Update framework-foundation to 1.1.4

Changes Unknown when pulling b8affdc8337bccd1746d89a3b8599e3d6381db29 on yiming187:maven_update into ** on ctripcorp:master**.

Current coverage is 22.36%

Merging #86 into master will decrease coverage by -28.37% as of 7a32186

@@            master     #86   diff @@
======================================
  Files           54      97     +43
  Stmts          956    1945    +989
  Branches        95     226    +131
  Methods          0       0
======================================
- Hit            485     435     -50
- Partial         26      33      +7
- Missed         445    1477   +1032

Review entire Coverage Diff as of 7a32186

Powered by Codecov. Updated on successful CI builds.


Convert minutes to weeks

Description
1 week = 7 days = 168 hours = 10080 minutes
Input  : 50400
Output : 5

Program added successfully :tada:
Thanks for your contribution :hugs:


2020-01-03 If DATADISPLAYLIST doesn't include a variable that is all EMPTY, i.e. all(is.na(x)) == TRUE, run_Mx_Sx_computation fails

This defect occurs because of code in each of the run_M#_Sx_computation.R modules that removes all empty columns from return_df. If there are NO columns that are entirely empty, the module will remove all columns from return_df and fail to return any results.
The fix is straightforward, in each of the modules change
### remove columns that are empty, note that this may remove columns incorporated into the SDEID/Profile computation
### but if they are empty, it should not impact the result of the computation if recomputed even if the SDEIDs themselves
### have different values
    j <- lapply(return_df, FUN=function(x) { all(is.na(x)) } )
    return_df <- return_df[,-match(names(j[j==TRUE]), names(return_df))]

to
### remove columns that are empty, note that this may remove columns incorporated into the SDEID/Profile computation
### but if they are empty, it should not impact the result of the computation if recomputed even if the SDEIDs themselves
### have different values
    j <- lapply(return_df, FUN=function(x) { all(is.na(x)) } )
### 2020-01-03/TGT/ add conditional logic for the following statement to only run when there are all empty columns
    if(length(j[j==TRUE])>0) { return_df <- return_df[,-match(names(j[j==TRUE]), names(return_df))] }

I've confirmed this fix with run_M1_SD_computation.R.
Note that this code block is MISSING from run_M4_SS_computation.R. Please incorporate.

With commit dce4ed8, this issue remains, cannot be closed and has not been addressed.

The rule for empty columns removal is changed as follows:
No completely empty columns, i.e. all(is.na(x))==TRUE, are removed from the output results.
If this needs to occur for either RETURNCOLS or DATADISPLAYLIST columns or results_list$data_out parameter columns, it will be managed in the wrapper scripts calling run_computation().


fix fix the merged rows is not equal to missed rows when do cumulative compaction

Proposed changes
Issue Number: close #xxx

dcheck fail. doris::Compaction::modify_rowsets, the merged rows(1986) is not equal to missed rows(0) in rowid conversion, tablet_id: 5957365, table_id:5957113
This problem was introduced by: #20153
Further comments
If this is a relatively large or complex change, kick off the discussion at<EMAIL_ADDRESS>by explaining why you chose the solution you did and what alternatives you considered, etc...

run buildall


Running the Docker command on Windows

Hi there!
Thanks for this super useful container!
I managed to build my custom firmware and add support for a non conventional (128x32) oled screen.
I struggled a little bit to run the docker command on Windows so I thought I would share the simple trick I found. You need to specify the full path in the command and you need to add a double forward back slashes (//) in front of the two paths. The command is then:
docker run --rm -it -v //c/Users/<user>/nodemcu-firmware://opt/nodemcu-firmware marcelstoer/nodemcu-build
I can submit a pull request with an updated README file if it helps.
Thanks!

Jason, thanks a bunch for this! Just the other day I got a support request via email from a guy who struggled with the exact same problem. As I don't use Windows myself my hands were tied and I couldn't help, very frustrating. I'll update the README accordingly.

double forward back slashes

Uuhhm, what's that? Wouldn't a forward back slash negate itself and thus become a pipe (|) :wink: ?

Hehehe :)
Silly me! Double forward slashes, of course!
It's horrible enough...

Jason, are you sure you need the // also in front of the "Docker path" i.e. the mount target? I think you only need them for the Windows path. It should be //c/Users/<user>/nodemcu-firmware:/opt/nodemcu-firmware no?

Hi there,
Sorry for the delay.
I just tested and I can use both: /opt... or //opt.... It does not make any difference for me.

Thanks for the confirmation! I updated README.md to document all the findings.

An addendum to this: I had to give Docker access to the drive containing my repository, in Settings/Shared Drives, before the "Not a git repository" error would go away.

First of all you need to give Docker access to the drive containing my repository, in Settings/Shared Drives!!! This helped me and then try to use the command line without --rm
docker run -it -v //c/users//nodemcu-firmware://opt/nodemcu-firmware marcelstoer/nodemcu-build
It checked on Windows 10!
Have a nice day.


We are not receiving the updates of the schema anymore... sigh... :angry:

the pull request action seems to fail to do its job but does not show as failing...
https://github.com/bids-standard/bids-matlab/pull/168/checks?check_run_id=1958560476
AKA: automation won't automate

Apparently we do... sometimes... #193


[Port] System.Private.MCG.dll

@yizhang82 I think you have confirmed before we don't need to port this library to corert. if this is true, could you please close this issue?

Won't be needed.


Add rudimentary Location return support for CompleteMultipartUpload

Based on AWS documentation: https://docs.aws.amazon.com/AmazonS3/latest/API/API_CompleteMultipartUpload.html
A location URL should be returned for the newly created object. This implementation adds this field to the output.
Both PathBucket and HostBucket are supported.

:warning: Please install the  to ensure uploads and comments are reliably processed by Codecov.
Codecov Report
Attention: Patch coverage is 83.33333% with 2 lines in your changes missing coverage. Please review.

Project coverage is 66.14%. Comparing base (edd0227) to head (8b10e3f).

Files with missing lines
Patch %
Lines

gofakes3.go
83.33%
1 Missing and 1 partial :warning:

:exclamation: Your organization needs to install the Codecov GitHub app to enable full functionality.
Additional details and impacted files
@@            Coverage Diff             @@
##           master      #95      +/-   ##
==========================================
+ Coverage   65.99%   66.14%   +0.15%
==========================================
  Files          28       28
  Lines        2697     2706       +9
==========================================
+ Hits         1780     1790      +10
+ Misses        651      649       -2
- Partials      266      267       +1

:umbrella: View full report in Codecov by Sentry.
:loudspeaker: Have feedback on the report? Share it here.


Button press no box

When pressing "Free domains", it begins to scroll you down but stops when it reaches the end of the website (obviously). It does not have a box around "Free domains" however.
Solution: Extend website with whitespace (temporary) or add a small footer at the end

This is the apple one, obviously we havent got that much info but maybe some small text with the discord, reddit links?
I'm sure the issue will be "fixed" automatically with more work being done on the website

Works as intended.
Adding unnecessary whitespace to the bottom would not look nice, it would also not work for all screen sizes.

I should also add if you look in the backend branch, the Navbar is being changed from scrolling to pages as there will be more pages than just the home page.


When will you provide the  training code and put it on GitHub?

Thanks for your great work!I found that you provided the verification code of the controllable captioning model , but there is no training  code. I want to know if you are convenient to provide the code of training part or put it on GitHub.

Hi @whut2962575697,
thank you for your interest in our work!
The training code is now available. I hope it will be useful.

Thanks for your reply！We are very grateful that you can provide us with the training code which will be very useful to us.
Thank you and best regards.
Yours sincerely,
Xin He
<EMAIL_ADDRESS>From: Marcella Cornia
Date: 2019-07-23 00:33
To: aimagelab/show-control-and-tell
CC: Clark He; Mention
Subject: Re: [aimagelab/show-control-and-tell] When will you provide the training code and put it on GitHub? (#6)
Hi @whut2962575697,
thank you for your interest in our work!
The training code is now available. I hope it will be useful.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub, or mute the thread.


Switch to PG

closes #68

closes  #73

I had an issue with the docker config on this repository, fixed locally by appending <IP_ADDRESS>:8000 to the Django runserver command to expose the Django server outside my container. I noticed that this fix is also included here, so I won't duplicate this work. Thanks @vosi


Add release manifests for multicluster

Signed-off-by: Lan Luo<EMAIL_ADDRESS>

Codecov Report

Merging #3197 (6fa318c) into feature/multi-cluster (1ef45bb) will increase coverage by 0.05%.
The diff coverage is n/a.

@@                    Coverage Diff                    @@
##           feature/multi-cluster    #3197      +/-   ##
=========================================================
+ Coverage                  41.03%   41.08%   +0.05%
=========================================================
  Files                        192      189       -3
  Lines                      22890    22695     -195
=========================================================
- Hits                        9392     9324      -68
+ Misses                     12551    12424     -127
  Partials                     947      947

Flag
Coverage Δ

unit-tests
41.08% <ø> (+0.05%)
:arrow_up:

Flags with carried forward coverage won't be shown. Click here to find out more.

Impacted Files
Coverage Δ

pkg/apiserver/storage/ram/store.go
76.86% <0.00%> (-1.50%)
:arrow_down:

pkg/agent/openflow/pipeline.go
21.21% <0.00%> (-0.07%)
:arrow_down:

...multicluster/commonarea/mock_remote_common_area.go

...ster/commonarea/mock_remote_common_area_manager.go

.../multicluster/mock_membercluster_status_manager.go

pkg/controller/grouping/controller.go
56.89% <0.00%> (+1.72%)
:arrow_up:


RevB USB comms speed limited

Should have routed the traces as a differential pair.

Rerouted tracks for USB d+/-.
New board works at 921600 baud. May work at higher baud rates but not tested.


Array mul  lacking min/max data

Have I written custom code (as opposed to using a stock example script provided in TensorFlow): no
OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Linux Ubuntu 16.04
Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device:N/A
TensorFlow installed from (source or binary):source
TensorFlow version (use command below):1.10.1
Python version:2.7.3
Bazel version (if compiling from source):0.12.0
GCC/Compiler version (if compiling from source):c++11
CUDA/cuDNN version:7.5.18
GPU model and memory:TITAN,12GB
Exact command to reproduce:
./bazel-bin/tensorflow/contrib/lite/python/tflite_convert --output_file=~/foo.cc   --graph_def_file!/frozen_inference_graph.pb   --inference_type=QUANTIZED_UINT8   --input_arrays=Imageholder --output_arrays=ResizeBilinear_2   --mean_values=127.5   --std_dev_values=127.5

Describe the problem
I modified deeplabv3+(mobilenetv2) model, and train/eval a quantized model with "tf.contrib.quantize.create_training_graph(quant_delay=0)
tf.contrib.quantize.create_eval_graph". The graph show as below:
Placeholder->mul->sub->mobilenetv2
When I convert this frozen model to tflite quantized model, I use input_node = Placeholer. There are error occurred. Does any one know how to solve this problem?
Source code / logs
2018-09-07 10:59:40.172018: F tensorflow/contrib/lite/toco/tooling_util.cc:1635] Array mul, which is an input to the Sub operator producing the output array sub, is lacking min/max data, which is necessary for quantization. If accuracy matters, either target a non-quantized output format, or run quantized training with your model from a floating point checkpoint to change the input graph to contain min/max information. If you don't care about accuracy, you can pass --default_ranges_min= and --default_ranges_max= for easy experimentation.
tf.estimator package not installed.
Aborted (core dumped)

Over to Suharsh to advise.

