text:
RTSP source in detectnet-camera example

Hi everybody.
I want to get images from my Hikvision camera. RTSP link is: rtsp://192.168.196.243:554/Streaming/Channels/102
I have successfully used this pipeline in my OpenCV(3.3.1) app, but it does not work in detectnet-camera example, which i`ve built on my x86_64 PC(Cuda 8.0, OpenCV 3.3.1, TensorRT version 2.1, build 2102
)
I have added this line to code cloned from this repo:
ss<< "rtspsrc location=rtsp://192.168.196.243:554/Streaming/Channels/102 protocols=udp latency=0 ! decodebin ! videoconvert ! appsink name=mysink ";
Those lines were in the output.
`[gstreamer] initialized gstreamer, version 1.8.3.0
[gstreamer] gstreamer decoder pipeline string:
rtspsrc location=rtsp://192.168.196.243:554/Streaming/Channels/102 protocols=udp latency=0 ! decodebin ! videoconvert ! appsink name=mysink
detectnet-camera:  successfully initialized video device
width:  1280
height:  720
depth:  24 (bpp)
[gstreamer] gstreamer transitioning pipeline to GST_STATE_PLAYING
[gstreamer] gstreamer changed state from NULL to READY ==> mysink
[gstreamer] gstreamer changed state from NULL to READY ==> videoconvert0
[gstreamer] gstreamer changed state from NULL to READY ==> typefind
[gstreamer] gstreamer changed state from NULL to READY ==> decodebin0
[gstreamer] gstreamer changed state from NULL to READY ==> rtspsrc0
[gstreamer] gstreamer changed state from NULL to READY ==> pipeline0
[gstreamer] gstreamer changed state from READY to PAUSED ==> videoconvert0
[gstreamer] gstreamer changed state from READY to PAUSED ==> typefind
[gstreamer] gstreamer msg progress ==> rtspsrc0
[gstreamer] gstreamer changed state from READY to PAUSED ==> rtspsrc0
[gstreamer] gstreamer changed state from READY to PAUSED ==> pipeline0
[gstreamer] gstreamer msg new-clock ==> pipeline0
[gstreamer] gstreamer changed state from PAUSED to PLAYING ==> videoconvert0
[gstreamer] gstreamer msg progress ==> rtspsrc0
[gstreamer] gstreamer changed state from PAUSED to PLAYING ==> rtspsrc0
[gstreamer] gstreamer msg progress ==> rtspsrc0
detectnet-camera:  camera open for streaming
detectnet-camera:  failed to capture frame
detectnet-camera:  failed to convert from NV12 to RGBA
detectNet::Detect( 0x(nil), 1280, 720 ) -> invalid parameters
[cuda]   cudaNormalizeRGBA((float4*)imgRGBA, make_float2(0.0f, 255.0f), (float4*)imgRGBA, make_float2(0.0f, 1.0f), camera->GetWidth(), camera->GetHeight())
[cuda]      invalid device pointer (error 17) (hex 0x11)
[cuda]      /home/aprentis/jetson-inference/detectnet-camera/detectnet-camera.cpp:247
[cuda]   registered 14745600 byte openGL texture for interop access (1280x720)
`
What`s wrong? Thanks in advance!

As it says, 'detectnet-camera: failed to convert from NV12 to RGBA'
There is a conversion to RGBAf format from NV12 or RGB in the file gstCamera.cpp: in function ConvertRGBA. Either you modify the check for onboard camera or change your pipeline to use 'nvvidconv' to generate the output in 'NV12' format instead of using videoconvert.
Hope this works.

I had the same issue and managed to fix it using @omaralvarez's Pull Request described here: https://github.com/dusty-nv/jetson-inference/issues/88
After taking in the pull request code, in detectnet-camera I replaced the line:
gstCamera* camera = gstCamera::Create(DEFAULT_CAMERA);
With:
gstPipeline* pipeline = gstPipeline::Create(
		"rtspsrc location=rtsp://wowzaec2demo.streamlock.net/vod/mp4:BigBuckBunny_115k.mov ! queue ! rtph264depay ! h264parse ! queue ! omxh264dec ! appsink name=mysink",
		240,
		160,
		12
);

(Replacing the RTSP address, height & width accordingly + #include "gstPipeline.h" at the top)

Yes, that will work fine because he gets rid of the onboard camera check and hence, there is just one conversion method, RGBtoRGBAf which is the one you should use.

Could you post the full code on how to use gstPipeline?

Dear,
is it possible to see full code for this? I'm new to GStreamer and i'm looking for several days already for a simple an clear example how to play a rtsp stream from an IP camera from the imagenet-camera source code.
your help is much appreciated

The full code for gstPipeline is in the Pull Request:
https://github.com/dusty-nv/jetson-inference/pull/93/commits/2717e8914dad03116641247ed2dd9ebc88379d4c

