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dnn: introduce dnn operand (in c code) to hold operand infos within network 

the info can be saved in dnn operand object without regenerating again and again,
and it is also needed for layer split/merge, and for memory reuse.

to make things step by step, this patch just focuses on c code,
the change within python script will be added later.

Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
tags/n4.3
Guo, Yejun Pedro Arthur 5 years ago
parent
20a12448aa
commit
09a455a246
6 changed files with 236 additions and 134 deletions
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  1. +108
    -118
      libavfilter/dnn/dnn_backend_native.c
  2. +53
    -1
      libavfilter/dnn/dnn_backend_native.h
  3. +23
    -1
      libavfilter/dnn/dnn_backend_native_layer_pad.c
  4. +3
    -1
      libavfilter/dnn/dnn_backend_native_layer_pad.h
  5. +1
    -1
      tests/dnn/Makefile
  6. +48
    -12
      tests/dnn/dnn-layer-pad-test.c

+ 108
- 118
libavfilter/dnn/dnn_backend_native.c View File

@@ -30,77 +30,30 @@
static DNNReturnType set_input_output_native(void *model, DNNInputData *input, const char *input_name, const char **output_names, uint32_t nb_output)
{
ConvolutionalNetwork *network = (ConvolutionalNetwork *)model;
InputParams *input_params;
ConvolutionalParams *conv_params;
DepthToSpaceParams *depth_to_space_params;
LayerPadParams *pad_params;
int cur_width, cur_height, cur_channels;
int32_t layer;

if (network->layers_num <= 0 || network->layers[0].type != INPUT){
if (network->layers_num <= 0 || network->operands_num <= 0)
return DNN_ERROR;
}
else{
input_params = (InputParams *)network->layers[0].params;
input_params->width = cur_width = input->width;
input_params->height = cur_height = input->height;
input_params->channels = cur_channels = input->channels;
if (input->data){
av_freep(&input->data);
}
av_assert0(input->dt == DNN_FLOAT);
network->layers[0].output = input->data = av_malloc(cur_height * cur_width * cur_channels * sizeof(float));
if (!network->layers[0].output){
return DNN_ERROR;
}
}

for (layer = 1; layer < network->layers_num; ++layer){
switch (network->layers[layer].type){
case CONV:
conv_params = (ConvolutionalParams *)network->layers[layer].params;
if (conv_params->input_num != cur_channels){
return DNN_ERROR;
}
cur_channels = conv_params->output_num;

if (conv_params->padding_method == VALID) {
int pad_size = (conv_params->kernel_size - 1) * conv_params->dilation;
cur_height -= pad_size;
cur_width -= pad_size;
}
break;
case DEPTH_TO_SPACE:
depth_to_space_params = (DepthToSpaceParams *)network->layers[layer].params;
if (cur_channels % (depth_to_space_params->block_size * depth_to_space_params->block_size) != 0){
return DNN_ERROR;
}
cur_channels = cur_channels / (depth_to_space_params->block_size * depth_to_space_params->block_size);
cur_height *= depth_to_space_params->block_size;
cur_width *= depth_to_space_params->block_size;
break;
case MIRROR_PAD:
pad_params = (LayerPadParams *)network->layers[layer].params;
cur_height = cur_height + pad_params->paddings[1][0] + pad_params->paddings[1][1];
cur_width = cur_width + pad_params->paddings[2][0] + pad_params->paddings[2][1];
cur_channels = cur_channels + pad_params->paddings[3][0] + pad_params->paddings[3][1];
break;
default:
return DNN_ERROR;
}
if (network->layers[layer].output){
av_freep(&network->layers[layer].output);
}

if (cur_height <= 0 || cur_width <= 0)
return DNN_ERROR;

network->layers[layer].output = av_malloc(cur_height * cur_width * cur_channels * sizeof(float));
if (!network->layers[layer].output){
return DNN_ERROR;
}
}
av_assert0(input->dt == DNN_FLOAT);