The issue is that the contrib/quantize rewriter is not very robust to any arbitrary model yet. In particular the Mul and Sub at the start of your network need quantization information for TOCO. This can be resolved by either manually adding a FakeQuantWithMinMaxVars node after mul and sub, see how the rewriter does it: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/quantize/python/quantize.py#L475
That being said this can be complicated, if you goal is to just get a smaller and faster model, I recommend trying the --post_training_quantize flag to tflite_convert. With that you keep the inference_type=FLOAT and pass a floating point version of your model (no need to call the contrib/quantize tool). That may provide sufficient speedup for your use case.

Nagging Assignee @suharshs: It has been 15 days with no activity and this issue has an assignee. Please update the label and/or status accordingly.

Automatically closing due to lack of recent activity. Please update the issue when new information becomes available, and we will reopen the issue. Thanks!

In feature_extractor.py, there is a function named '_preprocess_zero_mean_unit_range'. 'return (2.0 / 255.0) * tf.to_float(inputs) - 1.0' here will cause this problem. Make this function do nothing, just return the input and declare the input as  'input_image = tf.placeholder(tf.float32, [1, None, None, 3], name=_INPUT_NAME)' will help

@suharshs I'm facing the similar problem due to a layer consistent with several basic tensorflow ops (like Add, Mul, Sigmoid, etc.) Is there any update on the contrib/quantize tool? Or we still have to manually add FakeQuantWithMinMaxVars?
Thanks!

The issue is that the contrib/quantize rewriter is not very robust to any arbitrary model yet. In particular the Mul and Sub at the start of your network need quantization information for TOCO. This can be resolved by either manually adding a FakeQuantWithMinMaxVars node after mul and sub, see how the rewriter does it: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/quantize/python/quantize.py#L475
That being said this can be complicated, if you goal is to just get a smaller and faster model, I recommend trying the --post_training_quantize flag to tflite_convert. With that you keep the inference_type=FLOAT and pass a floating point version of your model (no need to call the contrib/quantize tool). That may provide sufficient speedup for your use case.

I'm wondering how to manually adding a FakeQuantWithMinMaxVars node. I've looked into the link you provided and still cannot find that code .


Fixed missing version in WFS App from scratch example

The :version: attribute setting needed to precede the inclusion, 'tis all.
Fixes RHOARDOC-1213

merged: 75e1c49b0e7e71b89cc68b72ac4372037c6d1088


RuntimeError: Not compiled with GPU support Stereo-RCNN/lib/model/csrc/nms.h:22

When running python3 demo.py I met a problem running nvidia-docker
with cuda 9.0 torch 1.01 torchvision 0.4.0
After python3 setup.py build develop
RuntimeError: Not compiled with GPU support (nms at /home/yuhengq/Stereo-RCNN/lib/model/csrc/nms.h:22)

I met a problem running nvidia-docker with cuda 9.0 torch 1.01 torchvision 0.4.0
After python3 setup.py build develop, I run python3 demo.py which return the error
line 121, in forward
keep_idx_i_left = nms(proposals_single_left, scores_single.squeeze(1), nms_thresh)
RuntimeError: Not compiled with GPU support (nms at /home/yuhengq/Stereo-RCNN/lib/model/csrc/nms.h:22)

When python3 setup.py build develop
No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda' running install

hi，i've met the same problem with you, could you please tell me how did you fix it? thanks in advance

@Hexuanfang It has been a long time. I am sorry I can't remember all details clearly.
I think this is a Cuda configuration or installation issue. Try to verify your cuda version and installation. BTW, I suggest you configure this with conda, which definitely saves your time.

thanks a lot
---Original---
From: "Yuheng<EMAIL_ADDRESS>Date: Mon, May 11, 2020 04:00 AM
To<EMAIL_ADDRESS>Cc: "Mention"<mention<EMAIL_ADDRESS>Subject: Re: [HKUST-Aerial-Robotics/Stereo-RCNN] RuntimeError: Not compiled with GPU support Stereo-RCNN/lib/model/csrc/nms.h:22 (#43)
@Hexuanfang It has been a long time. I am sorry I can't remember all details clearly.
I think this is a Cuda configuration or installation issue. Try to verify your cuda version and installation. BTW, I suggest you configure this with conda, which definitely saves your time.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub, or unsubscribe.


Consolidation of register-with-ledger step

The register-with-ledger step is an operation that must be performed after the eservice is built and the ledger is up to make sure that the transaction processor can validate the enclave attestation (i.e., the proof data) properly.
Right now there are multiple issues with this step.

we don't call the registration for sawtooth in sim mode -- rather, we start sawtooth in debug mode (to accept empty proof data)
in ccf, following the merge of #406 , we call it in two different places, for sim and hw mode respectively

the former works behind a proxy, because the registration is called from the ccf container with empty data
the latter does not work behind a proxy because the registration is called (similarly to the sawtooth one) from a different container. In this case, there are proxy and/or certificate issues, which are fully explained in #413 .

In an ideal scenario,

the registration step is called from a single place -- a good candidate is where it is right now for sawtooth, and for ccf in hw mode only.
the ccf container does not need to be aware of the SGX_MODE variable
#413 partially addresses these issues, by solving the proxy issue -- so the registration step can be consolidated.
Other PRs in the pipeline will take care of refactoring the docker/docker-compose files, containers and variables.

The proposed evolution of the docker containers should support a canonical registration "at the right point" in the process.
We should also move from putting "register" as a Makefile target & should just use single script that can load modules for registering CCF and/or some other ledger.

Tracked via 467, 463. @cmickeyb  @bvavala  If you are OK, I will close this Issue

Closed, after merge of https://github.com/hyperledger-labs/private-data-objects/pull/467,.


[Feature Request] Do not automatically continue gathering when revisit procs

Is your feature request related to a problem? Please describe.
When revisit procs and my effects are cleared, pandora continues gathering without giving me a chance to reapply my (manually chosen) buffs first, and I have to hurry to turn it off, reapply my buffs, and then start gathering again.
Describe the solution you'd like
I would like pandora's auto-gathering to treat revisit triggering as if I opened a new node. If "remember item between nodes" is disabled, it should wait for me to select an item to gather. If not, it should trigger any configured actions and then begin gathering again.
Describe alternatives you've considered
Alternatively, it would be acceptable to simply remember what action buffs I had active before revisit and apply them again, then continue gathering. This would be potentially less useful, as it would assume that the exact same item is desired, and it's possible that the "original" node hits were sufficient to gather the desired materials, at which point I usually prefer to gather a handful of crystals instead, but it would at least work as desired in most cases.
Additional context
If the alternative is implemented, single-hit buffs could complicate matters. Since GP is restored, it may be desirable to use the visit-long version instead.

It technically does already treat it like a new node. Revisit is a tricky one because the best way to detect it is through chat messages, so what Pandora does is when it detects a certain message type at 0 durability it will run the same code that is run on opening a node. So to that effect, if you've already turned off Remember Item then it should not gather until you click it again on a revisit. If it's not doing this then it's a bug which is really hard to test given the nature of revisit (I've been trying to proc it whilst writing this with no luck).
Pandora gathering logic isn't super deep and is designed for mindless gathering. The intention was to emulate the quick gather from the game but using abilities to increase yield. Anything beyond a bug fix at this point is beyond the scope of the feature and would be better suited in a plugin of its own.
I will continue to try and proc revisit in the meantime to investigate the Remember Item claim.

It's definitely not working for me, I'm afraid. I have all options disabled except for pandora gathering itself, and opening a new node waits for me to select an item, but twice now I've gotten revist to proc and pandora has simply continued gathering without pause. I didn't know there was handling in place for it, or I'd have filed this as a bug to start with.


Uses CLLocationCoordinate2D for coordinates

Replaces latitude and longitude properties to use CLLocationCoordinate2D.
CoreLocation framework has built-in validation of the coordinate by using CLLocationCoordinate2DIsValid.
Includes unit test, code coverage increased from 95% to 96.15%.
I've "linted" the JSON also.
Sorry for the whitespaces, my Xcode 9 is not properly trimming those.

This makes so much sense! How did I not think to use a CLLocationCoordinate2D in the first place is beyond me...
Thank you for the PR 👍


use shellcheck JSON output

Thanks!
https://github.com/euclio/neomake/blob/329d77437e2a9c2c56facb8028d1af4f5028b00c/tests/integration.vader#L183 fails, but it is also a not so good test by itself.  I will look into fixing it (removing shellcheck there) later.

https://github.com/neomake/neomake/pull/2304 (merged).
Please rebase.

Rebased.

Given the above issues: what is the benefit of using the json output?  Only having the length easily?

Yeah, being able to calculate the length is the main reason.

Re endColumn support in general: https://github.com/koalaman/shellcheck/issues/966

And regarding tab size: https://github.com/koalaman/shellcheck/issues/1048

Left a comment at https://github.com/koalaman/shellcheck/issues/1048#issuecomment-461550298.

Since endColumn support is on its way out, I think the downsides of this outweigh the benefits. Closing.

Agreed.

Since endColumn support is on its way out

Do you have a source?

https://github.com/koalaman/shellcheck/issues/966, but I didn't realize that it was from over a year ago. I guess it's no longer accurate.


Cannot display Epics/Features on a Preview Plan

I configured my full backlog (Epic>Feature>US) with start dates and iterations, and wanted to use the "Plan" preview feature.
Unfortunately the preview plan view only display user stories, not epics and features.
My goal is to display the Epic and Feature view rollup, as per documentation.
I also checked Settings>Fields>Show child rollup data.
When I go Settings>Teams, I can only select the only value "Stories" in the Backlog dropdown, I guess I should be able to select either Epic or Feature.
Is this a bug or is there something else to activate in projects?

Document Details
⚠ Do not edit this section. It is required for docs.microsoft.com ➟ GitHub issue linking.

ID: 9ef517e1-f614-a2e5-d968-e32bdaac6183
Version Independent ID: babf6a50-0147-74bb-0e20-b1939de397af
Content: Review team delivery plans - Azure Boards
Content Source: docs/boards/plans/review-team-plans.md
Product: devops
Technology: devops-agile
GitHub Login: @KathrynEE
Microsoft Alias: kaelli

@glautrou - It looks like you have a question with the product itself, rather than an issue with the documentation. To make sure it gets in front of the right people, please submit your bug here.

