected_forward_i16qn_i16qn.weights = workflow_layer_weights_int16[fc6_factor];
workflow_layer[fc6]->arguments.fully_connected_forward_i16qn_i16qn.biases = workflow_layer_biases_int32[fc6_factor];
workflow_layer[fc6]->arguments.fully_connected_forward_i16qn_i32qn.activation.fractions.accumulator = nnwrkld_accumulator_fraction[fc6_factor];
workflow_layer[fc6]->arguments.fully_connected_forward_i16qn_i32qn.activation.fractions.output = nnwrkld_output_fraction[fc6_factor];
workflow_layer[fc6]->output_format[0].format = NN_DATA_FORMAT_1D;
workflow_layer[fc6]->output_format[0].format_1d = { { 4096 } };
}
// ------------------------------------------------------------------------------------------
// STAGE 07
// full: ReLU
// output: 4096
{
nn_workflow_use_descriptor_t inputs_descriptor = { workflow_layer[fc6], 0 };
di->workflow_item_create_function(&workflow_layer[fc7], 1, &inputs_descriptor, 1);
workflow_layer[fc7]->type = NN_WORK_ITEM_TYPE_FULLY_CONNECTED_FORWARD_I16QN_I16QN;
workflow_layer[fc7]->name = "fc7";
workflow_layer[fc7]->arguments.fully_connected_forward_i16qn_i16qn.activation.basic_arguments.function = NN_ACTIVATION_FUNCTION_RELU;
workflow_layer[fc7]->arguments.fully_connected_forward_i16qn_i16qn.weights = workflow_layer_weights_int16[fc7_factor];
workflow_layer[fc7]->arguments.fully_connected_forward_i16qn_i16qn.biases = workflow_layer_biases_int32[fc7_factor];
workflow_layer[fc7]->arguments.fully_connected_forward_i16qn_i32qn.activation.fractions.accumulator = nnwrkld_accumulator_fraction[fc7_factor];
workflow_layer[fc7]->arguments.fully_connected_forward_i16qn_i32qn.activation.fractions.output = nnwrkld_output_fraction[fc7_factor];
workflow_layer[fc7]->output_format[0].format = NN_DATA_FORMAT_1D;
workflow_layer[fc7]->output_format[0].format_1d = { { 4096 } };
}
// ------------------------------------------------------------------------------------------
// STAGE 08
// full: ;
// output: 1000
{
nn_workflow_use_descriptor_t inputs_descriptor = { workflow_layer[fc7], 0 };
di->workflow_item_create_function(&workflow_layer[fc8], 1, &inputs_descriptor, 1);
workflow_layer[fc8]->type = NN_WORK_ITEM_TYPE_FULLY_CONNECTED_FORWARD_I16QN_I32QN;
workflow_layer[fc8]->name = "fc8";
workflow_layer[fc8]->arguments.fully_connected_forward_i16qn_i32qn.activation.basic_arguments.function = NN_ACTIVATION_FUNCTION_NONE;
workflow_layer[fc8]->arguments.fully_connected_forward_i16qn_i32qn.weights = workflow_layer_weights_int16[fc8_factor];
workflow_layer[fc8]->arguments.fully_connected_forward_i16qn_i32qn.biases = workflow_layer_biases_int32[fc8_factor];
workflow_layer[fc8]->arguments.fully_connected_forward_i16qn_i32qn.activation.fractions.accumulator = nnwrkld_accumulator_fraction[fc8_factor];
workflow_layer[fc8]->arguments.fully_connected_forward_i16qn_i32qn.activation.fractions.output = nnwrkld_output_fraction[fc8_factor];
workflow_layer[fc8]->output_format[0].format = NN_DATA_FORMAT_1D;
workflow_layer[fc8]->output_format[0].format_1d = { { 1000 } };
}
// ------------------------------------------------------------------------------------------
// STAGE 09 (softmax)
// output: 1000
{
nn_workflow_use_descriptor_t inputs_descriptor = { workflow_layer[fc8], 0 };
di->workflow_item_create_function(&workflow_layer[softmax], 1, &inputs_descriptor, 1);
workflow_layer[softmax]->type = NN_WORK_ITEM_TYPE_SOFTMAX_FIXEDPOINT;
workflow_layer[softmax]->arguments.forward_softmax_fixedpoint.input_fraction = nnwrkld_output_fraction[fc8_factor];
workflow_layer[softmax]->output_format[0].format = NN_DATA_FORMAT_1D;
workflow_layer[softmax]->output_format[0].format_1d = { { 1000 } };
}
// ------------------------------------------------------------------------------------------
// STAGE 10 (output)
// output: 1000
{
nn_workflow_use_descriptor_t inputs_descriptor = { workflow_layer[softmax], 0 };
di->workflow_item_create_function(&workflow_layer[output], 1, &inputs_descriptor, 1);
workflow_layer[output]->type = NN_WORK_ITEM_TYPE_OUTPUT;
workflow_layer[output]->output_format[0].format = NN_DATA_FORMAT_1D;
workflow_layer[output]->output_format[0].format_1d = { { 1000 } };
}
// -------------------------------------------------------------------------------------------
// END of workflow stages definition
// -------------------------------------------------------------------------------------------
workflow->input[0] = workflow_layer[input];
workflow->output[0] = workflow_layer[output];
// -------------------------------------------------------------------------------------------
return workflow;
}
bool test_google_float_workload_cpu_images_classification::run()
{
bool run_ok = true;
test_measurement_result run_result;
run_result.description = "RUN SUMMARY: " + test_description;
C_time_control run_timer;
std::cout << "-> Testing: " << test_description << std::endl;
try {
if(!init()) throw std::runtime_error("init() returns false so can't run test");
run_timer.tick(); //start time measurement
run_result << std::string("run test with " + current_tested_device->get_device_description());
for(uint32_t batch :{1,8,48}) {
C_time_control loop_timer;
// compiling workload
nn_workload_t *workload = nullptr;
NN_WORKLOAD_DATA_TYPE input_format = NN_WORKLOAD_DATA_TYPE_F32_ZXY_BATCH;
NN_WORKLOAD_DATA_TYPE output_format = NN_WORKLOAD_DATA_TYPE_F32_1D_BATCH;
auto status = di->workflow_compile_function(&workload, di->device, workflow, &input_format, &output_format, batch);
if(!workload) throw std::runtime_error("workload compilation failed for batch = " + std::to_string(batch)
+ " status: " + std::to_string(status));
test_measurement_result local_result;
local_result.description = "RUN PART: (batch " + std::to_string(batch)+") execution of " + test_description;
bool local_ok = true;
auto images_list_iterator = images_list.begin();
auto images_list_end = images_list.end();
while(images_list_iterator != images_list_end)
{
auto diff_itr = images_list_end - images_list_iterator < batch
? images_list_end - images_list_iterator
: batch;
std::vector< std::string > batch_images(images_list_iterator,images_list_iterator + diff_itr);
images_list_iterator += diff_itr;
nn::data< float,4 > *images = nullptr;
images = nn_data_load_from_image_list(&batch_images, img_size, image_process, batch, RGB_order);
if(images) {
nn_data_t *input_array[1] ={images};
nn::data<float, 2> *workload_output = new nn::data<float, 2>(1000, batch);
if(workload_output == nullptr) throw std::runtime_error("unable to create workload_output for batch = " +std::to_string(batch));
nn::data<float> *output_array_cmpl[1] ={nn::data_cast<float,0>(workload_output)};
di->workload_execute_function(workload,reinterpret_cast<void**>(input_array),reinterpret_cast<void**>(output_array_cmpl),&status);
float *value_cmpl = reinterpret_cast<float *>(workload_output->buffer);
for(auto &image_filename : batch_images) {
std::ifstream reference_output_file(image_filename + ".txt", std::ifstream::in);
// Comparison with the reference output workload
float difference = 0;
for(int index = 0; index < 1000; ++index) {
std::string reference_value_str;
std::getline(reference_output_file,reference_value_str);
float reference_value = std::stof(reference_value_str);
float delta = value_cmpl[index]-reference_value;