/**
* as the first step, suppose network->operands[0] is the input operand.
*/
network->operands[0].dims[0] = 1;
network->operands[0].dims[1] = input->height;
network->operands[0].dims[2] = input->width;
network->operands[0].dims[3] = input->channels;
network->operands[0].type = DOT_INPUT;
network->operands[0].data_type = DNN_FLOAT;
network->operands[0].isNHWC = 1;

av_freep(&network->operands[0].data);
network->operands[0].length = calculate_operand_data_length(&network->operands[0]);
network->operands[0].data = av_malloc(network->operands[0].length);
if (!network->operands[0].data)
return DNN_ERROR;

input->data = network->operands[0].data;
return DNN_SUCCESS;
}

@@ -119,6 +72,7 @@ DNNModel *ff_dnn_load_model_native(const char *model_filename)
ConvolutionalParams *conv_params;
DepthToSpaceParams *depth_to_space_params;
LayerPadParams *pad_params;
int32_t operand_index = 0;

model = av_malloc(sizeof(DNNModel));
if (!model){
@@ -131,7 +85,7 @@ DNNModel *ff_dnn_load_model_native(const char *model_filename)
}
file_size = avio_size(model_file_context);

network = av_malloc(sizeof(ConvolutionalNetwork));
network = av_mallocz(sizeof(ConvolutionalNetwork));
if (!network){
avio_closep(&model_file_context);
av_freep(&model);
@@ -139,32 +93,33 @@ DNNModel *ff_dnn_load_model_native(const char *model_filename)
}
model->model = (void *)network;

network->layers_num = 1 + (int32_t)avio_rl32(model_file_context);
network->layers_num = (int32_t)avio_rl32(model_file_context);
dnn_size = 4;

network->layers = av_malloc(network->layers_num * sizeof(Layer));
network->layers = av_mallocz(network->layers_num * sizeof(Layer));
if (!network->layers){
av_freep(&network);
avio_closep(&model_file_context);
av_freep(&model);
ff_dnn_free_model_native(&model);
return NULL;
}

for (layer = 0; layer < network->layers_num; ++layer){
network->layers[layer].output = NULL;
network->layers[layer].params = NULL;
}
network->layers[0].type = INPUT;
network->layers[0].params = av_malloc(sizeof(InputParams));
if (!network->layers[0].params){
/**
* Operands should be read from model file, the whole change will be huge.
* to make things step by step, we first mock the operands, instead of reading from model file.
*/
network->operands_num = network->layers_num + 1;
network->operands = av_mallocz(network->operands_num * sizeof(DnnOperand));
if (!network->operands){
avio_closep(&model_file_context);
ff_dnn_free_model_native(&model);
return NULL;
}

for (layer = 1; layer < network->layers_num; ++layer){
for (layer = 0; layer < network->layers_num; ++layer){
layer_type = (int32_t)avio_rl32(model_file_context);
dnn_size += 4;
network->layers[layer].input_operand_indexes[0] = operand_index++;
network->layers[layer].output_operand_index = operand_index;
switch (layer_type){
case CONV:
conv_params = av_malloc(sizeof(ConvolutionalParams));
@@ -258,14 +213,35 @@ DNNModel *ff_dnn_load_model_native(const char *model_filename)

#define CLAMP_TO_EDGE(x, w) ((x) < 0 ? 0 : ((x) >= (w) ? (w - 1) : (x)))

static void convolve(const float *input, float *output, const ConvolutionalParams *conv_params, int width, int height)
static int convolve(DnnOperand *operands, const int32_t *input_operand_indexes, int32_t output_operand_index, const ConvolutionalParams *conv_params)
{
float *output;
int32_t input_operand_index = input_operand_indexes[0];
int number = operands[input_operand_index].dims[0];
int height = operands[input_operand_index].dims[1];
int width = operands[input_operand_index].dims[2];
int channel = operands[input_operand_index].dims[3];
const float *input = operands[input_operand_index].data;

int radius = conv_params->kernel_size >> 1;
int src_linesize = width * conv_params->input_num;
int filter_linesize = conv_params->kernel_size * conv_params->input_num;
int filter_size = conv_params->kernel_size * filter_linesize;
int pad_size = (conv_params->padding_method == VALID) ? (conv_params->kernel_size - 1) / 2 * conv_params->dilation : 0;

