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Code Issues Releases Activity

lavfi/dnn: Remove DNN native backend

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According to discussion in
https://etherpad.mit.edu/p/FF_dev_meeting_20221202 and the proposal in
http://ffmpeg.org/pipermail/ffmpeg-devel/2022-December/304534.html,
the DNN native backend should be removed at first step.
All the DNN native backend related codes are deleted.

Signed-off-by: Ting Fu <ting.fu@intel.com>
This commit is contained in:
Ting Fu 2023-04-27 17:43:46 +08:00 committed by Guo Yejun
parent a9fb141719
commit 78f95f1088
37 changed files with 4 additions and 4440 deletions
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3
libavfilter/Makefile
View File

@ -635,9 +635,6 @@ SKIPHEADERS-$(CONFIG_VULKAN) += vulkan.h vulkan_filter.h
TOOLS = graph2dot
TESTPROGS = drawutils filtfmts formats integral
TESTPROGS-$(CONFIG_DNN) += dnn-layer-avgpool dnn-layer-conv2d dnn-layer-dense \
dnn-layer-depth2space dnn-layer-mathbinary \
dnn-layer-mathunary dnn-layer-maximum dnn-layer-pad \
TOOLS-$(CONFIG_LIBZMQ) += zmqsend

10
libavfilter/dnn/Makefile
View File

@ -3,16 +3,6 @@ OBJS-$(CONFIG_DNN) += dnn/dnn_io_proc.o
OBJS-$(CONFIG_DNN) += dnn/queue.o
OBJS-$(CONFIG_DNN) += dnn/safe_queue.o
OBJS-$(CONFIG_DNN) += dnn/dnn_backend_common.o
OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native.o
OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native_layers.o
OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native_layer_avgpool.o
OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native_layer_dense.o
OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native_layer_pad.o
OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native_layer_conv2d.o
OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native_layer_depth2space.o
OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native_layer_maximum.o
OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native_layer_mathbinary.o
OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native_layer_mathunary.o
DNN-OBJS-$(CONFIG_LIBTENSORFLOW) += dnn/dnn_backend_tf.o
DNN-OBJS-$(CONFIG_LIBOPENVINO) += dnn/dnn_backend_openvino.o

561
libavfilter/dnn/dnn_backend_native.c
View File

@ -1,561 +0,0 @@
/*
* Copyright (c) 2018 Sergey Lavrushkin
*
* This file is part of FFmpeg.
*
* FFmpeg is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public
* License as published by the Free Software Foundation; either
* version 2.1 of the License, or (at your option) any later version.
*
* FFmpeg is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with FFmpeg; if not, write to the Free Software
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
*/
/**
* @file
* DNN native backend implementation.
*/
#include "dnn_backend_native.h"
#include "libavutil/avassert.h"
#include "dnn_backend_native_layer_conv2d.h"
#include "dnn_backend_native_layers.h"
#include "dnn_io_proc.h"
#include "dnn_backend_common.h"
#define OFFSET(x) offsetof(NativeContext, x)
#define FLAGS AV_OPT_FLAG_FILTERING_PARAM
static const AVOption dnn_native_options[] = {
{ "conv2d_threads", "threads num for conv2d layer", OFFSET(options.conv2d_threads), AV_OPT_TYPE_INT, { .i64 = 0 }, INT_MIN, INT_MAX, FLAGS },
{ "async", "use DNN async inference", OFFSET(options.async), AV_OPT_TYPE_BOOL, { .i64 = 0 }, 0, 1, FLAGS },
{ NULL },
};
static const AVClass dnn_native_class = {
.class_name = "dnn_native",
.item_name = av_default_item_name,
.option = dnn_native_options,
.version = LIBAVUTIL_VERSION_INT,
.category = AV_CLASS_CATEGORY_FILTER,
};
static int execute_model_native(Queue *lltask_queue);
static int extract_lltask_from_task(TaskItem *task, Queue *lltask_queue)
{
NativeModel *native_model = task->model;
NativeContext *ctx = &native_model->ctx;
LastLevelTaskItem *lltask = av_malloc(sizeof(*lltask));
if (!lltask) {
av_log(ctx, AV_LOG_ERROR, "Unable to allocate space for LastLevelTaskItem\n");
return AVERROR(ENOMEM);
}
task->inference_todo = 1;
task->inference_done = 0;
lltask->task = task;
if (ff_queue_push_back(lltask_queue, lltask) < 0) {
av_log(ctx, AV_LOG_ERROR, "Failed to push back lltask_queue.\n");
av_freep(&lltask);
return AVERROR(ENOMEM);
}
return 0;
}
static int get_input_native(void *model, DNNData *input, const char *input_name)
{
NativeModel *native_model = model;
NativeContext *ctx = &native_model->ctx;
for (int i = 0; i < native_model->operands_num; ++i) {
DnnOperand *oprd = &native_model->operands[i];
if (strcmp(oprd->name, input_name) == 0) {
if (oprd->type != DOT_INPUT) {
av_log(ctx, AV_LOG_ERROR, "Found \"%s\" in model, but it is not input node\n", input_name);
return AVERROR(EINVAL);
}
input->dt = oprd->data_type;
av_assert0(oprd->dims[0] == 1);
input->height = oprd->dims[1];
input->width = oprd->dims[2];
input->channels = oprd->dims[3];
return 0;
}
}
// do not find the input operand
av_log(ctx, AV_LOG_ERROR, "Could not find \"%s\" in model\n", input_name);
return AVERROR(EINVAL);
}
static int get_output_native(void *model, const char *input_name, int input_width, int input_height,
const char *output_name, int *output_width, int *output_height)
{
int ret = 0;
NativeModel *native_model = model;
NativeContext *ctx = &native_model->ctx;
TaskItem task;
DNNExecBaseParams exec_params = {
.input_name = input_name,
.output_names = &output_name,
.nb_output = 1,
.in_frame = NULL,
.out_frame = NULL,
};
ret = ff_dnn_fill_gettingoutput_task(&task, &exec_params, native_model, input_height, input_width, ctx);
if (ret != 0) {
goto err;
}
ret = extract_lltask_from_task(&task, native_model->lltask_queue);
if (ret != 0) {
av_log(ctx, AV_LOG_ERROR, "unable to extract last level task from task.\n");
goto err;
}
ret = execute_model_native(native_model->lltask_queue);
*output_width = task.out_frame->width;
*output_height = task.out_frame->height;
err:
av_frame_free(&task.out_frame);
av_frame_free(&task.in_frame);
return ret;
}
// Loads model and its parameters that are stored in a binary file with following structure:
// layers_num,layer_type,layer_parameterss,layer_type,layer_parameters...
// For CONV layer: activation_function, input_num, output_num, kernel_size, kernel, biases
// For DEPTH_TO_SPACE layer: block_size
DNNModel *ff_dnn_load_model_native(const char *model_filename, DNNFunctionType func_type, const char *options, AVFilterContext *filter_ctx)
{
#define DNN_NATIVE_MAGIC "FFMPEGDNNNATIVE"
DNNModel *model = NULL;
// sizeof - 1 to skip the terminating '\0' which is not written in the file
char buf[sizeof(DNN_NATIVE_MAGIC) - 1];
int version, header_size, major_version_expected = 1;
NativeModel *native_model = NULL;
AVIOContext *model_file_context;
int file_size, dnn_size, parsed_size;
int32_t layer;
DNNLayerType layer_type;
if (avio_open(&model_file_context, model_filename, AVIO_FLAG_READ) < 0){
return NULL;
}
file_size = avio_size(model_file_context);
model = av_mallocz(sizeof(DNNModel));
if (!model){
goto fail;
}
/**
* check file header with string and version
*/
if (avio_read(model_file_context, buf, sizeof(buf)) != sizeof(buf) ||
memcmp(buf, DNN_NATIVE_MAGIC, sizeof(buf)))
goto fail;
dnn_size = sizeof(buf);
version = (int32_t)avio_rl32(model_file_context);
dnn_size += 4;
if (version != major_version_expected) {
goto fail;
}
// currently no need to check minor version
version = (int32_t)avio_rl32(model_file_context);
dnn_size += 4;
header_size = dnn_size;
native_model = av_mallocz(sizeof(NativeModel));
if (!native_model){
goto fail;
}
model->model = native_model;
native_model->ctx.class = &dnn_native_class;
model->options = options;
if (av_opt_set_from_string(&native_model->ctx, model->options, NULL, "=", "&") < 0)
goto fail;
native_model->model = model;
if (native_model->ctx.options.async) {
av_log(&native_model->ctx, AV_LOG_WARNING, "Async not supported. Rolling back to sync\n");
native_model->ctx.options.async = 0;
}
#if !HAVE_PTHREAD_CANCEL
if (native_model->ctx.options.conv2d_threads > 1){
av_log(&native_model->ctx, AV_LOG_WARNING, "'conv2d_threads' option was set but it is not supported "
"on this build (pthread support is required)\n");
}
#endif
avio_seek(model_file_context, file_size - 8, SEEK_SET);
native_model->layers_num = (int32_t)avio_rl32(model_file_context);
native_model->operands_num = (int32_t)avio_rl32(model_file_context);
dnn_size += 8;
avio_seek(model_file_context, header_size, SEEK_SET);
native_model->layers = av_mallocz(native_model->layers_num * sizeof(Layer));
if (!native_model->layers){
goto fail;
}
native_model->operands = av_mallocz(native_model->operands_num * sizeof(DnnOperand));
if (!native_model->operands){
goto fail;
}
native_model->task_queue = ff_queue_create();
if (!native_model->task_queue) {
goto fail;
}
native_model->lltask_queue = ff_queue_create();
if (!native_model->lltask_queue) {
goto fail;
}
for (layer = 0; layer < native_model->layers_num; ++layer){
layer_type = (int32_t)avio_rl32(model_file_context);
dnn_size += 4;
if (layer_type >= DLT_COUNT) {
goto fail;
}
native_model->layers[layer].type = layer_type;
parsed_size = ff_layer_funcs[layer_type].pf_load(&native_model->layers[layer], model_file_context, file_size, native_model->operands_num);
if (!parsed_size) {
goto fail;
}
dnn_size += parsed_size;
}
for (int32_t i = 0; i < native_model->operands_num; ++i){
DnnOperand *oprd;
int32_t name_len;
int32_t operand_index = (int32_t)avio_rl32(model_file_context);
dnn_size += 4;
if (operand_index >= native_model->operands_num) {
goto fail;
}
oprd = &native_model->operands[operand_index];
name_len = (int32_t)avio_rl32(model_file_context);
dnn_size += 4;
avio_get_str(model_file_context, name_len, oprd->name, sizeof(oprd->name));
dnn_size += name_len;
oprd->type = (int32_t)avio_rl32(model_file_context);
dnn_size += 4;
oprd->data_type = (int32_t)avio_rl32(model_file_context);
dnn_size += 4;
for (int32_t dim = 0; dim < 4; ++dim) {
oprd->dims[dim] = (int32_t)avio_rl32(model_file_context);
dnn_size += 4;
}
if (oprd->type == DOT_INPUT && oprd->dims[0] != 1)
goto fail;
oprd->isNHWC = 1;
}
avio_closep(&model_file_context);
if (dnn_size != file_size){
ff_dnn_free_model_native(&model);
return NULL;
}
model->get_input = &get_input_native;
model->get_output = &get_output_native;
model->filter_ctx = filter_ctx;
model->func_type = func_type;
return model;
fail:
ff_dnn_free_model_native(&model);
avio_closep(&model_file_context);
return NULL;
}
static int execute_model_native(Queue *lltask_queue)
{
NativeModel *native_model = NULL;
NativeContext *ctx = NULL;
int32_t layer;
DNNData input, output;
DnnOperand *oprd = NULL;
LastLevelTaskItem *lltask = NULL;
TaskItem *task = NULL;
int ret = 0;
lltask = ff_queue_pop_front(lltask_queue);
if (!lltask) {
av_log(NULL, AV_LOG_ERROR, "Failed to get LastLevelTaskItem\n");
ret = AVERROR(EINVAL);
goto err;
}
task = lltask->task;
native_model = task->model;
ctx = &native_model->ctx;
if (native_model->layers_num <= 0 || native_model->operands_num <= 0) {
av_log(ctx, AV_LOG_ERROR, "No operands or layers in model\n");
ret = AVERROR(EINVAL);
goto err;
}
for (int i = 0; i < native_model->operands_num; ++i) {
oprd = &native_model->operands[i];
if (strcmp(oprd->name, task->input_name) == 0) {
if (oprd->type != DOT_INPUT) {
av_log(ctx, AV_LOG_ERROR, "Found \"%s\" in model, but it is not input node\n", task->input_name);
ret = AVERROR(EINVAL);
goto err;
}
break;
}
oprd = NULL;
}
if (!oprd) {
av_log(ctx, AV_LOG_ERROR, "Could not find \"%s\" in model\n", task->input_name);
ret = AVERROR(EINVAL);
goto err;
}
oprd->dims[1] = task->in_frame->height;
oprd->dims[2] = task->in_frame->width;
av_freep(&oprd->data);
oprd->length = ff_calculate_operand_data_length(oprd);
if (oprd->length <= 0) {
av_log(ctx, AV_LOG_ERROR, "The input data length overflow\n");
ret = AVERROR(EINVAL);
goto err;
}
oprd->data = av_malloc(oprd->length);
if (!oprd->data) {
av_log(ctx, AV_LOG_ERROR, "Failed to malloc memory for input data\n");
ret = AVERROR(ENOMEM);
goto err;
}
input.height = oprd->dims[1];
input.width = oprd->dims[2];
input.channels = oprd->dims[3];
input.data = oprd->data;
input.dt = oprd->data_type;
if (task->do_ioproc) {
if (native_model->model->frame_pre_proc != NULL) {
native_model->model->frame_pre_proc(task->in_frame, &input, native_model->model->filter_ctx);
} else {
ff_proc_from_frame_to_dnn(task->in_frame, &input, ctx);
}
}
if (task->nb_output != 1) {
// currently, the filter does not need multiple outputs,
// so we just pending the support until we really need it.
avpriv_report_missing_feature(ctx, "multiple outputs");
ret = AVERROR(ENOSYS);
goto err;
}
for (layer = 0; layer < native_model->layers_num; ++layer){
DNNLayerType layer_type = native_model->layers[layer].type;
ret = ff_layer_funcs[layer_type].pf_exec(native_model->operands,
native_model->layers[layer].input_operand_indexes,
native_model->layers[layer].output_operand_index,
native_model->layers[layer].params,
&native_model->ctx);
if (ret != 0) {
av_log(ctx, AV_LOG_ERROR, "Failed to execute model\n");
goto err;
}
}
for (uint32_t i = 0; i < task->nb_output; ++i) {
DnnOperand *oprd = NULL;
const char *output_name = task->output_names[i];
for (int j = 0; j < native_model->operands_num; ++j) {
if (strcmp(native_model->operands[j].name, output_name) == 0) {
oprd = &native_model->operands[j];
break;
}
}
if (oprd == NULL) {
av_log(ctx, AV_LOG_ERROR, "Could not find output in model\n");
ret = AVERROR(EINVAL);
goto err;
}
output.data = oprd->data;
output.height = oprd->dims[1];
output.width = oprd->dims[2];
output.channels = oprd->dims[3];
output.dt = oprd->data_type;
if (task->do_ioproc) {
if (native_model->model->frame_post_proc != NULL) {
native_model->model->frame_post_proc(task->out_frame, &output, native_model->model->filter_ctx);
} else {
ff_proc_from_dnn_to_frame(task->out_frame, &output, ctx);
}
} else {
task->out_frame->width = output.width;
task->out_frame->height = output.height;
}
}
task->inference_done++;
err:
av_freep(&lltask);
return ret;
}
int ff_dnn_execute_model_native(const DNNModel *model, DNNExecBaseParams *exec_params)
{
NativeModel *native_model = model->model;
NativeContext *ctx = &native_model->ctx;
TaskItem *task;
int ret = 0;
ret = ff_check_exec_params(ctx, DNN_NATIVE, model->func_type, exec_params);
if (ret != 0) {
return ret;
}
task = av_malloc(sizeof(*task));
if (!task) {
av_log(ctx, AV_LOG_ERROR, "unable to alloc memory for task item.\n");
return AVERROR(ENOMEM);
}
ret = ff_dnn_fill_task(task, exec_params, native_model, ctx->options.async, 1);
if (ret != 0) {
av_freep(&task);
return ret;
}
if (ff_queue_push_back(native_model->task_queue, task) < 0) {
av_freep(&task);
av_log(ctx, AV_LOG_ERROR, "unable to push back task_queue.\n");
return AVERROR(ENOMEM);
}
ret = extract_lltask_from_task(task, native_model->lltask_queue);
if (ret != 0) {
av_log(ctx, AV_LOG_ERROR, "unable to extract last level task from task.\n");
return ret;
}
return execute_model_native(native_model->lltask_queue);
}
int ff_dnn_flush_native(const DNNModel *model)
{
NativeModel *native_model = model->model;
if (ff_queue_size(native_model->lltask_queue) == 0) {
// no pending task need to flush
return 0;
}
// for now, use sync node with flush operation
// Switch to async when it is supported
return execute_model_native(native_model->lltask_queue);
}
DNNAsyncStatusType ff_dnn_get_result_native(const DNNModel *model, AVFrame **in, AVFrame **out)
{
NativeModel *native_model = model->model;
return ff_dnn_get_result_common(native_model->task_queue, in, out);
}
int32_t ff_calculate_operand_dims_count(const DnnOperand *oprd)
{
int32_t result = 1;
for (int i = 0; i < 4; ++i)
result *= oprd->dims[i];
return result;
}
int32_t ff_calculate_operand_data_length(const DnnOperand* oprd)
{
// currently, we just support DNN_FLOAT
uint64_t len = sizeof(float);
for (int i = 0; i < 4; i++) {
len *= oprd->dims[i];
if (len > INT32_MAX)
return 0;
}
return len;
}
void ff_dnn_free_model_native(DNNModel **model)
{
NativeModel *native_model;
ConvolutionalParams *conv_params;
int32_t layer;
if (*model)
{
if ((*model)->model) {
native_model = (*model)->model;
if (native_model->layers) {
for (layer = 0; layer < native_model->layers_num; ++layer){
if (native_model->layers[layer].type == DLT_CONV2D){
conv_params = (ConvolutionalParams *)native_model->layers[layer].params;
av_freep(&conv_params->kernel);
av_freep(&conv_params->biases);
}
av_freep(&native_model->layers[layer].params);
}
av_freep(&native_model->layers);
}
if (native_model->operands) {
for (uint32_t operand = 0; operand < native_model->operands_num; ++operand)
av_freep(&native_model->operands[operand].data);
av_freep(&native_model->operands);
}
while (ff_queue_size(native_model->lltask_queue) != 0) {
LastLevelTaskItem *item = ff_queue_pop_front(native_model->lltask_queue);
av_freep(&item);
}
ff_queue_destroy(native_model->lltask_queue);
while (ff_queue_size(native_model->task_queue) != 0) {
TaskItem *item = ff_queue_pop_front(native_model->task_queue);
av_frame_free(&item->in_frame);
av_frame_free(&item->out_frame);
av_freep(&item);
}
ff_queue_destroy(native_model->task_queue);
av_freep(&native_model);
}
av_freep(model);
}
}

