#include "libavutil/avassert.h"
#include "dnn_backend_native_layer_conv2d.h"
#include "dnn_backend_native_layers.h"
+#include "dnn_io_proc.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 },
+ { NULL },
+};
+
+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 DNNReturnType execute_model_native(const DNNModel *model, const char *input_name, AVFrame *in_frame,
+ const char **output_names, uint32_t nb_output, AVFrame *out_frame,
+ int do_ioproc);
static DNNReturnType get_input_native(void *model, DNNData *input, const char *input_name)
{
NativeModel *native_model = (NativeModel *)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)
+ 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 DNN_ERROR;
+ }
input->dt = oprd->data_type;
av_assert0(oprd->dims[0] == 1);
input->height = oprd->dims[1];
}
// do not find the input operand
+ av_log(ctx, AV_LOG_ERROR, "Could not find \"%s\" in model\n", input_name);
return DNN_ERROR;
}
-static DNNReturnType set_input_native(void *model, DNNData *input, const char *input_name)
+static DNNReturnType 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)
{
+ DNNReturnType ret;
NativeModel *native_model = (NativeModel *)model;
- DnnOperand *oprd = NULL;
+ NativeContext *ctx = &native_model->ctx;
+ AVFrame *in_frame = av_frame_alloc();
+ AVFrame *out_frame = NULL;
- if (native_model->layers_num <= 0 || native_model->operands_num <= 0)
+ if (!in_frame) {
+ av_log(ctx, AV_LOG_ERROR, "Could not allocate memory for input frame\n");
return DNN_ERROR;
-
- /* inputs */
- for (int i = 0; i < native_model->operands_num; ++i) {
- oprd = &native_model->operands[i];
- if (strcmp(oprd->name, input_name) == 0) {
- if (oprd->type != DOT_INPUT)
- return DNN_ERROR;
- break;
- }
- oprd = NULL;
}
- if (!oprd)
- return DNN_ERROR;
-
- oprd->dims[0] = 1;
- oprd->dims[1] = input->height;
- oprd->dims[2] = input->width;
- oprd->dims[3] = input->channels;
+ out_frame = av_frame_alloc();
- av_freep(&oprd->data);
- oprd->length = calculate_operand_data_length(oprd);
- if (oprd->length <= 0)
- return DNN_ERROR;
- oprd->data = av_malloc(oprd->length);
- if (!oprd->data)
+ if (!out_frame) {
+ av_log(ctx, AV_LOG_ERROR, "Could not allocate memory for output frame\n");
+ av_frame_free(&in_frame);
return DNN_ERROR;
+ }
- input->data = oprd->data;
+ in_frame->width = input_width;
+ in_frame->height = input_height;
- return DNN_SUCCESS;
+ ret = execute_model_native(native_model->model, input_name, in_frame, &output_name, 1, out_frame, 0);
+ *output_width = out_frame->width;
+ *output_height = out_frame->height;
+
+ av_frame_free(&out_frame);
+ av_frame_free(&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, const char *options)
+DNNModel *ff_dnn_load_model_native(const char *model_filename, const char *options, void *userdata)
{
DNNModel *model = NULL;
char header_expected[] = "FFMPEGDNNNATIVE";
if (!native_model){
goto fail;
}
+
+ 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;
model->model = (void *)native_model;
+ native_model->model = model;
+
+#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);
return NULL;
}
- model->set_input = &set_input_native;
model->get_input = &get_input_native;
- model->options = options;
+ model->get_output = &get_output_native;
+ model->userdata = userdata;
return model;
return NULL;
}
-DNNReturnType ff_dnn_execute_model_native(const DNNModel *model, DNNData *outputs, const char **output_names, uint32_t nb_output)
+static DNNReturnType execute_model_native(const DNNModel *model, const char *input_name, AVFrame *in_frame,