@glautrou - To show Epics and Features, you need to add your team backlog for Epics and your team backlog for Features.  SImilar to that shown below. If you have added these different backlog levels and don't see the Epics and Features, then please report it as a bug.  The only other setting you might check is that the Team Settings supports features and backlogs - see second image. I just confirmed that if the backlog levels are not enabled for the team, then the Plans won't allow selection of those not selected. I'll add that clarification to the article.


Start experimental bundle

For now, simply take the cpp_visyv1_i16 experiment and organise it according to our source code conventions.

I have simply copied the source files from  cpp_visyv1_i16 to new directories and internally changed just what was needed for the translator to compile. Nothing really works yet, but this will be our starting point for trying things.


[cherry pick 1.18] scheduler-suite: pod-affinity, pod-anti-affinity, pod-topology-spread

What type of PR is this?
/kind feature
What this PR does / why we need it: This PR cherry picks the recent scheduler-suite additions to 1.18.
Which issue(s) this PR fixes:

Fixes #
Special notes for your reviewer:
Does this PR introduce a user-facing change?:

NONE

Additional documentation e.g., KEPs (Kubernetes Enhancement Proposals), usage docs, etc.:

/cc @tosi3k

/lgtm
/assign @wojtek-t

cc @ahg-g

/assign @mborsz

/lgtm
/approve


Implement light and dark themes

Now that there is a basic scheme and layout with #2 it is now time to implement light and dark theme toggling

Light and dark themes work now with 48cd82e


Verbose mode for react-native init fails for react-native-cli ver: 0.1.10

While using the latest version of react-native-cli (0.1.10), react-native init fails when invoked with verbose mode.
This will walk you through creating a new React Native project in C:\tmp\AwesomeProject
events.js:141
      throw er; // Unhandled 'error' event
      ^

Error: spawn npm ENOENT
    at exports._errnoException (util.js:837:11)
    at Process.ChildProcess._handle.onexit (internal/child_process.js:178:32)
    at onErrorNT (internal/child_process.js:344:16)
    at doNTCallback2 (node.js:429:9)
    at process._tickCallback (node.js:343:17)
    at Function.Module.runMain (module.js:477:11)
    at startup (node.js:117:18)
    at node.js:951:3

But with 0.1.9 version of react-native-cli, the verbose mode works fine.

Hey @RanjithNair, current version of CLI is 0.2.0 - could you check if the issue still happens? I couldn't reproduce, but it might be due to some system conditions I don't have prepared.

I can answer that one.  Absolutely yes, this thing is still a FAIL.  I'm using  react-native-cli --version = react-native-cli: 0.2.0.  When I type $ react-native init IconExplorer and I'm still seeing the Error: spawn npm ENOENT, sigh.  (Windows 10)   It seems to work okay withOUT the --verbose flag, But that gets the user back to a place where there are two and three minute delays during the init with zero / zip / nada feedback that anything is going on.  Why o why did someone thing hiding functional feedback was a useful thing?   In fact I can't imagine a piece of software on the planet where a delay of more then 30 seconds without user feedback of some sort is acceptable.
full error:
`$ react-native init IconExplorer --verbose
This will walk you through creating a new React Native project in C:\IconExplorer
Installing react-native package from npm...
events.js:154
throw er; // Unhandled 'error' event
^
Error: spawn npm ENOENT
at exports._errnoException (util.js:890:11)
at Process.ChildProcess._handle.onexit (internal/child_process.js:182:32)
at onErrorNT (internal/child_process.js:348:16)
at _combinedTickCallback (internal/process/next_tick.js:74:11)
at process._tickCallback (internal/process/next_tick.js:98:9)
`
I see somebody pulled an issue https://github.com/facebook/react-native/pull/6708 on the verbose issue.

Looks like this is fixed now.


Added a one-liner for helping push to openshift

First off why is this needed in the ofCourse repo? ofcourse openshift --help should work fine. This seems more like a personal note that you would want to keep on your computer or in your content repo.
Second off even though you might not always use it there's also a --domain option that allows you to specify a OpenShift domain that you have access to.

Closing because this isn't a general script. If you want, you can add this sample command to the docs.


Addition: PlayerOnGroundChangeEvent

This pull request allows people to more easily track the on-ground value of a player, which can be helpful for fall damage implementations. Additionally, it provides context for whether the client claims they are on the ground or if the server does.

I'd recommend against making this a cancellable event, as I think that could lead to a lot of movement jitter.

I'd recommend against making this a cancellable event, as I think that could lead to a lot of movement jitter.

I think I made this cancellable when I thought you could set the value yourself (kind of like how you can set if a player is sprinting or not), but you can't. I also didn't look far enough to see that onGround is always just based on what the client says, so I'll be removing that part of the event too.


[fixes #1489] Add meta flag for 'unsafe' queries

Add meta flag for 'unsafe' queries
This closes issue #1489

Fixed sails bot?

How 'bout meow?

Hey guys, any input on this pull request?
@mikermcneil @sgress454

@mdconaway Sorry it's taken so long to circle back to this.  I think we're all in agreement that it's worthwhile to have the ability to turn off normalization of attributes in criteria.  @mikermcneil and I were hoping it was something that could be done at a slightly higher level than what you're proposing, because otherwise it's easy to miss things that don't touch your specific use-case.  For example, in your PR you added the new meta argument to one of the normalizeCriteria calls in forge-stage-two-query, but missed the other one, probably because you weren't doing a "populate where" query.
Having said that, and looking at your PR, I'm not sure there is a way to do this at much of a higher level.  What @mikermcneil proposed avoids having to alter normalize-constraint, but you still have to normalize the sort clause as you've done, plus there's all the files you have to touch just to pass meta down.
We'll put our heads together for a bit and get back to you...

Hey @mdconaway!  So went over this a bit with Scott and meow I'm really seeing the big picture:
• skipping criteria normalization (skipConstraintNormalization)
• removing sort normalizations that enable sorting by nested key (separate thing)
• turning off attribute->column name mapping (separate thing)
I'm thinking we should split this up a bit first-- can we make this PR about skipCriteriaNormalization first?  It'd be good to knock this out one piece at a time.  (Then once we've got the more fine-grained / testable stuff together, we could always add some kind of "unsafe" flag for aggregating the options)
That said, I'm wondering: would it make sense for this use case to just use .native()?

@sgress454 and @mikermcneil,
I believe it may help clarify things a bit if I explain our greater system architecture and expand on the behavior we are actually looking for based on this pull request.
My team and I work in a large (read: global) private network where we develop a great number of applications for the various departments in our organization.  We have been working very hard to achieve a few specific things:

Build a pure micro-service infrastructure
Language unification (Writing all of our backend code in JavaScript to enable fluid transition of front-end and back-end devs throughout the organization)
12-factor server development patterns to enable automated cloud deployment of our APIs
Well defined REST data interchange formats (we use Ember REST Adapter specifications)
Maximize DRY, and minimize custom code in our API servers
Build pure API servers, and well-designed SPA's to interact with the API servers

We currently run dozens of separate Sails.js server instances, and in each instance we handle all of our CRUD interaction using this library:
https://github.com/mdconaway/sails-ember-rest
Which is a Sails 1.0 compatible library I have developed and now currently maintain that enables the Ember REST Adapter to just work with Sails.js and Waterline.
What the sails-ember-rest library achieves for our team is that we don't typically "write" controllers. Many of our applications have dozens of data models, and those models can ride on Mongo, MySQL, or both in a single micro-service.  No matter the model, or its underlying DB adapter, we handle all of our crud actions in our controller files with the following code:
import { controller } from 'sails-ember-rest';

export default new controller();

We have even added the ability to execute policy-like events after a CRUD action through the idea of "service interrupts" introduced by the sails-ember-rest library here.
As a result, 90% of the code in most of our micro-services is just policies and model schemas. This has a huge benefit to us, because it means that the CRUD actions for our services have very predictable behavior, and the only custom code we are typically writing per service is for special actions like file uploads, and policies that control who can acccess what data at what action end-point.
Any use of .native() is typically a non-starter for us, because it means we would have to discard the sails-ember-rest controller and build something that will continually progress towards divergent behavior. (Though we do use things like .native() in custom endpoints that do complex analytics using things like the Mongo aggregation pipeline)
Most of the time, matching left hand side query values to their corresponding schema values is totally acceptable in our services, but there are outliers.  We do have some models that are extremely complicated, and as a result we need to do searches and sorts against nested key values.  With that being said, we absolutely do not want server behavior when searching a nested key to diverge from the behavior when searching a root key and we do not want to make our front-end SPA's aware of the backend DB type as that would have an impact on their model hooks and a whole other cascade of things like our common paginator route in Ember. We need to keep all of the other features of the waterline language, such as the query forge expanding incoming queries like:
{
  a: ['b', 'c']
}

into:
{
  a: {
    in: ['b', 'c']
  }
}

We also do not want to lose the power of waterline which allows us to transition models to completely different databases overnight if we see performance gains using a different DB type without changing any application level code.  Writing anything with .native() creates potentially large amounts of refactor work for a server in the event that we decide to move to a new database.
When we can land a merge into waterline that allows us to selectively disable left-hand-side key validation as a query traverses the forge, I will also be adding a way to toggle that feature to the sails-ember-rest library so that we can use that query feature without having to write custom controller actions.
The TLDR version:
-We need a way to disable left-hand-side key validation on query and sort objects while still retaining all of the other niceties of the waterline query forge.
-We specifically don't want anything in the query forge to be skipped
I hope that helps clarify things a bit!  Please let me know if you have any questions about what I have explained above as I haven't finished my morning coffee yet.

@sgress454 @mikermcneil ,
If you guys are ok with it, I can modify this pull request to also take care of "populate where."
I am willing to personally support (with code and unit test writing) other internal refactors of waterline later, but my team's near term need is primarily just allowing the loose left-hand keys while retaining everything else in waterline.
If you guys can accept the unsafe flag for now (or want me to change it to another name so that you can reserve it for another purpose) it would be highly beneficial if we could merge this sooner rather than later.  I am more than willing to put together a war-plan with you guys over slack or some other service so that we can collectively refactor some of the other query forge internals over a longer timeline that isn't as mission critical for our team.
<- will work for merge

@mdconaway really appreciate you taking the time to detail the background here, and staying positive :)

we need to do searches and sorts against nested key values

I recognize you're going for a short-term solution here since you need this asap, and your fork seems to be an appropriate solution for that.  So first off, don't worry-- it's totally fine for you to use that (I don't anticipate there being any more major upheavals in how the files related to the query forge are structured any time in the near to medium-term future).