149
libavfilter/dnn/dnn_backend_native.h
View File

@ -1,149 +0,0 @@
/*
* Copyright (c) 2018 Sergey Lavrushkin
*
* This file is part of FFmpeg.
*
* FFmpeg is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public
* License as published by the Free Software Foundation; either
* version 2.1 of the License, or (at your option) any later version.
*
* FFmpeg is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with FFmpeg; if not, write to the Free Software
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
*/
/**
* @file
* DNN inference functions interface for native backend.
*/
#ifndef AVFILTER_DNN_DNN_BACKEND_NATIVE_H
#define AVFILTER_DNN_DNN_BACKEND_NATIVE_H
#include "../dnn_interface.h"
#include "libavformat/avio.h"
#include "libavutil/opt.h"
#include "queue.h"
/**
* the enum value of DNNLayerType should not be changed,
* the same values are used in convert_from_tensorflow.py
* and, it is used to index the layer execution/load function pointer.
*/
typedef enum {
DLT_INPUT = 0,
DLT_CONV2D = 1,
DLT_DEPTH_TO_SPACE = 2,
DLT_MIRROR_PAD = 3,
DLT_MAXIMUM = 4,
DLT_MATH_BINARY = 5,
DLT_MATH_UNARY = 6,
DLT_AVG_POOL = 7,
DLT_DENSE = 8,
DLT_COUNT
} DNNLayerType;
typedef enum {DOT_INPUT = 1, DOT_OUTPUT = 2, DOT_INTERMEDIATE = DOT_INPUT | DOT_OUTPUT} DNNOperandType;
typedef enum {VALID, SAME, SAME_CLAMP_TO_EDGE} DNNPaddingParam;
typedef enum {RELU, TANH, SIGMOID, NONE, LEAKY_RELU} DNNActivationFunc;
typedef struct Layer{
DNNLayerType type;
/**
* 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 InputParams{
int height, width, channels;
} InputParams;
typedef struct NativeOptions{
uint8_t async;
uint32_t conv2d_threads;
} NativeOptions;
typedef struct NativeContext {
const AVClass *class;
NativeOptions options;
} NativeContext;
// Represents simple feed-forward convolutional network.
typedef struct NativeModel{
NativeContext ctx;
DNNModel *model;
Layer *layers;
int32_t layers_num;
DnnOperand *operands;
int32_t operands_num;
Queue *task_queue;
Queue *lltask_queue;
} NativeModel;
DNNModel *ff_dnn_load_model_native(const char *model_filename, DNNFunctionType func_type, const char *options, AVFilterContext *filter_ctx);
int ff_dnn_execute_model_native(const DNNModel *model, DNNExecBaseParams *exec_params);
DNNAsyncStatusType ff_dnn_get_result_native(const DNNModel *model, AVFrame **in, AVFrame **out);
int ff_dnn_flush_native(const DNNModel *model);
void ff_dnn_free_model_native(DNNModel **model);
// NOTE: User must check for error (return value <= 0) to handle
// case like integer overflow.
int32_t ff_calculate_operand_data_length(const DnnOperand *oprd);
int32_t ff_calculate_operand_dims_count(const DnnOperand *oprd);
#endif

147
libavfilter/dnn/dnn_backend_native_layer_avgpool.c
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@ -1,147 +0,0 @@
/*
* Copyright (c) 2020
*
* This file is part of FFmpeg.
*
* FFmpeg is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public
* License as published by the Free Software Foundation; either
* version 2.1 of the License, or (at your option) any later version.
*
* FFmpeg is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with FFmpeg; if not, write to the Free Software
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
*/
/**
* @file
* DNN native backend implementation.
*/
#include "libavutil/avassert.h"
#include "dnn_backend_native_layer_avgpool.h"
int ff_dnn_load_layer_avg_pool(Layer *layer, AVIOContext *model_file_context, int file_size, int operands_num)
{
AvgPoolParams *avgpool_params;
int dnn_size = 0;
avgpool_params = av_malloc(sizeof(*avgpool_params));
if(!avgpool_params)
return 0;
avgpool_params->strides = (int32_t)avio_rl32(model_file_context);
avgpool_params->padding_method = (int32_t)avio_rl32(model_file_context);
avgpool_params->kernel_size = (int32_t)avio_rl32(model_file_context);
dnn_size += 12;
if (dnn_size > file_size || avgpool_params->kernel_size <= 0 || avgpool_params->strides <=0){
av_freep(&avgpool_params);
return 0;
}
layer->params = avgpool_params;
layer->input_operand_indexes[0] = (int32_t)avio_rl32(model_file_context);
layer->output_operand_index = (int32_t)avio_rl32(model_file_context);
dnn_size += 8;
if (layer->input_operand_indexes[0] >= operands_num || layer->output_operand_index >= operands_num) {
return 0;
}
return dnn_size;
}
int ff_dnn_execute_layer_avg_pool(DnnOperand *operands, const int32_t *input_operand_indexes,
int32_t output_operand_index, const void *parameters, NativeContext *ctx)
{
float *output;
int height_end, width_end, height_radius, width_radius, output_height, output_width, kernel_area;
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;
const AvgPoolParams *avgpool_params = parameters;
int kernel_strides = avgpool_params->strides;
int src_linesize = width * channel;
DnnOperand *output_operand = &operands[output_operand_index];
/**
* When padding_method = SAME, the tensorflow will only padding the hald number of 0 pixels
* except the remainders.
* Eg: assuming the input height = 1080, the strides = 11, so the remainders = 1080 % 11 = 2
* and if ksize = 5: it will fill (5 - 2) >> 1 = 1 line before the first line of input image,
* and 5 - 2 - 1 = 2 lines after the last line of input image.
* and if ksize = 7: it will fill (7 - 2) >> 1 = 2 lines before the first line of input image,
* and 7 - 2 - 2 = 3 lines after the last line of input image.
*/
if (avgpool_params->padding_method == SAME) {
height_end = height;
width_end = width;
height_radius = avgpool_params->kernel_size - ((height - 1) % kernel_strides + 1);
width_radius = avgpool_params->kernel_size - ((width - 1) % kernel_strides + 1);
height_radius = height_radius < 0 ? 0 : height_radius >> 1;
width_radius = width_radius < 0 ? 0 : width_radius >> 1;
output_height = ceil(height / (kernel_strides * 1.0));
output_width = ceil(width / (kernel_strides * 1.0));
} else {
av_assert0(avgpool_params->padding_method == VALID);
height_end = height - avgpool_params->kernel_size + 1;
width_end = width - avgpool_params->kernel_size + 1;
height_radius = 0;
width_radius = 0;
output_height = ceil((height - avgpool_params->kernel_size + 1) / (kernel_strides * 1.0));
output_width = ceil((width - avgpool_params->kernel_size + 1) / (kernel_strides * 1.0));
}
output_operand->dims[0] = number;
output_operand->dims[1] = output_height;
output_operand->dims[2] = output_width;
// not support pooling in channel dimension now
output_operand->dims[3] = channel;
output_operand->data_type = operands[input_operand_index].data_type;
output_operand->length = ff_calculate_operand_data_length(output_operand);
if (output_operand->length <= 0) {
av_log(ctx, AV_LOG_ERROR, "The output data length overflow\n");
return AVERROR(EINVAL);
}
output_operand->data = av_realloc(output_operand->data, output_operand->length);
if (!output_operand->data) {
av_log(ctx, AV_LOG_ERROR, "Failed to reallocate memory for output\n");
return AVERROR(ENOMEM);
}
output = output_operand->data;
for (int y = 0; y < height_end; y += kernel_strides) {
for (int x = 0; x < width_end; x += kernel_strides) {
for (int n_channel = 0; n_channel < channel; ++n_channel) {
output[n_channel] = 0.0;
kernel_area = 0;
for (int kernel_y = 0; kernel_y < avgpool_params->kernel_size; ++kernel_y) {
for (int kernel_x = 0; kernel_x < avgpool_params->kernel_size; ++kernel_x) {
float input_pel;
int y_pos = y + (kernel_y - height_radius);
int x_pos = x + (kernel_x - width_radius);
if (x_pos < 0 || x_pos >= width || y_pos < 0 || y_pos >= height) {
input_pel = 0.0;
} else {
kernel_area++;
input_pel = input[y_pos * src_linesize + x_pos * channel + n_channel];
}
output[n_channel] += input_pel;
}
}
output[n_channel] /= kernel_area;
}
output += channel;
}
}
return 0;
}

69
libavfilter/dnn/dnn_backend_native_layer_avgpool.h
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@ -1,69 +0,0 @@
/*
* Copyright (c) 2020
*
* This file is part of FFmpeg.
*
* FFmpeg is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public
* License as published by the Free Software Foundation; either
* version 2.1 of the License, or (at your option) any later version.
*
* FFmpeg is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with FFmpeg; if not, write to the Free Software
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
*/
/**
* @file
* DNN inference functions interface for native backend.
*/
#ifndef AVFILTER_DNN_DNN_BACKEND_NATIVE_LAYER_AVGPOOL_H
#define AVFILTER_DNN_DNN_BACKEND_NATIVE_LAYER_AVGPOOL_H
#include "dnn_backend_native.h"
typedef struct AvgPoolParams{
int32_t strides, kernel_size;
DNNPaddingParam padding_method;
} AvgPoolParams;
/**
* @brief Load Average Pooling Layer.
*
* It assigns the Average Pooling layer with AvgPoolParams
* after parsing from the model file context.
*
* @param layer pointer to the DNN layer instance
* @param model_file_context pointer to model file context
* @param file_size model file size to check if data is read
* correctly from the model file
* @param operands_num operand count of the whole model to
* check if data is read correctly from the model file
* @return number of bytes read from the model file
* @retval 0 if out of memory or an error occurs
*/
int ff_dnn_load_layer_avg_pool(Layer *layer, AVIOContext *model_file_context, int file_size, int operands_num);
/**
* @brief Execute the Average Pooling Layer.
* Padding in channel dimensions is currently not supported.
*
* @param operands all operands for the model
* @param input_operand_indexes input operand indexes for this layer
* @param output_operand_index output operand index for this layer
* @param parameters average pooling parameters
* @param ctx pointer to Native model context for logging
* @retval 0 if the execution succeeds
* @retval AVERROR(ENOMEM) if memory allocation fails
* @retval AVERROR(EINVAL) for invalid arguments
*/
int ff_dnn_execute_layer_avg_pool(DnnOperand *operands, const int32_t *input_operand_indexes,
int32_t output_operand_index, const void *parameters, NativeContext *ctx);
#endif

265
libavfilter/dnn/dnn_backend_native_layer_conv2d.c
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@ -1,265 +0,0 @@
/*
* Copyright (c) 2018 Sergey Lavrushkin
*
* This file is part of FFmpeg.
*
* FFmpeg is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public
* License as published by the Free Software Foundation; either
* version 2.1 of the License, or (at your option) any later version.
*
* FFmpeg is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with FFmpeg; if not, write to the Free Software
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
*/
#include "libavutil/avassert.h"
#include "libavutil/thread.h"
#include "libavutil/cpu.h"
#include "dnn_backend_native_layer_conv2d.h"
#define CLAMP_TO_EDGE(x, w) ((x) < 0 ? 0 : ((x) >= (w) ? (w - 1) : (x)))
//struct to pass parameters
typedef struct ThreadCommonParam{
DnnOperand *operands;
const int32_t *input_operand_indexes;
int32_t output_operand_index;
const void *parameters;
NativeContext *ctx;
float *output_data;
} ThreadCommonParam;
typedef struct ThreadParam{
ThreadCommonParam *thread_common_param;
int thread_start, thread_end;
#if HAVE_PTHREAD_CANCEL
pthread_t thread;
#endif
} ThreadParam;
int ff_dnn_load_layer_conv2d(Layer *layer, AVIOContext *model_file_context, int file_size, int operands_num)
{
ConvolutionalParams *conv_params;
int kernel_size;
int dnn_size = 0;
conv_params = av_malloc(sizeof(*conv_params));
if (!conv_params)
return 0;
conv_params->dilation = (int32_t)avio_rl32(model_file_context);
conv_params->padding_method = (int32_t)avio_rl32(model_file_context);
conv_params->activation = (int32_t)avio_rl32(model_file_context);
conv_params->input_num = (int32_t)avio_rl32(model_file_context);
conv_params->output_num = (int32_t)avio_rl32(model_file_context);
conv_params->kernel_size = (int32_t)avio_rl32(model_file_context);
conv_params->has_bias = (int32_t)avio_rl32(model_file_context);
dnn_size += 28;
kernel_size = conv_params->input_num * conv_params->output_num *
conv_params->kernel_size * conv_params->kernel_size;
dnn_size += kernel_size * 4;
if (conv_params->has_bias)
dnn_size += conv_params->output_num * 4;
if (dnn_size > file_size || conv_params->input_num <= 0 ||
conv_params->output_num <= 0 || conv_params->kernel_size <= 0){
av_freep(&conv_params);
return 0;
}
conv_params->kernel = av_malloc_array(kernel_size, sizeof(*conv_params->kernel));
if (!conv_params->kernel) {
av_freep(&conv_params);
return 0;
}
for (int i = 0; i < kernel_size; ++i) {
conv_params->kernel[i] = av_int2float(avio_rl32(model_file_context));
}
conv_params->biases = NULL;
if (conv_params->has_bias) {
conv_params->biases = av_malloc_array(conv_params->output_num, sizeof(*conv_params->biases));
if (!conv_params->biases){
av_freep(&conv_params->kernel);
av_freep(&conv_params);
return 0;
}
for (int i = 0; i < conv_params->output_num; ++i){
conv_params->biases[i] = av_int2float(avio_rl32(model_file_context));
}
}
layer->params = conv_params;
layer->input_operand_indexes[0] = (int32_t)avio_rl32(model_file_context);
layer->output_operand_index = (int32_t)avio_rl32(model_file_context);
dnn_size += 8;
if (layer->input_operand_indexes[0] >= operands_num || layer->output_operand_index >= operands_num) {
return 0;
}
return dnn_size;
}
static void * dnn_execute_layer_conv2d_thread(void *threadarg)
{
//pass parameters
ThreadParam *thread_param = threadarg;
ThreadCommonParam *thread_common_param = thread_param->thread_common_param;
DnnOperand *operands = thread_common_param->operands;
int32_t input_operand_index = thread_common_param->input_operand_indexes[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;
const ConvolutionalParams *conv_params = thread_common_param->parameters;
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;
float *output = thread_common_param->output_data;
output += (conv_params->output_num) * (width - 2 * pad_size) * (thread_param->thread_start - pad_size);
av_assert0(channel == conv_params->input_num);
for (int y = thread_param->thread_start; y < thread_param->thread_end; ++y) {
for (int x = pad_size; x < width - pad_size; ++x) {
for (int n_filter = 0; n_filter < conv_params->output_num; ++n_filter) {
if (conv_params->has_bias)
output[n_filter] = conv_params->biases[n_filter];
else
output[n_filter] = 0.f;
for (int ch = 0; ch < conv_params->input_num; ++ch) {
for (int kernel_y = 0; kernel_y < conv_params->kernel_size; ++kernel_y) {
for (int kernel_x = 0; kernel_x < conv_params->kernel_size; ++kernel_x) {
float input_pel;
if (conv_params->padding_method == SAME_CLAMP_TO_EDGE) {
int y_pos = CLAMP_TO_EDGE(y + (kernel_y - radius) * conv_params->dilation, height);
int x_pos = CLAMP_TO_EDGE(x + (kernel_x - radius) * conv_params->dilation, width);
input_pel = input[y_pos * src_linesize + x_pos * conv_params->input_num + ch];
} else {
int y_pos = y + (kernel_y - radius) * conv_params->dilation;
int x_pos = x + (kernel_x - radius) * conv_params->dilation;
input_pel = (x_pos < 0 || x_pos >= width || y_pos < 0 || y_pos >= height) ? 0.0 :
input[y_pos * src_linesize + x_pos * conv_params->input_num + ch];
}
output[n_filter] += input_pel * conv_params->kernel[n_filter * filter_size + kernel_y * filter_linesize +
kernel_x * conv_params->input_num + ch];
}
}
}
switch (conv_params->activation){
case RELU:
output[n_filter] = FFMAX(output[n_filter], 0.0);
break;
case TANH:
output[n_filter] = 2.0f / (1.0f + exp(-2.0f * output[n_filter])) - 1.0f;
break;
case SIGMOID:
output[n_filter] = 1.0f / (1.0f + exp(-output[n_filter]));
break;
case NONE:
break;
case LEAKY_RELU:
output[n_filter] = FFMAX(output[n_filter], 0.0) + 0.2 * FFMIN(output[n_filter], 0.0);
}
}
output += conv_params->output_num;
}
}
return NULL;
}
int ff_dnn_execute_layer_conv2d(DnnOperand *operands, const int32_t *input_operand_indexes,
int32_t output_operand_index, const void *parameters, NativeContext *ctx)
{
#if HAVE_PTHREAD_CANCEL
int thread_num = (ctx->options.conv2d_threads <= 0 || ctx->options.conv2d_threads > av_cpu_count())
? (av_cpu_count() + 1) : (ctx->options.conv2d_threads);
int ret = 0, thread_stride;
ThreadParam *thread_param;
#else
ThreadParam thread_param = { 0 };
#endif
ThreadCommonParam thread_common_param;
const ConvolutionalParams *conv_params = parameters;
int height = operands[input_operand_indexes[0]].dims[1];
int width = operands[input_operand_indexes[0]].dims[2];
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];
void *tmp;
output_operand->dims[0] = operands[input_operand_indexes[0]].dims[0];
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->data_type = operands[input_operand_indexes[0]].data_type;
output_operand->length = ff_calculate_operand_data_length(output_operand);
if (output_operand->length <= 0) {
av_log(ctx, AV_LOG_ERROR, "The output data length overflow\n");
return AVERROR(EINVAL);
}
tmp = av_realloc(output_operand->data, output_operand->length);
if (!tmp) {
av_log(ctx, AV_LOG_ERROR, "Failed to reallocate memory for output\n");
return AVERROR(ENOMEM);
}
output_operand->data = tmp;
thread_common_param.output_data = output_operand->data;
thread_common_param.operands = operands;
thread_common_param.input_operand_indexes = input_operand_indexes;
thread_common_param.output_operand_index = output_operand_index;
thread_common_param.parameters = parameters;
thread_common_param.ctx = ctx;
#if HAVE_PTHREAD_CANCEL
thread_param = av_malloc_array(thread_num, sizeof(*thread_param));
if (!thread_param)
return AVERROR(ENOMEM);
thread_stride = (height - pad_size * 2) / thread_num;
//create threads
for (int i = 0; i < thread_num; i++){
int thread_ret = 0;
thread_param[i].thread_common_param = &thread_common_param;
thread_param[i].thread_start = thread_stride * i + pad_size;
thread_param[i].thread_end = (i == thread_num - 1) ? (height - pad_size) : (thread_param[i].thread_start + thread_stride);
thread_ret = pthread_create(&thread_param[i].thread, NULL,
dnn_execute_layer_conv2d_thread, &thread_param[i]);
if (thread_ret) {
thread_num = i;
ret = AVERROR(thread_ret);
break;
}
}
for (int i = 0; i < thread_num; i++){
pthread_join(thread_param[i].thread, NULL);
}
//release memory
av_freep(&thread_param);
return ret;
#else
thread_param.thread_common_param = &thread_common_param;
thread_param.thread_start = pad_size;
thread_param.thread_end = height - pad_size;
dnn_execute_layer_conv2d_thread(&thread_param);
return 0;
#endif
}