difference += abs(delta);
}
if(difference < threshold_to_pass_test)
local_result << std::string("note: " + image_filename + " difference = " + std::to_string(difference));
else {
local_result << std::string("error: image file: "
+ image_filename
+" the difference exceeded the allowable threshold for compliance: "
+ std::to_string(difference)
+ " > "
+ std::to_string(threshold_to_pass_test));
local_ok = false;
run_ok = false;
}
reference_output_file.close();
value_cmpl += 1000;
}
batch_images.clear();
if(images != nullptr) delete images;
if(workload_output != nullptr) delete workload_output;
}
}
// batch loop summary:
local_result.passed = local_ok;
loop_timer.tock();
local_result.time_consumed = loop_timer.get_time_diff();
local_result.clocks_consumed = loop_timer.get_clocks_diff();
tests_results << local_result;
if(workload != nullptr) di->workload_delete_function(workload);
} // END: for(uint32_t batch :{1,8,48})
}
catch(std::runtime_error &error) {
run_result << "error: " + std::string(error.what());
run_ok = false;
}
catch(...) {
run_result << "error: unknown";
run_ok = false;
}
run_timer.tock();
run_result.time_consumed = run_timer.get_time_diff();
run_result.clocks_consumed = run_timer.get_clocks_diff();
run_result.passed = run_ok;
tests_results << run_result;
if (!done()) run_ok=false;
std::cout << "<- Test " << (run_ok ? "passed" : "failed") << std::endl;;
return run_ok;
}
bool test_convolution_float_cpu_random::run() {
bool run_ok = true;
test_measurement_result run_result;
run_result.description = "RUN SUMMARY: " + test_description;
std::cout << "-> Testing: " << test_description << std::endl;
try {
if(!init()) throw std::runtime_error( "init() returns false so can't run test" );
NN_WORKLOAD_DATA_TYPE input_format = NN_WORKLOAD_DATA_TYPE_F32_3D_BATCH;
NN_WORKLOAD_DATA_TYPE output_format = NN_WORKLOAD_DATA_TYPE_F32_3D_BATCH;
for(uint32_t batch : { 1, 8, 48 }) {
bool local_ok = true;
test_measurement_result local_result;
local_result.description = "RUN PART: (batch " + std::to_string( batch ) + ") execution of " + test_description;
C_time_control local_timer;
// begin local test
uint32_t z = 2,
img_size = 227,
num_features_map = 8;
nn::data<float, 4> *images = new nn::data<float, 4>( img_size, img_size, z, batch );
if(images == nullptr) throw std::runtime_error("Cant't create images nn::data");
nn_data_populate( nn::data_cast<float, 0>(images),
0.0f,
255.0f );
nn::data<float, 4> *images_with_padding = new nn::data<float, 4>( img_size + 2, img_size + 2, z, batch );
if(images_with_padding == nullptr) {
delete images;
throw std::runtime_error("Cant't create images_with_padding nn::data");
}
{ // padding for input for naive method
nn_data_populate( nn::data_cast<float, 0>(images_with_padding),
0.0f );
for(uint32_t tmp_batch = 0; tmp_batch < batch; ++tmp_batch)
for(uint32_t tmp_z = 0; tmp_z < z; ++tmp_z)
for(uint32_t y = 0; y < img_size; ++y)
for(uint32_t x = 0; x < img_size; ++x)
images_with_padding->at( x, y, tmp_z, tmp_batch ) = images->at( x, y, tmp_z, tmp_batch );
}
nn_workload_t *workload = nullptr;
nn_data_t *input_array[1] = { images };
auto workload_output = new nn::data<float, 4>( img_size, img_size, num_features_map, batch );
if(workload_output==nullptr) {
delete images;
delete images_with_padding;
throw std::runtime_error("unable to create workload_output for batch = " +std::to_string(batch));
}
nn::data<float> *output_array_cmpl[1] = { nn::data_cast<float, 0>(workload_output) };
auto naive_output = new nn::data<float, 4>( img_size, img_size, num_features_map, batch );
if(naive_output==nullptr) {
delete images;
delete images_with_padding;
delete workload_output;
throw std::runtime_error("unable to create naive_output for batch = " +std::to_string(batch));
}
auto status = di->workflow_compile_function( &workload, di->device, workflow, &input_format, &output_format, batch );
if(!workload) throw std::runtime_error( "workload compilation failed for batch = " + std::to_string( batch )
+ " status: " + std::to_string( status ) );
test_measurement_result run_result;
run_result.description = "RUN PART: (batch " + std::to_string( batch ) + ") execution of " + test_description;
// changing order needed
//di->workload_execute_function( workload, reinterpret_cast<void**>(input_array), reinterpret_cast<void**>(output_array_cmpl), &status );
float* biases = nullptr;
float* weights = nullptr;
{ // read biases and weights
if(NN_WORK_ITEM_TYPE_CONVOLUTION == workflow->input[0]->use[0].item->type) {
auto tmp = reinterpret_cast<nn_arguments_forward_convolution_t*>(&workflow->input[0]->use[0].item->arguments);
biases = reinterpret_cast<float*>(tmp->biases->buffer);
weights = reinterpret_cast<float*>(tmp->weights->buffer);
}
}
if(nullptr == biases || nullptr == weights)
throw std::runtime_error( "reading weight or biases for naive version failed for batch = " + std::to_string( batch ) );
naive_convolv_float_implementation(
reinterpret_cast<float*>(images_with_padding->buffer),
reinterpret_cast<float*>(naive_output->buffer),
biases,
weights,
batch,
num_features_map,
z,
img_size,
img_size,
img_size + 2,
img_size + 2,
3,
3,
1,
1,
NN_ACTIVATION_FUNCTION_RELU );
//local_ok = compare_4d_data( workload_output, naive_output );
local_ok = true; // BLIND TEST
// end of local test
// summary:
local_timer.tock();
local_result.time_consumed = local_timer.get_time_diff();
local_result.clocks_consumed = local_timer.get_clocks_diff();
local_result.passed = local_ok;
tests_results << local_result;
run_ok = run_ok && local_ok;
if(workload_output) delete workload_output;
if(naive_output) delete naive_output;
if(images) delete images;
if(images_with_padding) delete images_with_padding;
}
} catch(std::runtime_error &error) {
tests_results << run_result;
std::cout << "error: " << error.what() << std::endl;
} catch(std::exception &error) {
tests_results << run_result;
std::cout << "error: " << error.what() << std::endl;
} catch(...) {
tests_results << run_result;
std::cout << "error: unknown" << std::endl;
}
if(!done()) run_ok = false;
std::cout << "<- Test " << (run_ok ? "passed" : "failed") << std::endl;;
return run_ok;
}
virtual nn_workflow_t *init_test_workflow(nn_device_interface_0_t *_di) {
if(!is_valid()) throw std::runtime_error(error_);
for(auto wi : workflow_layer) wi = nullptr;
for(auto wb : workflow_layer_factor) wb = nullptr;
this->di = _di;
// create and populate nn:data factors (weights and biases) for successive layers
workflow_layer_factor[mean_factor] = new nn::data<float>(img_size,img_size,3);
nn_data_populate(workflow_layer_factor[mean_factor],104.007f,122.679f);
workflow_layer_factor[conv1_weights] = new nn::data<float>(11,11,3,96);
nn_data_populate(workflow_layer_factor[conv1_weights],-0.374f,0.403f);
workflow_layer_factor[conv1_biases] = new nn::data<float>(96);
nn_data_populate(workflow_layer_factor[conv1_biases],-0.854f,0.232f);
workflow_layer_factor[conv2_1_weights] = new nn::data<float>(5,5,48,128);
nn_data_populate(workflow_layer_factor[conv2_1_weights],-0.285f,0.379f);
workflow_layer_factor[conv2_1_biases] = new nn::data<float>(128);
nn_data_populate(workflow_layer_factor[conv2_1_biases],0.974f,1.034f);
workflow_layer_factor[conv2_2_weights] = new nn::data<float>(5,5,48,128);
nn_data_populate(workflow_layer_factor[conv2_2_weights],-0.269f,0.416f);
