static bool ult_perform_test(
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 pool_stride_x,
uint_least32_t pool_stride_y,
uint_least32_t pool_size_x,
uint_least32_t pool_size_y,
uint8_t accumulator_fraction,
uint8_t output_fraction,
uint_least32_t center_x,
uint_least32_t center_y,
NN_ACTIVATION_FUNCTION activation,
NN_POOLING_MODE mode)
{
uint32_t IFMBlock = 16;
uint32_t OFMOutBlock = 16;
nn_workload_item* work_item = nullptr;
nn_workload_item* work_items[12];
nn_workload_item* input_item = nullptr;
nn_workload_item* input_items[12];
std::fill_n(work_items, 12, nullptr);
bool passed = false;
int16_t* input = 0;
int16_t* output = 0;
int32_t* biases = 0;
int16_t* kernel = 0;
int16_t* input_ref = 0;
int16_t* output_ref = 0;
int32_t* biases_ref = 0;
int16_t* kernel_ref = 0;
uint32_t NoWItems = 1;
uint_least32_t output_feature_map_width = (((input_feature_map_width - kernel_width) / kernel_stride_x + 1) - pool_size_x) / pool_stride_x + 1;
uint_least32_t output_feature_map_height = (((input_feature_map_height - kernel_height) / kernel_stride_y + 1) - pool_size_y) / pool_stride_y + 1;
uint_least32_t output_feature_map_width_int = (input_feature_map_width - kernel_width) / kernel_stride_x + 1;
uint_least32_t output_feature_map_height_int = (input_feature_map_height - kernel_height) / kernel_stride_y + 1;
num_output_feature_maps += (C_simd_width - (num_output_feature_maps % C_simd_width)) % C_simd_width;
// Allocate naive and optimized buffers.
ult_nn_convolution_fixedpoint_comp_both_alloc(
input,
output,
biases,
kernel,
input_ref,
output_ref,
biases_ref,
kernel_ref,
num_output_feature_maps,
num_input_feature_maps,
output_feature_map_width,
output_feature_map_height,
input_feature_map_width,
input_feature_map_height,
kernel_width,
kernel_height,
center_x,
center_y);
// Initialize both buffers.
ult_nn_convolution_fixedpoint_comp_both_initialize_matrices(
input,
output,
biases,
kernel,
input_ref,
output_ref,
biases_ref,
kernel_ref,
num_output_feature_maps,
num_input_feature_maps,
output_feature_map_width,
output_feature_map_height,
input_feature_map_width,
input_feature_map_height,
kernel_width,
kernel_height,
center_x,
center_y);
// Naive maxpooling.
ult_nn_maxpooling_naive_pooling_int16_fixedpoint(
input_ref,
output_ref,
biases_ref,
kernel_ref,
num_output_feature_maps,
num_input_feature_maps,
output_feature_map_width,
output_feature_map_height,
output_feature_map_width_int,
output_feature_map_height_int,
input_feature_map_width,
input_feature_map_height,
kernel_width,
kernel_height,
kernel_stride_x,
kernel_stride_y,
pool_size_x,
pool_size_y,
pool_stride_x,
pool_stride_y,
accumulator_fraction,
output_fraction,
center_x,
center_y,
activation);
nn_workflow_t *workflow = nullptr;
nn_device_description_t device_description;
nn_device_interface_0_t device_interface_0;
test_setup(device_description, device_interface_0);
// shorter name for function calls
nn_device_interface_0_t &di = device_interface_0;
nn_workflow_item_t
*workflow_input = nullptr
, *workflow_output = nullptr
, *workflow_convolution = nullptr;
fill_workflow(
&workflow,
&di,
&workflow_input,
&workflow_output,
&workflow_convolution,
biases,
kernel,
num_input_feature_maps,
num_output_feature_maps,
input_feature_map_width,
input_feature_map_height,
kernel_width,
kernel_height,
kernel_stride_x,
kernel_stride_y,
center_x,
center_y,
accumulator_fraction,
output_fraction,
activation);
// attaching input/output to workflow
workflow->input[0] = workflow_input;
workflow->output[0] = workflow_output;
nn::data<int16_t, 3>* input_datas[] = { new nn::data<int16_t, 3>((int16_t *)input, num_input_feature_maps, input_feature_map_width, input_feature_map_height ) };
nn::data<int16_t, 3>* output_datas[] = { new nn::data<int16_t, 3>((int16_t *)output, num_output_feature_maps, output_feature_map_width, output_feature_map_height) };
if (num_input_feature_maps == 4)
IFMBlock = 4;
// compile workflow
NN_API_STATUS status;
nn_workload_t *workload;
NN_WORKLOAD_DATA_TYPE io_format = NN_WORKLOAD_DATA_TYPE_I16_ZXY;
EXPECT_EQ(NN_API_STATUS_OK, di.workflow_compile_function(&workload, di.device, workflow, &io_format, &io_format, 1));
EXPECT_EQ(NN_API_STATUS_OK, di.workload_execute_function(workload, (void **)input_datas, (void **)output_datas, &status));
// delete workload
EXPECT_EQ(NN_API_STATUS_OK, di.workload_delete_function(workload));
// delete workflow items
EXPECT_EQ(NN_API_STATUS_OK, di.workflow_item_delete_function(workflow_output));
EXPECT_EQ(NN_API_STATUS_OK, di.workflow_item_delete_function(workflow_convolution));
EXPECT_EQ(NN_API_STATUS_OK, di.workflow_item_delete_function(workflow_input));
// delete workflow
EXPECT_EQ(NN_API_STATUS_OK, di.workflow_delete_function(workflow));
test_teardown(device_description, device_interface_0);
// Basic check between optimized and naive versions.