As far as what we merge into core, I have to make sure whatever we do here also lines up with the plan going forward, which includes supporting this kind of usage.
I'm currently in the middle of a vaguely-related effort: changing the default behavior so that it avoids destructively mutating arguments from userland by default (but still allowing destructive mutation if configured to do so, via a meta key).  Thus I'm trying my best not to conflict with what's in this PR, but it looks like it might be unavoidable...  I just took another look through to get a sense of which of my changes might cause a problem, and the issue is that I also need to send through meta to some of the deeper-nested utility functions.  So before I do that and mess anything up, I wanted to figure out what do to support your use case longer term.
I'm wondering if it'd maybe be cleaner/faster to just to go ahead and take the plunge into allowing dot notation (foo.bar.baz) instead of attribute names for constraints.  Or something equivalent anyway.  (And obviously this would only be usable for adapters that support it.)
But first, just to make 100% sure:
• is the nested foo.bar.baz-style syntax for eq constraints the only blocker right now?
• is it just for looking up stuff within type: 'json' and type: 'ref' attributes?
• how attached are you to dot notation as far as syntax?  e.g. rather than dog.collar.color: 'red', another option would be something like dog: { whose: { collar: {whose: {color: 'red'}} }.  I'm not saying that's better or worse per se, but I'm just trying to get a sense.  As you probably noticed when exploring the stage 3 query stuff, there are a few edge cases (e.g. through tables) that make anything that relies on dots a little worrisome (and potentially bug-prone)
I'm heading out the door right now, but I'm planning to take a deeper dive into this tomorrow while I have some time, so we can hopefully have a resolution by the end of this weekend.
(refs https://github.com/balderdashy/waterline/issues/1489)

@mdunisch I should add: besides lookups inside type: ‘json’ and type: ‘ref’ attributes, I figure lookups inside out-of-schema attributes would fit in the same category.
The important thing to me is that we fail with a straightforward error message if someone tries to use this syntax to do a lookup within an association (whether singular or plural).  ie I want to make sure what you’re suggesting is purely for embeds.
Doing this inside an association is a different story (although eerily inverse to this) and involves a WHERE sub query, which is only supported in databases with a concept of joins and foreign keys.
I think it could be pretty nice to use dot notation for both use cases— but we need to be able to offer up purposeful errors for when people inevitably try to do that in the mean time between when this works for embeds and when it works for associations.
We also have to consider embedded arrays, though it’s pretty easy just to mimic MongoDB there
Some things to consider, using a pseudo-real example:
await Facility.find({
  //for embedded dicts:
  ‘address.city’: ‘Austin’,
  // what about embedded arrays?
  //...
  //for singular associations, deep:
  // (which might contain nested embeds as well)
  ‘organization.createdBy.name’: ‘Lee’,
  // for plural associations?
})

Hey @mikermcneil ,
Thanks for the input!
I would say that our most common use case for dot notation is searching and sorting against embedded objects within sails-mongo/MongoDB in production and sails-disk/NeDB for our local dev/test servers.  That being said, if you are open to allowing waterline to accept dot notation queries/sorts against attributes with type 'json' or type 'ref' that will likely solve all of our outstanding issues related to this pull request.  It also seems like that would actually be the more sane approach as well, since the type of json kind of implicitly means that you would need to send a dot notation query to use it for anything meaningful.
It could be nice to allow dot notation searches against values that are not within the schema, but we typically try to keep at least our root level keys defined so we could usually declare something like 'json' at the schema level.  For other teams that have really loose schemas for a reason, I can see them arguing that they would want to use dot notation outside of the schema.
Regarding this syntax:
{dog: { whose: { collar: {whose: {color: 'red'}} }}}

We could probably make that work as well, but it seems more verbose and it would require a refactoring of some of our code in other places like Ember.  I am not opposed to it, but I would need to have the deeper significance of this syntax explained to fully endorse it.
To this point I have never really used where objects against relationships (many<->many) but I think that would be a really powerful thing for us to take advantage of in our MySQL apps.  Specifically reducing some parent record set by searching some child record set in one query.  Is this currently supported in Waterline ~0.13, or is it something that is on the development timeline?
And now for something totally unrelated
sails-mysql:
I have noticed that the sails-mysql adapter doesn't support waterline 'or' syntax.  Is this true or am I totally missing something?
sails-mongo:
I noticed the following pull request over in the sails-mongo repo and I think it is something that would be useful for the community.
https://github.com/balderdashy/sails-mongo/pull/464
I don't believe it can be merged into sails-mongo as is, because it would force all 'contains' queries to be case insensitive.  What plans do you guys have regarding case insensitive searches as part of the waterline query syntax for adapters like sails-mongo?  Is there anything I can help with in order to make a better pull request for this feature?  I don't mind contributing to this one if you point me in the right direction.

I have noticed that the sails-mysql adapter doesn't support waterline 'or' syntax. Is this true or am I totally missing something?

It should, and does, unless I'm missing something

What plans do you guys have regarding case insensitive searches as part of the waterline query syntax for adapters like sails-mongo?

Let's bring that discussion to the other PR.  My focus as far as open source stuff this weekend is pretty saturated with the forge stuff, and then there are some priorities we need to look at from flagship, but Scott or I will try to take a look at that other PR in the upcoming weeks.  In general, our plan is to make Waterline query syntax completely defer to the adapter as far as case-sensitivity (prior to 0.13, we made a decision on that in core, and it caused performance issues for folks working on large projects with databases like postgresql.  That's why we moved to avoid any kind of core normalization related to case-sensitivity in the latest version)

So re: this kind of querying:
There's a lot of things supported by Mongo in this department.  My instinct is to avoid most of them (that's what native queries are for).
So I took a look at the subset of what's also supported elsewhere these days (e.g. PostgreSQL, MySQL).  And, like the last time I looked a couple of years ago, it's all over the map  -_-
So to begin with, we've got to make a call on how this works at the adapter level.  For now, I think we're best off supporting arbitrary deep targets for constraints, with the expectation that the RHS of the constraint syntax is standard Waterline query language.  If the data queried at runtime happens to be an array, that's ok-- but it's up to the adapter as far as what that actually means-- and database-specific syntax (e.g. $all will not be allowed).  Other than that, deep targets are fine as long as the deep target begins with an out-of-schema attribute or an attribute with type:'json' or type: 'ref'.  For example the constraint 'foo.bar.baz': … is ok as long as the foo attribute fits that definition, and as long as the RHS is a valid Waterline constraint.
@mdconaway would that do the trick?

That sounds good to me, provided this also supports 'in' queries like:
{
    'foo.bar.baz': ['a', 'b', 'c']
}

And other thing like date comparisons using >= and <= etc.

@mdconaway righto, it'd support anything you can currently do with a shallow target.
  'foo.bar.baz': ['a', 'b', 'c']

technically would be recognized as shorthand notation and expand into
'foo.bar.baz': {
  in: ['a', 'b', 'c']
}

but it would still work

@mdconaway something to be aware of though:
if you were to write 'foo.bar.baz': ['a', 'b', 'c'] intending it to be a Mongo array deep equality check, you'd be disappointed/surprised.  (The same thing could be said for shallow targets though, because that's the way it works today if you did foo: ['a','b','c'].  Since we don't know if foo is an array or not, we can't really do anything smarter-- and I think that's probably for the best)

No complaints here! I don't think I've ever found a good use case for array equality as of yet.

I believe https://github.com/balderdashy/waterline/commit/897b891a41ed7008e5b54ff7dfa447c50af4206f does the trick as far as the where clause.
I'm open to reusing the same meta key to allow this stuff in the sort clause, but I'd like to get this working w/ just where first, for clarity.
@mdconaway would you mind updating your PR so that it contains just the tests? (it's ok that any involving sort will fail at the moment).  Also, if you wouldn't mind taking this (just the where clause bit) for a spin with your db, it'd be good to know it actually works once the code actually makes it past all of the checks and balances :)

Removed all changes except tests.  What is the near to mid-term requirement for dealing with dot notation sorting?

What is the near to mid-term requirement for dealing with dot notation sorting?

First off I just want to make sure what we have so far actually works in Mongo.
Assuming that's good, then I think all we have to do is basically make the changes you did in fs3q and then bring in the comparable stuff in normalizeSortClause()

@mdconaway well I went ahead and tried it out and a cursory test worked for me:

@mdconaway ok I just did some basic tests as of https://github.com/balderdashy/waterline/commit/7f43bee1fd3bd21bb470b44408ec141599c19edc and this is now working for me for both the sort and where use cases.  Would you take it for a spin when you have a moment?

@mdconaway actually nevermind-- I tested this with .populate() and noticed that currently, sort doesn't work when it's in the subcriteria.

 I'll try to test it out later today. If it's not in NPM and accessible through our NPM mirror I will have to run the test at home so I have access to GitHub.
Is this flag necessary to enable functionality?
{enableExperimentalDeepTargets:true}

Hey @mikermcneil ,
Just got done running some tests here, and it looks like commit f0337a2af7b767e73334db9a95e3c3b01c0a9f1a fits the bill for our typical use cases.  Are you planning on leaving the
{
    enableExperimentalDeepTargets:true
}

meta flag around for a while? Or is it safe to assume the meta flag won't make it to main?

around for a while?

@mdconaway I imagine we'll turn the feature on by default eventually, I feel pretty good about what's there.  Probably doesn't make sense to enable it by default until there is an adapter-level concept of whether an adapter supports this or not though  (e.g. mysql should be able to say that it doesn't, and Postgresql and Mongo should be able to say that they do)
But yeah, while I'm not 100% comfortable documenting this and calling it "officially supported" yet, the only blockers IMO are the other missing accoutrements.  (Also it'd be nice if this worked for select and omit too, but that could always be added later.)

Hi @mikermcneil I like this one, any info if it will stay as a meta option?

Get an error when trying to find in nested data in postman on an api created in sales
trying to do
{
"job.description": "u"
}

job is an object and description is a property of that object. How can this be done?

Deep Populate not work in Sails V1.1


Center the site page without a flicker

Extend the extrahead and footer block in layout.html instead of
overriding versions.html

Did not know I can extend a block in a Sphinx template until now.

Center the page before the document body is available
Remove the cloak which is not needed anymore
Fork me on GitHub -> Fork me at GitHub

@delegacy PTAL

Current coverage is 64.36% (diff: 100%)

Merging #313 into master will not change coverage

@@             master       #313   diff @@
==========================================
  Files           317        317
  Lines         12753      12753
  Methods           0          0
  Messages          0          0
  Branches       1908       1908
==========================================
  Hits           8209       8209
  Misses         3660       3660
  Partials        884        884

Powered by Codecov. Last update 9b197b3...2328244

👍

@delegacy Thanks for reviewing again! 👍


scootapi/client: include run status; add testcase

Note: because of revert shenanigans, just look at the second commit for now

Current coverage is 73.64%

Merging #124 into master will increase coverage by 0.04%

@@             master       #124   diff @@
==========================================
  Files            56         56
  Lines          2409       2409
  Methods           0          0
  Messages          0          0
  Branches          0          0
==========================================
+ Hits           1773       1774     +1
+ Misses          544        543     -1
  Partials         92         92

Powered by Codecov. Last update 31d0a22...3380bca

OK, then I think I'll just close this because it's accidentally submitted.


