Integrating Tao Models (detectnet_v2) into Deepstream SDK

Accelerated Computing Intelligent Video Analytics TAO Toolkit
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1

Please provide the following information when requesting support.

• Hardware: Nano
• Network Type: (Detectnet_v2)
Deepstream version 5.0

Hello, I’m learning how to integrate a custom model, trained by tao toolkit into deepstream.

I wanted someone to check my knowledge in terms of what files I need to help deploy this model into deepstream. Please check below:

  1. The .etlt model (which I’ll export using my computer and placing it on the nano using tao export)
  2. the calibration cache file (using tao export)
  3. the label file (using tao export)
  4. the config file to update the config_infer_primary.txt (using tao export)

Question 1:

Are these the only files I need to be able to directly use in deepstream, I assume deepstream on the nano will generate the fp16.engine file by itself?

Question 2:

Secondly, if i generate a calibration cache file using tao export, do i need to specify the batch number and root to images for calibration within the tao export command?

Question 3:

Which example of deepstream 5.0 can I use to modify and use the custom trained detectnetv2 model? As I don’t really know where to start to integrate these files mentioned above into deepstream.

Thanks alot! I appreciate your time.

2

  1. You can refer to facenet(which is based on detectnet_v2 network) config file, deepstream_tao_apps/config_infer_primary_facenet.txt at master · NVIDIA-AI-IOT/deepstream_tao_apps · GitHub . To run deepstream under int8 mode, the calibration cache file is needed.
    The mode is set according to deepstream_tao_apps/config_infer_primary_facenet.txt at master · NVIDIA-AI-IOT/deepstream_tao_apps · GitHub

  2. You can specify the --batches and --cal_image_dir
    DetectNet_v2

  3. You can refer to GitHub - NVIDIA-AI-IOT/deepstream_tao_apps: Sample apps to demonstrate how to deploy models trained with TAO on DeepStream

3

Thanks I will attempt to transfer my model over to the facenet example to see if it works.

Quick question,

If i’m running detectnet v2 on the jetson nano, I’m aware that it cannot run in int8 mode and usually prefers the fp16 mode. Thus, do I need to generate the calib cache file for fp16 in tao toolkit?

Thanks.

4

For fp16 mode, the calibration cache file is not needed to set.

5

Thanks. I understand a bit better now in terms of integrating tao models into deep stream.

I have a question about the deepstream_tao_apps, since it’s mainly written in C++. Is there anything already available in the deepstream sdk just like the one on the 'getting started with deepstream for video analytics on jetson nano course on the Nvidia DLI website?

As I prefer to edit in Python and customise it to perform a live stream with the custom tao model.

Thank You.

6

Okay so I’ve used the deepstream sdk method using deepstream-test1-usbcam-rtsp-out (deepstream_test_1_usb.py)

I’ve approprately updated the config file which is shown below:

################################################################################
# SPDX-FileCopyrightText: Copyright (c) 2019-2021 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
################################################################################

# Following properties are mandatory when engine files are not specified:
#   int8-calib-file(Only in INT8)
#   Caffemodel mandatory properties: model-file, proto-file, output-blob-names
#   UFF: uff-file, input-dims, uff-input-blob-name, output-blob-names
#   ONNX: onnx-file
#
# Mandatory properties for detectors:
#   num-detected-classes
#
# Optional properties for detectors:
#   cluster-mode(Default=Group Rectangles), interval(Primary mode only, Default=0)
#   custom-lib-path,
#   parse-bbox-func-name
#
# Mandatory properties for classifiers:
#   classifier-threshold, is-classifier
#
# Optional properties for classifiers:
#   classifier-async-mode(Secondary mode only, Default=false)
#
# Optional properties in secondary mode:
#   operate-on-gie-id(Default=0), operate-on-class-ids(Defaults to all classes),
#   input-object-min-width, input-object-min-height, input-object-max-width,
#   input-object-max-height
#
# Following properties are always recommended:
#   batch-size(Default=1)
#
# Other optional properties:
#   net-scale-factor(Default=1), network-mode(Default=0 i.e FP32),
#   model-color-format(Default=0 i.e. RGB) model-engine-file, labelfile-path,
#   mean-file, gie-unique-id(Default=0), offsets, process-mode (Default=1 i.e. primary),
#   custom-lib-path, network-mode(Default=0 i.e FP32)
#
# The values in the config file are overridden by values set through GObject
# properties.