68
libavfilter/dnn/dnn_backend_native_layer_conv2d.h
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@ -1,68 +0,0 @@
/*
* Copyright (c) 2018 Sergey Lavrushkin
*
* This file is part of FFmpeg.
*
* FFmpeg is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public
* License as published by the Free Software Foundation; either
* version 2.1 of the License, or (at your option) any later version.
*
* FFmpeg is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with FFmpeg; if not, write to the Free Software
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
*/
#ifndef AVFILTER_DNN_DNN_BACKEND_NATIVE_LAYER_CONV2D_H
#define AVFILTER_DNN_DNN_BACKEND_NATIVE_LAYER_CONV2D_H
#include "dnn_backend_native.h"
typedef struct ConvolutionalParams{
int32_t input_num, output_num, kernel_size;
DNNActivationFunc activation;
DNNPaddingParam padding_method;
int32_t dilation;
int32_t has_bias;
float *kernel;
float *biases;
} ConvolutionalParams;
/**
* @brief Load the 2D Convolution Layer.
*
* It assigns the 2D convolution layer with ConvolutionalParams
* after parsing from the model file context.
*
* @param layer pointer to the DNN layer instance
* @param model_file_context pointer to model file context
* @param file_size model file size to check if data is read
* correctly from the model file
* @param operands_num operand count of the whole model to
* check if data is read correctly from the model file
* @return number of bytes read from the model file
* @retval 0 if out of memory or an error occurs
*/
int ff_dnn_load_layer_conv2d(Layer *layer, AVIOContext *model_file_context, int file_size, int operands_num);
/**
* @brief Execute the 2D Convolution Layer.
*
* @param operands all operands for the model
* @param input_operand_indexes input operand indexes for this layer
* @param output_operand_index output operand index for this layer
* @param parameters convolution parameters
* @param ctx pointer to Native model context for logging
* @retval 0 if the execution succeeds
* @retval AVERROR(ENOMEM) if memory allocation fails
* @retval AVERROR(EINVAL) for invalid arguments
*/
int ff_dnn_execute_layer_conv2d(DnnOperand *operands, const int32_t *input_operand_indexes,
int32_t output_operand_index, const void *parameters, NativeContext *ctx);
#endif

151
libavfilter/dnn/dnn_backend_native_layer_dense.c
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@ -1,151 +0,0 @@
/*
* Copyright (c) 2020
*
* This file is part of FFmpeg.
*
* FFmpeg is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public
* License as published by the Free Software Foundation; either
* version 2.1 of the License, or (at your option) any later version.
*
* FFmpeg is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with FFmpeg; if not, write to the Free Software
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
*/
#include "libavutil/avassert.h"
#include "dnn_backend_native_layer_dense.h"
int ff_dnn_load_layer_dense(Layer *layer, AVIOContext *model_file_context, int file_size, int operands_num)
{
DenseParams *dense_params;
int kernel_size;
int dnn_size = 0;
dense_params = av_malloc(sizeof(*dense_params));
if (!dense_params)
return 0;
dense_params->activation = (int32_t)avio_rl32(model_file_context);
dense_params->input_num = (int32_t)avio_rl32(model_file_context);
dense_params->output_num = (int32_t)avio_rl32(model_file_context);
dense_params->has_bias = (int32_t)avio_rl32(model_file_context);
dnn_size += 16;
kernel_size = dense_params->input_num * dense_params->output_num;
dnn_size += kernel_size * 4;
if (dense_params->has_bias)
dnn_size += dense_params->output_num * 4;
if (dnn_size > file_size || dense_params->input_num <= 0 ||
dense_params->output_num <= 0){
av_freep(&dense_params);
return 0;
}
dense_params->kernel = av_malloc(kernel_size * sizeof(float));
if (!dense_params->kernel) {
av_freep(&dense_params);
return 0;
}
for (int i = 0; i < kernel_size; ++i) {
dense_params->kernel[i] = av_int2float(avio_rl32(model_file_context));
}
dense_params->biases = NULL;
if (dense_params->has_bias) {
dense_params->biases = av_malloc(dense_params->output_num * sizeof(float));
if (!dense_params->biases){
av_freep(&dense_params->kernel);
av_freep(&dense_params);
return 0;
}
for (int i = 0; i < dense_params->output_num; ++i){
dense_params->biases[i] = av_int2float(avio_rl32(model_file_context));
}
}
layer->params = dense_params;
layer->input_operand_indexes[0] = (int32_t)avio_rl32(model_file_context);
layer->output_operand_index = (int32_t)avio_rl32(model_file_context);
dnn_size += 8;
if (layer->input_operand_indexes[0] >= operands_num || layer->output_operand_index >= operands_num) {
return 0;
}
return dnn_size;
}
int ff_dnn_execute_layer_dense(DnnOperand *operands, const int32_t *input_operand_indexes,
int32_t output_operand_index, const void *parameters, NativeContext *ctx)
{
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;
const DenseParams *dense_params = parameters;
int src_linesize = width * channel;
DnnOperand *output_operand = &operands[output_operand_index];
output_operand->dims[0] = number;
output_operand->dims[1] = height;
output_operand->dims[2] = width;
output_operand->dims[3] = dense_params->output_num;
output_operand->data_type = operands[input_operand_index].data_type;
output_operand->length = ff_calculate_operand_data_length(output_operand);
if (output_operand->length <= 0) {
av_log(ctx, AV_LOG_ERROR, "The output data length overflow\n");
return AVERROR(EINVAL);
}
output_operand->data = av_realloc(output_operand->data, output_operand->length);
if (!output_operand->data) {
av_log(ctx, AV_LOG_ERROR, "Failed to reallocate memory for output\n");
return AVERROR(ENOMEM);
}
output = output_operand->data;
av_assert0(channel == dense_params->input_num);
for (int y = 0; y < height; ++y) {
for (int x = 0; x < width; ++x) {
for (int n_filter = 0; n_filter < dense_params->output_num; ++n_filter) {
if (dense_params->has_bias)
output[n_filter] = dense_params->biases[n_filter];
else
output[n_filter] = 0.f;
for (int ch = 0; ch < dense_params->input_num; ++ch) {
float input_pel;
input_pel = input[y * src_linesize + x * dense_params->input_num + ch];
output[n_filter] += input_pel * dense_params->kernel[n_filter*dense_params->input_num + ch];
}
switch (dense_params->activation){
case RELU:
output[n_filter] = FFMAX(output[n_filter], 0.0);
break;
case TANH:
output[n_filter] = 2.0f / (1.0f + exp(-2.0f * output[n_filter])) - 1.0f;
break;
case SIGMOID:
output[n_filter] = 1.0f / (1.0f + exp(-output[n_filter]));
break;
case NONE:
break;
case LEAKY_RELU:
output[n_filter] = FFMAX(output[n_filter], 0.0) + 0.2 * FFMIN(output[n_filter], 0.0);
}
}
output += dense_params->output_num;
}
}
return 0;
}

65
libavfilter/dnn/dnn_backend_native_layer_dense.h
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@ -1,65 +0,0 @@
/*
* Copyright (c) 2020
*
* This file is part of FFmpeg.
*
* FFmpeg is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public
* License as published by the Free Software Foundation; either
* version 2.1 of the License, or (at your option) any later version.
*
* FFmpeg is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with FFmpeg; if not, write to the Free Software
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
*/
#ifndef AVFILTER_DNN_DNN_BACKEND_NATIVE_LAYER_DENSE_H
#define AVFILTER_DNN_DNN_BACKEND_NATIVE_LAYER_DENSE_H
#include "dnn_backend_native.h"
typedef struct DenseParams{
int32_t input_num, output_num;
DNNActivationFunc activation;
int32_t has_bias;
float *kernel;
float *biases;
} DenseParams;
/**
* @brief Load the Densely-Connected Layer.
*
* It assigns the densely connected layer with DenseParams
* after parsing from the model file context.
*
* @param layer pointer to the DNN layer instance
* @param model_file_context pointer to model file context
* @param file_size model file size to check if data is read
* correctly from the model file
* @param operands_num operand count of the whole model to
* check if data is read correctly from the model file
* @return number of bytes read from the model file
* @retval 0 if out of memory or an error occurs
*/
int ff_dnn_load_layer_dense(Layer *layer, AVIOContext *model_file_context, int file_size, int operands_num);
/**
* @brief Execute the Densely-Connected Layer.
*
* @param operands all operands for the model
* @param input_operand_indexes input operand indexes for this layer
* @param output_operand_index output operand index for this layer
* @param parameters dense layer parameters
* @param ctx pointer to Native model context for logging
* @retval 0 if the execution succeeds
* @retval AVERROR(ENOMEM) if memory allocation fails
* @retval AVERROR(EINVAL) for invalid arguments
*/
int ff_dnn_execute_layer_dense(DnnOperand *operands, const int32_t *input_operand_indexes,
int32_t output_operand_index, const void *parameters, NativeContext *ctx);
#endif

102
libavfilter/dnn/dnn_backend_native_layer_depth2space.c
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@ -1,102 +0,0 @@
/*
* Copyright (c) 2018 Sergey Lavrushkin
*
* This file is part of FFmpeg.
*
* FFmpeg is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public
* License as published by the Free Software Foundation; either
* version 2.1 of the License, or (at your option) any later version.
*
* FFmpeg is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with FFmpeg; if not, write to the Free Software
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
*/
/**
* @file
* DNN native backend implementation.
*/
#include "dnn_backend_native.h"
#include "dnn_backend_native_layer_depth2space.h"
int ff_dnn_load_layer_depth2space(Layer *layer, AVIOContext *model_file_context, int file_size, int operands_num)
{
DepthToSpaceParams *params;
int dnn_size = 0;
params = av_malloc(sizeof(*params));
if (!params)
return 0;
params->block_size = (int32_t)avio_rl32(model_file_context);
dnn_size += 4;
layer->input_operand_indexes[0] = (int32_t)avio_rl32(model_file_context);
layer->output_operand_index = (int32_t)avio_rl32(model_file_context);
dnn_size += 8;
layer->params = params;
if (layer->input_operand_indexes[0] >= operands_num || layer->output_operand_index >= operands_num) {
return 0;
}
return dnn_size;
}
int ff_dnn_execute_layer_depth2space(DnnOperand *operands, const int32_t *input_operand_indexes,
int32_t output_operand_index, const void *parameters, NativeContext *ctx)
{
float *output;
const DepthToSpaceParams *params = parameters;
int block_size = params->block_size;
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->data_type = operands[input_operand_index].data_type;
output_operand->length = ff_calculate_operand_data_length(output_operand);
if (output_operand->length <= 0) {
av_log(ctx, AV_LOG_ERROR, "The output data length overflow\n");
return AVERROR(EINVAL);
}
output_operand->data = av_realloc(output_operand->data, output_operand->length);
if (!output_operand->data) {
av_log(ctx, AV_LOG_ERROR, "Failed to reallocate memory for output\n");
return AVERROR(ENOMEM);
}
output = output_operand->data;
for (y = 0; y < height; ++y){
for (x = 0; x < width; ++x){
for (by = 0; by < block_size; ++by){
for (bx = 0; bx < block_size; ++bx){
for (ch = 0; ch < new_channels; ++ch){
output[by * by_linesize + x * x_linesize + bx * new_channels + ch] = input[ch];
}
input += new_channels;
}
}
}
output += output_linesize;
}
return 0;
}

72
libavfilter/dnn/dnn_backend_native_layer_depth2space.h
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@ -1,72 +0,0 @@
/*
* Copyright (c) 2018 Sergey Lavrushkin
*
* This file is part of FFmpeg.
*
* FFmpeg is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public
* License as published by the Free Software Foundation; either
* version 2.1 of the License, or (at your option) any later version.
*
* FFmpeg is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with FFmpeg; if not, write to the Free Software
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
*/
/**
* @file
* DNN inference functions interface for native backend.
*/
#ifndef AVFILTER_DNN_DNN_BACKEND_NATIVE_LAYER_DEPTH2SPACE_H
#define AVFILTER_DNN_DNN_BACKEND_NATIVE_LAYER_DEPTH2SPACE_H
#include "../dnn_interface.h"
#include "libavformat/avio.h"
typedef struct DepthToSpaceParams{
int block_size;
} DepthToSpaceParams;
/**
* @brief Load the Depth to Space Layer.
*
* It assigns the depth to space layer with DepthToSpaceParams
* after parsing from the model file context.
*
* @param layer pointer to the DNN layer instance
* @param model_file_context pointer to model file context
* @param file_size model file size to check if data is read
* correctly from the model file
* @param operands_num operand count of the whole model to
* check if data is read correctly from the model file
* @return number of bytes read from the model file
* @retval 0 if an error occurs or out of memory
*/
int ff_dnn_load_layer_depth2space(Layer *layer, AVIOContext *model_file_context, int file_size, int operands_num);
/**
* @brief Execute the Depth to Space Layer.
*
* It rearranges the input data from depth into spatial
* form by applying Depth to Space transformation.
*
* @param operands all operands for the model
* @param input_operand_indexes input operand indexes for this layer
* @param output_operand_index output operand index for this layer
* @param parameters depth to space layer parameters
* @param ctx pointer to Native model context for logging
* @retval 0 if the execution succeeds
* @retval AVERROR(ENOMEM) if memory allocation fails
* @retval AVERROR(EINVAL) for invalid arguments
*/
int ff_dnn_execute_layer_depth2space(DnnOperand *operands, const int32_t *input_operand_indexes,
int32_t output_operand_index, const void *parameters, NativeContext *ctx);
#endif

193
libavfilter/dnn/dnn_backend_native_layer_mathbinary.c
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@ -1,193 +0,0 @@
/*
* Copyright (c) 2020
*
* This file is part of FFmpeg.
*
* FFmpeg is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public
* License as published by the Free Software Foundation; either
* version 2.1 of the License, or (at your option) any later version.
*
* FFmpeg is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with FFmpeg; if not, write to the Free Software
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
*/
/**
* @file
* DNN native backend implementation.
*/
#include "dnn_backend_native.h"
#include "dnn_backend_native_layer_mathbinary.h"
typedef float (*FunType)(float src0, float src1);
static float sub(float src0, float src1)
{
return src0 - src1;
}
static float add(float src0, float src1)
{
return src0 + src1;
}
static float mul(float src0, float src1)
{
return src0 * src1;
}
static float realdiv(float src0, float src1)
{
return src0 / src1;
}
static float minimum(float src0, float src1)
{
return FFMIN(src0, src1);
}
static float floormod(float src0, float src1)
{
return (float)((int)(src0) % (int)(src1));
}
static void math_binary_commutative(FunType pfun, const DnnLayerMathBinaryParams *params, const DnnOperand *input, DnnOperand *output, DnnOperand *operands, const int32_t *input_operand_indexes)
{
int dims_count;
const float *src;
float *dst;
dims_count = ff_calculate_operand_dims_count(output);
src = input->data;
dst = output->data;
if (params->input0_broadcast || params->input1_broadcast) {
for (int i = 0; i < dims_count; ++i) {
dst[i] = pfun(params->v, src[i]);
}
} else {
const DnnOperand *input1 = &operands[input_operand_indexes[1]];
const float *src1 = input1->data;
for (int i = 0; i < dims_count; ++i) {
dst[i] = pfun(src[i], src1[i]);
}
}
}
static void math_binary_not_commutative(FunType pfun, const DnnLayerMathBinaryParams *params, const DnnOperand *input, DnnOperand *output, DnnOperand *operands, const int32_t *input_operand_indexes)
{
int dims_count;
const float *src;
float *dst;
dims_count = ff_calculate_operand_dims_count(output);
src = input->data;
dst = output->data;
if (params->input0_broadcast) {
for (int i = 0; i < dims_count; ++i) {
dst[i] = pfun(params->v, src[i]);
}
} else if (params->input1_broadcast) {
for (int i = 0; i < dims_count; ++i) {
dst[i] = pfun(src[i], params->v);
}
} else {
const DnnOperand *input1 = &operands[input_operand_indexes[1]];
const float *src1 = input1->data;
for (int i = 0; i < dims_count; ++i) {
dst[i] = pfun(src[i], src1[i]);
}
}
}
int ff_dnn_load_layer_math_binary(Layer *layer, AVIOContext *model_file_context, int file_size, int operands_num)
{
DnnLayerMathBinaryParams params = { 0 };
int dnn_size = 0;
int input_index = 0;
params.bin_op = (int32_t)avio_rl32(model_file_context);
dnn_size += 4;
params.input0_broadcast = (int32_t)avio_rl32(model_file_context);
dnn_size += 4;
if (params.input0_broadcast) {
params.v = av_int2float(avio_rl32(model_file_context));
} else {
layer->input_operand_indexes[input_index] = (int32_t)avio_rl32(model_file_context);
if (layer->input_operand_indexes[input_index] >= operands_num) {
return 0;
}
input_index++;
}
dnn_size += 4;
params.input1_broadcast = (int32_t)avio_rl32(model_file_context);
dnn_size += 4;
if (params.input1_broadcast) {
params.v = av_int2float(avio_rl32(model_file_context));
} else {
layer->input_operand_indexes[input_index] = (int32_t)avio_rl32(model_file_context);
if (layer->input_operand_indexes[input_index] >= operands_num) {
return 0;
}
input_index++;
}
dnn_size += 4;
layer->output_operand_index = (int32_t)avio_rl32(model_file_context);
dnn_size += 4;
if (layer->output_operand_index >= operands_num) {
return 0;
}
layer->params = av_memdup(&params, sizeof(params));
if (!layer->params)
return 0;
return dnn_size;
}
int ff_dnn_execute_layer_math_binary(DnnOperand *operands, const int32_t *input_operand_indexes,
int32_t output_operand_index, const void *parameters, NativeContext *ctx)
{
const DnnOperand *input = &operands[input_operand_indexes[0]];
DnnOperand *output = &operands[output_operand_index];
const DnnLayerMathBinaryParams *params = parameters;
for (int i = 0; i < 4; ++i)
output->dims[i] = input->dims[i];
output->data_type = input->data_type;
output->length = ff_calculate_operand_data_length(output);
if (output->length <= 0) {
av_log(ctx, AV_LOG_ERROR, "The output data length overflow\n");
return AVERROR(EINVAL);
}
output->data = av_realloc(output->data, output->length);
if (!output->data) {
av_log(ctx, AV_LOG_ERROR, "Failed to reallocate memory for output\n");
return AVERROR(ENOMEM);
}
switch (params->bin_op) {
case DMBO_SUB:
math_binary_not_commutative(sub, params, input, output, operands, input_operand_indexes);
return 0;
case DMBO_ADD:
math_binary_commutative(add, params, input, output, operands, input_operand_indexes);
return 0;
case DMBO_MUL:
math_binary_commutative(mul, params, input, output, operands, input_operand_indexes);
return 0;
case DMBO_REALDIV:
math_binary_not_commutative(realdiv, params, input, output, operands, input_operand_indexes);
return 0;
case DMBO_MINIMUM:
math_binary_commutative(minimum, params, input, output, operands, input_operand_indexes);
return 0;
case DMBO_FLOORMOD:
math_binary_not_commutative(floormod, params, input, output, operands, input_operand_indexes);
return 0;
default:
av_log(ctx, AV_LOG_ERROR, "Unmatch math binary operator\n");
return AVERROR(EINVAL);
}
}

