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dnn/native: rename struct ConvolutionalNetwork to NativeModel
Signed-off-by: Ting Fu <ting.fu@intel.com> Reviewed-by: Guo, Yejun <yejun.guo@intel.com>
This commit is contained in:
Ting Fu
Guo, Yejun
parent
b2266961c0
commit
a6e830ae7f
@ -30,10 +30,10 @@
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static DNNReturnType get_input_native(void *model, DNNData *input, const char *input_name)
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{
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ConvolutionalNetwork *network = (ConvolutionalNetwork *)model;
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NativeModel *native_model = (NativeModel *)model;
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for (int i = 0; i < network->operands_num; ++i) {
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DnnOperand *oprd = &network->operands[i];
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for (int i = 0; i < native_model->operands_num; ++i) {
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DnnOperand *oprd = &native_model->operands[i];
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if (strcmp(oprd->name, input_name) == 0) {
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if (oprd->type != DOT_INPUT)
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return DNN_ERROR;
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@ -52,15 +52,15 @@ static DNNReturnType get_input_native(void *model, DNNData *input, const char *i
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static DNNReturnType set_input_output_native(void *model, DNNData *input, const char *input_name, const char **output_names, uint32_t nb_output)
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{
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ConvolutionalNetwork *network = (ConvolutionalNetwork *)model;
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NativeModel *native_model = (NativeModel *)model;
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DnnOperand *oprd = NULL;
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if (network->layers_num <= 0 || network->operands_num <= 0)
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if (native_model->layers_num <= 0 || native_model->operands_num <= 0)
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return DNN_ERROR;
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/* inputs */
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for (int i = 0; i < network->operands_num; ++i) {
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oprd = &network->operands[i];
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for (int i = 0; i < native_model->operands_num; ++i) {
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oprd = &native_model->operands[i];
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if (strcmp(oprd->name, input_name) == 0) {
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if (oprd->type != DOT_INPUT)
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return DNN_ERROR;
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@ -88,24 +88,24 @@ static DNNReturnType set_input_output_native(void *model, DNNData *input, const
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input->data = oprd->data;
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/* outputs */
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network->nb_output = 0;
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av_freep(&network->output_indexes);
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network->output_indexes = av_mallocz_array(nb_output, sizeof(*network->output_indexes));
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if (!network->output_indexes)
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native_model->nb_output = 0;
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av_freep(&native_model->output_indexes);
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native_model->output_indexes = av_mallocz_array(nb_output, sizeof(*native_model->output_indexes));
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if (!native_model->output_indexes)
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return DNN_ERROR;
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for (uint32_t i = 0; i < nb_output; ++i) {
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const char *output_name = output_names[i];
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for (int j = 0; j < network->operands_num; ++j) {
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oprd = &network->operands[j];
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for (int j = 0; j < native_model->operands_num; ++j) {
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oprd = &native_model->operands[j];
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if (strcmp(oprd->name, output_name) == 0) {
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network->output_indexes[network->nb_output++] = j;
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native_model->output_indexes[native_model->nb_output++] = j;
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break;
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}
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}
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}
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if (network->nb_output != nb_output)
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if (native_model->nb_output != nb_output)
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return DNN_ERROR;
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return DNN_SUCCESS;
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@ -122,7 +122,7 @@ DNNModel *ff_dnn_load_model_native(const char *model_filename, const char *optio
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char *buf;
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size_t size;
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int version, header_size, major_version_expected = 1;
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ConvolutionalNetwork *network = NULL;
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NativeModel *native_model = NULL;
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AVIOContext *model_file_context;
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int file_size, dnn_size, parsed_size;
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int32_t layer;
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@ -167,29 +167,29 @@ DNNModel *ff_dnn_load_model_native(const char *model_filename, const char *optio
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dnn_size += 4;
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header_size = dnn_size;
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network = av_mallocz(sizeof(ConvolutionalNetwork));
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if (!network){
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native_model = av_mallocz(sizeof(NativeModel));
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if (!native_model){
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goto fail;
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}
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model->model = (void *)network;
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model->model = (void *)native_model;
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avio_seek(model_file_context, file_size - 8, SEEK_SET);
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network->layers_num = (int32_t)avio_rl32(model_file_context);
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network->operands_num = (int32_t)avio_rl32(model_file_context);
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native_model->layers_num = (int32_t)avio_rl32(model_file_context);
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native_model->operands_num = (int32_t)avio_rl32(model_file_context);
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dnn_size += 8;
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avio_seek(model_file_context, header_size, SEEK_SET);
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network->layers = av_mallocz(network->layers_num * sizeof(Layer));
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if (!network->layers){
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native_model->layers = av_mallocz(native_model->layers_num * sizeof(Layer));
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if (!native_model->layers){
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goto fail;
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}
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network->operands = av_mallocz(network->operands_num * sizeof(DnnOperand));
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if (!network->operands){
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native_model->operands = av_mallocz(native_model->operands_num * sizeof(DnnOperand));
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if (!native_model->operands){
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goto fail;
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}
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for (layer = 0; layer < network->layers_num; ++layer){
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for (layer = 0; layer < native_model->layers_num; ++layer){
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layer_type = (int32_t)avio_rl32(model_file_context);
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dnn_size += 4;