[property]
gpu-id=0
net-scale-factor=0.00392156862745098
offsets=0.0;0.0;0.0
# model-file=../../../../samples/models/Primary_Detector/resnet10.caffemodel
# proto-file=../../../../samples/models/Primary_Detector/resnet10.prototxt
tlt-model-key=tlt_encode
tlt-encoded-model=../../../../samples/models/Primary_Detector/resnet18_detector.etlt 
model-engine-file=../../../../samples/models/Primary_Detector/resnet18.etlt_b1_gpu0_fp16.engine
labelfile-path=../../../../samples/models/Primary_Detector/labels.txt
int8-calib-file=../../../../samples/models/Primary_Detector/cal_trt.bin
force-implicit-batch-dim=1
infer-dims=3;1088;1920
batch-size=1
network-mode=2
network-type=0
num-detected-classes=3
interval=0
gie-unique-id=1
uff-input-order=0
output-blob-names=output_cov/Sigmoid;output_bbox/BiasAdd
uff-input-blob-name=input_1
model-color-format=0
maintain-aspect-ratio=0
output-tensor-meta=0
#scaling-filter=0
#scaling-compute-hw=0

[class-attrs-all]
pre-cluster-threshold=0.2
eps=0.2
minBoxes=3
group-threshold=1

I’ve placed the etlt and labels in the correct directory and tried to run the program. The error I got is shown below:

Creating Pipeline 
 
Creating Source 
 
Creating Video Converter 

Creating H264 Encoder
Creating H264 rtppay
Playing cam /dev/video0 
Adding elements to Pipeline 

Linking elements in the Pipeline 


 *** DeepStream: Launched RTSP Streaming at rtsp://localhost:8554/ds-test ***


Starting pipeline 

Opening in BLOCKING MODE 
0:00:00.503103662   803   0x7f78005b60 WARN                 nvinfer gstnvinfer.cpp:635:gst_nvinfer_logger:<primary-inference> NvDsInferContext[UID 1]: Warning from NvDsInferContextImpl::initialize() <nvdsinfer_context_impl.cpp:1161> [UID = 1]: Warning, OpenCV has been deprecated. Using NMS for clustering instead of cv::groupRectangles with topK = 20 and NMS Threshold = 0.5
ERROR: Deserialize engine failed because file path: /opt/nvidia/deepstream/deepstream-6.0/sources/deepstream_python_apps/dli_apps/deepstream-test1-usbcam-rtsp-out/../../../../samples/models/Primary_Detector/resnet18.etlt_b1_gpu0_fp16.engine open error
0:00:02.534765217   803   0x7f78005b60 WARN                 nvinfer gstnvinfer.cpp:635:gst_nvinfer_logger:<primary-inference> NvDsInferContext[UID 1]: Warning from NvDsInferContextImpl::deserializeEngineAndBackend() <nvdsinfer_context_impl.cpp:1889> [UID = 1]: deserialize engine from file :/opt/nvidia/deepstream/deepstream-6.0/sources/deepstream_python_apps/dli_apps/deepstream-test1-usbcam-rtsp-out/../../../../samples/models/Primary_Detector/resnet18.etlt_b1_gpu0_fp16.engine failed
0:00:02.535991850   803   0x7f78005b60 WARN                 nvinfer gstnvinfer.cpp:635:gst_nvinfer_logger:<primary-inference> NvDsInferContext[UID 1]: Warning from NvDsInferContextImpl::generateBackendContext() <nvdsinfer_context_impl.cpp:1996> [UID = 1]: deserialize backend context from engine from file :/opt/nvidia/deepstream/deepstream-6.0/sources/deepstream_python_apps/dli_apps/deepstream-test1-usbcam-rtsp-out/../../../../samples/models/Primary_Detector/resnet18.etlt_b1_gpu0_fp16.engine failed, try rebuild
0:00:02.536045966   803   0x7f78005b60 INFO                 nvinfer gstnvinfer.cpp:638:gst_nvinfer_logger:<primary-inference> NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::buildModel() <nvdsinfer_context_impl.cpp:1914> [UID = 1]: Trying to create engine from model files
NvDsInferCudaEngineGetFromTltModel: Failed to open TLT encoded model file /opt/nvidia/deepstream/deepstream-6.0/sources/deepstream_python_apps/dli_apps/deepstream-test1-usbcam-rtsp-out/../../../../samples/models/Primary_Detector/resnet18_detector.etlt 
ERROR: Failed to create network using custom network creation function
ERROR: Failed to get cuda engine from custom library API
0:00:03.022170464   803   0x7f78005b60 ERROR                nvinfer gstnvinfer.cpp:632:gst_nvinfer_logger:<primary-inference> NvDsInferContext[UID 1]: Error in NvDsInferContextImpl::buildModel() <nvdsinfer_context_impl.cpp:1934> [UID = 1]: build engine file failed