54
libavfilter/dnn/dnn_backend_native_layer_mathbinary.h
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@ -1,54 +0,0 @@
/*
* Copyright (c) 2020
*
* This file is part of FFmpeg.
*
* FFmpeg is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public
* License as published by the Free Software Foundation; either
* version 2.1 of the License, or (at your option) any later version.
*
* FFmpeg is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with FFmpeg; if not, write to the Free Software
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
*/
/**
* @file
* DNN inference functions interface for native backend.
*/
#ifndef AVFILTER_DNN_DNN_BACKEND_NATIVE_LAYER_MATHBINARY_H
#define AVFILTER_DNN_DNN_BACKEND_NATIVE_LAYER_MATHBINARY_H
#include "libavformat/avio.h"
#include "dnn_backend_native.h"
typedef enum {
DMBO_SUB = 0,
DMBO_ADD = 1,
DMBO_MUL = 2,
DMBO_REALDIV = 3,
DMBO_MINIMUM = 4,
DMBO_FLOORMOD = 5,
DMBO_COUNT
} DNNMathBinaryOperation;
typedef struct DnnLayerMathBinaryParams{
DNNMathBinaryOperation bin_op;
int input0_broadcast;
int input1_broadcast;
float v;
} DnnLayerMathBinaryParams;
int ff_dnn_load_layer_math_binary(Layer *layer, AVIOContext *model_file_context, int file_size, int operands_num);
int ff_dnn_execute_layer_math_binary(DnnOperand *operands, const int32_t *input_operand_indexes,
int32_t output_operand_index, const void *parameters, NativeContext *ctx);
#endif

156
libavfilter/dnn/dnn_backend_native_layer_mathunary.c
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@ -1,156 +0,0 @@
/*
* Copyright (c) 2020
*
* This file is part of FFmpeg.
*
* FFmpeg is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public
* License as published by the Free Software Foundation; either
* version 2.1 of the License, or (at your option) any later version.
*
* FFmpeg is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with FFmpeg; if not, write to the Free Software
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
*/
/**
* @file
* DNN native backend implementation.
*/
#include <math.h>
#include "dnn_backend_native.h"
#include "dnn_backend_native_layer_mathunary.h"
int ff_dnn_load_layer_math_unary(Layer *layer, AVIOContext *model_file_context, int file_size, int operands_num)
{
DnnLayerMathUnaryParams *params;
int dnn_size = 0;
params = av_malloc(sizeof(*params));
if(!params)
return 0;
params->un_op = (int32_t)avio_rl32(model_file_context);
dnn_size += 4;
layer->params = params;
layer->input_operand_indexes[0] = (int32_t)avio_rl32(model_file_context);
layer->output_operand_index = (int32_t)avio_rl32(model_file_context);
dnn_size += 8;
if (layer->input_operand_indexes[0] >= operands_num || layer->output_operand_index >= operands_num) {
return 0;
}
return dnn_size;
}
int ff_dnn_execute_layer_math_unary(DnnOperand *operands, const int32_t *input_operand_indexes,
int32_t output_operand_index, const void *parameters, NativeContext *ctx)
{
const DnnOperand *input = &operands[input_operand_indexes[0]];
DnnOperand *output = &operands[output_operand_index];
const DnnLayerMathUnaryParams *params = parameters;
int dims_count;
const float *src;
float *dst;
for (int i = 0; i < 4; ++i)
output->dims[i] = input->dims[i];
output->data_type = input->data_type;
output->length = ff_calculate_operand_data_length(output);
if (output->length <= 0) {
av_log(ctx, AV_LOG_ERROR, "The output data length overflow\n");
return AVERROR(EINVAL);
}
output->data = av_realloc(output->data, output->length);
if (!output->data) {
av_log(ctx, AV_LOG_ERROR, "Failed to reallocate memory for output\n");
return AVERROR(ENOMEM);
}
dims_count = ff_calculate_operand_dims_count(output);
src = input->data;
dst = output->data;
switch (params->un_op) {
case DMUO_ABS:
for (int i = 0; i < dims_count; ++i)
dst[i] = FFABS(src[i]);
return 0;
case DMUO_SIN:
for (int i = 0; i < dims_count; ++i)
dst[i] = sin(src[i]);
return 0;
case DMUO_COS:
for (int i = 0; i < dims_count; ++i)
dst[i] = cos(src[i]);
return 0;
case DMUO_TAN:
for (int i = 0; i < dims_count; ++i)
dst[i] = tan(src[i]);
return 0;
case DMUO_ASIN:
for (int i = 0; i < dims_count; ++i)
dst[i] = asin(src[i]);
return 0;
case DMUO_ACOS:
for (int i = 0; i < dims_count; ++i)
dst[i] = acos(src[i]);
return 0;
case DMUO_ATAN:
for (int i = 0; i < dims_count; ++i)
dst[i] = atan(src[i]);
return 0;
case DMUO_SINH:
for (int i = 0; i < dims_count; ++i)
dst[i] = sinh(src[i]);
return 0;
case DMUO_COSH:
for (int i = 0; i < dims_count; ++i)
dst[i] = cosh(src[i]);
return 0;
case DMUO_TANH:
for (int i = 0; i < dims_count; ++i)
dst[i] = tanh(src[i]);
return 0;
case DMUO_ASINH:
for (int i = 0; i < dims_count; ++i)
dst[i] = asinh(src[i]);
return 0;
case DMUO_ACOSH:
for (int i = 0; i < dims_count; ++i)
dst[i] = acosh(src[i]);
return 0;
case DMUO_ATANH:
for (int i = 0; i < dims_count; ++i)
dst[i] = atanh(src[i]);
return 0;
case DMUO_CEIL:
for (int i = 0; i < dims_count; ++i)
dst[i] = ceil(src[i]);
return 0;
case DMUO_FLOOR:
for (int i = 0; i < dims_count; ++i)
dst[i] = floor(src[i]);
return 0;
case DMUO_ROUND:
for (int i = 0; i < dims_count; ++i)
dst[i] = round(src[i]);
return 0;
case DMUO_EXP:
for (int i = 0; i < dims_count; ++i)
dst[i] = exp(src[i]);
return 0;
default:
av_log(ctx, AV_LOG_ERROR, "Unmatch math unary operator\n");
return AVERROR(EINVAL);
}
}

92
libavfilter/dnn/dnn_backend_native_layer_mathunary.h
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@ -1,92 +0,0 @@
/*
* Copyright (c) 2020
*
* This file is part of FFmpeg.
*
* FFmpeg is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public
* License as published by the Free Software Foundation; either
* version 2.1 of the License, or (at your option) any later version.
*
* FFmpeg is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with FFmpeg; if not, write to the Free Software
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
*/
/**
* @file
* DNN inference functions interface for native backend.
*/
#ifndef AVFILTER_DNN_DNN_BACKEND_NATIVE_LAYER_MATHUNARY_H
#define AVFILTER_DNN_DNN_BACKEND_NATIVE_LAYER_MATHUNARY_H
#include "libavformat/avio.h"
#include "dnn_backend_native.h"
typedef enum {
DMUO_ABS = 0,
DMUO_SIN = 1,
DMUO_COS = 2,
DMUO_TAN = 3,
DMUO_ASIN = 4,
DMUO_ACOS = 5,
DMUO_ATAN = 6,
DMUO_SINH = 7,
DMUO_COSH = 8,
DMUO_TANH = 9,
DMUO_ASINH = 10,
DMUO_ACOSH = 11,
DMUO_ATANH = 12,
DMUO_CEIL = 13,
DMUO_FLOOR = 14,
DMUO_ROUND = 15,
DMUO_EXP = 16,
DMUO_COUNT
} DNNMathUnaryOperation;
typedef struct DnnLayerMathUnaryParams{
DNNMathUnaryOperation un_op;
} DnnLayerMathUnaryParams;
/**
* @brief Load the Unary Math Layer.
*
* It assigns the unary math layer with DnnLayerMathUnaryParams
* after parsing from the model file context.
*
* @param layer pointer to the DNN layer instance
* @param model_file_context pointer to model file context
* @param file_size model file size to check if data is read
* correctly from the model file
* @param operands_num operand count of the whole model to
* check if data is read correctly from the model file
* @return number of bytes read from the model file
* @retval 0 if out of memory or an error occurs
*/
int ff_dnn_load_layer_math_unary(Layer *layer, AVIOContext *model_file_context, int file_size, int operands_num);
/**
* @brief Execute the Unary Math Layer.
*
* It applies the unary operator parsed while
* loading to the given input operands.
*
* @param operands all operands for the model
* @param input_operand_indexes input operand indexes for this layer
* @param output_operand_index output operand index for this layer
* @param parameters unary math layer parameters
* @param ctx pointer to Native model context for logging
* @retval 0 if the execution succeeds
* @retval AVERROR(ENOMEM) if memory allocation fails
* @retval AVERROR(EINVAL) for invalid arguments
*/
int ff_dnn_execute_layer_math_unary(DnnOperand *operands, const int32_t *input_operand_indexes,
int32_t output_operand_index, const void *parameters, NativeContext *ctx);
#endif

83
libavfilter/dnn/dnn_backend_native_layer_maximum.c
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@ -1,83 +0,0 @@
/*
* Copyright (c) 2019 Guo Yejun
*
* This file is part of FFmpeg.
*
* FFmpeg is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public
* License as published by the Free Software Foundation; either
* version 2.1 of the License, or (at your option) any later version.
*
* FFmpeg is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with FFmpeg; if not, write to the Free Software
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
*/
/**
* @file
* DNN native backend implementation.
*/
#include "dnn_backend_native.h"
#include "dnn_backend_native_layer_maximum.h"
int ff_dnn_load_layer_maximum(Layer *layer, AVIOContext *model_file_context, int file_size, int operands_num)
{
DnnLayerMaximumParams *params;
int dnn_size = 0;
params = av_malloc(sizeof(*params));
if (!params)
return 0;
params->val.u32 = avio_rl32(model_file_context);
dnn_size += 4;
layer->params = params;
layer->input_operand_indexes[0] = (int32_t)avio_rl32(model_file_context);
layer->output_operand_index = (int32_t)avio_rl32(model_file_context);
dnn_size += 8;
if (layer->input_operand_indexes[0] >= operands_num || layer->output_operand_index >= operands_num) {
return 0;
}
return dnn_size;
}
int ff_dnn_execute_layer_maximum(DnnOperand *operands, const int32_t *input_operand_indexes,
int32_t output_operand_index, const void *parameters, NativeContext *ctx)
{
const DnnOperand *input = &operands[input_operand_indexes[0]];
DnnOperand *output = &operands[output_operand_index];
const DnnLayerMaximumParams *params = parameters;
int dims_count;
const float *src;
float *dst;
for (int i = 0; i < 4; ++i)
output->dims[i] = input->dims[i];
output->data_type = input->data_type;
output->length = ff_calculate_operand_data_length(output);
if (output->length <= 0) {
av_log(ctx, AV_LOG_ERROR, "The output data length overflow\n");
return AVERROR(EINVAL);
}
output->data = av_realloc(output->data, output->length);
if (!output->data) {
av_log(ctx, AV_LOG_ERROR, "Failed to reallocate memory for output\n");
return AVERROR(ENOMEM);
}
dims_count = ff_calculate_operand_dims_count(output);
src = input->data;
dst = output->data;
for (int i = 0; i < dims_count; ++i)
dst[i] = FFMAX(src[i], params->val.y);
return 0;
}

44
libavfilter/dnn/dnn_backend_native_layer_maximum.h
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@ -1,44 +0,0 @@
/*
* Copyright (c) 2019 Guo Yejun
*
* This file is part of FFmpeg.
*
* FFmpeg is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public
* License as published by the Free Software Foundation; either
* version 2.1 of the License, or (at your option) any later version.
*
* FFmpeg is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with FFmpeg; if not, write to the Free Software
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
*/
/**
* @file
* DNN inference functions interface for native backend.
*/
#ifndef AVFILTER_DNN_DNN_BACKEND_NATIVE_LAYER_MAXIMUM_H
#define AVFILTER_DNN_DNN_BACKEND_NATIVE_LAYER_MAXIMUM_H
#include "libavformat/avio.h"
#include "dnn_backend_native.h"
typedef struct DnnLayerMaximumParams{
union {
uint32_t u32;
float y;
}val;
} DnnLayerMaximumParams;
int ff_dnn_load_layer_maximum(Layer *layer, AVIOContext *model_file_context, int file_size, int operands_num);
int ff_dnn_execute_layer_maximum(DnnOperand *operands, const int32_t *input_operand_indexes,
int32_t output_operand_index, const void *parameters, NativeContext *ctx);
#endif

268
libavfilter/dnn/dnn_backend_native_layer_pad.c
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@ -1,268 +0,0 @@
/*
* Copyright (c) 2019 Guo Yejun
*
* This file is part of FFmpeg.
*
* FFmpeg is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public
* License as published by the Free Software Foundation; either
* version 2.1 of the License, or (at your option) any later version.
*
* FFmpeg is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with FFmpeg; if not, write to the Free Software
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
*/
#include <string.h>
#include "libavutil/avassert.h"
#include "dnn_backend_native_layer_pad.h"
int ff_dnn_load_layer_pad(Layer *layer, AVIOContext *model_file_context, int file_size, int operands_num)
{
LayerPadParams *params;
int dnn_size = 0;
params = av_malloc(sizeof(*params));
if (!params)
return 0;
params->mode = (int32_t)avio_rl32(model_file_context);
dnn_size += 4;
for (int i = 0; i < 4; ++i) {
params->paddings[i][0] = avio_rl32(model_file_context);
params->paddings[i][1] = avio_rl32(model_file_context);
dnn_size += 8;
}
layer->input_operand_indexes[0] = (int32_t)avio_rl32(model_file_context);
layer->output_operand_index = (int32_t)avio_rl32(model_file_context);
dnn_size += 8;
layer->params = params;
if (layer->input_operand_indexes[0] >= operands_num || layer->output_operand_index >= operands_num) {
return 0;
}
return dnn_size;
}
static int before_get_buddy(int given, int paddings, LayerPadModeParam mode)
{
if (mode == LPMP_SYMMETRIC) {
return (2 * paddings - 1 - given);
} else if (mode == LPMP_REFLECT) {
return (2 * paddings - given);
} else {
av_assert0(!"should not reach here");
return 0;
}
}
static int after_get_buddy(int given, int border, LayerPadModeParam mode)
{
if (mode == LPMP_SYMMETRIC) {
int offset = given - border;
return (border - 1 - offset);
} else if (mode == LPMP_REFLECT) {
int offset = given - border;
return (border - 2 - offset);
} else {
av_assert0(!"should not reach here");
return 0;
}
}
int ff_dnn_execute_layer_pad(DnnOperand *operands, const int32_t *input_operand_indexes,
int32_t output_operand_index, const void *parameters, NativeContext *ctx)
{
int32_t before_paddings;
int32_t after_paddings;
float* output;
const LayerPadParams *params = parameters;
// 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];
int new_channel = channel + params->paddings[3][0] + params->paddings[3][1];
int c_stride = channel;
int wc_stride = c_stride * width;
int hwc_stride = wc_stride * height;
int new_c_stride = new_channel;
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->data_type = operands[input_operand_index].data_type;
output_operand->length = ff_calculate_operand_data_length(output_operand);
if (output_operand->length <= 0) {
av_log(ctx, AV_LOG_ERROR, "The output data length overflow\n");
return AVERROR(EINVAL);
}
output_operand->data = av_realloc(output_operand->data, output_operand->length);
if (!output_operand->data) {
av_log(ctx, AV_LOG_ERROR, "Failed to reallocate memory for output\n");
return AVERROR(ENOMEM);
}
output = output_operand->data;
// copy the original data
for (int n = 0; n < number; n++) {
for (int h = 0; h < height; h++) {
for (int w = 0; w < width; w++) {
const float *src = input + n * hwc_stride + h * wc_stride + w * c_stride;
float *dst = output + (n + params->paddings[0][0]) * new_hwc_stride
+ (h + params->paddings[1][0]) * new_wc_stride
+ (w + params->paddings[2][0]) * new_c_stride
+ params->paddings[3][0];
memcpy(dst, src, channel * sizeof(float));
}
}
}
// handle the first dimension
before_paddings = params->paddings[0][0];
after_paddings = params->paddings[0][1];
for (int n = 0; n < before_paddings; n++) {
float *dst = output + n * new_hwc_stride;
if (params->mode == LPMP_CONSTANT) {
for (int i = 0; i < new_hwc_stride; i++) {
dst[i] = params->constant_values;
}
}
else {
int buddy = before_get_buddy(n, before_paddings, params->mode);
float *src = output + buddy * new_hwc_stride;
memcpy(dst, src, new_hwc_stride * sizeof(float));
}
}
for (int n = 0; n < after_paddings; n++) {
int given = number + before_paddings + n;
float *dst = output + given * new_hwc_stride;
if (params->mode == LPMP_CONSTANT) {
for (int i = 0; i < new_hwc_stride; i++) {
dst[i] = params->constant_values;
}
} else {
int buddy = after_get_buddy(given, number + before_paddings, params->mode);
float *src = output + buddy * new_hwc_stride;
memcpy(dst, src, new_hwc_stride * sizeof(float));
}
}
// handle the second dimension
before_paddings = params->paddings[1][0];
after_paddings = params->paddings[1][1];
for (int n = 0; n < new_number; n++) {
float *start = output + n * new_hwc_stride;
for (int h = 0; h < before_paddings; h++) {
float *dst = start + h * new_wc_stride;
if (params->mode == LPMP_CONSTANT) {
for (int i = 0; i < new_wc_stride; i++) {
dst[i] = params->constant_values;
}
} else {
int buddy = before_get_buddy(h, before_paddings, params->mode);
float *src = start + buddy * new_wc_stride;
memcpy(dst, src, new_wc_stride * sizeof(float));
}
}
for (int h = 0; h < after_paddings; h++) {
int given = height + before_paddings + h;
float *dst = start + given * new_wc_stride;
if (params->mode == LPMP_CONSTANT) {
for (int i = 0; i < new_wc_stride; i++) {
dst[i] = params->constant_values;
}
} else {
int buddy = after_get_buddy(given, height + before_paddings, params->mode);
float *src = start + buddy * new_wc_stride;
memcpy(dst, src, new_wc_stride * sizeof(float));
}
}
}
// handle the third dimension
before_paddings = params->paddings[2][0];
after_paddings = params->paddings[2][1];
for (int n = 0; n < new_number; n++) {
for (int h = 0; h < new_height; h++) {
float *start = output + n * new_hwc_stride + h * new_wc_stride;
for (int w = 0; w < before_paddings; w++) {
float *dst = start + w * new_c_stride;
if (params->mode == LPMP_CONSTANT) {
for (int i = 0; i < new_c_stride; i++) {
dst[i] = params->constant_values;
}
} else {
int buddy = before_get_buddy(w, before_paddings, params->mode);
float *src = start + buddy * new_c_stride;
memcpy(dst, src, new_c_stride * sizeof(float));
}
}
for (int w = 0; w < after_paddings; w++) {
int given = width + before_paddings + w;
float *dst = start + given * new_c_stride;
if (params->mode == LPMP_CONSTANT) {
for (int i = 0; i < new_c_stride; i++) {
dst[i] = params->constant_values;
}
} else {
int buddy = after_get_buddy(given, width + before_paddings, params->mode);
float *src = start + buddy * new_c_stride;
memcpy(dst, src, new_c_stride * sizeof(float));
}
}
}
}
// handle the fourth dimension
before_paddings = params->paddings[3][0];
after_paddings = params->paddings[3][1];
for (int n = 0; n < new_number; n++) {
for (int h = 0; h < new_height; h++) {
for (int w = 0; w < new_width; w++) {
float *start = output + n * new_hwc_stride + h * new_wc_stride + w * new_c_stride;
for (int c = 0; c < before_paddings; c++) {
float *dst = start + c;
if (params->mode == LPMP_CONSTANT) {
*dst = params->constant_values;
} else {
int buddy = before_get_buddy(c, before_paddings, params->mode);
float *src = start + buddy;
*dst = *src;
}
}
for (int c = 0; c < after_paddings; c++) {
int given = channel + before_paddings + c;
float *dst = start + given;
if (params->mode == LPMP_CONSTANT) {
*dst = params->constant_values;
} else {
int buddy = after_get_buddy(given, channel + before_paddings, params->mode);
float *src = start + buddy;
*dst = *src;
}
}
}
}
}
return 0;
}