DnnOperand *output_operand = &operands[output_operand_index];
output_operand->dims[0] = number;
output_operand->dims[1] = height - pad_size * 2;
output_operand->dims[2] = width - pad_size * 2;
output_operand->dims[3] = conv_params->output_num;
output_operand->length = calculate_operand_data_length(output_operand);
output_operand->data = av_realloc(output_operand->data, output_operand->length);
if (!output_operand->data)
return -1;
output = output_operand->data;

av_assert0(channel == conv_params->input_num);

for (int y = pad_size; y < height - pad_size; ++y) {
for (int x = pad_size; x < width - pad_size; ++x) {
for (int n_filter = 0; n_filter < conv_params->output_num; ++n_filter) {
@@ -311,16 +287,36 @@ static void convolve(const float *input, float *output, const ConvolutionalParam
output += conv_params->output_num;
}
}
return 0;
}

static void depth_to_space(const float *input, float *output, int block_size, int width, int height, int channels)
static int depth_to_space(DnnOperand *operands, const int32_t *input_operand_indexes, int32_t output_operand_index, int block_size)
{
float *output;
int32_t input_operand_index = input_operand_indexes[0];
int number = operands[input_operand_index].dims[0];
int height = operands[input_operand_index].dims[1];
int width = operands[input_operand_index].dims[2];
int channels = operands[input_operand_index].dims[3];
const float *input = operands[input_operand_index].data;

int y, x, by, bx, ch;
int new_channels = channels / (block_size * block_size);
int output_linesize = width * channels;
int by_linesize = output_linesize / block_size;
int x_linesize = new_channels * block_size;

DnnOperand *output_operand = &operands[output_operand_index];
output_operand->dims[0] = number;
output_operand->dims[1] = height * block_size;
output_operand->dims[2] = width * block_size;
output_operand->dims[3] = new_channels;
output_operand->length = calculate_operand_data_length(output_operand);
output_operand->data = av_realloc(output_operand->data, output_operand->length);
if (!output_operand->data)
return -1;
output = output_operand->data;

for (y = 0; y < height; ++y){
for (x = 0; x < width; ++x){
for (by = 0; by < block_size; ++by){
@@ -334,58 +330,38 @@ static void depth_to_space(const float *input, float *output, int block_size, in
}
output += output_linesize;
}
return 0;
}

DNNReturnType ff_dnn_execute_model_native(const DNNModel *model, DNNData *outputs, uint32_t nb_output)
{
ConvolutionalNetwork *network = (ConvolutionalNetwork *)model->model;
int cur_width, cur_height, cur_channels;
int32_t layer;
InputParams *input_params;
ConvolutionalParams *conv_params;
DepthToSpaceParams *depth_to_space_params;
LayerPadParams *pad_params;

if (network->layers_num <= 0 || network->layers[0].type != INPUT || !network->layers[0].output){
if (network->layers_num <= 0 || network->operands_num <= 0)
return DNN_ERROR;
if (!network->operands[0].data)
return DNN_ERROR;
}
else{
input_params = (InputParams *)network->layers[0].params;
cur_width = input_params->width;
cur_height = input_params->height;
cur_channels = input_params->channels;
}

