use deepstream to run yolov5s ERROR: <main:707>: Failed to set pipeline to PAUSED；ERROR: Failed to create network using custom network creation function

[eljinwei]

I am trying to run YOLOV5 on Jetson Nano. I have converted the yolov5_last.pt file into onnx format. Then I updated the “config_infer_primary_yoloV5.txt” with following settings:

but when i run it using deepstream-app -c deepstream_app_config.txt, it gives me the following error:
Using winsys: x11
ERROR: Deserialize engine failed because file path: /home/jetson/DeepStream-Yolo-new/model_b1_gpu0_fp32.engine open error
0:00:02.752670502 10643 0x7f140022a0 WARN nvinfer gstnvinfer.cpp:635:gst_nvinfer_logger:<primary_gie> NvDsInferContext[UID 1]: Warning from NvDsInferContextImpl::deserializeEngineAndBackend() <nvdsinfer_context_impl.cpp:1889> [UID = 1]: deserialize engine from file :/home/jetson/DeepStream-Yolo-new/model_b1_gpu0_fp32.engine failed
0:00:02.753780996 10643 0x7f140022a0 WARN nvinfer gstnvinfer.cpp:635:gst_nvinfer_logger:<primary_gie> NvDsInferContext[UID 1]: Warning from NvDsInferContextImpl::generateBackendContext() <nvdsinfer_context_impl.cpp:1996> [UID = 1]: deserialize backend context from engine from file :/home/jetson/DeepStream-Yolo-new/model_b1_gpu0_fp32.engine failed, try rebuild
0:00:02.753828601 10643 0x7f140022a0 INFO nvinfer gstnvinfer.cpp:638:gst_nvinfer_logger:<primary_gie> NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::buildModel() <nvdsinfer_context_impl.cpp:1914> [UID = 1]: Trying to create engine from model files
WARNING: [TRT]: onnx2trt_utils.cpp:366: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
ERROR: [TRT]: [graph.cpp::computeInputExecutionUses::549] Error Code 9: Internal Error (/0/model.11/Floor_1: IUnaryLayer cannot be used to compute a shape tensor)
ERROR: [TRT]: ModelImporter.cpp:773: While parsing node number 145 [Resize -> "/0/model.11/Resize_output_0"]:
ERROR: [TRT]: ModelImporter.cpp:774: --- Begin node ---
ERROR: [TRT]: ModelImporter.cpp:775: input: "/0/model.10/act/Mul_output_0"
input: ""
input: ""
input: "/0/model.11/Concat_1_output_0"
output: "/0/model.11/Resize_output_0"
name: "/0/model.11/Resize"
op_type: "Resize"
attribute {
name: "coordinate_transformation_mode"
s: "asymmetric"
type: STRING
}
attribute {
name: "cubic_coeff_a"
f: -0.75
type: FLOAT
}
attribute {
name: "mode"
s: "nearest"
type: STRING
}
attribute {
name: "nearest_mode"
s: "floor"
type: STRING
}

ERROR: [TRT]: ModelImporter.cpp:776: --- End node ---
ERROR: [TRT]: ModelImporter.cpp:779: ERROR: ModelImporter.cpp:179 In function parseGraph:
[6] Invalid Node - /0/model.11/Resize
[graph.cpp::computeInputExecutionUses::549] Error Code 9: Internal Error (/0/model.11/Floor_1: IUnaryLayer cannot be used to compute a shape tensor)

Could not parse the ONNX file

Failed to build CUDA engine
ERROR: Failed to create network using custom network creation function
ERROR: Failed to get cuda engine from custom library API
0:00:03.694147612 10643 0x7f140022a0 ERROR nvinfer gstnvinfer.cpp:632:gst_nvinfer_logger:<primary_gie> NvDsInferContext[UID 1]: Error in NvDsInferContextImpl::buildModel() <nvdsinfer_context_impl.cpp:1934> [UID = 1]: build engine file failed
0:00:03.695261701 10643 0x7f140022a0 ERROR nvinfer gstnvinfer.cpp:632:gst_nvinfer_logger:<primary_gie> NvDsInferContext[UID 1]: Error in NvDsInferContextImpl::generateBackendContext() <nvdsinfer_context_impl.cpp:2020> [UID = 1]: build backend context failed
0:00:03.695322327 10643 0x7f140022a0 ERROR nvinfer gstnvinfer.cpp:632:gst_nvinfer_logger:<primary_gie> NvDsInferContext[UID 1]: Error in NvDsInferContextImpl::initialize() <nvdsinfer_context_impl.cpp:1257> [UID = 1]: generate backend failed, check config file settings
0:00:03.695384360 10643 0x7f140022a0 WARN nvinfer gstnvinfer.cpp:841:gst_nvinfer_start:<primary_gie> error: Failed to create NvDsInferContext instance
0:00:03.695414204 10643 0x7f140022a0 WARN nvinfer gstnvinfer.cpp:841:gst_nvinfer_start:<primary_gie> error: Config file path: /home/jetson/DeepStream-Yolo-new/config_infer_primary_yoloV5.txt, NvDsInfer Error: NVDSINFER_CONFIG_FAILED
** ERROR: main:707: Failed to set pipeline to PAUSED
Quitting
ERROR from primary_gie: Failed to create NvDsInferContext instance
Debug info: /dvs/git/dirty/git-master_linux/deepstream/sdk/src/gst-plugins/gst-nvinfer/gstnvinfer.cpp(841): gst_nvinfer_start (): /GstPipeline:pipeline/GstBin:primary_gie_bin/GstNvInfer:primary_gie:
Config file path: /home/jetson/DeepStream-Yolo-new/config_infer_primary_yoloV5.txt, NvDsInfer Error: NVDSINFER_CONFIG_FAILED
App run failed

could anyone suggest what is the problem. I am exactly following the instructions but still getting the error. Thanks!

