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* readme.md updated
* Added customModels.md
* Added multipleInferences.md
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Marcos Luciano
2020-12-21 11:49:09 -03:00
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# Editing default model to your custom model
How to edit DeepStream files to your custom model
##
* [Requirements](#requirements)
* [Editing default model](#editing-default-model)
* [Compiling edited model](#compiling-edited-model)
* [Understanding and editing deepstream_app_config](#understanding-and-editing-deepstream_app_config)
* [Understanding and editing config_infer_primary](#understanding-and-editing-config_infer_primary)
* [Testing model](#testing-model)
* [Custom functions in your model](#custom-functions-in-your-model)
##
### Requirements
* [NVIDIA DeepStream SDK 5.0.1](https://developer.nvidia.com/deepstream-sdk)
* [DeepStream-Yolo Native](https://github.com/marcoslucianops/DeepStream-Yolo/tree/master/native)
* [Pre-treined YOLO model](https://github.com/AlexeyAB/darknet)
##
### Editing default model
1. Donwload [my native folder](https://github.com/marcoslucianops/DeepStream-Yolo/tree/master/native), rename to yolo and move to your deepstream/sources folder.
2. Copy and remane your obj.names file to labels.txt to deepstream/sources/yolo directory
3. Copy your yolo.cfg and yolo.weights files to deepstream/sources/yolo directory.
4. Edit config_infer_primary.txt for your model (example for YOLOv4)
```
[property]
...
# CFG
custom-network-config=yolo.cfg
# Weights
model-file=yolo.weights
# Model labels file
labelfile-path=labels.txt
...
```
Note: if you want to use YOLOv2 or YOLOv2-Tiny models, change deepstream_app_config.txt
```
[primary-gie]
enable=1
gpu-id=0
gie-unique-id=1
nvbuf-memory-type=0
config-file=config_infer_primary_yoloV2.txt
```
Note: config_infer_primary.txt uses cluster-mode=4 and NMS = 0.45 (via code) when beta_nms isn't available (when beta_nms is available, NMS = beta_nms), while config_infer_primary_yoloV2.txt uses cluster-mode=2 and nms-iou-threshold=0.45 to set NMS.
##
### Compiling edited model
1. Check your CUDA version (nvcc --version)
2. Go to deepstream/sources/yolo directory
3. Type command (example for CUDA 10.2 version):
```
CUDA_VER=10.2 make -C nvdsinfer_custom_impl_Yolo
```
##
### Understanding and editing deepstream_app_config
To understand and edit deepstream_app_config.txt file, read the [DeepStream SDK Development Guide - Configuration Groups](https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_ref_app_deepstream.html#configuration-groups)
##
* Edit tiled-display
```
[tiled-display]
enable=1
# If you have 1 stream use 1/1 (rows/columns), if you have 4 streams use 2/2 or 4/1 or 1/4 (rows/columns)
rows=1
columns=1
# Resolution of tiled display
width=1280
height=720
gpu-id=0
nvbuf-memory-type=0
```
##
* Edit source
Example for 1 source:
```
[source0]
enable=1
# 1=Camera (V4L2), 2=URI, 3=MultiURI, 4=RTSP, 5=Camera (CSI; Jetson only)
type=3
# Stream URL
uri=rtsp://192.168.1.2/Streaming/Channels/101/httppreview
# Number of sources copy (if > 1, you need edit rows/columns in tiled-display section and batch-size in streammux section and config_infer_primary.txt; need type=3 for more than 1 source)
num-sources=1
gpu-id=0
cudadec-memtype=0
```
Example for 1 duplcated source:
```
[source0]
enable=1
type=3
uri=rtsp://192.168.1.2/Streaming/Channels/101/httppreview
num-sources=2
gpu-id=0
cudadec-memtype=0
```
Example for 2 sources:
```
[source0]
enable=1
type=3
uri=rtsp://192.168.1.2/Streaming/Channels/101/httppreview
num-sources=1
gpu-id=0
cudadec-memtype=0
[source1]
enable=1
type=3
uri=rtsp://192.168.1.3/Streaming/Channels/101/httppreview
num-sources=1
gpu-id=0
cudadec-memtype=0
```
##
* Edit sink