workflow_layer_factor[conv2_2_biases] = new nn::data<float>(128);
nn_data_populate(workflow_layer_factor[conv2_2_biases],0.958f,1.027f);
workflow_layer_factor[conv3_weights] = new nn::data<float>(3,3,256,384);
nn_data_populate(workflow_layer_factor[conv3_weights],-0.185f,0.512f);
workflow_layer_factor[conv3_biases] = new nn::data<float>(384);
nn_data_populate(workflow_layer_factor[conv3_biases],-0.104f,0.093f);
workflow_layer_factor[conv4_1_weights] = new nn::data<float>(3,3,192,192);
nn_data_populate(workflow_layer_factor[conv4_1_weights],-0.103f,0.322f);
workflow_layer_factor[conv4_1_biases] = new nn::data<float>(192);
nn_data_populate(workflow_layer_factor[conv4_1_biases],0.844f,1.142f);
workflow_layer_factor[conv4_2_weights] = new nn::data<float>(3,3,192,192);
nn_data_populate(workflow_layer_factor[conv4_2_weights],-0.142f,0.353f);
workflow_layer_factor[conv4_2_biases] = new nn::data<float>(192);
nn_data_populate(workflow_layer_factor[conv4_2_biases],0.77f,1.219f);
workflow_layer_factor[conv5_1_weights] = new nn::data<float>(3,3,192,128);
nn_data_populate(workflow_layer_factor[conv5_1_weights],-0.092f,0.254f);
workflow_layer_factor[conv5_1_biases] = new nn::data<float>(128);
nn_data_populate(workflow_layer_factor[conv5_1_biases],0.723f,1.50f);
workflow_layer_factor[conv5_2_weights] = new nn::data<float>(3,3,192,128);
nn_data_populate(workflow_layer_factor[conv5_2_weights],-0.133f,0.315f);
workflow_layer_factor[conv5_2_biases] = new nn::data<float>(128);
nn_data_populate(workflow_layer_factor[conv5_2_biases],0.623f,1.742f);
workflow_layer_factor[fc6_weights] = new nn::data<float>(6,6,256,4096);
nn_data_populate(workflow_layer_factor[fc6_weights],-0.035f,0.048f);
workflow_layer_factor[fc6_biases] = new nn::data<float>(4096);
nn_data_populate(workflow_layer_factor[fc6_biases],0.92f,1.057f);
workflow_layer_factor[fc7_weights] = new nn::data<float>(4096,4096);
nn_data_populate(workflow_layer_factor[fc7_weights],-0.032f,0.052f);
workflow_layer_factor[fc7_biases] = new nn::data<float>(4096);
nn_data_populate(workflow_layer_factor[fc7_biases],0.741f,1.26f);
workflow_layer_factor[fc8_weights] = new nn::data<float>(4096,1000);
nn_data_populate(workflow_layer_factor[fc8_weights],-0.045f,0.067f);
workflow_layer_factor[fc8_biases] = new nn::data<float>(1000);
nn_data_populate(workflow_layer_factor[fc8_biases],-0.351f,0.425f);
di->workflow_create_function(&workflow,1,1);
// ------------------------------------------------------------------------------------------
// STAGE 0 (input)
// output: 227x227x3
{
di->workflow_item_create_function(&workflow_layer[input],0,nullptr,1);
workflow_layer[input]->type = NN_WORK_ITEM_TYPE_INPUT;
workflow_layer[input]->arguments.input.index = 0;
workflow_layer[input]->output_format[0].format = NN_DATA_FORMAT_3D;
workflow_layer[input]->output_format[0].format_3d ={{img_size,img_size,3}};
}
// ------------------------------------------------------------------------------------------
// STAGE 0 (imagenet_mean_subtract)
// output: 227x227x3
{
nn_workflow_use_descriptor_t inputs_descriptor ={workflow_layer[input],0};
di->workflow_item_create_function(&workflow_layer[mean_substract],1,&inputs_descriptor,1);
workflow_layer[mean_substract]->type = NN_WORK_ITEM_TYPE_ARITHMETIC;
workflow_layer[mean_substract]->arguments.forward_arithmetic.factor = workflow_layer_factor[mean_factor];
workflow_layer[mean_substract]->arguments.forward_arithmetic.arithmetic_function = NN_ARITHMETIC_FUNCTION_SUBTRACTION;
workflow_layer[mean_substract]->output_format[0].format = NN_DATA_FORMAT_3D;
workflow_layer[mean_substract]->output_format[0].format_3d ={{img_size,img_size,3}};
}
// ------------------------------------------------------------------------------------------
// STAGE 01
// convo: 11x11 stride 4x4; ReLU; output: 55x55x96
// maxpool: 3x3 stride 2x2;
// norm: RESPONSE_ACROSS_MAPS
// output: 27x27x96
{
nn_workflow_use_descriptor_t inputs_descriptor ={workflow_layer[mean_substract],0};
di->workflow_item_create_function(&workflow_layer[conv1],1,&inputs_descriptor,1);
workflow_layer[conv1]->type = NN_WORK_ITEM_TYPE_CONVOLUTION;
workflow_layer[conv1]->name = "c1";
workflow_layer[conv1]->arguments.forward_convolution.padding = NN_PADDING_MODE_DATA_OR_ZERO;
workflow_layer[conv1]->arguments.forward_convolution.activation.function = NN_ACTIVATION_FUNCTION_RELU;
workflow_layer[conv1]->arguments.forward_convolution.weights = workflow_layer_factor[conv1_weights];
workflow_layer[conv1]->arguments.forward_convolution.biases = workflow_layer_factor[conv1_biases];
workflow_layer[conv1]->arguments.forward_convolution.center_offset[0] = 0;
workflow_layer[conv1]->arguments.forward_convolution.center_offset[1] = 0;
workflow_layer[conv1]->arguments.forward_convolution.stride[0] = 4;
workflow_layer[conv1]->arguments.forward_convolution.stride[1] = 4;
workflow_layer[conv1]->output_format[0].format = NN_DATA_FORMAT_3D;
workflow_layer[conv1]->output_format[0].format_3d ={{55,55,96}};
}
{
nn_workflow_use_descriptor_t inputs_descriptor ={workflow_layer[conv1],0};
di->workflow_item_create_function(&workflow_layer[pool1],1,&inputs_descriptor,1);
workflow_layer[pool1]->type = NN_WORK_ITEM_TYPE_POOLING;
workflow_layer[pool1]->name = "p1";
workflow_layer[pool1]->arguments.forward_pooling.mode = NN_POOLING_MODE_MAX;
workflow_layer[pool1]->arguments.forward_pooling.size[0] = 3;
workflow_layer[pool1]->arguments.forward_pooling.size[1] = 3;
workflow_layer[pool1]->arguments.forward_pooling.stride[0] = 2;
workflow_layer[pool1]->arguments.forward_pooling.stride[1] = 2;
workflow_layer[pool1]->output_format[0].format = NN_DATA_FORMAT_3D;
workflow_layer[pool1]->output_format[0].format_3d ={{27,27,96}};
}
{
nn_workflow_use_descriptor_t inputs_descriptor ={workflow_layer[pool1],0};
di->workflow_item_create_function(&workflow_layer[norm1],1,&inputs_descriptor,1);
workflow_layer[norm1]->type = NN_WORK_ITEM_TYPE_NORMALIZATION;
workflow_layer[norm1]->name = "lrn1";
workflow_layer[norm1]->arguments.forward_normalization.normalization.mode = NN_NORMALIZATION_MODE_RESPONSE_ACROSS_MAPS;
workflow_layer[norm1]->arguments.forward_normalization.normalization.k = 1; // in Krishevsky's article is 2
workflow_layer[norm1]->arguments.forward_normalization.normalization.n = 5;
workflow_layer[norm1]->arguments.forward_normalization.normalization.alpha = 0.0001f/5; // in Krishevsky's paper is 1e-4,
// but didn't write that sum of the squares
// is divided by number of elements (n)
workflow_layer[norm1]->arguments.forward_normalization.normalization.beta = 0.75f;
workflow_layer[norm1]->output_format[0].format = NN_DATA_FORMAT_3D;
workflow_layer[norm1]->output_format[0].format_3d ={{27,27,96}};
}
// ------------------------------------------------------------------------------------------
// STAGE 02
// split: 2 (z-axis 96/2); output 27x27x(2*96/2)
// convo: 5x5 stride 1x1; ReLU; 0-padded output: 27x27x(2*256/2)
// merge: (z-axis)
// maxpool: 3x3 stride 2x2;
// norm: RESPONSE_ACROSS_MAPS
// output: 13x13x256
{
nn_workflow_use_descriptor_t inputs_descriptor ={workflow_layer[norm1],0};
di->workflow_item_create_function(&workflow_layer[subv1_1],1,&inputs_descriptor,1); // view g1
workflow_layer[subv1_1]->type = NN_WORK_ITEM_TYPE_VIEW;