Hi, thank you for the code.
but I have some issues when trying to compile
(original jetson-inference is compiling and working)
I'm running on a jetson TX2 with jetpack 4.2.2.
Can you give me any direction where to start looking for a solution?
Thank you in advance.
nvidia@tx2:~/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/build$ make -j2
[  5%] Building NVCC (Device) object CMakeFiles/jetson-inference.dir/util/cuda/jetson-inference_generated_cudaYUV-YV12.cu.o
[  5%] Building NVCC (Device) object CMakeFiles/jetson-inference.dir/jetson-inference_generated_imageNet.cu.o
:0:7: warning: ISO C++11 requires whitespace after the macro name
:0:7: warning: ISO C++11 requires whitespace after the macro name
:0:7: warning: ISO C++11 requires whitespace after the macro name
:0:7: warning: ISO C++11 requires whitespace after the macro name
:0:7: warning: ISO C++11 requires whitespace after the macro name
:0:7: warning: ISO C++11 requires whitespace after the macro name
:0:7: warning: ISO C++11 requires whitespace after the macro name
:0:7: warning: ISO C++11 requires whitespace after the macro name
:0:7: warning: ISO C++11 requires whitespace after the macro name
:0:7: warning: ISO C++11 requires whitespace after the macro name
:0:7: warning: ISO C++11 requires whitespace after the macro name
:0:7: warning: ISO C++11 requires whitespace after the macro name
[  7%] Building NVCC (Device) object CMakeFiles/jetson-inference.dir/util/cuda/jetson-inference_generated_cudaFont.cu.o
:0:7: warning: ISO C++11 requires whitespace after the macro name
[ 10%] Building NVCC (Device) object CMakeFiles/jetson-inference.dir/util/cuda/jetson-inference_generated_cudaNormalize.cu.o
:0:7: warning: ISO C++11 requires whitespace after the macro name
:0:7: warning: ISO C++11 requires whitespace after the macro name
:0:7: warning: ISO C++11 requires whitespace after the macro name
:0:7: warning: ISO C++11 requires whitespace after the macro name
:0:7: warning: ISO C++11 requires whitespace after the macro name
:0:7: warning: ISO C++11 requires whitespace after the macro name
:0:7: warning: ISO C++11 requires whitespace after the macro name
:0:7: warning: ISO C++11 requires whitespace after the macro name
:0:7: warning: ISO C++11 requires whitespace after the macro name
:0:7: warning: ISO C++11 requires whitespace after the macro name
[ 12%] Building NVCC (Device) object CMakeFiles/jetson-inference.dir/util/cuda/jetson-inference_generated_cudaOverlay.cu.o
:0:7: warning: ISO C++11 requires whitespace after the macro name
:0:7: warning: ISO C++11 requires whitespace after the macro name
:0:7: warning: ISO C++11 requires whitespace after the macro name
:0:7: warning: ISO C++11 requires whitespace after the macro name
:0:7: warning: ISO C++11 requires whitespace after the macro name
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/util/cuda/cudaOverlay.cu(29): warning: variable "thick" was declared but never referenced
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/util/cuda/cudaOverlay.cu(8): warning: function "eq_less" was declared but never referenced
:0:7: warning: ISO C++11 requires whitespace after the macro name
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/util/cuda/cudaOverlay.cu(29): warning: variable "thick" was declared but never referenced
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/util/cuda/cudaOverlay.cu(8): warning: function "eq_less" was declared but never referenced
:0:7: warning: ISO C++11 requires whitespace after the macro name
[ 15%] Building NVCC (Device) object CMakeFiles/jetson-inference.dir/util/cuda/jetson-inference_generated_cudaRGB.cu.o
:0:7: warning: ISO C++11 requires whitespace after the macro name
:0:7: warning: ISO C++11 requires whitespace after the macro name
:0:7: warning: ISO C++11 requires whitespace after the macro name
[ 17%] Building NVCC (Device) object CMakeFiles/jetson-inference.dir/util/cuda/jetson-inference_generated_cudaResize.cu.o
:0:7: warning: ISO C++11 requires whitespace after the macro name
:0:7: warning: ISO C++11 requires whitespace after the macro name
:0:7: warning: ISO C++11 requires whitespace after the macro name
:0:7: warning: ISO C++11 requires whitespace after the macro name
:0:7: warning: ISO C++11 requires whitespace after the macro name
:0:7: warning: ISO C++11 requires whitespace after the macro name
:0:7: warning: ISO C++11 requires whitespace after the macro name
:0:7: warning: ISO C++11 requires whitespace after the macro name
:0:7: warning: ISO C++11 requires whitespace after the macro name
[ 20%] Building NVCC (Device) object CMakeFiles/jetson-inference.dir/util/cuda/jetson-inference_generated_cudaYUV-NV12.cu.o
:0:7: warning: ISO C++11 requires whitespace after the macro name
:0:7: warning: ISO C++11 requires whitespace after the macro name
:0:7: warning: ISO C++11 requires whitespace after the macro name
:0:7: warning: ISO C++11 requires whitespace after the macro name
[ 22%] Building NVCC (Device) object CMakeFiles/jetson-inference.dir/util/cuda/jetson-inference_generated_cudaYUV-YUYV.cu.o
:0:7: warning: ISO C++11 requires whitespace after the macro name
:0:7: warning: ISO C++11 requires whitespace after the macro name
:0:7: warning: ISO C++11 requires whitespace after the macro name
:0:7: warning: ISO C++11 requires whitespace after the macro name
:0:7: warning: ISO C++11 requires whitespace after the macro name
:0:7: warning: ISO C++11 requires whitespace after the macro name
:0:7: warning: ISO C++11 requires whitespace after the macro name
:0:7: warning: ISO C++11 requires whitespace after the macro name
Scanning dependencies of target jetson-inference
[ 25%] Building CXX object CMakeFiles/jetson-inference.dir/detectNet.cpp.o
[ 27%] Building CXX object CMakeFiles/jetson-inference.dir/imageNet.cpp.o
In file included from /home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/imageNet.h:9:0,
from /home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/imageNet.cpp:5:
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/tensorNet.h:51:46: error: ‘vector’ in namespace ‘std’ does not name a template type
const char* input_blob, const std::vectorstd::string& output_blobs,
^~~~~~
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/tensorNet.h:51:52: error: expected ‘,’ or ‘...’ before ‘<’ token
const char* input_blob, const std::vectorstd::string& output_blobs,
^
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/tensorNet.h:93:20: error: ‘vector’ in namespace ‘std’ does not name a template type
const std::vectorstd::string& outputs,
^~~~~~
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/tensorNet.h:93:26: error: expected ‘,’ or ‘...’ before ‘<’ token
const std::vectorstd::string& outputs,
^
In file included from /home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/imageNet.h:9:0,
from /home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/imageNet.cpp:5:
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/tensorNet.h:170:7: error: ‘vector’ in namespace ‘std’ does not name a template type
std::vector mOutputs;
^~~~~~
In file included from /home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/imageNet.cpp:5:0:
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/imageNet.h:117:7: error: ‘vector’ in namespace ‘std’ does not name a template type
std::vectorstd::string mClassSynset; // 1000 class ID's (ie n01580077, n04325704)
^~~~~~
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/imageNet.h:118:7: error: ‘vector’ in namespace ‘std’ does not name a template type
std::vectorstd::string mClassDesc;
^~~~~~
In file included from /home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/imageNet.cpp:5:0:
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/imageNet.h: In member function ‘const char* imageNet::GetClassDesc(uint32_t) const’:
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/imageNet.h:90:68: error: ‘mClassDesc’ was not declared in this scope
inline const char* GetClassDesc( uint32_t index ) const  { return mClassDesc[index].c_str(); }
^~~~~~~~~~
In file included from /home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/detectNet.h:9:0,
from /home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/detectNet.cpp:5:
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/tensorNet.h:51:46: error: ‘vector’ in namespace ‘std’ does not name a template type
const char* input_blob, const std::vectorstd::string& output_blobs,
^~~~~~
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/tensorNet.h:51:52: error: expected ‘,’ or ‘...’ before ‘<’ token
const char* input_blob, const std::vectorstd::string& output_blobs,
^
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/tensorNet.h:93:20: error: ‘vector’ in namespace ‘std’ does not name a template type
const std::vectorstd::string& outputs,
^~~~~~
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/tensorNet.h:93:26: error: expected ‘,’ or ‘...’ before ‘<’ token
const std::vectorstd::string& outputs,
^
In file included from /home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/detectNet.h:9:0,
from /home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/detectNet.cpp:5:
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/tensorNet.h:170:7: error: ‘vector’ in namespace ‘std’ does not name a template type
std::vector mOutputs;
^~~~~~
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/imageNet.h:90:68: note: suggested alternative: ‘GetClassDesc’
inline const char* GetClassDesc( uint32_t index ) const  { return mClassDesc[index].c_str(); }
^~~~~~~~~~
GetClassDesc
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/imageNet.h: In member function ‘const char* imageNet::GetClassSynset(uint32_t) const’:
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/imageNet.h:95:70: error: ‘mClassSynset’ was not declared in this scope
inline const char* GetClassSynset( uint32_t index ) const  { return mClassSynset[index].c_str(); }
^~~~~~~~~~~~
In file included from /home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/detectNet.cpp:5:0:
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/detectNet.h: In member function ‘uint32_t detectNet::GetMaxBoundingBoxes() const’:
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/detectNet.h:122:56: error: ‘mOutputs’ was not declared in this scope
inline uint32_t GetMaxBoundingBoxes() const  { return mOutputs[1].dims.w * mOutputs[1].dims.h * mOutputs[1].dims.c; }
^~~~~~~~
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/imageNet.h:95:70: note: suggested alternative: ‘GetClassSynset’
inline const char* GetClassSynset( uint32_t index ) const  { return mClassSynset[index].c_str(); }
^~~~~~~~~~~~
GetClassSynset
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/detectNet.h:122:56: note: suggested alternative: ‘puts’
inline uint32_t GetMaxBoundingBoxes() const  { return mOutputs[1].dims.w * mOutputs[1].dims.h * mOutputs[1].dims.c; }
^~~~~~~~
puts
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/detectNet.h: In member function ‘uint32_t detectNet::GetNumClasses() const’:
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/detectNet.h:127:51: error: ‘mOutputs’ was not declared in this scope
inline uint32_t GetNumClasses() const   { return mOutputs[0].dims.c; }
^~~~~~~~
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/detectNet.h:127:51: note: suggested alternative: ‘puts’
inline uint32_t GetNumClasses() const   { return mOutputs[0].dims.c; }
^~~~~~~~
puts
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/imageNet.cpp: In member function ‘bool imageNet::init(const char*, const char*, const char*, const char*, const char*, const char*, uint32_t)’:
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/imageNet.cpp:128:19: error: ‘mOutputs’ was not declared in this scope
mOutputClasses = mOutputs[0].dims.c;
^~~~~~~~
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/detectNet.cpp: In static member function ‘static detectNet* detectNet::Create(const char*, const char*, const char*, float, const char*, const char*, const char*, uint32_t)’:
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/detectNet.cpp:56:7: error: ‘vector’ is not a member of ‘std’
std::vectorstd::string output_blobs;
^~~~~~
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/imageNet.cpp:128:19: note: suggested alternative: ‘output’
mOutputClasses = mOutputs[0].dims.c;
^~~~~~~~
output
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/detectNet.cpp:56:25: error: expected primary-expression before ‘>’ token
std::vectorstd::string output_blobs;
^
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/imageNet.cpp:130:36: error: ‘mClassSynset’ was not declared in this scope
if( !loadClassInfo(class_path) || mClassSynset.size() != mOutputClasses || mClassDesc.size() != mOutputClasses )
^~~~~~~~~~~~
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/detectNet.cpp:56:27: error: ‘output_blobs’ was not declared in this scope
std::vectorstd::string output_blobs;
^~~~~~~~~~~~
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/imageNet.cpp:130:36: note: suggested alternative: ‘GetClassSynset’
if( !loadClassInfo(class_path) || mClassSynset.size() != mOutputClasses || mClassDesc.size() != mOutputClasses )
^~~~~~~~~~~~
GetClassSynset
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/imageNet.cpp:130:77: error: ‘mClassDesc’ was not declared in this scope
if( !loadClassInfo(class_path) || mClassSynset.size() != mOutputClasses || mClassDesc.size() != mOutputClasses )
^~~~~~~~~~
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/detectNet.cpp:56:27: note: suggested alternative: ‘input_blob’
std::vectorstd::string output_blobs;
^~~~~~~~~~~~
input_blob
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/imageNet.cpp:130:77: note: suggested alternative: ‘GetClassDesc’
if( !loadClassInfo(class_path) || mClassSynset.size() != mOutputClasses || mClassDesc.size() != mOutputClasses )
^~~~~~~~~~
GetClassDesc
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/imageNet.cpp: In member function ‘bool imageNet::loadClassInfo(const char*)’:
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/imageNet.cpp:231:4: error: ‘mClassSynset’ was not declared in this scope
mClassSynset.push_back(a);
^~~~~~~~~~~~
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/detectNet.cpp: At global scope:
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/detectNet.cpp:197:29: error: variable or field ‘mergeRect’ declared void
static void mergeRect( std::vector& rects, const float6& rect )
^~~~~~
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/detectNet.cpp:197:29: error: ‘vector’ is not a member of ‘std’
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/detectNet.cpp:197:42: error: expected primary-expression before ‘>’ token
static void mergeRect( std::vector& rects, const float6& rect )
^
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/detectNet.cpp:197:45: error: ‘rects’ was not declared in this scope
static void mergeRect( std::vector& rects, const float6& rect )
^~~~~
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/detectNet.cpp:197:45: note: suggested alternative: ‘gets’
static void mergeRect( std::vector& rects, const float6& rect )
^~~~~
gets
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/detectNet.cpp:197:52: error: expected primary-expression before ‘const’
static void mergeRect( std::vector& rects, const float6& rect )
^~~~~
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/detectNet.cpp: In member function ‘bool detectNet::Detect(float*, uint32_t, uint32_t, float*, int*, float*)’:
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/detectNet.cpp:247:43: error: ‘mOutputs’ was not declared in this scope
void* inferenceBuffers[] = { mInputCUDA, mOutputs[OUTPUT_CVG].CUDA, mOutputs[OUTPUT_BBOX].CUDA };
^~~~~~~~
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/imageNet.cpp:231:4: note: suggested alternative: ‘GetClassSynset’
mClassSynset.push_back(a);
^~~~~~~~~~~~
GetClassSynset
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/imageNet.cpp:232:4: error: ‘mClassDesc’ was not declared in this scope
mClassDesc.push_back(b);
^~~~~~~~~~
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/detectNet.cpp:247:43: note: suggested alternative: ‘puts’
void* inferenceBuffers[] = { mInputCUDA, mOutputs[OUTPUT_CVG].CUDA, mOutputs[OUTPUT_BBOX].CUDA };
^~~~~~~~
puts
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/imageNet.cpp:232:4: note: suggested alternative: ‘GetClassDesc’
mClassDesc.push_back(b);
^~~~~~~~~~
GetClassDesc
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/imageNet.cpp:245:4: error: ‘mClassSynset’ was not declared in this scope
mClassSynset.push_back(a);
^~~~~~~~~~~~
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/detectNet.cpp:267:49: error: ‘class nvinfer1::Dims3’ has no member named ‘w’
const float cell_width  = /width/ mInputDims.w / ow;
^
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/detectNet.cpp:268:50: error: ‘class nvinfer1::Dims3’ has no member named ‘h’
const float cell_height = /height/ mInputDims.h / oh;
^
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/detectNet.cpp:270:56: error: ‘class nvinfer1::Dims3’ has no member named ‘w’
const float scale_x = float(width) / float(mInputDims.w);
^
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/detectNet.cpp:271:57: error: ‘class nvinfer1::Dims3’ has no member named ‘h’
const float scale_y = float(height) / float(mInputDims.h);
^
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/detectNet.cpp:280:7: error: ‘vector’ is not a member of ‘std’
std::vector< std::vector > rects;
^~~~~~
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/detectNet.cpp:280:20: error: ‘vector’ is not a member of ‘std’
std::vector< std::vector > rects;
^~~~~~
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/detectNet.cpp:280:33: error: expected primary-expression before ‘>’ token
std::vector< std::vector > rects;
^
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/detectNet.cpp:280:35: error: expected primary-expression before ‘>’ token
std::vector< std::vector > rects;
^
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/detectNet.cpp:280:37: error: ‘rects’ was not declared in this scope
std::vector< std::vector > rects;
^~~~~
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/imageNet.cpp:245:4: note: suggested alternative: ‘GetClassSynset’
mClassSynset.push_back(a);
^~~~~~~~~~~~
GetClassSynset
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/imageNet.cpp:246:4: error: ‘mClassDesc’ was not declared in this scope
mClassDesc.push_back(str);
^~~~~~~~~~
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/detectNet.cpp:280:37: note: suggested alternative: ‘gets’
std::vector< std::vector > rects;
^~~~~
gets
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/detectNet.cpp:307:6: error: ‘mergeRect’ was not declared in this scope
mergeRect( rects[z], make_float6(x1, y1, x2, y2, coverage, z) );
^~~~~~~~~
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/imageNet.cpp:246:4: note: suggested alternative: ‘GetClassDesc’
mClassDesc.push_back(str);
^~~~~~~~~~
GetClassDesc
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/imageNet.cpp:252:56: error: ‘mClassSynset’ was not declared in this scope
printf("imageNet -- loaded %zu class info entries\n", mClassSynset.size());
^~~~~~~~~~~~
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/imageNet.cpp:252:56: note: suggested alternative: ‘GetClassSynset’
printf("imageNet -- loaded %zu class info entries\n", mClassSynset.size());
^~~~~~~~~~~~
GetClassSynset
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/imageNet.cpp: In member function ‘int imageNet::Classify(float*, uint32_t, uint32_t, float*)’:
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/imageNet.cpp:286:43: error: ‘mOutputs’ was not declared in this scope
void* inferenceBuffers[] = { mInputCUDA, mOutputs[0].CUDA };
^~~~~~~~
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/imageNet.cpp:286:43: note: suggested alternative: ‘puts’
void* inferenceBuffers[] = { mInputCUDA, mOutputs[0].CUDA };
^~~~~~~~
puts
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/imageNet.cpp:303:49: error: ‘mClassDesc’ was not declared in this scope
printf("class %04zu - %f  (%s)\n", n, value, mClassDesc[n].c_str());
^~~~~~~~~~
/home/nvidia/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/imageNet.cpp:303:49: note: suggested alternative: ‘GetClassDesc’
printf("class %04zu - %f  (%s)\n", n, value, mClassDesc[n].c_str());
^~~~~~~~~~
GetClassDesc
CMakeFiles/jetson-inference.dir/build.make:125: recipe for target 'CMakeFiles/jetson-inference.dir/detectNet.cpp.o' failed
make[2]: *** [CMakeFiles/jetson-inference.dir/detectNet.cpp.o] Error 1
make[2]: *** Waiting for unfinished jobs....
CMakeFiles/jetson-inference.dir/build.make:149: recipe for target 'CMakeFiles/jetson-inference.dir/imageNet.cpp.o' failed
make[2]: *** [CMakeFiles/jetson-inference.dir/imageNet.cpp.o] Error 1
CMakeFiles/Makefile2:67: recipe for target 'CMakeFiles/jetson-inference.dir/all' failed
make[1]: *** [CMakeFiles/jetson-inference.dir/all] Error 2
Makefile:129: recipe for target 'all' failed
make: *** [all] Error 2
nvidia@tx2:~/jetson-inference-2717e8914dad03116641247ed2dd9ebc88379d4c/build$