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@ -197,25 +197,25 @@ DNNModel *ff_dnn_load_model_native(const char *model_filename, const char *optio
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goto fail;
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}
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network->layers[layer].type = layer_type;
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parsed_size = layer_funcs[layer_type].pf_load(&network->layers[layer], model_file_context, file_size, network->operands_num);
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native_model->layers[layer].type = layer_type;
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parsed_size = layer_funcs[layer_type].pf_load(&native_model->layers[layer], model_file_context, file_size, native_model->operands_num);
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if (!parsed_size) {
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goto fail;
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}
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dnn_size += parsed_size;
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}
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for (int32_t i = 0; i < network->operands_num; ++i){
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for (int32_t i = 0; i < native_model->operands_num; ++i){
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DnnOperand *oprd;
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int32_t name_len;
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int32_t operand_index = (int32_t)avio_rl32(model_file_context);
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dnn_size += 4;
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if (operand_index >= network->operands_num) {
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if (operand_index >= native_model->operands_num) {
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goto fail;
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}
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oprd = &network->operands[operand_index];
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oprd = &native_model->operands[operand_index];
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name_len = (int32_t)avio_rl32(model_file_context);
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dnn_size += 4;
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@ -257,25 +257,25 @@ fail:
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DNNReturnType ff_dnn_execute_model_native(const DNNModel *model, DNNData *outputs, uint32_t nb_output)
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{
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ConvolutionalNetwork *network = (ConvolutionalNetwork *)model->model;
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NativeModel *native_model = (NativeModel *)model->model;
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int32_t layer;
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uint32_t nb = FFMIN(nb_output, network->nb_output);
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uint32_t nb = FFMIN(nb_output, native_model->nb_output);
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if (network->layers_num <= 0 || network->operands_num <= 0)
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if (native_model->layers_num <= 0 || native_model->operands_num <= 0)
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return DNN_ERROR;
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if (!network->operands[0].data)
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if (!native_model->operands[0].data)
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return DNN_ERROR;
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for (layer = 0; layer < network->layers_num; ++layer){
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DNNLayerType layer_type = network->layers[layer].type;
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layer_funcs[layer_type].pf_exec(network->operands,
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network->layers[layer].input_operand_indexes,
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network->layers[layer].output_operand_index,
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network->layers[layer].params);
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for (layer = 0; layer < native_model->layers_num; ++layer){
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DNNLayerType layer_type = native_model->layers[layer].type;
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layer_funcs[layer_type].pf_exec(native_model->operands,
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native_model->layers[layer].input_operand_indexes,
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native_model->layers[layer].output_operand_index,
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native_model->layers[layer].params);
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}
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for (uint32_t i = 0; i < nb; ++i) {
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DnnOperand *oprd = &network->operands[network->output_indexes[i]];
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DnnOperand *oprd = &native_model->operands[native_model->output_indexes[i]];
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outputs[i].data = oprd->data;
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outputs[i].height = oprd->dims[1];
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outputs[i].width = oprd->dims[2];
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@ -309,34 +309,34 @@ int32_t calculate_operand_data_length(const DnnOperand* oprd)
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void ff_dnn_free_model_native(DNNModel **model)
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{
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ConvolutionalNetwork *network;
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NativeModel *native_model;
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ConvolutionalParams *conv_params;
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int32_t layer;
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if (*model)
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{
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if ((*model)->model) {
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network = (ConvolutionalNetwork *)(*model)->model;
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if (network->layers) {
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for (layer = 0; layer < network->layers_num; ++layer){
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if (network->layers[layer].type == DLT_CONV2D){
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conv_params = (ConvolutionalParams *)network->layers[layer].params;
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native_model = (NativeModel *)(*model)->model;
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if (native_model->layers) {
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for (layer = 0; layer < native_model->layers_num; ++layer){
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if (native_model->layers[layer].type == DLT_CONV2D){
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conv_params = (ConvolutionalParams *)native_model->layers[layer].params;
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av_freep(&conv_params->kernel);
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av_freep(&conv_params->biases);
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}
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av_freep(&network->layers[layer].params);
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av_freep(&native_model->layers[layer].params);
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}
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av_freep(&network->layers);
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av_freep(&native_model->layers);
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}
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if (network->operands) {
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for (uint32_t operand = 0; operand < network->operands_num; ++operand)
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av_freep(&network->operands[operand].data);
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av_freep(&network->operands);
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if (native_model->operands) {
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for (uint32_t operand = 0; operand < native_model->operands_num; ++operand)
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av_freep(&native_model->operands[operand].data);
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av_freep(&native_model->operands);
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}
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av_freep(&network->output_indexes);
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av_freep(&network);
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av_freep(&native_model->output_indexes);
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av_freep(&native_model);
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}
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av_freep(model);
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}
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@ -107,14 +107,14 @@ typedef struct InputParams{
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} InputParams;
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// Represents simple feed-forward convolutional network.