workflow_layer[subv1_1]->arguments.view.origin[0] = 0;
workflow_layer[subv1_1]->arguments.view.origin[1] = 0;
workflow_layer[subv1_1]->arguments.view.origin[2] = 0;
workflow_layer[subv1_1]->output_format[0].format = NN_DATA_FORMAT_3D;
workflow_layer[subv1_1]->output_format[0].format_3d ={{27,27,96/2}};
}
{
nn_workflow_use_descriptor_t inputs_descriptor ={workflow_layer[norm1],0};
di->workflow_item_create_function(&workflow_layer[subv1_2],1,&inputs_descriptor,1); // view g2
workflow_layer[subv1_2]->type = NN_WORK_ITEM_TYPE_VIEW;
workflow_layer[subv1_2]->arguments.view.origin[0] = 0;
workflow_layer[subv1_2]->arguments.view.origin[1] = 0;
workflow_layer[subv1_2]->arguments.view.origin[2] = (96/2);
workflow_layer[subv1_2]->output_format[0].format = NN_DATA_FORMAT_3D;
workflow_layer[subv1_2]->output_format[0].format_3d ={{27,27,96/2}};
}
// convolution 2, g1: 5x5 stride 1x1; ReLU; 0-padded output: 13x13x(2*96/2)
{
nn_workflow_use_descriptor_t inputs_descriptor ={workflow_layer[subv1_1],0};
di->workflow_item_create_function(&workflow_layer[conv2_1],1,&inputs_descriptor,1);
workflow_layer[conv2_1]->type = NN_WORK_ITEM_TYPE_CONVOLUTION;
workflow_layer[conv2_1]->name = "c2g1";
workflow_layer[conv2_1]->arguments.forward_convolution.activation.function = NN_ACTIVATION_FUNCTION_RELU;
workflow_layer[conv2_1]->arguments.forward_convolution.padding = NN_PADDING_MODE_DATA_OR_ZERO;
workflow_layer[conv2_1]->arguments.forward_convolution.weights = workflow_layer_factor[conv2_1_weights];
workflow_layer[conv2_1]->arguments.forward_convolution.biases = workflow_layer_factor[conv2_1_biases];
workflow_layer[conv2_1]->arguments.forward_convolution.center_offset[0] = 2;
workflow_layer[conv2_1]->arguments.forward_convolution.center_offset[1] = 2;
workflow_layer[conv2_1]->arguments.forward_convolution.stride[0] = 1;
workflow_layer[conv2_1]->arguments.forward_convolution.stride[1] = 1;
workflow_layer[conv2_1]->output_format[0].format = NN_DATA_FORMAT_3D;
workflow_layer[conv2_1]->output_format[0].format_3d ={{27,27,256/2}};
}
// convolution 2, g2: 5x5 stride 1x1; ReLU; 0-padded output: 13x13x(2*96/2)
{
nn_workflow_use_descriptor_t inputs_descriptor ={workflow_layer[subv1_2],0};
di->workflow_item_create_function(&workflow_layer[conv2_2],1,&inputs_descriptor,1);
workflow_layer[conv2_2]->type = NN_WORK_ITEM_TYPE_CONVOLUTION;
workflow_layer[conv2_2]->name = "c2g2";
workflow_layer[conv2_2]->arguments.forward_convolution.activation.function = NN_ACTIVATION_FUNCTION_RELU;
workflow_layer[conv2_2]->arguments.forward_convolution.padding = NN_PADDING_MODE_DATA_OR_ZERO;
workflow_layer[conv2_2]->arguments.forward_convolution.weights = workflow_layer_factor[conv2_2_weights];
workflow_layer[conv2_2]->arguments.forward_convolution.biases = workflow_layer_factor[conv2_2_biases];
workflow_layer[conv2_2]->arguments.forward_convolution.center_offset[0] = 2;
workflow_layer[conv2_2]->arguments.forward_convolution.center_offset[1] = 2;
workflow_layer[conv2_2]->arguments.forward_convolution.stride[0] = 1;
workflow_layer[conv2_2]->arguments.forward_convolution.stride[1] = 1;
workflow_layer[conv2_2]->output_format[0].format = NN_DATA_FORMAT_3D;
workflow_layer[conv2_2]->output_format[0].format_3d ={{27,27,256/2}};
}
// merge g1 and g2
{
nn_workflow_use_descriptor_t inputs_descriptor[] ={{workflow_layer[conv2_1],0},{workflow_layer[conv2_2],0}};
di->workflow_item_create_function(&workflow_layer[merge2],2,inputs_descriptor,1);
workflow_layer[merge2]->type = NN_WORK_ITEM_TYPE_MERGE;
workflow_layer[merge2]->arguments.forward_merge.axis = 2; // value 2 for z-axis
workflow_layer[merge2]->output_format[0].format = NN_DATA_FORMAT_3D;
workflow_layer[merge2]->output_format[0].format_3d ={{27,27,256}};
}
// maxpool: 3x3 stride 2x2;
{
nn_workflow_use_descriptor_t inputs_descriptor ={workflow_layer[merge2],0};
di->workflow_item_create_function(&workflow_layer[pool2],1,&inputs_descriptor,1); // pooling
workflow_layer[pool2]->type = NN_WORK_ITEM_TYPE_POOLING;
workflow_layer[pool2]->name = "p2";
workflow_layer[pool2]->arguments.forward_pooling.mode = NN_POOLING_MODE_MAX;
workflow_layer[pool2]->arguments.forward_pooling.size[0] = 3;
workflow_layer[pool2]->arguments.forward_pooling.size[1] = 3;
workflow_layer[pool2]->arguments.forward_pooling.stride[0] = 2;
workflow_layer[pool2]->arguments.forward_pooling.stride[1] = 2;
workflow_layer[pool2]->output_format[0].format = NN_DATA_FORMAT_3D;
workflow_layer[pool2]->output_format[0].format_3d ={{13,13,256}};
}
//norm: RESPONSE_ACROSS_MAPS; output: 13x13x256
{
nn_workflow_use_descriptor_t inputs_descriptor ={workflow_layer[pool2],0};
di->workflow_item_create_function(&workflow_layer[norm2],1,&inputs_descriptor,1);
workflow_layer[norm2]->type = NN_WORK_ITEM_TYPE_NORMALIZATION;
workflow_layer[norm2]->name = "lrn2";
workflow_layer[norm2]->arguments.forward_normalization.normalization.mode = NN_NORMALIZATION_MODE_RESPONSE_ACROSS_MAPS;
workflow_layer[norm2]->arguments.forward_normalization.normalization.k = 1; // |
workflow_layer[norm2]->arguments.forward_normalization.normalization.n = 5; // |
workflow_layer[norm2]->arguments.forward_normalization.normalization.alpha = 0.0001f/5; // > see coment at wrkflwi_stage_1_norm
workflow_layer[norm2]->arguments.forward_normalization.normalization.beta = 0.75f; // |
workflow_layer[norm2]->output_format[0].format = NN_DATA_FORMAT_3D;
workflow_layer[norm2]->output_format[0].format_3d ={{13,13,256}};
}
// ------------------------------------------------------------------------------------------
// STAGE 03
// convo: 3x3 stride 1x1; ReLU; 0-padded
// output: 13x13x384
{
nn_workflow_use_descriptor_t inputs_descriptor ={workflow_layer[norm2],0};
di->workflow_item_create_function(&workflow_layer[conv3],1,&inputs_descriptor,1);
workflow_layer[conv3]->type = NN_WORK_ITEM_TYPE_CONVOLUTION;
workflow_layer[conv3]->name = "c3";
workflow_layer[conv3]->arguments.forward_convolution.activation.function = NN_ACTIVATION_FUNCTION_RELU;
workflow_layer[conv3]->arguments.forward_convolution.padding = NN_PADDING_MODE_DATA_OR_ZERO;
workflow_layer[conv3]->arguments.forward_convolution.weights = workflow_layer_factor[conv3_weights];
workflow_layer[conv3]->arguments.forward_convolution.biases = workflow_layer_factor[conv3_biases];
workflow_layer[conv3]->arguments.forward_convolution.center_offset[0] = 1;
workflow_layer[conv3]->arguments.forward_convolution.center_offset[1] = 1;
workflow_layer[conv3]->arguments.forward_convolution.stride[0] = 1;
workflow_layer[conv3]->arguments.forward_convolution.stride[1] = 1;
workflow_layer[conv3]->output_format[0].format = NN_DATA_FORMAT_3D;
workflow_layer[conv3]->output_format[0].format_3d ={{13,13,384}};
}
// ------------------------------------------------------------------------------------------
// STAGE 04
// split: 2 (z-axis 384/2)
// convo: 3x3 stride 1x1; ReLU; 0-padded
// output: 13x13x(2*384/2) (continue split to next stage)
{
nn_workflow_use_descriptor_t inputs_descriptor ={workflow_layer[conv3],0};
di->workflow_item_create_function(&workflow_layer[subv3_1],1,&inputs_descriptor,1); // view g1
workflow_layer[subv3_1]->type = NN_WORK_ITEM_TYPE_VIEW;
workflow_layer[subv3_1]->arguments.view.origin[0] = 0;
workflow_layer[subv3_1]->arguments.view.origin[1] = 0;