Hi,
I managed to get a RTSP stream from an IP camera with pipeline.
But I don't get how to make it work with the gstCamera that is used in the jetson-inference example
how should i make the pipeline as video source of the gstCamera?
I'm really sorry if i ask supid questions but this is completely new to me.
When I start program now, I have my IP camera live image on top of the onboard camera of my jetson...
complete c++ code:
#include "gstCamera.h"
#include "glDisplay.h"
#include "detectNet.h"
#include "commandLine.h"
#include <signal.h>
bool signal_recieved = false;
void sig_handler(int signo)
{
if( signo == SIGINT )
{
printf("received SIGINT\n");
signal_recieved = true;
}
}
int usage()
{
printf("usage: detectnet-camera [-h] [--network NETWORK] [--threshold THRESHOLD]\n");
printf("                        [--camera CAMERA] [--width WIDTH] [--height HEIGHT]\n\n");
printf("Locate objects in a live camera stream using an object detection DNN.\n\n");
printf("optional arguments:\n");
printf("  --help            show this help message and exit\n");
printf("  --network NETWORK pre-trained model to load (see below for options)\n");
printf("  --overlay OVERLAY detection overlay flags (e.g. --overlay=box,labels,conf)\n");
printf("                    valid combinations are:  'box', 'labels', 'conf', 'none'\n");
printf("  --alpha ALPHA     overlay alpha blending value, range 0-255 (default: 120)\n");
printf("  --camera CAMERA   index of the MIPI CSI camera to use (e.g. CSI camera 0),\n");
printf("                    or for VL42 cameras the /dev/video device to use.\n");
printf("                    by default, MIPI CSI camera 0 will be used.\n");
printf("  --width WIDTH     desired width of camera stream (default is 1280 pixels)\n");
printf("  --height HEIGHT   desired height of camera stream (default is 720 pixels)\n");
printf("  --threshold VALUE minimum threshold for detection (default is 0.5)\n\n");
printf("%s\n", detectNet::Usage());

return 0;

}
int main( int argc, char** argv )
{
/*
* parse command line
*/
commandLine cmdLine(argc, argv);
if( cmdLine.GetFlag("help") )
	return usage();

/*
 * attach signal handler
 */
if( signal(SIGINT, sig_handler) == SIG_ERR )
	printf("\ncan't catch SIGINT\n");

    /**Added by me for rtsp streaming */

    GstElement *pipeline;
    GstBus *bus;
    GstMessage *msg;

      /* Initialize GStreamer */
      gst_init (&argc, &argv);

      /* Build the pipeline */
      pipeline = gst_parse_launch ("playbin uri=rtsp://admin:admin@192.168.1.18:554/cam/realmonitor?channel=1&subtype=1&unicast=true&proto=Onvif",NULL);

      /* Start playing IP Camera */
      gst_element_set_state (pipeline, GST_STATE_PLAYING);

      /*
       How to set pipeline as gstCamera source instead of onboard cam of TX2?
       */

/* create the camera device */
gstCamera* camera = gstCamera::Create(cmdLine.GetInt("width", gstCamera::DefaultWidth),cmdLine.GetInt("height", gstCamera::DefaultHeight),0);

if( !camera )
{
	printf("\ndetectnet-camera:  failed to initialize camera device\n");
	return 0;
}

printf("\ndetectnet-camera:  successfully initialized camera device\n");
printf("    width:  %u\n", camera->GetWidth());
printf("   height:  %u\n", camera->GetHeight());
printf("    depth:  %u (bpp)\n\n", camera->GetPixelDepth());

/*
 * create detection network
 */
detectNet* net = detectNet::Create(argc, argv);

if( !net )
{
	printf("detectnet-camera:   failed to load detectNet model\n");
	return 0;
}

// parse overlay flags
const uint32_t overlayFlags = detectNet::OverlayFlagsFromStr(cmdLine.GetString("overlay", "box,labels,conf"));

/*
 * create openGL window
 */
glDisplay* display = glDisplay::Create();

if( !display )
	printf("detectnet-camera:  failed to create openGL display\n");

/*
 * start streaming
 */
if( !camera->Open() )
{
	printf("detectnet-camera:  failed to open camera for streaming\n");
	return 0;
}

printf("detectnet-camera:  camera open for streaming\n");

/*
 * processing loop
 */
float confidence = 0.0f;

while( !signal_recieved )
{
	// capture RGBA image
	float* imgRGBA = NULL;

	if( !camera->CaptureRGBA(&imgRGBA, 1000) )
		printf("detectnet-camera:  failed to capture RGBA image from camera\n");

	// detect objects in the frame
	detectNet::Detection* detections = NULL;

	const int numDetections = net->Detect(imgRGBA, camera->GetWidth(), camera->GetHeight(), &detections, overlayFlags);

	if( numDetections > 0 )
	{
		printf("%i objects detected\n", numDetections);

		for( int n=0; n < numDetections; n++ )
		{
			printf("detected obj %i  class #%u (%s)  confidence=%f\n", n, detections[n].ClassID, net->GetClassDesc(detections[n].ClassID), detections[n].Confidence);
			printf("bounding box %i  (%f, %f)  (%f, %f)  w=%f  h=%f\n", n, detections[n].Left, detections[n].Top, detections[n].Right, detections[n].Bottom, detections[n].Width(), detections[n].Height());
		}
	}

	// update display
	if( display != NULL )
	{
		// render the image
		display->RenderOnce(imgRGBA, camera->GetWidth(), camera->GetHeight());

		// update the status bar
		char str[256];
		sprintf(str, "TensorRT %i.%i.%i | %s | Network %.0f FPS", NV_TENSORRT_MAJOR, NV_TENSORRT_MINOR, NV_TENSORRT_PATCH, precisionTypeToStr(net->GetPrecision()), net->GetNetworkFPS());
		display->SetTitle(str);

		// check if the user quit
		if( display->IsClosed() )
			signal_recieved = true;
	}

	// print out timing info
	net->PrintProfilerTimes();
}

/*
 * destroy resources
 */
printf("detectnet-camera:  shutting down...\n");

SAFE_DELETE(camera);
SAFE_DELETE(display);
SAFE_DELETE(net);

printf("detectnet-camera:  shutdown complete.\n");
return 0;

}

I've updated the jetson inference code with alinabee revisions, when I run sudo make I get the following error:
QMutex: No such file or directory
#include  compilation terminated.
Any idea how to get this dependency installed on my Jetson Nano?
Thanks

I also get this:
QMutex: No such file or directory
#include compilation terminated.
Any ideas gratefully received!

Here is what I did to get my external Hikvision IP camera feed working with detectnet example.
## Install v4l2loopback utilities and kernel driver
$ sudo apt install v4l2loopback-utils v4l2loopback-dkms

## Install ffmpeg
$ sudo apt install ffmpeg

## Load the v4l2loopback driver. This will create /dev/video0 device
$ sudo modprobe v4l2loopback

## Using ffmpeg pull rtsp stream from camera and push it to the video device created by
## v4l2loopback kernel module.
$ ffmpeg -thread_queue_size 512 \
    -i rtsp://camuser:campassword@192.168.1.13/Streaming/channels/502 \
    -vcodec rawvideo  -vf scale=640:480 -f v4l2 \
    -threads 0 -pix_fmt yuyv422 /dev/video0

## Now you can run the detectnet or imagenet example against the video device
## make sure to match height and width specified in ffmpeg command here.
## I was getting gstreamer error when the size is not matched.
$ detectnet-camera.py  \
   --network=ped-100 \
   --width=640 --height=360 \
   --camera=/dev/video0 \
   --threshold=1.8 --overlay=box

I tried that also...I've been trying to get IP camera to work for quite a while with no luck...I followed your patch and go a gstreamer error below..The stream runs great in browser but nothing on my TX2. .Any ideas? Any Hints? Best,
Sudo modprobe v4l2loopback
ffmpeg -thread_queue_size 512 -i http://192.168.1.9:8888/ir.mjpeg -vcodec rawvideo -vf scale=320:240 -f v4l2 -threads 0 -pix_fmt yuv420p /dev/video1
./detectnet-camera --width=320 --height=240 --camera=/dev/video1  --overlay=box
OpenGL] glDisplay -- X screen 0 resolution:  1280x1024
[OpenGL] glDisplay -- display device initialized
[gstreamer] opening gstCamera for streaming, transitioning pipeline to GST_STATE_PLAYING
[gstreamer] gstreamer changed state from NULL to READY ==> mysink
[gstreamer] gstreamer changed state from NULL to READY ==> videoconvert1
[gstreamer] gstreamer changed state from NULL to READY ==> capsfilter1
[gstreamer] gstreamer changed state from NULL to READY ==> videoconvert0
[gstreamer] gstreamer changed state from NULL to READY ==> capsfilter0
[gstreamer] gstreamer changed state from NULL to READY ==> v4l2src0
[gstreamer] gstreamer changed state from NULL to READY ==> pipeline0
[gstreamer] gstreamer changed state from READY to PAUSED ==> videoconvert1
[gstreamer] gstreamer changed state from READY to PAUSED ==> capsfilter1
[gstreamer] gstreamer changed state from READY to PAUSED ==> videoconvert0
[gstreamer] gstreamer changed state from READY to PAUSED ==> capsfilter0
[gstreamer] gstreamer stream status CREATE ==> src
[gstreamer] gstreamer changed state from READY to PAUSED ==> v4l2src0
[gstreamer] gstreamer changed state from READY to PAUSED ==> pipeline0
[gstreamer] gstreamer msg new-clock ==> pipeline0
[gstreamer] gstreamer stream status ENTER ==> src
[gstreamer] gstreamer msg stream-start ==> pipeline0
[gstreamer] gstreamer changed state from PAUSED to PLAYING ==> videoconvert1
[gstreamer] gstreamer changed state from PAUSED to PLAYING ==> capsfilter1
[gstreamer] gstreamer changed state from PAUSED to PLAYING ==> videoconvert0
[gstreamer] gstreamer changed state from PAUSED to PLAYING ==> capsfilter0
[gstreamer] gstreamer changed state from PAUSED to PLAYING ==> v4l2src0
[gstreamer] gstCamera onEOS
[gstreamer] gstreamer v4l2src0 ERROR Internal data stream error.
[gstreamer] gstreamer Debugging info: gstbasesrc.c(3055): gst_base_src_loop (): /GstPipeline:pipeline0/GstV4l2Src:v4l2src0:
streaming stopped, reason not-negotiated (-4)
[gstreamer] gstreamer changed state from READY to PAUSED ==> mysink
detectnet-camera:  camera open for streaming

Here is what I did to get my external Hikvision IP camera feed working with detectnet example.
## Install v4l2loopback utilities and kernel driver
$ sudo apt install v4l2loopback-utils v4l2loopback-dkms

## Install ffmpeg
$ sudo apt install ffmpeg

## Load the v4l2loopback driver. This will create /dev/video0 device
$ sudo modprobe v4l2loopback

## Using ffmpeg pull rtsp stream from camera and push it to the video device created by
## v4l2loopback kernel module.
$ ffmpeg -thread_queue_size 512 \
    -i rtsp://camuser:campassword@192.168.1.13/Streaming/channels/502 \
    -vcodec rawvideo  -vf scale=640:480 -f v4l2 \
    -threads 0 -pix_fmt yuyv422 /dev/video0

## Now you can run the detectnet or imagenet example against the video device
## make sure to match height and width specified in ffmpeg command here.
## I was getting gstreamer error when the size is not matched.
$ detectnet-camera.py  \
   --network=ped-100 \
   --width=640 --height=360 \
   --camera=/dev/video0 \
   --threshold=1.8 --overlay=box

I use the stream from my Raspberry Pi  running MotionEyeOS using this method.  Thanks for the solution.  I did tweak the ffmpeg command line slightly.
$ ffmpeg -thread_queue_size 512 -i http://192.168.0.142:8081/ -vcodec rawvideo -vf scale=1280:720 -f v4l2 -threads 0 -pix_fmt yuyv422 /dev/video2
$ detectnet-camera.py
--network=ped-100
--width=1280 --height=720
--camera=/dev/video2
--threshold=1.8 --overlay=box

Here is what I did to get my external Hikvision IP camera feed working with detectnet example.
## Install v4l2loopback utilities and kernel driver
$ sudo apt install v4l2loopback-utils v4l2loopback-dkms

## Install ffmpeg
$ sudo apt install ffmpeg

## Load the v4l2loopback driver. This will create /dev/video0 device
$ sudo modprobe v4l2loopback

## Using ffmpeg pull rtsp stream from camera and push it to the video device created by
## v4l2loopback kernel module.
$ ffmpeg -thread_queue_size 512 \
    -i rtsp://camuser:campassword@192.168.1.13/Streaming/channels/502 \
    -vcodec rawvideo  -vf scale=640:480 -f v4l2 \
    -threads 0 -pix_fmt yuyv422 /dev/video0

## Now you can run the detectnet or imagenet example against the video device
## make sure to match height and width specified in ffmpeg command here.
## I was getting gstreamer error when the size is not matched.
$ detectnet-camera.py  \
   --network=ped-100 \
   --width=640 --height=360 \
   --camera=/dev/video0 \
   --threshold=1.8 --overlay=box

That worked fine for me, thanks! But I had some issues that were solved wrapping up the rtsp url with "rtsp://...". Here is a video of everything working together: https://youtu.be/PLBffle0CcQ

Hello, how can I put rtsp in "videoOutput"? I need steram detectnet from jetson nano over lan by RTSP.