Not sure what I missed and why deepstream doesn’t build the engine for me on the Jetson nano.

Jetson Nano Version:
Deepstream version is 6.0.1
Jetpack version is 4.6.1-b110
Tensorrt version is 8.2.1.8-1+cuda10.2
model: detectnetv2 using resnset18

PC:
tao toolkit version on pc for model export : 4.0.0-tf1.15.5

Additionally, since I’m allowing deepstream sdk generate the engine on the nano, there shouldn’t be an issue with incompatibility with tensorrt and cuda since it’s using the versions based on the nano.

Update

So i reflashed my jetson nano with deepstream 5.1 since detectnet_V2 tlt models are apparently compatible with that version of deepstream. The deepstream app works but again the exact same error as before appears in failing to open TLT encoded model file. My question: ** How can I let the jetson nano see the tlt-model-key?** I have a feeling it’s not opening the etlt file because of it, does the jetson need to install additional libraries? should I be connecting it the NGC cloud? etc…

7

The official link is GitHub - NVIDIA-AI-IOT/deepstream_tao_apps: Sample apps to demonstrate how to deploy models trained with TAO on DeepStream.
So, please run with this repo.

For example, facenet is based on detectnet_v2 network. You can refer to it.
To set the key: deepstream_tao_apps/config_infer_primary_facenet.txt at master · NVIDIA-AI-IOT/deepstream_tao_apps · GitHub

8

Okay.

Where do I find the documentation to help me alter the C++ source code to accept a USB camera input rather than a video file?

Also what is the purpose of the deepstream-python-apps? Because it’s a very similar setup to the link you provided and built on top of deepstream sdk. Is it not compatible with custom trained tao models?

Apologies for me asking simple questions, I’m just trying to help the audience out as well so there is clarity on what researchers/developers should be doing (as there is alot of nvidia documentation to get your head around)

Thanks!

9

Hi,
Officially TAO user guide is expecting customer to run with GitHub - NVIDIA-AI-IOT/deepstream_tao_apps: Sample apps to demonstrate how to deploy models trained with TAO on DeepStream 1
We do not know/verify when using deepstream-python-apps repo.

For using USB camera with deepstream-python-apps, could you search in deepstream forum or ask the question directly in deepstream forum (DeepStream SDK - NVIDIA Developer Forums)? I believe there are similar topics there.

For example,
https://forums.developer.nvidia.com/search?q=%22usb%20camera%22%20%20%20%23intelligent-video-analytics%3Adeepstream-sdk%20%20order%3Alatest
→ Deepstream 6.01 - Python app error - USB Camera ,
Deepstream4.0 TLT, How to run the application with USB video source - #2

1 Like
10

I made the python deepstream work in the end with my own model, it runs super slow, but it’s a start.

Can I ask is the tao models i.e. detectnet, yolo etc… compatible with deepstream 6.2 and jetpack 5?

11

There is no update from you for a period, assuming this is not an issue anymore. Hence we are closing this topic. If need further support, please open a new one. Thanks

TAO models is compatible with Deepstream 6.2.
You can refer to GitHub - NVIDIA-AI-IOT/deepstream_tao_apps: Sample apps to demonstrate how to deploy models trained with TAO on DeepStream

For python deepstream, since it is not official repo for tao models, please raise the issues you found in deepstream forum.

12

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