[theme] Multiple non nested Themes class name conflicts

[x] The issue is present in the latest release.
[x] I have searched the issues of this repository and believe that this is not a duplicate.

Current Behavior 😯
When multiple Themes are provided to different parts of the document the classnames conflict and only the second theme gets used.
Expected Behavior 🤔
The correct Theme gets applied to the correct parts
Steps to Reproduce 🕹

https://codesandbox.io/s/serverless-pond-ky2mg
expected: a red and a blue Paper element
Without Styles Provider both Paper elements are red
StylesProvider and disableGlobal=false: both Paper elements are red
StylesProvider and disableGlobal=true:  red and blue Paper
StylesProvider with generateClassname=undefined red and blue Paper

Context 🔦
I was trying to reproduce an other bug and found this unexpected behavior.

I think this is intentional. From the docs:

The generated class names of the @material-ui/core components behave differently. When the following conditions are met, the class names are deterministic:

Only one theme provider is used (No theme nesting)
The style sheet has a name that starts with Mui (all Material-UI components).
The disableGlobal option of the class name generator is false (the default).

So you either need to use disableGlobal: true, or add a base theme which will contain the two themes (which might be a good idea anyway if the themes have a lot in common).

Duplicate of https://github.com/mui-org/material-ui/issues/27000

@mnemanja this is not a duplicate of the other bug. One ist neasted themes, one is wit non nested themes.
I can see this is probably intendend like @michal-perlakowski pointed out. But a warning would be nice.

Sorry I misread. Agree, removing the duplicated label.

@mnemanja it's the second time someone conflicts our usernames 😄 Sorry for the spam 😃

oh sorry that was not intende


Too long Changelog for zone.

Hi, your work is realy great thank you.
We have zone for monitoring and using DNSUI api for make changes in it.
So, we have already more than 30000 changes and web is very slow because of that.
Can we limit the number of showing changes if we whant to see only records or can we separate changelog to diffenet scope?

We're starting to hit those limitations as well, so I'm trying to find a way to filter (or better, search in) changelogs.
I had a look in the table schemas. The changeset table stores a record for each changelog entry; each
changelog is then made up of one or more rows from the change table. However, the juicy details are stored there as serialized PHP objects, so they can't be searched through.
The (useful) information available in changeset is just author_id, change_date and comment. This leaves us with the following options:

paginate results
search through comment
limit to date range
filter by author (least useful, IMO)

I'd like to implement one or more of the above; @thomas-pike, what would be your preference here?

I think that primarily, to solve the problem of page load becoming slow, the most important thing is to implement pagination. Search and date range options definitely would be nice to have though. Any contributions you are able to make will be gratefully accepted.
Apologies for the use of serialized PHP objects, that could definitely have been done better.

Here's the pull request: https://github.com/operasoftware/dns-ui/pull/190


spike: foundation for nextjs-publishing

Wondering how/why this PR got added to master in https://github.com/dendronhq/dendron/commit/16b0e5c59e3ee11530199b5c9a11a58f05e14a93

Merged this into master for ease of development. PR updates here -> https://github.com/dendronhq/dendron/pull/1114


feat: add orgs and scp visualization capability

What does this PR do?
Completion checklist

[ ] Additions and changes have unit tests
[ ] Unit tests, Pylint, security testing, and Integration tests are passing. GitHub actions does this automatically
[ ] The pull request has been appropriately labeled using the provided PR labels (major, minor, fix, maintenance, documentation)

LGTM


browser redirect to the login page

when browser is opened, it then redirect to the login page

Did you use proxy?


Adding project badges to README

Project badges help users to figure out popularity, build stability, dependencies security, and are a great addition to the README to give a quick overview on the project status in general.

This project has been un-maintained, closing PR.


Remove leftover comment

Remove a left over comment

Codecov Report

Merging #13629 (d458f9c) into master (8f97988) will decrease coverage by 26.17%.
The diff coverage is n/a.

@@              Coverage Diff              @@
##             master   #13629       +/-   ##
=============================================
- Coverage     43.76%   17.59%   -26.18%
+ Complexity     9049     2666     -6383
=============================================
  Files          1378     1378
  Lines         79869    79869
  Branches       9713     9713
=============================================
- Hits          34957    14050    -20907
- Misses        41962    64574    +22612
+ Partials       2950     1245     -1705

Impacted Files
Coverage Δ

...uxio/master/file/meta/AsyncUfsAbsentPathCache.java
13.70% <ø> (-59.68%)
:arrow_down:

...mon/src/main/java/alluxio/shell/CommandReturn.java
0.00% <0.00%> (-100.00%)
:arrow_down:

...mon/src/main/java/alluxio/util/ExceptionUtils.java
0.00% <0.00%> (-100.00%)
:arrow_down:

...n/src/main/java/alluxio/wire/AlluxioProxyInfo.java
0.00% <0.00%> (-100.00%)
:arrow_down:

.../src/main/java/alluxio/wire/AlluxioMasterInfo.java
0.00% <0.00%> (-100.00%)
:arrow_down:

...src/main/java/alluxio/job/meta/JobIdGenerator.java
0.00% <0.00%> (-100.00%)
:arrow_down:

...n/src/main/java/alluxio/stress/BaseParameters.java
0.00% <0.00%> (-100.00%)
:arrow_down:

...src/main/java/alluxio/client/UnderStorageType.java
0.00% <0.00%> (-100.00%)
:arrow_down:

...main/java/alluxio/underfs/options/ListOptions.java
0.00% <0.00%> (-100.00%)
:arrow_down:

...main/java/alluxio/worker/block/io/BlockReader.java
0.00% <0.00%> (-100.00%)
:arrow_down:

... and 658 more

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
Powered by Codecov. Last update 8f97988...d458f9c. Read the comment docs.

alluxio-bot, merge this please

@HelloHorizon FYI

alluxio-bot, cherry-pick this to branch-2.6 please


Some improvements to the logs

The current logs are somewhat confusing, and missing some very useful information. This is an attempt
to clean it up in a few ways:

Log the number of items from each dg. Each datagatherer can return
the number of items it collected so that the logs tell us a bit more about
what was found in the cluster, and can help find where any items have
been missed.

Log a more informative error message when giving up on uploading
readings to the server. This would be the last message before
the pod exits, so if the pod ends up in CrashLoopBackoff it's
important to highlight this as the reason.

Remove the "Running Agent" log message, it's not clear what it means

Put [] around the body as if the body is empty the log message
can read like the following message is the body.

2024/05/10 16:33:43 retrying in 25.555756126s after error: received response with status code 404. Body:
W0510 16:33:43.832278   10875 reflector.go:535] pkg/mod/k8s.io/client-go@v0.28.3/tools/cache/reflector.go:229: failed to list route.openshift.io/v1, Resource=routes: the server could not find the requested resource

Remove "using parent stop channel" from a log message as it's not
clear what it means. It used to be conditional, and now it isn't,
so it's not providing any information, and it's confusing as to
what it might be trying to tell you if you aren't familiar with that
code.

There are couple of code cleanups included as well:

Fix a crash bug when REST config can't be loaded.
discoveryClient is uninitialized, so the pointer can't be deferenced,
so return an empty struct if there's an error.

Remove the informer context in the dynamic dg. The informer
context is created, but not passed to anything, so this code doesn't do anything.

The changes are split into a few logical commits if we want to break it up and review them independently,
as I know these might not be the best approaches for all of these things.

Hey, I've taken a look at your PR. I've went ahead with reviewing this as part of the handover effort started this morning. I'll let Olu do a final review as I don't know much about this code base.

Log a more informative error message when giving up on uploading readings to the server. This would be the last message before the pod exits, so if the pod ends up in CrashLoopBackoff it's important to highlight this as the reason.

I wasn't able to reproduce the agent giving up, or I haven't waited long enough, I guess the backoff threshold is high:
$ export HTTPS_PROXY=foo
$ go run . agent -c config.yaml --client-id XXXXX -k /tmp/key --venafi-cloud -p 5s 2>&1 | grep api.venafi.cloud
2024/05/14 14:54:23 Posting data to: https://api.venafi.cloud/
2024/05/14 14:54:23 retrying in 17.654784782s after error: post to server failed: Post "https://api.venafi.cloud/v1/oauth/token/serviceaccount": proxyconnect tcp: dial tcp: lookup foo: no such host
2024/05/14 14:54:41 Posting data to: https://api.venafi.cloud/
2024/05/14 14:54:41 retrying in 56.057860693s after error: post to server failed: Post "https://api.venafi.cloud/v1/oauth/token/serviceaccount": proxyconnect tcp: dial tcp: lookup foo: no such host
2024/05/14 14:55:37 Posting data to: https://api.venafi.cloud/
2024/05/14 14:55:37 retrying in 42.620269206s after error: post to server failed: Post "https://api.venafi.cloud/v1/oauth/token/serviceaccount": proxyconnect tcp: dial tcp: lookup foo: no such host
2024/05/14 14:56:19 Posting data to: https://api.venafi.cloud/
2024/05/14 14:56:19 retrying in 51.204385623s after error: post to server failed: Post "https://api.venafi.cloud/v1/oauth/token/serviceaccount": proxyconnect tcp: dial tcp: lookup foo: no such host
2024/05/14 14:57:11 Posting data to: https://api.venafi.cloud/
2024/05/14 14:57:11 retrying in 2m16.713596561s after error: post to server failed: Post "https://api.venafi.cloud/v1/oauth/token/serviceaccount": proxyconnect tcp: dial tcp: lookup foo: no such host

(I've hidden all the useless messages about missing resources)
About the REST config bug, well spotted. I wasn't able to reproduce the panic (if that's how you spotted this)
Anyways, I think all the changes you made are sensible.

Hey, I've taken a look at your PR. I've went ahead with reviewing this as part of the handover effort started this morning. I'll let Olu do a final review as I don't know much about this code base.

Thanks!

Log a more informative error message when giving up on uploading readings to the server. This would be the last message before the pod exits, so if the pod ends up in CrashLoopBackoff it's important to highlight this as the reason.

I wasn't able to reproduce the agent giving up, or I haven't waited long enough, I guess the backoff threshold is high:

I believe the default timeout is 15m before it gives up.

About the REST config bug, well spotted. I wasn't able to reproduce the panic (if that's how you spotted this)

It was. Running locally without a kubeconfig file should reproduce.

Anyways, I think all the changes you made are sensible.

Thanks.

Thanks all, I pushed an update with the suggested changes.

Thank you :+1: LGTM, I'll merge the changes to master


Additional GZDoom Games

The Forestale: https://steamdb.info/app/817220/
Fury's Sky: https://steamdb.info/app/1118550/
Hedon: https://steamdb.info/app/1072150/
Lycanthorn II - Rain of Beasts: https://steamdb.info/app/1546440/
Selaco: https://steamdb.info/app/1592280/
Shrine: https://steamdb.info/app/1271050/
Shrine II: https://steamdb.info/app/1417010/
Additional information
Here's a list of additional games on Steam that use the GZDoom Engine, several of which are free.

