Hi，
After I compile deepstream-test1 directly, the program can’t run on my Jetson Nano 2G. However, after removing the deep learning model nvinfer from the codes, the program can run. Is it because my nano 2G memory is too small, so the deep learning model can’t run? Or other reasons?
What kind of error have you met?
When I enter the command line ./deepstream-test1-app sample_720p.h264, the window deepstream-test1-app was showed. However, the window only draws the title and the border, the image is not displayed in it. The canvas of the window we saw is the desktop of Jetson Nano. The output of this program is as follows:
jetson@jetson-desktop:/opt/nvidia/deepstream/deepstream/sources/apps/sample_apps/deepstream-test1$ ./deepstream-test1-app sample_720p.h264
Now playing: sample_720p.h264
Using winsys: x11
Opening in BLOCKING MODE
0:00:11.198570435 8436 0x55934fcad0 INFO nvinfer gstnvinfer.cpp:619:gst_nvinfer_logger:<primary-nvinference-engine> NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::deserializeEngineAndBackend() <nvdsinfer_context_impl.cpp:1702> [UID = 1]: deserialized trt engine from :/opt/nvidia/deepstream/deepstream-5.1/samples/models/Primary_Detector/resnet10.caffemodel_b1_gpu0_fp16.engine
INFO: [Implicit Engine Info]: layers num: 3
0 INPUT kFLOAT input_1 3x368x640
1 OUTPUT kFLOAT conv2d_bbox 16x23x40
2 OUTPUT kFLOAT conv2d_cov/Sigmoid 4x23x40
0:00:11.218911904 8436 0x55934fcad0 INFO nvinfer gstnvinfer.cpp:619:gst_nvinfer_logger:<primary-nvinference-engine> NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::generateBackendContext() <nvdsinfer_context_impl.cpp:1806> [UID = 1]: Use deserialized engine model: /opt/nvidia/deepstream/deepstream-5.1/samples/models/Primary_Detector/resnet10.caffemodel_b1_gpu0_fp16.engine
0:00:11.316350994 8436 0x55934fcad0 INFO nvinfer gstnvinfer_impl.cpp:313:notifyLoadModelStatus:<primary-nvinference-engine> [UID 1]: Load new model:dstest1_pgie_config.txt sucessfully
Running...
NvMMLiteOpen : Block : BlockType = 261
NVMEDIA: Reading vendor.tegra.display-size : status: 6
NvMMLiteBlockCreate : Block : BlockType = 261
ERROR: [TRT]: ../rtSafe/cuda/reformat.cu (925) - Cuda Error in NCHWToNCHHW2: 719 (unspecified launch failure)
ERROR: [TRT]: FAILED_EXECUTION: std::exception
ERROR: Failed to enqueue inference batch
ERROR: Infer context enqueue buffer failed, nvinfer error:NVDSINFER_TENSORRT_ERROR
0:00:12.668284961 8436 0x5593091de0 WARN nvinfer gstnvinfer.cpp:1225:gst_nvinfer_input_queue_loop:<primary-nvinference-engine> error: Failed to queue input batch for inferencing
ERROR: Failed to make stream wait on event, cuda err_no:4, err_str:cudaErrorLaunchFailure
ERROR: Preprocessor transform input data failed., nvinfer error:NVDSINFER_CUDA_ERROR
ERROR from element primary-nvinference-engine: Failed to queue input batch for inferencing
0:00:12.668905003 8436 0x5593091de0 WARN nvinfer gstnvinfer.cpp:1225:gst_nvinfer_input_queue_loop:<primary-nvinference-engine> error: Failed to queue input batch for inferencing
ERROR: Failed to make stream wait on event, cuda err_no:4, err_str:cudaErrorLaunchFailure
ERROR: Preprocessor transform input data failed., nvinfer error:NVDSINFER_CUDA_ERROR
0:00:12.669423588 8436 0x5593091de0 WARN nvinfer gstnvinfer.cpp:1225:gst_nvinfer_input_queue_loop:<primary-nvinference-engine> error: Failed to queue input batch for inferencing
ERROR: Failed to make stream wait on event, cuda err_no:4, err_str:cudaErrorLaunchFailure
ERROR: Preprocessor transform input data failed., nvinfer error:NVDSINFER_CUDA_ERROR
0:00:12.669702021 8436 0x5593091de0 WARN nvinfer gstnvinfer.cpp:1225:gst_nvinfer_input_queue_loop:<primary-nvinference-engine> error: Failed to queue input batch for inferencing
Error details: /dvs/git/dirty/git-master_linux/deepstream/sdk/src/gst-plugins/gst-nvinfer/gstnvinfer.cpp(1225): gst_nvinfer_input_queue_loop (): /GstPipeline:dstest1-pipeline/GstNvInfer:primary-nvinference-engine
Returned, stopping playback
Frame Number = 0 Number of objects = 0 Vehicle Count = 0 Person Count = 0
** (deepstream-test1-app:8436): WARNING **: 14:51:19.402: Use gst_egl_image_allocator_alloc() to allocate from this allocator
0:00:13.099598980 8436 0x5593091c00 WARN nvinfer gstnvinfer.cpp:1984:gst_nvinfer_output_loop:<primary-nvinference-engine> error: Internal data stream error.
0:00:13.099640229 8436 0x5593091c00 WARN nvinfer gstnvinfer.cpp:1984:gst_nvinfer_output_loop:<primary-nvinference-engine> error: streaming stopped, reason error (-5)
Frame Number = 1 Number of objects = 0 Vehicle Count = 0 Person Count = 0
** (deepstream-test1-app:8436): WARNING **: 14:51:19.417: Use gst_egl_image_allocator_alloc() to allocate from this allocator
Frame Number = 2 Number of objects = 0 Vehicle Count = 0 Person Count = 0
** (deepstream-test1-app:8436): WARNING **: 14:51:19.429: Use gst_egl_image_allocator_alloc() to allocate from this allocator
Frame Number = 3 Number of objects = 0 Vehicle Count = 0 Person Count = 0
** (deepstream-test1-app:8436): WARNING **: 14:51:19.442: Use gst_egl_image_allocator_alloc() to allocate from this allocator
I just tried with Nano board with deepstream-test1-app, it can run and I can see the video displayed.
Can you provide platform information?
| **• | Hardware Platform (Jetson / GPU)** |
|---|---|
| **• | DeepStream Version** |
| **• | JetPack Version (valid for Jetson only)** |
| **• | TensorRT Version** |
| **• | NVIDIA GPU Driver Version (valid for GPU only)** |
Thank you so much! My running environment is as follows:
| **• | Hardware Platform: Jetson Nano 2G ** |
|---|---|
| **• | DeepStream Version: DeepStream 5.1 ** |
| **• | JetPack Version UNKNOWN [L4T 32.4.4] (this information is from JTOP)** |
| **• | TensorRT Version: 7.1.3.0 ** |
| **• | NVIDIA GPU Driver Version (valid for GPU only)** |
The information displayed in jtop is shown in the figure below：
Deepstream5.1 is based on JetPack 4.5.1 GA(corresponding to L4T 32.5.1 release) .
Please install Deepstream5.1 and the dependencies correctly.
https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_Quickstart.html#jetson-setup
Thank you very much for your guidance! I ran this program successfully according to the method you provided. Thanks again!