Hi @niyazFattahov, jetson-inference/jetson-utils doesn't support RTSP output, as it requires support for special server code.  Otherwise I would add it if it were simple.
Note that DeepStream supports RTSP output and has support for the RTSP server if you need that.

What about this: https://github.com/GStreamer/gst-rtsp-server/blob/1.14.5/examples/test-launch.c
can it be used somehow?
I tested rtsp only from csi camera with gstreamer like this:
./test-launch "videotestsrc ! nvvidconv ! nvv4l2h264enc ! h264parse ! rtph264pay name=pay0 pt=96"
(https://forums.developer.nvidia.com/t/jetson-nano-faq/82953)
thank you.

In theory, yes, some similar code / dependencies would need to be integrated into the videoOutput class in order to support RTSP output.

(## Install v4l2loopback utilities and kernel driver
$ sudo apt install v4l2loopback-utils v4l2loopback-dkms
Install ffmpeg
$ sudo apt install ffmpeg
Load the v4l2loopback driver. This will create /dev/video0 device
$ sudo modprobe v4l2loopback
Using ffmpeg pull rtsp stream from camera and push it to the video device created by
v4l2loopback kernel module.
$ ffmpeg -thread_queue_size 512
-i rtsp://camuser:campassword@192.168.1.13/Streaming/channels/502
-vcodec rawvideo  -vf scale=640:480 -f v4l2
-threads 0 -pix_fmt yuyv422 /dev/video0
Now you can run the detectnet or imagenet example against the video device
make sure to match height and width  specified in ffmpeg command here.
I was getting gstreamer error when the size is not matched.
$ detectnet-camera.py
--network=ped-100
--width=640 --height=360
--camera=/dev/video0
--threshold=1.8 --overlay=box #)
@neildotwilliams @dusty-nv
I've tried this on Jetson TX2 but I got this error
(aititx2-2@aititx22-desktop:~$ ffmpeg -thread_queue_size 2631 -i "rtsp://aititx2-2@aititx22-desktop@192.168.10.76:1935/profile" -vcodec rawvideo -vf scale=1280:720 -f v4l2 -threads 0 -pix_fmt yuyv422 /dev/video0
ffmpeg version 3.4.8-0ubuntu0.2 Copyright (c) 2000-2020 the FFmpeg developers
built with gcc 7 (Ubuntu/Linaro 7.5.0-3ubuntu1~18.04)
configuration: --prefix=/usr --extra-version=0ubuntu0.2 --toolchain=hardened --libdir=/usr/lib/aarch64-linux-gnu --incdir=/usr/include/aarch64-linux-gnu --enable-gpl --disable-stripping --enable-avresample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librubberband --enable-librsvg --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-omx --enable-openal --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-chromaprint --enable-frei0r --enable-libopencv --enable-libx264 --enable-shared
libavutil      55. 78.100 / 55. 78.100
libavcodec     57.107.100 / 57.107.100
libavformat    57. 83.100 / 57. 83.100
libavdevice    57. 10.100 / 57. 10.100
libavfilter     6.107.100 /  6.107.100
libavresample   3.  7.  0 /  3.  7.  0
libswscale      4.  8.100 /  4.  8.100
libswresample   2.  9.100 /  2.  9.100
libpostproc    54.  7.100 / 54.  7.100
[rtsp @ 0x559b6b26b0] max delay reached. need to consume packet
[rtsp @ 0x559b6b26b0] RTP: missed 2 packets
[rtsp @ 0x559b6b26b0] max delay reached. need to consume packet
[rtsp @ 0x559b6b26b0] RTP: missed 2 packets
[rtsp @ 0x559b6b26b0] max delay reached. need to consume packet
[rtsp @ 0x559b6b26b0] RTP: missed 1 packets
Input #0, rtsp, from 'rtsp://aititx2-2@aititx22-desktop@192.168.10.76:1935/profile':
Metadata:
title           : Unnamed
comment         : N/A
Duration: N/A, start: 0.000000, bitrate: N/A
Stream #0:0: Video: h264 (Constrained Baseline), yuv420p(progressive), 720x1280, 90k tbr, 90k tbn, 180k tbc
Stream #0:1: Audio: aac (LC), 32000 Hz, stereo, fltp
Stream mapping:
Stream #0:0 -> #0:0 (h264 (native) -> rawvideo (native))
Press [q] to stop, [?] for help
[v4l2 @ 0x559b7204e0] Frame rate very high for a muxer not efficiently supporting it.
Please consider specifying a lower framerate, a different muxer or -vsync 2
[v4l2 @ 0x559b7204e0] ioctl(VIDIOC_G_FMT): Invalid argument
Could not write header for output file #0 (incorrect codec parameters ?): Invalid argument
Error initializing output stream 0:0 --
Conversion failed!
)
please help! I use RSTP to connect to my android phone camera.

authors: LSAMIJN alinabee aprentis bkanaki dusty-nv e-mily engineer1982 flurpo jonwilliams84 kanakiyab linusali neildotwilliams nikever niyazFattahov sms720

repo: dusty-nv/jetson-inference


text:
Does it work with latest Stencil v1?

Hello @dutscher and thank you so much for creating this package! :heart:
I see that it supports Stencil v2 but our organization can't quite move to v2 just yet, so we're on the latest version of v1. Would this package work with the latest v1?
Thanks for any information.

storybook runs alone and stencil is connected via proxy. so i promise you that your "old" stencil will work.
give it a try, clone and set down the stencil version in package.json. thr sample component and scss should run also under older version.

Ah okay, that's great. I'll give it a try!

please let me know if it really works :)

source: gharchive/issue

repo: dutscher/stencil-storybook

license: MIT

license_type: permissive

license_source: github-api


text:
[Feature Request] Set output device

It would be pretty awesome, if deezer-enhanced would provide the posibility to select the output device. I have the GoXLR, which has 5 Output channels and everytime the audio starts again I have to go to pavoucontrol and select the music channel. (Default is System).

I see, sounds great, I'll look into it!

This issue is probably upstream.
https://github.com/electron/electron/issues/27581
https://github.com/electron/electron/issues/7470

I'm closing this because the error never occurred again. I don't know what it was, but it seems to be gone now.

repo: duzda/deezer-enhanced


text:
关于dva组件状态的问题

在使用dva过程中，如果在一个列表页面点击添加按钮，弹出一个modal窗口，窗口是否显示保存在model的state中，如果在model里的控件例如 ant.design的穿梭框（Transfer）需要靠model里state的值改变选中结果，这样每次选中改变后就会调用 reducers ，这样整个modal窗口就会重新弹出。 如何解决呢

应该是使用方式不对，贴下代码

全部数据走 model，不要走 component 的 state 。混用会出现一些奇怪的问题，除非你很明白整套机制。

@sorrycc 数据走的就是model，我把代码贴上来
弹出的modal 组件
`import React, { PropTypes } from 'react';
import { Form, Input, Modal, InputNumber, Select, Transfer } from 'antd';
import { connect } from 'dva';
const FormItem = Form.Item;
const Option = Select.Option;
const formItemLayout = {
labelCol: {
span: 6
},
wrapperCol: {
span: 14
}
};
const formItemLayout2 = {
labelCol: {
span: 8
},
wrapperCol: {
span: 12
}
};
var SetSpotsModal = ({cardtype, dispatch, form, type, item = {}, onOk, onCancel}) => {
const { getFieldProps, validateFields, getFieldsValue } = form;
const modalOpts = {
title: cardtype.title,
visible : cardtype.spotsVisible,
maskClosable: false,
onOk: handleOk,
onCancel,
};
let allSpots=[];
if(cardtype.allSpots!=null&&cardtype.allSpots.length>0){
for(var i=0;i<cardtype.allSpots.length;i++){
allSpots.push({cardtype.allSpots[i].name});
}
}
function handleOk() {
form.validateFields((errors) => {
if (errors) {
return;
}
const data = { ...form.getFieldsValue(), cardTypeId: item.cardTypeId };
onOk(data);
});
}
function handleChange(targetKeys, direction, moveKeys) {
dispatch({
type: 'card/type/tranfser/success',
payload: {
targetKeys: targetKeys,
},
});
}
return (
<Modal {...modalOpts}>

<FormItem
label="卡类型："
{...formItemLayout}
hasFeedback
>
{cardtype.cardTypeName}

<FormItem
label="景区："
{...formItemLayout}
hasFeedback
>
<Select showSearch
placeholder="请选择景区" multiple
{...getFieldProps('scenicIds',{rules: [{ required: true, type: 'array',message: '请选择景区'}],initialValue: item['scenicIds']==null?[]:item['scenicIds']})}
>
{allSpots}

<Transfer
dataSource={cardtype.mockData}
targetKeys={cardtype.targetKeys}
onChange={handleChange}
render={item => item.title}
/>
  </Form>
</Modal>

);
}
function mapStateToProps(state) {
return { cardtype: state.cardtype };
}
SetSpotsModal = Form.create()(SetSpotsModal);
export default connect(mapStateToProps)(SetSpotsModal);
`
model
`import { call, put } from 'dva/effects';
import { hashHistory } from 'dva/router';
import { query, create, del, info, edit,getScenicspots,spotsinfo,setCardTtypeSpots} from '../services/cardtype';
import { message } from 'antd';
export default {
namespace: 'cardtype',
state: {
list: [],
tableLoading: false,
currentItem: {},
modalType: 'create',
total: null,
title: '新增卡分类',
modalVisible : false,
spotsVisible:false,
currentSize: 10,
current: 1,
allSpots:[],
cardTypeName:'',
targetKeys: [],
mockData:[],
},
subscriptions: [
function(dispatch) {
hashHistory.listen(location => {
if (location.pathname === '/card/type') {
dispatch.dispatch({
type: 'card/type/tranfser',
});
dispatch.dispatch({
type: 'card/type/query',
payload: {'name':''},
});
dispatch.dispatch({
type: 'card/type/allSpots',
payload: {'name':''},
});
}
});
},
],
effects: {
*'card/type/tranfser' {
const targetKeys = [];
const mockData = [];
for (let i = 0; i < 20; i++) {
const data = {
key: i,
title: 内容${i + 1},
description: 内容${i + 1}的描述,
chosen: Math.random() * 2 > 1,
};
if (data.chosen) {
targetKeys.push(data.key);
}
mockData.push(data);
}
yield put({
type: 'card/type/tranfser/success',
payload: {
targetKeys: targetKeys,
mockData: mockData,
},
});
},
// 卡分类查询
*['card/type/query']({ payload }) {
yield put({ type: 'card/type/showLoading' });
const result = yield call(query, payload);
if (result.body && result.body.success) {
yield put({
type: 'card/type/query/success',
payload: {
list: result.body.page.list,
total: result.body.page.total,
currentSize: result.body.page.pageSize,
current: result.body.page.pageNum,
},
});
}
},
// 增加卡分类
*['card/type/create']({ payload }) {
    yield put({ type: 'card/type/hideModal' });
    yield put({ type: 'card/type/showLoading' });
    const result = yield call(create, payload);
    if (result.body && result.body.success) {
      message.success("添加卡分类成功");
      const result = yield call(query, {name:''});
      if (result && result.body.success) {
        yield put({
          type: 'card/type/query/success',
          payload: {
            list: result.body.page.list,
            total: result.body.page.total,
            currentSize: result.body.page.pageSize,
            current: result.body.page.pageNum,
          },
        });
      }
    }else if (result.body && !result.body.success) {
      message.success(result.body.errorMsg);
    }
    yield put({ type: 'card/type/hideLoading' });
},