for (layer = 1; layer < network->layers_num; ++layer){
if (!network->layers[layer].output){
return DNN_ERROR;
}
for (layer = 0; layer < network->layers_num; ++layer){
switch (network->layers[layer].type){
case CONV:
conv_params = (ConvolutionalParams *)network->layers[layer].params;
convolve(network->layers[layer - 1].output, network->layers[layer].output, conv_params, cur_width, cur_height);
cur_channels = conv_params->output_num;
if (conv_params->padding_method == VALID) {
int pad_size = (conv_params->kernel_size - 1) * conv_params->dilation;
cur_height -= pad_size;
cur_width -= pad_size;
}
convolve(network->operands, network->layers[layer].input_operand_indexes,
network->layers[layer].output_operand_index, conv_params);
break;
case DEPTH_TO_SPACE:
depth_to_space_params = (DepthToSpaceParams *)network->layers[layer].params;
depth_to_space(network->layers[layer - 1].output, network->layers[layer].output,
depth_to_space_params->block_size, cur_width, cur_height, cur_channels);
cur_height *= depth_to_space_params->block_size;
cur_width *= depth_to_space_params->block_size;
cur_channels /= depth_to_space_params->block_size * depth_to_space_params->block_size;
depth_to_space(network->operands, network->layers[layer].input_operand_indexes,
network->layers[layer].output_operand_index, depth_to_space_params->block_size);
break;
case MIRROR_PAD:
pad_params = (LayerPadParams *)network->layers[layer].params;
dnn_execute_layer_pad(network->layers[layer - 1].output, network->layers[layer].output,
pad_params, 1, cur_height, cur_width, cur_channels);
cur_height = cur_height + pad_params->paddings[1][0] + pad_params->paddings[1][1];
cur_width = cur_width + pad_params->paddings[2][0] + pad_params->paddings[2][1];
cur_channels = cur_channels + pad_params->paddings[3][0] + pad_params->paddings[3][1];
dnn_execute_layer_pad(network->operands, network->layers[layer].input_operand_indexes,
network->layers[layer].output_operand_index, pad_params);
break;
case INPUT:
return DNN_ERROR;
@@ -395,14 +371,24 @@ DNNReturnType ff_dnn_execute_model_native(const DNNModel *model, DNNData *output
// native mode does not support multiple outputs yet
if (nb_output > 1)
return DNN_ERROR;
outputs[0].data = network->layers[network->layers_num - 1].output;
outputs[0].height = cur_height;
outputs[0].width = cur_width;
outputs[0].channels = cur_channels;

/**
* as the first step, suppose network->operands[network->operands_num - 1] is the output operand.
*/
outputs[0].data = network->operands[network->operands_num - 1].data;
outputs[0].height = network->operands[network->operands_num - 1].dims[1];
outputs[0].width = network->operands[network->operands_num - 1].dims[2];
outputs[0].channels = network->operands[network->operands_num - 1].dims[3];

return DNN_SUCCESS;
}

int32_t calculate_operand_data_length(DnnOperand* operand)
{
// currently, we just support DNN_FLOAT
return operand->dims[0] * operand->dims[1] * operand->dims[2] * operand->dims[3] * sizeof(float);
}

void ff_dnn_free_model_native(DNNModel **model)
{
ConvolutionalNetwork *network;
@@ -413,7 +399,6 @@ void ff_dnn_free_model_native(DNNModel **model)
{
network = (ConvolutionalNetwork *)(*model)->model;
for (layer = 0; layer < network->layers_num; ++layer){
av_freep(&network->layers[layer].output);
if (network->layers[layer].type == CONV){
conv_params = (ConvolutionalParams *)network->layers[layer].params;
av_freep(&conv_params->kernel);
@@ -422,6 +407,11 @@ void ff_dnn_free_model_native(DNNModel **model)
av_freep(&network->layers[layer].params);
}
av_freep(&network->layers);

for (uint32_t operand = 0; operand < network->operands_num; ++operand)
av_freep(&network->operands[operand].data);
av_freep(&network->operands);