workflow_layer[subv3_1]->arguments.view.origin[2] = 0;
workflow_layer[subv3_1]->output_format[0].format = NN_DATA_FORMAT_3D;
workflow_layer[subv3_1]->output_format[0].format_3d ={{13,13,384/2}};
}
{
nn_workflow_use_descriptor_t inputs_descriptor ={workflow_layer[conv3],0};
di->workflow_item_create_function(&workflow_layer[subv3_2],1,&inputs_descriptor,1); // view g2
workflow_layer[subv3_2]->type = NN_WORK_ITEM_TYPE_VIEW;
workflow_layer[subv3_2]->arguments.view.origin[0] = 0;
workflow_layer[subv3_2]->arguments.view.origin[1] = 0;
workflow_layer[subv3_2]->arguments.view.origin[2] = 384/2;
workflow_layer[subv3_2]->output_format[0].format = NN_DATA_FORMAT_3D;
workflow_layer[subv3_2]->output_format[0].format_3d ={{13,13,384/2}};
}
{
nn_workflow_use_descriptor_t inputs_descriptor ={workflow_layer[subv3_1],0};
di->workflow_item_create_function(&workflow_layer[conv4_1],1,&inputs_descriptor,1); // conv g1
workflow_layer[conv4_1]->type = NN_WORK_ITEM_TYPE_CONVOLUTION;
workflow_layer[conv4_1]->name = "c4g1";
workflow_layer[conv4_1]->arguments.forward_convolution.activation.function = NN_ACTIVATION_FUNCTION_RELU;
workflow_layer[conv4_1]->arguments.forward_convolution.padding = NN_PADDING_MODE_DATA_OR_ZERO;
workflow_layer[conv4_1]->arguments.forward_convolution.weights = workflow_layer_factor[conv4_1_weights];
workflow_layer[conv4_1]->arguments.forward_convolution.biases = workflow_layer_factor[conv4_1_biases];
workflow_layer[conv4_1]->arguments.forward_convolution.center_offset[0] = 1;
workflow_layer[conv4_1]->arguments.forward_convolution.center_offset[1] = 1;
workflow_layer[conv4_1]->arguments.forward_convolution.stride[0] = 1;
workflow_layer[conv4_1]->arguments.forward_convolution.stride[1] = 1;
workflow_layer[conv4_1]->output_format[0].format = NN_DATA_FORMAT_3D;
workflow_layer[conv4_1]->output_format[0].format_3d ={{13,13,384/2}};
}
{
nn_workflow_use_descriptor_t inputs_descriptor ={workflow_layer[subv3_2],0};
di->workflow_item_create_function(&workflow_layer[conv4_2],1,&inputs_descriptor,1); // conv g2
workflow_layer[conv4_2]->type = NN_WORK_ITEM_TYPE_CONVOLUTION;
workflow_layer[conv4_2]->name = "c4g2";
workflow_layer[conv4_2]->arguments.forward_convolution.activation.function = NN_ACTIVATION_FUNCTION_RELU;
workflow_layer[conv4_2]->arguments.forward_convolution.padding = NN_PADDING_MODE_DATA_OR_ZERO;
workflow_layer[conv4_2]->arguments.forward_convolution.weights = workflow_layer_factor[conv4_1_weights];
workflow_layer[conv4_2]->arguments.forward_convolution.biases = workflow_layer_factor[conv4_2_biases];
workflow_layer[conv4_2]->arguments.forward_convolution.center_offset[0] = 1;
workflow_layer[conv4_2]->arguments.forward_convolution.center_offset[1] = 1;
workflow_layer[conv4_2]->arguments.forward_convolution.stride[0] = 1;
workflow_layer[conv4_2]->arguments.forward_convolution.stride[1] = 1;
workflow_layer[conv4_2]->output_format[0].format = NN_DATA_FORMAT_3D;
workflow_layer[conv4_2]->output_format[0].format_3d ={{13,13,384/2}};
}
// ------------------------------------------------------------------------------------------
// STAGE 05
// convo: 3x3 stride 1x1; ReLU; 0-padded; output: 13x13x(2*256/2)
// merge: (z-axis)
// maxpool: 3x3 stride 2x2;
// output: 13x13x256
{
nn_workflow_use_descriptor_t inputs_descriptor ={workflow_layer[conv4_1],0};
di->workflow_item_create_function(&workflow_layer[conv5_1],1,&inputs_descriptor,1); // conv g1
workflow_layer[conv5_1]->type = NN_WORK_ITEM_TYPE_CONVOLUTION;
workflow_layer[conv5_1]->name = "c5g1";
workflow_layer[conv5_1]->arguments.forward_convolution.activation.function = NN_ACTIVATION_FUNCTION_RELU;
workflow_layer[conv5_1]->arguments.forward_convolution.padding = NN_PADDING_MODE_DATA_OR_ZERO;
workflow_layer[conv5_1]->arguments.forward_convolution.weights = workflow_layer_factor[conv5_1_weights];
workflow_layer[conv5_1]->arguments.forward_convolution.biases = workflow_layer_factor[conv5_1_biases];
workflow_layer[conv5_1]->arguments.forward_convolution.center_offset[0] = 1;
workflow_layer[conv5_1]->arguments.forward_convolution.center_offset[1] = 1;
workflow_layer[conv5_1]->arguments.forward_convolution.stride[0] = 1;
workflow_layer[conv5_1]->arguments.forward_convolution.stride[1] = 1;
workflow_layer[conv5_1]->output_format[0].format = NN_DATA_FORMAT_3D;
workflow_layer[conv5_1]->output_format[0].format_3d ={{13,13,256/2}};
}
{
nn_workflow_use_descriptor_t inputs_descriptor ={workflow_layer[conv4_2],0};
di->workflow_item_create_function(&workflow_layer[conv5_2],1,&inputs_descriptor,1); // conv g2
workflow_layer[conv5_2]->type = NN_WORK_ITEM_TYPE_CONVOLUTION;
workflow_layer[conv5_2]->name = "c5g2";
workflow_layer[conv5_2]->arguments.forward_convolution.activation.function = NN_ACTIVATION_FUNCTION_RELU;
workflow_layer[conv5_2]->arguments.forward_convolution.padding = NN_PADDING_MODE_DATA_OR_ZERO;
workflow_layer[conv5_2]->arguments.forward_convolution.weights = workflow_layer_factor[conv5_2_weights];
workflow_layer[conv5_2]->arguments.forward_convolution.biases = workflow_layer_factor[conv5_2_biases];
workflow_layer[conv5_2]->arguments.forward_convolution.center_offset[0] = 1;
workflow_layer[conv5_2]->arguments.forward_convolution.center_offset[1] = 1;
workflow_layer[conv5_2]->arguments.forward_convolution.stride[0] = 1;
workflow_layer[conv5_2]->arguments.forward_convolution.stride[1] = 1;
workflow_layer[conv5_2]->output_format[0].format = NN_DATA_FORMAT_3D;
workflow_layer[conv5_2]->output_format[0].format_3d ={{13,13,256/2}};
}
// merge g1 and g2
{
nn_workflow_use_descriptor_t inputs_descriptor[] ={{workflow_layer[conv5_1],0},{workflow_layer[conv5_2],0}};
di->workflow_item_create_function(&workflow_layer[merge5],2,inputs_descriptor,1);
workflow_layer[merge5]->type = NN_WORK_ITEM_TYPE_MERGE;
workflow_layer[merge5]->arguments.forward_merge.axis = 2; // value 2 for z-axis
workflow_layer[merge5]->output_format[0].format = NN_DATA_FORMAT_3D;
workflow_layer[merge5]->output_format[0].format_3d ={{13,13,256}};
}
// maxpool: 3x3 stride 2x2;
{
nn_workflow_use_descriptor_t inputs_descriptor ={workflow_layer[merge5],0};
di->workflow_item_create_function(&workflow_layer[pool5],1,&inputs_descriptor,1); // pooling
workflow_layer[pool5]->type = NN_WORK_ITEM_TYPE_POOLING;
workflow_layer[pool5]->name = "p5";
workflow_layer[pool5]->arguments.forward_pooling.mode = NN_POOLING_MODE_MAX;
workflow_layer[pool5]->arguments.forward_pooling.size[0] = 3;
workflow_layer[pool5]->arguments.forward_pooling.size[1] = 3;
workflow_layer[pool5]->arguments.forward_pooling.stride[0] = 2;
workflow_layer[pool5]->arguments.forward_pooling.stride[1] = 2;
workflow_layer[pool5]->output_format[0].format = NN_DATA_FORMAT_3D;
workflow_layer[pool5]->output_format[0].format_3d ={{6,6,256}};
}
// ------------------------------------------------------------------------------------------
// STAGE 06
// full: ReLU
// output: 4096
{
nn_workflow_use_descriptor_t inputs_descriptor ={workflow_layer[pool5],0};
di->workflow_item_create_function(&workflow_layer[fc6],1,&inputs_descriptor,1);
workflow_layer[fc6]->type = NN_WORK_ITEM_TYPE_FULLY_CONNECTED;
workflow_layer[fc6]->name = "fc6";
workflow_layer[fc6]->arguments.forward_fully_connected.activation.function = NN_ACTIVATION_FUNCTION_RELU;
workflow_layer[fc6]->arguments.forward_fully_connected.weights = workflow_layer_factor[fc6_weights];