Hedon is already native and Selaco isn't out yet, but I'll look at the others when I have time. Do you have a list of the commands needed for each one?

Hedon runs an older version of the engine, it would be useful to have the option to use modern vanilla gzdoom.
Simply running gzdoom while in the game's folder is enough. Here's a list of subfolders containing the game:
The Forestale: ./
Fury's Sky: ./
Hedon: ./hgzdoom
Lycanthorn II - Rain of Beasts: ./
Selaco: Unknown, but probably ./
Shrine: ./
Shrine II: ./shrine2
So to launch Shrine would be simply gzdoom, but Shrine II would need to be cd ./shrine2 && gzdoom

From my testing, it was required to add the iwad command so that gzdoom would not show up the GUI launcher.
I've implemented these in this commit: https://github.com/luxtorpeda-dev/packages/commit/32c69019206772dcec5ea4ed8eb8c28881284a4c

One side effect of specifying -iwad is that the default configuration file for the game isn't always loaded.
The Forestale: Seems to work.
Fury's Sky: Seems to work.
Hedon: Non-functional, should be -iwad hgzdoom/Hedon.ipk3
Lycanthorn II - Rain of Beasts: Config isn't being loaded -config gzdoom.ini
Shrine: Config isn't being loaded, specify with -config gzdoom.ini
Shrine II: Config isn't being loaded, specify with -config shrine2/gzdoom.ini

Ok, this should work: https://github.com/luxtorpeda-dev/packages/commit/9d065e569f4fe873b187bf9dfe6bfbd34c22b343

The Forestale: Tested working.
Fury's Sky: Tested working.
Hedon: See below.
Lycanthorn II - Rain of Beasts: Tested working.
Shrine: Tested working.
Shrine II: Tested working.
Hedon right now is mostly working, but there's a few small problems:

The path I gave earlier -iwad hgzdoom/Hedon.ipk3 is for the linux depot of the game. For some reason the Windows version has the flies in -iwad ./Hedon.ipk3 instead. Despite this incorrect path the game autodiscovers the files and runs anyways.
Hedon doesn't ship with a configuration file, but rather it's generated on launch. I have ran Hedon in Proton to generate the file, hedon-steamuser.ini. I suggest including this file as gzdoom.ini and specifying -config gzdoom.ini so it works out of the box.

@JoshuaFern I've fixed the path here: https://github.com/luxtorpeda-dev/packages/commit/dfe4e064bdbf12f030f45d8bbf0a366c41915ae0
For the config, are you saying that the normal gzdoom does not generate that file? And this one is needed for the game to function properly?

For the config, are you saying that the normal gzdoom does not generate that file? And this one is needed for the game to function properly?

Most games just ship their customized gzdoom.ini along with the game files. Hedon uses a lightly modified fork of gzdoom called hgzdoom to generate the configuration on launch similar to how gzdoom does normally - but with two differences. One, the file is named hedon-steamuser.ini rather than the usual gzdoom.ini, and two, the file generated is not the standard generic config for gzdoom but rather a config specific to Hedon.
To ensure Hedon works properly out of the box we can't rely on hedon-steamuser.ini being in the directory, as the game must be launched for it to generate - so instead I propose shipping the hgzdoom generated file that I supplied and naming it gzdoom.ini so it won't conflict in the event that the user wants to play without Luxtorpeda.

@JoshuaFern I did this in: https://github.com/luxtorpeda-dev/packages/commit/325b48c530eec3538647a813a36c5a82bc1b6b26

Great, the configuration file exists, now Hedon just needs to use it with -config gzdoom.ini
Also I like the way you copied hedon.ini to gzdoom.ini only if the file doesn't exist, this allows users to easily modify the config without Luxtorpeda trying to replace it every launch. Maybe rename hedon.ini to hedon_template.ini so folks don't get confused and think it's shipped with the game. They could end up manually modifying hedon.ini and get confused why it's not having an effect in-game.

@JoshuaFern Done in https://github.com/luxtorpeda-dev/packages/commit/01cee1cc650d6601ee8b754dc5731bc32cc29f24

Working perfectly now, thanks.


"erc20-with-balance" usability

Case
As quoted as a representative strategy in the official doc, "erc20-with-balance" strategy allows the space owner to check the min amount of token required to vote.
During the test, if I add the parameter “minBalance” and set the value to "10" where the wallet has only 5 token at snapshot, it still gives 1 voting power.

The link is too long, I'll put it in this hyperlink: erc20-with-balance
As instructed by the official doc, using USDC with decimal 6 and passed the "minbalance" to test.

Other Common strategies

erc20-with-balance is an example of strategy that checks whether the participant has a minimum amount of token required to vote and assigns all the votes to 1. You need to add the parameter “minBalance” and set it equal to the minimum balance required to vote on a proposal. This value is set to 0 by default.

Maybe the playground link is wrong, i can't see any minbalance in it, i tried adding minBalance and it works. check here the user got 1 USDT, and if you remove minBalance you should see that voting power will be 1

Thanks! I think it works.
For people who may seek help like this strategy, I'll comment as below:

The token address doesn't need to be checksummed.
make sure the param is “minBalance”  not “minbalance”


Nan value encountered  while testing DCVC-FM code

Hi, thank you for sharing the repository. When I test the DCVC-FM code with HEVC Class D dataset, pytorch=2.0.0, python=3.10.13, numpy=1.26.4, nan value encountered, how to solve the problem?
Traceback (most recent call last):
File "DCVC-FM/src/models/image_model.py", line 166, in encode
encoded = self.forward_one_frame(x, q_index)
File "DCVC-FM/src/models/image_model.py", line 149, in forward_one_frame
bits_y = self.get_y_gaussian_bits(y_for_bit, scales_hat)
File "DCVC-FM/src/models/common_model.py", line 74, in get_y_gaussian_bits
gaussian = torch.distributions.normal.Normal(mu, sigma)
File "/home/anaconda3/envs/dcvc-fm/lib/python3.10/site-packages/torch/distributions/normal.py", line 56, in init
super().init(batch_shape, validate_args=validate_args)
File "/home/anaconda3/envs/dcvc-fm/lib/python3.10/site-packages/torch/distributions/distribution.py", line 62, in init
raise ValueError(
ValueError: Expected parameter scale (Tensor of shape (1, 256, 32, 48)) of distribution Normal(loc: torch.Size([1, 256, 32, 48]), scale: torch.Size([1, 256, 32, 48])) to satisfy the constraint GreaterThan(lower_bound=0.0), but found invalid values:
tensor([[[[nan, nan, nan,  ..., nan, nan, nan],
[nan, nan, nan,  ..., nan, nan, nan],
[nan, nan, nan,  ..., nan, nan, nan],
...,
[nan, nan, nan,  ..., nan, nan, nan],
[nan, nan, nan,  ..., nan, nan, nan],
[nan, nan, nan,  ..., nan, nan, nan]]]], device='cuda:0')

Hi, I experience a similar problem during testing as you did. Did you find a solution for this problem? My code crashes at the exact same position as yours with all nan values in the scale tensor.

Hello, I also encountered similar problem when training and finetuning image_model of DCVC-FM. But I can test DCVC-FM code on HEVC dataset except class F. Do you have the corresponding solution ?


Fixed empty and leastSize for OpenSSLStream

The previous implementations were sometimes incorrect, because
SSL_pending only returns the current pending bytes without blocking, and
even if the underlying stream has data available, it might not be
application bytes. Using SSL_peek will block until application
bytes are available or the end of stream has been reached.
See http://permalink.gmane.org/gmane.comp.encryption.openssl.user/6536

maybe related, maybe OT: I have trouble with a vibe.d https server. More precisely, a POST file upload to a server running on AWS.
With some OS/browser/connection combinations, the server logs
"Reading from TLS stream was unsuccessful with ret 0" from openssl.d line 279 and the browser gets no response.
If one combination fails, it fails reproducible. For some unknown reason, connecting to a VPN can fix it. After disconnecting from the VPN, the error occures again.
I tried this PR and exept the log line is now triggered in line 280 nothing changed.

It turned out that vibe.d is not to blame for the "Reading from TLS stream was unsuccessful with ret 0" errors we experienced. In fact no POST request with sufficient large body did not made it to the server (EC2 instance). We tried a clean instance and different favours of gnu/linux as well as a variant of python's SimpleHTTPServer... A "first usage of wireshark" investigation seemed to indicate that the request arrived crippled inside the instance (reason unknown).
After switching to another provider, everthing works fine.

Sorry for letting this sit here for so long! I somehow associated this with a very obscure solution, but apparently that was another PR for the same bug. This one makes a lot of sense on the other hand. I've cherry picked the change to master as ee35d735c0eee9d6b45d34270f1af5db7ac07679.


v1.0.* broke yeelink.curtain.ctmt2 support

Device model / 设备型号
yeelink.curtain.ctmt2
Component version / 插件版本
1.0.1
HA core version / HA版本
2024.11.3
Integrated mode / 集成方式
Local (本地模式)
The problem / 问题详情
The new version of the integration broke curtain support:
The operating mode selection is no longer available.
Statuses, such as open/closed curtains, are no longer displayed.
A phantom device has appeared.

Entity attributes / 实体属性
For real device
device_class: curtain
friendly_name: Шторы гостиная Curtain
supported_features: 15

For phantom device
restored: true
device_class: curtain
friendly_name: Xiaomi Miot Штора
supported_features: 15

Home Assistant Logs / 系统日志
No response

Try master branch.
wget -O - https://get.hacs.vip | DOMAIN=xiaomi_miot ARCHIVE_TAG=master bash -

@al-one After the latest update, my sliding curtains turned into roller blinds.

@al-one After the latest update, my sliding curtains turned into roller blinds.

Fixed in master branch.


DOCS update framework and cms milestone links

+1

Do I need to rebase this?

Do I need to rebase this?

please


Comparison between 2D and 3D models

I ran a few experiments with both 2D and 3D models and the purpose of this issue is to document the results and draw comparisons.

