Em qui, 29 de ago de 2019 às 02:57, Guo, Yejun <yejun....@intel.com> escreveu:
>
> 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....@intel.com>
> ---
> libavfilter/dnn/dnn_backend_native.c | 226
> ++++++++++++-------------
> libavfilter/dnn/dnn_backend_native.h | 54 +++++-
> libavfilter/dnn/dnn_backend_native_layer_pad.c | 24 ++-
> libavfilter/dnn/dnn_backend_native_layer_pad.h | 4 +-
> tests/dnn/Makefile | 2 +-
> tests/dnn/dnn-layer-pad-test.c | 60 +++++--
> 6 files changed, 236 insertions(+), 134 deletions(-)
>
> diff --git a/libavfilter/dnn/dnn_backend_native.c
> b/libavfilter/dnn/dnn_backend_native.c
> index d52abc6..daa4f50 100644
> --- a/libavfilter/dnn/dnn_backend_native.c
> +++ b/libavfilter/dnn/dnn_backend_native.c
> @@ -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);
> }
> diff --git a/libavfilter/dnn/dnn_backend_native.h
> b/libavfilter/dnn/dnn_backend_native.h
> index b6f9533..87b4394 100644
> --- a/libavfilter/dnn/dnn_backend_native.h
> +++ b/libavfilter/dnn/dnn_backend_native.h
> @@ -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
> diff --git a/libavfilter/dnn/dnn_backend_native_layer_pad.c
> b/libavfilter/dnn/dnn_backend_native_layer_pad.c
> index 5417d73..c2905a7 100644
> --- a/libavfilter/dnn/dnn_backend_native_layer_pad.c
> +++ b/libavfilter/dnn/dnn_backend_native_layer_pad.c
> @@ -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;
> }
> diff --git a/libavfilter/dnn/dnn_backend_native_layer_pad.h
> b/libavfilter/dnn/dnn_backend_native_layer_pad.h
> index 0fbe652..7cc8213 100644
> --- a/libavfilter/dnn/dnn_backend_native_layer_pad.h
> +++ b/libavfilter/dnn/dnn_backend_native_layer_pad.h
> @@ -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
> diff --git a/tests/dnn/Makefile b/tests/dnn/Makefile
> index 0e050ea..fabed75 100644
> --- a/tests/dnn/Makefile
> +++ b/tests/dnn/Makefile
> @@ -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))
> diff --git a/tests/dnn/dnn-layer-pad-test.c b/tests/dnn/dnn-layer-pad-test.c
> index 28a49eb..1fb2be1 100644
> --- a/tests/dnn/dnn-layer-pad-test.c
> +++ b/tests/dnn/dnn-layer-pad-test.c
> @@ -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, ¶ms, 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, ¶ms);
> +
> + 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, ¶ms, 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, ¶ms);
>
> - 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, ¶ms, 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, ¶ms);
>
> - 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;
>
> }
> --
> 2.7.4
>
Pushed, thanks!
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