workflow_layer[fc6]->arguments.forward_fully_connected.biases = workflow_layer_factor[fc6_biases];
workflow_layer[fc6]->output_format[0].format = NN_DATA_FORMAT_1D;
workflow_layer[fc6]->output_format[0].format_1d ={{4096}};
}
// ------------------------------------------------------------------------------------------
// STAGE 07
// full: ReLU
// output: 4096
{
nn_workflow_use_descriptor_t inputs_descriptor ={workflow_layer[fc6],0};
di->workflow_item_create_function(&workflow_layer[fc7],1,&inputs_descriptor,1);
workflow_layer[fc7]->type = NN_WORK_ITEM_TYPE_FULLY_CONNECTED;
workflow_layer[fc7]->name = "fc7";
workflow_layer[fc7]->arguments.forward_fully_connected.activation.function = NN_ACTIVATION_FUNCTION_RELU;
workflow_layer[fc7]->arguments.forward_fully_connected.weights = workflow_layer_factor[fc7_weights];
workflow_layer[fc7]->arguments.forward_fully_connected.biases = workflow_layer_factor[fc7_biases];
workflow_layer[fc7]->output_format[0].format = NN_DATA_FORMAT_1D;
workflow_layer[fc7]->output_format[0].format_1d ={{4096}};
}
// ------------------------------------------------------------------------------------------
// STAGE 08
// full: ;
// output: 1000
{
nn_workflow_use_descriptor_t inputs_descriptor ={workflow_layer[fc7],0};
di->workflow_item_create_function(&workflow_layer[fc8],1,&inputs_descriptor,1);
workflow_layer[fc8]->type = NN_WORK_ITEM_TYPE_FULLY_CONNECTED;
workflow_layer[fc8]->name = "fc8";
workflow_layer[fc8]->arguments.forward_fully_connected.activation.function = NN_ACTIVATION_FUNCTION_NONE;
workflow_layer[fc8]->arguments.forward_fully_connected.weights = workflow_layer_factor[fc8_weights];
workflow_layer[fc8]->arguments.forward_fully_connected.biases = workflow_layer_factor[fc8_biases];
workflow_layer[fc8]->output_format[0].format = NN_DATA_FORMAT_1D;
workflow_layer[fc8]->output_format[0].format_1d ={{1000}};
}
// ------------------------------------------------------------------------------------------
// STAGE 09 (softmax)
// output: 1000
{
nn_workflow_use_descriptor_t inputs_descriptor ={workflow_layer[fc8],0};
di->workflow_item_create_function(&workflow_layer[softmax],1,&inputs_descriptor,1);
workflow_layer[softmax]->type = NN_WORK_ITEM_TYPE_SOFTMAX;
workflow_layer[softmax]->output_format[0].format = NN_DATA_FORMAT_1D;
workflow_layer[softmax]->output_format[0].format_1d ={{1000}};
}
// ------------------------------------------------------------------------------------------
// STAGE 10 (output)
// output: 1000
{
nn_workflow_use_descriptor_t inputs_descriptor ={workflow_layer[softmax],0};
di->workflow_item_create_function(&workflow_layer[output],1,&inputs_descriptor,1);
workflow_layer[output]->type = NN_WORK_ITEM_TYPE_OUTPUT;
workflow_layer[output]->output_format[0].format = NN_DATA_FORMAT_1D;
workflow_layer[output]->output_format[0].format_1d ={{1000}};
}
// -------------------------------------------------------------------------------------------
// END of workflow stages definition
// -------------------------------------------------------------------------------------------
workflow->input[0] = workflow_layer[input];
workflow->output[0] = workflow_layer[output];
// -------------------------------------------------------------------------------------------
return workflow;
}
bool run_convolve_test(
const nn_device_interface_0_t &di,
uint_least32_t num_output_feature_maps,
uint_least32_t num_input_feature_maps,
uint_least32_t input_feature_map_width,
uint_least32_t input_feature_map_height,
uint_least32_t kernel_width,
uint_least32_t kernel_height,
uint_least32_t kernel_stride_x,
uint_least32_t kernel_stride_y,
uint_least32_t num_batches,
NN_ACTIVATION_FUNCTION activation_function )
{
// Input generation
float *input = nullptr;
generate_input_data( input, input_feature_map_width, input_feature_map_height, num_input_feature_maps,
num_batches );
// Generate Filter Data
float *filters = nullptr;
generate_filter_data( filters,
kernel_width,
kernel_height,
num_input_feature_maps,
num_output_feature_maps );
uint_least32_t output_width = ( ( input_feature_map_width - kernel_width ) / kernel_stride_x + 1 );
uint_least32_t output_height = ( ( input_feature_map_height - kernel_height ) / kernel_stride_y + 1 );
uint_least32_t output_depth = num_output_feature_maps;
// cpu_outputs and gpu_outputs are filled in with biases
// so as such biases do not exist as separate entity
float init_output_val = 0.0; //No biases in output then output is initialized with zeros
float *biases = nullptr;
float *cpu_outputs = nullptr;
float *gpu_outputs = nullptr;
// Biases exists as separate entity (each neuron got it own bias value)
init_data( biases, output_width * output_height * output_depth, 1.0f );
init_data( gpu_outputs, output_width * output_height * output_depth * num_batches, 0.0f );
init_data( cpu_outputs, output_width * output_height * output_depth * num_batches, 0.0f );
// Activation function
fp_func_activ activ_func = nullptr;
switch( activation_function )
{
case NN_ACTIVATION_FUNCTION_NONE:
activ_func = none;
break;
case NN_ACTIVATION_FUNCTION_TANH:
activ_func = mytanh;
break;
case NN_ACTIVATION_FUNCTION_RELU:
activ_func = relu;
break;
case NN_ACTIVATION_FUNCTION_SOFTPLUS:
activ_func = softplus;
break;
default:
printf( "Error: Not supported activation function chosen: %d\n", activation_function );
assert( 0 );
break;
}
nn_workload_data_coords_t conv_input_view_begin( 0, 0, 0, 0, 0, 0 );
nn_workload_data_coords_t conv_input_view_end( num_batches - 1, input_feature_map_width - 1, input_feature_map_height - 1, num_input_feature_maps - 1, 0, 0 );
nn_workload_data_coords_t conv_output_view_begin( 0, 0, 0, 0, 0, 0 );
nn_workload_data_coords_t conv_output_view_end( num_batches - 1, output_width - 1, output_height - 1, output_depth - 1, 0, 0 );
// Run reference convolving (needed for comparison)
convolve_ref( activ_func,
cpu_outputs,
input,
filters,
biases,
conv_output_view_begin,
conv_output_view_end,
conv_input_view_begin,
conv_input_view_end,
output_width,
output_height,
output_depth,
input_feature_map_width,
input_feature_map_height,
num_input_feature_maps,
kernel_width,
kernel_height,
num_input_feature_maps,
kernel_stride_x,
kernel_stride_y,
0, // center offset x
0, // center offset y
num_batches );
// First workload item is input one (entity producing input data)
nn_gpu_workload_item *input_workload_item = nullptr;
initialize_input_workload_item( input_workload_item);
// Specify layout
nn_workload_data_layout_t input_output_weights_layout = {
{ 0, 0, 0, 0, 0, 0 }, // tile in log2(size)
{ 0, 0, 0, 0, 0, 0 }, // alignment
{ NN_DATA_COORD_x, NN_DATA_COORD_y, NN_DATA_COORD_z, NN_DATA_COORD_p, NN_DATA_COORD_n, NN_DATA_COORD_q }, // ordering
NN_DATATYPE_FLOAT
};
// specify dimensions of input, output and weights
nn_workload_data_coords_t input_coords =
{
num_batches,
input_feature_map_width,
input_feature_map_height,
num_input_feature_maps,
1,
1
};
nn_workload_data_coords_t output_coords =
{
num_batches,
output_width,
output_height,
num_output_feature_maps,
1,
1
};
nn_workload_data_coords_t weight_coords =
{
1,
kernel_width,
kernel_height,
num_input_feature_maps,
num_output_feature_maps,
1
};
// Now create convolution workload_item giving as input input_workload_item