 Config file used for 2D model
{
    "command": "train",
    "gpu_ids": [1],
    "path_output": "outputs/2d-model_ccrop=64-64_N=full",
    "model_name": "modified_unet",
    "debugging": true,
    "object_detection_params": {
        "object_detection_path": null,
        "safety_factor": [1.0, 1.0, 1.0]
    },
    "wandb": {
        "wandb_api_key": "bf043c64b6c6b4abcc1ee7d8501c300b19945028",
        "project_name": "centerline-detection",
        "group_name": "ivadomed-2d-unet",
        "run_name": "ccrop=64-64_N=full",
        "log_grads_every": 5000
    },
    "loader_parameters": {
        "path_data": ["/home/GRAMES.POLYMTL.CA/u114716/duke/temp/janvalosek/canproco_T2w_centerline_2023-01-23/data_processed"],
        "target_suffix": ["_seg_centerline"],
        "extensions": [".nii.gz"],
        "roi_params": {
            "suffix": null,
            "slice_filter_roi": null
        },
        "contrast_params": {
            "training_validation": ["T2w"],
            "testing": ["T2w"],
            "balance": {}
        },
        "slice_filter_params": {
            "filter_empty_mask": false,
            "filter_empty_input": true
        },
        "slice_axis": "axial",
        "multichannel": false,
        "soft_gt": false,
        "bids_validate": false
    },
    "split_dataset": {
        "fname_split": null,
        "random_seed": 100,
        "split_method" : "participant_id",
        "data_testing": {"data_type": null, "data_value":[]},
        "balance": null,
        "train_fraction": 0.6,
        "test_fraction": 0.2
    },
    "training_parameters": {
        "batch_size": 64,
        "loss": {
            "name": "DiceLoss"
        },
        "training_time": {
            "num_epochs": 200,
            "early_stopping_patience": 75,
            "early_stopping_epsilon": 0.001
        },
        "scheduler": {
            "initial_lr": 1e-4,
            "lr_scheduler": {
                "name": "CosineAnnealingLR",
                "base_lr": 1e-5,
                "max_lr": 1e-3
            }
        },
        "balance_samples": {"applied": false, "type": "gt"},
        "mixup_alpha": null,
        "transfer_learning": {
            "retrain_model": null,
            "retrain_fraction": 1.0,
            "reset": true
        }
    },
    "default_model": {
        "name": "Unet",
        "dropout_rate": 0.25,
        "bn_momentum": 0.9,
        "is_2d": true,
        "final_activation": "relu",
        "depth": 4
    },
    "uncertainty": {
        "epistemic": false,
        "aleatoric": false,
        "n_it": 0
    },
    "postprocessing": {
        "remove_noise": {"thr": -1},
        "keep_largest": {},
        "binarize_prediction": {"thr": 0.5},
        "uncertainty": {"thr": -1, "suffix": "_unc-vox.nii.gz"},
        "fill_holes": {},
        "remove_small": {"unit": "vox", "thr": 3}
    },
    "evaluation_parameters": {},
    "Modified3DUNet": {
        "applied": false,
        "length_3D": [320, 320, 56],
        "stride_3D": [320, 320, 28],
        "attention": false,
        "n_filters": 8
    },
    "transformation": {
        "Resample": {
            "hspace": 0.8,
            "wspace": 0.8,
            "dspace": 0.8
        },
        "CenterCrop": {
            "size": [64, 64]
        },
        "RandomReverse": {
            "applied_to": ["im", "gt"],
            "dataset_type": ["training"]
        },
        "RandomAffine": {
            "degrees": 5,
            "scale": [0.1, 0.1],
            "translate": [0.03, 0.03],
            "applied_to": ["im", "gt"],
            "dataset_type": ["training"]
        },
        "ElasticTransform": {
			"alpha_range": [28.0, 30.0],
			"sigma_range":  [3.5, 4.5],
			"p": 0.1,
            "applied_to": ["im", "gt"],
            "dataset_type": ["training"]
        },
        "NormalizeInstance": {"applied_to": ["im"]}
    }
}

 Config file used for 3D model

{
    "command": "train",
    "gpu_ids": [2],
    "path_output": "outputs/3d-model_ccrop=320-256-64_len=320-256-64_str=160-128-32_N=full",
    "model_name": "modified_unet",
    "debugging": true,
    "object_detection_params": {
        "object_detection_path": null,
        "safety_factor": [1.0, 1.0, 1.0]
    },
    "wandb": {
        "wandb_api_key": "bf043c64b6c6b4abcc1ee7d8501c300b19945028",
        "project_name": "centerline-detection",
        "group_name": "ivadomed-3d-unet",
        "run_name": "ccrop=320-256-64_len=320-256-64_str=160-128-32_N=full",
        "log_grads_every": 4000
    },
    "loader_parameters": {
        "path_data": ["/home/GRAMES.POLYMTL.CA/u114716/duke/temp/janvalosek/canproco_T2w_centerline_2023-01-23/data_processed"],
        "target_suffix": ["_seg_centerline"],
        "extensions": [".nii.gz"],
        "roi_params": {
            "suffix": null,
            "slice_filter_roi": null
        },
        "contrast_params": {
            "training_validation": ["T2w"],
            "testing": ["T2w"],
            "balance": {}
        },
        "slice_filter_params": {
            "filter_empty_mask": false,
            "filter_empty_input": false
        },
        "slice_axis": "sagittal",
        "multichannel": false,
        "soft_gt": false,
        "bids_validate": false
    },
    "split_dataset": {
        "fname_split": null,
        "random_seed": 7,
        "split_method" : "participant_id",
        "data_testing": {"data_type": null, "data_value":[]},
        "balance": null,
        "train_fraction": 0.6,
        "test_fraction": 0.2
    },
    "training_parameters": {
        "batch_size": 2,
        "loss": {
            "name": "DiceLoss"
        },
        "training_time": {
            "num_epochs": 400,
            "early_stopping_patience": 50,
            "early_stopping_epsilon": 0.001
        },
        "scheduler": {
            "initial_lr": 1e-4,
            "lr_scheduler": {
                "name": "CosineAnnealingLR",
                "base_lr": 1e-5,
                "max_lr": 1e-3
            }
        },
        "balance_samples": {"applied": false, "type": "gt"},
        "transfer_learning": {
            "retrain_model": null,
            "retrain_fraction": 1.0,
            "reset": true
        }
    },
    "default_model": {
        "name": "Unet",
        "dropout_rate": 0.25,
        "bn_momentum": 0.1,
        "is_2d": false,
        "final_activation": "relu"
    },
    "uncertainty": {
        "epistemic": false,
        "aleatoric": false,
        "n_it": 0
    },
    "postprocessing": {
        "remove_noise": {"thr": -1},
        "keep_largest": {},
        "binarize_prediction": {"thr": 0.5},
        "uncertainty": {"thr": -1, "suffix": "_unc-vox.nii.gz"},
        "fill_holes": {},
        "remove_small": {"unit": "vox", "thr": 3}
    },
    "evaluation_parameters": {},
    "Modified3DUNet": {
        "applied": true,
        "length_3D": [320, 256, 64],
        "stride_3D": [160, 128, 32],
        "attention": false,
        "n_filters": 8
    },
    "transformation": {
        "Resample": {
            "hspace": 0.8,
            "wspace": 0.8,
            "dspace": 0.8
        },
        "CenterCrop": {
            "size": [320, 256, 64],
            "dataset_type": ["training", "validation"]
        },
        "RandomAffine": {
            "degrees": 5,
            "scale": [0.15, 0.15, 0.15],
            "translate": [0.1, 0.1, 0.1],
            "applied_to": ["im", "gt"],
            "dataset_type": ["training"]
        },
        "RandomBiasField": {
            "coefficients": 0.5,
            "order": 3,
            "p": 0.25,
            "applied_to": ["im"],
            "dataset_type": ["training"]
        },
        "RandomReverse": {
            "applied_to": ["im", "gt"],
            "dataset_type": ["training"]
        },
        "HistogramClipping": {
            "min_percentile": 3,
            "max_percentile": 97,
            "applied_to": ["im"]
        },
        "NormalizeInstance": {"applied_to": ["im"]}
    }
}

The average test Dice from the 2D model was 0.302 and that of the 3D model was 0.493. Shown below is a gif of how the predictions look like:

 Comparison 1
The GT centerline (from sct_get_centerline) is shown in white, the prediction from 2D model is in green and prediction from 3D model is red.

 Comparison 2
The GT centerline (from sct_get_centerline) is shown in white, the prediction from 2D model is in green and prediction from 3D model is red.

Observations:

In both cases, the model is not predicting the centerline at the thoracic regions. This issue still persists and I am trying to think of ways to overcome that.
What's good is that the models are also predicting the centerline even when it's not defined in the GT. This suggests that there is a scope in improving the model's predictions such that it cover the centerline better than the OptiC which sct_get_centerline uses.

This is very cool @naga-karthik ! However I don't trust the sagittal view for QCing, because of the reason mentioned here https://github.com/neuropoly/idea-projects/issues/15#issuecomment-1402319717. It is possible that the centerline is partly on another sagittal slice, hence it makes the comparison meaningless. My suggestion:

First step: Establish a fair QC benchmark (based on https://github.com/neuropoly/idea-projects/issues/15#issuecomment-1402319717 or on the existing SCT QC for centerline-- there is already one showing axial view)
Second step: Compare models


Move meshNetworks network1,2 under networks:

In response to a cherrypick label: new pull request created: #596


_pickle.UnpicklingError: invalid load key, '\x09'

Hi, I am using my own dataset on Colab by uploading 2 .png file manually to the /content/fine_tune/train_data, and get following error for 4.4. Aspect Ratio Bucketing and Cache Latents:
found 12 images.
loading existing metadata: /content/fine_tune/meta_clean.json
load VAE: /content/pre_trained_model/Anything-v3-pruned.ckpt
Traceback (most recent call last):
File "prepare_buckets_latents.py", line 179, in
main(args)
File "prepare_buckets_latents.py", line 57, in main
vae = model_util.load_vae(args.model_name_or_path, weight_dtype)
File "/usr/local/lib/python3.8/dist-packages/library/model_util.py", line 1112, in load_vae
else torch.load(vae_id, map_location="cuda"))
File "/usr/local/lib/python3.8/dist-packages/torch/serialization.py", line 795, in load
return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args)
File "/usr/local/lib/python3.8/dist-packages/torch/serialization.py", line 1002, in _legacy_load
magic_number = pickle_module.load(f, **pickle_load_args)
_pickle.UnpicklingError: invalid load key, '\x09'.

hello, this is happened because aria2c can't get filename when downloading from huggingface, and also i forgot to create conditional if-else to load certain model extensions instead of naming all of them as .ckpt
already fixed for 3 colab, please dont use dreambooth colab because i haven't touch it for 5 days.


When I use the cityscapes dataset for training, I encounter an error when loading the model. How to solve this problem?

2021-12-23 11:41:25,981 Segmentron INFO: train_fine data num 2975
2021-12-23 11:41:26,012 Segmentron INFO: train_fine data num 2975
2021-12-23 11:41:26,017 Segmentron INFO: val_fine data num 500
2021-12-23 11:41:26,763 Segmentron INFO: DSSyncBatchNorm is effective!
Traceback (most recent call last):
File "tools/train_semi.py", line 284, in
trainer = Trainer(args)
File "tools/train_semi.py", line 79, in init
self.model = get_segmentation_model().to(self.device)
File "/user/lwx1055260/ID2450_SimpleBaseline_for_pytorch/segmentron/models/model_zoo.py", line 27, in get_segmentation_model
load_model_pretrain(model)
File "/user/lwx1055260/ID2450_SimpleBaseline_for_pytorch/segmentron/models/model_zoo.py", line 34, in load_model_pretrain
assert os.path.exists(cfg.TRAIN.PRETRAINED_MODEL_PATH)
AssertionError
THCudaCheck FAIL file=/pytorch/aten/src/THC/THCCachingHostAllocator.cpp line=278 error=4 : driver shutting down

please download the pre-trained model. For more details check https://github.com/jianlong-yuan/SimpleBaseline/blob/main/segmentron/models/backbones/build.py .