43
libavfilter/dnn/dnn_backend_native_layer_pad.h
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@ -1,43 +0,0 @@
/*
* Copyright (c) 2019 Guo Yejun
*
* This file is part of FFmpeg.
*
* FFmpeg is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public
* License as published by the Free Software Foundation; either
* version 2.1 of the License, or (at your option) any later version.
*
* FFmpeg is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with FFmpeg; if not, write to the Free Software
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
*/
/**
* @file
* layer pad (equivalent to tf.pad) for native backend.
*/
#ifndef AVFILTER_DNN_DNN_BACKEND_NATIVE_LAYER_PAD_H
#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;
typedef struct LayerPadParams{
int32_t paddings[4][2];
LayerPadModeParam mode;
float constant_values;
} LayerPadParams;
int ff_dnn_load_layer_pad(Layer *layer, AVIOContext *model_file_context, int file_size, int operands_num);
int ff_dnn_execute_layer_pad(DnnOperand *operands, const int32_t *input_operand_indexes,
int32_t output_operand_index, const void *parameters, NativeContext *ctx);
#endif

42
libavfilter/dnn/dnn_backend_native_layers.c
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@ -1,42 +0,0 @@
/*
* Copyright (c) 2019 Guo Yejun
*
* This file is part of FFmpeg.
*
* FFmpeg is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public
* License as published by the Free Software Foundation; either
* version 2.1 of the License, or (at your option) any later version.
*
* FFmpeg is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with FFmpeg; if not, write to the Free Software
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
*/
#include <string.h>
#include "dnn_backend_native_layers.h"
#include "dnn_backend_native_layer_pad.h"
#include "dnn_backend_native_layer_conv2d.h"
#include "dnn_backend_native_layer_depth2space.h"
#include "dnn_backend_native_layer_maximum.h"
#include "dnn_backend_native_layer_mathbinary.h"
#include "dnn_backend_native_layer_mathunary.h"
#include "dnn_backend_native_layer_avgpool.h"
#include "dnn_backend_native_layer_dense.h"
const LayerFunc ff_layer_funcs[DLT_COUNT] = {
{NULL, NULL},
{ff_dnn_execute_layer_conv2d, ff_dnn_load_layer_conv2d},
{ff_dnn_execute_layer_depth2space, ff_dnn_load_layer_depth2space},
{ff_dnn_execute_layer_pad, ff_dnn_load_layer_pad},
{ff_dnn_execute_layer_maximum, ff_dnn_load_layer_maximum},
{ff_dnn_execute_layer_math_binary, ff_dnn_load_layer_math_binary},
{ff_dnn_execute_layer_math_unary, ff_dnn_load_layer_math_unary},
{ff_dnn_execute_layer_avg_pool, ff_dnn_load_layer_avg_pool},
{ff_dnn_execute_layer_dense, ff_dnn_load_layer_dense},
};

38
libavfilter/dnn/dnn_backend_native_layers.h
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@ -1,38 +0,0 @@
/*
* Copyright (c) 2019 Guo Yejun
*
* This file is part of FFmpeg.
*
* FFmpeg is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public
* License as published by the Free Software Foundation; either
* version 2.1 of the License, or (at your option) any later version.
*
* FFmpeg is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with FFmpeg; if not, write to the Free Software
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
*/
#ifndef AVFILTER_DNN_DNN_BACKEND_NATIVE_LAYERS_H
#define AVFILTER_DNN_DNN_BACKEND_NATIVE_LAYERS_H
#include <stdint.h>
#include "dnn_backend_native.h"
typedef int (*LAYER_EXEC_FUNC)(DnnOperand *operands, const int32_t *input_operand_indexes,
int32_t output_operand_index, const void *parameters, NativeContext *ctx);
typedef int (*LAYER_LOAD_FUNC)(Layer *layer, AVIOContext *model_file_context, int file_size, int operands_num);
typedef struct LayerFunc {
LAYER_EXEC_FUNC pf_exec;
LAYER_LOAD_FUNC pf_load;
}LayerFunc;
extern const LayerFunc ff_layer_funcs[DLT_COUNT];
#endif

366
libavfilter/dnn/dnn_backend_tf.c
View File

@ -24,17 +24,13 @@
*/
#include "dnn_backend_tf.h"
#include "dnn_backend_native.h"
#include "dnn_backend_native_layer_conv2d.h"
#include "dnn_backend_native_layer_depth2space.h"
#include "libavformat/avio.h"
#include "libavutil/avassert.h"
#include "libavutil/avstring.h"
#include "libavutil/cpu.h"
#include "libavutil/opt.h"
#include "libavcodec/defs.h"
#include "../internal.h"
#include "dnn_backend_native_layer_pad.h"
#include "dnn_backend_native_layer_maximum.h"
#include "dnn_io_proc.h"
#include "dnn_backend_common.h"
#include "safe_queue.h"
@ -481,363 +477,6 @@ static int load_tf_model(TFModel *tf_model, const char *model_filename)
#define NAME_BUFFER_SIZE 256
static int add_conv_layer(TFModel *tf_model, TF_Operation *transpose_op, TF_Operation **cur_op,
ConvolutionalParams* params, const int layer)
{
TFContext *ctx = &tf_model->ctx;
TF_Operation *op;
TF_OperationDescription *op_desc;
TF_Output input;
int64_t strides[] = {1, 1, 1, 1};
TF_Tensor *kernel_tensor = NULL, *biases_tensor = NULL;
int64_t dims[4];
int dims_len;
char name_buffer[NAME_BUFFER_SIZE];
int32_t size;
size = params->input_num * params->output_num * params->kernel_size * params->kernel_size;
input.index = 0;
snprintf(name_buffer, NAME_BUFFER_SIZE, "conv_kernel%d", layer);
op_desc = TF_NewOperation(tf_model->graph, "Const", name_buffer);
TF_SetAttrType(op_desc, "dtype", TF_FLOAT);
dims[0] = params->output_num;
dims[1] = params->kernel_size;
dims[2] = params->kernel_size;
dims[3] = params->input_num;
dims_len = 4;
kernel_tensor = TF_AllocateTensor(TF_FLOAT, dims, dims_len, size * sizeof(float));
memcpy(TF_TensorData(kernel_tensor), params->kernel, size * sizeof(float));
TF_SetAttrTensor(op_desc, "value", kernel_tensor, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
goto err;
}
op = TF_FinishOperation(op_desc, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
goto err;
}
snprintf(name_buffer, NAME_BUFFER_SIZE, "transpose%d", layer);
op_desc = TF_NewOperation(tf_model->graph, "Transpose", name_buffer);
input.oper = op;
TF_AddInput(op_desc, input);
input.oper = transpose_op;
TF_AddInput(op_desc, input);
TF_SetAttrType(op_desc, "T", TF_FLOAT);
TF_SetAttrType(op_desc, "Tperm", TF_INT32);
op = TF_FinishOperation(op_desc, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
goto err;
}
snprintf(name_buffer, NAME_BUFFER_SIZE, "conv2d%d", layer);
op_desc = TF_NewOperation(tf_model->graph, "Conv2D", name_buffer);
input.oper = *cur_op;
TF_AddInput(op_desc, input);
input.oper = op;
TF_AddInput(op_desc, input);
TF_SetAttrType(op_desc, "T", TF_FLOAT);
TF_SetAttrIntList(op_desc, "strides", strides, 4);
TF_SetAttrString(op_desc, "padding", "VALID", 5);
*cur_op = TF_FinishOperation(op_desc, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
goto err;
}
snprintf(name_buffer, NAME_BUFFER_SIZE, "conv_biases%d", layer);
op_desc = TF_NewOperation(tf_model->graph, "Const", name_buffer);
TF_SetAttrType(op_desc, "dtype", TF_FLOAT);
dims[0] = params->output_num;
dims_len = 1;
biases_tensor = TF_AllocateTensor(TF_FLOAT, dims, dims_len, params->output_num * sizeof(float));
memcpy(TF_TensorData(biases_tensor), params->biases, params->output_num * sizeof(float));
TF_SetAttrTensor(op_desc, "value", biases_tensor, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
goto err;
}
op = TF_FinishOperation(op_desc, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
goto err;
}
snprintf(name_buffer, NAME_BUFFER_SIZE, "bias_add%d", layer);
op_desc = TF_NewOperation(tf_model->graph, "BiasAdd", name_buffer);
input.oper = *cur_op;
TF_AddInput(op_desc, input);
input.oper = op;
TF_AddInput(op_desc, input);
TF_SetAttrType(op_desc, "T", TF_FLOAT);
*cur_op = TF_FinishOperation(op_desc, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
goto err;
}
snprintf(name_buffer, NAME_BUFFER_SIZE, "activation%d", layer);
switch (params->activation){
case RELU:
op_desc = TF_NewOperation(tf_model->graph, "Relu", name_buffer);
break;
case TANH:
op_desc = TF_NewOperation(tf_model->graph, "Tanh", name_buffer);
break;
case SIGMOID:
op_desc = TF_NewOperation(tf_model->graph, "Sigmoid", name_buffer);
break;
default:
avpriv_report_missing_feature(ctx, "convolutional activation function %d", params->activation);
return AVERROR(ENOSYS);
}
input.oper = *cur_op;
TF_AddInput(op_desc, input);
TF_SetAttrType(op_desc, "T", TF_FLOAT);
*cur_op = TF_FinishOperation(op_desc, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
goto err;
}
return 0;
err:
TF_DeleteTensor(kernel_tensor);
TF_DeleteTensor(biases_tensor);
av_log(ctx, AV_LOG_ERROR, "Failed to add conv layer %d\n", layer);
return DNN_GENERIC_ERROR;
}
static int add_depth_to_space_layer(TFModel *tf_model, TF_Operation **cur_op,
DepthToSpaceParams *params, const int layer)
{
TFContext *ctx = &tf_model->ctx;
TF_OperationDescription *op_desc;
TF_Output input;
char name_buffer[NAME_BUFFER_SIZE];
snprintf(name_buffer, NAME_BUFFER_SIZE, "depth_to_space%d", layer);
op_desc = TF_NewOperation(tf_model->graph, "DepthToSpace", name_buffer);
input.oper = *cur_op;
input.index = 0;
TF_AddInput(op_desc, input);
TF_SetAttrType(op_desc, "T", TF_FLOAT);
TF_SetAttrInt(op_desc, "block_size", params->block_size);
*cur_op = TF_FinishOperation(op_desc, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
av_log(ctx, AV_LOG_ERROR, "Failed to add depth_to_space to layer %d\n", layer);
return DNN_GENERIC_ERROR;
}
return 0;
}
static int add_pad_layer(TFModel *tf_model, TF_Operation **cur_op,
LayerPadParams *params, const int layer)
{
TFContext *ctx = &tf_model->ctx;
TF_Operation *op;
TF_Tensor *tensor;
TF_OperationDescription *op_desc;
TF_Output input;
int32_t *pads;
int64_t pads_shape[] = {4, 2};
char name_buffer[NAME_BUFFER_SIZE];
snprintf(name_buffer, NAME_BUFFER_SIZE, "pad%d", layer);
op_desc = TF_NewOperation(tf_model->graph, "Const", name_buffer);
TF_SetAttrType(op_desc, "dtype", TF_INT32);
tensor = TF_AllocateTensor(TF_INT32, pads_shape, 2, 4 * 2 * sizeof(int32_t));
pads = (int32_t *)TF_TensorData(tensor);
pads[0] = params->paddings[0][0];
pads[1] = params->paddings[0][1];
pads[2] = params->paddings[1][0];
pads[3] = params->paddings[1][1];
pads[4] = params->paddings[2][0];
pads[5] = params->paddings[2][1];
pads[6] = params->paddings[3][0];
pads[7] = params->paddings[3][1];
TF_SetAttrTensor(op_desc, "value", tensor, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
TF_DeleteTensor(tensor);
av_log(ctx, AV_LOG_ERROR, "Failed to set value for pad of layer %d\n", layer);
return DNN_GENERIC_ERROR;
}
op = TF_FinishOperation(op_desc, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
TF_DeleteTensor(tensor);
av_log(ctx, AV_LOG_ERROR, "Failed to add pad to layer %d\n", layer);
return DNN_GENERIC_ERROR;
}
op_desc = TF_NewOperation(tf_model->graph, "MirrorPad", "mirror_pad");
input.oper = *cur_op;
input.index = 0;
TF_AddInput(op_desc, input);
input.oper = op;
TF_AddInput(op_desc, input);
TF_SetAttrType(op_desc, "T", TF_FLOAT);
TF_SetAttrType(op_desc, "Tpaddings", TF_INT32);
TF_SetAttrString(op_desc, "mode", "SYMMETRIC", 9);
*cur_op = TF_FinishOperation(op_desc, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
TF_DeleteTensor(tensor);
av_log(ctx, AV_LOG_ERROR, "Failed to add mirror_pad to layer %d\n", layer);
return DNN_GENERIC_ERROR;
}
return 0;
}
static int add_maximum_layer(TFModel *tf_model, TF_Operation **cur_op,
DnnLayerMaximumParams *params, const int layer)
{
TFContext *ctx = &tf_model->ctx;
TF_Operation *op;
TF_Tensor *tensor;
TF_OperationDescription *op_desc;
TF_Output input;
float *y;
char name_buffer[NAME_BUFFER_SIZE];
snprintf(name_buffer, NAME_BUFFER_SIZE, "maximum/y%d", layer);
op_desc = TF_NewOperation(tf_model->graph, "Const", name_buffer);
TF_SetAttrType(op_desc, "dtype", TF_FLOAT);
tensor = TF_AllocateTensor(TF_FLOAT, NULL, 0, TF_DataTypeSize(TF_FLOAT));
y = (float *)TF_TensorData(tensor);
*y = params->val.y;
TF_SetAttrTensor(op_desc, "value", tensor, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
TF_DeleteTensor(tensor);
av_log(ctx, AV_LOG_ERROR, "Failed to set value for maximum/y of layer %d", layer);
return DNN_GENERIC_ERROR;
}
op = TF_FinishOperation(op_desc, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
TF_DeleteTensor(tensor);
av_log(ctx, AV_LOG_ERROR, "Failed to add maximum/y to layer %d\n", layer);
return DNN_GENERIC_ERROR;
}
snprintf(name_buffer, NAME_BUFFER_SIZE, "maximum%d", layer);
op_desc = TF_NewOperation(tf_model->graph, "Maximum", name_buffer);
input.oper = *cur_op;
input.index = 0;
TF_AddInput(op_desc, input);
input.oper = op;
TF_AddInput(op_desc, input);
TF_SetAttrType(op_desc, "T", TF_FLOAT);
*cur_op = TF_FinishOperation(op_desc, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
TF_DeleteTensor(tensor);
av_log(ctx, AV_LOG_ERROR, "Failed to add maximum to layer %d\n", layer);
return DNN_GENERIC_ERROR;
}
return 0;
}
static int load_native_model(TFModel *tf_model, const char *model_filename)
{
TFContext *ctx = &tf_model->ctx;
int32_t layer;
TF_OperationDescription *op_desc;
TF_Operation *op;
TF_Operation *transpose_op;
TF_Tensor *tensor = NULL;
TF_Output input;
int32_t *transpose_perm;
int64_t transpose_perm_shape[] = {4};
int64_t input_shape[] = {1, -1, -1, -1};
int layer_add_res;
DNNModel *model = NULL;
NativeModel *native_model;
model = ff_dnn_load_model_native(model_filename, DFT_PROCESS_FRAME, NULL, NULL);
if (!model){
av_log(ctx, AV_LOG_ERROR, "Failed to load native model\n");
return AVERROR(EINVAL);
}
native_model = model->model;
tf_model->graph = TF_NewGraph();
tf_model->status = TF_NewStatus();
#define CLEANUP_ON_ERROR(tf_model) \
{ \
TF_DeleteTensor(tensor); \
TF_DeleteGraph(tf_model->graph); \
TF_DeleteStatus(tf_model->status); \
av_log(ctx, AV_LOG_ERROR, "Failed to set value or add operator to layer\n"); \
return DNN_GENERIC_ERROR; \
}
op_desc = TF_NewOperation(tf_model->graph, "Placeholder", "x");
TF_SetAttrType(op_desc, "dtype", TF_FLOAT);
TF_SetAttrShape(op_desc, "shape", input_shape, 4);
op = TF_FinishOperation(op_desc, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
CLEANUP_ON_ERROR(tf_model);
}
op_desc = TF_NewOperation(tf_model->graph, "Const", "transpose_perm");
TF_SetAttrType(op_desc, "dtype", TF_INT32);
tensor = TF_AllocateTensor(TF_INT32, transpose_perm_shape, 1, 4 * sizeof(int32_t));
transpose_perm = (int32_t *)TF_TensorData(tensor);
transpose_perm[0] = 1;
transpose_perm[1] = 2;
transpose_perm[2] = 3;
transpose_perm[3] = 0;
TF_SetAttrTensor(op_desc, "value", tensor, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
CLEANUP_ON_ERROR(tf_model);
}
transpose_op = TF_FinishOperation(op_desc, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
CLEANUP_ON_ERROR(tf_model);
}
for (layer = 0; layer < native_model->layers_num; ++layer){
switch (native_model->layers[layer].type){
case DLT_INPUT:
layer_add_res = 0;
break;
case DLT_CONV2D:
layer_add_res = add_conv_layer(tf_model, transpose_op, &op,
(ConvolutionalParams *)native_model->layers[layer].params, layer);
break;
case DLT_DEPTH_TO_SPACE:
layer_add_res = add_depth_to_space_layer(tf_model, &op,
(DepthToSpaceParams *)native_model->layers[layer].params, layer);
break;
case DLT_MIRROR_PAD:
layer_add_res = add_pad_layer(tf_model, &op,
(LayerPadParams *)native_model->layers[layer].params, layer);
break;
case DLT_MAXIMUM:
layer_add_res = add_maximum_layer(tf_model, &op,
(DnnLayerMaximumParams *)native_model->layers[layer].params, layer);
break;
default:
CLEANUP_ON_ERROR(tf_model);
}
if (layer_add_res != 0){
CLEANUP_ON_ERROR(tf_model);
}
}
op_desc = TF_NewOperation(tf_model->graph, "Identity", "y");
input.oper = op;
input.index = 0;
TF_AddInput(op_desc, input);
TF_FinishOperation(op_desc, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
CLEANUP_ON_ERROR(tf_model);
}
ff_dnn_free_model_native(&model);
return 0;
}
DNNModel *ff_dnn_load_model_tf(const char *model_filename, DNNFunctionType func_type, const char *options, AVFilterContext *filter_ctx)
{
DNNModel *model = NULL;
@ -867,10 +506,9 @@ DNNModel *ff_dnn_load_model_tf(const char *model_filename, DNNFunctionType func_
}
if (load_tf_model(tf_model, model_filename) != 0){
if (load_native_model(tf_model, model_filename) != 0){
av_log(ctx, AV_LOG_ERROR, "Failed to load TensorFlow model: \"%s\"\n", model_filename);
goto err;
}
}
if (ctx->options.nireq <= 0) {
ctx->options.nireq = av_cpu_count() / 2 + 1;

