Normalize company/product names

[x] Github -> GitHub #34971
[x] Linkedin -> LinkedIn #34972
[ ] Javascript -> JavaScript #35183
[ ] Typescript -> TypeScript #35183
[ ] Jquery, JQuery -> jQuery #35184
[ ] ReactJS, React JS -> React
[ ] Angular JS, AngularJS -> Angular
[ ] Wordpress -> WordPress

Not sure about Angular and Angular JS, but feel free to contribute with more findings by editing this post.

Before you create a PR to change ReactJS or React JS to React, let's get some consensus with some collaborators first.

That's why I opened that issue.. not touching the ones that are in doubt.. or needs to be addressed.

IMHO we should always, refer what the brand guidelines or styling is used on their official docs, website etc. is used.
Example: https://reactjs.org/ -> React

React makes it painless to create interactive UIs. Design simple views for each state in your a ...

This removed all ambiguity.

IMHO we should always, refer to the brand guidelines or styling that is used on their official docs, website etc.
Example: https://reactjs.org/ -> React

React makes it painless to create interactive UIs. Design simple views for each state in your a ...

This removes all ambiguity.

@raisedadead Shall I change all spellings to "React" from other variations as suggested by @lipis and submit in individual PRs with fewer number of files changed so that not many files are changed at a time so as to make merging easy?

Yes please. But you should wait for confirmation from @lipis in case they have already started work on it.

@aravindvnair99 Definitely wait until @lipis responds, because we don't want to have two PRs with duplicates on them.  Maybe, the two of you can split the work by each taking a different set of languages.  Just communicate with each other to make sure you are both using the same technique to find them all.

@lipis @RandellDawson @raisedadead WordPress is the right spelling and as per the guidelines mentioned here, it should be spelled using proper case.
Meta rules 1 as given on WordPress website for both .com and .org:

Spell WordPress using the proper case. WordPress

@aravindvnair99 Definitely wait until @lipis responds, because we don't want to have two PRs with duplicates on them. Maybe, the two of you can split the work by each taking a different set of languages. Just communicate with each other to make sure you are both using the same technique to find them all.

Yes please. But you should wait for confirmation from @lipis in case they have already started work on it.

@RandellDawson @raisedadead Yes, I'll await a response from @lipis . Would love to help! Thanks for responding. :)

Roundup of what I could find regarding the naming guidelines:

Angular and AngularJS

AngularJS was the first version and all successive versions are Angular. So technically and theoretically, both are different. We could make changes only for Angularjs / AngularJs / AngularjS to AngularJS.
Reference links:

Stack Overflow
Angular official website
AngularJS official website

WordPress

WordPress is the right spelling and as per the guidelines mentioned here, it should be spelled using proper case, i.e. "W" and "P" capital letters.
Meta rules 1 as given on WordPress website for both .com and .org:

Spell WordPress using the proper case. WordPress

React

React is the right term as used by Facebook everywhere and should be changed from Reactjs / ReactJS / ReactjS.
Reference links:

GitHub
React official website
Stack Overflow

AngularJS was the first version and all successive versions are Angular. So technically and theoretically, both are different. We could make changes only for Angularjs / AngularJs / AngularjS to AngularJS.

I think this one is going to be tricky, because it is going to be based on context.  It could be that an article was written concerning the latest version of Angular, but the author used Angularjs by mistake.  It would be wrong to automatically name it AngularJS, because it really should actually be Angular.  This means there is no easy regex you can write to replace these easily like React or WordPress.  It is my opinion that if the number of Angularjs or AngularJs findings are high that we just wait for individual users to make the corrections as they come across them in the normal reading of the articles.  That being said, you are more than welcome to try, but I think your time my be better spent on issues we currently have on GitHub.

Hey guys, I haven't started anything from the ones that don't have PR number next to it.. so feel free to work on them! and maybe it's a good idea to edit the initial comment and write your name next to it, on who is working on what.
React and WordPress are easy, but for Angular I'm not sure what should we do. I'm also not familiar if there are courses for older versions of Angular, if not then we could just drop all the JS references. Also I think it's preferable to have just Angular in all cases, than randomly (and wrongfully) seeing AngularJS. It's all Angular now :)

I also added another tricky one: freeCodeCamp.

Added more: npm, Node.js

@raisedadead How large do we want to let this one issue get?  There are probably hundreds of other product names which are not consistent throughout the guide and curriculum.  If we can narrow it down to the big hitters, then maybe a better approach here is instead of trying to fix all of these in replacement PRs, we have the sweeper.js and Probot add a special label Possible Invalid Product Name to any existing/future PR which happens to find one of these misspelled versions of the words.  We could take it a step further and have the script/Bot add a review comment requesting a possible change and reference to the word in question.  These would look like the same review request changes we manually do now for other PRs.
We found a line in this file which possible contains an incorrect spelling of a product name.  Please see the following line and fix if applicable.

**Moderators**:  If you do not agree with the suggestion, please dismiss the request.  You can view a list of agreed upon product names in issue #35185.

The above message is just a first-pass suggestion.
Doing it in this way, would put the burden on the OP/reviewer to check if named needs to be changed and can simply dismiss the requested changes if not applicable.  This would be a good way of dealing with names like 'freeCodeCamp' or 'Angular/AngularJS`.
These kind of replacement PRs, which end up touching so many files, always end up with merge conflicts because of the shear number of PRs we have right now.  To get them merged quickly involves having to review many other PRs first as not to cause further merge conflicts on earlier PRs (if we were just to merge these out of order).  The longer it takes to review/merge these kind of PRs, the more newer PRs with changes can end up with merge conflicts once we finally do merge this kind of PRs.

An example of a PR which involves a lot of prep reviewing before even getting to reviewing/merge of it is PR #35183.  @cmccormack and I have started the task of reviewing 104 earlier PRs which also make changes to the same files.  Making it even more complex is, some of the earlier PRs also have multiple PRs which will have to be reviewed first, because of similar replacement PRs.

@lipis Cool. I'll take up WordPress then for now. We can take one at a time so as to progress in the most efficient way.
@RandellDawson @raisedadead I would suggest we send few files at a time in one pull request so as to not create serious merge conflicts or have too many open PRs. And these PRs could be merged faster as it's just few spelling changes as opposed to serious code changes.
So, how do I go about? Shall I edit few files at a time? I personally feel that would be better for ease of maintaining and merging. And for ease of tracking, a new label could be made for this.

@aravindvnair99 Before creating the actual PR for each language for the WordPress, can you first report back how many files it will change for each language?

After speaking with @raisedadead, we have decided to put this issue on hold and only allow one more set of PRs (1 for each language) to address the cases where Wordpress should be changed to WordPress.
@aravindvnair99  There are 45 files which have Wordpress.  They are all Guide articles.  You have permission to go ahead and create the necessary PRs, but make sure you make them for each language and indicate which language in the PR title.
The mods will continue to work through the existing open PRs already created (#35183, #35203, #35204, #35206) along with the PRs @aravindvnair99 will create for WordPress, but we will not accept any further PRs like this until the total number of open PRs gets down to around 500.
I will tag each of you when we reach this milestone and then we can discuss the best approach from there.
ATTN Moderators:  Please do not close this issue as we do not want to lose track of this issue.  We will tackle it again when the number of open PRs is below 500.
Thank you for all your work and patience regarding this issue.