[eljinwei]

[application]
enable-perf-measurement=1
perf-measurement-interval-sec=5

[tiled-display]
enable=1
rows=1
columns=1
width=1280
height=720
gpu-id=0
nvbuf-memory-type=0

[source0]
enable=1
type=3
uri=file:///opt/nvidia/deepstream/deepstream/samples/streams/sample_1080p_h264.mp4
num-sources=1
gpu-id=0
cudadec-memtype=0

[sink0]
enable=1
type=2
sync=0
gpu-id=0
nvbuf-memory-type=0

[osd]
enable=1
gpu-id=0
border-width=5
text-size=15
text-color=1;1;1;1;
text-bg-color=0.3;0.3;0.3;1
font=Serif
show-clock=0
clock-x-offset=800
clock-y-offset=820
clock-text-size=12
clock-color=1;0;0;0
nvbuf-memory-type=0

[streammux]
gpu-id=0
live-source=0
batch-size=1
batched-push-timeout=40000
width=1920
height=1080
enable-padding=0
nvbuf-memory-type=0

[primary-gie]
enable=1
gpu-id=0
gie-unique-id=1
nvbuf-memory-type=0
config-file=config_infer_primary_yoloV5.txt

[tests]
file-loop=0

[property]
gpu-id=0
net-scale-factor=0.0039215697906911373
model-color-format=0
onnx-file=last.pt.onnx
model-engine-file=model_b1_gpu0_fp32.engine
#int8-calib-file=calib.table
labelfile-path=labels_wheat.txt
batch-size=1
network-mode=0
num-detected-classes=2
interval=0
gie-unique-id=1
process-mode=1
network-type=0
cluster-mode=2
maintain-aspect-ratio=1
symmetric-padding=1
#workspace-size=2000
parse-bbox-func-name=NvDsInferParseYolo
#parse-bbox-func-name=NvDsInferParseYoloCuda
custom-lib-path=nvdsinfer_custom_impl_Yolo/libnvdsinfer_custom_impl_Yolo.so
engine-create-func-name=NvDsInferYoloCudaEngineGet

[class-attrs-all]
nms-iou-threshold=0.45
pre-cluster-threshold=0.25
topk=300

[eljinwei]

ubuntu18.04
CUDA：10.2
CUDNN:8.2.1.32
TensorRT:8.2.1.8
OPENCV：4.1.1
jetpack:4.6.1
Deepstream6.0

[eljinwei]

@marcoslucianops can you help me ? thanks!

[marcoslucianops]

You need to export the ONNX file without the --dynamic and you need to set --opset 12 or lower for the old Jetson Nano board.

[eljinwei]

Hi Marcos, &nbsp; &nbsp;I am a newcomer and am learning to deploy my model using your open-source project DeepStream-YOLO. I would be greateful for your help! I have read the code in `nvdsinfer_custom_impl_Yolo`, and `nvdsparsedbox_Yolo.cpp` parses the output of the YOLO model to generate bounding boxes and text, but it does not involve drawing the bounding boxes onto the original video. I would like to understand how this pipeline is organized, but I have not found the relevant code in the project files. Perhaps you could provide some guidance? The official examples, such as `deepstream-test1`, have a `deepstream_test1_app.c` file where you can see how the pipeline is designed, but in the DeepStream-YOLO project, I have not found the corresponding code, nor have I found where `nvosd` is used subsequently. I would appreciate it if you could clarify my doubts; Thanks! &nbsp; &nbsp; What I would like to do is to save the video frames with detection boxes drawn after detection. My idea is to locate the OSD pad and add a callback function (I'm not sure if this approach is feasible). However, I am currently facing the issues mentioned above. The code in the project is all about constructing the TRT (TensorRT) engine and parsing the output layers. I am not sure where the parsed data goes after that, so I am writing to you for assistance（I am currently able to run the engine using deepstream-yolo project and invoke the camera for detection.）. I wonder if you could provide me with some guidance, which I would greatly appreciate!

…

------------------ 原始邮件 ------------------ 发件人: "marcoslucianops/DeepStream-Yolo" ***@***.***&gt;; 发送时间:&nbsp;2024年11月14日(星期四) 晚上10:58 ***@***.***&gt;; ***@***.******@***.***&gt;; 主题:&nbsp;Re: [marcoslucianops/DeepStream-Yolo] use deepstream to run yolov5s ERROR: <main:707&gt;: Failed to set pipeline to PAUSED；ERROR: Failed to create network using custom network creation function (Issue #583) You need to export the ONNX file without the --dynamic and you need to set --opset 12 or lower for the old Jetson Nano board. — Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you authored the thread.Message ID: ***@***.***&gt;