2
libavfilter/dnn_interface.h
View File

@ -32,7 +32,7 @@
#define DNN_GENERIC_ERROR FFERRTAG('D','N','N','!')
typedef enum {DNN_NATIVE, DNN_TF, DNN_OV} DNNBackendType;
typedef enum {DNN_TF = 1, DNN_OV} DNNBackendType;
typedef enum {DNN_FLOAT = 1, DNN_UINT8 = 4} DNNDataType;

197
libavfilter/tests/dnn-layer-avgpool.c
View File

@ -1,197 +0,0 @@
/*
* Copyright (c) 2020
*
* This file is part of FFmpeg.
*
* FFmpeg is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public
* License as published by the Free Software Foundation; either
* version 2.1 of the License, or (at your option) any later version.
*
* FFmpeg is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with FFmpeg; if not, write to the Free Software
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
*/
#include <stdio.h>
#include "libavfilter/dnn/dnn_backend_native_layer_avgpool.h"
#define EPSON 0.00001
static int test_with_same(void)
{
// the input data and expected data are generated with below python code.
/*
import tensorflow as tf
import numpy as np
x = tf.placeholder(tf.float32, shape=[1, None, None, 3])
y = tf.layers.average_pooling2d(x, pool_size=[2,2], strides=[1,1], padding='VALID')
data = np.random.rand(1, 5, 6, 3);
sess=tf.Session()
sess.run(tf.global_variables_initializer())
output = sess.run(y, feed_dict={x: data})
print("input:")
print(data.shape)
print(list(data.flatten()))
print("output:")
print(output.shape)
print(list(output.flatten()))
*/
AvgPoolParams params;
DnnOperand operands[2];
int32_t input_indexes[1];
float input[1*5*6*3] = {
0.7461309859908424, 0.7567538372797069, 0.07662743569678687, 0.8882112610336333, 0.9720443314026668, 0.3337200343220823, 0.4421032129780248,
0.14940809044964876, 0.6773177061961277, 0.9778844630669781, 0.6522650522626998, 0.0317651530878591, 0.31259897552911364, 0.6235936821891896,
0.40016094349542775, 0.4599222930032276, 0.7893807222960093, 0.8475986363538283, 0.5058802717647394, 0.7827005363222633, 0.3032188123727916,
0.8983728631302361, 0.20622408444965523, 0.22966072303869878, 0.09535751273161308, 0.8760709100995375, 0.9982324154558745, 0.7904595468621013,
0.13883671508879347, 0.9332751439533138, 0.0010861680752152214, 0.3607210449251048, 0.6600652759586171, 0.7629572058138805, 0.29441975810476106,
0.2683471432889405, 0.22574580829831536, 0.8893251976212904, 0.3907737043801005, 0.6421829842863968, 0.6670373870457297, 0.9383850793160277,
0.4120458907436003, 0.3589847212711481, 0.48047736550128983, 0.6428192648418949, 0.0313661686292348, 0.429357100401472, 0.5123413386514056,
0.8492446404097114, 0.9045286128486804, 0.8123708563814285, 0.3943245008451698, 0.9576713003177785, 0.5985610965938726, 0.9350833279543561,
0.8010079897491659, 0.45882114217642866, 0.35275037908941487, 0.4555844661432271, 0.12352455940255314, 0.37801756635035544, 0.2824056214573083,
0.6229462823245029, 0.7235305681391472, 0.5408259266122064, 0.12142224381781208, 0.34431198802873686, 0.7112823816321276, 0.6307144385115417,
0.8136734589018082, 0.842095618140585, 0.8602767724004784, 0.6649236853766185, 0.5184782829419623, 0.9119607270982825, 0.3084111974561645,
0.39460705638161364, 0.17710447526170836, 0.1715485945814199, 0.17277563576521882, 0.40188232428735704, 0.22847985411491878, 0.4135361701550696,
0.24621846601980057, 0.6576588108454774, 0.6063336087333997, 0.6452342242996931, 0.7071689702737508, 0.1973416063225648
};
float expected_output[] = {
0.75964886, 0.6794307, 0.23580676, 0.5810112, 0.5509369, 0.55973274, 0.5764512, 0.45414522, 0.6601476, 0.52050734, 0.44385415,
0.50631666, 0.38414115, 0.5170288, 0.544043, 0.61143976, 0.5419003, 0.5579729, 0.5680455, 0.6363218, 0.4655096, 0.51198983,
0.5270792, 0.66168886, 0.48517057, 0.3513146, 0.7103355, 0.48667657, 0.34504217, 0.7318065, 0.5221889, 0.4746775, 0.69765306,
0.78766406, 0.34437215, 0.6130092, 0.48132777, 0.7110491, 0.6464378, 0.40914366, 0.4391975, 0.5392131, 0.45033398, 0.37297475,
0.43326652, 0.4748823, 0.48711336, 0.64649844, 0.51921225, 0.60038865, 0.8538945, 0.7215426, 0.60399896, 0.89988345, 0.707405,
0.5652921, 0.54241943, 0.41785273, 0.30268195, 0.3263432, 0.3313644, 0.37539417, 0.35238582, 0.34811732, 0.48849532, 0.56799453,
0.41089734, 0.63070333, 0.5892633, 0.6379743, 0.7604212, 0.5197186, 0.88611877, 0.48666745, 0.45654267, 0.5445326, 0.2399799,
0.28369135, 0.28949338, 0.20001422, 0.2931559, 0.3240504, 0.44306934, 0.5099349, 0.44572634, 0.68241394, 0.40183762, 0.6452342,
0.707169, 0.1973416
};
float *output;
params.strides = 1;
params.kernel_size = 2;
params.padding_method = SAME;
operands[0].data = input;
operands[0].dims[0] = 1;
operands[0].dims[1] = 5;
operands[0].dims[2] = 6;
operands[0].dims[3] = 3;
operands[1].data = NULL;
input_indexes[0] = 0;
ff_dnn_execute_layer_avg_pool(operands, input_indexes, 1, &params, NULL);
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;
}
static int test_with_valid(void)
{
// the input data and expected data are generated with below python code.
/*
import tensorflow as tf
import numpy as np
x = tf.placeholder(tf.float32, shape=[1, None, None, 3])
y = tf.layers.average_pooling2d(x, pool_size=[2,2], strides=[1,1], padding='VALID')
data = np.random.rand(1, 5, 6, 3);
sess=tf.Session()
sess.run(tf.global_variables_initializer())
output = sess.run(y, feed_dict={x: data})
print("input:")
print(data.shape)
print(list(data.flatten()))
print("output:")
print(output.shape)
print(list(output.flatten()))
*/
AvgPoolParams params;
DnnOperand operands[2];
int32_t input_indexes[1];
float input[1*5*6*3] = {
0.5046741692941682, 0.9273653202485155, 0.8193878359859937, 0.1904059431360905, 0.8664919633253656, 0.7484625128286059, 0.984534184632278,
0.31900804890072254, 0.3259426099940872, 0.05388974903570376, 0.7356610151331133, 0.46710858713311965, 0.718553768817036, 0.062478421853278676,
0.7813224786584609, 0.4826837517658389, 0.9748095400220147, 0.8078547703898341, 0.11976750668368585, 0.8713586777195065, 0.41447321551284355,
0.9818788239089807, 0.4335715767584073, 0.4059793452147419, 0.3677205907204525, 0.47919995923571, 0.8341395256258882, 0.7059726374074609,
0.5478504551919791, 0.8622900484790175, 0.8343709722511167, 0.05089827275068537, 0.6465283980840416, 0.544539116066677, 0.39812057257884337,
0.9578115576866337, 0.25012888117580145, 0.579333516024662, 0.5556732133051457, 0.6119862111181243, 0.0018736758772316398, 0.9795490254040474,
0.4488085008883018, 0.28947489777011737, 0.4834108668633247, 0.9280490084385024, 0.9895821458049648, 0.31777618554697606, 0.42679693258977847,
0.74447844466923, 0.9752225305081498, 0.17564130841849335, 0.22382692067314292, 0.009602884447469373, 0.5144884415025782, 0.031622570708844555,
0.8277532752502512, 0.4111593210409763, 0.5272084646575664, 0.28856508082905297, 0.11317726946036655, 0.7203328275540273, 0.8310055019972384,
0.8535951508685228, 0.40230347305233227, 0.2819703265132867, 0.6243143957791139, 0.7512463693822311, 0.7523056340495644, 0.8838077258040928,
0.5472240664033092, 0.2550538284454935, 0.5560317774456567, 0.8966847087518931, 0.6728358284165321, 0.30361297147530875, 0.464343925441822,
0.34507695659461224, 0.6333175615390685, 0.26661369038523497, 0.9926748632253231, 0.9994267301382666, 0.8684917986974414, 0.3598754806113009,
0.49550268625464666, 0.03652458679973214, 0.13469081713137177, 0.4579424049273835, 0.48641107969110353, 0.9670250266945365
};
float expected_output[1*4*5*3] = {
0.44918162, 0.7746969, 0.5970757, 0.63113487, 0.5245679, 0.578631, 0.52802926, 0.52042985, 0.6223702, 0.57819676, 0.34922206,
0.6893124, 0.64503694, 0.37157673, 0.7983793, 0.49094033, 0.47153437, 0.5889187, 0.6025985, 0.30103004, 0.6757697, 0.6126377,
0.5765268, 0.62440413, 0.7237974, 0.5832023, 0.7004543, 0.49533707, 0.35433105, 0.6472913, 0.44694072, 0.28500956, 0.6628852,
0.39628282, 0.38472247, 0.6456326, 0.58590746, 0.60042334, 0.47854072, 0.7081889, 0.7219026, 0.5818187, 0.5276401, 0.56669396,
0.49804622, 0.4463231, 0.4799649, 0.5335578, 0.36531678, 0.4946247, 0.6143306, 0.6498792, 0.5644355, 0.6163815, 0.7432098,
0.5146416, 0.38221055, 0.6153918, 0.45535153, 0.5272688
};
float *output;
params.strides = 1;
params.kernel_size = 2;
params.padding_method = VALID;
operands[0].data = input;
operands[0].dims[0] = 1;
operands[0].dims[1] = 5;
operands[0].dims[2] = 6;
operands[0].dims[3] = 3;
operands[1].data = NULL;
input_indexes[0] = 0;
ff_dnn_execute_layer_avg_pool(operands, input_indexes, 1, &params, NULL);
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;
}
int main(int argc, char **argv)
{
if (test_with_same())
return 1;
if (test_with_valid())
return 1;
return 0;
}

248
libavfilter/tests/dnn-layer-conv2d.c
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@ -1,248 +0,0 @@
/*
* Copyright (c) 2019 Guo Yejun
*
* This file is part of FFmpeg.
*
* FFmpeg is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public
* License as published by the Free Software Foundation; either
* version 2.1 of the License, or (at your option) any later version.
*
* FFmpeg is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with FFmpeg; if not, write to the Free Software
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
*/
#include <stdio.h>
#include <string.h>
#include <math.h>
#include "libavfilter/dnn/dnn_backend_native_layer_conv2d.h"
#define EPSON 0.00001
static int test_with_same_dilate(void)
{
// the input data and expected data are generated with below python code.
/*
x = tf.placeholder(tf.float32, shape=[1, None, None, 3])
y = tf.layers.conv2d(x, 2, 3, activation=tf.nn.tanh, padding='same', dilation_rate=(2, 2), bias_initializer=tf.keras.initializers.he_normal())
data = np.random.rand(1, 5, 6, 3);
sess=tf.Session()
sess.run(tf.global_variables_initializer())
weights = dict([(var.name, sess.run(var)) for var in tf.trainable_variables()])
kernel = weights['conv2d/kernel:0']
kernel = np.transpose(kernel, [3, 0, 1, 2])
print("kernel:")
print(kernel.shape)
print(list(kernel.flatten()))
bias = weights['conv2d/bias:0']
print("bias:")
print(bias.shape)
print(list(bias.flatten()))
output = sess.run(y, feed_dict={x: data})
print("input:")
print(data.shape)
print(list(data.flatten()))
print("output:")
print(output.shape)
print(list(output.flatten()))
*/
ConvolutionalParams params;
DnnOperand operands[2];
int32_t input_indexes[1];
float input[1*5*6*3] = {
0.7012556460308194, 0.4233847954643357, 0.19515900664313612, 0.16343083004926495, 0.5758261611052848, 0.9510767434014871, 0.11014085055947687,
0.906327053637727, 0.8136794715542507, 0.45371764543639526, 0.5768443343523952, 0.19543668786046986, 0.15648326047898609, 0.2099500241141279,
0.17658777090552413, 0.059335724777169196, 0.1729991838469117, 0.8150514704819208, 0.4435535466703049, 0.3752188477566878, 0.749936650421431,
0.6823494635284907, 0.10776389679424747, 0.34247481674596836, 0.5147867256244629, 0.9063709728129032, 0.12423605800856818, 0.6064872945412728,
0.5891681538551459, 0.9865836236466314, 0.9002163879294677, 0.003968273184274618, 0.8628374809643967, 0.1327176268279583, 0.8449799925703798,
0.1937671869354366, 0.41524410152707425, 0.02038786604756837, 0.49792466069597496, 0.8881874553848784, 0.9683921035597336, 0.4122972568010813,
0.843553550993252, 0.9588482762501964, 0.5190350762645546, 0.4283584264145317, 0.09781496073714646, 0.9501058833776156, 0.8665541760152776,
0.31669272550095806, 0.07133074675453632, 0.606438007334886, 0.7007157020538224, 0.4827996264130444, 0.5167615606392761, 0.6385043039312651,
0.23069664707810555, 0.058233497329354456, 0.06323892961591071, 0.24816458893245974, 0.8646369065257812, 0.24742185893094837, 0.09991225948167437,
0.625700606979606, 0.7678541502111257, 0.6215834594679912, 0.5623003956582483, 0.07389123942681242, 0.7659100715711249, 0.486061471642225,
0.9947455699829012, 0.9094911797643259, 0.7644355876253265, 0.05384315321492239, 0.13565394382783613, 0.9810628204953316, 0.007386389078887889,
0.226182754156241, 0.2609021390764772, 0.24182802076928933, 0.13264782451941648, 0.2035816485767682, 0.005504188177612557, 0.7014619934040155,
0.956215988391991, 0.5670398541013633, 0.9809764721750784, 0.6886338100487461, 0.5758152317218274, 0.7137823176776179
};
float expected_output[1*5*6*2] = {
-0.9480655, -0.7169147, -0.9404794, -0.5567385, -0.8991124, -0.8306558, -0.94487447, -0.8932543, -0.88238764, -0.7301602,
-0.8974813, -0.7026703, -0.8858988, -0.53203243, -0.92881465, -0.5648504, -0.8871471, -0.7000097, -0.91754407, -0.79684794,
-0.760465, -0.117928326, -0.88302773, -0.8975289, -0.70615053, 0.19231977, -0.8318776, -0.386184, -0.80698484, -0.8556624,
-0.7336671, -0.6168619, -0.7658234, -0.63449603, -0.73314047, -0.87502456, -0.58158904, -0.4184259, -0.52618927, -0.13613208,
-0.5093187, -0.21027721, -0.39455596, -0.44507834, -0.22269244, -0.73400885, -0.77655095, -0.74408925, -0.57313335, -0.15333457,
-0.74620694, -0.34858236, -0.42586932, -0.5240488, 0.1634339, -0.2447881, -0.57927346, -0.62732303, -0.82287043, -0.8474058
};
float *output;
float kernel[2*3*3*3] = {
0.26025516, 0.16536498, -0.24351254, 0.33892477, -0.34005195, 0.35202783, 0.34056443, 0.01422739, 0.13799345, 0.29489166,
0.2781723, 0.178585, 0.22122234, 0.044115514, 0.13134438, 0.31705368, 0.22527462, -0.021323413, 0.115134746, -0.18216397,
-0.21197563, -0.027848959, -0.01704529, -0.12401503, -0.23415318, -0.12661739, -0.35338148, 0.20049328, -0.076153606,
-0.23642601, -0.3125769, -0.025851756, -0.30006272, 0.050762743, 0.32003498, 0.3052225, -0.0017385483, 0.25337684, -0.25664508,
0.27846587, -0.3112659, 0.2066065, 0.31499845, 0.113178134, 0.09449363, -0.11828774, -0.12671001, -0.36259216, 0.2710235,
-0.19676702, 0.023612618, -0.2596915, -0.34949252, -0.108270735
};
float bias[2] = { -1.6574852, -0.72915393 };
NativeContext ctx;
ctx.class = NULL;
ctx.options.conv2d_threads = 1;
params.activation = TANH;
params.has_bias = 1;
params.biases = bias;
params.dilation = 2;
params.input_num = 3;
params.kernel = kernel;
params.kernel_size = 3;
params.output_num = 2;
params.padding_method = SAME;
operands[0].data = input;
operands[0].dims[0] = 1;
operands[0].dims[1] = 5;
operands[0].dims[2] = 6;
operands[0].dims[3] = 3;
operands[1].data = NULL;
input_indexes[0] = 0;
ff_dnn_execute_layer_conv2d(operands, input_indexes, 1, &params, &ctx);
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;
}
static int test_with_valid(void)
{
// the input data and expected data are generated with below python code.
/*
x = tf.placeholder(tf.float32, shape=[1, None, None, 3])
y = tf.layers.conv2d(x, 2, 3, activation=tf.nn.tanh, padding='valid', bias_initializer=tf.keras.initializers.he_normal())
data = np.random.rand(1, 5, 6, 3);
sess=tf.Session()
sess.run(tf.global_variables_initializer())
weights = dict([(var.name, sess.run(var)) for var in tf.trainable_variables()])
kernel = weights['conv2d/kernel:0']
kernel = np.transpose(kernel, [3, 0, 1, 2])
print("kernel:")
print(kernel.shape)
print(list(kernel.flatten()))
bias = weights['conv2d/bias:0']
print("bias:")
print(bias.shape)
print(list(bias.flatten()))
output = sess.run(y, feed_dict={x: data})
print("input:")
print(data.shape)
print(list(data.flatten()))
print("output:")
print(output.shape)
print(list(output.flatten()))
*/
ConvolutionalParams params;
DnnOperand operands[2];
int32_t input_indexes[1];
float input[1*5*6*3] = {
0.26126657468269665, 0.42762216215337556, 0.7466274030131497, 0.802550266787863, 0.3709323443076644, 0.5919817068197668, 0.49274512279324967,
0.7170132295090351, 0.0911793215410649, 0.5134213878288361, 0.670132600785118, 0.49417034512633484, 0.03887389460089885, 0.436785102836845,
0.1490231658611978, 0.6413606121498127, 0.8595987991375995, 0.9132593077586231, 0.7075959004873255, 0.17754995944845464, 0.5212507214937141,
0.35379732738215475, 0.25205107358505296, 0.3928792840544273, 0.09485294189485782, 0.8685115437448666, 0.6489046799288605, 0.509253797582924,
0.8993255536791972, 0.18740056466602373, 0.34237617336313986, 0.3871438962989183, 0.1488532571774911, 0.5187002331293636, 0.8137098818752955,
0.521761863717401, 0.4622312310118274, 0.29038411334638825, 0.16194915718170566, 0.5175999923925211, 0.8852230040101133, 0.0218263385047206,
0.08482355352852367, 0.3463638568376264, 0.28627127120619733, 0.9553293378948409, 0.4803391055970835, 0.841635695030805, 0.3556828280031952,
0.06778527221541808, 0.28193560357091596, 0.8399957619031576, 0.03305536359456385, 0.6625039162109645, 0.9300552020023897, 0.8551529138204146,
0.6133216915522418, 0.222427800857393, 0.1315422686800336, 0.6189144989185527, 0.5346184916866876, 0.8348888624532548, 0.6544834567840291,
0.2844062293389934, 0.28780026600883324, 0.5372272015684924, 0.6250226011503823, 0.28119106062279453, 0.49655812908420094, 0.6451488959145951,
0.7362580606834843, 0.44815578616664087, 0.6454760235835586, 0.6794062414265861, 0.045378883014935756, 0.9008388543865096, 0.7949752851269782,
0.4179928876222264, 0.28733419007048644, 0.996902319501908, 0.5690851338677467, 0.9511814013279738, 0.025323788678181636, 0.5594359732604794,
0.1213732595086251, 0.7172624313368294, 0.6759328959074691, 0.07252138454885071, 0.17557735158403442, 0.5988895455048769
};
float expected_output[1*3*4*2] = {
-0.556947, -0.42143887, -0.092070885, 0.27404794, -0.41886684, 0.0862887, -0.25001016, -0.342721, 0.020730592, 0.04016919, -0.69839877,
-0.06136704, 0.14186388, -0.11655602, -0.23489095, -0.3845829, -0.19017771, 0.1595885, -0.18308741, -0.3071209, -0.5848686, -0.22509028,
-0.6023201, -0.14448485
};
float *output;
float kernel[2*3*3*3] = {
-0.25291282, 0.22402048, 0.028642118, -0.14615723, -0.27362752, -0.34801802, -0.2759148, 0.19594926, -0.25029412, 0.34606284, 0.10376671,
-0.1015394, 0.23616093, 0.2134214, 0.35285157, 0.05893758, 0.0024731457, -0.17143056, 0.35758412, 0.2186206, -0.28384736, -0.21206513,
-0.20871592, 0.27070445, 0.25878823, 0.11136332, -0.33737376, 0.08353335, -0.34290665, 0.041805506, -0.09738535, 0.3284936, -0.16838405,
-0.032494456, -0.29193437, 0.033259362, -0.09272635, -0.2802651, -0.28648436, 0.3542878, 0.2432127, -0.24551713, 0.27813476, 0.21024024,
-0.013690501, -0.1350077, -0.07826337, -0.34563828, 0.3220685, -0.07571727, 0.19420576, 0.20783454, 0.18738335, 0.16672492
};
float bias[2] = { -0.4773722, -0.19620377 };
NativeContext ctx;
ctx.class = NULL;
ctx.options.conv2d_threads = 1;
params.activation = TANH;
params.has_bias = 1;
params.biases = bias;
params.dilation = 1;
params.input_num = 3;
params.kernel = kernel;
params.kernel_size = 3;
params.output_num = 2;
params.padding_method = VALID;
operands[0].data = input;
operands[0].dims[0] = 1;
operands[0].dims[1] = 5;
operands[0].dims[2] = 6;
operands[0].dims[3] = 3;
operands[1].data = NULL;
input_indexes[0] = 0;
ff_dnn_execute_layer_conv2d(operands, input_indexes, 1, &params, &ctx);
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;
}
int main(int argc, char **argv)
{
if (test_with_valid())
return 1;
if (test_with_same_dilate())
return 1;
return 0;
}