@RandellDawson the idea of making the bot is pretty nice one and I guess not hard to make and we don't have to update all the changes before installing it, it can be done from now on for all upcoming results. The main reason is that if it's not automated we will have to do these changes once in a while to keep it consistent.

@lipis I agree.  The long term plan is to get the bot to do something like I proposed above.  The short term plan is to wait until we get down to below 500 open PRs and then do a mass update on some of these others.

I agree with @RandellDawson on this. While we are very interested in making things more consistent, it should not come at the cost of clogging things up. We should do this two steps:

Automated linting as proposed in the bot/scripts/tests.
Making large sweeps in PRs once we reach a manageable no of open PRs.

This way, everyone wins and we have lesser chore like work to do.

@raisedadead @RandellDawson Should we block this issue further till we have less PRs?

@raisedadead Should we close this issue now?


Typo "[ASP.Net Core]"→"[ASP.NET Core]"

https://docs.microsoft.com/en-us/azure/bot-service/dotnet/bot-builder-dotnet-sdk-quickstart?view=azure-bot-service-4.0

Fixing in private repo

Fixing in private repo


Rip out bloom filter stuff?

https://github.com/ZcashFoundation/zebra/blob/master/zebra-sync/src/utils/bloom_filter.rs

There are related later BIPs that we also don't do, if there's any code for them lying around:
https://github.com/bitcoin/bips/blob/master/bip-0157.mediawiki
https://github.com/bitcoin/bips/blob/master/bip-0158.mediawiki

Some version of this is required for BIP37, which is included in Zcash, so we cannot remove it.


doc(Guides):EEE thru Kafka; exec names

Current Behavior
Please refer to Run section in the guide End to End Encryption through Kafka. The docker commands threw following errors
error: exec: "ockam_kafka_bob": executable file not found in $PATH
error: exec: "ockam_kafka_alice": executable file not found in $PATH
Proposed Changes
Following changes to the docker commands worked for me:
docker run --rm --interactive --tty ghcr.io/build-trust/examples/kafka bob
docker run --rm --interactive --tty ghcr.io/build-trust/examples/kafka alice
Checks

[X] All commits in this Pull Request are signed and Verified by Github.
[X] All commits in this Pull Request follow the Ockam commit message convention.
[X] I accept the Ockam Community Code of Conduct.
[X] I have accepted the Ockam Contributor License Agreement by adding my Git/Github details in a row at the end of the CONTRIBUTORS.csv file in a separate pull request to the build-trust/ockam-contributors repository.

@sanjayatpilcrow thank you for sending this 🎉

@mrinalwadhwa Most welcome!

@mrinalwadhwa, seems I messed up the commit message and Commit-check is failing. Would you suggest me to update commit-message with new PR?

@sanjayatpilcrow no need, I'll fix the commit message for you as part of the merge.
But we need this part:

[ ] Accept the Ockam Contributor License Agreement by adding my Git/Github details in a row at the end of the CONTRIBUTORS.csv file in a separate pull request to the build-trust/ockam-contributors repository.

@mrinalwadhwa CONTRIBUTORS.csv updated

@sanjayatpilcrow thank you for an awesome first contribution 🥳

@mrinalwadhwa :relieved: Thank you for the nice words!


Propagating the MPI compile definitions to all relevant targets

Without this change, the fix for issue #3475, introduced in PR #3496, does break under certain circumstances.

IMHO refactoring the build system for the parcelports will be much easier after we have "fixed" all other targets. Just now it would require to refactor pretty much everything in one go.
I have the first commits for the CMake refactoring lined up and will submit the first PR later today.


Added support for hiding vulnerability description

Hi @Duy-L ,
Thanks for the PR, actually we have one issue with this change, the button for collapse moves as we collapse the description.
e.g.

The same issue is with hints too, can we do something such that the button for collapse stays on the very right and doesn't move with action? If so can you please correct at both the places.
thanks,
Karan


A question about “soft_rasterize_cuda”