av_freep(&network);
av_freep(model);
}


+ 53
- 1
libavfilter/dnn/dnn_backend_native.h View File

@@ -36,12 +36,60 @@ typedef enum {RELU, TANH, SIGMOID, NONE, LEAKY_RELU} DNNActivationFunc;

typedef enum {VALID, SAME, SAME_CLAMP_TO_EDGE} DNNConvPaddingParam;

typedef enum {DOT_INPUT, DOT_INTERMEDIATE, DOT_OUTPUT} DNNOperandType;

typedef struct Layer{
DNNLayerType type;
float *output;
/**
* a layer can have multiple inputs and one output.
* 4 is just a big enough number for input operands (increase it if necessary),
* do not use 'int32_t *input_operand_indexes', so we don't worry about mem leaks.
*/
int32_t input_operand_indexes[4];
int32_t output_operand_index;
void *params;
} Layer;

typedef struct DnnOperand{
/**
* there are two memory layouts, NHWC or NCHW, so we use dims,
* dims[0] is Number.
*/
int32_t dims[4];

/**
* input/output/intermediate operand of the network
*/
DNNOperandType type;

/**
* support different kinds of data type such as float, half float, int8 etc,
* first support float now.
*/
DNNDataType data_type;

/**
* NHWC if 1, otherwise NCHW.
* let's first support NHWC only, this flag is for extensive usage.
*/
int8_t isNHWC;

/**
* to avoid possible memory leak, do not use char *name
*/
char name[128];

/**
* data pointer with data length in bytes.
* usedNumbersLeft is only valid for intermediate operand,
* it means how many layers still depend on this operand,
* todo: the memory can be reused when usedNumbersLeft is zero.
*/
void *data;
int32_t length;
int32_t usedNumbersLeft;
}DnnOperand;

typedef struct ConvolutionalParams{
int32_t input_num, output_num, kernel_size;
DNNActivationFunc activation;
@@ -63,6 +111,8 @@ typedef struct DepthToSpaceParams{
typedef struct ConvolutionalNetwork{
Layer *layers;
int32_t layers_num;
DnnOperand *operands;
int32_t operands_num;
} ConvolutionalNetwork;

DNNModel *ff_dnn_load_model_native(const char *model_filename);
@@ -71,4 +121,6 @@ DNNReturnType ff_dnn_execute_model_native(const DNNModel *model, DNNData *output

void ff_dnn_free_model_native(DNNModel **model);

int32_t calculate_operand_data_length(DnnOperand *operand);

#endif

+ 23
- 1
libavfilter/dnn/dnn_backend_native_layer_pad.c View File

@@ -48,12 +48,21 @@ static int after_get_buddy(int given, int border, LayerPadModeParam mode)
}
}

void dnn_execute_layer_pad(const float *input, float *output, const LayerPadParams *params, int number, int height, int width, int channel)
int dnn_execute_layer_pad(DnnOperand *operands, const int32_t *input_operand_indexes, int32_t output_operand_index,
const LayerPadParams *params)
{
int32_t before_paddings;
int32_t after_paddings;
float* output;

// suppose format is <N, H, W, C>
int32_t input_operand_index = input_operand_indexes[0];
int number = operands[input_operand_index].dims[0];
int height = operands[input_operand_index].dims[1];
int width = operands[input_operand_index].dims[2];
int channel = operands[input_operand_index].dims[3];
const float *input = operands[input_operand_index].data;

int new_number = number + params->paddings[0][0] + params->paddings[0][1];
int new_height = height + params->paddings[1][0] + params->paddings[1][1];
int new_width = width + params->paddings[2][0] + params->paddings[2][1];
@@ -67,6 +76,17 @@ void dnn_execute_layer_pad(const float *input, float *output, const LayerPadPara
int new_wc_stride = new_c_stride * new_width;
int new_hwc_stride = new_wc_stride * new_height;