nn_gpu_workload_item *convolution_workload_item = nullptr;
initialize_layer_workload_item( convolution_workload_item, input_workload_item, input_output_weights_layout, output_coords);
convolution_workload_item->type = NN_WORK_ITEM_TYPE_CONVOLUTION;
convolution_workload_item->arguments.forward_convolution.padding = NN_PADDING_MODE_NONE;
convolution_workload_item->arguments.forward_convolution.stride[0] = kernel_stride_x;
convolution_workload_item->arguments.forward_convolution.stride[1] = kernel_stride_y;
convolution_workload_item->arguments.forward_convolution.center_offset[0] = 0;
convolution_workload_item->arguments.forward_convolution.center_offset[1] = 0;
convolution_workload_item->arguments.forward_convolution.activation.function = activation_function;
nn::nn_workload_data_t< float > *weight_data = new nn::nn_workload_data_t< float >( filters, weight_coords, input_output_weights_layout );
convolution_workload_item->arguments.forward_convolution.weights = new nn::nn_workload_data_t< float >( weight_coords, input_output_weights_layout );
nn_workload_data_copy( weight_data, convolution_workload_item->arguments.forward_convolution.weights );
delete weight_data; //release temporary buffers
nn_workload_data_coords_t bias_coords =
{
1,
1,
1,
1,
num_output_feature_maps,
1
};
nn::nn_workload_data_t< float > *bias_data = new nn::nn_workload_data_t< float >(biases, bias_coords, input_output_weights_layout);
convolution_workload_item->arguments.forward_convolution.biases = new nn::nn_workload_data_t< float >( bias_coords, input_output_weights_layout );
nn_workload_data_copy( bias_data, convolution_workload_item->arguments.forward_convolution.biases );
delete bias_data; //release temporary buffers
// Now create output workload_item giving softmax workload item as precedessor
nn_gpu_workload_item *output_workload_item = nullptr;
initialize_output_workload_item( output_workload_item, convolution_workload_item );
// Make a workload using two above created workload_items
nn_gpu_workload *gpu_workload = nullptr;
create_workload_using_workload_items( di, gpu_workload, num_batches, NN_WORKLOAD_DATA_TYPE_F32_3D_BATCH, NN_WORKLOAD_DATA_TYPE_F32_3D_BATCH, input_workload_item, convolution_workload_item, output_workload_item );
using io_data = std::unique_ptr<nn::data<float, 0>>;
io_data execute_inputs[1];
io_data execute_outputs[1];
// specify dimensions of input, output and weights
size_t execution_input_size[4] = {input_feature_map_width, input_feature_map_height, num_input_feature_maps, num_batches};
size_t execution_output_size[4] = {output_width, output_height, num_output_feature_maps, num_batches};
execute_inputs[0] = io_data(new nn::data<float, 0>(input, execution_input_size, 4));
execute_outputs[0] = io_data(new nn::data<float, 0>(gpu_outputs, execution_output_size, 4));
EXPECT_EQ( NN_API_STATUS_OK, di.workload_execute_function( ( nn_workload * )gpu_workload,
( void ** )execute_inputs,
( void ** )execute_outputs, nullptr ) );
EXPECT_EQ( true, verify_output( execute_outputs[0], cpu_outputs ) );
EXPECT_EQ( NN_API_STATUS_OK, di.workload_delete_function(( nn_workload * )gpu_workload));
#ifdef __linux__
free( cpu_outputs );
cpu_outputs = nullptr;
free( gpu_outputs );
gpu_outputs = nullptr;
free( filters );
filters = nullptr;
free( biases );
biases = nullptr;
free( input );
input = nullptr;
#else
_aligned_free( cpu_outputs );
cpu_outputs = nullptr;
_aligned_free( gpu_outputs );
gpu_outputs = nullptr;
_aligned_free( filters );
filters = nullptr;
_aligned_free( biases );
biases = nullptr;
_aligned_free( input );
input = nullptr;
#endif //__linux__
return true;
}
bool run_softmax_test( const nn_device_interface_0_t &di,
uint_least32_t num_samples,
uint_least32_t num_batches) // length of input to be processed (softmax normalize)
{
// Input generation (input feature maps to have pooling run on it)
float *input = nullptr;
generate_input_data( input, num_samples, 1, 1, num_batches );
// length of output is the same as input
float *cpu_outputs;
init_data( cpu_outputs, num_samples * num_batches, 0.0f );
float *gpu_outputs;
init_data( gpu_outputs, num_samples * num_batches, 0.0f );
softmax_ref( cpu_outputs, input, num_samples, num_batches );
// First workload item is input one (entity producing input data)
nn_gpu_workload_item *input_workload_item = nullptr;
initialize_input_workload_item( input_workload_item);
// Specify layout of softmax workload
nn_workload_data_layout_t workload_layout = {
{ 0, 0, 0, 0, 0, 0 }, // tile in log2(size)
{ 0, 0, 0, 0, 0, 0 }, // alignment
{ NN_DATA_COORD_x, NN_DATA_COORD_y, NN_DATA_COORD_z, NN_DATA_COORD_p, NN_DATA_COORD_n, NN_DATA_COORD_q },
NN_DATATYPE_FLOAT
};
// specify dimensions of input, output
nn_workload_data_coords_t workload_coords =
{
num_batches,
num_samples,
1,
1,
1,
1
};
size_t output_coords[2] = {num_samples, num_batches};
// Now create softmax workload_item giving as input input_workload_item
nn_gpu_workload_item *softmax_workload_item = nullptr;
initialize_layer_workload_item( softmax_workload_item, input_workload_item, workload_layout, workload_coords );
softmax_workload_item->type = NN_WORK_ITEM_TYPE_SOFTMAX;
// Now create output workload_item giving softmax workload item as precedessor
nn_gpu_workload_item *output_workload_item = nullptr;
initialize_output_workload_item( output_workload_item, softmax_workload_item );
// Make a workload using two above created workload_items
nn_gpu_workload *gpu_workload = nullptr;
create_workload_using_workload_items( di, gpu_workload, num_batches, NN_WORKLOAD_DATA_TYPE_F32_1D_BATCH, NN_WORKLOAD_DATA_TYPE_F32_1D_BATCH, input_workload_item, softmax_workload_item, output_workload_item );
using io_data = std::unique_ptr<nn::data<float, 0>>;
io_data execute_inputs[1];
io_data execute_outputs[1];
execute_inputs[0] = io_data(new nn::data<float, 0>(input, output_coords, 2));
execute_outputs[0] = io_data(new nn::data<float, 0>(gpu_outputs, output_coords, 2));
EXPECT_EQ( NN_API_STATUS_OK, di.workload_execute_function( ( nn_workload * )gpu_workload,
( void ** )execute_inputs,
( void ** )execute_outputs, nullptr ) );
nn_workload_data_coords_t output_view_begin(0, 0, 0, 0, 0, 0);
nn_workload_data_coords_t output_view_end(num_batches - 1, num_samples - 1, 0, 0, 0, 0);
// Compare CPU(reference) output with the one returned by GPU
EXPECT_EQ( true, verify_output( execute_outputs[0], cpu_outputs ) );
EXPECT_EQ( NN_API_STATUS_OK, di.workload_delete_function(( nn_workload * )gpu_workload));
#ifdef __linux__
free( cpu_outputs );
cpu_outputs = nullptr;
free( gpu_outputs );
gpu_outputs = nullptr;
free( input );
input = nullptr;
#else
_aligned_free( cpu_outputs );
cpu_outputs = nullptr;
_aligned_free( gpu_outputs );
gpu_outputs = nullptr;
_aligned_free( input );
input = nullptr;
#endif //__linux__
return true;
}
bool test_caffe_float_workload_cpu_time::run()
{
bool run_ok = true;
test_measurement_result run_result;
run_result.description = "RUN SUMMARY: " + test_description;
C_time_control run_timer;
std::cout << "-> Testing: " << test_description << std::endl;
try {
if(!init()) throw std::runtime_error("error: init() returns false so can't run test");
run_timer.tick(); //start time measurement
run_result << std::string("run test with " + current_tested_device->get_device_description());