Hi,When I run this code, I have the following problem:
/home/ls/anaconda3/envs/qwt_py38/bin/python /home/qwt/code/3DLocalMultiViewDesc-master/scripts/main_mvdesc.py
Using /home/qwt/.cache/torch_extensions as PyTorch extensions root...
Detected CUDA files, patching ldflags
Emitting ninja build file /home/qwt/.cache/torch_extensions/soft_rasterize_cuda/build.ninja...
Building extension module soft_rasterize_cuda...
Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)
[1/3] /usr/local/cuda/bin/nvcc --generate-dependencies-with-compile --dependency-output soft_rasterize_cuda_kernel.cuda.o.d -DTORCH_EXTENSION_NAME=soft_rasterize_cuda -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE="gcc" -DPYBIND11_STDLIB="libstdcpp" -DPYBIND11_BUILD_ABI="cxxabi1011" -isystem /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include -isystem /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include -isystem /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/TH -isystem /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/THC -isystem /usr/local/cuda/include -isystem /home/ls/anaconda3/envs/qwt_py38/include/python3.6m -D_GLIBCXX_USE_CXX11_ABI=0 -D__CUDA_NO_HALF_OPERATORS -D__CUDA_NO_HALF_CONVERSIONS_ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_86,code=compute_86 -gencode=arch=compute_86,code=sm_86 --compiler-options '-fPIC' -std=c++14 -c /home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda_kernel.cu -o soft_rasterize_cuda_kernel.cuda.o
FAILED: soft_rasterize_cuda_kernel.cuda.o
/usr/local/cuda/bin/nvcc --generate-dependencies-with-compile --dependency-output soft_rasterize_cuda_kernel.cuda.o.d -DTORCH_EXTENSION_NAME=soft_rasterize_cuda -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE="gcc" -DPYBIND11_STDLIB="libstdcpp" -DPYBIND11_BUILD_ABI="cxxabi1011" -isystem /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include -isystem /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include -isystem /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/TH -isystem /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/THC -isystem /usr/local/cuda/include -isystem /home/ls/anaconda3/envs/qwt_py38/include/python3.6m -D_GLIBCXX_USE_CXX11_ABI=0 -D__CUDA_NO_HALF_OPERATORS -D__CUDA_NO_HALF_CONVERSIONS_ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_86,code=compute_86 -gencode=arch=compute_86,code=sm_86 --compiler-options '-fPIC' -std=c++14 -c /home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda_kernel.cu -o soft_rasterize_cuda_kernel.cuda.o
nvcc fatal   : Unsupported gpu architecture 'compute_86'
[2/3] c++ -MMD -MF soft_rasterize_cuda.o.d -DTORCH_EXTENSION_NAME=soft_rasterize_cuda -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE="_gcc" -DPYBIND11_STDLIB="_libstdcpp" -DPYBIND11_BUILD_ABI="_cxxabi1011" -isystem /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include -isystem /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include -isystem /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/TH -isystem /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/THC -isystem /usr/local/cuda/include -isystem /home/ls/anaconda3/envs/qwt_py38/include/python3.6m -D_GLIBCXX_USE_CXX11_ABI=0 -fPIC -std=c++14 -c /home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp -o soft_rasterize_cuda.o
FAILED: soft_rasterize_cuda.o
c++ -MMD -MF soft_rasterize_cuda.o.d -DTORCH_EXTENSION_NAME=soft_rasterize_cuda -DTORCH_API_INCLUDE_EXTENSION_H -DPYBIND11_COMPILER_TYPE="_gcc" -DPYBIND11_STDLIB="_libstdcpp" -DPYBIND11_BUILD_ABI="_cxxabi1011" -isystem /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include -isystem /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include -isystem /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/TH -isystem /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/THC -isystem /usr/local/cuda/include -isystem /home/ls/anaconda3/envs/qwt_py38/include/python3.6m -D_GLIBCXX_USE_CXX11_ABI=0 -fPIC -std=c++14 -c /home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp -o soft_rasterize_cuda.o
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp: In function ‘void soft_rasterize_forward(at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, float, float, float, float, float, float, int, bool, bool, bool, at::Tensor, at::Tensor, at::Tensor, at::Tensor)’:
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:51:39: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
#define CHECK_CUDA(x) AT_CHECK(x.type().is_cuda(), #x, " must be a CUDA tensor")
^
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:53:24: note: in expansion of macro ‘CHECK_CUDA’
#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
^~~~~~~~~~
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:79:5: note: in expansion of macro ‘CHECK_INPUT’
CHECK_INPUT(mvps);
^~~~~~~~~~~
In file included from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:3:0,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/ATen.h:9,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:8,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
from /home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:1:
/home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:303:30: note: declared here
DeprecatedTypeProperties & type() const {
^~~~
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:51:23: error: ‘AT_CHECK’ was not declared in this scope
#define CHECK_CUDA(x) AT_CHECK(x.type().is_cuda(), #x, " must be a CUDA tensor")
^
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:53:24: note: in expansion of macro ‘CHECK_CUDA’
#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
^~~~~~~~~~
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:79:5: note: in expansion of macro ‘CHECK_INPUT’
CHECK_INPUT(mvps);
^~~~~~~~~~~
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:51:23: note: suggested alternative: ‘DCHECK’
#define CHECK_CUDA(x) AT_CHECK(x.type().is_cuda(), #x, " must be a CUDA tensor")
^
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:53:24: note: in expansion of macro ‘CHECK_CUDA’
#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
^~~~~~~~~~
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:79:5: note: in expansion of macro ‘CHECK_INPUT’
CHECK_INPUT(mvps);
^~~~~~~~~~~
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:51:39: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
#define CHECK_CUDA(x) AT_CHECK(x.type().is_cuda(), #x, " must be a CUDA tensor")
^
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:53:24: note: in expansion of macro ‘CHECK_CUDA’
#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
^~~~~~~~~~
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:80:5: note: in expansion of macro ‘CHECK_INPUT’
CHECK_INPUT(vertices);
^~~~~~~~~~~
In file included from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:3:0,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/ATen.h:9,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:8,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
from /home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:1:
/home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:303:30: note: declared here
DeprecatedTypeProperties & type() const {
^~~~
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:51:39: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
#define CHECK_CUDA(x) AT_CHECK(x.type().is_cuda(), #x, " must be a CUDA tensor")
^
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:53:24: note: in expansion of macro ‘CHECK_CUDA’
#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
^~~~~~~~~~
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:81:5: note: in expansion of macro ‘CHECK_INPUT’
CHECK_INPUT(radii);
^~~~~~~~~~~
In file included from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:3:0,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/ATen.h:9,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:8,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
from /home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:1:
/home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:303:30: note: declared here
DeprecatedTypeProperties & type() const {
^~~~
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:51:39: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
#define CHECK_CUDA(x) AT_CHECK(x.type().is_cuda(), #x, " must be a CUDA tensor")
^
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:53:24: note: in expansion of macro ‘CHECK_CUDA’
#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
^~~~~~~~~~
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:82:5: note: in expansion of macro ‘CHECK_INPUT’
CHECK_INPUT(colors);
^~~~~~~~~~~
In file included from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:3:0,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/ATen.h:9,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:8,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
from /home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:1:
/home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:303:30: note: declared here
DeprecatedTypeProperties & type() const {
^~~~
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:51:39: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
#define CHECK_CUDA(x) AT_CHECK(x.type().is_cuda(), #x, " must be a CUDA tensor")
^
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:53:24: note: in expansion of macro ‘CHECK_CUDA’
#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
^~~~~~~~~~
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:83:5: note: in expansion of macro ‘CHECK_INPUT’
CHECK_INPUT(locks);
^~~~~~~~~~~
In file included from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:3:0,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/ATen.h:9,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:8,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
from /home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:1:
/home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:303:30: note: declared here
DeprecatedTypeProperties & type() const {
^~~~
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:51:39: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
#define CHECK_CUDA(x) AT_CHECK(x.type().is_cuda(), #x, " must be a CUDA tensor")
^
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:53:24: note: in expansion of macro ‘CHECK_CUDA’
#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
^~~~~~~~~~
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:84:5: note: in expansion of macro ‘CHECK_INPUT’
CHECK_INPUT(weights);
^~~~~~~~~~~
In file included from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:3:0,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/ATen.h:9,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:8,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
from /home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:1:
/home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:303:30: note: declared here
DeprecatedTypeProperties & type() const {
^~~~
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:51:39: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
#define CHECK_CUDA(x) AT_CHECK(x.type().is_cuda(), #x, " must be a CUDA tensor")
^
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:53:24: note: in expansion of macro ‘CHECK_CUDA’
#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
^~~~~~~~~~
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:85:5: note: in expansion of macro ‘CHECK_INPUT’
CHECK_INPUT(color_map);
^~~~~~~~~~~
In file included from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:3:0,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/ATen.h:9,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:8,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
from /home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:1:
/home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:303:30: note: declared here
