Please provide complete information as applicable to your setup.
**• Hardware Platform (Jetson / GPU)GPU
**• DeepStream Version 5.1
• JetPack Version (valid for Jetson only)
**• TensorRT Version 7.1.2
**• NVIDIA GPU Driver Version (valid for GPU only) 11.1
**• Issue Type( questions, new requirements, bugs) question
• How to reproduce the issue ? (This is for bugs. Including which sample app is using, the configuration files content, the command line used and other details for reproducing)
• Requirement details( This is for new requirement. Including the module name-for which plugin or for which sample application, the function description)
Dear professor:
I hope to get the last layer output of an engine. I checked the sample app such as: "deepstream_infer_tensor_meta_test.cpp", "deepstream_user_metadata_app.c" and "gstnvifer.cpp".
Especially, in the plugins, the code “gstnvinfer_meta_utils.cpp”， I can find the meta, and the function of this .cpp is given in the DOCS. However, I am hard to read the meta directly. I use the carcolor classification. So I hope I can print the engine output as the label id.
The attachment is the code I find. I hope use “count<< data<< endl”. I tried to find the define of the meta. but I can not find in the DOCS.
Please Help me.
/* Attaches the raw tensor output to the GstBuffer as metadata. */
void
attach_tensor_output_meta (GstNvInfer *nvinfer, GstMiniObject * tensor_out_object,
GstNvInferBatch *batch, NvDsInferContextBatchOutput *batch_output)
{
NvDsBatchMeta batch_meta = (nvinfer->process_full_frame) ?
batch->frames[0].frame_meta->base_meta.batch_meta :
batch->frames[0].obj_meta->base_meta.batch_meta;
/ Create and attach NvDsInferTensorMeta for each frame/object. Also
- increment the refcount of GstNvInferTensorOutputObject. */
for (size_t j = 0; j < batch->frames.size(); j++) {
GstNvInferFrame &frame = batch->frames[j];
NvDsInferTensorMeta *meta = new NvDsInferTensorMeta;
meta->unique_id = nvinfer->unique_id;
meta->num_output_layers = nvinfer->output_layers_info->size ();
meta->output_layers_info = nvinfer->output_layers_info->data ();
meta->out_buf_ptrs_host = new void *[meta->num_output_layers];
meta->out_buf_ptrs_dev = new void *[meta->num_output_layers];
meta->gpu_id = nvinfer->gpu_id;
meta->priv_data = gst_mini_object_ref (tensor_out_object);
meta->network_info = nvinfer->network_info;
for (unsigned int i = 0; i < meta->num_output_layers; i++)
{
NvDsInferLayerInfo & info = meta->output_layers_info[i];
meta->out_buf_ptrs_dev[i] =
(uint8_t *) batch_output->outputDeviceBuffers[i] +
info.inferDims.numElements * get_element_size (info.dataType) * j;
meta->out_buf_ptrs_host[i] =
(uint8_t *) batch_output->hostBuffers[info.bindingIndex] +
info.inferDims.numElements * get_element_size (info.dataType) * j;
}
NvDsUserMeta *user_meta = nvds_acquire_user_meta_from_pool (batch_meta);
user_meta->user_meta_data = meta;
user_meta->base_meta.meta_type = (NvDsMetaType) NVDSINFER_TENSOR_OUTPUT_META;
user_meta->base_meta.release_func = release_tensor_output_meta;
user_meta->base_meta.copy_func = nullptr;
user_meta->base_meta.batch_meta = batch_meta;
if (nvinfer->process_full_frame)
{
nvds_add_user_meta_to_frame (frame.frame_meta, user_meta);
} else
{
nvds_add_user_meta_to_obj (frame.obj_meta, user_meta);
}
}
}