// 删除用户
*['card/type/del']({ payload }) {
  yield put({ type: 'card/type/showLoading' });
  const result = yield call(del, payload);
  if (result.body && result.body.success) {
    message.success("删除卡分类成功");
    const result = yield call(query, {name:''});
    if (result.body && result.body.success) {
      yield put({
        type: 'card/type/query/success',
        payload: {
          list: result.body.page.list,
          total: result.body.page.total,
          currentSize: result.body.page.pageSize,
          current: result.body.page.pageNum,
        },
      });
    }
  }
  yield put({ type: 'card/type/hideLoading' });
},

// 获取卡分类信息
*['card/type/info']({ payload }) {
  const result = yield call(info, payload.id);
  if (result.body && result.body.success) {
    yield put({
      type: 'card/type/showModal',
      payload: {
        currentItem: result.body.data,
        modalType: 'edit',
      },
    });
  }
},
//获取所有景区

*'card/type/allSpots' {
const result = yield call(getScenicspots);
if (result.body && result.body.success) {
yield put({
type: 'card/type/allSpots/success',
payload: {
allSpots:result.body.data,
},
});
}
},
//卡设置景区
*['card/type/spotsinfo']({ payload }) {
const result = yield call(spotsinfo, payload.id);
if (result.body && result.body.success) {
yield put({
type: 'card/type/showSpotsModal',
payload: {
currentItem: result.body.data,
title:'关联景区',
cardTypeName:payload.name,
},
});
}
},
// 更新卡分类信息
*['card/type/setSpots']({ payload }) {
const result = yield call(setCardTtypeSpots, payload);
if (result.body && result.body.success) {
yield put({ type: 'card/type/hideSpotsModal' });
yield put({ type: 'card/type/showLoading' });
message.success("设置景区成功");
const result = yield call(query, {name:''});
if (result.body && result.body.success) {
yield put({ type: 'card/type/hideLoading' });
yield put({
type: 'card/type/query/success',
payload: {
list: result.body.page.list,
total: result.body.page.total,
currentSize: result.body.page.pageSize,
current: result.body.page.pageNum,
},
});
}
}else if(result.body && !result.body.success) {
message.success(result.body.errorMsg);
}
    },

// 更新卡分类信息
*['card/type/edit']({ payload }) {
  yield put({ type: 'card/type/hideModal' });
  yield put({ type: 'card/type/showLoading' });
  const result = yield call(edit, payload);
  if (result.body && result.body.success) {
    message.success("编辑卡分类成功");
    const result = yield call(query, {name:''});
    if (result.body && result.body.success) {
      yield put({
        type: 'card/type/query/success',
        payload: {
          list: result.body.page.list,
          total: result.body.page.total,
          currentSize: result.body.page.pageSize,
          current: result.body.page.pageNum,
        },
      });
    }
  }else if(result.body && !result.body.success) {
    message.success(result.body.errorMsg);
  }
  yield put({ type: 'card/type/hideLoading' });
},

},
reducers: {
['card/type/tranfser/success'](state, action) {
return { ...state, ...action.payload};
},
['card/type/showLoading'](state, action) {
return { ...state, tableLoading: true };
},
['card/type/hideLoading'](state, action) {
return { ...state, tableLoading: false };
},
['card/type/query/success'](state, action) {
return { ...state, ...action.payload, tableLoading: false};
},
['card/type/showModal'](state, action) {
return { ...state, ...action.payload, modalVisible: true};
},
['card/type/hideModal'](state, action) {
return { ...state, modalVisible: false};
},
['card/type/showSpotsModal'](state, action) {
return { ...state, ...action.payload, spotsVisible: true};
},
['card/type/hideSpotsModal'](state, action) {
return { ...state, spotsVisible: false};
},
['card/type/allSpots/success'](state, action) {
return { ...state, ...action.payload};
},
},
}
`

@sorrycc 问题我找到了
按你的方法
// 解决 Form.create initialValue 的问题   // 每次创建一个全新的组件, 而不做diff   // 如果你使用了redux, 请移步 http://react-component.github.io/form/examples/redux.html   const UserModalGen = () =>     <UserModal {...userModalProps} />;
每次创建全新的modal组件如果更新model数据组件就会重新绘制，就会造成modal重新弹出
还成手动控制就行了

借此问题提出一下组件的疑问，user-dashboard demo中的User.jsx中有这样一段：
// 解决 Form.create initialValue 的问题
  // 每次创建一个全新的组件, 而不做diff
  // 如果你使用了redux, 请移步 http://react-component.github.io/form/examples/redux.html
  const UserModalGen = () =>
    <UserModal {...userModalProps} />;

  return (
    <MainLayout location={location}>
      <div className={styles.normal}>
        <UserSearch {...userSearchProps} />
        <UserList {...userListProps} />
        <UserModalGen />
      </div>
    </MainLayout>
  );

为什么User.jsx中引入的三个子组件里面只有UserModal组件需要重新包装一下，而UserSearch却没有，看注释说每次会生成一个全新的组件，为啥会这样，求大神指导一下，非常感谢

@wonyun
// 解决 Form.create initialValue 的问题
// 每次创建一个全新的组件, 而不做diff
// 如果你使用了redux, 请移步 http://react-component.github.io/form/examples/redux.html

antd2.0.0已经没有此问题

authors: codering nikogu sorrycc wonyun yrcy0418

repo: dvajs/dva

license: mit

license_source: bigquery


text:
can we make the structure of notion the same as zotero collections?

Right now, all items are in the same folder in Notero, can we make the structure of notion the same as zotero collections
(The same item can exist in different folders)

Hi @fredericky123, thanks for the feedback!
There was some discussion around this topic in #6, and the decision was made to sync to a single Notion database for now. To achieve something similar to what you're asking, the idea we had was to sync a Collection property into Notion as described in #30. Then you could make as many different views of that database as you wanted based on the Collection property or any combination of other properties. This felt like the more "Notion way" of doing things.
Do you think having a Collection property in Notion as described in #30 would meet your needs?

Thanks @dvanoni ! This is exactly what I want. Have it already been realized? How to configure it?
By the way, when enable the collections to sync, can we have a toogle to sync all collections? Right now, we have to choose each single collection, since I have a lot of collections, it seems time-consuming to do this.

@fredericky123, unfortunately this functionality isn't built yet. Keep an eye on #30, and I'll be sure to post an update when it's ready.
I pulled out your second question into its own issue so we can track that separately: #63
I'm going to close this issue as I believe we should have these points covered by #30 and #63. Feel free to add your input on those!

repo: dvanoni/notero


text:
Something wrong with the torch version

I followed the steps in readme but I encounted the following errors in SFT.
[WARNING]  async_io requires the dev libaio .so object and headers but these were not found.
[WARNING]  async_io: please install the libaio-dev package with apt
[WARNING]  If libaio is already installed (perhaps from source), try setting the CFLAGS and LDFLAGS environment variables to where it can be found.
[WARNING]  Please specify the CUTLASS repo directory as environment variable $CUTLASS_PATH
[WARNING]  sparse_attn requires a torch version >= 1.5 and < 2.0 but detected 2.3
[WARNING]  using untested triton version (2.2.0), only 1.0.0 is known to be compatible
[2024-05-19 04:59:05,039] [INFO] [comm.py:637:init_distributed] cdb=None
[2024-05-19 04:59:05,082] [INFO] [real_accelerator.py:203:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2024-05-19 04:59:05,108] [INFO] [real_accelerator.py:203:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[WARNING]  async_io requires the dev libaio .so object and headers but these were not found.
[WARNING]  async_io requires the dev libaio .so object and headers but these were not found.
[WARNING]  async_io: please install the libaio-dev package with apt
[WARNING]  If libaio is already installed (perhaps from source), try setting the CFLAGS and LDFLAGS environment variables to where it can be found.
[WARNING]  Please specify the CUTLASS repo directory as environment variable $CUTLASS_PATH
[WARNING]  sparse_attn requires a torch version >= 1.5 and < 2.0 but detected 2.3
[WARNING]  using untested triton version (2.2.0), only 1.0.0 is known to be compatible
[WARNING]  async_io: please install the libaio-dev package with apt
[WARNING]  If libaio is already installed (perhaps from source), try setting the CFLAGS and LDFLAGS environment variables to where it can be found.
[WARNING]  Please specify the CUTLASS repo directory as environment variable $CUTLASS_PATH
[WARNING]  sparse_attn requires a torch version >= 1.5 and < 2.0 but detected 2.3
[WARNING]  using untested triton version (2.2.0), only 1.0.0 is known to be compatible
[rank5]: Traceback (most recent call last):
[rank5]:   File "/home/zhangyan/Dynathink/LongLoRA/fine-tune.py", line 211, in
[rank5]:     train()
[rank5]:   File "/home/zhangyan/Dynathink/LongLoRA/fine-tune.py", line 106, in train
[rank5]:     model_args, training_args = parser.parse_args_into_dataclasses()
[rank5]:                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank5]:   File "/home/zhangyan/anaconda3/envs/longlora/lib/python3.12/site-packages/transformers/hf_argparser.py", line 347, in parse_args_into_dataclasses
[rank5]:     raise ValueError(f"Some specified arguments are not used by the HfArgumentParser: {remaining_args}")
[rank5]: ValueError: Some specified arguments are not used by the HfArgumentParser: [' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ']
In requirements.txt the version of torch is >=2.0.0, but the errors show that I need to install torch<2.0.0. How can I deal with the problem?

the same problem

repo: dvlab-research/LongLoRA


text:
Chore: Improved the overall cart UI including empty cart

Issue # 15
I divided the main section into 80-20%
Implemented bootstrap card styling to make it more attractive.
Iterated the same items in the pricing module to reflect the pricing more clearly.
PREVIOUSLY

NOW
(1/2)

(2/2)

Please have a look at the PR

Great work @AqibAliMughal.

source: gharchive/pull-request


text:
is the new "high quality" camera supported?

hi,
rpi2dng barfs with images from the new raspberry camera. is this supposed to work? here's a raw (captured with raspi-still --raw) in  case this helps.
https://rm.ignorelist.com/s/yrWY8362a6wNmjC
.rm

the sensor is a "IMX477R"

Not yet... Will look into it when I have time

Here are some infos about other tools that do the dng conversion from the HQ Camera Raw Images: https://www.raspberrypi.org/forums/viewtopic.php?p=1665327#p1665327

authors: dword1511 rmalchow tilllt


text:
Feature/26 fix payscale ordering

updates pay scale labels
retrieves in correct order

 Fix pay_scale ordering

 Correct naming of pay scale options

repo: dxw/teacher-vacancy-service


text:
Update preinstalled software list for TCC agents

This PR was created by an automated build in TeamCity Cloud

Closing this PR as obsolete, there is a more recent one


text:
[Snyk] Security upgrade @backstage/theme from 0.0.0-use.local to 0.1.1

Snyk has created this PR to fix 4 vulnerabilities in the yarn dependencies of this project.
Snyk changed the following file(s):

plugins/example-todo-list/package.json

Note for zero-installs users
If you are using the Yarn feature zero-installs that was introduced in Yarn V2, note that this PR does not update the .yarn/cache/ directory meaning this code cannot be pulled and immediately developed on as one would expect for a zero-install project - you will need to run yarn to update the contents of the ./yarn/cache directory.
If you are not using zero-install you can ignore this as your flow should likely be unchanged.

⚠ Warning
Failed to update the yarn.lock, please update manually before merging.

Vulnerabilities that will be fixed with an upgrade:

Issue
Score

Asymmetric Resource Consumption (Amplification) SNYK-JS-BODYPARSER-7926860
  112

Cross-site Scripting SNYK-JS-EXPRESS-7926867
  80

Cross-site Scripting SNYK-JS-SEND-7926862
  80

Cross-site Scripting SNYK-JS-SERVESTATIC-7926865
  80

[!IMPORTANT]

Check the changes in this PR to ensure they won't cause issues with your project.
Max score is 1000. Note that the real score may have changed since the PR was raised.
This PR was automatically created by Snyk using the credentials of a real user.

Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.
For more information:
🧐 View latest project report
📜 Customise PR templates
🛠 Adjust project settings
📚 Read about Snyk's upgrade logic

Learn how to fix vulnerabilities with free interactive lessons:
🦉 Cross-site Scripting

🎉 Snyk hasn't found any issues so far.
✅ code/snyk check is completed. No issues were found. (View Details)

repo: dylansnyk/backstage


text:
[Snyk] Security upgrade @backstage/plugin-auth-node from 0.0.0-use.local to 0.1.0

Snyk has created this PR to fix 1 vulnerabilities in the yarn dependencies of this project.
Snyk changed the following file(s):

plugins/catalog-backend/package.json

Note for zero-installs users
If you are using the Yarn feature zero-installs that was introduced in Yarn V2, note that this PR does not update the .yarn/cache/ directory meaning this code cannot be pulled and immediately developed on as one would expect for a zero-install project - you will need to run yarn to update the contents of the ./yarn/cache directory.
If you are not using zero-install you can ignore this as your flow should likely be unchanged.

⚠ Warning
Failed to update the yarn.lock, please update manually before merging.

Vulnerabilities that will be fixed with an upgrade:

Issue
Score

Cross-site Scripting (XSS) SNYK-JS-COOKIE-8163060
  44

[!IMPORTANT]

Check the changes in this PR to ensure they won't cause issues with your project.
Max score is 1000. Note that the real score may have changed since the PR was raised.
This PR was automatically created by Snyk using the credentials of a real user.

Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.
For more information:
🧐 View latest project report
📜 Customise PR templates
🛠 Adjust project settings
📚 Read about Snyk's upgrade logic

Learn how to fix vulnerabilities with free interactive lessons:
🦉 Cross-site Scripting (XSS)

🎉 Snyk hasn't found any issues so far.
✅ security/snyk check is completed. No issues were found. (View Details)
✅ license/snyk check is completed. No issues were found. (View Details)


text:
Initial benchmark setup

It is advisable to have solid benchmarks from the beginning to quickly and easily assess the performance impact of different implementations.
For this we could leverage jmh.
Some example JMH integrations can be found here: https://github.com/FasterXML/jackson-benchmarks/tree/2.15
The idea of this issue is to provide an initial setup to enable the project to write easy and reliable micro benchmarks.

I started a branch for this this morning. We've been mostly using the tests to measure performance but i think it makes more sense to get a basic micro benchmark setup working. JMH will provide more control and also i'd like to separate out things like parsing, compilation, and runtime performance. Will ping you when i get a PR started @danielperano

I think this has been solved by https://github.com/dylibso/chicory/pull/280 in the meantime.

authors: bhelx thomasdarimont

repo: dylibso/chicory


text:
Improve Configuration and Validation sections of the docs

Summary
Fixes #911 and #968 by adding documentation on some Dynaconf parameters and reworking the Validation section of the docs.
Details
Not much detail to give, as context is present in the 2 linked issues.
Aside from the obvious additions to configuration.md and validation.md, I updated the Pyhon version in CONTRIBUTING.md to 3.8+ and added .DS_Store file to .gitignore. In particular, any of these 2 unrelated changes could be dropped from this PR if needed :).
Questions

Links between Markdown files don't seem to work on my local docs (make docs). Did I do something wrong or do they get fixed when deployed to the web?

Notes
Please feel free to ask any changes in this PR. The idea is to improve Dynaconf.

Maybe I have to do absolute links (without the ./ at the start?

Thanks @pedro-psb for your input and patience. I pushed some changes, you can look at the diff here.
Please don't hesitate to re-ask for changes, and please mark as solved any discussions you think should be closed.

Hey @pedro-psb, I'll look at your comments during the weekend. My bad with the links though, I could've sworn that I went through all of them :S.

@pedro-psb sorry this is taking so long, I don't have much time mid-week to tackle this PR :S. I've now re-read and it looks like its better now. I'll look check all links from the dev deploy once it's ready.

repo: dynaconf/dynaconf


text:
md formatting

Added some python syntax highlighting to code blocks.

@ginotrombetti can you review this when you get a chance?

repo: dynata/rex-sdk-python


text:
Add color override for OOTB Honeycomb Tiles

When viewing certain honeycomb tiles, like synthetics, the chart displays only green/red based on the success/failure of the synthetic monitor. If a monitor is disabled and it's last run was successful, the cell shows green. This request is to override that green color for disabled monitor (last run was good) or red color for disabled monitor (last run was bad) to grey. This would depend on the status of the monitor. The field is called status and I can filter the chart on that, but not sure if "status" is exposed where the powerup could just color those cells grey. Hopefully it is...

this doesn't seem possible without making some sort of api call

authors: LucasHocker TechShady


text:
No handler for module nn.MulConstant! for ResNet

Hi,
when trying to profile a ReNet trained on CIFAR10, I get:
In 5 module of nn.Sequential:
In 1 module of nn.Sequential:
In 1 module of nn.Sequential:
In 2 module of nn.ConcatTable:
In 2 module of nn.Sequential:
In 2 module of nn.Concat:
/home/pedro/repositories/Torch7-profiling/src/profiler.lua:37: No handler for module nn.MulConstant!
stack traceback:
        [C]: in function 'assert'
        /home/pedro/repositories/Torch7-profiling/src/profiler.lua:37: in function 'compute_ops'
        /home/pedro/repositories/Torch7-profiling/src/profiler.lua:19: in function </home/pedro/repositories/Torch7-profiling/src/profiler.lua:18>
        [C]: in function 'xpcall'
        /home/pedro/torch/install/share/lua/5.1/nn/Container.lua:63: in function 'rethrowErrors'
        /home/pedro/torch/install/share/lua/5.1/nn/Concat.lua:14: in function </home/pedro/torch/install/share/lua/5.1/nn/Concat.lua:9>
        [C]: in function 'xpcall'
        /home/pedro/torch/install/share/lua/5.1/nn/Container.lua:63: in function 'rethrowErrors'
        /home/pedro/torch/install/share/lua/5.1/nn/Sequential.lua:44: in function </home/pedro/torch/install/share/lua/5.1/nn/Sequential.lua:41>
        [C]: in function 'xpcall'
        ...
        /home/pedro/torch/install/share/lua/5.1/nn/Container.lua:63: in function 'rethrowErrors'
        /home/pedro/torch/install/share/lua/5.1/nn/Sequential.lua:44: in function </home/pedro/torch/install/share/lua/5.1/nn/Sequential.lua:41>
        [C]: in function 'xpcall'
        /home/pedro/torch/install/share/lua/5.1/nn/Container.lua:63: in function 'rethrowErrors'
        /home/pedro/torch/install/share/lua/5.1/nn/Sequential.lua:44: in function 'forward'
        /home/pedro/repositories/Torch7-profiling/src/profiler.lua:10: in function 'count_ops'
        profile-model.lua:87: in main chunk
        [C]: in function 'dofile'
        ...edro/torch/install/lib/luarocks/rocks/trepl/scm-1/bin/th:145: in main chunk
        [C]: at 0x00405d50 ```

Thanks,

Added nn.MulConstant with this commit. Let me know if it is working.

Sorry for the late reply. It works! Thanks.


text:
KC and mariadb upgrade

Check following resources:

https://github.com/keycloak/keycloak/tree/main/quarkus/container

https://github.com/keycloak/keycloak-containers/tree/main/docker-compose-examples

https://github.com/MariaDB/mariadb-docker/ issues/94
https://mariadb.org/mariadb-server-docker-official-images-healthcheck-without-mysqladmin/
https://github.com/MariaDB/mariadb-docker/ pull/508

Try to send a PR to upstream now that seems that the project is active again:

https://github.com/Frankniesten/Limesurvey-SAML-Authentication/ pull/6

Beware that LS seems to create responses' tables using MyISAM but seems that now can be configured:

https://github.com/LimeSurvey/LimeSurvey/ pull/1043
https://mariadb.com/kb/en/converting-tables-from-myisam-to-innodb/

repo: e-ucm/docker-limesurvey


text:
Downloading Dashboard Comprised of Google Charts extends past width of PDF

I have a simple div tag denoted with the id: dashboard and my code is as the following:
//Download PDF of Dashboard
$('#options-menu-download-as-pdf').on('click', function () {
    var dashboard = document.getElementById('dashboard');

    var options = {
        filename: 'Report.pdf',
        html2canvas: {
            scale: 2
        },
        pagebreak: {
            mode: ['avoid-all', 'css', 'legacy']
        },
        jsPDF: {
            format: [500, 200],
            unit: 'mm',
            orientation: 'landscape'
        }
    }
    html2pdf().from(dashboard).set(options).save();
});

This does everything I would like except I would like to be able to assign the width and height dynamically (I can do this already by window size or the current h/w of the dashboard in HTML but that could lead to poorly rendered PDFs if the user has their browser window not at full screen).
It appears as though the charts themselves cannot be manipulated to fit within the h/w of the pdf so the get cut off, whereas my grid styling actually fits to the dimensions of he document.
I looked at this thread: https://github.com/eKoopmans/html2pdf.js/issues/44 that discusses a 'fit-to-width' option. I am missing an example of that in documentation so I am not sure if that is an option with html2pdf, the underlying jsPDF, or even a CSS option.

How about: $("#Elementd).css("height", "297mm");
Or have you already found a solution?


text:
'HelloWorldPubSubType' object has no attribute 'setName'. Did you mean: 'set_name'?

E:\test\build\Release>python HelloWorldExample.py -p publisher
Creating publisher.
Traceback (most recent call last):
File "E:\test\build\Release\HelloWorldExample.py", line 206, in
writer = Writer(args.domain, args.machine)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "E:\test\build\Release\HelloWorldExample.py", line 113, in init
self.topic_data_type.setName("HelloWorldDataType")
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'HelloWorldPubSubType' object has no attribute 'setName'. Did you mean: 'set_name'?

Hi @SunLiangcan, thanks for your report.
I am assuming you are using the Fast DDS python version from master branch.
Fast DDS, and Fast DDS Python, are near to a mayor release (v3.0.0 and v2.0.0, respectively). That entails several API breaks and refactors. For that reason, and until these versions are released, it is strongly advisable to use the latest stable branch, 2.14.x (and python v1.4.2).
It seems that performing the TopicDataType refactor in the python repository we missed updating that example.
We will fix it and come back to you, sorry for the inconvenience.

ok,use 1.4.2 no problem

Hi @SunLiangcan, we have already fixed it (#183) in master

authors: JesusPoderoso SunLiangcan

repo: eProsima/Fast-DDS-python


text:
[13307] is_metatraffic documentation

#126 extended public API but no documentation was included. This PR solves the issue.
Merge after #140 (only last commit is relevant)

@JLBuenoLopez-eProsima I think this one has to be rebased

authors: JLBuenoLopez-eProsima MiguelCompany

repo: eProsima/Fast-DDS-statistics-backend


text:
图片展示的时候有间隔，有待美化

图片随机打乱后，在展示时有缝隙。

这是高分屏系统按倍数放大的兼容性问题...
暂时没有时间改了,so sorry~

authors: eatage reckcn

repo: eatage/VerificationCode


text:
Avoid deleting parent backup failed caused by 404

Catch NotFound exception in trove API level, and return directly to avoid dropping out while deleting parent backup that has multiple children backups.
Signed-off-by: Fan Zhang zh.f@outlook.com

commit message 里加上 issue 的信息。

repo: eayunstack/trove


text:
Allow settings-only mode

There should be a way to only show settings (and even user-defined lists of settings) in HomeKit as a multi-switch accessory.
This would allow voice control of settings such as speech enhancement or night mode without requiring any other visibility of Sonos in HK.
Ideally, I would be able to configure SonosZP to show a single tile that allowed me to turn on or off Night Mode and Speech Enhancement and any subwoofer I had paired to a given zone/surround setup. This would allow me to automate the "evening" Sonos settings along with lights and other HK devices.

You can change the settings from Siri: use Eve or another decent HomeKit app to create a scene with the setting.  Then recall that scene from Siri.
Only exposing a single setting is beyond the scope of Homebridge ZP.  If you don’t like to see the accessories exposed by Homebridge ZP, don’t use that plugin.  You could probably use a plugin like homebridge-commander to expose dummy switches that issue zp commands to change a setting.

authors: SamTheGeek ebaauw

repo: ebaauw/homebridge-zp


text:
Country number is not supported

Hi, I was trying to use the SMS spammer on my own number. But it seems my Indian number is not supported. Can you help me with adding my country code to the script? What changes I must do and where.