DeprecatedTypeProperties & type() const {
^~~~
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:51:39: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
#define CHECK_CUDA(x) AT_CHECK(x.type().is_cuda(), #x, " must be a CUDA tensor")
^
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:53:24: note: in expansion of macro ‘CHECK_CUDA’
#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
^~~~~~~~~~
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:86:5: note: in expansion of macro ‘CHECK_INPUT’
CHECK_INPUT(depth_map);
^~~~~~~~~~~
In file included from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:3:0,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/ATen.h:9,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:8,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
from /home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:1:
/home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:303:30: note: declared here
DeprecatedTypeProperties & type() const {
^~~~
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:51:39: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
#define CHECK_CUDA(x) AT_CHECK(x.type().is_cuda(), #x, " must be a CUDA tensor")
^
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:53:24: note: in expansion of macro ‘CHECK_CUDA’
#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
^~~~~~~~~~
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:87:5: note: in expansion of macro ‘CHECK_INPUT’
CHECK_INPUT(pseudo_depth_map);
^~~~~~~~~~~
In file included from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:3:0,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/ATen.h:9,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:8,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
from /home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:1:
/home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:303:30: note: declared here
DeprecatedTypeProperties & type() const {
^~~~
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp: In function ‘void soft_rasterize_backward(at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, float, float, float, float, float, float, int, bool, bool, at::Tensor)’:
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:51:39: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
#define CHECK_CUDA(x) AT_CHECK(x.type().is_cuda(), #x, " must be a CUDA tensor")
^
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:53:24: note: in expansion of macro ‘CHECK_CUDA’
#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
^~~~~~~~~~
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:132:5: note: in expansion of macro ‘CHECK_INPUT’
CHECK_INPUT(mvps);
^~~~~~~~~~~
In file included from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:3:0,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/ATen.h:9,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:8,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
from /home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:1:
/home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:303:30: note: declared here
DeprecatedTypeProperties & type() const {
^~~~
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:51:23: error: ‘AT_CHECK’ was not declared in this scope
#define CHECK_CUDA(x) AT_CHECK(x.type().is_cuda(), #x, " must be a CUDA tensor")
^
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:53:24: note: in expansion of macro ‘CHECK_CUDA’
#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
^~~~~~~~~~
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:132:5: note: in expansion of macro ‘CHECK_INPUT’
CHECK_INPUT(mvps);
^~~~~~~~~~~
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:51:23: note: suggested alternative: ‘DCHECK’
#define CHECK_CUDA(x) AT_CHECK(x.type().is_cuda(), #x, " must be a CUDA tensor")
^
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:53:24: note: in expansion of macro ‘CHECK_CUDA’
#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
^~~~~~~~~~
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:132:5: note: in expansion of macro ‘CHECK_INPUT’
CHECK_INPUT(mvps);
^~~~~~~~~~~
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:51:39: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
#define CHECK_CUDA(x) AT_CHECK(x.type().is_cuda(), #x, " must be a CUDA tensor")
^
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:53:24: note: in expansion of macro ‘CHECK_CUDA’
#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
^~~~~~~~~~
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:133:5: note: in expansion of macro ‘CHECK_INPUT’
CHECK_INPUT(vertices);
^~~~~~~~~~~
In file included from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:3:0,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/ATen.h:9,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:8,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
from /home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:1:
/home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:303:30: note: declared here
DeprecatedTypeProperties & type() const {
^~~~
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:51:39: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
#define CHECK_CUDA(x) AT_CHECK(x.type().is_cuda(), #x, " must be a CUDA tensor")
^
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:53:24: note: in expansion of macro ‘CHECK_CUDA’
#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
^~~~~~~~~~
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:134:5: note: in expansion of macro ‘CHECK_INPUT’
CHECK_INPUT(colors);
^~~~~~~~~~~
In file included from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:3:0,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/ATen.h:9,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:8,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
from /home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:1:
/home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:303:30: note: declared here
DeprecatedTypeProperties & type() const {
^~~~
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:51:39: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
#define CHECK_CUDA(x) AT_CHECK(x.type().is_cuda(), #x, " must be a CUDA tensor")
^
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:53:24: note: in expansion of macro ‘CHECK_CUDA’
#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
^~~~~~~~~~
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:135:5: note: in expansion of macro ‘CHECK_INPUT’
CHECK_INPUT(weights);
^~~~~~~~~~~
In file included from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:3:0,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/ATen.h:9,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:8,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
from /home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:1:
/home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:303:30: note: declared here
DeprecatedTypeProperties & type() const {
^~~~
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:51:39: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
#define CHECK_CUDA(x) AT_CHECK(x.type().is_cuda(), #x, " must be a CUDA tensor")
^
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:53:24: note: in expansion of macro ‘CHECK_CUDA’
#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
^~~~~~~~~~
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:136:5: note: in expansion of macro ‘CHECK_INPUT’
CHECK_INPUT(grad_color_map);
^~~~~~~~~~~
In file included from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:3:0,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/ATen.h:9,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:8,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
from /home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:1:
/home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:303:30: note: declared here
DeprecatedTypeProperties & type() const {
^~~~
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:51:39: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
#define CHECK_CUDA(x) AT_CHECK(x.type().is_cuda(), #x, " must be a CUDA tensor")
^
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:53:24: note: in expansion of macro ‘CHECK_CUDA’
#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
^~~~~~~~~~
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:137:5: note: in expansion of macro ‘CHECK_INPUT’
CHECK_INPUT(grad_depth_map);
^~~~~~~~~~~
In file included from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:3:0,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/ATen.h:9,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:8,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
from /home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:1:
/home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:303:30: note: declared here
DeprecatedTypeProperties & type() const {
^~~~
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:51:39: warning: ‘at::DeprecatedTypeProperties& at::Tensor::type() const’ is deprecated: Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device(). [-Wdeprecated-declarations]
#define CHECK_CUDA(x) AT_CHECK(x.type().is_cuda(), #x, " must be a CUDA tensor")
^
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:53:24: note: in expansion of macro ‘CHECK_CUDA’
#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
^~~~~~~~~~
/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:138:5: note: in expansion of macro ‘CHECK_INPUT’
CHECK_INPUT(grad_mvps);
^~~~~~~~~~~
In file included from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/Tensor.h:3:0,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/Context.h:4,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/ATen.h:9,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/csrc/api/include/torch/all.h:8,
from /home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/torch/extension.h:4,
from /home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/soft_rasterize_cuda.cpp:1:
/home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/include/ATen/core/TensorBody.h:303:30: note: declared here
DeprecatedTypeProperties & type() const {
^~~~
ninja: build stopped: subcommand failed.
Traceback (most recent call last):
File "/home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/utils/cpp_extension.py", line 1673, in _run_ninja_build
env=env)
File "/home/ls/anaconda3/envs/qwt_py38/lib/python3.6/subprocess.py", line 438, in run
output=stdout, stderr=stderr)
subprocess.CalledProcessError: Command '['ninja', '-v']' returned non-zero exit status 1.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/qwt/code/3DLocalMultiViewDesc-master/scripts/main_mvdesc.py", line 21, in
from models.mvdesc import RenderModel, MV_MODELS
File "/home/qwt/code/3DLocalMultiViewDesc-master/models/mvdesc.py", line 18, in
from soft_renderer.transform import MultiViewRenderer
File "/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/transform.py", line 17, in
from soft_renderer.cuda.jit import soft_rasterize_cuda as src
File "/home/qwt/code/3DLocalMultiViewDesc-master/soft_renderer/cuda/jit.py", line 8, in
verbose=True)
File "/home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/utils/cpp_extension.py", line 1091, in load
keep_intermediates=keep_intermediates)
File "/home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/utils/cpp_extension.py", line 1302, in _jit_compile
is_standalone=is_standalone)
File "/home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/utils/cpp_extension.py", line 1407, in _write_ninja_file_and_build_library
error_prefix=f"Error building extension '{name}'")
File "/home/ls/anaconda3/envs/qwt_py38/lib/python3.6/site-packages/torch/utils/cpp_extension.py", line 1683, in _run_ninja_build
raise RuntimeError(message) from e
RuntimeError: Error building extension 'soft_rasterize_cuda'
What should I do about it?Thank you.