131
libavfilter/tests/dnn-layer-dense.c
View File

@ -1,131 +0,0 @@
/*
* Copyright (c) 2020
*
* This file is part of FFmpeg.
*
* FFmpeg is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public
* License as published by the Free Software Foundation; either
* version 2.1 of the License, or (at your option) any later version.
*
* FFmpeg is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with FFmpeg; if not, write to the Free Software
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
*/
#include <stdio.h>
#include <string.h>
#include <math.h>
#include "libavfilter/dnn/dnn_backend_native_layer_dense.h"
#define EPSON 0.00001
static int test(void)
{
// the input data and expected data are generated with below python code.
/*
x = tf.placeholder(tf.float32, shape=[1, None, None, 3])
y = tf.layers.dense(input_x, 3, activation=tf.nn.sigmoid, bias_initializer=tf.keras.initializers.he_normal())
data = np.random.rand(1, 5, 6, 3);
sess=tf.Session()
sess.run(tf.global_variables_initializer())
weights = dict([(var.name, sess.run(var)) for var in tf.trainable_variables()])
kernel = weights['dense/kernel:0']
kernel = np.transpose(kernel, [1, 0])
print("kernel:")
print(kernel.shape)
print(list(kernel.flatten()))
bias = weights['dense/bias:0']
print("bias:")
print(bias.shape)
print(list(bias.flatten()))
output = sess.run(y, feed_dict={x: data})
print("input:")
print(data.shape)
print(list(data.flatten()))
print("output:")
print(output.shape)
print(list(output.flatten()))
*/
DenseParams params;
DnnOperand operands[2];
int32_t input_indexes[1];
float input[1*5*6*3] = {
0.5552418686576308, 0.20653189262022464, 0.31115120939398877, 0.5897014433221428, 0.37340078861060655, 0.6470921693941893, 0.8039950367872679, 0.8762700891949274,
0.6556655583829558, 0.5911096107039339, 0.18640250865290997, 0.2803248779238966, 0.31586613136402053, 0.9447300740056483, 0.9443980824873418, 0.8158851991115941,
0.5631010340387631, 0.9407402251929046, 0.6485434876551682, 0.5631376966470001, 0.17581924875609634, 0.7033802439103178, 0.04802402495561675, 0.9183681450194972,
0.46059317944364, 0.07964160481596883, 0.871787076270302, 0.973743142324361, 0.15923146943258415, 0.8212946080584571, 0.5415954459227064, 0.9552813822803975,
0.4908552668172057, 0.33723691635292274, 0.46588057864910026, 0.8994239961321776, 0.09845220457674186, 0.1713400292123486, 0.39570294912818826, 0.08018956486392803,
0.5290478278169032, 0.7141906125920976, 0.0320878067840098, 0.6412406575332606, 0.0075712007102423096, 0.7150828462386156, 0.1311989216968138, 0.4706847944253756,
0.5447610794883336, 0.3430923933318001, 0.536082357943209, 0.4371629342483694, 0.40227962985019927, 0.3553806249465469, 0.031806622424259245, 0.7053916426174,
0.3261570237309813, 0.419500213292063, 0.3155691223480851, 0.05664028113178088, 0.3636491555914486, 0.8502419746667123, 0.9836596530684955, 0.1628681802975801,
0.09410832912479894, 0.28407218939480294, 0.7983417928813697, 0.24132158596506748, 0.8154729498062224, 0.29173768373895637, 0.13407102008052096, 0.18705786678800385,
0.7167943621295573, 0.09222004247174376, 0.2319220738766018, 0.17708964382285064, 0.1391440370249517, 0.3254088083499256, 0.4013916894718289, 0.4819742663322323,
0.15080103744648077, 0.9302407847555013, 0.9397597961319524, 0.5719200825550793, 0.9538938024682824, 0.9583882089203861, 0.5168861091262276, 0.1926396841842669,
0.6781176744337578, 0.719366447288566
};
float expected_output[1*5*6*3] = {
-0.3921688, -0.9243112, -0.29659146, -0.64000785, -0.9466343, -0.62125254, -0.71759033, -0.9171336, -0.735589, -0.34365994,
-0.92100817, -0.23903961, -0.8962277, -0.9521279, -0.90962386, -0.7488303, -0.9563761, -0.7701762, -0.40800542, -0.87684774,
-0.3339763, -0.6354543, -0.97068924, -0.6246325, -0.6992075, -0.9706726, -0.6818918, -0.51864433, -0.9592881, -0.51187396,
-0.7423632, -0.89911884, -0.7457824, -0.82009757, -0.96402895, -0.8235518, -0.61980766, -0.94494647, -0.5410502, -0.8281218,
-0.95508635, -0.8201453, -0.5937325, -0.8679507, -0.500767, -0.39430764, -0.93967676, -0.32183182, -0.58913624, -0.939717,
-0.55179894, -0.55004454, -0.9214453, -0.4889004, -0.75294703, -0.9118363, -0.7200309, -0.3248641, -0.8878874, -0.18977344,
-0.8873837, -0.9571257, -0.90145934, -0.50521654, -0.93739635, -0.39051685, -0.61143184, -0.9591179, -0.605999, -0.40008977,
-0.92219675, -0.26732883, -0.19607787, -0.9172511, -0.07068595, -0.5409857, -0.9387041, -0.44181606, -0.4705004, -0.8899935,
-0.37997037, -0.66105115, -0.89754754, -0.68141997, -0.6324047, -0.886776, -0.65066385, -0.8334821, -0.94801456, -0.83297
};
float *output;
float kernel[3*3] = {
0.56611896, -0.5144603, -0.82600045, 0.19219112, 0.3835776, -0.7475352, 0.5209291, -0.6301091, -0.99442935};
float bias[3] = {-0.3654299, -1.5711838, -0.15546428};
params.activation = TANH;
params.has_bias = 1;
params.biases = bias;
params.input_num = 3;
params.kernel = kernel;
params.output_num = 3;
operands[0].data = input;
operands[0].dims[0] = 1;
operands[0].dims[1] = 5;
operands[0].dims[2] = 6;
operands[0].dims[3] = 3;
operands[1].data = NULL;
input_indexes[0] = 0;
ff_dnn_execute_layer_dense(operands, input_indexes, 1, &params, NULL);
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;
}
int main(int argc, char **argv)
{
if (test())
return 1;
return 0;
}

102
libavfilter/tests/dnn-layer-depth2space.c
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@ -1,102 +0,0 @@
/*
* Copyright (c) 2019 Guo Yejun
*
* This file is part of FFmpeg.
*
* FFmpeg is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public
* License as published by the Free Software Foundation; either
* version 2.1 of the License, or (at your option) any later version.
*
* FFmpeg is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with FFmpeg; if not, write to the Free Software
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
*/
#include <stdio.h>
#include <string.h>
#include <math.h>
#include "libavfilter/dnn/dnn_backend_native.h"
#include "libavfilter/dnn/dnn_backend_native_layer_depth2space.h"
#define EPSON 0.00001
static int test(void)
{
// the input data and expected data are generated with below python code.
/*
x = tf.placeholder(tf.float32, shape=[1, None, None, 4])
y = tf.depth_to_space(x, 2)
data = np.random.rand(1, 5, 3, 4);
sess=tf.Session()
sess.run(tf.global_variables_initializer())
output = sess.run(y, feed_dict={x: data})
print("input:")
print(data.shape)
print(list(data.flatten()))
print("output:")
print(output.shape)
print(list(output.flatten()))
*/
DepthToSpaceParams params;
DnnOperand operands[2];
int32_t input_indexes[1];
float input[1*5*3*4] = {
0.09771065121566602, 0.6336807372403175, 0.5142416549709786, 0.8027206567330333, 0.2154276025069397, 0.12112878462616772, 0.913936596765778,
0.38881443647542646, 0.5850447615898835, 0.9311499327398275, 0.3613660929428246, 0.5420722002125493, 0.6002131190230359, 0.44800665702299525,
0.7271322557896777, 0.3869293511885826, 0.5144404769364138, 0.6910844856987723, 0.6142102742269762, 0.6249991371621018, 0.45663376215836626,
0.19523477129943423, 0.2483895888532045, 0.64326768256278, 0.5485877602998981, 0.45442067849873546, 0.529374943304256, 0.30439850391811885,
0.11961343361340993, 0.2909643484561082, 0.9810970344127848, 0.8886928489786549, 0.6112237084436409, 0.8852482695156674, 0.9110868043114374,
0.21242780027585217, 0.7101536973207572, 0.9709717457443375, 0.2702666770969332, 0.7718295953780221, 0.3957005164588574, 0.24383544252475453,
0.040143453532367035, 0.26358051835323115, 0.013130251443791319, 0.3016550481482074, 0.03582340459943956, 0.718025513612361, 0.09844204177633753,
0.04433767496953056, 0.6221895044119757, 0.6190414032940228, 0.8963550834625371, 0.5642449700064629, 0.2482982014723497, 0.17824909294583013,
0.024401882408643272, 0.21742800875253465, 0.6794724473181843, 0.4814830479242237
};
float expected_output[1*10*6*1] = {
0.097710654, 0.63368076, 0.2154276, 0.12112878, 0.58504474, 0.93114996, 0.51424164, 0.80272067, 0.9139366, 0.38881445,
0.3613661, 0.5420722, 0.6002131, 0.44800666, 0.5144405, 0.6910845, 0.45663378, 0.19523478, 0.72713226, 0.38692936,
0.61421025, 0.62499917, 0.24838959, 0.6432677, 0.54858774, 0.4544207, 0.11961343, 0.29096434, 0.6112237, 0.88524824,
0.52937496, 0.3043985, 0.98109704, 0.88869286, 0.9110868, 0.2124278, 0.7101537, 0.97097176, 0.3957005, 0.24383545,
0.013130251, 0.30165505, 0.27026668, 0.7718296, 0.040143453, 0.26358053, 0.035823405, 0.7180255, 0.09844204,
0.044337675, 0.8963551, 0.564245, 0.024401883, 0.21742801, 0.6221895, 0.6190414, 0.2482982, 0.17824909, 0.67947245, 0.48148304
};
float *output;
operands[0].data = input;
operands[0].dims[0] = 1;
operands[0].dims[1] = 5;
operands[0].dims[2] = 3;
operands[0].dims[3] = 4;
operands[1].data = NULL;
input_indexes[0] = 0;
params.block_size = 2;
ff_dnn_execute_layer_depth2space(operands, input_indexes, 1, &params, NULL);
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;
}
int main(int argc, char **argv)
{
return test();
}

214
libavfilter/tests/dnn-layer-mathbinary.c
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@ -1,214 +0,0 @@
/*
* Copyright (c) 2020
*
* This file is part of FFmpeg.
*
* FFmpeg is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public
* License as published by the Free Software Foundation; either
* version 2.1 of the License, or (at your option) any later version.
*
* FFmpeg is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with FFmpeg; if not, write to the Free Software
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
*/
#include <stdio.h>
#include <string.h>
#include <math.h>
#include "libavfilter/dnn/dnn_backend_native_layer_mathbinary.h"
#include "libavutil/avassert.h"
#define EPSON 0.00005
static float get_expected(float f1, float f2, DNNMathBinaryOperation op)
{
switch (op)
{
case DMBO_SUB:
return f1 - f2;
case DMBO_ADD:
return f1 + f2;
case DMBO_MUL:
return f1 * f2;
case DMBO_REALDIV:
return f1 / f2;
case DMBO_MINIMUM:
return (f1 < f2) ? f1 : f2;
case DMBO_FLOORMOD:
return (float)((int)(f1) % (int)(f2));
default:
av_assert0(!"not supported yet");
return 0.f;
}
}
static int test_broadcast_input0(DNNMathBinaryOperation op)
{
DnnLayerMathBinaryParams params;
DnnOperand operands[2];
int32_t input_indexes[1];
float input[1*1*2*3] = {
-3, 2.5, 2, -2.1, 7.8, 100
};
float *output;
params.bin_op = op;
params.input0_broadcast = 1;
params.input1_broadcast = 0;
params.v = 7.28;
operands[0].data = input;
operands[0].dims[0] = 1;
operands[0].dims[1] = 1;
operands[0].dims[2] = 2;
operands[0].dims[3] = 3;
operands[1].data = NULL;
input_indexes[0] = 0;
ff_dnn_execute_layer_math_binary(operands, input_indexes, 1, &params, NULL);
output = operands[1].data;
for (int i = 0; i < sizeof(input) / sizeof(float); i++) {
float expected_output = get_expected(params.v, input[i], op);
if (fabs(output[i] - expected_output) > EPSON) {
printf("op %d, at index %d, output: %f, expected_output: %f (%s:%d)\n",
op, i, output[i], expected_output, __FILE__, __LINE__);
av_freep(&output);
return 1;
}
}
av_freep(&output);
return 0;
}
static int test_broadcast_input1(DNNMathBinaryOperation op)
{
DnnLayerMathBinaryParams params;
DnnOperand operands[2];
int32_t input_indexes[1];
float input[1*1*2*3] = {
-3, 2.5, 2, -2.1, 7.8, 100
};
float *output;
params.bin_op = op;
params.input0_broadcast = 0;
params.input1_broadcast = 1;
params.v = 7.28;
operands[0].data = input;
operands[0].dims[0] = 1;
operands[0].dims[1] = 1;
operands[0].dims[2] = 2;
operands[0].dims[3] = 3;
operands[1].data = NULL;
input_indexes[0] = 0;
ff_dnn_execute_layer_math_binary(operands, input_indexes, 1, &params, NULL);
output = operands[1].data;
for (int i = 0; i < sizeof(input) / sizeof(float); i++) {
float expected_output = get_expected(input[i], params.v, op);
if (fabs(output[i] - expected_output) > EPSON) {
printf("op %d, at index %d, output: %f, expected_output: %f (%s:%d)\n",
op, i, output[i], expected_output, __FILE__, __LINE__);
av_freep(&output);
return 1;
}
}
av_freep(&output);
return 0;
}
static int test_no_broadcast(DNNMathBinaryOperation op)
{
DnnLayerMathBinaryParams params;
DnnOperand operands[3];
int32_t input_indexes[2];
float input0[1*1*2*3] = {
-3, 2.5, 2, -2.1, 7.8, 100
};
float input1[1*1*2*3] = {
-1, 2, 3, -21, 8, 10.0
};
float *output;
params.bin_op = op;
params.input0_broadcast = 0;
params.input1_broadcast = 0;
operands[0].data = input0;
operands[0].dims[0] = 1;
operands[0].dims[1] = 1;
operands[0].dims[2] = 2;
operands[0].dims[3] = 3;
operands[1].data = input1;
operands[1].dims[0] = 1;
operands[1].dims[1] = 1;
operands[1].dims[2] = 2;
operands[1].dims[3] = 3;
operands[2].data = NULL;
input_indexes[0] = 0;
input_indexes[1] = 1;
ff_dnn_execute_layer_math_binary(operands, input_indexes, 2, &params, NULL);
output = operands[2].data;
for (int i = 0; i < sizeof(input0) / sizeof(float); i++) {
float expected_output = get_expected(input0[i], input1[i], op);
if (fabs(output[i] - expected_output) > EPSON) {
printf("op %d, at index %d, output: %f, expected_output: %f (%s:%d)\n",
op, i, output[i], expected_output, __FILE__, __LINE__);
av_freep(&output);
return 1;
}
}
av_freep(&output);
return 0;
}
static int test(DNNMathBinaryOperation op)
{
if (test_broadcast_input0(op))
return 1;
if (test_broadcast_input1(op))
return 1;
if (test_no_broadcast(op))
return 1;
return 0;
}
int main(int argc, char **argv)
{
if (test(DMBO_SUB))
return 1;
if (test(DMBO_ADD))
return 1;
if (test(DMBO_MUL))
return 1;
if (test(DMBO_REALDIV))
return 1;
if (test(DMBO_MINIMUM))
return 1;
if (test(DMBO_FLOORMOD))
return 1;
return 0;
}