Can you help me with adding my country code to the script? What changes I must do and where.

Hello, it is unlikely that I will be able to add support for Indian phone numbers🤷

authors: aakash-priyadarshi ebankoff


text:
Allow port 80 for certificate renewal

Let's Encrypt requires port 80 to be open for certificate renewal. This commit adds port 80 to the firewall rules for coturn when using TLS.

OH! Sorry somehow I missed this PR completely.
Will work on it right away.

Moved to PR #10

authors: BrutalBirdie louim

repo: ebbba-org/ansible-role-coturn


text:
Fix BAM to FASTQ - d5410c6e-612d-421a-a66f-2de5e04dd050

SOP: https://ebi-ait.github.io/hca-ebi-wrangler-central/SOPs/update_ena_runs_SOP.html

I started to download the files from DCP Data Browser (https://data.humancellatlas.org/explore/projects/abe1a013-af7a-45ed-8c26-f3793c24a1f4/get-curl-command) to the EBI cluster into the nfs/production/hca/d5410c6e-612d-421a-a66f-2de5e04dd050 folder. 126 files/ 3.09 TB so it is going to take a while.

Started checksum generation on the EBI cluster to /nfs/production/hca/d5410c6e-612d-421a-a66f-2de5e04dd050/md5_checksum.txt.

Started uploading the files to ENA's FTP server into /d5410c6e-612d-421a-a66f-2de5e04dd050 folder.

Checksum calculation is done and stored here: /nfs/production/hca/d5410c6e-612d-421a-a66f-2de5e04dd050/md5_checksum.txt.

Files has been submitted to ENA and we got back the accessions:
<?xml version='1.0' encoding='UTF-8'?>
<RECEIPT receiptDate="2021-11-29T16:08:45.085Z" submissionFile="submission.xml" success="true">
     <RUN accession="ERR7441145" alias="sequencingRun_90b34757-474f-42c3-9d31-683a5a0a84bd_1" status="PRIVATE" />
     <RUN accession="ERR7441146" alias="sequencingRun_0f14c412-5014-4ac0-9a71-858b2f047777_1" status="PRIVATE" />
     <RUN accession="ERR7441147" alias="sequencingRun_082e87ac-5cf6-4bad-bedb-5f6591b8f566_1" status="PRIVATE" />
     <RUN accession="ERR7441148" alias="sequencingRun_157ba915-28d7-4d80-89ad-71c8291dbc05_1" status="PRIVATE" />
     <RUN accession="ERR7441149" alias="sequencingRun_cc5a78a1-539f-4dec-80b6-62f35dcafd89_1" status="PRIVATE" />
     <RUN accession="ERR7441150" alias="sequencingRun_01f7c3d0-d4be-432d-aa25-8c7fbce20b49_1" status="PRIVATE" />
     <RUN accession="ERR7441151" alias="sequencingRun_dc31f31d-ab56-4025-9834-99be638a2d50_1" status="PRIVATE" />
     <RUN accession="ERR7441152" alias="sequencingRun_b6dec4a6-2d9b-40ac-80c4-41dce01aea46_1" status="PRIVATE" />
     <RUN accession="ERR7441153" alias="sequencingRun_51fb7eb7-a422-482e-a98e-c9e6f9628e97_1" status="PRIVATE" />
     <RUN accession="ERR7441154" alias="sequencingRun_d8c08782-6f69-4314-947c-1afe6928cbce_1" status="PRIVATE" />
     <RUN accession="ERR7441155" alias="sequencingRun_b3ce1085-08dc-42ff-a609-6968315327a8_1" status="PRIVATE" />
     <RUN accession="ERR7441156" alias="sequencingRun_6a0f0064-ba67-43d6-985e-68d8edcf8c0b_1" status="PRIVATE" />
     <RUN accession="ERR7441157" alias="sequencingRun_afd0ea55-e710-4b46-bb05-2423e491b6f5_1" status="PRIVATE" />
     <RUN accession="ERR7441158" alias="sequencingRun_13a062ba-2b8e-43a1-bc2a-bc17f650b37d_1" status="PRIVATE" />
     <RUN accession="ERR7441159" alias="sequencingRun_6d273f72-f55c-4c8e-b91e-29e762194c3f_1" status="PRIVATE" />
     <RUN accession="ERR7441160" alias="sequencingRun_37cad11b-c8c9-4d1f-b715-498b0f8d4b35_1" status="PRIVATE" />
     <RUN accession="ERR7441161" alias="sequencingRun_0b52914d-687b-44d1-9a70-a95df55ed502_1" status="PRIVATE" />
     <RUN accession="ERR7441162" alias="sequencingRun_3a20b6a5-6652-4486-86bc-842c7c31c343_1" status="PRIVATE" />
     <RUN accession="ERR7441163" alias="sequencingRun_44b8ad82-1109-4543-a534-a85b34c2c301_1" status="PRIVATE" />
     <RUN accession="ERR7441164" alias="sequencingRun_548a75b4-ba45-4700-b7bb-656c3995c316_1" status="PRIVATE" />
     <RUN accession="ERR7441165" alias="sequencingRun_2c2c943c-1c0e-462c-b630-8a91a1f0fb94_1" status="PRIVATE" />
     <RUN accession="ERR7441166" alias="sequencingRun_83b474d3-c20f-48f6-95a0-b0fa2269f14d_1" status="PRIVATE" />
     <SUBMISSION accession="ERA7498444" alias="SUBMISSION-29-11-2021-16:08:43:157" />
     <MESSAGES />
     <ACTIONS>ADD</ACTIONS>
</RECEIPT>

Filed ticket to ENA HelpDesk: [ENA DATA STATUS #549358]
Submitter: Broker
Name: Karoly Erdos
Email: karoly@ebi.ac.uk
CCEmails: wrangler-team@data.humancellatlas.org
Subject: Suppressing old ENA runs
Query is related to: Suppression
I work on: Humans
Organisms classification: Not applicable
The work is: Other/not sure (Raw sequencing reads)
Message Body:
Hi,

Could you please suppress the following ENA runs from this study (ERP120466 / PRJEB37165):

ERR4336830
ERR4336831
ERR4336832
ERR4336833
ERR4336834
ERR4336835
ERR4336836
ERR4336837
ERR4336838
ERR4336839
ERR4336840
ERR4336841
ERR4336842
ERR4336843
ERR4336844
ERR4336845
ERR4336846
ERR4336847
ERR4336848
ERR4336849
ERR4336850
ERR4336851

These were BAM files and they had been replaced with FASTQ files.
Please DO NOT delete/suppress the experiment and the new FASTQ files.

Many thanks,

Karoly

We have to monitor ENA when are the new FASTQ files are available in the browser. Normally it takes at least 48 hours.
Project to check: https://www.ebi.ac.uk/ena/browser/view/PRJEB37165

Bam files have not yet been deleted as per Monday Dec 6th, 10:20 AM

@ke4  to confirm if bam files are deleted in the ENA study today.

authors: ESapenaVentura aaclan-ebi ke4 ofanobilbao

repo: ebi-ait/hca-ebi-wrangler-central


text:
Wrong ontology for norbornene and premutilin

These relationships are wrong because alkenes are acyclic while norbornene and premutilin are cyclic.
norbornene (CHEBI:52286) is a alkene (CHEBI:32878)
premutilin (CHEBI:142455) is a alkene

Thanks. Now corrected.

repo: ebi-chebi/ChEBI


text:
#9 Get block

Not ready for merging yet.
Created PR for reviewing and discussion.

@carte7000 done

repo: ecadlabs/tezos-ts


text:
No incident recorded in the database

it seems that the incidents are not saved in DB
A critical error is visible in the logs
INFO:bot.incident.incident:Creating incident channel: inc-202210281751-test-4
ERROR:bot.incident.incident:Error sending message to incident digest channel: The request to the Slack API failed. (url: https://www.slack.com/api/chat.postMessage)
The server responded with: {'ok': False, 'error': 'not_in_channel'}
INFO:bot.incident.incident:Sending message to digest channel for: inc-202210281751-test-4
INFO:bot.incident.incident:Writing incident entry to database for inc-202210281751-test-4...
CRITICAL:bot.incident.incident:Error writing entry to database: local variable 'digest_message' referenced before assignment
ERROR:bot.models.incident:Incident update failed for inc-202210281751-test-4: No row was found when one was required```

False positive : incident-bot was not added to the incident digest channel, which means it was not able to post a message.
Then everything failed afterward

repo: echoboomer/incident-bot


text:
Fix link error related to Go 1.23 release

Since release of Go 1.23 go:linkname is a problem and the hook into testing.(*common).logDepth does not longer work. Therefore (although ugly), we have to disable this feature inside of github.com/echocat/slf4g/sdk/testlog for now.

Pull Request Test Coverage Report for Build 10410896027
Details

0 of 0   changed or added relevant lines in 0 files are covered.
No unchanged relevant lines lost coverage.
Overall coverage remained the same at 97.987%

Totals

Change from base Build 10342024026:
0.0%

Covered Lines:
3992

Relevant Lines:
4074

💛  - Coveralls

authors: blaubaer coveralls


text:
Update GitHub Actions

Due to the migration, some actions do not work anymore

Currently, we have three actions:

Deploy to maven central: Is obsolete due to mandatory Jenkins usage --> Delete
Deploy to java Model: Is still needed. However, in the current state, it is still referencing the admin shell io packages --> Update
Run Tests: is still needed. Most likely an update to also cover the main branch is sufficient.


text:
"org.eclipse.tracecompass.incubator.trace.server.jersey.rest.core.id" could not be found in the registry

Hi,
Just trying to follow the steps on the README file to run a demo on a Linux machine using the Trace extension on Theia.

I cloned the repo
I have the prerequisite of Java 11 as required
I completed the section Build the extension and example application
And the problem arise on the next step on Try the trace extension section, when I run the command: yarn start:server then the following error message is saved on the log file at (theia-trace-extension/trace-compass-server/configuration/):

!ENTRY org.eclipse.osgi 4 0 2023-04-20 23:02:54.573
!MESSAGE Application error
!STACK 1
java.lang.RuntimeException: Application "org.eclipse.tracecompass.incubator.trace.server.jersey.rest.core.id" could not be found in the registry. The applications available are: org.eclipse.equinox.app.error.

	at org.eclipse.equinox.internal.app.EclipseAppContainer.startDefaultApp(EclipseAppContainer.java:252)
	at org.eclipse.equinox.internal.app.MainApplicationLauncher.run(MainApplicationLauncher.java:33)
	at org.eclipse.core.runtime.internal.adaptor.EclipseAppLauncher.runApplication(EclipseAppLauncher.java:136)
	at org.eclipse.core.runtime.internal.adaptor.EclipseAppLauncher.start(EclipseAppLauncher.java:104)
	at org.eclipse.core.runtime.adaptor.EclipseStarter.run(EclipseStarter.java:402)
	at org.eclipse.core.runtime.adaptor.EclipseStarter.run(EclipseStarter.java:255)
	at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
	at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
	at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
	at java.base/java.lang.reflect.Method.invoke(Method.java:566)
	at org.eclipse.equinox.launcher.Main.invokeFramework(Main.java:659)
	at org.eclipse.equinox.launcher.Main.basicRun(Main.java:596)
	at org.eclipse.equinox.launcher.Main.run(Main.java:1467)
	at org.eclipse.equinox.launcher.Main.main(Main.java:1440)

What is the error about and the solution for it?
Thank you.

Thanks for having reported this. I just pushed a PR (linked herein) to fix the Java version in the README. Using 17 instead of 11 works locally for me, and should be the required version AFAIK.

authors: marco-miller santimchp

repo: eclipse-cdt-cloud/theia-trace-extension


text:
Update README files for default prop values cleanup

As part of https://github.com/eclipse-pass/main/issues/1066

@markpatton I realized I have to fix the ITs for pass-support after merging the pass-core change for default prop values.  I'll fix them and then reopen this PR.