Hi,
In the error log, there is a line saying
nvcc fatal : Unsupported gpu architecture 'compute_86'
So you may have a check at your GPU compute capability and the installed cuda version.
The code was developed with an Nvidia 1080Ti GPU.


Update progress block

⚠ This repo is Continuously Deployed: make sure you follow the guidance ⚠
What
Rework progress block.
Why
Trello card

This PR has been superseded by #4421 but we'll retain this for now as it contains some code that might be useful in future.


How to shape/clip to have rounded edges (like the screenshots)

shape = RoundedCornerShape(topStart = 16.dp, topEnd = 16.dp)

@BobbyESP hi! You can set shape of your Surface.

Yes, i realized it, thank you!


Do not rebuild existing site by default; provide option

cc @adaoraul @mattr- opinions?
This addresses #34. I suggest a bug fix release 0.4.2 after possible merge.
Restore the behavior of the previous released version (0.4.1)
and do not always generate the Jekyll site at startup, because
this fails on read-only file systems.
By default, the site is now only generated when the destination path
is empty. (This reverts a behavior change introduced with 1088b1b.)
Also provide a configuration option to force a build at startup.

In general, I'm ok with this. I wonder what happens when the site is first pushed to a host like Heroku though.

I'm not sure I understand what you mean...
AFAIK currently launching the app fails because of the (re)build, you would need to also push the previously generated site.


Cannot login via G Suite (white screen: accounts.google.com/signin/continue)

My university uses a self-hosted login page (shib). After I specify my<EMAIL_ADDRESS>address, Google redirects to shib.university.edu/....

Interestingly, Duo Security says cookies aren't enabled so I can't remember 2FA for 30 days. But the stock Android 7.0+GApps university login workflow has the same issue, so this isn't the cause of this problem.

After approving the 2fa, it redirects to a white screen. Logcat:

2018-09-29 03:55:27.917 10734-10734/? D/GmsAuthLoginBrowser: pageFinished: https://accounts.google.com/signin/continue?sarp=1&plt=redacted&PersistentCookie=1&scc=0

I think microG doesn't recognize Google Apps external login URLs and doesn't realize it needs to close the WebView. Code at https://github.com/microg/android_packages_apps_GmsCore/blob/1e444dc4aaae56c381e3e6621f8c0d188963ca03/play-services-core/src/main/java/org/microg/gms/auth/login/LoginActivity.java#L111
Sidenote: This URL is not identical to the "Pidgin Hangouts OAuth destination" ( https://www.youtube.com/watch?v=hlDhp-eNLMU).

switching from lineageOS webview engine to Google Webview (f-droid Chromium doesn't supply webview) doesn't help.

I visited https://accounts.google.com/EmbeddedSetup?tmpl=new_account in a private desktop Firefox session, recorded network logs with "Persist logs" across page-changes checked.
https://www.google.com/accounts/signin/continue?sarp=1&plt=redacted&PersistentCookie=1&scc=0

redirects via location: header to

https://accounts.google.com/signin/continue?sarp=1&plt=redacted&PersistentCookie=1&scc=0

has several set-cookie: GAPS, oauth_token, user_id headers.
Does not redirect anywhere on Firefox desktop.

I think you need to add an extra case to your code, to fetch said cookies from this URL.


Is this supported by react-native 0.22

Does anyone know if this works with React Native 0.22?

Yes, I have it implemented in a an app running .22, are you experiencing issue?

Yeah the build was failing but let me investigate a little further.

So it's working now, i probably forgot to close the packager :p


examples: Use autobins

Or is there a reason to use autobins = false?

Because some examples might have specific dependencies such as the #152; or is that can still be made to work with autobins?

Because some examples might have specific dependencies such as the #152; or is that can still be made to work with autobins?

Because some examples might have specific dependencies

Just keep the explicit definitions for binaries that need additional settings. These things mix just fine.

Because some examples might have specific dependencies

Just keep the explicit definitions for binaries that need additional settings. These things mix just fine.

Oh cool, I wasn't aware of that. Do you mind sending a PR to do the same thing for gtk-rs? :)

Oh cool, I wasn't aware of that. Do you mind sending a PR to do the same thing for gtk-rs? :)

Yes, I will also backport run_future in the coming days

Yes, I will also backport run_future in the coming days

Thank you!

Thank you!

Yes, I will also backport run_future in the coming days (Edit: Yes=No, I don't mind, will do laughing)

That would be great, thanks :)

Yes, I will also backport run_future in the coming days (Edit: Yes=No, I don't mind, will do laughing)

That would be great, thanks :)


Fails to build with exceptions disabled.

Have I missed something in the docs?

Albeit, I think the only proper replacement for it is std::abort();

Oh, joy. std::terminate, std::abort, std::exit and std::quick_exit all are not constexpr. The optional implementation is constexpr. In other words, goddamnit C++.

Get the fixes by pulling from latest: https://github.com/ThePhD/sol2/commit/0a165dc5b4b1df019d78cae3ea6837301549b396

Slow reply: All fixed for me.


add feature to copy caserunner.java to caserunner dynamic compiling template file.

add feature to copy caserunner.java to caserunner dynamic compiling template file.
before start compiling ate, the file caserunner.java needs to be copied to caserunner under resource folder to have the latest code as the dynamic compiling template.
need to support

eclipse development environment
maven compiling environment

this might be a solution, https://deors.wordpress.com/2011/10/08/annotation-processors/

http://hannesdorfmann.com/annotation-processing/annotationprocessing101/


Benchmarking

Hello,
In benchmarks you have to be really careful about what you're measuring. For example when you’re measuring how fast you can extract a value from a Promise by blocking on its result, using your get implementation, what actually happens there is that your operation is going to be faster than Scala’s Await.result because Scala uses its BlockContext to signal to the underlying thread-pool that a blocking op occurs, so you have extra overhead because Await.result calls blocking, which involves a ThreadLocal.get + an extra closure being created and then executed.
And if it actually runs on a BlockingContext enabled thread-pool (like Scala's global), it also means it’s going to add an extra thread to the pool.
For measuring Monix, as I've said on the channel, by calling runAsync(Scheduler) it gives you back a CancelableFuture, which for actually async results (as those created by Task.create) is powered by an underlying Scala Promise. But this is only for convenience. If you want to measure its actual performance, then you need to work with runAsync(Callback).
First of all you need a callback implementation:
import monix.eval.Callback
import java.util.concurrent.locks.AbstractQueuedSynchronizer
import scala.concurrent.duration.{Duration, FiniteDuration}
import scala.util.{Failure, Success, Try}

final class AwaitableCallback[A] extends Callback[A] {
  /** Keeps track of the completion status:
    *
    *  - 0: initial value, waiting for result
    *  - 1: completed with success
    *  - 2: completed with failure
    */
  private[this] var state = 0
  private[this] var success: A = _
  private[this] var error: Throwable = _
  private[this] val latch = new OneShotLatch

  def onSuccess(value: A): Unit = {
    this.success = value
    this.state = 1
    latch.releaseShared(1)
  }

  def onError(ex: Throwable): Unit = {
    this.error = ex
    this.state = 2
    latch.releaseShared(1)
  }

  def value: Option[Try[A]] =
    state match {
      case 0 => None
      case 1 => Some(Success(success))
      case _ => Some(Failure(error))
    }

  def blockForResult: A = {
    tryAwait(Duration.Inf)
    state match {
      case 1 => success
      case _ => throw error
    }
  }

  private def tryAwait(atMost: Duration): Boolean = {
    import Duration.Undefined
    if (state != 0) true else {
      atMost match {
        case e if e eq Undefined =>
          throw new IllegalArgumentException("cannot wait for Undefined period")
        case Duration.Inf =>
          latch.acquireSharedInterruptibly(1)
        case f: FiniteDuration if f > Duration.Zero =>
          latch.tryAcquireSharedNanos(1, f.toNanos)
        case _ =>
          () // Do nothing
      }

      state != 0
    }
  }

  private final class OneShotLatch extends AbstractQueuedSynchronizer {
    override protected def tryAcquireShared(ignored: Int): Int =
      if (getState != 0) 1 else -1

    override protected def tryReleaseShared(ignore: Int): Boolean = {
      setState(1)
      true
    }
  }
}