@jianlong-yuan  what is PRETRAINED_MODEL_PATH: "/**/best_model.pth" ?


Clean up IPC socket files

Delete socket files in /tmp/
Will need to fix #1 first

Doesn't seem to work on raspberry pi.

Related: https://github.com/hamon-in/librtcdcpp/issues/4


v1.3 Add Flag feature

This PR implements the flag feature.

Add flag as a wrapper for boolean
Add flagged field to Applicant
Added code in internal list implementation to sort by flag status
Edit UI to display the flag status
Edit storage to save the flag field
Update relevant tests and JSON files to test this feature.

Resolves #115

Codecov Report

Merging #117 (4a262d1) into master (218da18) will decrease coverage by 3.01%.
The diff coverage is 25.58%.

@@             Coverage Diff              @@
##             master     #117      +/-   ##
============================================
- Coverage     73.16%   70.14%   -3.02%
- Complexity      445      449       +4
============================================
  Files            76       79       +3
  Lines          1323     1407      +84
  Branches        140      148       +8
============================================
+ Hits            968      987      +19
- Misses          321      383      +62
- Partials         34       37       +3

Impacted Files
Coverage Δ

...va/seedu/linkedout/logic/commands/FlagCommand.java
0.00% <0.00%> (ø)

...eedu/linkedout/logic/parser/FlagCommandParser.java
0.00% <0.00%> (ø)

.../seedu/linkedout/logic/parser/LinkedoutParser.java
89.47% <0.00%> (-10.53%)
:arrow_down:

src/main/java/seedu/linkedout/model/Linkedout.java
81.48% <0.00%> (-10.19%)
:arrow_down:

src/main/java/seedu/linkedout/model/Model.java
100.00% <ø> (ø)

.../main/java/seedu/linkedout/model/ModelManager.java
94.00% <0.00%> (-6.00%)
:arrow_down:

...linkedout/model/applicant/UniqueApplicantList.java
82.60% <0.00%> (-10.08%)
:arrow_down:

...rc/main/java/seedu/linkedout/ui/ApplicantCard.java
0.00% <0.00%> (ø)

...ain/java/seedu/linkedout/model/applicant/Flag.java
42.85% <42.85%> (ø)

...ava/seedu/linkedout/model/applicant/Applicant.java
86.56% <61.90%> (-11.31%)
:arrow_down:

... and 2 more

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
Powered by Codecov. Last update 218da18...4a262d1. Read the comment docs.


maintain: update dependencies

Please add safety to the exceptions for the ci in the commit hook config. Thx.


Type not suggested when implementing an interface's overloaded method

TypeScript Version:  2.5.2 / nightly (2.6.0-dev.20170910)
Code
interface MyInterface {
	exec(action: 'action_1'): void;
	exec(action: 'action_2'): void;
	exec(action: string): void;
}

const test: MyInterface = {
	exec(arg) {
		// `arg` type is `any`
	}
};

Expected behavior:
I would expect arg type to be "action1"|"action2"|string.
Actual behavior:
arg type is any.

Contextual types do not flow from overloads. this issue has been reported in https://github.com/Microsoft/TypeScript/issues/16019 and there is a proposed change for it in https://github.com/Microsoft/TypeScript/pull/17819

Declining due to lack of interest.


Addresses #5694

Microsoft.AspNetCore.StaticFiles": "1.1.0"
to project,json
Add
app.UseStaticFiles();
in the
public void Configure(IApplicationBuilder app)
from Startup class.
Addresses #5694

Hi @ignatandrei, I'm your friendly neighborhood .NET Foundation Pull Request Bot (You can call me DNFBOT). Thanks for your contribution!

In order for us to evaluate and accept your PR, we ask that you sign a contribution license agreement. It's all electronic and will take just minutes. I promise there's no faxing. https://cla2.dotnetfoundation.org.

TTYL, DNFBOT;

@ignatandrei thanks for the PR! Can you quickly sign the CLA and address the review feedback so we can look into merging?

I am getting now the approval from my company ( I think it will be 3-4 days from now) . If you need faster , then please reject the pull request and I will re-submit after I get the approval.
Sorry for that
Andrei

@ignatandrei no worries, a few days is no problem at all.

(If you'd like, you can still send us an update to the PR in the meantime.)

@ignatandrei, Thanks for signing the contribution license agreement so quickly! Actual humans will now validate the agreement and then evaluate the PR.
Thanks, DNFBOT;

I have signed the document, I have also pushed to my fork .


Modifying a property does not trigger HMR

Version
3.0.11
Reproduction link
https://github.com/CHOYSEN/vue-hmr-issue
Steps to reproduce
Modify the placeholders of two el-input components in App.vue
What is expected?
The placeholders of two el-input components have been modified correctly
What is actually happening?
The el-input component within the el-main component has not been modified

I'm not sure if it was the HMR, because from the response it worked.
If I modify the placeholders of these two components:
import { createHotContext as __vite__createHotContext } from "/@vite/client";import.meta.hot = __vite__createHotContext("/src/App.vue");const _sfc_main = {}
import { resolveComponent as _resolveComponent, createVNode as _createVNode, withCtx as _withCtx, Fragment as _Fragment, openBlock as _openBlock, createBlock as _createBlock } from "/node_modules/.vite/vue.js?v=fccc817f"

function _sfc_render(_ctx, _cache) {
  const _component_el_input = _resolveComponent("el-input")
  const _component_el_main = _resolveComponent("el-main")

  return (_openBlock(), _createBlock(_Fragment, null, [
    _createVNode(_component_el_input, { placeholder: "hey!" }),
    _createVNode(_component_el_main, null, {
      default: _withCtx(() => [
        _createVNode(_component_el_input, { placeholder: "hey!" })
      ]),
      _: 1 /* STABLE */
    })
  ], 64 /* STABLE_FRAGMENT */))
}

_sfc_main.render = _sfc_render
_sfc_main.__file = "C:/Users/CHOYSEN/Desktop/vite-hmr-issue/src/App.vue"
export default _sfc_main
_sfc_main.__hmrId = "7ba5bd90"
typeof __VUE_HMR_RUNTIME__ !== 'undefined' && __VUE_HMR_RUNTIME__.createRecord(_sfc_main.__hmrId, _sfc_main)
export const _rerender_only = true
import.meta.hot.accept(({ default: updated, _rerender_only }) => {
  if (_rerender_only) {
    __VUE_HMR_RUNTIME__.rerender(updated.__hmrId, updated.render)
  } else {
    __VUE_HMR_RUNTIME__.reload(updated.__hmrId, updated)
  }
})

Please open an issue in element first.
Read https://new-issue.vuejs.org/?repo=vuejs/vue#why-repro and open an issue here if you reproduce it without element

@CHOYSEN
I tried your repo and everything works fine.

@edison1105

@edison1105
It may cause by element-plus. I'm not sure.

i have the problem too


List of Subreddits related to param.

Just added a new link to Reddit class and method to allow someone to get a list of subreddits related to a search query. I want it in order to get a list of recommended subreddits related to frequently commented subreddits of a user.

Ignore the first commit, VS didn't pick up the changes to the Reddit class for some reason, so had to re-commit.

 I can't read the diff and I can't review this pull request.

Cool, not sure why it did that, I'll fix it when I get back into the house.

Hey man, so I've had a look in Visual Studio and I can't see what it's done at all. I literally forked the project, pulled it, added a variable and a method and changed nothing else. I don't know what it would have done to the line endings, I suppose you wouldn't have any ideas on how to fix it? (Sorry by the way, pretty new to Git so not entirely sure what to do, sorry again for the inconvenience).

It's probably your git configuration that's messed up.
Check your EOL settings with git config --global core.autocrlf
If core.autocrlf is false set it to true with git config --global core.autocrlf true.

According to Git Bash core.autocrlf is set to true, so not entirely sure what's going on at all.


Update Vulkan-Headers to 1.3.236

Updated Vulkan-Headers to 1.3.236, disabled codegen for three extensions* which will need more work and fixed compilation errors.
*
"VK_EXT_descriptor_buffer",
"VK_NV_copy_memory_indirect"
"VK_NV_memory_decompression"
Issues for the extensions to follow-up on:

https://github.com/LunarG/gfxreconstruct/issues/917
https://github.com/LunarG/gfxreconstruct/issues/920

CI gfxreconstruct build # 2240 running.

CI gfxreconstruct-extended build queued with queue ID 43961.

CI gfxreconstruct-extended build # 134 running.

CI gfxreconstruct-extended build queued with queue ID 44133.

CI gfxreconstruct-extended build # 135 running.

CI gfxreconstruct build queued with queue ID 44397.

CI gfxreconstruct build # 2246 running.

CI gfxreconstruct build # 2246 passed.


Setting the size of the editor

I could not find an option to set the minimum size of the editor. When you're in tree mode it fits to its contents but in Code or Text mode the editor is very small...

Hi,
You can define outer div height to make it large like below


Clarifying passive mode and removing reference to LPS which is consumer

@dansimp The PR that introduces a clarification is ready for review. Thanks.

@Dansimp Following up for updates.

@Dansimp Following up for updates.


Separate optional components to plugins

 Issue by Cloudef
Tuesday Aug 18, 2015 at 13:17 GMT
Originally opened as https://github.com/Cloudef/wlc/issues/47

Link against these plugins and dlopen the plugins instead. Just like orbment / bemenu does.

 Comment by Cloudef
Tuesday Aug 18, 2015 at 22:44 GMT

Started working on feature/modular branch.

 Comment by Cloudef
Tuesday Aug 18, 2015 at 13:18 GMT

#46


Encryption

Once the client is working for unencrypted smtp, we should start work on starttls and ssl encryption and whatnot, those are important if sail is to actually be used

Implicit TLS is expected on 465, and is the recommended method of encrypted mail submission. (fastmail article about ssl,tls, and starttls)
STARTTLS on 587 (for submission) is being deprecated and shouldn't be used? It was common before, but now is being phased out? In a transition period (me too)

However, to maximize the use of encryption for
submission, it is desirable to support both mechanisms for Message
Submission over TLS for a transition period of several years.  As a
result, clients and servers SHOULD implement both STARTTLS on
port 587 and Implicit TLS on port 465 for this transition period.

RF 8314 Section 3.3

("submission" here is a user submitting mail to their "home server" to then be sent onwards to the destination. it is not the transfer of mail between SMTP servers)
It appears that the only encryption offered to use for mail transfer is good ol' STARTTLS on 25. Which feels weird, but eh.


add referrer

Something I had forgotten to add to my initial commits was referrer. This allows Plausible to see what the traffic is coming from and that's kinda an important detail for some.

dont mess with the indentation

Blame VS Code for not picking up the .prettierrc