Example for 1 source or 1 duplicated source:
```
[sink0]
enable=1
# 1=Fakesink, 2=EGL (nveglglessink), 3=Filesink, 4=RTSP, 5=Overlay (Jetson only)
type=2
# Indicates how fast the stream is to be rendered (0=As fast as possible, 1=Synchronously)
sync=0
# The ID of the source whose buffers this sink must use
source-id=0
gpu-id=0
nvbuf-memory-type=0
```
Example for 2 sources:
```
[sink0]
enable=1
type=2
sync=0
source-id=0
gpu-id=0
nvbuf-memory-type=0
[sink1]
enable=1
type=2
sync=0
source-id=1
gpu-id=0
nvbuf-memory-type=0
```
##
* Edit streammux
Example for 1 source:
```
[streammux]
gpu-id=0
# Boolean property to inform muxer that sources are live
live-source=1
# Number of sources
batch-size=1
# Time out in usec, to wait after the first buffer is available to push the batch even if the complete batch is not formed
batched-push-timeout=40000
# Resolution of streammux
width=1920
height=1080
enable-padding=0
nvbuf-memory-type=0
```
Example for 1 duplicated source or 2 sources:
```
[streammux]
gpu-id=0
live-source=0
batch-size=2
batched-push-timeout=40000
width=1920
height=1080
enable-padding=0
nvbuf-memory-type=0
```
##
* Edit primary-gie
```
[primary-gie]
enable=1
gpu-id=0
gie-unique-id=1
nvbuf-memory-type=0
config-file=config_infer_primary.txt
```
* You can remove [tracker] section, if you don't use it.
##
### Understanding and editing config_infer_primary
To understand and edit config_infer_primary.txt file, read the [NVIDIA DeepStream Plugin Manual - Gst-nvinfer File Configuration Specifications](https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_plugin_gst-nvinfer.html#gst-nvinfer-file-configuration-specifications)
##
* Edit model-color-format accoding number of channels in yolo.cfg (1=GRAYSCALE, 3=RGB)
```
# 0=RGB, 1=BGR, 2=GRAYSCALE
model-color-format=0
```
##
* Edit model-engine-file (example for batch-size=1 and network-mode=2)
```
model-engine-file=model_b1_gpu0_fp16.engine
```
##
* Edit batch-size
```
# Number of sources
batch-size=1
```
##
* Edit network-mode
```
# 0=FP32, 1=INT8, 2=FP16
network-mode=0
```
##
* Edit num-detected-classes according number of classes in yolo.cfg
```
num-detected-classes=80
```
##
* Edit network-type
```
# 0=Detector, 1=Classifier, 2=Segmentation
network-type=0
```
##
* Add/edit interval (FPS increase if > 0)
```
# Interval of detection
interval=0
```
##
* Change pre-cluster-threshold (optional)
```
[class-attrs-all]
# CONF_THRESH
pre-cluster-threshold=0.25
```
##
### Testing model
To run your custom YOLO model, use command
```
deepstream-app -c deepstream_app_config.txt
```
##
### Custom functions in your model
You can get metadata from deepstream in Python and C++. For C++, you need edit deepstream-app or deepstream-test code. For Python your need install and edit [this](https://github.com/NVIDIA-AI-IOT/deepstream_python_apps).
You need manipulate NvDsObjectMeta ([Python](https://docs.nvidia.com/metropolis/deepstream/python-api/PYTHON_API/NvDsMeta/NvDsObjectMeta.html) [C++](https://docs.nvidia.com/metropolis/deepstream/sdk-api/Meta/_NvDsObjectMeta.html)), NvDsFrameMeta ([Python](https://docs.nvidia.com/metropolis/deepstream/python-api/PYTHON_API/NvDsMeta/NvDsFrameMeta.html) [C++](https://docs.nvidia.com/metropolis/deepstream/sdk-api/Meta/_NvDsFrameMeta.html)) and NvOSD_RectParams ([Python](https://docs.nvidia.com/metropolis/deepstream/python-api/PYTHON_API/NvDsOSD/NvOSD_RectParams.html) [C++](https://docs.nvidia.com/metropolis/deepstream/sdk-api/OSD/Data_Structures/_NvOSD_FrameRectParams.html)) to get label, position, etc. of bboxs.
In C++ deepstream-app application, your code need be in analytics_done_buf_prob function.
In C++/Python deepstream-test application, your code need be in osd_sink_pad_buffer_probe/tiler_src_pad_buffer_probe function.
Python is slightly slower than C (about 5-10%).