for (layer = 0; layer < network->layers_num; ++layer){
layer_type = (int32_t)avio_rl32(model_file_context);
dnn_size += 4;
+ network->layers[layer].type = layer_type;
switch (layer_type){
- case CONV:
+ case DLT_CONV2D:
conv_params = av_malloc(sizeof(ConvolutionalParams));
if (!conv_params){
avio_closep(&model_file_context);
network->layers[layer].input_operand_indexes[0] = (int32_t)avio_rl32(model_file_context);
network->layers[layer].output_operand_index = (int32_t)avio_rl32(model_file_context);
dnn_size += 8;
- network->layers[layer].type = CONV;
network->layers[layer].params = conv_params;
break;
- case DEPTH_TO_SPACE:
+ case DLT_DEPTH_TO_SPACE:
depth_to_space_params = av_malloc(sizeof(DepthToSpaceParams));
if (!depth_to_space_params){
avio_closep(&model_file_context);
network->layers[layer].input_operand_indexes[0] = (int32_t)avio_rl32(model_file_context);
network->layers[layer].output_operand_index = (int32_t)avio_rl32(model_file_context);
dnn_size += 8;
- network->layers[layer].type = DEPTH_TO_SPACE;
network->layers[layer].params = depth_to_space_params;
break;
- case MIRROR_PAD:
+ case DLT_MIRROR_PAD:
pad_params = av_malloc(sizeof(LayerPadParams));
if (!pad_params){
avio_closep(&model_file_context);
network->layers[layer].input_operand_indexes[0] = (int32_t)avio_rl32(model_file_context);
network->layers[layer].output_operand_index = (int32_t)avio_rl32(model_file_context);
dnn_size += 8;
- network->layers[layer].type = MIRROR_PAD;
network->layers[layer].params = pad_params;
break;
- case MAXIMUM:
+ case DLT_MAXIMUM:
maximum_params = av_malloc(sizeof(*maximum_params));
if (!maximum_params){
avio_closep(&model_file_context);
}
maximum_params->val.u32 = avio_rl32(model_file_context);
dnn_size += 4;
- network->layers[layer].type = MAXIMUM;
network->layers[layer].params = maximum_params;
network->layers[layer].input_operand_indexes[0] = (int32_t)avio_rl32(model_file_context);
network->layers[layer].output_operand_index = (int32_t)avio_rl32(model_file_context);
for (layer = 0; layer < network->layers_num; ++layer){
switch (network->layers[layer].type){
- case CONV:
+ case DLT_CONV2D:
conv_params = (ConvolutionalParams *)network->layers[layer].params;
convolve(network->operands, network->layers[layer].input_operand_indexes,
network->layers[layer].output_operand_index, conv_params);
break;
- case DEPTH_TO_SPACE:
+ case DLT_DEPTH_TO_SPACE:
depth_to_space_params = (DepthToSpaceParams *)network->layers[layer].params;
depth_to_space(network->operands, network->layers[layer].input_operand_indexes,
network->layers[layer].output_operand_index, depth_to_space_params->block_size);
break;
- case MIRROR_PAD:
+ case DLT_MIRROR_PAD:
pad_params = (LayerPadParams *)network->layers[layer].params;
dnn_execute_layer_pad(network->operands, network->layers[layer].input_operand_indexes,
network->layers[layer].output_operand_index, pad_params);
break;
- case MAXIMUM:
+ case DLT_MAXIMUM:
maximum_params = (DnnLayerMaximumParams *)network->layers[layer].params;
dnn_execute_layer_maximum(network->operands, network->layers[layer].input_operand_indexes,
network->layers[layer].output_operand_index, maximum_params);
break;
- case INPUT:
+ case DLT_INPUT:
return DNN_ERROR;
}
}
{
network = (ConvolutionalNetwork *)(*model)->model;
for (layer = 0; layer < network->layers_num; ++layer){
- if (network->layers[layer].type == CONV){
+ if (network->layers[layer].type == DLT_CONV2D){
conv_params = (ConvolutionalParams *)network->layers[layer].params;
av_freep(&conv_params->kernel);
av_freep(&conv_params->biases);
for (layer = 0; layer < conv_network->layers_num; ++layer){
switch (conv_network->layers[layer].type){
- case INPUT:
+ case DLT_INPUT:
layer_add_res = DNN_SUCCESS;
break;
- case CONV:
+ case DLT_CONV2D:
layer_add_res = add_conv_layer(tf_model, transpose_op, &op,
(ConvolutionalParams *)conv_network->layers[layer].params, layer);
break;
- case DEPTH_TO_SPACE:
+ case DLT_DEPTH_TO_SPACE:
layer_add_res = add_depth_to_space_layer(tf_model, &op,
(DepthToSpaceParams *)conv_network->layers[layer].params, layer);
break;
- case MIRROR_PAD:
+ case DLT_MIRROR_PAD:
layer_add_res = add_pad_layer(tf_model, &op,
(LayerPadParams *)conv_network->layers[layer].params, layer);
break;
- case MAXIMUM:
+ case DLT_MAXIMUM:
layer_add_res = add_maximum_layer(tf_model, &op,
(DnnLayerMaximumParams *)conv_network->layers[layer].params, layer);
break;