We are also going to replace Task.create with Task.unsafeCreate, because Task.create has extra overhead for safety reasons:
def signalValue[A](value: A): Task[A] =
  Task.unsafeCreate { (context, callback) =>
    callback.onSuccess(value)
  }

Speaking of safety, Monix has this feature where it does execution in batches, so it executes multiple cycles on the same thread, but then it jumps threads when its internal counter goes over a threshold. The purpose is to preserve liveness and not keep a thread occupied forever. Such a jump should happen if you do enough flatMap calls in a loop (e.g. over 1024), but for a benchmark there's no point in having such a feature enabled:
import monix.execution.ExecutionModel.SynchronousExecution
import monix.execution.Scheduler.global

implicit val scheduler = global.withExecutionModel(SynchronousExecution)

And then blocking for that result:
val awaitable = new AwaitableCallback[String]
signalValue("hello").runAsync(awaitable)
val result: String = awaitable.blockForResult

Also as a word of warning, this never happens in actual code:
for (int i = 0; i < 100; i++)
  f = f.flatMap(flatMapF);

What happens in actual code are recursive loops:
def loop[A](n: Int, task: Task[A]): Task[A] =
  task.flatMap { a =>
    if (n <= 0) Task.now(a)
    else loop(n-1, task)
  }

And yes, it does make a difference because your sample does more strict evaluation than what happens in practice.
And if you're actually interested in the performance of flatMap, you have to do it enough times to make sure that it dominates the throughput of the benchmark, otherwise with an efficient flatMap you're going to measure how fast you're blocking for a result. So instead of 100, make that 10000 at least.
And another thought: Task makes it possible to describe an entire program and then call runAsync only once at the edges of that program, something that you cannot do with a strict Future/Promise implementation. This means that the performance of Task.runAsync is less relevant than the performance of its flatMap.

@alexandru thanks for the feedback!

In benchmarks you have to be really careful about what you're measuring. For example when you’re measuring how fast you can extract a value from a Promise by blocking on its result, using your get implementation, what actually happens there is that your operation is going to be faster than Scala’s Await.result because Scala uses its BlockContext to signal to the underlying thread-pool that a blocking op occurs, so you have extra overhead because Await.result calls blocking, which involves a ThreadLocal.get + an extra closure being created and then executed.
And if it actually runs on a BlockingContext enabled thread-pool (like Scala's global), it also means it’s going to add an extra thread to the pool.

These are good points, but there isn't much that can be done to workaround them. The purpose of the benchmarks is to compare the throughput and allocation rate of the future implementations in a fair way, so it needs to take into consideration the full cycle: from creating the composition to actually returning the value. For instance, async execution models defer part of the execution overhead to a thread pool, if the benchmark doesn't wait for the final result it'll take into consideration only the overhead to enqueue the task.
Considering that all futures are always satisfied by the end of the benchmark, well-optimized implementations should be able to return the value without much overhead.

For measuring Monix, as I've said on the channel, by calling runAsync(Scheduler) it gives you back a CancelableFuture, which for actually async results (as those created by Task.create) is powered by an underlying Scala Promise. But this is only for convenience. If you want to measure its actual performance, then you need to work with runAsync(Callback).

Would it make sense to have something like this built-in in Monix as a more lightweight way of blocking on the result? I'm not comfortable with adding the specialized callback just for the purpose of benchmarking. For instance, for the sake of fairness, if I add this callback I'd need to implement something similar for Scala futures to work around its inefficiency to return satisfied futures.

We are also going to replace Task.create with Task.unsafeCreate, because Task.create has extra overhead for safety reasons:

Sounds good. One question, though: I'm wondering if it's fair to set the result of the task upon construction. For the other implementations, the value of the Promise is set only at the end of the benchmark and it implies extra overhead. If I change the call that sets the Promise result to immediately after its creation, most future implementations would have much better performance.

Speaking of safety, Monix has this feature where it does execution in batches, so it executes multiple cycles on the same thread, but then it jumps threads when its internal counter goes over a threshold. The purpose is to preserve liveness and not keep a thread occupied forever. Such a jump should happen if you do enough flatMap calls in a loop (e.g. over 1024), but for a benchmark there's no point in having such a feature enabled:

The project actually has two benchmarks: one uses the sync execution model and the other one the async model. I think it's important to have results for both modes.

Also as a word of warning, this never happens in actual code:

It does and it's normally the common case. Most future compositions that I see are long chains of transformations (map, flatMap, etc) and not recursive. That's what this benchmark simulates.

And if you're actually interested in the performance of flatMap, you have to do it enough times to make sure that it dominates the throughput of the benchmark, otherwise with an efficient flatMap you're going to measure how fast you're blocking for a result. So instead of 100, make that 10000 at least.

I guess the question here is more about the objective of these benchmarks. From a comparison standpoint, what matters is overall throughput including the operation to get the future result.

And another thought: Task makes it possible to describe an entire program and then call runAsync only once at the edges of that program, something that you cannot do with a strict Future/Promise implementation. This means that the performance of Task.runAsync is less relevant than the performance of its flatMap.

Isn't it the other way around? The overhead to create a Task should be minimal (allocation) and most of the work would be done by runAsync.

@alexandru have you had some time to take a look at this? I'm inclined to remove the Monix benchmarks since I'm not sure it's a fair and correct comparison.

@alexandru I've compiled the jmh results into a spreadsheet in case you want to see the results for the current implementation of the Monix benchmark. Also, you can run the benchmarks using the future-benchmark/run.sh, it only requires maven installed.

I've just removed the Monix benchmarks


3pMedia: search results text

Context

In 3pMedia tab, it can be confusing what is being displayed after entering a search term. The subheader above the results displays Trending no matter if you have searched or not.
Summary

When searching for 3pMedia, we want display the subheading just above the results with the text Search Results since this more inline with what is being displayed. As opposed to showing a subheading of Trending. Category name still takes precedent when filtered by a category.
Relevant Technical Choices

To-do

User-facing changes

Testing Instructions

[ ] This is a non-user-facing change and requires no QA

This PR can be tested by following these steps:

In 3pMedia tab, enter a search term. You should see Search Results instead of the Trending text.
If you select a category to filter by, the subheading should be the Category text. ie. "Wallpapers`

Reviews
Does this PR have a security-related impact?

no
Does this PR change what data or activity we track or use?

no
Does this PR have a legal-related impact?

no
Checklist

[x] This PR addresses an existing issue and I have linked this PR to it in ZenHub
[x] I have tested this code to the best of my abilities
[x] I have verified accessibility to the best of my abilities (docs)
[x] I have verified i18n and l10n (translation, right-to-left layout) to the best of my abilities
[x] This code is covered by automated tests (unit, integration, and/or e2e) to verify it works as intended (docs)
[x] I have added documentation where necessary
[x] I have added a matching Type: XYZ label to the PR

Fixes #3903

An edge case for sure, but if i have a search term and then i select a filter (like Wallpapers) then the text should update to "Wallpapers" not remain "Search Results" since it's not cactus wallpaper it's just wallpaper.