Apparently github has some issues with reopened PRs and rebase and merge.  Closed this PR and opened a new PR https://github.com/eclipse-pass/pass-support/pull/130

repo: eclipse-pass/pass-support


text:
fix(tutorials): link to perquisites doesn't work

What
There is a link to prerequisites in the Skills required section. This link doesn't work, and ends up in a Page not found.

Why
The link doesn't work.

closed by #623


text:
fix(service marketplace):  list all active services

Description
Service marketplace should have all the active services but now with implemented solution we can see all the services.
Changelog entry:
- fixed service marketplace to display all active available servicesp[#1143](https://github.com/eclipse-tractusx/portal-frontend/issues/1143)
Why
Service Marketplace hasn't have the all the active services available throughout from all the service provider. Now, for each service provider marketplace we can observe all the services.
Issue
https://github.com/eclipse-tractusx/portal-frontend/issues/1143
Checklist

[x] I have performed a self-review of my own code
[x] I have successfully tested my changes locally

@ma3u  @oyo  can you please review this PR ?
currently, I can't add you in this PR as reviewer. However, I have already created the ticket to become tractusx contributor: https://github.com/eclipse-tractusx/sig-infra/issues/547 . Thank you!

@evegufy  I have updated the dependency file but the error message still persists and not any clue with the error message.
signal need to update DEPENDENCIES.

@manojava-gk  I have introduced enum for frequently used string for sorting type . can you have a look please ? :)

@Usmanfee Just to maintain consistency use PascalCase instead. See other examples in the page.

@manojava-gk  we are also use camelcase format in e.g. PAGES and OVERLAYS enum . I am using camelcase since this is the pattern backend support.

@Usmanfee one more thing, constants file is a place where we host most common things in the app. I do not think this sorting type is a generic one. it is very specific to the back end api. I prefer to define this in the specific api types's file.
In future we can define this in the constants when all apis sorting types are unified.
CC: @oyo

@manojava-gk  I have updated the code based on your suggestion. could you please have a look now ? Thank you :)

Looks fine now

@oyo  Thanks for your feedback :) . I have resolved your comments with suggested changes.

@evegufy  I have reverted changelog changes. @oyo  could you please re-approve and merge it again ? Thank you

authors: Usmanfee manojava-gk

repo: eclipse-tractusx/portal-frontend


text:
chore(docker hub): clean up after registry move

Description
change registry in package.json
Why
moved to docker hub
Issue
https://github.com/eclipse-tractusx/portal-frontend/pull/19
Checklist

[x] I have performed a self-review of my own code
[x] I have successfully tested my changes locally

@oyo could you please check if this script is still needed? It contains references to the old registry.

@oyo could you please check if this script is still needed? It contains references to the old registry.

@evegufy That is a very old script initially meant to build and push images to the azure container registry that we had before ghcr. It's not used any more and we can delete it.

authors: evegufy oyo


text:
Separate Issuer Component (removed from MIW)

Description
Remove Issuer Component from MIW.
Issuer Component to be transferred to separate component,
Harmonize approach with other dataspaces, GAIA-X and EDC and reduce restrictions.
Impact

Additional information

[ ] I'm willing to contribute to this feature

Won't do in PI11

replaced with new feature https://github.com/eclipse-tractusx/sig-release/issues/416

authors: jjeroch stefan-ettl stephanbcbauer

repo: eclipse-tractusx/sig-release


text:
Story "Introduce Supply Chain Domain Ontology"

          # Story "Introduce Supply Chain Domain Ontology"

Repository https://github.com/catenax-ng/product-ontology
Statement
As an ESS use case developer, I want to have a supply-chain domain ontology such that I can formulate my use case roles, permissions and skills with a vocabulary based on BPN entities paired with abstract material flows.
Acceptance Criteria

Domain Ontology Exists
Domain Ontology Validates

Estimation
5 SP
Originally posted by @drcgjung in https://github.com/eclipse-tractusx/sig-release/issues/280#issuecomment-1770735415

wrong issue place @igorsvetlov could you please raise the issue under https://github.com/catenax-ng/product-ontology

@FaGru3n please re-open for now. We'll discuss with @drcgjung where to place this one and other related stories.


text:
Update esmf-sdk version

update esmf-sdk version to 2.2.3

PR:139 will close this issue.

Hi @richaashara ,
thanks for your feedback and PR. We will take a look over the PR.

Hi @richaashara ,
Thanks for the PR. This PR is incomplete since unit tests are failing.
We are also working on ESMF SDK vesion update.

Updated to 2.4.2. Issue will be closed.

authors: richaashara shijinrajbosch tunacicek

repo: eclipse-tractusx/sldt-semantic-hub


text:
fix: get rid of ghcr.io image references

I've fount multiple docker image links referencing ghcr.io registry. Please get rid of them and reference the DockerHub images instead. Some examples:

trivy.yml worfklow
values.yaml in main chart
values.yaml in backend and frontend chart

It's okay to build for both registries but DockerHub is preferred and should be used in cases mentioned above. See TRG4.05.

Hi @almadigabor I removed the main references and only left the ghcr repository in cases where we build on both registry based on repository. Although the 3 mentioned have been changed!
Please find attached links:
Trivy: https://github.com/eclipse-tractusx/traceability-foss/blob/main/.github/workflows/trivy.yml
Backend Values: https://github.com/eclipse-tractusx/traceability-foss/blob/main/charts/traceability-foss/charts/backend/values.yaml
Frontend Values: https://github.com/eclipse-tractusx/traceability-foss/blob/main/charts/traceability-foss/charts/frontend/values.yaml
Main Values: https://github.com/eclipse-tractusx/traceability-foss/blob/main/charts/traceability-foss/values.yaml
Please let me know if you need anything else.
Thanks in advance!

Hey! Looks good!

authors: almadigabor ds-mwesener

repo: eclipse-tractusx/traceability-foss


text:
Clarify requirements of L3 services and L3 clients

The current up-l3 specs combine the requirements for the service with the requirements for clients communicating with those services. We have found it difficult to parse what we are expected to implement for client code in up-cpp vs what is a behavior expected of the service. For example, it is not clear in the USubscription spec whether the state diagram is something the client does when subscribing to a topic, or if it represents an internal state per-topic within the service.
I would recommend splitting the specs into separate documents. One could focus on the requirements of the service itself, such as its states, behaviors, inputs, and outputs. The other(s) could cover how clients interact with those services - what RPC methods are available, what data they're expected to send, what steps they need to follow, are they expected to subscribe for notifications, etc. The client specs could be defined entirely in terms of layer 2 components and operations, abstracting any protocol details below layer 2.
After splitting these specs, the file tree would probably be something like this:
up-l3
├── usubscription
│   └── v3
│       ├── README.adoc
│       ├── service.adoc
│       ├── client_publisher.adoc
│       └── client_subscriber.adoc
└── utwin
    └── v2
        ├── README.adoc
        ├── service.adoc
        └── client.adoc

this has been done for uDiscovery, Need to open a separate issue for uSubscription.

authors: gregmedd stevenhartley

repo: eclipse-uprotocol/up-spec


text:
NPE on w2vec example

Running with snapshots. Code here
o.n.l.f.Nd4jBackend - Loaded [JCublasBackend] backend
o.n.n.NativeOpsHolder - Number of threads used for linear algebra: 32
o.n.l.a.o.e.DefaultOpExecutioner - Backend used: [CUDA]; OS: [Linux]
o.n.l.a.o.e.DefaultOpExecutioner - Cores: [64]; Memory: [26.7GB];
o.n.l.a.o.e.DefaultOpExecutioner - Blas vendor: [CUBLAS]
o.n.l.j.JCublasBackend - ND4J CUDA build version: 10.2.89
o.n.l.j.JCublasBackend - CUDA device 0: [GeForce RTX 2060 SUPER]; cc: [7.5]; Total memory: [8368685056]
o.n.l.j.JCublasBackend - CUDA device 1: [GeForce RTX 2060 SUPER]; cc: [7.5]; Total memory: [8370061312]
o.d.n.m.MultiLayerNetwork - Starting MultiLayerNetwork with WorkspaceModes set to [training: ENABLED; inference: ENABLED], cacheMode set to [NONE]
o.d.n.l.r.LSTM - cuDNN not found: use cuDNN for better GPU performance by including the deeplearning4j-cuda module. For more information, please refer to: https://deeplearning4j.org/docs/latest/deeplearning4j-config-cudnn
java.lang.ClassNotFoundException: org.deeplearning4j.cuda.recurrent.CudnnLSTMHelper
        at java.net.URLClassLoader.findClass(URLClassLoader.java:382)
        at java.lang.ClassLoader.loadClass(ClassLoader.java:418)
        at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:355)
        at java.lang.ClassLoader.loadClass(ClassLoader.java:351)
        at java.lang.Class.forName0(Native Method)
Exception in thread "main" java.lang.NullPointerException
        at org.nd4j.linalg.dataset.ExistingMiniBatchDataSetIterator.<init>(ExistingMiniBatchDataSetIterator.java:58)
        at org.nd4j.linalg.dataset.ExistingMiniBatchDataSetIterator.<init>(ExistingMiniBatchDataSetIterator.java:47)
        at org.deeplearning4j.examples.multigpu.advanced.w2vsentiment.ImdbReviewClassificationRNN.main(ImdbReviewClassificationRNN.java:106)

Is it reproducible on CPU?

Will check

Pure Java issue by the looks of it. Added null check to iterator constructor.

Closing. Also changed example to automatically run presave. Thank you @raver119


text:
"Cannot do backward pass: all epsilons not set" when using frozen layer

I have a convolution layer that, when its output feeds a frozen deconvolution2D layer, triggers the following exception:
java.lang.IllegalStateException: Cannot do backward pass: all epsilons not set. Layer "fc" (idx 1876), numInputs :1; numOutputs: 1
	at org.deeplearning4j.nn.graph.vertex.impl.LayerVertex.doBackward(LayerVertex.java:133)
	at org.deeplearning4j.nn.graph.ComputationGraph.calcBackpropGradients(ComputationGraph.java:2713)
	at org.deeplearning4j.nn.graph.ComputationGraph.computeGradientAndScore(ComputationGraph.java:1382)
	at org.deeplearning4j.nn.graph.ComputationGraph.computeGradientAndScore(ComputationGraph.java:1342)
	at org.deeplearning4j.optimize.solvers.BaseOptimizer.gradientAndScore(BaseOptimizer.java:170)
	at org.deeplearning4j.optimize.solvers.StochasticGradientDescent.optimize(StochasticGradientDescent.java:63)
	at org.deeplearning4j.optimize.Solver.optimize(Solver.java:52)
	at org.deeplearning4j.nn.graph.ComputationGraph.fitHelper(ComputationGraph.java:1166)
	at org.deeplearning4j.nn.graph.ComputationGraph.fit(ComputationGraph.java:1116)
	at org.deeplearning4j.nn.graph.ComputationGraph.fit(ComputationGraph.java:1083)
	at org.deeplearning4j.nn.graph.ComputationGraph.fit(ComputationGraph.java:1019)

Here is the creation of the frozen layer:
 graph.appendLayer("transpose",
                    new FrozenLayer(
                    new Deconvolution2D.Builder(new int[]{upFactor * 2, upFactor * 2}, new int[]{upFactor, upFactor},
                            new int[]{upFactor / 2, upFactor / 2})
                            .nOut(numClasses)
                            .hasBias(false)
                            .weightInit(linearInterpolationInit)
                            .build()
                    )
        );

if I remove the FrozenLayer (keeping an unfrozen Deconvolution2D layer), the exception doesn't show up.
Using DL4J beta 7.

@HGuillemet please check with debugger if it goes into org.deeplearning4j.nn.layers.FrozenLayer.java:159 at gradientAndScore() method

Also seems like "fc" layer is another layer and it's not provided here. Debugging requires full code or probably at least graph declaration and initialization.

@HGuillemet (and future readers)  I'll get to this after the release. I plan on doing a bug fix sprint for bugs like this and in samediff. Sorry for the wait.

@HGuillemet I'll be getting to this now that M1 is out. If you have any updates on this, let me know. Feedback is appreciated!

Closing this, we support backprop with: FrozenLayerWithBackprop - usages here. https://github.com/eclipse/deeplearning4j/blob/5e8951cd8ee8106bb393635f840c398a1759b2fa/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/FrozenLayerWithBackpropTest.java#L90 I assume this is for GANs.

authors: HGuillemet agibsonccc jljljl