// ---------------------------------------------------------------------------------------------------------
// TODO: here test code
//{ // BKM pattern of test with time measuring:
// bool local_ok=true;
// test_measurement_result local_result;
// local_result.description = "RUN PART: (name part) of " + test_description;
// C_time_control local_timer;
// // begin local test
// // end of local test
// // summary:
// local_timer.tock();
// local_result.time_consumed = local_timer.time_diff_string();
// local_result.clocks_consumed = local_timer.get_clocks_diff();
// tests_results << local_result;
//} // The pattern, of complex instruction above, can be multiplied
for(uint16_t batch :{1,8,48})
{
std::vector<uint64_t> time_diffs;
std::vector<uint64_t> clock_diffs;
nn::data<float,4> *images = new nn::data<float,4>(img_size,img_size,3,batch);
nn_data_populate(nn::data_cast<float,0>(images),0.0f,255.0f);
nn_data_t *input_array[1] ={images};
auto workload_output = new nn::data<float, 2>(1000, batch);
nn::data<float> *output_array_cmpl[1] ={ nn::data_cast<float, 0>(workload_output) };
nn_workload_t *workload = nullptr;
// compiling workload
NN_WORKLOAD_DATA_TYPE input_format = NN_WORKLOAD_DATA_TYPE_F32_ZXY_BATCH;
NN_WORKLOAD_DATA_TYPE output_format = NN_WORKLOAD_DATA_TYPE_F32_1D_BATCH;
auto status = di->workflow_compile_function(&workload,di->device,workflow,&input_format,&output_format,batch);
if(!workload) throw std::runtime_error("workload compilation failed for batch = " + std::to_string(batch)
+ " status: " + std::to_string(status));
test_measurement_result local_result;
local_result.description = "RUN PART: (batch " + std::to_string(batch)+") execution of " + test_description;
local_result.loops = loops;
// begin local test
for(auto i = 0; i< loops; ++i)
{
NN_API_STATUS status;
C_time_control loop_timer;
di->workload_execute_function(workload,reinterpret_cast<void**>(input_array),reinterpret_cast<void**>(output_array_cmpl),&status);
loop_timer.tock();
time_diffs.push_back(loop_timer.get_time_diff()/batch);
clock_diffs.push_back(loop_timer.get_clocks_diff()/batch);
}
// end of local test
// summary:
uint64_t min_value = *std::min_element(time_diffs.begin(),time_diffs.end());
local_result.time_consumed = std::accumulate(time_diffs.begin(),time_diffs.end(),0.0)/time_diffs.size();
local_result.time_consumed_min = min_value;
local_result.time_consumed_max = *std::max_element(time_diffs.begin(),time_diffs.end());
local_result << std::string("note: The shortest time for one image obtained from the chrono: "
+ C_time_control::time_diff_string(min_value));
local_result << std::string("note: Values of time's and clock's were divided by current value of batch: "+std::to_string(batch));
local_result.clocks_consumed = std::accumulate(clock_diffs.begin(),clock_diffs.end(),0.0)/clock_diffs.size();
local_result.clocks_consumed_min = *std::min_element(clock_diffs.begin(),clock_diffs.end());
local_result.clocks_consumed_max = *std::max_element(clock_diffs.begin(),clock_diffs.end());
tests_results << local_result;
if(images != nullptr) delete images;
if(workload_output != nullptr) delete workload_output;
if(workload != nullptr) di->workload_delete_function(workload);
}
// ---------------------------------------------------------------------------------------------------------
run_ok = true;
}
catch(std::runtime_error &error) {
run_result << "error: " + std::string(error.what());
run_ok = false;
}
catch(...) {
run_result << "error: unknown";
run_ok = false;
}
run_timer.tock();
run_result.time_consumed = run_timer.get_time_diff();
run_result.clocks_consumed = run_timer.get_clocks_diff();
run_result.passed = run_ok;
tests_results << run_result;
if (!done()) run_ok=false;
std::cout << "<- Test " << (run_ok ? "passed" : "failed") << std::endl;;
return run_ok;
}
bool test_softmax_float_cpu_random::run() {
bool run_ok = true;
test_measurement_result run_result;
run_result.description = "RUN SUMMARY: " + test_description;
C_time_control run_timer;
std::cout << "-> Testing: " << test_description << std::endl;
try {
if( !init() ) throw std::runtime_error( "init() returns false so can't run test" );
run_timer.tick(); //start time measurement
run_result << std::string( "run test with " + current_tested_device->get_device_description() );
NN_WORKLOAD_DATA_TYPE input_format = NN_WORKLOAD_DATA_TYPE_F32_1D_BATCH;
NN_WORKLOAD_DATA_TYPE output_format = NN_WORKLOAD_DATA_TYPE_F32_1D_BATCH;
const int softmax_size = 1000;
for( auto batch : { 1, 8, 48 } ) {
// ---------------------------------------------------------------------------------------------------------
{ // simple sample pattern of test with time measuring:
bool local_ok = true;
test_measurement_result local_result;
local_result.description = "RUN PART: (batch " + std::to_string( batch ) + ") execution of " + test_description;
C_time_control local_timer;
// begin local test
auto input = new nn::data<float>( softmax_size, batch );
if(input == nullptr) throw std::runtime_error("unable to create input for batch = " +std::to_string(batch));
auto workload_output = new nn::data<float>( softmax_size, batch );
if(workload_output == nullptr) throw std::runtime_error("unable to create workload_output for batch = " +std::to_string(batch));
nn_data_populate( workload_output, 0.0f );
nn_data_populate( input, 0.0f, 20.0f );
nn_workload_t *workload = nullptr;
nn_data_t *input_array[1] = { input };
nn::data<float> *output_array_cmpl[1] = { nn::data_cast<float, 0>(workload_output) };
auto status = di->workflow_compile_function( &workload, di->device, workflow, &input_format, &output_format, batch );
if( !workload ) throw std::runtime_error( "workload compilation failed for batch = " + std::to_string( batch )
+ " status: " + std::to_string( status ) );
di->workload_execute_function( workload, reinterpret_cast<void**>(input_array), reinterpret_cast<void**>(output_array_cmpl), &status );
auto naive_output = cpu_layer_softmax( input );
local_ok = compare_data(workload_output, naive_output);
// end of local test
// summary:
local_timer.tock();
local_result.time_consumed = local_timer.get_time_diff();
local_result.clocks_consumed = local_timer.get_clocks_diff();
local_result.passed = local_ok;
tests_results << local_result;
run_ok = run_ok && local_ok;
if( input ) delete input;
if( workload_output ) delete workload_output;
if( naive_output ) delete naive_output;
if( workload ) delete workload;
} // The pattern, of complex instruction above, can be multiplied
// END of run tests
// ---------------------------------------------------------------------------------------------------------
}
} catch( std::runtime_error &error ) {
run_result << "error: " + std::string( error.what() );
run_ok = false;
} catch( std::exception &error ) {
run_result << "error: " + std::string( error.what() );
run_ok = false;
} catch( ... ) {
run_result << "unknown error";
run_ok = false;
}
run_timer.tock();
run_result.time_consumed = run_timer.get_time_diff();
run_result.clocks_consumed = run_timer.get_clocks_diff();
run_result.passed = run_ok;
tests_results << run_result;
if( !done() ) run_ok = false;
std::cout << "<- Test " << (run_ok ? "passed" : "failed") << std::endl;;
return run_ok;
}