+ const char **output_names, uint32_t nb_output, AVFrame *out_frame,
+ int do_ioproc)
{
NativeModel *native_model = (NativeModel *)model->model;
+ NativeContext *ctx = &native_model->ctx;
int32_t layer;
+ DNNData input, output;
+ DnnOperand *oprd = NULL;
- if (native_model->layers_num <= 0 || native_model->operands_num <= 0)
+ if (native_model->layers_num <= 0 || native_model->operands_num <= 0) {
+ av_log(ctx, AV_LOG_ERROR, "No operands or layers in model\n");
return DNN_ERROR;
- if (!native_model->operands[0].data)
+ }
+
+ for (int i = 0; i < native_model->operands_num; ++i) {
+ 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 DNN_ERROR;
+ }
+ break;
+ }
+ oprd = NULL;
+ }
+ if (!oprd) {
+ av_log(ctx, AV_LOG_ERROR, "Could not find \"%s\" in model\n", input_name);
+ return DNN_ERROR;
+ }
+
+ oprd->dims[1] = in_frame->height;
+ oprd->dims[2] = in_frame->width;
+
+ av_freep(&oprd->data);
+ oprd->length = calculate_operand_data_length(oprd);
+ if (oprd->length <= 0) {
+ av_log(ctx, AV_LOG_ERROR, "The input data length overflow\n");
+ return DNN_ERROR;
+ }
+ oprd->data = av_malloc(oprd->length);
+ if (!oprd->data) {
+ av_log(ctx, AV_LOG_ERROR, "Failed to malloc memory for input data\n");
+ return DNN_ERROR;
+ }
+
+ 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 (do_ioproc) {
+ if (native_model->model->pre_proc != NULL) {
+ native_model->model->pre_proc(in_frame, &input, native_model->model->userdata);
+ } else {
+ proc_from_frame_to_dnn(in_frame, &input, ctx);
+ }
+ }
+
+ if (nb_output != 1) {
+ // currently, the filter does not need multiple outputs,
+ // so we just pending the support until we really need it.
+ av_log(ctx, AV_LOG_ERROR, "do not support multiple outputs\n");
return DNN_ERROR;
+ }
for (layer = 0; layer < native_model->layers_num; ++layer){
DNNLayerType layer_type = native_model->layers[layer].type;
- 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);
+ if (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) == DNN_ERROR) {
+ av_log(ctx, AV_LOG_ERROR, "Failed to execuet model\n");
+ return DNN_ERROR;
+ }
}
for (uint32_t i = 0; i < nb_output; ++i) {
}
}
- if (oprd == NULL)
+ if (oprd == NULL) {
+ av_log(ctx, AV_LOG_ERROR, "Could not find output in model\n");
return DNN_ERROR;
+ }
- outputs[i].data = oprd->data;
- outputs[i].height = oprd->dims[1];
- outputs[i].width = oprd->dims[2];
- outputs[i].channels = oprd->dims[3];
- outputs[i].dt = oprd->data_type;
+ 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 (do_ioproc) {
+ if (native_model->model->post_proc != NULL) {
+ native_model->model->post_proc(out_frame, &output, native_model->model->userdata);
+ } else {
+ proc_from_dnn_to_frame(out_frame, &output, ctx);
+ }
+ } else {
+ out_frame->width = output.width;
+ out_frame->height = output.height;
+ }
}
return DNN_SUCCESS;
}
+DNNReturnType ff_dnn_execute_model_native(const DNNModel *model, const char *input_name, AVFrame *in_frame,
+ const char **output_names, uint32_t nb_output, AVFrame *out_frame)
+{
+ NativeModel *native_model = (NativeModel *)model->model;
+ NativeContext *ctx = &native_model->ctx;
+
+ if (!in_frame) {
+ av_log(ctx, AV_LOG_ERROR, "in frame is NULL when execute model.\n");
+ return DNN_ERROR;
+ }
+
+ if (!out_frame) {
+ av_log(ctx, AV_LOG_ERROR, "out frame is NULL when execute model.\n");
+ return DNN_ERROR;
+ }
+
+ return execute_model_native(model, input_name, in_frame, output_names, nb_output, out_frame, 1);
+}
+
int32_t calculate_operand_dims_count(const DnnOperand *oprd)
{
int32_t result = 1;
projects / ffmpeg / blobdiff