DnnOperand *output_operand = &operands[output_operand_index];
output_operand->dims[0] = new_number;
output_operand->dims[1] = new_height;
output_operand->dims[2] = new_width;
output_operand->dims[3] = new_channel;
output_operand->length = calculate_operand_data_length(output_operand);
output_operand->data = av_realloc(output_operand->data, output_operand->length);
if (!output_operand->data)
return -1;
output = output_operand->data;

// copy the original data
for (int n = 0; n < number; n++) {
for (int h = 0; h < height; h++) {
@@ -208,4 +228,6 @@ void dnn_execute_layer_pad(const float *input, float *output, const LayerPadPara
}
}
}

return 0;
}

+ 3
- 1
libavfilter/dnn/dnn_backend_native_layer_pad.h View File

@@ -26,6 +26,7 @@
#define AVFILTER_DNN_DNN_BACKEND_NATIVE_LAYER_PAD_H

#include <stdint.h>
#include "dnn_backend_native.h"

typedef enum {LPMP_CONSTANT, LPMP_REFLECT, LPMP_SYMMETRIC} LayerPadModeParam;

@@ -35,6 +36,7 @@ typedef struct LayerPadParams{
float constant_values;
} LayerPadParams;

void dnn_execute_layer_pad(const float *input, float *output, const LayerPadParams *params, int number, int height, int width, int channel);
int dnn_execute_layer_pad(DnnOperand *operands, const int32_t *input_operand_indexes, int32_t output_operand_index,
const LayerPadParams *params);

#endif

+ 1
- 1
tests/dnn/Makefile View File

@@ -5,7 +5,7 @@ DNNTESTPROGS := $(DNNTESTPROGS:%=$(DNNTESTSDIR)/%-test$(EXESUF))
-include $(wildcard $(DNNTESTOBJS:.o=.d))

$(DNNTESTPROGS): %$(EXESUF): %.o $(FF_STATIC_DEP_LIBS)
$(LD) $(LDFLAGS) $(LDEXEFLAGS) $(LD_O) $(filter %.o,$^) $(FF_STATIC_DEP_LIBS) $(ELIBS)
$(LD) $(LDFLAGS) $(LDEXEFLAGS) $(LD_O) $(filter %.o,$^) $(FF_STATIC_DEP_LIBS) $(EXTRALIBS-avcodec) $(EXTRALIBS-avfilter) $(EXTRALIBS-avformat) $(EXTRALIBS-avutil) $(EXTRALIBS-swresample) $(EXTRALIBS)

testclean::
$(RM) $(addprefix $(DNNTESTSDIR)/,$(CLEANSUFFIXES) *-test$(EXESUF))

+ 48
- 12
tests/dnn/dnn-layer-pad-test.c View File

@@ -44,6 +44,8 @@ static int test_with_mode_symmetric(void)
*/

LayerPadParams params;
DnnOperand operands[2];
int32_t input_indexes[1];
float input[1*4*4*3] = {
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47
};
@@ -57,8 +59,7 @@ static int test_with_mode_symmetric(void)
27.0, 28.0, 29.0, 24.0, 25.0, 26.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 33.0, 34.0, 35.0, 30.0, 31.0, 32.0, 18.0, 19.0, 20.0, 15.0, 16.0, 17.0, 12.0,
13.0, 14.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 21.0, 22.0, 23.0, 18.0, 19.0, 20.0
};
float output[1*9*9*3];
memset(output, 0, sizeof(output));
float *output;

params.mode = LPMP_SYMMETRIC;
params.paddings[0][0] = 0;
@@ -70,15 +71,26 @@ static int test_with_mode_symmetric(void)
params.paddings[3][0] = 0;
params.paddings[3][1] = 0;

dnn_execute_layer_pad(input, output, &params, 1, 4, 4, 3);
operands[0].data = input;
operands[0].dims[0] = 1;
operands[0].dims[1] = 4;
operands[0].dims[2] = 4;
operands[0].dims[3] = 3;
operands[1].data = NULL;