passed = ult_nn_convolution_fixedpoint_comp_check_outputs(
output_datas[0],
output_ref,
num_output_feature_maps,
output_feature_map_width,
output_feature_map_height,
center_x,
center_y);
ult_nn_convolution_fixedpoint_comp_both_dealloc(
input,
output,
biases,
kernel,
input_ref,
output_ref,
biases_ref,
kernel_ref);
return passed;
}
static bool ult_perform_test(
uint32_t batch_size,
uint32_t feature_map_width,
uint32_t feature_map_height,
uint32_t num_feature_maps,
float coeff_alpha,
float coeff_beta,
float coeff_k,
uint32_t input_fraction,
uint32_t output_fraction,
NN_NORMALIZATION_MODE mode
)
{
bool return_value = true;
bool passed = true;
int16_t* input = 0;
int16_t* output = 0;
int16_t* input_ref = 0;
int16_t* output_ref = 0;
// Allocate naive and optimized buffers.
ult_lrn_comp_buffers_alloc(
input,
output,
input_ref,
output_ref,
num_feature_maps,
feature_map_width,
feature_map_height,
batch_size
);
// Initialize both buffers.
ult_lrn_comp_buffers_initialize(
input,
output,
input_ref,
output_ref,
num_feature_maps,
feature_map_width,
feature_map_height,
batch_size
);
// Naive normalization_lrn
ult_nn_lrn_fp_comp_naive(
input_ref,
output_ref,
num_feature_maps,
feature_map_width,
feature_map_height,
batch_size,
input_fraction,
output_fraction,
coeff_alpha,
coeff_beta,
coeff_k
);
nn_workflow_t *workflow = nullptr;
nn_device_description_t device_description;
nn_device_interface_0_t device_interface_0;
test_setup(device_description, device_interface_0);
// shorter name for function calls
nn_device_interface_0_t &di = device_interface_0;
nn_workflow_item_t
*workflow_input = nullptr
, *workflow_output = nullptr
, *workflow_normalization = nullptr;
fill_workflow(
&workflow,
&di,
&workflow_input,
&workflow_output,
&workflow_normalization,
num_feature_maps,
feature_map_width,
feature_map_height,
batch_size,
input_fraction,
output_fraction,
coeff_alpha,
coeff_beta,
coeff_k);
// attaching input/output to workflow
workflow->input[0] = workflow_input;
workflow->output[0] = workflow_output;
nn::data<int16_t, 3>* input_datas[] = { new nn::data<int16_t, 3>((int16_t *)input, num_feature_maps, feature_map_width, feature_map_height) };
nn::data<int16_t, 3>* output_datas[] = { new nn::data<int16_t, 3>((int16_t *)output, num_feature_maps, feature_map_width, feature_map_height) };
// compile workflow
NN_API_STATUS status;
nn_workload_t *workload;
NN_WORKLOAD_DATA_TYPE io_format = NN_WORKLOAD_DATA_TYPE_I16_ZXY;
EXPECT_EQ(NN_API_STATUS_OK, di.workflow_compile_function(&workload, di.device, workflow, &io_format, &io_format, 1));
EXPECT_EQ(NN_API_STATUS_OK, di.workload_execute_function(workload, (void **)input_datas, (void **)output_datas, &status));
// delete workload
EXPECT_EQ(NN_API_STATUS_OK, di.workload_delete_function(workload));
// delete workflow items
EXPECT_EQ(NN_API_STATUS_OK, di.workflow_item_delete_function(workflow_output));
EXPECT_EQ(NN_API_STATUS_OK, di.workflow_item_delete_function(workflow_normalization));
EXPECT_EQ(NN_API_STATUS_OK, di.workflow_item_delete_function(workflow_input));
// delete workflow
EXPECT_EQ(NN_API_STATUS_OK, di.workflow_delete_function(workflow));
test_teardown(device_description, device_interface_0);
//Basic check between optimized and naive versions.
passed = ult_nn_lrn_fp_check_outputs(
output_datas[0],
output_ref,
num_feature_maps,
feature_map_width,
feature_map_height,
batch_size);
ult_nn_lrn_fp_both_dealloc(
input,
output,
input_ref,
output_ref);
return passed;
}