Nice catch!


[DOCFIX] Reorder multipart upload docs.

Merged build finished. Test PASSed.

Test PASSed.
Refer to this link for build results (access rights to CI server needed):
https://amplab.cs.berkeley.edu/jenkins//job/Alluxio-Pull-Request-Builder/16815/
Test PASSed.


Fix for EBS volumes created when the instance has been disabled, plus some vars description improvement

Fixing #74 (EBS volumes will be created if enabled = false)
Fixing a wrong description provided for ebs_volume_encrypted in variables.tf
Changing some EBS related description to be clear those are "additional volumes"

what
Adding a local variable and a && to the creation of the EBS volumes we can avoid the creation of the additional volumes if the instance creation has been disabled.
references

Closes #74

/test all

/test all


Remove non-existent command from docs

Description
The docs reference the slumber show dir command which does not exist.
The similar slumber show paths command provides the required information.
Known Risks
None, this is a documentation only change.
QA
Ran oranda dev and observed the corrected docs.
Checklist

[x] Have you read CONTRIBUTING.md already?
[x] Did you update CHANGELOG.md?

Only user-facing changes belong in the changelog. Internal changes such as refactors should only be included if they'll impact users, e.g. via performance improvement.

[x] Did you remove all TODOs?

If there are unresolved issues, please open a follow-on issue and link to it in a comment so future work can be tracked

I agree with your tweaks @LucasPickering, I have applied them 👍


implement CommandRunner

struct CommandRunner
fn run(command: &str) -> io::Result

Command Runner is not implemented.


Allow using templates in annotations

I have a use case where I need to include helm templates in an annotation. Example:
  ingress:
    enabled: true
    annotations:
      nginx.ingress.kubernetes.io/custom-http-errors: "401,403,404,500,502,503"
      nginx.ingress.kubernetes.io/default-backend: "{{ .Release.Name }}-error-pages"

This does not work as of now - the "{{ .Release.Name }}"-part is not interpreted and written as is to the rendered manifest.

@pierluigilenoci I totally understand and asm aware that this is a restriction by helm.
But there are ways around this. Some charts (i.e. most Bitnami charts) process the input of such fields through helms tpl function, instead of toYaml to resolve any string templates within a parameter.
Helm mentions this "work-around" in their FAQ.

I noticed that this is already done in a couple of places:
https://github.com/oauth2-proxy/manifests/blob/main/helm/oauth2-proxy/ci/tpl-values.yaml
I'll create a PR for the annotations

@gtudan, these are the values files used in the CI for testing.


Show NEP141 Fungible Token Metadata

Story
As a NEAR user who uses or owns fungible token, I want to see token metadata on explorer so I can understand the status of the token easily.
The example is showing the metadata of ERC20 token in etherscan: https://etherscan.io/token/0xdac17f958d2ee523a2206206994597c13d831ec7

Acceptance Criteria

[ ] User can see NEP141 fungible token metadata in explorer
[ ] The UI is mobile compatible

NOTES

Users have requested to see the NEP141 token metadata in explorer

@think-in-universe Thanks for bringing this users request! Are there the most and the least important things on the page that you can highlight from your experience or feedback?
Just for reference, we had some initial work discussed in #516

@frol thanks Frol.
I think we can start with showing the NEP141 metadata especially the Overview section in etherscan.

NEP141 metadata: https://nomicon.io/Standards/FungibleToken/Metadata.html#reference-level-explanation

should show the details of nep-141 tokens on personal wallet page:

whats tokens holding
tokens transfers details

and should show the tokens transfers details on every transaction page.
yeah just like ethscan xd.


test

test feature02

pull request testing

declined

could you please merge feature 2? this is a test.

please modify ......

OK, I will merge it to the master branch.


Ella Yap 19447 - Sprint 3: Autonomous Coding (Alliance Specific Route Testing & Yellow Route Intake Coding)

By Friday, November 1, 2024, the following regarding autonomous should be done:

Finish testing the alliance specific autonomous route (without intake)
Review the code for the alliance specific autonomous route movement (without intake)
Begin coding the intake for the yellow autonomous route

Additional Objectives to be Completed:

Finish building a robot that is able to do every component of the game

10/28/2024
15 Minute Stand Up Meeting

10/31/2024

Continued motor testing for hang (testing if the motors can hold the weight of the robot)

11/1/2024

Researched PID
Discussed PID

11/2/2024

My team and I had our 15 minute Stand Up Meeting to discuss what was worked on, what will be worked on (what will be accomplished this sprint), and any obstacles
Discussed changing the angles of the sliders

11/3/2024

Learned how to file

11/6/2024

Continued testing the alliance specific autonomous route (without intake)
My team and I had our 15 minute Stand Up Meeting to discuss what was worked on, what will be worked on (what will be accomplished this sprint), and any obstacles
What will be worked on (individually for me)

Finish testing the alliance specific autonomous route (without intake)
Review the code for the alliance specific autonomous route movement (without intake)
Begin coding the intake for the yellow autonomous route
Team
Finish building a robot that is able to do every component of the game

11/7/2024

Cable management for the robot (to ensure the cables fall the exact same way every time)
Assisted with the 19447 Engineering Portfolio (more specifically, worked on the autonomous strategy category)

11/8/2024


Very nice work!

Hey @russellyang728
This is very nice work! I found and corrected a few bugs. And it would really be if you

[ ] put your code into functions

Greetings,
Tomasz

Hi @donotdespair ,
thank you for all the corrections you made. I updated all except the function. I will put code in function before final project submission.
Cheers


Add cp and cs request

Awesome job as always @eduhenke !
Just a code style thing that comes to my mind is to separate public and private methods in classes. Angular style guide uses to recommend to declare public methods above private ones, what do you think?

Also, can you update the dependencies on this PR?

About the public/private methods, how about adding a linter to this, so this restriction is enforced automatically?

About the public/private methods, how about adding a linter to this, so this restriction is enforced automatically?

I've added the suggestions :)

I've added the suggestions :)

About the public/private methods, how about adding a linter to this, so this restriction is enforced automatically?

Sure, that's a good idea!

About the public/private methods, how about adding a linter to this, so this restriction is enforced automatically?

Sure, that's a good idea!


[Autocomplete] Doesn't load correctly on the first input

Hi,
Using Symfony 7 and autocomplete 2.15 here, and I got this behaviour.
When the autocomplete field got the focus, it sends an ajax request and receives the 10 first results (as designed).
But when I enter something, like a single char or two, nothing happens. When I clear the field (hitting backspace), it makes a request for the page 2... and then when I enter a char again, this time it makes the correct request and displays the appropriate choices.