for (int i = 0; i < sizeof(output) / sizeof(float); i++) {
input_indexes[0] = 0;
dnn_execute_layer_pad(operands, input_indexes, 1, &params);

output = operands[1].data;
for (int i = 0; i < sizeof(expected_output) / sizeof(float); i++) {
if (fabs(output[i] - expected_output[i]) > EPSON) {
printf("at index %d, output: %f, expected_output: %f\n", i, output[i], expected_output[i]);
av_freep(&output);
return 1;
}
}

av_freep(&output);
return 0;

}
@@ -102,6 +114,8 @@ static int test_with_mode_reflect(void)
*/

LayerPadParams params;
DnnOperand operands[2];
int32_t input_indexes[1];
float input[3*2*2*3] = {
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35
};
@@ -110,8 +124,7 @@ static int test_with_mode_reflect(void)
12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0,
35.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0
};
float output[6*2*2*3];
memset(output, 0, sizeof(output));
float *output;

params.mode = LPMP_REFLECT;
params.paddings[0][0] = 1;
@@ -123,15 +136,26 @@ static int test_with_mode_reflect(void)
params.paddings[3][0] = 0;
params.paddings[3][1] = 0;

dnn_execute_layer_pad(input, output, &params, 3, 2, 2, 3);
operands[0].data = input;
operands[0].dims[0] = 3;
operands[0].dims[1] = 2;
operands[0].dims[2] = 2;
operands[0].dims[3] = 3;
operands[1].data = NULL;

input_indexes[0] = 0;
dnn_execute_layer_pad(operands, input_indexes, 1, &params);

for (int i = 0; i < sizeof(output) / sizeof(float); i++) {
output = operands[1].data;
for (int i = 0; i < sizeof(expected_output) / sizeof(float); i++) {
if (fabs(output[i] - expected_output[i]) > EPSON) {
printf("at index %d, output: %f, expected_output: %f\n", i, output[i], expected_output[i]);
av_freep(&output);
return 1;
}
}

av_freep(&output);
return 0;

}
@@ -155,6 +179,8 @@ static int test_with_mode_constant(void)
*/

LayerPadParams params;
DnnOperand operands[2];
int32_t input_indexes[1];
float input[1*2*2*3] = {
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11
};
@@ -163,8 +189,7 @@ static int test_with_mode_constant(void)
728.0, 728.0, 0.0, 1.0, 2.0, 728.0, 728.0, 728.0, 3.0, 4.0, 5.0, 728.0, 728.0,
728.0, 6.0, 7.0, 8.0, 728.0, 728.0, 728.0, 9.0, 10.0, 11.0, 728.0, 728.0
};
float output[1*3*2*6];
memset(output, 0, sizeof(output));
float *output;

params.mode = LPMP_CONSTANT;
params.constant_values = 728;
@@ -177,15 +202,26 @@ static int test_with_mode_constant(void)
params.paddings[3][0] = 1;
params.paddings[3][1] = 2;

dnn_execute_layer_pad(input, output, &params, 1, 2, 2, 3);
operands[0].data = input;
operands[0].dims[0] = 3;
operands[0].dims[1] = 2;
operands[0].dims[2] = 2;
operands[0].dims[3] = 3;
operands[1].data = NULL;

input_indexes[0] = 0;
dnn_execute_layer_pad(operands, input_indexes, 1, &params);

for (int i = 0; i < sizeof(output) / sizeof(float); i++) {
output = operands[1].data;
for (int i = 0; i < sizeof(expected_output) / sizeof(float); i++) {
if (fabs(output[i] - expected_output[i]) > EPSON) {
printf("at index %d, output: %f, expected_output: %f\n", i, output[i], expected_output[i]);
av_freep(&output);
return 1;
}
}

av_freep(&output);
return 0;

}


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