148
libavfilter/tests/dnn-layer-mathunary.c
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@ -1,148 +0,0 @@
/*
* Copyright (c) 2020
*
* This file is part of FFmpeg.
*
* FFmpeg is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public
* License as published by the Free Software Foundation; either
* version 2.1 of the License, or (at your option) any later version.
*
* FFmpeg is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with FFmpeg; if not, write to the Free Software
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
*/
#include <stdio.h>
#include <string.h>
#include <math.h>
#include "libavfilter/dnn/dnn_backend_native_layer_mathunary.h"
#include "libavutil/avassert.h"
#define EPS 0.00001
static float get_expected(float f, DNNMathUnaryOperation op)
{
switch (op)
{
case DMUO_ABS:
return (f >= 0) ? f : -f;
case DMUO_SIN:
return sin(f);
case DMUO_COS:
return cos(f);
case DMUO_TAN:
return tan(f);
case DMUO_ASIN:
return asin(f);
case DMUO_ACOS:
return acos(f);
case DMUO_ATAN:
return atan(f);
case DMUO_SINH:
return sinh(f);
case DMUO_COSH:
return cosh(f);
case DMUO_TANH:
return tanh(f);
case DMUO_ASINH:
return asinh(f);
case DMUO_ACOSH:
return acosh(f);
case DMUO_ATANH:
return atanh(f);
case DMUO_CEIL:
return ceil(f);
case DMUO_FLOOR:
return floor(f);
case DMUO_ROUND:
return round(f);
case DMUO_EXP:
return exp(f);
default:
av_assert0(!"not supported yet");
return 0.f;
}
}
static int test(DNNMathUnaryOperation op)
{
DnnLayerMathUnaryParams params;
DnnOperand operands[2];
int32_t input_indexes[1];
float input[1*1*3*3] = {
0.1, 0.5, 0.75, -3, 2.5, 2, -2.1, 7.8, 100};
float *output;
params.un_op = op;
operands[0].data = input;
operands[0].dims[0] = 1;
operands[0].dims[1] = 1;
operands[0].dims[2] = 3;
operands[0].dims[3] = 3;
operands[1].data = NULL;
input_indexes[0] = 0;
ff_dnn_execute_layer_math_unary(operands, input_indexes, 1, &params, NULL);
output = operands[1].data;
for (int i = 0; i < sizeof(input) / sizeof(float); ++i) {
float expected_output = get_expected(input[i], op);
int output_nan = isnan(output[i]);
int expected_nan = isnan(expected_output);
if ((!output_nan && !expected_nan && fabs(output[i] - expected_output) > EPS) ||
(output_nan && !expected_nan) || (!output_nan && expected_nan)) {
printf("at index %d, output: %f, expected_output: %f\n", i, output[i], expected_output);
av_freep(&output);
return 1;
}
}
av_freep(&output);
return 0;
}
int main(int agrc, char **argv)
{
if (test(DMUO_ABS))
return 1;
if (test(DMUO_SIN))
return 1;
if (test(DMUO_COS))
return 1;
if (test(DMUO_TAN))
return 1;
if (test(DMUO_ASIN))
return 1;
if (test(DMUO_ACOS))
return 1;
if (test(DMUO_ATAN))
return 1;
if (test(DMUO_SINH))
return 1;
if (test(DMUO_COSH))
return 1;
if (test(DMUO_TANH))
return 1;
if (test(DMUO_ASINH))
return 1;
if (test(DMUO_ACOSH))
return 1;
if (test(DMUO_ATANH))
return 1;
if (test(DMUO_CEIL))
return 1;
if (test(DMUO_FLOOR))
return 1;
if (test(DMUO_ROUND))
return 1;
if (test(DMUO_EXP))
return 1;
return 0;
}

71
libavfilter/tests/dnn-layer-maximum.c
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@ -1,71 +0,0 @@
/*
* Copyright (c) 2019 Guo Yejun
*
* This file is part of FFmpeg.
*
* FFmpeg is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public
* License as published by the Free Software Foundation; either
* version 2.1 of the License, or (at your option) any later version.
*
* FFmpeg is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with FFmpeg; if not, write to the Free Software
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
*/
#include <stdio.h>
#include <string.h>
#include <math.h>
#include "libavfilter/dnn/dnn_backend_native_layer_maximum.h"
#define EPSON 0.00001
static int test(void)
{
DnnLayerMaximumParams params;
DnnOperand operands[2];
int32_t input_indexes[1];
float input[1*1*2*3] = {
-3, 2.5, 2, -2.1, 7.8, 100
};
float *output;
params.val.y = 2.3;
operands[0].data = input;
operands[0].dims[0] = 1;
operands[0].dims[1] = 1;
operands[0].dims[2] = 2;
operands[0].dims[3] = 3;
operands[1].data = NULL;
input_indexes[0] = 0;
ff_dnn_execute_layer_maximum(operands, input_indexes, 1, &params, NULL);
output = operands[1].data;
for (int i = 0; i < sizeof(input) / sizeof(float); i++) {
float expected_output = input[i] > params.val.y ? input[i] : params.val.y;
if (fabs(output[i] - expected_output) > EPSON) {
printf("at index %d, output: %f, expected_output: %f\n", i, output[i], expected_output);
av_freep(&output);
return 1;
}
}
av_freep(&output);
return 0;
}
int main(int argc, char **argv)
{
if (test())
return 1;
return 0;
}

239
libavfilter/tests/dnn-layer-pad.c
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@ -1,239 +0,0 @@
/*
* Copyright (c) 2019 Guo Yejun
*
* This file is part of FFmpeg.
*
* FFmpeg is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public
* License as published by the Free Software Foundation; either
* version 2.1 of the License, or (at your option) any later version.
*
* FFmpeg is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with FFmpeg; if not, write to the Free Software
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
*/
#include <stdio.h>
#include <string.h>
#include <math.h>
#include "libavfilter/dnn/dnn_backend_native_layer_pad.h"
#define EPSON 0.00001
static int test_with_mode_symmetric(void)
{
// the input data and expected data are generated with below python code.
/*
x = tf.placeholder(tf.float32, shape=[1, None, None, 3])
y = tf.pad(x, [[0, 0], [2, 3], [3, 2], [0, 0]], 'SYMMETRIC')
data = np.arange(48).reshape(1, 4, 4, 3);
sess=tf.Session()
sess.run(tf.global_variables_initializer())
output = sess.run(y, feed_dict={x: data})
print(list(data.flatten()))
print(list(output.flatten()))
print(data.shape)
print(output.shape)
*/
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
};
float expected_output[1*9*9*3] = {
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, 6.0, 7.0, 8.0, 3.0,
4.0, 5.0, 0.0, 1.0, 2.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 9.0, 10.0, 11.0, 6.0, 7.0, 8.0, 6.0, 7.0, 8.0, 3.0, 4.0, 5.0, 0.0, 1.0, 2.0, 0.0, 1.0, 2.0, 3.0,
4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 9.0, 10.0, 11.0, 6.0, 7.0, 8.0, 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, 30.0, 31.0, 32.0, 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, 42.0, 43.0, 44.0, 39.0, 40.0, 41.0, 36.0, 37.0, 38.0, 36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 45.0, 46.0, 47.0, 42.0, 43.0,
44.0, 42.0, 43.0, 44.0, 39.0, 40.0, 41.0, 36.0, 37.0, 38.0, 36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 45.0, 46.0, 47.0, 42.0, 43.0, 44.0, 30.0, 31.0, 32.0,
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;
params.mode = LPMP_SYMMETRIC;
params.paddings[0][0] = 0;
params.paddings[0][1] = 0;
params.paddings[1][0] = 2;
params.paddings[1][1] = 3;
params.paddings[2][0] = 3;
params.paddings[2][1] = 2;
params.paddings[3][0] = 0;
params.paddings[3][1] = 0;
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;
input_indexes[0] = 0;
ff_dnn_execute_layer_pad(operands, input_indexes, 1, &params, NULL);
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;
}
static int test_with_mode_reflect(void)
{
// the input data and expected data are generated with below python code.
/*
x = tf.placeholder(tf.float32, shape=[3, None, None, 3])
y = tf.pad(x, [[1, 2], [0, 0], [0, 0], [0, 0]], 'REFLECT')
data = np.arange(36).reshape(3, 2, 2, 3);
sess=tf.Session()
sess.run(tf.global_variables_initializer())
output = sess.run(y, feed_dict={x: data})
print(list(data.flatten()))
print(list(output.flatten()))
print(data.shape)
print(output.shape)
*/
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
};
float expected_output[6*2*2*3] = {
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,
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;
params.mode = LPMP_REFLECT;
params.paddings[0][0] = 1;
params.paddings[0][1] = 2;
params.paddings[1][0] = 0;
params.paddings[1][1] = 0;
params.paddings[2][0] = 0;
params.paddings[2][1] = 0;
params.paddings[3][0] = 0;
params.paddings[3][1] = 0;
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;
ff_dnn_execute_layer_pad(operands, input_indexes, 1, &params, NULL);
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;
}
static int test_with_mode_constant(void)
{
// the input data and expected data are generated with below python code.
/*
x = tf.placeholder(tf.float32, shape=[1, None, None, 3])
y = tf.pad(x, [[0, 0], [1, 0], [0, 0], [1, 2]], 'CONSTANT', constant_values=728)
data = np.arange(12).reshape(1, 2, 2, 3);
sess=tf.Session()
sess.run(tf.global_variables_initializer())
output = sess.run(y, feed_dict={x: data})
print(list(data.flatten()))
print(list(output.flatten()))
print(data.shape)
print(output.shape)
*/
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
};
float expected_output[1*3*2*6] = {
728.0, 728.0, 728.0, 728.0, 728.0, 728.0, 728.0, 728.0, 728.0, 728.0, 728.0,
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;
params.mode = LPMP_CONSTANT;
params.constant_values = 728;
params.paddings[0][0] = 0;
params.paddings[0][1] = 0;
params.paddings[1][0] = 1;
params.paddings[1][1] = 0;
params.paddings[2][0] = 0;
params.paddings[2][1] = 0;
params.paddings[3][0] = 1;
params.paddings[3][1] = 2;
operands[0].data = input;
operands[0].dims[0] = 1;
operands[0].dims[1] = 2;
operands[0].dims[2] = 2;
operands[0].dims[3] = 3;
operands[1].data = NULL;
input_indexes[0] = 0;
ff_dnn_execute_layer_pad(operands, input_indexes, 1, &params, NULL);
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;
}
int main(int argc, char **argv)
{
if (test_with_mode_symmetric())
return 1;
if (test_with_mode_reflect())
return 1;
if (test_with_mode_constant())
return 1;
}

1
libavfilter/vf_derain.c
View File

@ -44,7 +44,6 @@ static const AVOption derain_options[] = {
{ "derain", "derain filter flag", 0, AV_OPT_TYPE_CONST, { .i64 = 0 }, 0, 0, FLAGS, "type" },
{ "dehaze", "dehaze filter flag", 0, AV_OPT_TYPE_CONST, { .i64 = 1 }, 0, 0, FLAGS, "type" },
{ "dnn_backend", "DNN backend", OFFSET(dnnctx.backend_type), AV_OPT_TYPE_INT, { .i64 = 1 }, 0, 1, FLAGS, "backend" },
{ "native", "native backend flag", 0, AV_OPT_TYPE_CONST, { .i64 = 0 }, 0, 0, FLAGS, "backend" },
#if (CONFIG_LIBTENSORFLOW == 1)
{ "tensorflow", "tensorflow backend flag", 0, AV_OPT_TYPE_CONST, { .i64 = 1 }, 0, 0, FLAGS, "backend" },
#endif

1
libavfilter/vf_dnn_processing.c
View File

@ -46,7 +46,6 @@ typedef struct DnnProcessingContext {
#define FLAGS AV_OPT_FLAG_FILTERING_PARAM | AV_OPT_FLAG_VIDEO_PARAM
static const AVOption dnn_processing_options[] = {
{ "dnn_backend", "DNN backend", OFFSET(backend_type), AV_OPT_TYPE_INT, { .i64 = 1 }, INT_MIN, INT_MAX, FLAGS, "backend" },
{ "native", "native backend flag", 0, AV_OPT_TYPE_CONST, { .i64 = 0 }, 0, 0, FLAGS, "backend" },
#if (CONFIG_LIBTENSORFLOW == 1)
{ "tensorflow", "tensorflow backend flag", 0, AV_OPT_TYPE_CONST, { .i64 = 1 }, 0, 0, FLAGS, "backend" },
#endif

1
libavfilter/vf_sr.c
View File

@ -47,7 +47,6 @@ typedef struct SRContext {
#define FLAGS AV_OPT_FLAG_FILTERING_PARAM | AV_OPT_FLAG_VIDEO_PARAM
static const AVOption sr_options[] = {
{ "dnn_backend", "DNN backend used for model execution", OFFSET(dnnctx.backend_type), AV_OPT_TYPE_INT, { .i64 = 1 }, 0, 1, FLAGS, "backend" },
{ "native", "native backend flag", 0, AV_OPT_TYPE_CONST, { .i64 = 0 }, 0, 0, FLAGS, "backend" },
#if (CONFIG_LIBTENSORFLOW == 1)
{ "tensorflow", "tensorflow backend flag", 0, AV_OPT_TYPE_CONST, { .i64 = 1 }, 0, 0, FLAGS, "backend" },
#endif

1
tests/Makefile
View File

@ -172,7 +172,6 @@ include $(SRC_PATH)/tests/fate/cover-art.mak
include $(SRC_PATH)/tests/fate/dca.mak
include $(SRC_PATH)/tests/fate/demux.mak
include $(SRC_PATH)/tests/fate/dfa.mak
include $(SRC_PATH)/tests/fate/dnn.mak
include $(SRC_PATH)/tests/fate/dnxhd.mak
include $(SRC_PATH)/tests/fate/dpcm.mak
include $(SRC_PATH)/tests/fate/dvvideo.mak

45
tests/fate/dnn.mak
View File

@ -1,45 +0,0 @@
DNNTESTSDIR := libavfilter/tests
FATE_DNN += fate-dnn-layer-pad
fate-dnn-layer-pad: $(DNNTESTSDIR)/dnn-layer-pad$(EXESUF)
fate-dnn-layer-pad: CMD = run $(DNNTESTSDIR)/dnn-layer-pad$(EXESUF)
fate-dnn-layer-pad: CMP = null
FATE_DNN += fate-dnn-layer-conv2d
fate-dnn-layer-conv2d: $(DNNTESTSDIR)/dnn-layer-conv2d$(EXESUF)
fate-dnn-layer-conv2d: CMD = run $(DNNTESTSDIR)/dnn-layer-conv2d$(EXESUF)
fate-dnn-layer-conv2d: CMP = null
FATE_DNN += fate-dnn-layer-dense
fate-dnn-layer-dense: $(DNNTESTSDIR)/dnn-layer-dense$(EXESUF)
fate-dnn-layer-dense: CMD = run $(DNNTESTSDIR)/dnn-layer-dense$(EXESUF)
fate-dnn-layer-dense: CMP = null
FATE_DNN += fate-dnn-layer-depth2space
fate-dnn-layer-depth2space: $(DNNTESTSDIR)/dnn-layer-depth2space$(EXESUF)
fate-dnn-layer-depth2space: CMD = run $(DNNTESTSDIR)/dnn-layer-depth2space$(EXESUF)
fate-dnn-layer-depth2space: CMP = null
FATE_DNN += fate-dnn-layer-mathbinary
fate-dnn-layer-mathbinary: $(DNNTESTSDIR)/dnn-layer-mathbinary$(EXESUF)
fate-dnn-layer-mathbinary: CMD = run $(DNNTESTSDIR)/dnn-layer-mathbinary$(EXESUF)
fate-dnn-layer-mathbinary: CMP = null
FATE_DNN += fate-dnn-layer-maximum
fate-dnn-layer-maximum: $(DNNTESTSDIR)/dnn-layer-maximum$(EXESUF)
fate-dnn-layer-maximum: CMD = run $(DNNTESTSDIR)/dnn-layer-maximum$(EXESUF)
fate-dnn-layer-maximum: CMP = null
FATE_DNN += fate-dnn-layer-mathunary
fate-dnn-layer-mathunary: $(DNNTESTSDIR)/dnn-layer-mathunary$(EXESUF)
fate-dnn-layer-mathunary: CMD = run $(DNNTESTSDIR)/dnn-layer-mathunary$(EXESUF)
fate-dnn-layer-mathunary: CMP = null
FATE_DNN += fate-dnn-layer-avgpool
fate-dnn-layer-avgpool: $(DNNTESTSDIR)/dnn-layer-avgpool$(EXESUF)
fate-dnn-layer-avgpool: CMD = run $(DNNTESTSDIR)/dnn-layer-avgpool$(EXESUF)
fate-dnn-layer-avgpool: CMP = null
FATE-$(CONFIG_DNN) += $(FATE_DNN)
fate-dnn: $(FATE_DNN)