How can i debug this ? I did more or less what is described on the demo page https://ux.symfony.com/autocomplete
I tried with Firefox 123 and Chrome but with the same results.
I will try to make a very simple project to reproduce and isolate the bug.

Fixed : it was in fact a combination of local factors. Oh, and the 'min_characters' option of the autocomplete field also help.


Check websocket before push

Hi,
I have a question. I have set my websockets, and it's work perfectly.
But how manage Ratchet.
I explain, i put pusher with ajax notifications. So in real time  we have  notifications, and we use the old method, for users who are not connected.
If accidentally the websocket crash, or something block or something  else, all ajax requests are becoming wrong.
Because ajax request calls a service that use the websocket.
          //La notification entraine un push
	$pusher = $this->container->get('gos_web_socket.wamp.pusher');
	$pusher->push(['msg' => 'bulles'], 'skies_notif', ['adherent' => $user->getId()]);

Is it possible to use a checking method to avoid freezing the website . Because it's very dangerous on the  production server
Example :
If  ($objectPusher->somethingCheck)
{
//return true
$pusher = $this->container->get('gos_web_socket.wamp.pusher');
$pusher->push(['msg' => 'bulles'], 'skies_notif', ['adherent' => $user->getId()])
}
else
{
//Pusher errors so we jump notifications in real time
}
Thanks for ur help.
Best regards.
Antoine

I have the same questions/problem. I need to check for websocket connection before pushing from one of my controllers. Because if the websocket is gone, the server returns error 500 for my AJAX request despite the fact, that the request itself worked just fine - only the push didn't work. I tried wrapping the push command with try/catch but to no avail. Is there a method somewhere which can check for a working websocket connection before the push?

What kind of exception is raised? We may simply catch her ?
Other solution is to using amqp pusher is a better way because of queuing
system, you can push notification without server behind, queue will be
autimatically consumed when you will start the web socket server.
Le jeu. 12 janv. 2017 09:21, AsfalothDE<EMAIL_ADDRESS>a écrit :

I have the same questions/problem. I need to check for websocket
connection before pushing from one of my controllers. Because if the
websocket is gone, the server returns error 500 for my AJAX request despite
the fact, that the request itself worked just fine - only the push didn't
work. I tried wrapping the push command with try/catch but to no avail. Is
there a method somewhere which can check for a working websocket connection
before the push?
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Hi ProPheT777, I tried catching the Exception. It is a BadResponseException and it is thrown by in vendor/gos/websocket-client/Wamp/Client.php at line 112. The message says "Could not open socket. Reason: Connection refused" which is correct of course, because the websocket is gone. I need to catch that exception or test for a valid connection first so my controller doesn't fail anymore.
Unfortunately I can't use the amqp pusher because of server limitations.

Ok, so catch the exception is enough? do we really need to expose an API like isConnected which is an edge case (From my point of view) ?

No, catching doesn't work, I don't know why. The exception is still thrown, despite my try/catch block. isConnected would be great if it's possible... I know it's an edge case but I still would be thankful for it...

Strange, I tested again to catch it and now it worked. Thank you anyway for your help! 👍

Hi, asfalothde,  can u show me ur code, I have always the problem.  When I tried catching my push,  he returns "Could not open socket" how u jump this for continue ?  Because its a try catch under my try catch.

 And its work ? I tried exactly the same code last time, and I have create the ticket for this problem with the conviction then isconnected méthod its better. OK I go back home and I will try again

I had the problem too at first. But I only catched the general exception, not the BadResponseException and that was it.

Nice one, i havent see the little difference. Have a good day =)

@AsfalothDE How did you define a $route in your below code? Please send me code so I can check that
try {     $pusher = $this->container->get('gos_web_socket.wamp.pusher');     $pusher->push($arrPush, $route, $arguments); } catch (BadResponseException $exception) { // Do whatever you want }


Test enhancement

Changed log

Using correct assertions when doing assertion works.
Add autoload-dev block in composer.json to load the test classes automatically via vendor/autoload.php.
Declaring the use PHPUnit\Framework\TestCase instead of using extends \PHPUnit\Framework\TestCase directly for all test methods.

Thanks again


Widgets do not appear in 1.6.0 version

Hello,
I have problem with widgets in 1.6.0 version. I can't create any. In 1.5.0 everything was ok. Old widgets don't work
Bug report grom android on attachement.
I'm using Xiaomi mi 8 se, android 9 and Nova launcher

There was a change to how widgets worked that very likely broke the old widgets.  However, you should be able to create new ones.  Is this screenshot from trying to create a new widget or launch an old one?  It looks like a widget configuration crash.

FWIW I was able to create a widget in the latest build.  I did not carry any over.  Sometimes we do need to start over.

@KBerstene this screenshot is taken after deleting old widgets and trying to create a new one.

@piotrdal Thanks for the info.  I think I have an idea of where this is originating from, but I'd like to gather a bit more info, if you don't mind.  It looks like you might have a service in a format that was unaccounted for in the widget configuration setup, specifically the service auto-populate method.  If I can narrow down what the service is (it doesn't seem to exist in my system), then I might be able to fix it.
I'm working in Lovelace, so I don't know if this is different for you.  If you go into Home Assistant and select Developer Tools, you should have a Services tab available.  If you type "log_event" in that service box, I am curious if it will auto-complete a domain and service (e.g. homeassistant.log_event).  If it does, could you please provide a screenshot and perhaps the name of the integration you're using that would have the log_event option?

@KBerstene I Hope it's what you need...
i'm using edgeos integration

@piotrdal That was exactly what I was looking for.  It looks like the edgeos log_events field doesn't conform to how the rest of the Home Assistant services are laid out.
For future notes:
In edgeos' services.yaml, the log_events field needs to be changed to have a description member rather than just being a string member itself.
However, this doesn't fix all possible cases for importing services to the app.  I'll have to look into what changes can be made to assure that this issue doesn't come up in the app if any other components are similarly non-conformist.

@KBerstene 👍  We shouldn't crash, but I don't think we should go too far out of way to handle custom components that don't follow the standard.  We might be able to add a check for this in HA it's self to have the custom component not load at all if not conforming.  Not sure how @balloob feels about that plan?

@JBassett I agree, we should just have it not load the service if it fails for whatever reason.  However, my only hesitation there is that we might want to add in some sort of "add custom field" button to be able to add an extra field to service data if the service you're trying to call doesn't auto-populate the fields.

@KBerstene commenting log_events in services.yaml from edgeos component enables creating widgets. Thanks a lot :)
Btw - on 1.5.0 it was working without any problems with edgeos component fully enabled

@piotrdal Awesome!  I'm glad that works.  You could try editing your custom_components/edgeos/services.yaml with the correction I proposed here and see if that would enable the widget configuration page to load the log_event service correctly.

Ok...hmmm...I don't know exactly how it can be changed... The services.yaml from edgeos component looks quite the same to me as the services.yaml from your link except for the comments...

I agree with @JBassett. We're not going to support formats that do not conform to our services format.
Home Assistant currently doesn't check it because it is provided by the developer, and so should follow our formats (but I guess it doesn't always).


Generated partial class missing using clauses

The type defined for inject attribute (or inferred from the field) namespace must be included in the using clause generation.

Issue fixed and included on package release version 1.0.4