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typedef struct ConvolutionalNetwork{
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typedef struct NativeModel{
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Layer *layers;
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int32_t layers_num;
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DnnOperand *operands;
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int32_t operands_num;
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int32_t *output_indexes;
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uint32_t nb_output;
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} ConvolutionalNetwork;
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} NativeModel;
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DNNModel *ff_dnn_load_model_native(const char *model_filename, const char *options);
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@ -487,15 +487,15 @@ static DNNReturnType load_native_model(TFModel *tf_model, const char *model_file
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int64_t transpose_perm_shape[] = {4};
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int64_t input_shape[] = {1, -1, -1, -1};
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DNNReturnType layer_add_res;
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DNNModel *native_model = NULL;
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ConvolutionalNetwork *conv_network;
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DNNModel *model = NULL;
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NativeModel *native_model;
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native_model = ff_dnn_load_model_native(model_filename, NULL);
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if (!native_model){
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model = ff_dnn_load_model_native(model_filename, NULL);
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if (!model){
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return DNN_ERROR;
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}
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conv_network = (ConvolutionalNetwork *)native_model->model;
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native_model = (NativeModel *)model->model;
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tf_model->graph = TF_NewGraph();
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tf_model->status = TF_NewStatus();
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@ -528,26 +528,26 @@ static DNNReturnType load_native_model(TFModel *tf_model, const char *model_file
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}
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transpose_op = TF_FinishOperation(op_desc, tf_model->status);
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for (layer = 0; layer < conv_network->layers_num; ++layer){
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switch (conv_network->layers[layer].type){
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for (layer = 0; layer < native_model->layers_num; ++layer){
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switch (native_model->layers[layer].type){
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case DLT_INPUT:
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layer_add_res = DNN_SUCCESS;
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break;
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case DLT_CONV2D:
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layer_add_res = add_conv_layer(tf_model, transpose_op, &op,
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(ConvolutionalParams *)conv_network->layers[layer].params, layer);
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(ConvolutionalParams *)native_model->layers[layer].params, layer);
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break;
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case DLT_DEPTH_TO_SPACE:
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layer_add_res = add_depth_to_space_layer(tf_model, &op,
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(DepthToSpaceParams *)conv_network->layers[layer].params, layer);
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(DepthToSpaceParams *)native_model->layers[layer].params, layer);
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break;
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case DLT_MIRROR_PAD:
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layer_add_res = add_pad_layer(tf_model, &op,
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(LayerPadParams *)conv_network->layers[layer].params, layer);
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(LayerPadParams *)native_model->layers[layer].params, layer);
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break;
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case DLT_MAXIMUM:
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layer_add_res = add_maximum_layer(tf_model, &op,
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(DnnLayerMaximumParams *)conv_network->layers[layer].params, layer);
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(DnnLayerMaximumParams *)native_model->layers[layer].params, layer);
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break;
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default:
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CLEANUP_ON_ERROR(tf_model);
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@ -567,7 +567,7 @@ static DNNReturnType load_native_model(TFModel *tf_model, const char *model_file
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CLEANUP_ON_ERROR(tf_model);
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}
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ff_dnn_free_model_native(&native_model);
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ff_dnn_free_model_native(&model);
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return DNN_SUCCESS;
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}
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